  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.
