HiP-AD：面向单一解码器自动驾驶的层次化多粒度规划与可变形注意力
HiP-AD: Hierarchical and Multi-Granularity Planning with Deformable Attention for Autonomous Driving in a Single Decoder
AI总结：
本文提出HiP-AD框架，通过多粒度规划查询与可变形注意力机制，在单一解码器中统一实现感知、预测和规划，显著提升端到端自动驾驶的闭环性能。
摘要：
尽管端到端自动驾驶（E2E-AD）技术近年来取得了显著进展，但在闭环评估中仍存在性能不尽如人意的问题。在查询设计与交互中利用规划信息的潜力尚未被充分挖掘。本文提出了一种多粒度规划查询表示，该表示融合了异构航路点，包括跨多种采样模式的空间、时间和驾驶风格航路点。它为轨迹预测提供了额外监督，增强了对自车的精确闭环控制。此外，我们显式利用规划轨迹的几何特性，通过可变形注意力根据物理位置有效检索相关图像特征。结合这些策略，我们提出了一种新颖的端到端自动驾驶框架，称为HiP-AD，它在统一解码器中同时执行感知、预测和规划。HiP-AD通过允许规划查询在BEV空间中与感知查询迭代交互，同时从透视视图动态提取图像特征，实现了全面的交互。实验表明，HiP-AD在闭环基准Bench2Drive上优于所有现有的端到端自动驾驶方法，并在真实世界数据集nuScenes上取得了具有竞争力的性能。
英文摘要：
Although end-to-end autonomous driving (E2E-AD) technologies have made significant progress in recent years, there remains an unsatisfactory performance on closed-loop evaluation. The potential of leveraging planning in query design and interaction has not yet been fully explored. In this paper, we introduce a multi-granularity planning query representation that integrates heterogeneous waypoints, including spatial, temporal, and driving-style waypoints across various sampling patterns. It provides additional supervision for trajectory prediction, enhancing precise closed-loop control for the ego vehicle. Additionally, we explicitly utilize the geometric properties of planning trajectories to effectively retrieve relevant image features based on physical locations using deformable attention. By combining these strategies, we propose a novel end-to-end autonomous driving framework, termed HiP-AD, which simultaneously performs perception, prediction, and planning within a unified decoder. HiP-AD enables comprehensive interaction by allowing planning queries to iteratively interact with perception queries in the BEV space while dynamically extracting image features from perspective views. Experiments demonstrate that HiP-AD outperforms all existing end-to-end autonomous driving methods on the closed-loop benchmark Bench2Drive and achieves competitive performance on the real-world dataset nuScenes.
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