Learning When to Drive in Intersections by Combining Reinforcement Learning and Model Predictive Control
This work addresses intersection navigation for automated vehicles, but it is incremental as it builds on existing methods like reinforcement learning and model predictive control.
The paper tackles the problem of automated vehicles negotiating intersections with other vehicles by proposing a decision algorithm that combines reinforcement learning and model predictive control, resulting in shorter training episodes and increased success rate compared to another controller.
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.