  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.
