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2020, Le Centre pour la Communication Scientifique Directe - HAL - Université de Nantes
HAL (Le Centre pour la Communication Scientifique Directe)
Embedded Perception & Decision-making for Safe Navigation in Uncertain, Dynamic and Human-populated Environments2020 •
2018 •
This chapter introduces the main technology bricks and some related socioeconomic aspects of automated driving. Vehicles automation technology is advancing at a vertiginous pace. However, the complexity behind some highly uncertain and dynamic driving scenarios imposes the need to distinguish between the different automation levels. This chapter starts from these considerations to elaborate thereafter on the maturity of the currently used technologies—situation awareness, risk assessment, decision-making, human–machine interaction, planning, control—and their near future possibilities. The introduction of connectivity among vehicles and with the digital world brings a number of new opportunities, when combined with automation, that are introduced with the focus on cooperative automated driving. After presenting this technological panorama, different relevant projects are described with the aim to understand the differences between the existing prototypes and the upcoming products an...
IOP Conference Series: Materials Science and Engineering
Current challenges in autonomous driving2017 •
Advances in Science, Technology and Engineering Systems Journal
The Ecosystem of the Next-Generation Autonomous Vehicles2021 •
2020 •
The advances in Information Technologies have led to more complex road safety applications. These systems provide multiple possibilities for improving road transport. The integrated system that this paper presents deals with two aspects that have been identified as key topics: safety and efficiency. To this end, the development and implementation of an integrated advanced driver assistance system (ADAS) for rural and intercity environments is proposed. The system focuses mainly on single-carriageways roads, given the complexity of these environments compared to motorways and the high number of severe and fatal accidents on them. The proposed system is based on advanced perception techniques, vehicle automation and communications between vehicles (V2V) and with the infrastructure (V2I). Sensor fusion architecture based on computer vision and laser scanner technologies are developed. It allows real time detection and classification of obstacles, and the identification of potential risks. The driver receives this information and some warnings generated by the system. In case, he does not react in a proper way, the vehicle could perform autonomous actions (both on speed control or steering maneuvers) to improve safety and/or efficiency. Furthermore, a multimodal V2V and V2I communication system, based on GeoNetworking, facilitates the flow of information between vehicles and assists in the detection and information broadcasting processes. All this, combined with vehicle positioning, detailed digital maps and advanced map-matching algorithms, establish the decision algorithms of different ADAS systems. The applications developed include: adaptive cruise control with consumption optimization, overtaking assistance system in single-carriageways roads that takes into account appropriate speed evolution and identifies most suitable road stretches for the 2246 Felipe Jiménez et al. / Transportation Research Procedia 14 (2016) 2245-2254 maneuver; assistance system in intersections with speed control during approximation maneuvers, and collision avoidance system with the possibility of evasive maneuvers. To this end, mathematical vehicle dynamics models have been used to ensure the stability, and propulsion system models are used to establish efficient patterns, Artificial Intelligence and simulation are used for experimentation and evaluation of algorithms to be implemented in the control unit. Finally, the system is designed to warn the driver if a risk is detected and, if necessary, to take control of the vehicle. The system has been implemented on a passenger car and has been tested in specific scenarios on a test track with satisfactory results.
Handbook on AI and Transportation - Chapter 20: Autonomous driving: present and emerging trends of technology, ethics, and law
Autonomous driving: present and emerging trends of technology, ethics, and law2023 •
Autonomous vehicles (AVs) have developed quickly in the past few years and are poised to define the future of mobility. This can be attributed to tailwinds such as the increase in computational power, advances in the artificial intelligence (AI) field, and a favorable regulatory environment created by the impending climate crisis. From a sustainability point of view, AVs can be a major enabling technology for various Sustainable Development Goals (SDGs). Nonetheless, their system complexity and increased dependence on intractable AI algorithms should be addressed and studied, as they could inhibit some of the SDGs. In this work, a comprehensive description of the different sensors and AI algorithms used in AVs is given, as well as their governance and ethical implications. The development of complex deep learning (DL) algorithms is seen as a key factor in the development of more complex AVs, and in the use of camera-based AVs over LIDAR/radar based platforms. When it comes to decision-making, reinforcement learning (RL) is the key machine learning subclass allowing for an increase in the complexity of the actions being automated. Nonetheless, both DL and RL require vast amounts of training data, which should be heterogeneous and exhaustive enough to allow for the safe deployment of the AV into the real world. This poses clear issues related to privacy and data governance, which should be addressed both by institutions and AV manufacturers. Lastly, the increased complexity of AVs raises ethical and accountability issues, and the lack of international safety standards, such as those existing for normal vehicles, is seen as a major issue to be tackled.
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