Behavior Planning at Urban Intersections through Hierarchical Reinforcement Learning

• URL: http://arxiv.org/abs/2011.04697v1
• Date: Mon, 9 Nov 2020 19:23:26 GMT
• Title: Behavior Planning at Urban Intersections through Hierarchical Reinforcement Learning
• Authors: Zhiqian Qiao, Jeff Schneider and John M. Dolan
• Abstract summary: In this work, we propose a behavior planning structure based on reinforcement learning (RL) which is capable of performing autonomous vehicle behavior planning with a hierarchical structure in simulated urban environments. Our algorithms can perform better than rule-based methods for elective decisions such as when to turn left between vehicles approaching from the opposite direction or possible lane-change when approaching an intersection due to lane blockage or delay in front of the ego car. Results also show that the proposed method converges to an optimal policy faster than traditional RL methods.
• Score: 25.50973559614565
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: For autonomous vehicles, effective behavior planning is crucial to ensure safety of the ego car. In many urban scenarios, it is hard to create sufficiently general heuristic rules, especially for challenging scenarios that some new human drivers find difficult. In this work, we propose a behavior planning structure based on reinforcement learning (RL) which is capable of performing autonomous vehicle behavior planning with a hierarchical structure in simulated urban environments. Application of the hierarchical structure allows the various layers of the behavior planning system to be satisfied. Our algorithms can perform better than heuristic-rule-based methods for elective decisions such as when to turn left between vehicles approaching from the opposite direction or possible lane-change when approaching an intersection due to lane blockage or delay in front of the ego car. Such behavior is hard to evaluate as correct or incorrect, but for some aggressive expert human drivers handle such scenarios effectively and quickly. On the other hand, compared to traditional RL methods, our algorithm is more sample-efficient, due to the use of a hybrid reward mechanism and heuristic exploration during the training process. The results also show that the proposed method converges to an optimal policy faster than traditional RL methods.

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