End-to-end motion planning models equipped with deep neural networks have shown great potential for enabling full autonomous driving. However, the oversized neural networks render them impractical for deployment on resource-constrained systems, which unavoidably requires more computational time and resources during reference.To handle this, knowledge distillation offers a promising approach that compresses models by enabling a smaller student model to learn from a larger teacher model. Nevertheless, how to apply knowledge distillation to compress motion planners has not been explored so far. In this paper, we propose PlanKD, the first knowledge distillation framework tailored for compressing end-to-end motion planners. First, considering that driving scenes are inherently complex, often containing planning-irrelevant or even noisy information, transferring such information is not beneficial for the student planner. Thus, we design an information bottleneck based strategy to only distill planning-relevant information, rather than transfer all information indiscriminately. Second, different waypoints in an output planned trajectory may hold varying degrees of importance for motion planning, where a slight deviation in certain crucial waypoints might lead to a collision. Therefore, we devise a safety-aware waypoint-attentive distillation module that assigns adaptive weights to different waypoints based on the importance, to encourage the student to accurately mimic more crucial waypoints, thereby improving overall safety. Experiments demonstrate that our PlanKD can boost the performance of smaller planners by a large margin, and significantly reduce their reference time.

翻译：配备深度神经网络的端到端运动规划模型在实现全自动驾驶方面展现出巨大潜力。然而，过大规模的神经网络使其难以部署在资源受限系统上，这必然会在推理过程中消耗更多计算时间和资源。为解决这一问题，知识蒸馏提供了一种有前景的方法，通过让较小的学生模型向较大的教师模型学习来压缩模型。尽管如此，如何将知识蒸馏应用于压缩运动规划器尚未得到探索。本文提出PlanKD，这是首个专为压缩端到端运动规划器设计的知识蒸馏框架。首先，考虑到驾驶场景本身具有复杂性，往往包含与规划无关甚至包含噪声的信息，不加区分地传递这些信息对学生规划器并无益处。为此，我们设计了一种基于信息瓶颈的策略，仅蒸馏与规划相关的信息，而非无差别地传递所有信息。其次，输出规划轨迹中不同路径点对运动规划的重要性可能不同——某些关键路径点的微小偏差就可能导致碰撞。因此，我们开发了一种安全感知的路径点注意力蒸馏模块，根据重要性为不同路径点分配自适应权重，促使学生更精确地模仿更关键的路径点，从而提升整体安全性。实验表明，我们的PlanKD能够大幅提升较小规划器的性能，并显著缩短其推理时间。