Learning When to Drive in Intersections by Combining Reinforcement Learning and Model Predictive Control
arXiv:1908.00177
The paper presents a two‑level decision system for autonomous vehicles at intersections, combining a high‑level reinforcement‑learning policy with a low‑level model predictive control planner to improve success rates and reduce training time.
Abstract
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.
6 pages, 5 figures, 1 table, Accepted to IEEE Intelligent Transport Systems Conference 2019