Improving Autonomous Driving Safety through a better Understanding of Traffic Scenes and of Potential Upcoming Collisions : A Bayesian & Machine Learning Approach (Invited Plenary Speech) - Archive ouverte HAL Access content directly
Conference Papers Year :

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

(1)
1

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.
Fichier principal
Vignette du fichier
Invited-talk-ICARCV2018.pdf (4.05 Mo) Télécharger le fichier
Origin : Files produced by the author(s)

Dates and versions

hal-01969802 , version 1 (04-01-2019)

Identifiers

  • HAL Id : hal-01969802 , version 1

Cite

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⟩
171 View
210 Download

Share

Gmail Facebook Twitter LinkedIn More