首个聚焦过渡级自动驾驶变道行为的高精度数据集，助力研究人车交互安全。
Introducing the transitional autonomous vehicle lane-changing dataset: Empirical Experiments
- 构建两组受控实验：变道执行与跟驰响应，模拟真实交通场景
- 采集152组变道数据，20Hz采样精度达厘米级，含自适应巡航车辆
- 适合研究自动驾驶决策、人车交互及交通流稳定性建模的团队
过渡级自动驾驶车辆（tAV）介于SAE L1-L2与完全自主之间，正越来越多地与人类驾驶车辆共行。在复杂操作如变道过程中，其交互可能引发新的交通动态，影响行车稳定与安全。评估此类行为需高分辨率轨迹数据，但现有数据稀缺。本文提出北卡罗来纳州过渡级自动驾驶变道数据集（NC-tALC），包含两组受控实验：第一组为tAV变道实验，测试其在配备自适应巡航控制（ACC）目标车环境下的变道行为；第二组为响应实验，两辆tAV作为跟驰车辆，回应另一辆tAV发起的切入行为，用于分析跟随动态。数据集共包含152次试验（72次变道，80次响应），以20 Hz频率采集，定位精度达厘米级RTK-GPS。该数据集为评估tAV在强制变道场景中的决策与交互提供了严谨的实证基础。
原文摘要 · Abstract (English)
Transitional autonomous vehicles (tAVs), which operate beyond SAE Level 1-2 automation but short of full autonomy, are increasingly sharing the road with human-driven vehicles (HDVs). As these systems interact during complex maneuvers such as lane changes, new patterns may emerge with implications for traffic stability and safety. Assessing these dynamics, particularly during mandatory lane changes, requires high-resolution trajectory data, yet datasets capturing tAV lane-changing behavior are scarce. This study introduces the North Carolina Transitional Autonomous Vehicle Lane-Changing (NC-tALC) Dataset, a high-fidelity trajectory dataset designed to characterize tAV interactions during lane-changing maneuvers. The dataset includes two controlled experimental series. In the first, tAV lane-changing experiments, a tAV executes lane changes in the presence of adaptive cruise control (ACC) equipped target vehicles, enabling analysis of lane-changing execution. In the second, tAV responding experiments, two tAVs act as followers and respond to cut-in maneuvers initiated by another tAV, enabling analysis of follower response dynamics. The dataset contains 152 trials (72 lane-changing and 80 responding trials) sampled at 20 Hz with centimeter-level RTK-GPS accuracy. The NC-tALC dataset provides a rigorous empirical foundation for evaluating tAV decision-making and interaction dynamics in controlled mandatory lane-changing scenarios.
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