A Comparative Conflict Resolution Dataset Derived from Argoverse-2: Scenarios with vs. without Autonomous Vehicles

As the deployment of autonomous vehicles (AVs) becomes increasingly prevalent, ensuring safe and smooth interactions between AVs and other human agents is of critical importance. In the urban environment, how vehicles resolve conflicts has significant impacts on both driving safety and traffic efficiency. To expedite the studies on evaluating conflict resolution in AV-involved and AV-free scenarios at unsignalized intersections, this paper presents a high-quality dataset derived from the open Argoverse-2 motion forecasting data. First, scenarios of interest are selected by applying a set of heuristic rules regarding post-encroachment time (PET), minimum distance, trajectory crossing, and speed variation. Next, the quality of the raw data is carefully examined. We found that position and speed data are not consistent in Argoverse-2 data and its improper processing induced unnecessary errors. To address these specific problems, we propose and apply a data processing pipeline to correct and enhance the raw data. As a result, 5k+ AV-involved scenarios and 16k+ AV-free scenarios with smooth and consistent position, speed, acceleration, and heading direction data are obtained. Further assessments show that this dataset comprises diverse and balanced conflict resolution regimes. This informative dataset provides a valuable resource for researchers and practitioners in the field of autonomous vehicle assessment and regulation.

随着自动驾驶汽车（Autonomous Vehicles, AVs）的部署日益普及，确保其与其他人类出行主体之间的安全顺畅交互至关重要。在城市道路环境中，车辆如何化解冲突对行车安全与交通效率均具有显著影响。为推进无信号交叉口场景下自动驾驶汽车参与及非参与场景的冲突解决评估研究，本文基于开源Argoverse-2运动预测数据构建了一套高质量数据集。首先，通过应用一系列基于侵闯后时间（post-encroachment time, PET）、最小间距、轨迹交叉及速度变化的启发式规则，筛选出目标研究场景。随后，对原始数据的质量开展了严格核查。研究发现，Argoverse-2数据中的位置与速度数据存在不一致问题，且对原始数据的不当处理会引入不必要的误差。为解决上述特定问题，本文提出并应用了一套数据处理流程，以校正并优化原始数据。最终，我们得到了5000余例含自动驾驶汽车参与的场景及16000余例无自动驾驶汽车参与的场景，其位置、速度、加速度及航向数据均平滑一致。进一步评估表明，该数据集涵盖了多样化且均衡的冲突解决场景。这套信息详实的数据集，可为自动驾驶汽车评估与监管领域的研究人员与从业者提供宝贵的研究资源。