Cutting edge Forum on Autonomous Driving, Contributions from Intelligent Robotics, AI and ITS AD’19 2019 IEEE/RSJ International Conference on Intelligent Robots and Systems Cutting edge Forum on Autonomous Driving Contributions from Intelligent Robotics, AI and ITS Macau, China, November 5th 11:00 12:30, Room LG ‐ R8 Research Director Christian Laugier, Professor Philippe Martinet, Professor Marcello H. Ang, NUS, INRIA, CHROMA project, Inria, CHORALE project mpeangh@nus.edu.sg, , Christian.Laugier@inrialpes.fr, Philippe.Martinet@inria.fr, http://guppy.mpe.nus.edu.sg/~mpeangh/ https://team.inria.fr/chroma/christian- http://www- laugier/ sop.inria.fr/members/Philippe.Martinet/ Professor Christoph Stiller, Professor M.A. Sotelo, University Karlsruher Institut für Technologie of Alcalà stiller@kit.edu miguel.sotelo@uah.es, http://www.mrt.kit.edu/mitarbeiter_stiller.php www.invett.es/sotelo This workshop is organized in the framework of the IEEE Robotics and Automation's Technical Committee on: Autonomous Ground Vehicles and Intelligent Transportation Systems TC-chairs: P. Martinet, C. Laugier, M.H. Ang, D. Wolf
Cutting edge Forum on Autonomous Driving, Contributions from Intelligent Robotics, AI and ITS AD’19 Christial Laugier , Inria Grenoble Rhône-Alpes, Grenoble, France Keynote title: Situation Awareness and Decision-making for Autonomous Driving Abstract: Motion Autonomy and Safety issues in Autonomous Vehicles are strongly dependent upon the capabilities and performances of both Embedded Perception and Decision-making systems. This talk presents how it is possible to address these important issues by mixing Bayesian and Machine Learning approaches. The talk will be illustrated using results obtained by INRIA Grenoble Rhône-Alpes (France) in the scope of several R&D projects conducted in collaboration with IRT Nanoelec (French Technological Research Institute) and with several industrial companies such as Toyota or Renault Keywords: Bayesian Perception, Situation Awareness, Decision-making, Machine Learning, Autonomous Driving
Cutting edge Forum on Autonomous Driving, Contributions from Intelligent Robotics, AI and ITS AD’19 Dinesh Manocha , Department of Computer Science, University of Maryland, USA Keynote title: Autonomous Driving: Simulation and Navigation Abstract: Autonomous driving has been an active area of research and development over the last decade. Despite considerable progress, there are many open challenges including automated driving in dense and urban scenes. In this talk, we give an overview of our recent work on simulation and navigation technologies for autonomous vehicles. This includes development of autonomous driving simulation, that use recent developments in physics-based simulation, robot motion planning, game engines, and behavior modeling along with data-driven simulation. We describe novel methods for interactive simulation of multiple vehicles with unique steering or acceleration limits taking into account vehicle dynamics constraints. We also present novel multi-agent simulation algorithms using reciprocal velocity obstacles that can model the behavior and trajectories of different traffic agents in dense scenarios, including cars, buses, bicycles and pedestrians. We also present novel methods for extracting trajectories from videos and use them for behavior modeling and safe navigation.We highlight the performance of our new algorithms in challenging urban driving scenarios and highlight areas for future research. Keywords: dense traffic, urban driving, traffic prediction, collision-free navigation, driver-behavior
Cutting edge Forum on Autonomous Driving, Contributions from Intelligent Robotics, AI and ITS AD’19 Danwei Wang , NTU, Singapore Keynote title: Security of Autonomous Vehicle under Cyber Attacks Abstract: Vehicle autonomy is taking human out of decision making process and the driving is based on some rules set beforehand. Such cyber systems are subject to attacks in various ways. For example, malwares to allow hijacking of operation of brake, displacement of GPS reading, or distortion of range measurement of Lidar. These are key sensors and components in an autonomous driving vehicle and their malfunctions can cause disastrous consequences. In this research, we study these problems and provide solutions to detect these malicious attacks. Some successful counter measures are implemented to ensure the continuous autonomous operation of driverless vehicle. Examples and experiments are used to illustrate the effectiveness of proposed solutions. Keywords : Control, mobile robot
Cutting edge Forum on Autonomous Driving, Contributions from Intelligent Robotics, AI and ITS AD’19 Discussion and questions
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