让自动驾驶看清车前视角的交通标志，实现导航与规则判断。
SignEye: Traffic Sign Interpretation from Vehicle First-Person View
- 基于车辆第一视角构建分步推理框架SignEye，理解交通标志的空间与语义
- 在Traffic-CN数据集上验证，可实现交通标志解读与导航辅助功能
- 适合需要实时更新导航、应对临时标志调整的自动驾驶系统
交通标志在自动驾驶系统中对评估车辆合规行为和提供导航指引至关重要。然而，现有研究仅关注基础标志识别，未考虑车辆自身空间位置，难以支持进一步的规则判定与路径引导。针对此问题，我们提出新任务：从车辆第一人称视角进行交通标志解读（TSI-FPV），并设计交通引导助手（TGA）应用场景，重新探索交通标志在自动驾驶中的补充作用，而非替代电子地图导航。TGA可在离线或临时标志变更等场景下自动更新并补足导航信息。为此，我们构建了空间与语义逻辑感知的分步推理框架SignEye，以及专用数据集Traffic-CN。实验表明，基于Traffic-CN训练的SignEye能有效实现TSI-FPV与TGA，且其提供的信息可超越现有主流自动驾驶技术。
原文摘要 · Abstract (English)
Traffic signs play a key role in assisting autonomous driving systems (ADS) by enabling the assessment of vehicle behavior in compliance with traffic regulations and providing navigation instructions. However, current works are limited to basic sign understanding without considering the egocentric vehicle's spatial position, which fails to support further regulation assessment and direction navigation. Following the above issues, we introduce a new task: traffic sign interpretation from the vehicle's first-person view, referred to as TSI-FPV. Meanwhile, we develop a traffic guidance assistant (TGA) scenario application to re-explore the role of traffic signs in ADS as a complement to popular autonomous technologies (such as obstacle perception). Notably, TGA is not a replacement for electronic map navigation; rather, TGA can be an automatic tool for updating it and complementing it in situations such as offline conditions or temporary sign adjustments. Lastly, a spatial and semantic logic-aware stepwise reasoning pipeline (SignEye) is constructed to achieve the TSI-FPV and TGA, and an application-specific dataset (Traffic-CN) is built. Experiments show that TSI-FPV and TGA are achievable via our SignEye trained on Traffic-CN. The results also demonstrate that the TGA can provide complementary information to ADS beyond existing popular autonomous technologies.
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