发表机构
University of Memphis（孟菲斯大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
针对端到端运动规划模型规模大难在资源受限平台部署的问题，提出BucketKD框架。该框架将关键环境变量离散化，并设计安全感知航路点注意力机制。实验表明，其在规划精度、安全性及压缩率方面表现出色，显著优于现有方法。
AI 中文摘要
端到端运动规划在自动驾驶中是一种很有前景的范式，可通过深度神经网络将原始传感器数据直接映射到控制命令。但其模型规模大阻碍在资源受限平台部署。本文提出BucketKD，一种基于桶的知识蒸馏框架，能产生紧凑且安全感知的端到端规划器。与依赖简化规划状态表示的现有方法不同，BucketKD将关键环境变量离散化为自适应桶，在保持效率的同时捕获更丰富场景语义。还设计了安全感知航路点注意力机制，通过考虑障碍物接近度和相对运动评估每个航路点的风险水平。在CARLA中使用Bench2Drive数据集进行的大量实验表明，BucketKD在规划精度和安全性方面显著优于现有方法，同时保持高压缩率。
英文摘要
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.
