BucketKD: Safety-Aware Knowledge Distillation for End-to-End Motion Planning

Jul 14, 2026

Researchers introduce BucketKD, a knowledge distillation framework designed to compress large end-to-end motion planning models for autonomous driving while maintaining safety. The method uses adaptive buckets to capture scene semantics and incorporates a safety-aware attention mechanism based on time-to-collision. Experiments in the CARLA simulator demonstrate that BucketKD achieves higher planning accuracy and safety compared to state-of-the-art methods, with strong model compression.

Why it matters: This approach could facilitate the deployment of safety-critical autonomous driving models on resource-constrained platforms without compromising performance.

Full story at: arXiv Robotics ↗