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Mixing Bayesian and Artificial Intelligence approaches for Autonomous Driving

Christian Laugier 1, 2
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 : Invited talk. Motion Autonomy and Safety issues in Autonomous Vehicles are strongly dependent upon the capabilities and performances of Embedded Perception and Decision-making systems. This talk presents how it is possible to address these important issues using Bayesian and Machine Learning approaches. The talk will be illustrated using results obtained in the scope of several R&D projects with IRT (French Technological Research Institute) Nanoelec and with industrial companies such as Toyota or Renault. The talk will be illustrated by experimental results obtained using the Inria & IRT Nanoelec Renault Zoe car.
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https://hal.inria.fr/hal-02434275
Contributor : Christian Laugier <>
Submitted on : Thursday, January 9, 2020 - 7:29:36 PM
Last modification on : Tuesday, February 11, 2020 - 6:23:55 PM

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  • HAL Id : hal-02434275, version 1

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Christian Laugier. Mixing Bayesian and Artificial Intelligence approaches for Autonomous Driving. Tech M&A 2019 - Minalogic Technical Conference, May 2019, Grenoble, France. ⟨hal-02434275⟩

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