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Improving Autonomous Driving Safety through a better Understanding of Traffic Scenes and of Potential Upcoming Collisions : A Bayesian & Machine Learning Approach (Invited Plenary Speech)

Christian Laugier 1
1 CHROMA - Robots coopératifs et adaptés à la présence humaine en environnements dynamiques
Inria Grenoble - Rhône-Alpes, CITI - CITI Centre of Innovation in Telecommunications and Integration of services
Abstract : Motion Autonomy and Safety issues in Autonomous Vehicles are strongly dependent upon the capabilities and performances of Embedded Perception and Situation Awareness systems. Recent benign and severe accidents (e.g. Tesla or Uber) have shown that the level of safety obtained using currently tested autonomous driving systems is still insufficient. This speech addresses this important perception and safety issue, and presents how it can be addressed using Bayesian and Machine Learning approaches. The approach is illustrated using results obtained in the scope of several Research and Development projects conducted in cooperation with the French IRT Nanoelec and with several industrial companies such as Toyota and Renault.
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https://hal.inria.fr/hal-01969802
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Submitted on : Friday, January 4, 2019 - 3:09:41 PM
Last modification on : Tuesday, November 19, 2019 - 12:51:44 PM
Document(s) archivé(s) le : Friday, April 5, 2019 - 2:43:42 PM

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Christian Laugier. Improving Autonomous Driving Safety through a better Understanding of Traffic Scenes and of Potential Upcoming Collisions : A Bayesian & Machine Learning Approach (Invited Plenary Speech). ICARCV 2018 - 15th International Conference on Control, Automation, Robotics and Vision, Nov 2018, Singapore, Singapore. pp.1-15. ⟨hal-01969802⟩

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