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.

翻译：基于学习的方法在自动驾驶领域取得了显著性能，越来越多基于数据驱动的研究正在决策与规划模块中展开。然而，神经网络的可靠性与稳定性仍面临诸多挑战。本文提出一种分层模仿方法，包含高层基于网格的行为规划器与低层轨迹规划器。该方法不仅可作为独立的数据驱动驾驶策略，还能便捷地嵌入基于规则的架构中。我们通过闭环仿真与真实道路驾驶场景对方法进行评估，结果表明该神经网络规划器在复杂城市自动驾驶场景中具有卓越表现。