Embedded Bayesian Perception for Autonomous Driving

Christian Laugier 1, 2, 3
2 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
3 E-MOTION - Geometry and Probability for Motion and Action
Inria Grenoble - Rhône-Alpes, LIG - Laboratoire d'Informatique de Grenoble
Abstract : The main issues addressed in this talk are the followings: •Embedded Perception is one of the major bottlenecks for Motion Autonomy, and Safety is still insufficient. New solutions have still to be investigated. •The current lack of Robustness, Efficiency and Software / Hardware integration of these systems is still an obstacle to a full deployment of autonomous driving technologies. These important issues have to be more deeply addressed. •An novel approach, called "Bayesian Perception", is proposed to improve the system Robustness and Efficiency in highly dynamic and uncertain environments. •Another important property of this approach is to include a "prediction step", which allows the system to continuously evaluate the "Risk of Collision" with the surrounding traffic participants (for safe navigation). •The Real-time implementation issue will also be discussed.
Type de document :
Communication dans un congrès
IS Auto Europe 2017, Apr 2017, Dusseldorf, Germany. 2017
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Contributeur : Christian Laugier <>
Soumis le : samedi 23 décembre 2017 - 15:19:35
Dernière modification le : lundi 28 janvier 2019 - 01:27:27


  • HAL Id : hal-01672171, version 1


Christian Laugier. Embedded Bayesian Perception for Autonomous Driving. IS Auto Europe 2017, Apr 2017, Dusseldorf, Germany. 2017. 〈hal-01672171〉



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