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

Pierre de Beaucorps (1) , Thomas Streubel (1) , Anne Verroust-Blondet (1) , Fawzi Nashashibi (1) , Benazouz Bradai (2) , Paulo Resende (2)

1 RITS - Robotics & Intelligent Transportation Systems
2 Valeo Driving Assistance Domain

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.

Domaines: Informatique [cs]; Intelligence artificielle [cs.AI]; Apprentissage [cs.LG]; Modélisation et simulation

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, IEEE, Jun 2017, Redondo Beach, United States. ⟨hal-01531516⟩