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Dynamic Scene Understanding and Upcoming Collision Prediction to improve Autonomous Driving Safety: A Bayesian Approach

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Abstract

Thanks to the recent strong involvement of the Web Giants (GAFA) and of numerous international industrial companies and startups in the fields of car production, mobile robotics and mobility services, the concepts of Autonomous Vehicles and of Future Mobility Services is progressively becoming a reality connected to a huge expected market. More and more pre-products and innovative mobility services are both proposed and intensively tested in real world conditions. This is for instance the case with the commercially available Autopilot system of Tesla, or with the concept of Robot Taxi currently under testing in some US and Asian cities by companies such as Uber, Waymo or nuTonomy. Several millions of miles have been cover in the last decade by autonomous or semi-autonomous vehicles operating in real traffic environments, but at the expense of some benign or serious accidents due to insufficient safety conditions. The objective of this talk is to give a brief analysis of the state of the art in the field of Autonomous Vehicles, before focusing on one of the current brake on the deployment of such a technology: The lack of robustness and of efficiency of current Embedded Perception and Decision-making systems. After having presented some new technologies and trends for addressing these important issues, the emphasis will put on “Bayesian approaches” that are increasingly used to obtain the required robustness in presence of both real world uncertainty and complex dynamic scenes. It will be shown that the concept of “Dynamic Occupancy Grids” is extremely useful for addressing the abovementioned robustness and efficiency requirements. The approach will be illustrated using interesting experimental results obtained at Inria and IRT Nanoelec (a French Technological Research Institute) in the scope of several collaborative projects and technological transfers with Toyota, Renault and with some Industrial partners of IRT Nanoelec.
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Dates and versions

hal-01970468 , version 1 (07-01-2019)

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  • HAL Id : hal-01970468 , version 1

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Christian Laugier. Dynamic Scene Understanding and Upcoming Collision Prediction to improve Autonomous Driving Safety: A Bayesian Approach. RWIA 2018 - International Conference on Robotic Welding, Intelligence and Automation, Dec 2018, Guangzhou, China. pp.1-26. ⟨hal-01970468⟩
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