Interpretable Motion Planner for Urban Driving via Hierarchical Imitation Learning
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Learning-based approaches have achieved impressive performance for autonomous driving and an increasing number of data-driven works are being studied in the decision-making and planning module. However, the reliability and the stability of the neural network is still full of challenges. In this paper, we introduce a hierarchical imitation method including a high-level grid-based behavior planner and a low-level trajectory planner, which is not only an individual data-driven driving policy and can also be easily embedded into the rule-based architecture. We evaluate our method both in closed-loop simulation and real world driving, and demonstrate the neural network planner has outstanding performance in complex urban autonomous driving scenarios.
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