This paper presents the development of a comprehensive dataset capturing interactions between Autonomous Vehicles (AVs) and traffic control devices, specifically traffic lights and stop signs. Derived from the Waymo Motion dataset, our work addresses a critical gap in the existing literature by providing real-world trajectory data on how AVs navigate these traffic control devices. We propose a methodology for identifying and extracting relevant interaction trajectory data from the Waymo Motion dataset, incorporating over 37,000 instances with traffic lights and 44,000 with stop signs. Our methodology includes defining rules to identify various interaction types, extracting trajectory data, and applying a wavelet-based denoising method to smooth the acceleration and speed profiles and eliminate anomalous values, thereby enhancing the trajectory quality. Quality assessment metrics indicate that trajectories obtained in this study have anomaly proportions in acceleration and jerk profiles reduced to near-zero levels across all interaction categories. By making this dataset publicly available, we aim to address the current gap in datasets containing AV interaction behaviors with traffic lights and signs. Based on the organized and published dataset, we can gain a more in-depth understanding of AVs' behavior when interacting with traffic lights and signs. This will facilitate research on AV integration into existing transportation infrastructures and networks, supporting the development of more accurate behavioral models and simulation tools.

翻译：本文介绍了一个综合性数据集的开发，该数据集记录了自动驾驶车辆与交通控制设备（特别是交通信号灯和停车标志）之间的交互行为。基于Waymo Motion数据集，本研究通过提供自动驾驶车辆如何通过这些交通控制设备的真实轨迹数据，填补了现有文献中的关键空白。我们提出了一种从Waymo Motion数据集中识别和提取相关交互轨迹数据的方法，涵盖超过37,000个交通信号灯交互实例和44,000个停车标志交互实例。该方法包括定义规则以识别各类交互类型、提取轨迹数据，并应用基于小波的去噪方法对加速度和速度曲线进行平滑处理以消除异常值，从而提升轨迹质量。质量评估指标表明，本研究获得的轨迹在所有交互类别中，其加速度和加加速度曲线的异常比例均降至接近零的水平。通过公开此数据集，我们旨在填补当前缺乏包含自动驾驶车辆与交通信号灯及标志交互行为的数据集的空白。基于整理和发布的数据集，我们可以更深入地理解自动驾驶车辆在与交通信号灯及标志交互时的行为。这将促进自动驾驶车辆融入现有交通基础设施和网络的研究，支持开发更精确的行为模型和仿真工具。