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 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. The dataset is openly available via https://github.com/RomainLITUD/conflict_resolution_dataset.

翻译：随着自动驾驶车辆（AV）部署日益普及，确保其与其他人类行为体之间的安全顺畅交互至关重要。在城市环境中，车辆如何消解冲突对行车安全和交通效率均具有显著影响。为加快评估交叉路口处涉及AV与无AV场景中冲突消解的研究，本文从开源Argoverse-2运动预测数据中提取出高质量数据集。首先，基于后侵入时间、最小距离、轨迹交叉及速度变化等一组启发式规则筛选目标场景；其次，对原始数据质量进行仔细核查。研究发现Argoverse-2数据中的位置与速度信息存在不一致性，且不当处理会引入不必要的误差。针对这些具体问题，我们提出并应用了一套数据处理管线以修正和增强原始数据。最终获得了5千余个涉及AV的场景与1.6万余个无AV场景，其位置、速度、加速度及航向角数据均平滑一致。进一步评估表明，该数据集包含多样且均衡的冲突消解模式。这一高信息量数据集为自动驾驶评估与监管领域的研究者和实践者提供了宝贵资源。数据集通过https://github.com/RomainLITUD/conflict_resolution_dataset 公开提供。