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.

翻译：基于学习的方法在自动驾驶领域已取得了显著成果。利用神经网络的强大能力与大量人类驾驶数据，驾驶行为的复杂模式与规则可被编码为模型，从而赋能自动驾驶系统。此外，越来越多的数据驱动研究聚焦于决策与运动规划模块。然而，神经网络的可靠性与稳定性仍充满不确定性。本文提出一种分层规划架构，包含高层基于栅格的行为规划器与低层轨迹规划器，该架构具有高度可解释性与可控性。其中，高层规划器负责寻找一致路径，低层规划器生成可行轨迹。我们通过闭环仿真与实际道路驾驶对方法进行评估，结果表明该神经网络规划器在复杂城市自动驾驶场景中展现出卓越性能。