Scipedia: Documents published in 2020

A Novel Surface Inset Permanent Magnet Synchronous Motor for Electric Vehicles

Scipedia content — Fri, 12 Feb 2021 12:43:04 +0100

Aiming to successfully meet the requirements of a large output torque and a wide range of flux weakening speed expansion in permanent magnet synchronous motors (PMSM) for electric vehicles, a novel surface insert permanent magnet synchronous motor (SIPMSM) is developed. The method of notching auxiliary slots between the magnetic poles in the rotor and unequal thickness magnetic poles is proposed to improve the performance of the motor. By analyzing the magnetic circuit characteristics of the novel SIPMSM, the notching auxiliary slots between the adjacent magnetic poles can affect the q-axis inductance, and the shape of magnetic pole effects the d-axis inductance of the motor. The combined action of the two factors not only weakens the cogging torque, but also improves the flux weakening capability of the motor. In this paper, the response surface methodology (RSM) is used to establish a mathematical model of the relationship between the structural parameters of the motor and the optimization objectives, and the optimal design of the motor is completed by solving the mathematical model. Experimental validation has been conducted to show the correctness and effectiveness of the proposed SIPMSM.

Document type: Article

Path Planning for Autonomous Vehicle Based on a Two-Layered Planning Model in Complex Environment

Scipedia content — Fri, 12 Feb 2021 12:41:48 +0100

The autonomous vehicle consists of perception, decision-making, and control system. The study of path planning method has always been a core and difficult problem, especially in complex environment, due to the effect of dynamic environment, the safety, smoothness, and real-time requirement, and the nonholonomic constraints of vehicle. To address the problem of travelling in complex environments which consists of lots of obstacles, a two-layered path planning model is presented in this paper. This method includes a high-level model that produces a rough path and a low-level model that provides precise navigation. In the high-level model, the improved Bidirectional Rapidly-exploring Random Tree (Bi-RRT) based on the steering constraint is used to generate an obstacle-free path while satisfying the nonholonomic constraints of vehicle. In low-level model, a Vector Field Histogram- (VFH-) guided polynomial planning algorithm in Frenet coordinates is introduced. Based on the result of VFH, the aim point chosen from improved Bi-RRT path is moved to the most suitable location on the basis of evaluation function. By applying quintic polynomial in Frenet coordinates, a real-time local path that is safe and smooth is generated based on the improved Bi-RRT path. To verify the effectiveness of the proposed planning model, the real autonomous vehicle has been placed in several driving scenarios with different amounts of obstacles. The two-layered real-time planning model produces flexible, smooth, and safe paths that enable the vehicle to travel in complex environment.

Document type: Article

CO3D MISSION DIGITAL SURFACE MODEL PRODUCTION PIPELINE

Scipedia content — Fri, 12 Feb 2021 12:40:04 +0100

Abstract. Earth Observation (EO) remote sensing missions are producing an increasing volume of data due to higher spatial and spectral resolutions, and higher frequency of acquisitions. Thus, in order to prepare the future of image processing pipelines, CNES has carried out Research & Development studies related to the use of Big Data and Cloud technologies for image processing chains made. Since mid-2019, CNES in partnership with Airbus Defense & Space, has started a new High Resolution Optical EO mission dedicated to very high resolution 3D observation called CO3D (“Constellation Optique 3D”). To achieve those objectives, a new image processing pipeline prototype is being developed taking in consideration the lessons learned from the previous studies. The paper will introduce this new image processing pipeline, the processing paradigms used to take advantage of big data technologies and the results of production benchmarks at a large scale. The on-going works to optimize the processing pipeline and Cloud cluster will be also discussed.

Document type: Article

Adaptive Traffic Signal Control Model on Intersections Based on Deep Reinforcement Learning

Scipedia content — Fri, 12 Feb 2021 12:38:30 +0100

Controlling traffic signals to alleviate increasing traffic pressure is a concept that has received public attention for a long time. However, existing systems and methodologies for controlling traffic signals are insufficient for addressing the problem. To this end, we build a truly adaptive traffic signal control model in a traffic microsimulator, i.e., “Simulation of Urban Mobility” (SUMO), using the technology of modern deep reinforcement learning. The model is proposed based on a deep "jats:italic"Q"/jats:italic"-network algorithm that precisely represents the elements associated with the problem: agents, environments, and actions. The real-time state of traffic, including the number of vehicles and the average speed, at one or more intersections is used as an input to the model. To reduce the average waiting time, the agents provide an optimal traffic signal phase and duration that should be implemented in both single-intersection cases and multi-intersection cases. The co-operation between agents enables the model to achieve an improvement in overall performance in a large road network. By testing with data sets pertaining to three different traffic conditions, we prove that the proposed model is better than other methods (e.g., "jats:italic"Q"/jats:italic"-learning method, longest queue first method, and Webster fixed timing control method) for all cases. The proposed model reduces both the average waiting time and travel time, and it becomes more advantageous as the traffic environment becomes more complex.

Document type: Article

An Interacting Multiple Model Approach for Target Intent Estimation at Urban Intersection for Application to Automated Driving Vehicle

Scipedia content — Fri, 12 Feb 2021 12:37:28 +0100

Research shows that urban intersections are a hotspot for traffic accidents which cause major human injuries. Predicting turning, passing, and stop maneuvers against surrounding vehicles is considered to be fundamental for advanced driver assistance systems (ADAS), or automated driving systems in urban intersections. In order to estimate the target intent in such situations, an interacting multiple model (IMM)-based intersection-target-intent estimation algorithm is proposed. A driver model is developed to represent the driver’s maneuvering on the intersection using an IMM-based target intent classification algorithm. The performance of the intersection-target-intent estimation algorithm is examined through simulation studies. It is demonstrated that the intention of a target vehicle is successfully predicted based on observations at an individual intersection by proposed algorithms.

Document type: Article

Research on the Influence of a High-Speed Railway on the Spatial Structure of the Western Urban Agglomeration Based on Fractal Theory—Taking the Chengdu–Chongqing Urban Agglomeration as an Example

Scipedia content — Fri, 12 Feb 2021 12:37:06 +0100

By shortening the transportation time between cities, high-speed rail shortens the spatial distance between cities and exerts a far-reaching influence on urban agglomerations’ spatial structures. In order to explore the influence of high-speed rail on the spatial reconstruction of an urban agglomeration in western China, this paper employs fractal theory to compare and analyze the spatial structure evolution of the Chengdu–Chongqing urban agglomeration in western China before and after the opening of a high-speed railway. The results show that after the completion of the high-speed railway, the intercity accessibility is improved. The Chengdu–Chongqing urban agglomeration’s spatial distribution shows a decreasing density from the central city to the surrounding areas. Furthermore, the urban system presents a trend of an agglomeration distribution. Therefore, strengthening the construction of high-speed rail channels between primary and medium-sized cities, as well as accelerating the construction of intercity railway networks and rapid transportation systems based on high-speed rail cities, would help develop urban agglomerations in western China.

Document type: Article

Data Analysis for Self-Driving Vehicles in Intelligent Transportation Systems

Scipedia content — Fri, 12 Feb 2021 12:35:24 +0100

International audience

Document type: Article

Visualizing the Intellectual Structure and Evolution of Intelligent Transportation Systems: A Systematic Analysis of Research Themes and Trends

Scipedia content — Fri, 12 Feb 2021 12:33:24 +0100

ording to the "i"United Nations"/i", 70% of the world’s population will live in cities by 2050. This growth will be reflected in the demand for better services that should be adjusted to the collective and individual needs of the population. Governments and organizations are working on defining and implementing strategies that will enable them to respond to these challenges. The main challenges are related to transport and its management, considering transportation as a core issue in the economy, sustainability, and development of the regions. In this way, the Intelligent Transportation Systems (ITS) play a key role in the response to these scenarios, being that they are the framework where the new hardware and software tools are integrated, allowing an efficient development of transportation systems management, attending to aspects such as: traffic management, communications between vehicles and infrastructures, and security, among others. Nevertheless, the concept of ITS evolves rapidly so it is necessary to understand its evolution. To do that, the current research develops a thematic analysis of ITS in literature, evaluating the intellectual structure and its evolution using "i"SciMAT"/i", quantifying the main bibliometric performance indicators, and identifying the main research areas, authors, journals, and countries. To this purpose, the publications related to ITS from 1993 to 2019 available in the "i"Web of Science (WoS) Core Collection"/i" were retrieved (7649 publications) and analyzed. Finally, one of the main results is the latest research themes map of ITS, considering its intellectual structure, evolution, and relationship. It assists in the definition and implementation of strategies, the identification of the scientific, academic, and business opportunities, and future research lines to consolidate the role of ITS in the new city models.

Document type: Article

Smart Cities Oriented Project Planning and Evaluation Methodology Driven by Citizen Perception—IoT Smart Mobility Case

Scipedia content — Fri, 12 Feb 2021 12:29:09 +0100

Smart Cities empower progress through technology integration directed with a strategic approach to sustainable development and citizen well-being. The creation of solid strategic planning boosts the development of infrastructure, innovation, and technology. However, the above can be compromised if citizens are not properly involved; therefore, it is relevant to enhance citizen participation when a new Smart City project appears on the horizon. This work presents a Smart Cities Oriented Project Planning and Evaluation (SCOPPE) Methodology that combines the citizen participation and the Minimum Viable Product creation through adaptive project management. Moreover, since the smart mobility projects represent the first step towards a Smart City, a case of study of an Intelligent Parking System (SEI-UVM) is presented following the SCOPPE Methodology. The application’s steps results lead us to key and useful information when defining, designing, and implementing the minimum viable product of the cornerstone device of the SEI-UVM: the Smart Vehicle Presence Sensor (SPIN-V). It is worthwhile to mention that the proposed SCOPPE Methodology could be extended to any Smart City project.

Document type: Article

Multimode Traffic Travel Behavior Characteristics Analysis and Congestion Governance Research

Scipedia content — Fri, 12 Feb 2021 12:28:20 +0100

The rapid aggregation of modern urban population and the rapid growth of car travel lead to traffic congestion, environmental pollution, and other problems. In view of the limited land resources in our country, it is impractical to meet residents’ travel demand by blindly increasing traffic supply. Therefore, addressing the urban road congestion problem for sustainable development of modern cities, the paper makes research on residents’ travel behavior characteristics and travel preference under the condition of multimodal transportation to formulate reasonable traffic demand management strategy for the guide on public traffic demand, bus priority strategy, and congestion management. The operation characteristic of each transportation mode is analyzed by comparing its related traffic and economic characteristics. Multimode traffic choice behavior is discussed by establishing multiple logistic regression models to analyze the main influencing factors to travelers’ social and economic attributes, travel characteristics, and preference based on travel survey data of urban residents. The paper proposes the development of an urban public transportation system and travelling mode shift from cars to public transportation as reasonable travel structure for congestion management and sustainable development of modern cities.

Document type: Article

A Fail-Operational Control Architecture Approach and Dead-Reckoning Strategy in Case of Positioning Failures

Scipedia content — Fri, 12 Feb 2021 12:26:53 +0100

Presently, in the event of a failure in Automated Driving Systems, control architectures rely on hardware redundancies over software solutions to assure reliability or wait for human interaction in takeover requests to achieve a minimal risk condition. As user confidence and final acceptance of this novel technology are strongly related to enabling safe states, automated fall-back strategies must be assured as a response to failures while the system is performing a dynamic driving task. In this work, a fail-operational control architecture approach and dead-reckoning strategy in case of positioning failures are developed and presented. A fail-operational system is capable of detecting failures in the last available positioning source, warning the decision stage to set up a fall-back strategy and planning a new trajectory in real time. The surrounding objects and road borders are considered during the vehicle motion control after failure, to avoid collisions and lane-keeping purposes. A case study based on a realistic urban scenario is simulated for testing and system verification. It shows that the proposed approach always bears in mind both the passenger&rsquo

s safety and comfort during the fall-back maneuvering execution.

Document type: Article

Environmental Impact Assessment of the Transportation Sector and Hybrid Vehicle Implications in Palestine

Scipedia content — Fri, 12 Feb 2021 12:26:34 +0100

During the last two decades, the development of sustainable transportation systems has been highlighted as a key element in solving environmental problems related to climate change and impacts on greenhouse gases. Globally, the transportation sector has become one of the main contributors to these environmental problems. Thus, the environmental impact assessment of this sector and the implications of new vehicle technologies have begun to be considered as first steps for any long-term future strategies in this sector. In Palestine, the lack of environmental data related to the transportation sector and the absence of studies that address the new vehicle technologies (such as hybrid vehicles) and their future implications make it difficult to set up any future strategies or plans. In this study, the current and the future environmental impacts of the transportation sector have been assessed, and the future implications of hybrid vehicles have been determined. The gross domestic product (GDP), population, and the number of vehicles for the period 1994–2018 have been used to develop an auto regressive integrated moving average (ARIMA) prediction model for the future number of vehicles. Then, the total traveled kilometers and the total consumed fuels (by diesel and gasoline vehicles) have been predicted. After that, the current and future (2020 and 2030) greenhouse gas (GHG) emissions, including CO"sub"2"/sub", N"sub"2"/sub"O, and CH"sub"4"/sub", have been estimated. Finally, the future implications of hybrid vehicles, based on two scenarios (10% and 20% hybrid vehicles) have been estimated. The results have showed that the estimated CO"sub"2"/sub", N"sub"2"/sub"O, and CH"sub"4"/sub" emissions from the transportation sector in 2020 are 4,842,164.5, 213.8, and 445.8 tons, which are very high, and even much higher than the total national emissions of 2014 (the only officially available data). Moreover, in 2030, replacing 20% of internal combustion engine vehicles (ICEVs) by hybrid vehicles would lead to 4.66% and 13.31% reductions in CO"sub"2"/sub" and N"sub"2"/sub"O, respectively, as compared to 100% ICEVs, while the CH"sub"4 "/sub"emissions will increase. However, the overall CO"sub"2"/sub"-equivalent will decrease by 5%; therefore, a more sustainable transport system will be achieved.

Document type: Article

STATISTICAL ANALYSIS OF DYNAMICAL QUANTITIES RELATED TO RUNNING SAFETY AND RIDE COMFORT OF A RAILWAY VEHICLE

Scipedia content — Thu, 11 Feb 2021 17:59:25 +0100

This paper investigates the variation of track geometrical parameters that lead to a local increase of specific dynamical quantities of a railway vehicle, possibly beyond their acceptable values. In particular, the changes of track geometry are investigated near track points where the running safety or ride comfort are significantly decreased during the vehicle motion due to track irregularities. The investigated dynamical quantities include the lateral and vertical forces at the wheel-rail contact as well as the acceleration of the vehicle body. The vehicle motion has been simulated using a non-linear model of a passenger car moving along a nominally tangent stiff track with random geometrical irregularities. The relationship between the local track condition and the maxima of the dynamical quantities was investigated with the statistical method proposed by the author. The performed analysis clearly identifies the characteristic variation of track irregularities that leads to a large increase of the investigated dynamical quantities at some track points.

Avoiding Traffic Congestion Externalities? The Value of Urgency

Scipedia content — Thu, 11 Feb 2021 14:43:15 +0100

In Becker (1965) and neoclassical microeconomic theory the value of time is a constant fraction of the hourly wage. When taken to data, however, this value departs from theoretical predictions, and appears to vary with the amount of time saved. By observing drivers on freeways opting to enter toll lanes with high-frequency, time-varying prices that secure a minimum level-of-service, we uncover a new and fundamental aspect of preferences for travel time savings related to urgency. The presence of preferences for urgency, which reflect the fact that individuals often face discrete penalties for being late, allows us to reconcile the pattern observed in the data with neoclassical theory. Using a rich, repeated-transaction data and individual-level hedonic estimation, we show that the value of urgency accounts for 87 percent of total willingness-to-pay for time savings. As a result, ignoring the value of urgency in cost-benefit analysis severely underestimates the true value of time savings that projects deliver, as such omission will typically ignore non-trivial welfare gains to a potentially large number of individuals.

Ability for a Stateful Path Computation Element (PCE) to Request and Obtain Control of a Label Switched Path (LSP)

Scipedia content — Thu, 11 Feb 2021 14:39:20 +0100

Stateful Path Computation Element (PCE) retains information about the placement of Multiprotocol Label Switching (MPLS) Traffic Engineering Label Switched Paths (TE LSPs). When a PCE has stateful control over LSPs it may send indications to LSP head-ends to modify the attributes (especially the paths) of the LSPs. A Path Computation Client (PCC) that has set up LSPs under local configuration may delegate control of those LSPs to a stateful PCE. There are use-cases in which a stateful PCE may wish to obtain control of locally configured LSPs of which it is aware but that have not been delegated to the PCE. This document describes an extension to the Path Computation Element communication Protocol (PCEP) to enable a PCE to make requests for such control.

Common YANG Data Types for Traffic Engineering

Scipedia content — Thu, 11 Feb 2021 14:37:35 +0100

This document defines a collection of common data types and groupings in YANG data modeling language. These derived common types and groupings are intended to be imported by modules that model Traffic Engineering (TE) configuration and state capabilities.

Unsettled Topics in the Application of Satellite Navigation to Air Traffic Management

Scipedia content — Thu, 11 Feb 2021 14:33:52 +0100

Scenarios and Simulation Results of PCE in a Native IP Network

Scipedia content — Thu, 11 Feb 2021 14:27:50 +0100

Requirements for providing the End to End(E2E) performance assurance are emerging within the service provider networks. While there are various technology solutions, there is no single solution that can fulfill these requirements for a native IP network. In particular, there is a need for a universal (E2E) solution that can cover both intra- and inter-domain scenarios. One feasible E2E traffic engineering solution is the addition of central control in a native IP network. This document describes various complex scenarios and simulation results when applying the Path Computation Element (PCE) in a native IP network. This solution, referred to as Centralized Control Dynamic Routing (CCDR), integrates the advantage of using distributed protocols and the power of a centralized control technology, providing traffic engineering for native IP networks in a manner that applies equally to intra- and inter-domain scenarios.

Impact of COVID-19 on Travel Behavior and Shared Mobility Systems

Scipedia content — Thu, 11 Feb 2021 14:19:20 +0100

IS-IS Application-Specific Link Attributes

Scipedia content — Thu, 11 Feb 2021 14:18:44 +0100

Existing traffic engineering related link attribute advertisements have been defined and are used in RSVP-TE deployments. Since the original RSVP-TE use case was defined, additional applications (e.g., Segment Routing Policy, Loop Free Alternate) that also make use of the link attribute advertisements have been defined . In cases where multiple applications wish to make use of these link attributes, the current advertisements do not support application-specific values for a given attribute, nor do they support indication of which applications are using the advertised value for a given link. This document introduces new link attribute advertisements that address both of these shortcomings.

YANG Data Model for Traffic Engineering (TE) Topologies

Scipedia content — Thu, 11 Feb 2021 14:13:44 +0100

This document defines a YANG data model for representing, retrieving and manipulating Traffic Engineering (TE) Topologies. The model serves as a base model that other technology specific TE Topology models can augment.

DfR of iBooster Gen 2 Valve body by changing the material

Scipedia content — Wed, 10 Feb 2021 09:20:09 +0100

The iBooster Gen2 is currently commercialized in three different motor versions: size1, size1+ and size2. Each motor size generates a maximum torque which corresponds to a maximum support force required to slowdown the vehicle. In all these configurations the material of the Valve Body is PET50GF. The possibility of future size3 motors leads to a decrease of the system reliability below of the safety requirements. Therefore, the goal of this project is to identify a new plastic material for the Valve Body able to withstand the size 3 load collective of the iBooster Gen2 without changing the geometry of this part.

DfR of iBooster Gen 2 Valve body by changing the material

Scipedia content — Tue, 09 Feb 2021 09:26:11 +0100

DfR of iBooster Gen 2 Valve body by changing the material

Scipedia content — Tue, 09 Feb 2021 09:03:48 +0100

Design of a dual 22kW AC charging station for electric vehicles

Scipedia content — Tue, 09 Feb 2021 09:02:05 +0100

Recent years have denoted an unexpected, but positive,increase of Electric Vehicles (EV) registrations in Europe, North America, China and Japan. New policies and regulations are pushing the automotive market to develop a robust and reliable electric mobility network: from its charging network infrastructure to the development of new electric vehicles. Many Original Equipment Manufacturers (OEMs) have launched (or are planning to) new electric vehicles to the market, showing a real commitment towardsthe electric revolution the future is expected the world to come. With a focus on Germanyand its current State-of-the-Art regarding EV Charging Infrastructureand EV registrations, public charging equipment seems not to be in accordance with the EV sales increase. Manufacturers and OEM are opting for developing its self-developed charging equipment (so AC Charging Stations), focusing on relieve the stress feeling the drivers have. This Thesis has as objectives to study the State-of-the-Art of the current solutions of 2x22kW AC Charging Stations that complies with the German Calibration Law, summarize the most relevant standards and regulations in order to develop a reliable AC EV Charging Station, list the requirements needed in order to produce a dual 22kW AC Charging Station and propose a solution according the mentionedlist. In order to propose an electrical-safe and competitive AC Charging Station, a market analysis of the current solutions is needed. Moreover, the understanding of the regulations and standards that definewhether an AC Charging Station is qualifiedto be placed in publicand/or private spaces is essentially necessary. Listing all the relevant points of the central documents and comparing it to the current solutions,gives the reader a valuableoverall view of the key points to develop a Charging Pillar. The results demonstrates a “Proof of Concept” for a 22kW AC Charging Stations, centering the solution on the thermal, mechanical and electrical requirements. Using standardized and approved components for electrical safety and charging infrastructure, the solution avoids possible technical disputeswhen validating the AC Charging Infrastructure. Concluding with the strategy that has been adopted, the solution of including all the electrical components inside a box ("IP44) avoids the designing issues it may take to develop a one housing solution. In addition, using standardized components increases significantly the development cost, which it tempts to start considering, in example, the idea todesign a self-developed charging controller

Design of a polite electric scooter

Scipedia content — Tue, 09 Feb 2021 08:59:14 +0100

Techno-Economic Aspects of Production, Storage and Distribution of Ammonia

Scipedia content — Mon, 08 Feb 2021 12:13:24 +0100

The cost of green ammonia is determined primarily by its production cost, but it is also influenced by the cost of distribution and storage. Production costs are a function of plant location, size, and whether the plant is islanded or semi-islanded, that is whether the power source is variable renewable energy (VRE) or grid electricity. Capital costs for a green ammonia plant consist of equipment for the production of hydrogen (electrolyzer) and nitrogen (air separation), ammonia synthesis (Haber–Bosch, compressors and separators) and storage. Operating costs are mainly due to power consumption. The electrolyzer dominates both capital and operating costs in the manufacture of green ammonia. Ammonia is stored in either pressurized or refrigerated vessels with the latter preferred for large scale storage. Distribution of ammonia may involve several transport modes depending on the location of the production and consumption sites. Inland transport can involve pipelines, trains, and trucks, and offshore shipping is generally done with medium, large or very large gas carrier vessels with refrigerated tanks. A case study to supply a fleet of 36 ultralarge container vessels (ULCVs) operating between the ports of Shanghai and Rotterdam is used to exemplify the combination of production, storage and transportation costs.

Cooperative Automated Driving for managing Transition Areas and the Operational Design Domain (ODD)

Scipedia content — Mon, 08 Feb 2021 11:39:46 +0100

When cooperative automated vehicles (CAVs) emerge on urban roads, there will be areas and situations where all levels of automation can be granted, and others where highly automated driving will not be allowed or is not feasible. Complex environments or temporary road configurations are examples of situations leading to takeover requests and are referred to as 'Transition Areas'. Such situations are assumed to cause negative impacts on traffic safety and efficiency, in particular with mixed traffic fleets. The TransAID project is developing a digital infrastructure and dedicated traffic management strategies to assist CAVs at transition areas, and preserve safe and smooth traffic flow. This paper explains the relevance of transition areas and the link to the operational design domain (ODD) of automated vehicles. By combining results from different projects with findings from stakeholder consultation workshops, ODD is discussed in detail and a conceptual structure to guide the discussion is provided.

The effects of explicit intention communication, conspicuous sensors, and pedestrian attitude in interactions with automated vehicles

Scipedia content — Mon, 08 Feb 2021 10:15:35 +0100

In this paper, we investigate the effect of an external human- machine interface (eHMI) and a conspicuous external vehicle appearance due to visible sensors on pedestrian interactions with automated vehicles (AVs). Recent research shows that AVs may need to explicitly communicate with the environ- ment due to the absence of a driver. Furthermore, in interac- tion situations, an AV that looks different and conspicuous owing to an extensive sensor system may potentially lead to hesitation stemming from mistrust in automation. Thus, we evaluated in a virtual reality study how pedestrian attitude, the presence/absence of an eHMI, and a conspicuous sensor system affect their willingness to cross the road. Results rec- ommend the use of an eHMI. A conspicuous appearance of automated-driving capability had no effect for the sample as a whole, although it led to more efficient crossing decisions for those with a more negative attitude towards AVs. Our findings contribute towards the effective design of future AV interfaces.

Determining Infrastructure- and Traffic Factors That Increase the Perceived Complexity of Driving Situations

Scipedia content — Mon, 08 Feb 2021 10:07:10 +0100

When designing experimental studies in the driving domain, an important decision is which driving scenarios to include. It is proposed that HMI need to be adaptive to the complexity of the driving situation, in order to avoid overloading the driver. To further study adaptive HMI a comprehensive list of factors that determine the perceived complexity of a driving situation is required, yet absent. In this, infrastructure- and traffic characteristics that may influence the perceived complexity of a driving situation were collected from literature. Next, four sets of driving scenarios of varying complexities were created and validated in an online survey. The results of this study include: 1) a list of infrastructure- and traffic characteristics that influence the overall complexity of a driving situation, and 2) validated scenarios of varying complexities. These outcomes help researchers and designers in setting up future driving studies.

The impact of a new mid-range aircraft with advanced technologies on air traffic emissions and climate

Scipedia content — Mon, 08 Feb 2021 10:05:54 +0100

viation is currently undoubtedly facing the deepest crisis ever. However, the industry is expected to return to its long-term growth trend with or without a certain offset from its historic trend line. Thus, the greening of air transport remains an important challenge, and technological and operational solutions need to be found as soon as possible. This includes the development of aircraft which make use of advanced technologies with improved environmental performance, as investigated in DLR’s project ATLAs. In this study we present research results on emission changes of a new advanced technology mid-range aircraft on fleet and global level and the corresponding implications on climate. The technologies under investigation are CO2-managed cabin, hybrid laminar flow control as well as functional-driven moveables for load alleviation. The approach combines the calculation of emission inventories for various technology combinations with an established climate response model. The results indicate that the implementation of the three mentioned technologies in a new mid-range aircraft with an expected Entry into Sevice in 2028 has the potential to reduce the fuel consumption in a representative airline sub-fleet by up to 7% and to reduce NOx emissions by even up to 12%, depending on how the technologies are combined. As a consequence, the climate impact can be reduced by up to 7.7%, taking the effects from CO2, H2 O, NOx and contrail cirrus into account.

Determining Infrastructure- and Traffic Factors that Increase the Perceived Complexity of Driving Situations

Scipedia content — Mon, 08 Feb 2021 09:48:51 +0100

Estudio de la gestión de la movilidad en grandes ciudades y análisis de los sistemas actuales basados en movilidad compartida

Scipedia content — Sun, 07 Feb 2021 13:48:12 +0100

[ES] El trabajo analiza la movilidad en grandes ciudades. Se estudian diferentes medios de transporte y se abordan las medidas adoptadas sobre movilidad compartida, así como cuál ha sido su impacto en cada caso. Se identifican también aquellas características que aportan valor al cliente. Las conclusiones sirven de base para el diseño de una aplicación que permita gestionar los diferentes medios de transporte de movilidad compartida, que sea de fácil uso y reúna las buenas prácticas de empresas que proporcionan servicios de movilidad. [EN] Nowadays, there are many mobility providers available on the market, like for example bike sharing apps, car sharing apps or even the so old and famous taxi cab. This makes people’s life easier and more connected, as now, travelling from one place to another can be easier, less time consuming and more economical than in the past few years. Social life can be enhanced this way, although not everything can be positive when regarding the pollution that transportation generates. As time passes, more and more people are being aware of this issue, so there is a need for sustainable mobility to develop in big cities in the form of car sharing and the increased use of public transport. Car sharing is something that has been going on for many years, not 10 or 20 years ago, but more than 60 years ago, car sharing was starting to get popular in the united states. Not everyone had the privilege of owning a car, and those who owned a car did share the car with, for example workmates and shared the cost of it. Car sharing became more important in the US with the oil crisis, this is were the government incentivised carpooling and started applying measures for the purpose. Nowadays, the problem is not an oil crisis, but immense traffic jams, and air pollution in cities, which translate to a waste of time and health hazards for those living inside those big cities. Governments have realised, that in a long term its even more expensive to treat people with health issues related to bad air quality than to eliminate the pollution that causes them. Therefore recently, governments are making big efforts to improve the air quality, so therefore improve people’s lives. Recent studies determined that for the last 10 years, the tendency for young people is to delay the purchase of a car and use shared mobility, although this kind of new method to travel hasn’t been fully accepted by people over a certain age. The challenge today is to make as much people as possible to acknowledge the benefits of shared mobility, not only regarding to carbon emissions, but also the benefits regarding reduced costs and time of travel. Shared mobility is something that everyone will have present in a few years, nowadays shared mobility is just an option, like for example, mobile phones on the 90’s, but currently, mobile phones are a need, as our daily life relies entirely on them. With shared mobility, something similar may happen, as with time, the need to travel is getting more and more important, so it is essential to give customers what they really want and give our world what it really needs. Lledó Jannone, P. (2019). Estudio de la gestión de la movilidad en grandes ciudades y análisis de los sistemas actuales basados en movilidad compartida. http://hdl.handle.net/10251/144239 TFGM

Análisis de la sostenibilidad de la electromovilidad para la planificación de rutas de técnicos de servicios

Scipedia content — Sun, 07 Feb 2021 13:48:00 +0100

[ES] La disponibilidad creciente de vehículos eléctricos en el mercado propone una nueva alternativa a propietarios de flotas que necesitan adquirir nuevos vehículos, y existe una necesidad de herramientas que apoyen la decisión de electrificar dichas flotas. En el contexto de la planificación de rutas de técnicos en un entorno urbano, muchos parámetros que tienen un impacto en el consumo de energía de los vehículos eléctricos cambian a diario. Por ello, se propone un modelo de simulación basado en agentes para generar diferentes escenarios que puedan proporcionar un enfoque más realista para el cálculo de los costes operacionales de varios grados de implementación de vehículos eléctricos, así como su huella medioambiental en términos de emisiones de CO2. El modelo de simulación propuesto integra una heurística ALNS para resolver el problema operacional de la planificación de rutas de técnicos. Además, el modelo de consumo de energía propuesto incluye un componente auxiliar debido a calefacción o aire acondicionado que no había sido considerado previamente en muchos estudios de optimización. Los resultados obtenidos en un caso proporcionado por una empresa demuestran que la introducción de electromovilidad en la logística urbana ofrece considerables beneficios económicos y medioambientales, aunque quizá el ahorro en costes operacionales no sea suficiente cuando es comparado con la gran diferencia en costes de adquisición. [EN] The growing availability of electric vehicles (EVs) in the market brings a new alternative to fleet managers that need to purchase new vehicles, and there is a need for tools to assess the decision of fleet electrification. In the context of the technician routing in an urban setting, many parameters that have an impact on the energy consumption of EVs change on a daily basis. Because of this, we propose an agent-based simulation model to generate different scenarios that can provide a more realistic approach to the computation of the operational costs of various degrees of EV implementation, as well as the environmental footprint of those fleets in terms of CO2 emissions. Our simulation model integrates an adaptive large neighborhood search heuristic to solve the operational technician routing problem. In addition, our energy consumption model includes an auxiliary component due to heating or air conditioning, which had not been included in many optimization studies. Our results on a case study show that the introduction of electro-mobility in urban logistics offers substantial economic and environmental benefits, although operational cost savings may not be enough when compared to the high difference in acquisition costs. Ramírez Martí, D. (2019). Análisis de la sostenibilidad de la electromovilidad para la planificación de rutas de técnicos de servicios. http://hdl.handle.net/10251/142245 TFGM

Mechanisms for outsourcing computation via a decentralized market

Scipedia content — Wed, 03 Feb 2021 21:57:27 +0100

the number of personal computing and IoT devices grows rapidly, so does the amount of computational power that is available at the edge. Since many of these devices are often idle, there is a vast amount of computational power that is currently untapped, and which could be used for outsourcing computation. Existing solutions for harnessing this power, such as volunteer computing (e.g., BOINC), are centralized platforms in which a single organization or company can control participation and pricing. By contrast, an open market of computational resources, where resource owners and resource users trade directly with each other, could lead to greater participation and more competitive pricing. To provide an open market, we introduce MODiCuM, a decentralized system for outsourcing computation. MODiCuM deters participants from misbehaving-which is a key problem in decentralized systems-by resolving disputes via dedicated mediators and by imposing enforceable fines. However, unlike other decentralized outsourcing solutions, MODiCuM minimizes computational overhead since it does not require global trust in mediation results. We provide analytical results proving that MODiCuM can deter misbehavior, and we evaluate the overhead of MODiCuM using experimental results based on an implementation of our platform.

Decentralizing Air Traffic Flow Management with Blockchain-based Reinforcement Learning

Scipedia content — Wed, 03 Feb 2021 21:48:50 +0100

We propose and implement a decentralized, intelligent air traffic flow management (ATFM) solution to improve the efficiency of air transportation in the ASEAN region as a whole. Our system, named BlockAgent, leverages the inherent synergy between multi-agent reinforcement learning (RL) for air traffic flow optimization; and the rising blockchain technology for a secure, transparent and decentralized coordination platform. As a result, BlockAgent does not require a centralized authority for effective ATFM operations. We have implemented several novel distributed coordination approaches for RL in BlockAgent. Empirical experiments with real air traffic data concerning regional airports have demonstrated the feasibility and effectiveness of our approach. To the best of our knowledge, this is the first work that considers blockchain-based, distributed RL for ATFM.

Near-Optimal Reactive Synthesis Incorporating Runtime Information

Scipedia content — Wed, 03 Feb 2021 21:45:29 +0100

We consider the problem of optimal reactive synthesis - compute a strategy that satisfies a mission specification in a dynamic environment, and optimizes a performance metric. We incorporate task-critical information, that is only available at runtime, into the strategy synthesis in order to improve performance. Existing approaches to utilising such time-varying information require online re-synthesis, which is not computationally feasible in real-time applications. In this paper, we pre-synthesize a set of strategies corresponding to candidate instantiations (pre-specified representative information scenarios). We then propose a novel switching mechanism to dynamically switch between the strategies at runtime while guaranteeing all safety and liveness goals are met. We also characterize bounds on the performance suboptimality. We demonstrate our approach on two examples - robotic motion planning where the likelihood of the position of the robot's goal is updated in real-time, and an air traffic management problem for urban air mobility.

Comment: Presented at ICRA2020

Urban Factories – Establishing resource-efficiency in production logistics systems in cities

Scipedia content — Wed, 03 Feb 2021 21:39:35 +0100

Cities are a hotspot for resource consumption and related impacts. This is induced, among others, by transportation, production and the use of products and services. Industrial production is commonly associated with negative impacts, e.g. on the environment or traffic. Through positive integration of production sites into urban surroundings, negative impacts can be eliminated, and even positive impacts achieved. To reach a higher degree of integration of different utilizations in cities, resource-efficiency, new conceptual approaches are required for urban factories, city authorities and further stakeholders. For this purpose, a methodology has been developed that describes the planning processes of the involved disciplines and their interdependencies concerning content and timing. Subsequently, an analysis of Urban Factories within a reference framework called the factory-city-system and its key resources is carried out in an exemplary case study. Measures to enhance resource-efficiency are thus dentified, exemplarily described and examined regarding their potential to raise resource-efficiency.

Supervised Machine Learning Model to Help Controllers Solving Aircraft Conflicts

Scipedia content — Wed, 03 Feb 2021 21:18:12 +0100

International audience; When two or more airplanes find themselves less than a minimum distance apart on their trajectory, it is called a conflict situation. To solve a conflict, air traffic controllers use various types of information and decide on actions pilots have to apply on the fly. With the increase of the air traffic, the controllers’ workload increases; making quick and accurate decisions is more and more complex for humans. Our research work aims at reducing the controllers’ workload and help them in making the most appropriate decisions. More specifically, our PhD goal is to develop a model that learns the best possible action(s) to solve aircraft conflicts based on past decisions or examples. As the first steps in this work, we present a Conflict Resolution Deep Neural Network (CR-DNN) model as well as the evaluation framework we will follow to evaluate our model and a data set we developed for evaluation.

Sustainable business model archetypes for the electric vehicle battery second use industry: towards a conceptual framework

Scipedia content — Wed, 03 Feb 2021 21:15:37 +0100

This paper explores sustainable business models (SBMs) evolution for the rapidly developing battery second use (B2U) market within the emerging electric vehicle (EV) industry. Previous work identified that SBMs and EV B2U are emerging as major research streams but there is paucity among literature to deliver an overarching framework or a holistic view between these fields and highlight fresh areas for future research. We adopted an inductive multiple-case study approach to unearth new knowledge by comprehending how B2U stakeholders undertake their sustainability-related business activities. These are not only focused on economic profitability but more importantly address wider social and environmental stakeholder value as part of prospective SBMs. The SBM archetypes were adopted as the major lens for our data analysis to study multiple cases of B2U stakeholder roles and comprehend further the scope and ultimate purpose of their operations. Major results indicate that the SBM archetypes as major sustainable innovation strategies have the potential to create a new conception of business models for sustainability in the EV B2U market. In turn, this creates and drives shared sustainable value for multiple stakeholders through cross-sectoral collaborations as part of an entire new and more SBMs. Finally, this study proposes the conceptual sustainable innovation business model (SIBM) framework for the EV B2U industry that includes such shared sustainable value creations which in turn drives forward business performance and sustainability at the same time, eventually creating the business case for sustainability within the EV industry.

Peer Reviewed

Document type: Article

Adaptive Strategies of Multi-Objective Optimization For Greener Networks

Scipedia content — Wed, 03 Feb 2021 21:10:41 +0100

Increasing energy costs and environmental issues related to the Internet and wired networks continue to be a major concern. Energy-efficient or power-aware networks continue to gain interest in the research community. Existing energy reduction approaches do not fully address all aspects of the problem. We consider the problem of reducing energy by turning off network links, while achieving acceptable load balance, by adjusting link weights. In this research, we optimize two objectives, which are minimizing network energy consumption by maximizing utilization of shortest paths, and at the same time achieving load-balance by minimizing network Maximum Link Utilization (MLU). Increasing utilization of shortest paths provides the opportunity to switch off nodes and links, thus saving network power. This research proposes a new approach that relies on live data collected from wired networks, and performs Multi Objective Optimization (MOO) using a Non-dominated Sorting Genetic Algorithm (NSGA-II) that applies alternative adaptive strategies in order to optimize both objectives. Research to date has focused on the link level or traffic load balance, to minimize energy consumption, while putting less focus on utilizing adaptive strategic techniques that optimize multi objectives problems. This work proposes a novel approach to select underutilized links to go to sleep using adaptive strategies of MOO that are aware of traffic changes. Re-computing the algorithm should take less than a minute, while network traffic is frequently updated every few minutes. The hybrid approach we proposed was able to reduce the power consumption by 35%, while reducing MLU by 31% for specific traffic pattern used in Abilene network topology.

Towards Transforming OpenFlow Rulesets to Fit Fixed-Function Pipelines

Scipedia content — Wed, 03 Feb 2021 20:53:14 +0100

Connectivity and resilience of remote operations: insights from air traffic management

Scipedia content — Wed, 03 Feb 2021 20:52:03 +0100

Multiform Logical Time & Space for Mobile Cyber-Physical System with Automated Driving Assistance System

Scipedia content — Wed, 03 Feb 2021 20:48:14 +0100

International audience; We study the use of Multiform Logical Time, as embodied in Esterel/SyncCharts and Clock Constraint Specification Language (CCSL), for the specification of assume-guarantee constraints providing safe driving rules related to time and space, in the context of Automated Driving Assistance Systems (ADAS). The main novelty lies in the use of logical clocks to represent the epochs of specific area encounters (when particular area trajectories just start overlapping for instance), thereby combining time and space constraints by CCSL to build safe driving rules specification. We propose the safe specification pattern at high-level that provide the required expressiveness for safe driving rules specification. In the pattern, multiform logical time provides the power of parameterization to express safe driving rules, before instantiation in further simulation contexts. We present an efficient way to irregularly update the constraints in the specification due to the context changes, where elements (other cars, road sections, traffic signs) may dynamically enter and exit the scene. In this way, we add constraints for the new elements and remove the constraints related to the disappearing elements rather than rebuild everything. The multi-lane highway scenario is used to illustrate how to irregularly and efficiently update the constraints in the specification while receiving a fresh scene.

Deep Learning for Pipeline Damage Detection: an Overview of the Concepts and a Survey of the State-of-the-Art

Scipedia content — Wed, 03 Feb 2021 20:47:14 +0100

Pipelines have been extensively implemented to transfer oil as well as gas products at wide distances as they are safe, and suitable. However, numerous sorts of damages may happen to the pipeline, for instance erosion, cracks, and dent. Hence, if these faults are not properly refit will result in the pipeline demolitions having leak or segregation which leads to tremendously environment risks. Deep learning methods aid operators to recognize the earliest phases of threats to the pipeline, supplying them time and information in order to handle the problem efficiently. This paper illustrates fundamental implications of deep learning comprising convolutional neural networks. Furthermore the usages of deep learning approaches for hampering pipeline detriment through the earliest diagnosis of threats are introduced.

FORESEE: Future proofing strategies FOr resilient transport networks against Extreme Events

Scipedia content — Wed, 03 Feb 2021 20:45:45 +0100

The overall objective of FORESEE, as an H2020 project inside the resilience to extreme events topic (natural, climate change and man-made), is to provide cost effective and reliable tools to improve the resilience of transport infrastructure networks, as the ability to reduce the probability of occurrence, magnitude and/or duration of possible disruptive events that may affect the security and/or the quality of the services provided by infrastructure operators. FORESEE will address through new innovative technologies, methodologies and resilient schemes the effectiveness of resilient measures to improve the ability to anticipate, absorb, adapt to, and/or rapidly recover from a potentially disruptive event."br

What drives the usage of shared transport services?: An impact analysis of supply and utilization of mobility services in German cities

Scipedia content — Wed, 03 Feb 2021 20:31:31 +0100

Shared mobility is widely discussed, yet only few travelers actually make use of shared services. Apart from personal characteristics, the supply and more specific the supply density of shared vehicles is assumed to be crucial for a widespread shared mobility usage. In this paper, we test this hypothesis. Moreover we provide insights into the impact of current mobility behavior on the usage intention for shared transport services. For this purpose, we combine existing transport usage data with the real supply of shared vehicles in selected cities in Germany. We investigate free-floating and station-based car- and bikesharing, free-floating e-scootersharing, as well as ridesharing. To do so, we collected data on the vehicles supplied per service for beginning of 2020. In a first step, we analyze group differences in terms of intended usage between people living in cities where the services are offered and those who live in cities without access to such services. This information is used in a second step when we analyze to what extent the supply density is driving usage intention for a specific trip purpose obtained from the first analysis step. Therefore, we apply logistic regression analyses that focus on socio-demographics, the users’ possession of mobility tools (e.g. driver’s license, car access, transit pass), their current transport behavior and the availability of services respectively.

Parallel Multi-Hypothesis Algorithm for Criticality Estimation in Traffic and Collision Avoidance

Scipedia content — Wed, 03 Feb 2021 20:24:50 +0100

Due to the current developments towards autonomous driving and vehicle active safety, there is an increasing necessity for algorithms that are able to perform complex criticality predictions in real-time. Being able to process multi-object traffic scenarios aids the implementation of a variety of automotive applications such as driver assistance systems for collision prevention and mitigation as well as fall-back systems for autonomous vehicles. We present a fully model-based algorithm with a parallelizable architecture. The proposed algorithm can evaluate the criticality of complex, multi-modal (vehicles and pedestrians) traffic scenarios by simulating millions of trajectory combinations and detecting collisions between objects. The algorithm is able to estimate upcoming criticality at very early stages, demonstrating its potential for vehicle safety-systems and autonomous driving applications. An implementation on an embedded system in a test vehicle proves in a prototypical manner the compatibility of the algorithm with the hardware possibilities of modern cars. For a complex traffic scenario with 11 dynamic objects, more than 86 million pose combinations are evaluated in 21 ms on the GPU of a Drive PX~2.

The need for data management plans to enable the resilience analysis of transport infrastructure systems

Scipedia content — Wed, 03 Feb 2021 20:23:55 +0100

To be helpful in developing recommendations to support the standardization of infrastructure resilience assessment, members of the FORESEE project have studied the data requirements of a case study through its lifecycle phases, and asset management perspectives. This paper introduces key results in these analysis, including concepts and objectives for infrastructure data management plans, to accomplish future resilience governance optimizations and enable the broad variety of assessment methods.

Towards more environmentally sustainable intercontinental freight transport

Scipedia content — Wed, 03 Feb 2021 20:02:43 +0100

In a world where the population and many economies are expanding rapidly the demand for freight transport keeps rising accordingly. As more goods are transported by a growing number of freight vehicles, in particular trucks and sea vessels, their already considerable negative environmental impact also rises. Technology advances, but demand growth (partially) counteracts its positive impact on fuel consumption and emissions. In road transport, CO2-emissions keep rising, while emissions of NOx and PM10 have been reduced, at least in those countries where the most advanced engine technologies are used, although locally serious problems may remain. In areas where such technologies are not available, more freight transport means higher emissions and negative health effects. Sea shipping sees increasing emission levels overall. Maritime transport and trucking dominate intercontinental freight transport. Modernisation of railways and roads offers opportunities to reduce emissions by using rail for part of the journey. In a market setting, this means that transport providers have to redesign transport chains. Some have done this already, while others are increasingly interested. To assess the potential, the following main research question was addressed: Is it possible to reduce emissions of CO2, NOx and PM10 by replacing the maritime leg of a transport service by road and/or rail transport in the corridor Antwerp (Belgium) – Shanghai (China) without logistic penalties? Various combinations of trucking, sea and rail transport were fed into a simulation model to estimate the accompanying emissions and trip times. The"br/"new services offer a complex range of positive and negative impacts; hence governments should carefully consider their support. In a simulation study only a very stylised representation of these services can be modelled. This leads to an advice for a more in-depth study to include additional (technical, service and cost) data.

Description of Non-Intrusive Sonar Array-Based Technology and its Application to Unique and Difficult Slurry and Paste Flow Measurements

Scipedia content — Wed, 03 Feb 2021 19:51:27 +0100

In this presentation, CiDRA’s patented technology platform and its applications will be described. CiDRA's non-invasive, passive sonar array-based flow meter technology performs two independent measurements – flow rate and fluid characterization. Firstly, the meter provides the volumetric flow rate of the mixture by measuring the speed at which naturally occurring structures such as turbulent eddies or density variations convect with the flow past an axial array of sensors. Secondly, the meter uses similar sonar-based processing techniques and naturally occurring sound in the process slurry to measure entrained air levels and in some cases fluid composition. The result is a unique ability to measure the flow rate and entrained air level of most fluids – clean liquids, high solids content slurries, pastes, and liquids and slurries with entrained air. Also to be presented is the application of this array-based technology platform in a variety of hydrotransport and minerals beneficiation applications. Examples of these situations include volume flow measurements in tailings lines, thickener discharge, high solids contents pipelines, slurry lines with magnetite and other magnetic ore, slurry lines with abrasive or corrosive materials, high pressure lines, and slurry and nonslurry lines exhibiting scale buildup. The operational advantages and value of these measurements, even in the presence of scale buildup, will be discussed. Recent developments in extending this technology to solve other unique measurement problems such as valve movement confirmation, non-invasive slurry profiling, and sanding detection will be covered.

Faster and More Accurate Measurement through Additive-Error Counters

Scipedia content — Wed, 03 Feb 2021 19:36:17 +0100

Counters are a fundamental building block for networking applications such as load balancing, traffic engineering, and intrusion detection, which require estimating flow sizes and identifying heavy hitter flows. Existing works suggest replacing counters with shorter multiplicative error \\emph{estimators} that improve the accuracy by fitting more of them within a given space. However, such estimators impose a computational overhead that degrades the measurement throughput. Instead, we propose \\emph{additive} error estimators, which are simpler, faster, and more accurate when used for network measurement. Our solution is rigorously analyzed and empirically evaluated against several other measurement algorithms on real Internet traces. For a given error target, we improve the speed of the uncompressed solutions by $5\\times$-$30\\times$, and the space by up to $4\\times$. Compared with existing state-of-the-art estimators, our solution is $ 9\\times$-$35\\times$ faster while being considerably more accurate.

Comment: To appear in IEEE INFOCOM 2020

Challenges of compressing hydrogen for pipeline transportation with centrifugal-compressors

Scipedia content — Wed, 03 Feb 2021 18:52:16 +0100

Stop & Go a Cooperative Maneuver for Automated Vehicles Based on Virtual and Real Environment

Scipedia content — Wed, 03 Feb 2021 18:42:28 +0100

[Resumen] La implementación de maniobras cooperativas entre vehículos automatizados es una necesidad dentro del progreso de los Sistemas Avanzado de Asistencia al Conductor (ADAS). Sin embargo, el desarrollo de estas estrategias en vehículos reales depende de la disponibilidad de un mínimo de plataformas experimentales, que involucran elevados costos y tiempos de pruebas. En este sentido, el presente trabajo presenta una herramienta para el diseño de la maniobra cooperativa Stop & Go, haciendo uso de un entorno virtual para la simulación de un vehículo líder, junto con un vehículo eléctrico automatizado que realiza el seguimiento dentro de un circuito cerrado. Para el diseño de la maniobra se establecerá comunicación V2V entre ambas plataformas, las cuales ejecutan una arquitectura general de conducción automatizada. El algoritmo de seguimiento está basado en un controlador de lógica difusa dependiente de la velocidad del vehículo líder y la distancia entre ambos coches. Los resultados demuestran la utilidad de combinar ambos entornos de prueba para la validación de maniobras cooperativas reduciendo el costo y el tiempo en comparación con pruebas reales [Abstract] The implementation of cooperative maneuvers between automated vehicles is a necessary for the improvement of the Advanced Driver Assistence Systems (ADAS). However, the development of these strategies in real vehicles depends on the availability of experimentals platforms, which involves high costs and a lot of testing time. In this line of thought, the present work shows a tool for the design of the Stop & Go cooperative maneuver, making use of a virtual environment for the simulation of a leading vehicle, along with an automated electric vehicle that performs the tracking within a closed circuit. For the design of the maneuver, a V2V communication system bet- ween the two platforms will be established, bearing in mind that they execute an automated driving general arhcitecture. The tracking algorithm is based on a fuzzy logic controller, dependent on the leading vehicle speed and the distance between the two vehicles. The results show the usefulness of combining the two test environments for the validation of the cooperative maneuver, reducing the cost and the time in comparison with the real test environment.

Simultaneous Stabilization of Traffic Flow on Two Connected Roads

Scipedia content — Wed, 03 Feb 2021 18:39:31 +0100

International audience; In this paper we develop a boundary state feedback control law for a cascaded traffic flow network system: one incoming and one outgoing road connected by a junction. The macroscopic traffic dynamics on each road segment are governed by Aw-Rascle-Zhang (ARZ) model, consisting of second-order nonlinear partial differential equations (PDEs) for traffic density and velocity. Different equilibrium road conditions are considered for the two segments. For stabilization of stop-and-go traffic congestion on the two roads, we consider a ramp metering located at the connecting junction. The traffic flow rate entering from the on-ramp to the mainline junction is actuated. The objective is to simultaneously stabilize the upstream and downstream traffic to given spatially-uniform constant steady states. We design a full state feedback control law for this under-actuated network of two systems of two hetero-directional linear first-order hyperbolic PDEs interconnected through the junction boundary. Exponential Convergence to steady states in L 2 sense is validated by a numerical simulation.

Ant Colony Systems for Optimizing Sequences of Airspace Partitions

Scipedia content — Wed, 03 Feb 2021 18:38:01 +0100

International audience; In this paper, we introduce an Ant Colony System algorithm which finds optimal or near-optimal sequences of airspace partitions, taking into account some constraints on the transitions between two successive airspace configurations. The transitions should be simple enough to allow air traffic controllers to maintain their situation awareness during the airspace configuration changes. For the same reason, once a sector is opened it should remain so for a minimum duration. The Ant Colony System (ACS) finds a sequence of airspace configurations minimizing a cost related to the workload and the usage of manpower resources, while satisfying the transition constraints. This approach shows good results in a limited time when compared with a previously proposed $A$ * algorithm on some instances from the french air traffic control center of Aix (East qualification zone) where the $A$ * algorithm exhibited high computation times.00

Optimizing Geometry Compression using Quantum Annealing

Scipedia content — Wed, 03 Feb 2021 18:30:55 +0100

The compression of geometry data is an important aspect of bandwidth-efficient data transfer for distributed 3d computer vision applications. We propose a quantum-enabled lossy 3d point cloud compression pipeline based on the constructive solid geometry (CSG) model representation. Key parts of the pipeline are mapped to NP-complete problems for which an efficient Ising formulation suitable for the execution on a Quantum Annealer exists. We describe existing Ising formulations for the maximum clique search problem and the smallest exact cover problem, both of which are important building blocks of the proposed compression pipeline. Additionally, we discuss the properties of the overall pipeline regarding result optimality and described Ising formulations.

Comment: 6 pages, 3 figure

A Distributed Metaheuristic Approach for Complexity Reduction in Air Traffic for Strategic 4D Trajectory Optimization

Scipedia content — Wed, 03 Feb 2021 18:29:57 +0100

International audience; This paper presents a new challenge on the strategic 4D trajectory optimization problem with the evaluation of air traffic complexity by using the geometric-based intrinsic complexity measure called König metric. The demonstration of König metric shows the potential that the algorithm can capture the disorganized the disorganized traffic which represents the difficulty of maintaining situational awareness as expected by the air traffic controller. We reformulate the optimization problem with two trajectory separation approaches including delaying flight departure time and allocating the new flight level subject to limited delay time of departure, limited changes of flight levels and fuel consumption constraints. We propose our solution to solve daily traffic demands in the regional French airspace. The resolution process uses the distributed metaheuristic algorithm to optimize aircraft trajectories in 4D environment with the objective of finding the optimal air traffic complexity. The experimental results shows the reduction of maximum complexity more than 95 % with average delay of 2.69 minutes. The optimized trajectories can save fuel more than 80000 kg. The proposed algorithm not only reduces the air traffic complexity but also maintain its distribution in traffic. The research results represent further steps towards taking other trajectory separations methods and aircraft trajectory uncertainties into account, developing our approach at the continental scale as well as adapting it in the pre-tactical and tactical planning phase.

A Morphable Face Albedo Model

Scipedia content — Wed, 03 Feb 2021 18:10:00 +0100

In this paper, we bring together two divergent strands of research: photometric face capture and statistical 3D face appearance modelling. We propose a novel lightstage capture and processing pipeline for acquiring ear-to-ear, truly intrinsic diffuse and specular albedo maps that fully factor out the effects of illumination, camera and geometry. Using this pipeline, we capture a dataset of 50 scans and combine them with the only existing publicly available albedo dataset (3DRFE) of 23 scans. This allows us to build the first morphable face albedo model. We believe this is the first statistical analysis of the variability of facial specular albedo maps. This model can be used as a plug in replacement for the texture model of the Basel Face Model (BFM) or FLAME and we make the model publicly available. We ensure careful spectral calibration such that our model is built in a linear sRGB space, suitable for inverse rendering of images taken by typical cameras. We demonstrate our model in a state of the art analysis-by-synthesis 3DMM fitting pipeline, are the first to integrate specular map estimation and outperform the BFM in albedo reconstruction.

Comment: CVPR 2020

The autonomous vehicle for urban collective transport: disrupting business models embedded in the smart city revolution

Scipedia content — Wed, 03 Feb 2021 18:02:17 +0100

The complexity of the automotive sector has grown dramatically in the last years. We are probably witnessing an automobile revolution, combining technology disruptions as well as regulation’s changes which pave the way for a new robomobility. Accordingly, new economic models are about to connect the fourfold product-service-structure-market leading to a responsible and sustainable mobility in connection with the development of smart cities. In this paper, we aim at characterizing the changes that are occurring and try to anticipate which players are about to lead the transition towards the new paradigm of robomobility.

BugDoc: Algorithms to Debug Computational Processes

Scipedia content — Wed, 03 Feb 2021 18:01:38 +0100

Data analysis for scientific experiments and enterprises, large-scale simulations, and machine learning tasks all entail the use of complex computational pipelines to reach quantitative and qualitative conclusions. If some of the activities in a pipeline produce erroneous outputs, the pipeline may fail to execute or produce incorrect results. Inferring the root cause(s) of such failures is challenging, usually requiring time and much human thought, while still being error-prone. We propose a new approach that makes use of iteration and provenance to automatically infer the root causes and derive succinct explanations of failures. Through a detailed experimental evaluation, we assess the cost, precision, and recall of our approach compared to the state of the art. Our experimental data and processing software is available for use, reproducibility, and enhancement.

Comment: To appear in SIGMOD 2020. arXiv admin note: text overlap with arXiv:2002.04640

A Burst and Congestion-Aware Routing Metric for RPL Protocol in IoT Network

Scipedia content — Wed, 03 Feb 2021 17:43:13 +0100

The packet loss and power consumption are the main issues considered once congestion occurs in any network, such as the Internet of Things (IoT) with a huge number of sensors and applications. Since IPv6 Routing Protocol for Low Power and Lossy Networks (RPL) is not initially designed for high stream traffic load, this restricts the application domain of RPL in several IoT scenarios such as burst traffic scenarios. The performance of RPL suffers in a network with burst traffic load, which leads to reducing the lifetime of the network and causing traffic congestion among the neighbour nodes. Therefore, to address this issue, we proposed a Burst and Congestion-Aware Metric for RPL called BCA-RPL, which calculates the rank, considering the number of packets. Also, the proposed mechanism includes congestion avoiding and load balancing techniques by switching the best parent selection to avoid the congested area. Our scheme is built and compared to the original RPL routing protocol for low power and lossy network with OF0 (OF0-RPL). Simulation results based on Cooja simulator shows BCA-RPL performs better than the original RPL-OF0 routing protocol in terms of packet loss, power consumption and packet delivery ratio (PDR) under burst traffic load. The BCA-RPL significantly improves the network where it decreases the packet loss around 50% and power consumption to an acceptable level with an improvement on the PDR of the IoT network.

A novel cost-effective Pressure Sensor based Smart Car park system

Scipedia content — Wed, 03 Feb 2021 17:37:54 +0100

With the increase in number of people using vehicles for transportation since last decade, traffic congestion is a major problem that requires to be solved effectively. Smart car park system is considered as one of the strategic solutions to this problem, which involves use of sensors to collect data. This paper proposes a novel low-cost smart car monitoring system to detect number of incoming and outgoing cars in and/or out of the car park using pressure sensors. This system also provides the data for the number of spaces available in the car park. Additionally, the paper also demonstrates the algorithm used to process the data obtained by the sensors to use it as a useful information.

Online Calibration of Microscopic Road Traffic Simulator

Scipedia content — Wed, 03 Feb 2021 17:22:32 +0100

Microscopic road traffic simulator is a powerful tool to analyze and evaluate various transportation systems due to its efficiency and risk-free operation. It is, therefore, widely used in traffic engineering field along with the gradual implementation of novel intelligent transportation systems. A reliable microscopic traffic simulator is able to accurately represent the real-world traffic situation when it is effectively calibrated with the combination of field data and proper simulation settings. Based on the existing theoretical calibration framework for the microscopic traffic simulator, this paper proposes an online calibration procedure using genetic algorithm as well as a specific implementation method to provide real-time performance measures that adequately mimic the field traffic situation. The proposed method was tested based on loop detector data demonstrating that real-time traffic modeling can be run in parallel with the real-world traffic process.

A Deeply Pipelined, Highly Parallel and Flexible LDPC Decoder

Scipedia content — Wed, 03 Feb 2021 17:18:57 +0100

International audience; A deeply pipelined and parallel LDPC decoder architecture is proposed in this paper. The main feature of this architecture is the ∆-update scheme, which relaxes the data dependency requirement and allows for deeper pipelines than typical decoders. The proposed architecture also has the flexibility to handle a large number of codes. Frame error rate performance is shown for three codes with different quantization parameters. Finally, the impact of pipeline depth on processing time and on the energy-delay product (EDP) is evaluated from post-synthesis results. The results show that the ability to have deeper pipelines can lead to large reductions in EDP.

Red Alert: a cognitive countermeasure to mitigate attentional tunneling

Scipedia content — Wed, 03 Feb 2021 17:17:40 +0100

International audience; Attentional tunneling, that is the inability to detect unexpected changes in the environment, has been shown to have critical consequences in air traffic control. The motivation of this study was to assess the design of a cognitive countermeasure dedicated to mitigate such failure of attention. The Red Alert cognitive countermeasure relies on a brief orange-red flash (300 ms) that masks the entire screen with a 15% opacity. Twenty-two air traffic controllers faced two demanding scenarios, with or without the cognitive countermeasure. The volunteers were not told about the Red Alert so as to assess the intuitiveness of the design without prior knowledge. Behavioral results indicated that the cognitive countermeasure reduced reaction time and improved the detection of the notification when compared to the classical operational design. Further analyses showed this effect was even stronger for half of our participants (91.7% detection rate) who intuitively understood the purpose of this design.

MIMIC-Extract

Scipedia content — Wed, 03 Feb 2021 17:11:14 +0100

Robust machine learning relies on access to data that can be used with standardized frameworks in important tasks and the ability to develop models whose performance can be reasonably reproduced. In machine learning for healthcare, the community faces reproducibility challenges due to a lack of publicly accessible data and a lack of standardized data processing frameworks. We present MIMIC-Extract, an open-source pipeline for transforming raw electronic health record (EHR) data for critical care patients contained in the publicly-available MIMIC-III database into dataframes that are directly usable in common machine learning pipelines. MIMIC-Extract addresses three primary challenges in making complex health records data accessible to the broader machine learning community. First, it provides standardized data processing functions, including unit conversion, outlier detection, and aggregating semantically equivalent features, thus accounting for duplication and reducing missingness. Second, it preserves the time series nature of clinical data and can be easily integrated into clinically actionable prediction tasks in machine learning for health. Finally, it is highly extensible so that other researchers with related questions can easily use the same pipeline. We demonstrate the utility of this pipeline by showcasing several benchmark tasks and baseline results.

Guideline to measure service provided by, and resilience of, transport infrastructure

Scipedia content — Wed, 03 Feb 2021 17:00:07 +0100

In order to optimally allocate resources to help ensure that transport infrastructure networks continue to provide acceptable levels of service immediately, or as fast as possible, following the occurrence of extreme events, the resilience of the infrastructure need to be estimated. In this paper, a guideline is presented, based on (Adey et al., 2019), that allows managers to measure the resilience of infrastructure networks. The guideline emphasizes that to this scope it is required to define clearly: (i) the transport system, and the way to consistently measure (ii) the service and (iii) the resilience. Particular attention is paid on the fact that resilience can be measured with various degrees of precision depending on the specific problem to be addressed, the time-frame at disposition and the expertise available. Guide are then given on how to do this either using simulation, indicators, or the percentage of fulfilment of the resilience indicators.

A Tele-Visit System for ACTIVAGE Project

Scipedia content — Wed, 03 Feb 2021 16:52:33 +0100

The document concerns the development of a remote communication system that allows a remote visit between older patients and healthcare professionals in situations that do not allow hospital transport.

Leveraging Personal Navigation Assistant Systems Using Automated Social Media Traffic Reporting

Scipedia content — Wed, 03 Feb 2021 16:37:51 +0100

Modern urbanization is demanding smarter technologies to improve a variety of applications in intelligent transportation systems to relieve the increasing amount of vehicular traffic congestion and incidents. Existing incident detection techniques are limited to the use of sensors in the transportation network and hang on human-inputs. Despite of its data abundance, social media is not well-exploited in such context. In this paper, we develop an automated traffic alert system based on Natural Language Processing (NLP) that filters this flood of information and extract important traffic-related bullets. To this end, we employ the fine-tuning Bidirectional Encoder Representations from Transformers (BERT) language embedding model to filter the related traffic information from social media. Then, we apply a question-answering model to extract necessary information characterizing the report event such as its exact location, occurrence time, and nature of the events. We demonstrate the adopted NLP approaches outperform other existing approach and, after effectively training them, we focus on real-world situation and show how the developed approach can, in real-time, extract traffic-related information and automatically convert them into alerts for navigation assistance applications such as navigation apps.

Comment: This paper is accepted for publication in IEEE Technology Engineering Management Society International Conference (TEMSCON'20), Metro Detroit, Michigan (USA)

DOPS: Learning to Detect 3D Objects and Predict Their 3D Shapes

Scipedia content — Wed, 03 Feb 2021 16:25:51 +0100

We propose DOPS, a fast single-stage 3D object detection method for LIDAR data. Previous methods often make domain-specific design decisions, for example projecting points into a bird-eye view image in autonomous driving scenarios. In contrast, we propose a general-purpose method that works on both indoor and outdoor scenes. The core novelty of our method is a fast, single-pass architecture that both detects objects in 3D and estimates their shapes. 3D bounding box parameters are estimated in one pass for every point, aggregated through graph convolutions, and fed into a branch of the network that predicts latent codes representing the shape of each detected object. The latent shape space and shape decoder are learned on a synthetic dataset and then used as supervision for the end-to-end training of the 3D object detection pipeline. Thus our model is able to extract shapes without access to ground-truth shape information in the target dataset. During experiments, we find that our proposed method achieves state-of-the-art results by ~5% on object detection in ScanNet scenes, and it gets top results by 3.4% in the Waymo Open Dataset, while reproducing the shapes of detected cars.

Comment: To appear in CVPR 2020

Modelling one-way electric carsharing in the city of Shanghai, China

Scipedia content — Wed, 03 Feb 2021 16:14:13 +0100

carsharing is developing rapidly worldwide, carsharing demand estimation becomes a more and more important issue, especially for an area that just introduces this service. Station-based one-way carsharing, as a new carsharing type, recently developed rapidly in China. Both policy-maker and operator want to know how the demand changes with increasing supply. To enrich understanding of these problems, this paper aims to make use of the muti-agent simulation tool (MATSim) to model and simulate one-way carsharing. The largest carsharing project in Shanghai, Evcard, is explicitly analyzed. Specifically, it intends to integrate the mobile phone GSM (Global System for Mobile Communications) data, point of interest data, network data and travel survey data to build a base simulation scenario with about 160,000 agents in the Jiading district, Shanghai. More data, for example the empirical data of operator, are used to calibrate the model. Some special functions, for example, the carsharing vehicles in simulation are pure battery electric vehicles, have been integrated into MATSim. Some preliminary results are presented and validated.

TensorFlow Data Validation: Data Analysis and Validation in Continuous ML Pipelines

Scipedia content — Wed, 03 Feb 2021 15:54:56 +0100

Facilitating Electric Vehicle Adoption with Vehicle Cost Calculators

Scipedia content — Wed, 03 Feb 2021 15:51:05 +0100

Consumer education regarding the costs of electric vehicles (EVs), particularly in comparison with similar gasoline vehicles, is important for adoption. However, the complexity of comparing gasoline and electricity prices, and balancing long-term return-on-investment from fuel and maintenance savings with purchase premiums for EVs, makes it difficult for consumers to assess potential economic advantages. Online vehicle cost calculators (VCCs) may help consumers navigate this complexity by providing tailored estimates of different types of vehicles costs for users and enabling comparisons across multiple vehicles. However, VCCs range widely and there has been virtually no behavioral research to identify functionalities and features that determine their usefulness in engaging and educating consumers and promoting EV adoption. This research draws on a behavioral theory, systematic review of available VCCs, and user research with three VCCs to articulate design recommendations for effective VCCs.View the NCST Project Webpage

Enabling automated driving by ICT infrastructure : a reference architecture

Scipedia content — Wed, 03 Feb 2021 15:29:55 +0100

Information and communication technology (ICT) is an enabler for establishing automated vehicles (AVs) in today's traffic systems. By providing complementary and/or redundant information via radio communication to the AV's perception by on-board sensors, higher levels of automated driving become more comfortable, safer, or even possible without interaction by the driver, especially in complex scenarios. Additionally, communication between vehicles and/or a central service can improve the efficiency of traffic flow. This paper presents a reference architecture for such an infrastructure-based support of AVs. The architecture combines innovative concepts and technologies from different technological fields like communication, IT environment and data flows, and cyber-security and privacy. Being the basis for the EU-funded project ICT4CART, exemplary implementations of this architecture will show its power for a variety of use cases on highways and in urban areas in test sites in Austria, Germany, and Italy, including cross-border interoperability.

Comment: Proceedings of 8th Transport Research Arena TRA 2020 (Conference cancelled), April 27-30, 2020, Helsinki, Finland

Data-driven Conflict Detection Enhancement in 3D Airspace with Machine Learning

Scipedia content — Tue, 02 Feb 2021 07:53:48 +0100

International audience; Trajectory prediction with Closest Point of Approach (CPA) concept is a fundamental element of aircraft Conflict Detection (CD) problem. Conventional motion-based CPA prediction model generally assumes that aircraft is flying in straight line with constant speed. But due to environment uncertainties and ground speed changes, this conventional method frequently lacks accuracy in the real world with a high rate of false alarms and missed detections. In this paper, we introduce a novel automated data-driven CD framework with Machine Learning (ML) for 3D CPA prediction in a lookahead time of less than 20 minutes. Firstly, a 3D CPA model with cylindrical norm is proposed as the baseline. Then, data preparation with Mode-S observation data in France is explained, including data collection and data processing, to convert raw Mode-S data to the close-to-reality dataset. Furthermore, feature engineering is applied to build up a feature set with 16 features. Finally, four prevailing ML models are used to predict the time, horizontal distance and vertical distance of CPA in 3D airspace. CD is conducted based on the predicted values. The prediction and CD results show that all proposed ML models outperform the baseline model. Especially, GBM and FFNNs could strongly enhance the performance of CD.

QEBA: Query-Efficient Boundary-Based Blackbox Attack

Scipedia content — Tue, 02 Feb 2021 07:29:18 +0100

Machine learning (ML), especially deep neural networks (DNNs) have been widely used in various applications, including several safety-critical ones (e.g. autonomous driving). As a result, recent research about adversarial examples has raised great concerns. Such adversarial attacks can be achieved by adding a small magnitude of perturbation to the input to mislead model prediction. While several whitebox attacks have demonstrated their effectiveness, which assume that the attackers have full access to the machine learning models; blackbox attacks are more realistic in practice. In this paper, we propose a Query-Efficient Boundary-based blackbox Attack (QEBA) based only on model's final prediction labels. We theoretically show why previous boundary-based attack with gradient estimation on the whole gradient space is not efficient in terms of query numbers, and provide optimality analysis for our dimension reduction-based gradient estimation. On the other hand, we conducted extensive experiments on ImageNet and CelebA datasets to evaluate QEBA. We show that compared with the state-of-the-art blackbox attacks, QEBA is able to use a smaller number of queries to achieve a lower magnitude of perturbation with 100% attack success rate. We also show case studies of attacks on real-world APIs including MEGVII Face++ and Microsoft Azure.

Comment: Accepted by CVPR 2020

Scalable Performance Awareness for In Situ Scientific Applications

Scipedia content — Tue, 02 Feb 2021 07:14:14 +0100

Part of the promise of exascale computing and the next generation of scientific simulation codes is the ability to bring together time and spatial scales that have traditionally been treated separately. This enables creating complex coupled simulations and in situ analysis pipelines, encompassing such things as "whole device" fusion models or the simulation of cities from sewers to rooftops. Unfortunately, the HPC analysis tools that have been built up over the preceding decades are ill suited to the debugging and performance analysis of such computational ensembles. In this paper, we present a new vision for performance measurement and understanding of HPC codes, MonitoringAnalytics (MONA). MONA is designed to be a flexible, high performance monitoring infrastructure that can perform monitoring analysis in place or in transit by embedding analytics and characterization directly into the data stream, without relying upon delivering all monitoring information to a central database for post-processing. It addresses the trade-offs between the prohibitively expensive capture of all performance characteristics and not capturing enough to detect the features of interest. We demonstrate several uses of MONA; capturing and indexing multi-executable performance profiles to enable later processing, extraction of performance primitives to enable the generation of customizable benchmarks and performance skeletons, and extracting communication and application behaviors to enable better control and placement for the current and future runs of the science ensemble. Relevant performance information based on a system for MONA built from ADIOS and SOSflow technologies is provided for DOE science applications and leadership machines.

The Ups and Downs of Paste Transport

Scipedia content — Tue, 02 Feb 2021 07:12:26 +0100

Mineral tailings pipelines have to traverse undulating terrain. Paste and high concentration tailings lines convey non-Newtonian slurries and usually contain coarse particles, i.e. " 20 m, that are conveyed as a burden. In presentations at previous Paste conferences papers have been presented that demonstrated that such flows, while appearing to behave homogenously in fact stratify and require higher transport pressure gradients and more care when conveying than their true homogenous paste counterparts, (Pullum and Graham 2000, Pullum 2003, Talmon and Mastbergen 2004). The flows are most readily described using a stratified model and a non-Newtonian version of this type of model has been shown to predict such flows quite well (Pullum et al., 2004). While suitable non-Newtonian models have been devised for transport in horizontal lines the effect of incline on such hybrid suspension flows is yet to be established. A new tilting pipeline rig has been constructed at CSIRO to investigate the behaviour of these complex suspensions and this paper describes this new test facility and reports on preliminary results obtained with a visco-plastic suspension, typical of many non-Newtonian co-disposal systems, e.g. (Houman 2003).

ADAM & EV: Developing an Adoption Dynamics Analysis Model for Electric Vehicles

Scipedia content — Tue, 02 Feb 2021 07:01:43 +0100

This work describes the development of an agent-based simulation that is an Adoption Dynamics Analysis Model for Electric Vehicles (ADAM & EV). ADAM & EV supports stakeholders in understanding the electric mobility landscape and the possible effects on policies. The model takes a consumer perspective by focusing in-depth on the consumer decision making process following the Theory of Planned Behaviour. In this work, we discuss the structure and interface of the model, and shortly discuss a few initial outcomes such as the consequence that stimulating BEVs increases emissions in the short-term when taking into account manufacturing emissions.

Towards Alleviating Traffic Congestion: Optimal Route Planning for Massive-Scale Trips

Scipedia content — Tue, 02 Feb 2021 06:56:18 +0100

We investigate the problem of optimal route planning for massive-scale trips: Given a traffic-aware road network and a set of trip queries Q, we aim to find a route for each trip such that the global travel time cost for all queries in Q is minimized. Our problem is designed for a range of applications such as traffic-flow management, route planning and congestion prevention in rush hours. The exact algorithm bears exponential time complexity and is computationally prohibitive for application scenarios in dynamic traffic networks. To address the challenge, we propose a greedy algorithm and an epsilon-refining algorithm. Extensive experiments offer insight into the accuracy and efficiency of our proposed algorithms.

5G Network Performance Experiments for Automated Car Functions

Scipedia content — Tue, 02 Feb 2021 06:22:43 +0100

This article discusses the results of supporting transition towards fully automated driving with remote operator support via the novel V2X channels. Automated passenger cars are equipped with multiple sensors (radars, cameras, LiDARs, inertia, GNSS, etc.), the operation of which is limited by weather, detection range, processing power and resolution. The study explores the use of a dedicated network for supporting automated driving needs. The MEC server latencies and bandwidths are compared between the Tampere, Finland test network and studies conducted in China to support remote passenger car operation. In China the main aim is to evaluate the network latencies in different communication planes, whereas the European focus is more on associated driving applications, thus making the two studies mutually complementary.5G revolutionizes connected driving, providing new avenues due to having lower and less latency variation and higher bandwidths. However, due to higher operating frequencies, network coverage is a challenge and one base station is limited to a few hundred meters and thus they deployed mainly to cities with a high population density. Therefore, the transport solutions are lacking so-called C-V2X (one form of 5G RAT) to enable data exchanges between vehicles (V2V) and also between vehicles and the digital infrastructure (V2I). The results of this study indicate that new edge-computing services do not cause a significant increase in latencies $(\\lt 100$ ms), but that latency variation (11 - 192 ms) remains a problem in the first new network configurations.

Network Adjustment: Channel Search Guided by FLOPs Utilization Ratio

Scipedia content — Tue, 02 Feb 2021 06:17:43 +0100

utomatic designing computationally efficient neural networks has received much attention in recent years. Existing approaches either utilize network pruning or leverage the network architecture search methods. This paper presents a new framework named network adjustment, which considers network accuracy as a function of FLOPs, so that under each network configuration, one can estimate the FLOPs utilization ratio (FUR) for each layer and use it to determine whether to increase or decrease the number of channels on the layer. Note that FUR, like the gradient of a non-linear function, is accurate only in a small neighborhood of the current network. Hence, we design an iterative mechanism so that the initial network undergoes a number of steps, each of which has a small 'adjusting rate' to control the changes to the network. The computational overhead of the entire search process is reasonable, i.e., comparable to that of re-training the final model from scratch. Experiments on standard image classification datasets and a wide range of base networks demonstrate the effectiveness of our approach, which consistently outperforms the pruning counterpart. The code is available at https://github.com/danczs/NetworkAdjustment.

Low-Overhead Near-Real-Time Flow Statistics Collection in SDN

Scipedia content — Tue, 02 Feb 2021 06:14:41 +0100

International audience; In Software-Defined Networking, near-real-time collection of flow-level statistics provided by OpenFlow (e.g. byte count) is needed for control and management applications like traffic engineering, heavy hitters detection, attack detection, etc. The practical way to do this near-real-time collection is a periodic collection at high frequency. However, periodic polling may generate a lot of overheads expressed by the number of OpenFlow request and reply messages on the control network. To handle these overheads, adaptive techniques based on the pull model were proposed. But we can do better by detaching from the classical OpenFlow request-reply model for the particular case of periodic statistics collection. In light of this, we propose a push and prediction based adaptive collection to handle efficiently periodic OpenFlow statistics collection while maintaining good accuracy. We utilize the Ryu Controller and Mininet to implement our solution and then we carry out intensive experiments using real-world traces. The results show that our proposed approach can reduce the number of pushed messages up to 75% compared to a fixed periodic collection with a very good accuracy represented by a collection error of less than 0.5%.

Automated Decision-making in Automated Driving: Striking a Balance between Individual Autonomy and General Road Safety

Scipedia content — Tue, 02 Feb 2021 06:14:23 +0100

In an attempt to increase road safety, car manufacturers turn their attention to the interior of the vehicle. If the driver falls asleep, or is intoxicated, this will be picked up by sensors and cameras inside the vehicle. If it is deemed unsafe for the driver to continue the trip, the vehicle will pull over and bring itself to a stop so as to prevent endangering other road users. This automated decision-making process is not only affecting the autonomy of the driver, it is also challenging law as it gives rise to many legal and ethical questions. When does the autonomy of the individual and its right to data protection weigh heavier than the public interest of road safety? This research aims to answer that question and fill the existing gap in legal literature.

DeepLine: AutoML Tool for Pipelines Generation using Deep Reinforcement Learning and Hierarchical Actions Filtering

Scipedia content — Tue, 02 Feb 2021 05:51:25 +0100

utomatic machine learning (AutoML) is an area of research aimed at automating machine learning (ML) activities that currently require human experts. One of the most challenging tasks in this field is the automatic generation of end-to-end ML pipelines: combining multiple types of ML algorithms into a single architecture used for end-to-end analysis of previously-unseen data. This task has two challenging aspects: the first is the need to explore a large search space of algorithms and pipeline architectures. The second challenge is the computational cost of training and evaluating multiple pipelines. In this study we present DeepLine, a reinforcement learning based approach for automatic pipeline generation. Our proposed approach utilizes an efficient representation of the search space and leverages past knowledge gained from previously-analyzed datasets to make the problem more tractable. Additionally, we propose a novel hierarchical-actions algorithm that serves as a plugin, mediating the environment-agent interaction in deep reinforcement learning problems. The plugin significantly speeds up the training process of our model. Evaluation on 56 datasets shows that DeepLine outperforms state-of-the-art approaches both in accuracy and in computational cost.

A Stochastic Theoretical Game Approach for Resource Allocation in Vehicular Fog Computing

Scipedia content — Tue, 02 Feb 2021 05:23:51 +0100

International audience; Mobile devices have usually limited capabilities in terms of computation power, battery lifetime, storage size and available bandwidth. Thus, to address these limitations and to continue supporting the ever-increasing application requirements, service providers use powerful servers in order to offer services through the cloud. However, due to latency and QoS limitations, cloud computing still does not solve all the problems of newly emerging mobile applications demands. Thus, a more recent development is to push the storage and processing capabilities to the edge of access network closer to end users, which introduce the new concept of fog computing. Fog computing is a decentralized computation framework which essentially extends cloud computing resources and services to the edge of access network [2].

HOG, LBP and SVM based Traffic Density Estimation at Intersection

Scipedia content — Tue, 02 Feb 2021 05:21:45 +0100

Increased amount of vehicular traffic on roads is a significant issue. High amount of vehicular traffic creates traffic congestion, unwanted delays, pollution, money loss, health issues, accidents, emergency vehicle passage and traffic violations that ends up in the decline in productivity. In peak hours, the issues become even worse. Traditional traffic management and control systems fail to tackle this problem. Currently, the traffic lights at intersections aren't adaptive and have fixed time delays. There's a necessity of an optimized and sensible control system which would enhance the efficiency of traffic flow. Smart traffic systems perform estimation of traffic density and create the traffic lights modification consistent with the quantity of traffic. We tend to propose an efficient way to estimate the traffic density on intersection using image processing and machine learning techniques in real time. The proposed methodology takes pictures of traffic at junction to estimate the traffic density. We use Histogram of Oriented Gradients (HOG), Local Binary Patterns (LBP) and Support Vector Machine (SVM) based approach for traffic density estimation. The strategy is computationally inexpensive and can run efficiently on raspberry pi board. Code is released at https://github.com/DevashishPrasad/Smart-Traffic-Junction.

Comment: paper accepted at IEEE PuneCon 2019

Mission-Aware Spatio-Temporal Deep Learning Model for UAS Instantaneous Density Prediction

Scipedia content — Tue, 02 Feb 2021 05:08:18 +0100

The number of daily sUAS operations in uncontrolled low altitude airspace is expected to reach into the millions in a few years. Therefore, UAS density prediction has become an emerging and challenging problem. In this paper, a deep learning-based UAS instantaneous density prediction model is presented. The model takes two types of data as input: 1) the historical density generated from the historical data, and 2) the future sUAS mission information. The architecture of our model contains four components: Historical Density Formulation module, UAS Mission Translation module, Mission Feature Extraction module, and Density Map Projection module. The training and testing data are generated by a python based simulator which is inspired by the multi-agent air traffic resource usage simulator (MATRUS) framework. The quality of prediction is measured by the correlation score and the Area Under the Receiver Operating Characteristics (AUROC) between the predicted value and simulated value. The experimental results demonstrate outstanding performance of the deep learning-based UAS density predictor. Compared to the baseline models, for simplified traffic scenario where no-fly zones and safe distance among sUASs are not considered, our model improves the prediction accuracy by more than 15.2% and its correlation score reaches 0.947. In a more realistic scenario, where the no-fly zone avoidance and the safe distance among sUASs are maintained using A* routing algorithm, our model can still achieve 0.823 correlation score. Meanwhile, the AUROC can reach 0.951 for the hot spot prediction.

Enhanced Demand and Capacity Balancing based on Alternative Trajectory Options and Traffic Volume Hotspot Detection

Scipedia content — Tue, 02 Feb 2021 05:03:16 +0100

Nowadays, regulations in Europe are applied at traffic volume (TV) level consisting in a reference location, i.e. a sector or an airport, and in some traffic flows, which act as directional traffic filters. This paper presents an enhanced demand and capacity balance (EDCB) formulation based on constrained capacities at traffic volume level. In addition, this approach considers alternative trajectories in order to capture the user driven preferences under the trajectory based operations scope. In fact, these alternative trajectories are assumed to be generated by the airspace users for those flights that cross regulated traffic volumes, where the demand is above the capacity. For every regulated trajectory the network manager requests two additional alternative trajectories to the airspace users, one for avoiding the regulated traffic volumes laterally and another for avoiding it vertically. This paper considers that the network manager allows more flexibility for the new alternative trajectories by removing restrictions in the Route Availability Document (RAD). All the regulated trajectories (and their alternatives) are considered together by the EDCB model in order to perform a centralised optimisation minimising the the cost deviation with respect to the initial traffic situation, considering fuel consumption, route charges and cost of delay. The EDCB model, based on Mixed-Integer Linear Programming (MILP), manages to balance the network applying ground delay, using alternative trajectories or both. A full day scenario over the ECAC area is simulated. The regulated traffic volumes are identified using historical data (based on 28th July of 2016) and the results show that the EDCB could reduce the minutes of delay by 70%. The cost of the regulations is reduced by 11.7%, due to the reduction of the delay, but also because of the savings in terms of fuel and route charges derived from alternative trajectories.

Peer Reviewed

Document type: Conference object

PointNet++ Grasping: Learning An End-to-end Spatial Grasp Generation Algorithm from Sparse Point Clouds

Scipedia content — Tue, 02 Feb 2021 04:31:51 +0100

Grasping for novel objects is important for robot manipulation in unstructured environments. Most of current works require a grasp sampling process to obtain grasp candidates, combined with local feature extractor using deep learning. This pipeline is time-costly, expecially when grasp points are sparse such as at the edge of a bowl. In this paper, we propose an end-to-end approach to directly predict the poses, categories and scores (qualities) of all the grasps. It takes the whole sparse point clouds as the input and requires no sampling or search process. Moreover, to generate training data of multi-object scene, we propose a fast multi-object grasp detection algorithm based on Ferrari Canny metrics. A single-object dataset (79 objects from YCB object set, 23.7k grasps) and a multi-object dataset (20k point clouds with annotations and masks) are generated. A PointNet++ based network combined with multi-mask loss is introduced to deal with different training points. The whole weight size of our network is only about 11.6M, which takes about 102ms for a whole prediction process using a GeForce 840M GPU. Our experiment shows our work get 71.43% success rate and 91.60% completion rate, which performs better than current state-of-art works.

Comment: Accepted at the International Conference on Robotics and Automation (ICRA) 2020

Discrete Event Simulation of Driver’s Routing Behavior Rule at a Road Intersection

Scipedia content — Tue, 02 Feb 2021 04:12:22 +0100

Several factors influence traffic congestion and overall traffic dynamics. Simulation modelling has been utilized to understand the traffic performance parameters during traffic congestions. This paper focuses on driver behavior of route selection by differentiating three distinguishable decisions, which are shortest distance routing, shortest time routing and less crowded road routing. This research generated 864 different scenarios to capture various traffic dynamics under collective driving behavior of route selection. Factors such as vehicle arrival rate, behaviors at system boundary and traffic light phasing were considered. The simulation results revealed that shortest time routing scenario offered the best solution considering all forms of interactions among the factors. Overall, this routing behavior reduces traffic wait time and total time (by 69.5% and 65.72%) compared to shortest distance routing.

On Urban Traffic Flow Benefits of Connected and Automated Vehicles

Scipedia content — Tue, 02 Feb 2021 04:06:23 +0100

utomated Vehicles are an integral part of Intelligent Transportation Systems (ITSs) and are expected to play a crucial role in the future mobility services. This paper investigates two classes of self-driving vehicles: (i) Level 4&5 Automated Vehicles (AVs) that rely solely on their on-board sensors for environmental perception tasks, and (ii) Connected and Automated Vehicles (CAVs), leveraging connectivity to further enhance perception via driving intention and sensor information sharing. Our investigation considers and quantifies the impact of each vehicle group in large urban road networks in Europe and in the USA. The key performance metrics are the traffic congestion, average speed and average trip time. Specifically, the numerical studies show that the traffic congestion can be reduced by up to a factor of four, while the average flow speeds of CAV group remains closer to the speed limits and can be up to 300% greater than the human-driven vehicles. Finally, traffic situations are also studied, indicating that even a small market penetration of CAVs will have a substantial net positive effect on the traffic flows.

Comment: Accepted to IEEE VTC-Spring 2020, Antwerp, Belgium

Research on the impact of high-speed railway operation on the lower culvert stability of municipal pipelines

Scipedia content — Tue, 02 Feb 2021 04:01:01 +0100

n urban natural gas high-pressure pipeline culvert is located below the subgrade of a high-speed railway passenger train ring line. Due to the loads generated by the operation of high-speed railway trains, the pipeline may be deformed or even damaged, causing safety accidents. Therefore, its research is of great significance. Based on the field survey and design data, FLAC3D was used to construct a high-simulation 3D mechanical model, and the deformation and stress state of the rock and soil around the lower natural gas pipeline culvert caused by high-speed railway train operation was calculated. The results show that after the operation of the high-speed railway train, the stress on the pipeline structure does not reach the ultimate strength, and the deformation of the pipeline culvert and surrounding rock and soil is small, which will not affect the use of natural gas pipeline culverts and the normal operation of high-speed railway lines. The research results provide references for the normal use of natural gas pipeline culverts and the safe operation of high-speed railways.

Experimental investigation on traffic congestion control and maintenance system using internet of things

Scipedia content — Tue, 02 Feb 2021 03:24:17 +0100

Photonic Physical Unclonable Functions: From the Concept to Fully Functional Device Operating in the Field

Scipedia content — Tue, 02 Feb 2021 02:39:44 +0100

The scope of this paper is to demonstrate a fully working and compact photonic Physical Unclonable Function (PUF) device capable of operating in real life scenarios as an authentication mechanism and random number generator. For this purpose, an extensive experimental investigation of a Polymer Optical Fiber (POF) and a diffuser as PUF tokens is performed and the most significant properties are evaluated using the proper mathematical tools. Two different software algorithms, the Random Binary Method (RBM) and Singular Value Decomposition (SVD), were tested for optimized key extraction and error correction codes have been incorporated for enhancing key reproducibility. By taking into consideration the limitations and overall performance derived by the experimental evaluation of the system, the designing details towards the implementation of a miniaturized, energy efficient and low-cost device are extensively discussed. The performance of the final device is thoroughly evaluated, demonstrating a long-term stability of 1 week, an operating temperature range of 50C, an exponentially large pool of unique Challenge-Response Pairs (CRPs), recovery after power failure and capability of generating NIST compliant true random numbers.

Performance Improvement of Hardware By Series Duplicating Data Buffer for High-level Synthesis

Scipedia content — Tue, 02 Feb 2021 02:37:26 +0100

To occupy appropriate the expanding market share of embedded image processing systems, it is important to quickly develop and launch a high-performance and low-power product onto the market tracking the short life cycle of recent products. To achieve high performance and low-power produce quickly, it is effective to develop the hardware module of high computational software processing using high-level synthesis technology automatically converting software to hardware. However, high-level synthesis cannot convert software not taking into hardware organization to the efficient hardware with high-performance and low-power. This paper proposes a software description method for high-level synthesis that replicates histograms in series and pipelines the pre and post processing across the histogram. The experimental result on the real machine demonstrates the effects of the proposed method.

The Optimal Deployment of Recharging Stations for Electric Vehicles Based on Mobility Flows and Electric Grid Specifications

Scipedia content — Tue, 02 Feb 2021 02:31:54 +0100

International audience; With the increasing interest in using electric vehicles (EVs) in future transportation systems, the need for deploying fast charging infrastructures becomes essential. In order to fulfil this need, it is important to anticipate EV future charging demands and requirements, and optimize their deployment. In this paper, we develop an optimization model to solve the problem of positioning fast-charging stations for EVs. The proposed model takes into account the different mobility flows and recharging demands as well as the constraints imposed by the available electric grid. In addition, the model considers the availability of alternative energy sources (i.e. photo-voltaic). For this purpose, we provide a mathematical formulation for the considered problem aiming at maximizing the covered recharging demand while respecting investment budget limits and the available capacities provided by the electric grid. Through a case study on Paris-Saclay area, we obtain the optimal locations for deploying EV charging stations as well as the number of chargers that need to be installed at each charging station. Results also highlight the benefits of integrating locally-produced photo-voltaic energy on EV recharging service.

Specific Air Traffic Management Cybersecurity Challenges

Scipedia content — Tue, 02 Feb 2021 02:23:55 +0100

The Automotive Take on Continuous Experimentation: A Multiple Case Study

Scipedia content — Tue, 02 Feb 2021 02:12:35 +0100

Recently, an increasingly growing number of companies is focusing on achieving self-driving systems towards SAE level 3 and higher. Such systems will have much more complex capabilities than today's advanced driver assistance systems (ADAS) like adaptive cruise control and lane-keeping assistance. For complex software systems in the Web-application domain, the logical successor for Continuous Integration and Deployment (CI/CD) is known as Continuous Experimentation (CE), where product owners jointly with engineers systematically run A/B experiments on possible new features to get quantifiable data about a feature's adoption from the users. While this methodology is increasingly adopted in software-intensive companies, our study is set out to explore advantages and challenges when applying CE during the development and roll-out of functionalities required for self-driving vehicles. This paper reports about the design and results from a multiple case study that was conducted at four companies including two automotive OEMs with a long history of developing vehicles, a Tier-1 supplier, and a start-up company within the area of automated driving systems. Unanimously, all expect higher quality and fast roll-out cycles to the fleet; as major challenges, however, safety concerns next to organizational structures are mentioned.