Bayesian Perception and Decision Making for next Cars Generation

Christian Laugier 1, 2, 3
1 E-MOTION - Geometry and Probability for Motion and Action
Inria Grenoble - Rhône-Alpes, LIG - Laboratoire d'Informatique de Grenoble
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
Abstract : Modern cars include more and more sophisticated electronics, sensors, processing and control components. These new components are used both for controlling the main functions of the vehicle and for providing the driver with Advanced Driving Assistance Systems (ADAS). Such ADAS functionalities are increasingly based on Robotics technologies for partly automating some driving functions such as adaptive cruise control, acceleration/braking in a traffic lane, lane keeping, parking assistance, or even simple collision avoidance or mitigation actions (including braking or airbag actuation). Most of the automotive constructors are now proposing ADAS options, and the degree of autonomy of cars is progressively increasing. But the ultimate challenge addressed by many Academic and Industrial Research Laboratories is the development of driverless cars. Impressive results have already been published and largely disseminated through the media, and many announcements concerning the future deployment of such vehicles have been done by most of the major Automotive Manufacturers and by multinational groups such as Google. This talk addresses both the socio-economic and technical issues which are behind the development of the next cars generation. These future cars will include both smart ADAS and Driverless Car functionalities. In the talk, an emphasis will be put on three main enabling technologies: (1) Robust Multi-sensors Embedded Perception system, (2) Situation Awareness & Collision Risk assessment in complex traffic situations, and (3) Decisional and Control System for generating safe Navigation and Maneuvering actions. All theses functionalities have to be robust in the presence of sensing errors, uncertainty and traffic hazards. It will be shown that “Bayesian Perception” and “Bayesian Decision” are two key paradigms for developing the above mentioned functionalities. Experimental results obtained on real equipped vehicles provided by Toyota and by Renault will be used to illustrate the talk.
Type de document :
Autre publication
Invited talk at Google Self-Driving Cars, Mountain View, May 12th 2015. 2015
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Contributeur : Christian Laugier <>
Soumis le : jeudi 28 janvier 2016 - 20:55:21
Dernière modification le : jeudi 11 janvier 2018 - 06:21:47


  • HAL Id : hal-01264215, version 1


Christian Laugier. Bayesian Perception and Decision Making for next Cars Generation. Invited talk at Google Self-Driving Cars, Mountain View, May 12th 2015. 2015. 〈hal-01264215〉



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