Abstract

International audience; Future autonomous vehicles and ADAS (Advanced Driver Assistance Systems) need real-time audio and video transmission together with control data traac (CDT). Audio/video stream delay analysis has been largely investigated in AVB (Audio Video Bridging) context, but not yet with the presence of the CDT in the new TSN context. In this paper we present a local delay analysis of AVB frames under hierarchical scheduling of credit-based shaping and time-aware shaping on TSN switches. We present the eeects of time aware shaping on AVB traac, how it changes the relative order of transmission of frames leading to bursts and worst case scenarios for lower priority streams. We also show that these bursts are upper-bounded by the Credit-Bases Shaper, hence the worst-case transmissions delay of a given stream is also upper-bounded. We present the analysis to compute the worst case delay for a frame, as well as the feasibility condition necessary for the analysis to be applied. Our methods (analysis and simulation) are applied to an automotive use case, which is deened within the Eurostars RETINA project, and where both control data traac and AVB traac must be guaranteed. CCS CONCEPTS • Computer systems organization →Embedded systems; • Networks →Network reliability;

Abstract

In this paper is presented the development of a project to extract, in real-time, information’s related with an Electric Vehicle (EV). This project was elaborated to extract data from an EV battery charging device developed at the University of Minho, and from an EV prototype, the VEECO (Veículo Eléctrico ECOlógico – Ecologic Electric Vehicle), developed in a cooperation project of ISEL (Lisbon Superior Institute of Engineering) and the Portuguese company VE. The main goal of this project consists in collecting and transmitting the extracted data to inform the EV driver about the performance and the real behavior of the EV. Thereby, it is created an open interface to manage, in real-time, the main data related with the EV, as the batteries SoC (State-of-Charge), the EV speed, and internal temperatures (like the temperatures of the batteries, motor and power electronics inverter), as well as to control the start and stop of the batteries charging process, and to optimize the charging program (to define the best algorithm to preserve the batteries lifespan). This interface also controls the discharging process of the batteries, in order to make possible to deliver back to the electrical power grid part of the stored energy in the batteries, which is defined by the concept Vehicle-to-Grid (V2G). In the paper are presented and described the two main parts of this work: the real-time information extraction system and the charging device. FEDER Funds - Operational Programme for Competitiveness Factors (COMPETE) Fundação para a Ciência e a Tecnologia (FCT) - PTDC/EEA-EEL/104569/2008, MITPT/EDAM-SMS/0030/2008.

Abstract

dvanced Driver Assistance Systems (ADAS) become standard and sometimes mandatory for vehicles, e.g. autonomous emergency braking. Future vehicles will include multiple ADAS that assist with safety-critical operations. For efficiency and effectiveness, these ADAS should share resources, information and functionalities. Additionally, ADAS performing safety-critical functionality require predictability for the execution of their processes. This paper presents the integration of a layer-based multi-sensor fusion and processing platform into a real-time system that also supports non-critical processes, i.e. a mixed-criticality system. A suitable system is selected, the behavior of the platform and its interfaces is described and tests are performed to validate the predictable behavior, by examining the difference in jitter and execution latency. The real-time layer-based platform is suitable for the development and testing of multiple integrated safety-critical ADAS. cop. 2015 IEEE.

Abstract

This paper proposes a methodology for leak detection and isolation (LDI) in pipelines based on data fusion from two approaches: a steady-state estimation and an Extended Kalman Filter (EKF). The proposed method considers only pressure head and flow rate measurements at the pipeline ends, which contain intrinsic sensor and process noise. The LDI system is tested in real-time by using an USB data acquisition device that is implemented in MATLAB environment. The effectiveness of the method is analyzed by considering: online detection, location as well as quantification of non-concurrent leaks at different positions. The leak estimation error average is less than 1% of the flow rate and less than 3% in the leakage position. Furthermore, the incorporation of a steady-state estimation shows that the solution of the LDI problem has improved significantly with respect to the one that only considers the EKF estimation. An experimental analysis was also performed on the effectiveness of the proposed approach for different sampling rates and for different leakage positions Peer Reviewed

Abstract

Effective ITS and traffic management purposes requires a complete and accurate information about current and predicted traffic states in the transport network. The current state-of-the-art in literature regarding traffic state estimation and prediction yields efforts which mostly focus on highways, which are not bluntly transferrable to an urban environment and do not maximize the utilization of all available traffic data."br" This paper describes the development and assessment of a data-driven traffic state estimation and prediction framework for application in an urban environment. It uses the intuitive relationship between past, current and future traffic states on neighboring links to train and improve estimation/prediction accuracy and fill the gaps on those links where no floating car data are available. Additionally, this framework is tested on the well-known Sioux Falls Scenario. When penetration rate of floating cars is 5%, on average 50% of the urban links are estimated within 5 km/h accuracy. For a prediction horizon of 5 minutes, it performs almost equal with a percentage of 49%.

Abstract

This paper presents the work done during the first year in the WP-E project ERAINT (Evaluation of the RPAS-ATM Interaction in Non-Segregated Airspace) that intends to evaluate by means of human-in-the-loop real-time simulations the interaction between a Remotely Piloted Aircraft System (RPAS) and the Air Traffic Management (ATM) when a Remotely Piloted Aircraft (RPA) is being operated in shared airspace. This interaction will be evaluated from three different perspectives. First, the separation management, its results are presented in this paper. Secondly, during the next year, the contingency management, also including loss of link situations and, lastly, the capacity impact of such operations in the overall ATM system. The used simulation infrastructure allows to simulate realistic exercises from both the RPAS Pilot-in-Command (PiC) and the Air Traffic Controller (ATCo) perspectives. Moreover, it permits to analyze the actual workload of the ATC and to evaluate several support tools and different RPAS levels of automation from the PiC and ATC sides. The simulation results and the usefulness of the support tools are presented for each selected concept of operations. Peer Reviewed

Abstract

The evolution of Advanced Driver Assistance Systems (ADASs) increasingly paves the way towards autonomous driving. The increase in sensor data to be processed as well as the complexity of processing algorithms demands more powerful processing platforms. Additionally, aspects like functional safety and platform security have to be taken into account. Algorithms should be re-usable to facilitate rapid development. Current solutions are very much focused on single sub-systems and incompatible with next generation processing platforms available for ADASs. In this paper we therefore propose a novel architecture for future ADAS development, taking into account the aforementioned aspects. We also outline tools to aid developers in dealing with the real-time and functional safety aspects of ADASs. The utilization of the presented architecture in the EU-funded project ADAS&ME is also described.

Abstract

The availability of functionality is a crucial aspect of mission- and safety-critical systems. This is for instance demonstrated by the pursuit to automate road transportation. Here, the driver is not obligated to be part of the control loop, thereby requiring the underlying system to remain operational even after a critical component failure. Advances in the field of mixed-criticality research have allowed to address this topic of fail-operational system behaviour more efficiently. For instance, general purpose computing platforms may relinquish the need for dedicated backup units, as their purpose can be redefined at runtime. Based on this, a deterministic and resource-efficient reconfiguration mechanism is developed, in order to address safety concerns with respect to availability in a generic manner. To find a configuration for this mechanism that can ensure all availability-related safety properties, a design-time method to automatically generate schedules for different modes of operations from declaratively defined requirements is established. To cope with the inherent computational complexity, heuristics are developed to effectively narrow the problem space. Subsequently, this method’s applicability and scalability are respectively evaluated qualitatively within an automotive case study and quantitatively by means of a tool performance analysis.

Abstract

technique capable of identifying electric vehicle (EV) mass in real-time has been a topic of research for several years due to the advantages it presents, such as the ability to dramatically improve range estimates, perform more effective torque vectoring for ABS/ESC, track delivery vehicle weight, etc.. Some crucial issues in mass identification impede an easy implementation of such an algorithm, however, and this work introduces a simple method to calculate EV mass on-the-fly using standard data available on most CAN buses and therefore without the need of additional sensors. The results presented here are achieved using an eight step technique suitable for accurate mass estimations during wide-open-throttle acceleration events. The algorithm’s instantaneous error is less than 10%, and converges to better than 3% absolute accuracy performance with subsequent measurements. A preliminary analysis of trips lacking hard acceleration presented in this paper show an inability to differentiate between loaded and unloaded conditions. Document type: Article