On the Road to Portability: Compressing End-to-End Motion Planner for Autonomous Driving

Kaituo Feng, Changsheng Li, Dongchun Ren, Ye Yuan, Guoren Wang; Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2024, pp. 15099-15108

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.