标题： OpenLKA：来自市场自动驾驶车辆的车道保持辅助开放数据集 显示英文标题

标题： OpenLKA: an open dataset of lane keeping assist from market autonomous vehicles

摘要： 车道保持辅助（LKA）系统已成为近期汽车型号的标准功能。 尽管被宣传为提供自动转向功能，但由于缺乏真实世界测试和全面数据，该系统的工作特性和安全性能仍鲜有研究。 为了填补这一空白，我们在佛罗里达州坦帕市对领先的美国汽车制造商的主流LKA系统进行了广泛测试。 通过一种创新的方法，我们收集了一个全面的数据集，包括带有LKA属性的完整控制器局域网（CAN）消息，以及配备先进视觉检测和轨迹规划算法的高质量前向摄像头的视频、感知和横向轨迹数据。 我们的测试涵盖了多种具有挑战性的条件，包括复杂的道路几何结构、恶劣天气、退化的车道标记及其组合。 一个视觉语言模型（VLM）进一步对视频进行标注，以捕捉天气、光照和交通特征。 基于这个数据集，我们呈现了LKA的操作特性和安全性能的经验概述。 关键发现表明：（i）LKA对模糊的车道线和低路面对比度容易产生问题；（ii）在车道转换（如并道、分道、交叉路口）中表现不佳，常常导致意外偏离或断开；（iii）转向扭矩限制导致在急转弯时频繁偏离，带来安全风险；（iv）LKA系统始终维持严格的车道居中，缺乏在狭窄弯道或靠近大型车辆（如卡车）时的适应性。 最后，我们展示了该数据集如何指导基础设施规划和自动驾驶技术的发展。 鉴于LKA的局限性，我们建议改进道路几何结构和路面维护。 此外，我们说明了该数据集如何通过VLM微调和思维链推理支持开发类似人类的LKA系统。 显示英文摘要

摘要： The Lane Keeping Assist (LKA) system has become a standard feature in recent car models. While marketed as providing auto-steering capabilities, the system's operational characteristics and safety performance remain underexplored, primarily due to a lack of real-world testing and comprehensive data. To fill this gap, we extensively tested mainstream LKA systems from leading U.S. automakers in Tampa, Florida. Using an innovative method, we collected a comprehensive dataset that includes full Controller Area Network (CAN) messages with LKA attributes, as well as video, perception, and lateral trajectory data from a high-quality front-facing camera equipped with advanced vision detection and trajectory planning algorithms. Our tests spanned diverse, challenging conditions, including complex road geometry, adverse weather, degraded lane markings, and their combinations. A vision language model (VLM) further annotated the videos to capture weather, lighting, and traffic features. Based on this dataset, we present an empirical overview of LKA's operational features and safety performance. Key findings indicate: (i) LKA is vulnerable to faint markings and low pavement contrast; (ii) it struggles in lane transitions (merges, diverges, intersections), often causing unintended departures or disengagements; (iii) steering torque limitations lead to frequent deviations on sharp turns, posing safety risks; and (iv) LKA systems consistently maintain rigid lane-centering, lacking adaptability on tight curves or near large vehicles such as trucks. We conclude by demonstrating how this dataset can guide both infrastructure planning and self-driving technology. In view of LKA's limitations, we recommend improvements in road geometry and pavement maintenance. Additionally, we illustrate how the dataset supports the development of human-like LKA systems via VLM fine-tuning and Chain of Thought reasoning.