A Safe Hierarchical Planning Framework for Complex Driving Scenarios based on Reinforcement Learning

Abstract

Autonomous vehicles need to handle various traffic conditions and make safe and efficient decisions and maneuvers. However, on the one hand, a single optimization/sampling-based motion planner cannot efficiently generate safe trajectories in real time, particularly when there are many interactive vehicles near by. On the other hand, end-to-end learning methods cannot assure the safety of the outcomes. To address this challenge, we propose a hierarchical behavior planning framework with a set of low-level safe controllers and a high-level reinforcement learning algorithm (H-CtRL) as a coordinator for the low-level controllers. Safety is guaranteed by the low-level optimization/sampling-based controllers, while the high-level reinforcement learning algorithm makes H-CtRL an adaptive and efficient behavior planner. To train and test our proposed algorithm, we built a simulator that can reproduce traffic scenes using real-world datasets. The proposed H-CtRL is proved to be effective in various realistic simulation scenarios, with satisfying performance in terms of both safety and efficiency.

References in corpus (9)

• End to End Learning for Self-Driving Cars
• INTERACTION Dataset: An INTERnational, Adversarial and Cooperative moTION Dataset in Interactive Driving Scenarios with Semantic Maps
• Combining Planning and Deep Reinforcement Learning in Tactical Decision Making for Autonomous Driving
• Virtual to Real Reinforcement Learning for Autonomous Driving
• Deep Imitation Learning for Autonomous Driving in Generic Urban Scenarios with Enhanced Safety
• Interpretable End-to-end Urban Autonomous Driving with Latent Deep Reinforcement Learning
• Hierarchical Reinforcement Learning Method for Autonomous Vehicle Behavior Planning
• Learning hierarchical behavior and motion planning for autonomous driving
• Reinforcement Learning based Control of Imitative Policies for Near-Accident Driving