Learning When to Drive in Intersections by Combining Reinforcement Learning and Model Predictive Control
Paper in proceedings, 2019

In this paper, we propose a decision making algorithm intended for automated vehicles that negotiate with other possibly non-automated vehicles in intersections. The decision algorithm is separated into two parts: a high-level decision module based on reinforcement learning, and a low-level planning module based on model predictive control. Traffic is simulated with numerous predefined driver behaviors and intentions, and the performance of the proposed decision algorithm was evaluated against another controller. The results show that the proposed decision algorithm yields shorter training episodes and an increased performance in success rate compared to the other controller.

Author

Tommy Tram

Chalmers, Electrical Engineering, Systems and control, Mechatronics

Zenuity

AI Innovation of Sweden

Ivo Batkovic

Zenuity

Chalmers, Electrical Engineering, Systems and control, Mechatronics

AI Innovation of Sweden

Mohammad Ali

Zenuity

Jonas Sjöberg

Chalmers, Electrical Engineering, Systems and control, Mechatronics

2019 IEEE Intelligent Transportation Systems Conference, ITSC 2019

3263-3268 8916922

Subject Categories

Vehicle Engineering

Robotics

Control Engineering

DOI

10.1109/ITSC.2019.8916922

More information

Latest update

6/18/2020