Field experiments on longitudinal characteristics of human driver behavior following an autonomous vehicle

Journal article, 2020

Although mixed traffic, including both autonomous vehicles (AV) and human-driven vehicles (HV), is expected to prevail in the foreseeable future, our current understanding of the longitudinal characteristics of mixed traffic is limited and, in particular, lacks evidence from field experiments. To bridge this gap, we designed and conducted a set of field experiments to reveal differences in car-following behaviors between a human driver following-AV and following-HV on both constant speed traffic characteristics with discrete speeds ({10,20,…,60}km/h) and dynamic car-following behaviors with continuous speeds (within 0–60 km/h) in both the indifferentiable and differentiable appearance settings of the AV. We recruited 10 drivers for the experiment (14 runs for each driver and collected position and speed data of the tested vehicles along their complete trajectories based on vehicle gaps, headways, and standard deviations of vehicle speed. A K-means clustering algorithm was applied to classify drivers based on their responses in following-AV vs. following-HV with both constant speed and dynamic speed characteristics. The analyses of the differentiable appearance setting show that different drivers exhibit different behaviors in following-AV vs. following-HV: some are AV-believers, some are AV-skeptics, and the others are insensitive. Yet in the indifferentiable appearance setting, there is no significant difference between following a lead AV and following a lead HV. This reveals that drivers’ response to the lead vehicle depends on their subjective trusts on AV technologies rather than the actual driving behavior. The results suggest that, depending on the characteristics and composition of the drivers, classic car-following behavior in pure HV traffic may need to be updated for modeling mixed traffic in the near future.