Interpretable Motion Planner for Urban Driving via Hierarchical Imitation Learning
This work addresses the problem of interpretability and control in autonomous driving systems for urban environments, representing an incremental improvement over existing data-driven methods.
The paper tackles the reliability and stability issues in neural network-based motion planning for autonomous driving by introducing a hierarchical architecture with a high-level grid-based behavior planner and a low-level trajectory planner, demonstrating outstanding performance in complex urban scenarios through closed-loop simulation and real-world driving.
Learning-based approaches have achieved remarkable performance in the domain of autonomous driving. Leveraging the impressive ability of neural networks and large amounts of human driving data, complex patterns and rules of driving behavior can be encoded as a model to benefit the autonomous driving system. Besides, an increasing number of data-driven works have been studied in the decision-making and motion planning module. However, the reliability and the stability of the neural network is still full of uncertainty. In this paper, we introduce a hierarchical planning architecture including a high-level grid-based behavior planner and a low-level trajectory planner, which is highly interpretable and controllable. As the high-level planner is responsible for finding a consistent route, the low-level planner generates a feasible trajectory. 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.