发表机构
North Carolina State University（北卡罗来纳州立大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本研究通过公共道路控制实验构建了NC-tALC数据集，刻画过渡自动驾驶车辆的强制变道行为，揭示其变道过程中的车距演变与碰撞风险规律，为相关评估与验证提供实证基准。
AI 中文摘要
本文提出了北卡罗来纳州过渡自动驾驶车辆变道（NC-tALC）数据集，并利用该数据集刻画过渡自动驾驶车辆（tAVs）的强制变道行为。它量化了变道过程中前后车距（lead--lag gaps）的演变情况，并研究了该操作过程中潜在碰撞风险的发展方式。研究团队在北卡罗来纳州 Apex 的一条公共道路上开展了包含78次强制变道试验的控制实地实验：四辆配备仪器的车辆营造出可重复的交通条件，同时改变变道车辆在候选目标间隙内的初始位置。研究人员对高分辨率RTK-GNSS/INS轨迹进行处理，以识别关键时间戳、计算前后车距和变道间隙，并采用基于时间间隙和速度的替代安全措施来估算交互作用。尽管初始条件存在显著差异，但前后车距始终收敛至接近车道交叉的相对狭窄范围。潜在碰撞风险随操作推进而增加，在物理进入车道附近达到峰值，且主要由与目标车道前车的交互作用主导。变道完成并不一定与碰撞风险消失同时发生。本研究是首批利用可重复公共道路实验对tAVs完整强制变道过程进行控制实证刻画的研究之一；NC-tALC数据集支持对整个操作过程中的行为和安全演变进行分析，而非仅针对间隙接受瞬间。该数据集和研究结果为评估自动变道行为、校准行为模型以及验证强制变道场景的仿真和安全评估方法提供了实证基准。
英文摘要
This paper presents the North Carolina Transitional Autonomous Vehicle Lane-Changing (NC-tALC) dataset and uses it to characterize mandatory lane-changing behavior of transitional automated vehicles (tAVs). It quantifies the evolution of lead--lag gaps throughout the lane-change process and examines how potential collision risk develops during the maneuver. A controlled field experiment comprising 78 mandatory lane-change trials was conducted on a public roadway in Apex, North Carolina. Four instrumented vehicles created repeatable traffic conditions while varying the lane changer's initial position within the candidate target gap. High-resolution RTK-GNSS/INS trajectories were processed to identify key timestamps, calculate lead, lag, and lane-change gaps, and estimate interactions using time-gap- and speed-based surrogate safety measures. Despite substantial differences in initial conditions, lead and lag gaps consistently converged toward a relatively narrow range near lane crossing. Potential collision risk increased as the maneuver progressed, peaked near physical lane entry, and was dominated by interactions with the target-lane leader. Lane-change completion did not necessarily coincide with the disappearance of collision risk. This study provides one of the first controlled empirical characterizations of the complete mandatory lane-change process of tAVs using repeatable public-road experiments. The NC-tALC dataset supports analysis of behavioral and safety evolution throughout the maneuver rather than only at the gap-acceptance instant. The dataset and findings provide empirical benchmarks for evaluating automated lane-changing behavior, calibrating behavioral models, and validating simulation and safety assessment methods for mandatory lane-change scenarios.
