Abstract

The Internet of Things (IoT) is one of the key ingredients for the realization of Smart Cities. IoT devices are essential components of the Smart Cities infrastructure, as they can provide information collected from the environment through sensors or allow other systems to reach out and act on the world through actuators. IoT data collection, however, is not limited to sensors and machines, but to data from social networks, and the web. Social networks have a huge impact on the amount of data being produced daily, becoming an increasingly central and important data source. The exploitation of these data sources, combined with the growing popularity of mobile devices, can lead to the development of better solutions to improve people’s quality of life. This paper discusses how to take advantage of the benefits of mobile devices and the vast range of information sources and services, such as traffic conditions, and narrow, closed or conditioned roads data. The proposed system uses a real-time collection, organization, and transmission of traffic and road conditions data to provide efficient and accurate information to drivers. With the purpose of supporting and improving traffic data collection and distribution, an Android application was developed to collect information about extraordinary events that take place in a city, providing warnings and alternative routes to drivers and helping them to improve their time management. The developed solution also exploits the existing gaps in other applications, implementing a more specific solution for the Madeira Island traffic condition problems. info:eu-repo/semantics/acceptedVersion Document type: Part of book or chapter of book

Abstract

Vehicle automation is one of the major challenges of nowadays’ transport system and its goals are to achieve the ideal energy efficiency, the minimum environment impact and the highest safety rate. In this context, IoSense is the project which will deploy new capabilities (Sensors, Components and Systems) through several demonstrators, one of them called “SmaBility” (Smart Mobility scenarios). So, the intelligent perception and decision making for safer and autonomous driving are the main objectives of the SmaBility demonstrator focus on the “Automated parking”. Then this chapter firstly lists the capabilities in the design, modelling and simulation area of each partner (TECNALIA, IFAT and VIF) involved on the title “From Parking Assistance to Automated Parking” within the Smability. In a second stage, several simulations considering a Time-of-Flight (ToF) camera, as the main perception technology, are explained at both levels: Sensor (ToF) and System (Automated parking). In parking assistance scenario (system level), a ToF camera, similar to the previous one analysed at sensor level, is considered as substitute for ultrasonic range sensors. The expected advantages of using such camera include faster answer, better resolution and object recognition capabilities. Combining depth information with a vehicle geometry model and ego-information (position, speed, steering angle), it is possible to estimate distance to collision point and time to collision (TTC) with great accuracy. Finally, summary and conclusions are reported. H2020 | ECSEL-IA, 692480, IoSense

Abstract

In order to have a complete Architecture of the ACTIVAGE platform, the device domain constituted of smart sensor nodes, gateways, connectivity and its associated protocols must be also considered. Therefore, it is imperative to identify the different devices required to implement the uses cases considered in the different Deployment sites (DSes). A systematic analysis is performed on the device, gateway, cloud and applications domains constituting each of the nine DSes. This analysis is based on several registration forms in order to gather key elements of each domain. The original goal was to collect information concerning only the device domain. However during the first months of the project, this goal changed to also include the three other domains in order to get an overall, homogenised, and rich technical information view of each DS. The additional information includes, the DS topology, the applications foreseen, the security and privacy mechanism to be implemented in the overall system, the servers used and their locations, and other information required to facilitate, in Task 3.2, the Security and Privacy assessments. Furthermore, this document gives a summary and classification of the different devices used in each DS allowing the identification of communalities, potential synergies and knowledge sharing between DSs. For the cases where, no suitable solutions are available, new devices could be prototyped in order to support a given use case with the right device or the right security protection level. Finally and in order to have a more complete document, the following aspects are also included: a list of key concepts in the device and gateway domains are explicitly defined to be shared and used in the overall project, a short introduction on the evolution of the communication systems are given in order to understand key IoT concepts at IoT architecture level and in particular at the device and gateway domains, including some security ones, and a brief market analysis on the home automation and health care devices.

Abstract

Fusion is becoming a classic topic in Intelligent Transport System (ITS) society. The lack of trustworthy sensors requires the combination of several devices to provide reliable detections. In this paper a novel approach, that takes advantage of the Joint Probabilistic Data Association technique (JPDA) for data association, is presented. The approach uses one of the most powerful techniques of Multiple Target Tracking theory and adapts it to fulfill the strong requirements of road safety applications. The different test performed proved that a powerful association technique can enhance the capacity of Advance Driver Assistance Systems. Two main sensors are used for pedestrian detection: laser scanner and computer vision. Furthermore, the approach takes advantage of the availability of other information sources i.e. context information and online information (GPS). The detections are fused using JPDA, enhancing the capacities of classical pedestrian detection systems, mainly based in visual information. The test performed also showed that JPDA improved the results offered by other data association techniques, e.g. Global Nearest Neighbors. This work was supported by the Spanish Government through the Cicyt projects (GRANT TRA2010-20225-C03- 01) and (GRANT TRA2011-29454-C03-02). CAM through SEGAUTO-II ( S2009/DPI-1509) .

Abstract

Long haulage road transport consumes considerable amounts of energy in today's world. Predictive control strategies based on digital maps can significantly lower the portion being wasted in traditional cruise control operated highway driving. Such control strategies rely on high quality stored road grade information. This paper describes a newly developed method to estimate the road grade using sensors commonly found on standard heavy duty vehicles. The method utilizes a piecewise linear road model derived from highway design methodologies. The estimation method has been implemented and evaluated experimentally, and is shown to give better results compared to an existing method. © 2010 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. QC 20120213

Abstract

Railway switches are crucial for normal operation and during disruptions of the railroad system since they allow trains to use alternative routes. Switches moving parts are subject to high deterioration and prone to malfunctioning, representing a potential safety hazard. Thus frequent inspection, maintenance and renewal are required. Models to optimize the railroad system operation and reduce costs are possible on the basis of inspections vehicles, online condition monitoring, inspection standardization and data-based models. This paper presents a switch condition now- and forecasting model based on continuous monitored data (switch engine current during blades movement). The model is capable of identifying unusual behavior due to emerging failures without the need of manually set switch-specific thresholds. In this approach no labelled training data set of historic switch failures is required for training the model. Its output combined with maintenance information and the switch functional model sheds light on switch degradation modes, helping to optimize maintenance actions.

Abstract

The lane-level localization of vehicles with low-cost sensors is a challenging task. In situations in which Global Navigation Satellite Systems (GNSSs) suffer from weak observation geometry or from the influence of reflected signals, the fusion of heterogeneous information presents a suitable approach for improving the localization accuracy. We propose a solution based on a monocular front-facing camera, a low-cost inertial measurement unit (IMU), and a single-frequency GNSS receiver. The sensor data fusion is implemented as a tightly coupled Kalman filter that corrects the IMU-based trajectory with GNSS observations while employing European Geostationary Overlay Service correction data. Further, we consider vision-based complementary data that serve as an additional source of information. In contrast to other approaches, the camera is not used to infer the motion of the vehicle, but rather for directly correcting the localization results under the usage of map information. More specifically, the so-called camera-to-map alignment is done by comparing virtual 3D views (candidates) created from projected map data with lane geometry features that are extracted from the camera image. One strength of the proposed solution is its compatibility with state-of-the-art map data, which are publicly available from different sources. We validate the approach on real-world data recorded in The Netherlands and show that it presents a promising and cost-efficient means to support future advanced driver assistance systems.

Abstract

The automatic detection of road related information using data from sensors while driving has many potential applications such as traffic congestion detection or automatic routable map generation. This paper focuses on the automatic detection of road elements based on GPS data from on-vehicle systems. A new algorithm is developed that uses the total variation distance instead of the statistical moments to improve the classification accuracy. The algorithm is validated for detecting traffic lights, roundabouts, and street-crossings in a real scenario and the obtained accuracy (0.75) improves the best results using previous approaches based on statistical moments based features (0.71). Each road element to be detected is characterized as a vector of speeds measured when a driver goes through it. We first eliminate the speed samples in congested traffic conditions which are not comparable with clear traffic conditions and would contaminate the dataset. Then, we calculate the probability mass function for the speed (in 1 m/s intervals) at each point. The total variation distance is then used to find the similarity among different points of interest (which can contain a similar road element or a different one). Finally, a k-NN approach is used for assigning a class to each unlabelled element. The research leading to these results has received funding from the “HERMES-Smart Driver” Project TIN2013-46801-C4-2-R (MINECO), funded by the Spanish Agencia Estatal de Investigación (AEI), and the “Analytics Using Sensor Data for Flatcity” Project TIN2016-77158-C4-1-R (MINECO/ERDF, EU) funded by the SpanishAgencia Estatal de Investigación (AEI) and the European Regional Development Fund (ERDF). Document type: Article