CoT-VLM4Tar：用于交通异常解决的思维链引导视觉语言模型
CoT-VLM4Tar: Chain-of-Thought Guided Vision-Language Models for Traffic Anomaly Resolution
AI总结：
针对复杂交通异常的实时处理难题，提出CoT-VLM4Tar框架，利用思维链引导视觉语言模型分析推理并生成解决方案，结合CARLA模拟器闭环测试验证了其有效性。
摘要：
随着城市化进程的加速，现代城市交通系统变得日益复杂，导致交通异常频发。这些异常不仅包括常见的交通拥堵，还包含更具挑战性的问题，如幽灵交通拥堵、交叉路口死锁以及事故责任分析，它们严重影响了交通流量、车辆安全和整体运输效率。目前，现有的解决方案主要依赖于交警的人工干预或基于人工智能的检测系统。然而，由于资源不足，这些方法往往存在响应延迟和管理不一致的问题；而AI检测系统尽管在一定程度上提升了效率，但在以实时和精确的方式处理复杂交通异常方面仍显不足。为了解决这些问题，我们提出了CoT-VLM4Tar（用于交通异常解决的思维链视觉语言模型），这一创新方法引入了新的思维链来引导VLM分析、推理并生成交通异常的解决方案，以提供更合理有效的方案。为了评估我们方法的性能和有效性，我们开发了一个基于CARLA模拟器的闭环测试框架。此外，为了确保VLM生成的解决方案与CARLA模拟器无缝集成，我们实现了一个集成模块，将这些解决方案转换为可执行命令。我们的结果证明了VLM在解决实时交通异常方面的有效性，为其集成到自主交通管理系统提供了概念验证。
英文摘要：
With the acceleration of urbanization, modern urban traffic systems are becoming increasingly complex, leading to frequent traffic anomalies. These anomalies encompass not only common traffic jams but also more challenging issues such as phantom traffic jams, intersection deadlocks, and accident liability analysis, which severely impact traffic flow, vehicular safety, and overall transportation efficiency. Currently, existing solutions primarily rely on manual intervention by traffic police or artificial intelligence-based detection systems. However, these methods often suffer from response delays and inconsistent management due to inadequate resources, while AI detection systems, despite enhancing efficiency to some extent, still struggle to handle complex traffic anomalies in a real-time and precise manner. To address these issues, we propose CoT-VLM4Tar: (Chain of Thought Visual-Language Model for Traffic Anomaly Resolution), this innovative approach introduces a new chain-of-thought to guide the VLM in analyzing, reasoning, and generating solutions for traffic anomalies with greater reasonable and effective solution, and to evaluate the performance and effectiveness of our method, we developed a closed-loop testing framework based on the CARLA simulator. Furthermore, to ensure seamless integration of the solutions generated by the VLM with the CARLA simulator, we implement an itegration module that converts these solutions into executable commands. Our results demonstrate the effectiveness of VLM in the resolution of real-time traffic anomalies, providing a proof-of-concept for its integration into autonomous traffic management systems.
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。
