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

© 2019 IEEE. 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.

Författare

Tommy Tram

Chalmers, Elektroteknik, System- och reglerteknik, Mekatronik

AI Innovation of Sweden

Zenuity

Ivo Batkovic

Zenuity

Chalmers, Elektroteknik, System- och reglerteknik, Mekatronik

AI Innovation of Sweden

Mohammad Ali

Zenuity

Jonas Sjöberg

Chalmers, Elektroteknik, System- och reglerteknik, Mekatronik

2019 IEEE Intelligent Transportation Systems Conference, ITSC 2019

3263-3268 8916922

Ämneskategorier

Farkostteknik

Robotteknik och automation

Reglerteknik

DOI

10.1109/ITSC.2019.8916922

Mer information

Senast uppdaterat

2020-04-23