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Bayesian & AI driven Embedded Perception and Decision-making. Application to Autonomous Navigation in Complex, Dynamic, Uncertain and Human-populated Environments. Synoptic of Research Activity, Period 2004-20 and beyond

Christian Laugier 1, 2, 3, 4
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
LIG - Laboratoire d'Informatique de Grenoble, Inria Grenoble - Rhône-Alpes
Abstract : Robust perception & Decision-making for safe navigation in open and dynamic environments populated by human beings is an open and challenging scientific problem. Traditional approaches do not provide adequate solutions for these problems, mainly because these environments are partially unknown, open and subject to strong constraints to be satisfied (in particular high dynamicity and uncertainty). This means that the proposed solutions have to take simultaneously into account characteristics such as real-time processing, temporary occultation or false detections, dynamic changes in the scene, prediction of the future dynamic behaviors of the surrounding moving entities, continuous assessment of the collision risk, or decision-making for safe navigation. This research report presents how we have addressed this problem over the two last decades, as well as an outline of our Bayesian & IA approach for solving the Embedded Perception and Decision-making problems.
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https://hal.inria.fr/hal-03147594
Contributeur : Christian Laugier <>
Soumis le : samedi 20 février 2021 - 11:56:11
Dernière modification le : mercredi 24 février 2021 - 03:19:05

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  • HAL Id : hal-03147594, version 1

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Christian Laugier. Bayesian & AI driven Embedded Perception and Decision-making. Application to Autonomous Navigation in Complex, Dynamic, Uncertain and Human-populated Environments. Synoptic of Research Activity, Period 2004-20 and beyond. [Research Report] INRIA Grenoble - Rhone-Alpes; LIG (Laboratoire informatique de Grenoble). 2021. ⟨hal-03147594⟩

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