Knowledge Distillation for End-to-End Motion Planning in Autonomous Driving

The author proposes PlanKD, a knowledge distillation method tailored for compressing end-to-end motion planners. By focusing on distilling planning-relevant features and incorporating a safety-aware waypoint-attentive mechanism, PlanKD offers a portable and safe solution for resource-limited deployment.