Decision-making for automated vehicles at intersections adapting human-like behavior

Abstract : Learning from human driver’s strategies for solving complex and potentially dangerous situations including interaction with other road users has the potential to improve decision-making methods for automated vehicles. In this paper, we focus on simple unsignalized intersections and roundabouts in presence of another vehicle. We propose a human-like decision-making algorithm for these scenarios built up from human drivers recordings. The algorithm includes a risk assessment to avoid collisions in the intersection area. Three road topologies with different interaction scenarios were presented to human participants on a previously developed simulation tool. The same scenarios have been used to validate our decision-making process. The algorithm showed promising results with no collisions in all setups and the ability to successfully determine to go before or after another vehicle.
Type de document :
Communication dans un congrès
IV'17 - IEEE Intelligent Vehicles Symposium, Jun 2017, Redondo Beach, United States. 2017, 〈http://iv2017.org/〉
Liste complète des métadonnées

Littérature citée [13 références]  Voir  Masquer  Télécharger

https://hal.inria.fr/hal-01531516
Contributeur : Anne Verroust-Blondet <>
Soumis le : vendredi 2 juin 2017 - 15:11:27
Dernière modification le : jeudi 26 avril 2018 - 10:27:48
Document(s) archivé(s) le : mercredi 13 décembre 2017 - 09:18:38

Fichier

Paper_author_decision_making.p...
Fichiers produits par l'(les) auteur(s)

Identifiants

  • HAL Id : hal-01531516, version 1

Collections

Citation

Pierre De Beaucorps, Thomas Streubel, Anne Verroust-Blondet, Fawzi Nashashibi, Benazouz Bradai, et al.. Decision-making for automated vehicles at intersections adapting human-like behavior. IV'17 - IEEE Intelligent Vehicles Symposium, Jun 2017, Redondo Beach, United States. 2017, 〈http://iv2017.org/〉. 〈hal-01531516〉

Partager

Métriques

Consultations de la notice

306

Téléchargements de fichiers

322