Car-Following (CF), as a fundamental driving behaviour, has significant influences on the safety and efficiency of traffic flow. Investigating how human drivers react differently when following autonomous vs. human-driven vehicles (HV) is thus critical for mixed traffic flow. Research in this field can be expedited with trajectory datasets collected by Autonomous Vehicles (AVs). However, trajectories collected by AVs are noisy and not readily applicable for studying CF behaviour. This paper extracts and enhances two categories of CF data, HV-following-AV (H-A) and HV-following-HV (H-H), from the open Lyft level-5 dataset. First, CF pairs are selected based on specific rules. Next, the quality of raw data is assessed by anomaly analysis. Then, the raw CF data is corrected and enhanced via motion planning, Kalman filtering, and wavelet denoising. As a result, 29k+ H-A and 42k+ H-H car-following segments are obtained, with a total driving distance of 150k+ km. A diversity assessment shows that the processed data cover complete CF regimes for calibrating CF models. This open and ready-to-use dataset provides the opportunity to investigate the CF behaviours of following AVs vs. HVs from real-world data. It can further facilitate studies on exploring the impact of AVs on mixed urban traffic.

翻译：跟驰行为作为一种基础驾驶行为，对交通流的安全性与效率具有重要影响。研究人类驾驶员在跟随自动驾驶车辆与人类驾驶车辆时的差异化反应，对于混合交通流研究至关重要。利用自动驾驶车辆采集的轨迹数据集可加速该领域研究进展，但此类轨迹存在噪声干扰，难以直接用于跟驰行为分析。本文从开放的Lyft level-5数据集中提取并优化了两类跟驰数据：人类车辆跟随自动驾驶车辆及人类车辆跟随人类车辆。首先基于特定规则筛选跟驰配对，继而通过异常分析评估原始数据质量，随后采用运动规划、卡尔曼滤波与小波去噪方法对原始跟驰数据进行校正与增强。最终获得了2.9万余段人类车辆跟随自动驾驶车辆与4.2万余段人类车辆跟随人类车辆的跟驰片段，总行驶里程超过15万公里。多样性评估表明，处理后的数据覆盖了完整的跟驰场景，可用于标定跟驰模型。该开放即用数据集为基于真实数据研究跟随自动驾驶车辆与人类驾驶车辆的跟驰行为提供了契机，并可进一步推动探索自动驾驶车辆对城市混合交通流影响的相关研究。