SignEye：基于车辆第一视角的交通标志解释
SignEye: Traffic Sign Interpretation from Vehicle First-Person View
AI总结：
提出基于车辆第一视角的交通标志解释（TSI-FPV）任务及交通引导助手（TGA）应用，构建分步推理流水线SignEye与数据集Traffic-CN，实验证明其能有效为自动驾驶系统提供补充信息。
摘要：
交通标志在辅助自动驾驶系统（ADS）方面发挥着关键作用，它能够评估车辆行为是否符合交通法规，并提供导航指令。然而，当前的研究仅局限于基础的标志理解，未考虑自车（egocentric vehicle）的空间位置，无法支持进一步的法规评估和方向导航。针对上述问题，我们引入了一项新任务：基于车辆第一视角的交通标志解释，简称为TSI-FPV。同时，我们开发了交通引导助手（TGA）场景应用，以重新探讨交通标志在ADS中的作用，作为流行自动驾驶技术（如障碍物感知）的补充。值得注意的是，TGA并非电子地图导航的替代品；相反，TGA可以作为一种自动工具用于更新电子地图，并在离线条件或临时标志调整等情况下对其进行补充。最后，我们构建了空间与语义逻辑感知的分步推理流水线（SignEye）来实现TSI-FPV和TGA，并构建了特定应用的数据集（Traffic-CN）。实验表明，通过在Traffic-CN上训练的SignEye可以实现TSI-FPV和TGA。结果还表明，TGA能够为ADS提供超越现有流行自动驾驶技术的补充信息。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
