BucketKD: A Safety-Aware Bucket-Based Knowledge Distillation Framework for End-to-End Motion Planning

• URL: http://arxiv.org/abs/2607.10565v1
• Date: Sun, 12 Jul 2026 04:45:02 GMT
• Title: BucketKD: A Safety-Aware Bucket-Based Knowledge Distillation Framework for End-to-End Motion Planning
• Abstract summary: BucketKD is a bucket-based knowledge distillation framework that yields compact and safety-aware end-to-end planners.<n>We design a safety-aware waypoint attention mechanism that evaluates each waypoint's risk level.<n>Experiments in CARLA show that BucketKD significantly outperforms the state-of-the-art in both planning accuracy and safety.
• Score: 5.6534573088753595
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: End-to-end motion planning has emerged as a promising paradigm in autonomous driving, directly mapping raw sensor data to control commands via deep neural networks. Despite its advantages, its large model size hinders deployment in resource-constrained platforms. In this paper, we present BucketKD, a bucket-based knowledge distillation framework that yields compact and safety-aware end-to-end planners. Compared to the state-of-the-art approach, which relies on simplified planning state representations, BucketKD discretizes critical environmental variables into adaptive buckets that capture richer scene semantics while preserving efficiency. In addition, we design a safety-aware waypoint attention mechanism that evaluates each waypoint's risk level by accounting for both obstacle proximity and relative motion through a time-to-collision (TTC) formulation widely used in transportation research. This enables the student model to better retain safety-critical behaviors during distillation. Extensive experiments in CARLA using the Bench2Drive dataset show that BucketKD significantly outperforms the state-of-the-art in both planning accuracy and safety while maintaining strong compression ratios.

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