基于法规数据库与视觉语言模型的交通法规感知路径规划
Traffic Regulation-aware Path Planning with Regulation Databases and Vision-Language Models
AI总结：
本文提出一种将交通法规数据库与视觉语言模型相结合的自动驾驶路径规划框架，通过RGB摄像头感知环境并生成法规感知决策，在仿真和真实车辆测试中验证了其合法性、安全性与可行性。
摘要：
本文提出并测试了一个将交通法规合规性集成到自动驾驶系统（ADS）中的框架。该框架使ADS能够遵循交通法规，并根据驾驶环境做出明智决策。系统利用RGB摄像头输入和视觉语言模型（VLM）生成描述性文本，以支持法规感知的决策过程，确保合法和安全的驾驶行为。这些信息与机器可读的ADS法规数据库相结合，在法律约束范围内指导未来的驾驶规划。关键特性包括：1）支持ADS决策的法规数据库；2）利用传感器输入进行法规感知路径规划的自动化流程；3）在仿真和真实世界环境中的验证。特别是，真实世界车辆测试不仅评估了该框架的性能，还评估了VLM通过集成检测、推理和规划来解决复杂驾驶问题的潜力和挑战。这项工作提升了ADS的合法性、安全性和公众信任，代表了该领域向前迈出的重要一步。
英文摘要：
This paper introduces and tests a framework integrating traffic regulation compliance into automated driving systems (ADS). The framework enables ADS to follow traffic laws and make informed decisions based on the driving environment. Using RGB camera inputs and a vision-language model (VLM), the system generates descriptive text to support a regulation-aware decision-making process, ensuring legal and safe driving practices. This information is combined with a machine-readable ADS regulation database to guide future driving plans within legal constraints. Key features include: 1) a regulation database supporting ADS decision-making, 2) an automated process using sensor input for regulation-aware path planning, and 3) validation in both simulated and real-world environments. Particularly, the real-world vehicle tests not only assess the framework's performance but also evaluate the potential and challenges of VLMs to solve complex driving problems by integrating detection, reasoning, and planning. This work enhances the legality, safety, and public trust in ADS, representing a significant step forward in the field.
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。
