提出导航引导框架，让自动驾驶系统更懂全局路线。
Unveiling the Surprising Efficacy of Navigation Understanding in End-to-End Autonomous Driving
- 用真实导航模式构建序列化导航表示，融合路径与实时转向指令。
- 在复杂场景下导航跟随准确率提升，模型性能超越现有方法。
- 适合研究端到端自动驾驶与多模态规划的开发者使用。
全局导航信息与局部场景理解是自动驾驶系统的两大核心。然而实验表明，多数端到端自动驾驶系统过度依赖局部理解，未能有效利用全局导航信息，其规划能力与导航输入相关性弱，在复杂场景中难以实现导航跟随。为此，我们提出序列化导航引导（SNG）框架，基于真实世界导航模式构建全局导航信息的高效表征，包含用于约束长期轨迹的导航路径和用于实时决策的逐级转向（TBT）信息。我们构建了SNG-QA数据集，一个基于SNG的视觉问答数据集，用于对齐全局与局部规划。此外，我们设计了SNG-VLA模型，通过融合局部与全局规划实现高效建模。该模型在不依赖感知任务辅助损失的情况下，达到当前最优性能。
原文摘要 · Abstract (English)
Global navigation information and local scene understanding are two crucial components of autonomous driving systems. However, our experimental results indicate that many end-to-end autonomous driving systems tend to over-rely on local scene understanding while failing to utilize global navigation information. These systems exhibit weak correlation between their planning capabilities and navigation input, and struggle to perform navigation-following in complex scenarios. To overcome this limitation, we propose the Sequential Navigation Guidance (SNG) framework, an efficient representation of global navigation information based on real-world navigation patterns. The SNG encompasses both navigation paths for constraining long-term trajectories and turn-by-turn (TBT) information for real-time decision-making logic. We constructed the SNG-QA dataset, a visual question answering (VQA) dataset based on SNG that aligns global and local planning. Additionally, we introduce an efficient model SNG-VLA that fuses local planning with global planning. The SNG-VLA achieves state-of-the-art performance through precise navigation information modeling without requiring auxiliary loss functions from perception tasks. Project page: SNG-VLA
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