Learning hierarchical behavior and motion planning for autonomous driving

• URL: http://arxiv.org/abs/2005.03863v1
• Date: Fri, 8 May 2020 05:34:55 GMT
• Title: Learning hierarchical behavior and motion planning for autonomous driving
• Authors: Jingke Wang, Yue Wang, Dongkun Zhang, Yezhou Yang, Rong Xiong
• Abstract summary: We introduce hierarchical behavior and motion planning (HBMP) to explicitly model the behavior in learning-based solution. We transform HBMP problem by integrating a classical sampling-based motion planner. In addition, we propose a sharable representation for input sensory data across simulation platforms and real-world environment.
• Score: 32.78069835190924
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Learning-based driving solution, a new branch for autonomous driving, is expected to simplify the modeling of driving by learning the underlying mechanisms from data. To improve the tactical decision-making for learning-based driving solution, we introduce hierarchical behavior and motion planning (HBMP) to explicitly model the behavior in learning-based solution. Due to the coupled action space of behavior and motion, it is challenging to solve HBMP problem using reinforcement learning (RL) for long-horizon driving tasks. We transform HBMP problem by integrating a classical sampling-based motion planner, of which the optimal cost is regarded as the rewards for high-level behavior learning. As a result, this formulation reduces action space and diversifies the rewards without losing the optimality of HBMP. In addition, we propose a sharable representation for input sensory data across simulation platforms and real-world environment, so that models trained in a fast event-based simulator, SUMO, can be used to initialize and accelerate the RL training in a dynamics based simulator, CARLA. Experimental results demonstrate the effectiveness of the method. Besides, the model is successfully transferred to the real-world, validating the generalization capability.

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