用于端到端自动驾驶的即插即用交通元素感知
Plug-and-Play Traffic Element Awareness for End-to-End Autonomous Driving
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中文总结 AI 辅助
该研究首次系统探究端到端自动驾驶的交通元素感知，通过补充标注构建统一基础设施，以即插即用方式集成信号，在多范式多数据集上提升性能，在NAVSIM-v2上达到新SOTA。
中文摘要 AI 辅助
交通元素如交通信号灯和道路标志在人类驾驶决策中起着基础性作用，自然应影响端到端驾驶性能。然而，现有的端到端驾驶研究主要关注动态道路参与者（如车辆和行人），而交通元素的作用在很大程度上未被探索。学术界仍缺乏量化其影响的系统研究，主要原因是公开数据集很少提供结构化的交通元素标注，且现代驾驶系统的架构和训练范式差异很大。在这项工作中，我们首次对端到端自动驾驶的交通元素感知进行系统研究。我们通过为多个公开驾驶数据集补充全面的交通元素标注，构建了统一的研究基础设施。为支持不同的模型家族，我们采用了极简且通用的集成设计，以即插即用的方式将交通元素信号融入现有管道，仅需极少的架构修改。我们在现代范式上评估该设计，包括感知-预测-规划管道、视觉-语言-动作模型（VLA）、基于回归的规划器、基于扩散的策略以及轨迹评分框架，涉及nuScenes、NAVSIM-v1、NAVSIM-v2和Bench2Drive数据集。在所有范式和数据集上，这种简单的集成均持续提升驾驶性能，表明交通元素感知为端到端驾驶系统提供了鲁棒且可泛化的信号。值得注意的是，在具有挑战性的NAVSIM-v2基准上，我们的方法显著改进了最先进的架构和数据管道，建立了新的最先进水平。
英文摘要
Traffic elements such as traffic lights and road signs play a fundamental role in human driving decisions and should naturally influence end-to-end driving performance. However, existing end-to-end driving research predominantly focuses on dynamic road participants (e.g., vehicles and pedestrians), while the role of traffic elements remains largely unexplored. The community still lacks a systematic study quantifying their impact, largely because public datasets rarely provide structured traffic-element annotations and modern driving systems vary widely in architecture and training paradigm. In this work, we present the first systematic investigation of traffic element awareness for end-to-end autonomous driving. We construct a unified research infrastructure by augmenting multiple public driving datasets with comprehensive traffic-element annotations. To support diverse model families, we adopt a minimal and universal integration design that incorporates traffic-element signals into existing pipelines in a plug-and-play manner with negligible architectural modification. We evaluate this design across modern paradigms, including perception-prediction-planning pipelines, vision-language-action models (VLA), regression-based planners, diffusion-based policies, and trajectory-scoring frameworks, on nuScenes, NAVSIM-v1, NAVSIM-v2, and Bench2Drive. Across all paradigms and datasets, this simple integration consistently improves driving performance, demonstrating that traffic element awareness provides a robust and generalizable signal for end-to-end driving systems. Notably, on the challenging NAVSIM-v2 benchmark, our approach significantly improves state-of-the-art architectures and data pipelines, establishing a new state of the art.
发表机构
- Institute for AI Industry Research (AIR), Tsinghua University（清华大学人工智能产业研究院（AIR））
- Bosch Corporate Research（博世企业研究中心）
机构由 AI 辅助整理，请以论文原文为准。
