Behavioral adaptation of human drivers in car-following interactions with automated vehicles

The emergence of Automated Vehicles (AVs) will result in mixed traffic with Human-Driven Vehicles (HDVs), creating a complex traffic environment. While literature supports the effect of AVs on human driving behavior, the influence of different AV driving styles in this context remains largely unexplored. This study investigated interactions between HDVs and AVs varying in driving style (cautious vs. aggressive) and appearance (recognizable vs. unrecognizable) at Market Penetration Rates (MPR) of 0% to 75% in 25% increments within a highway context. Car-following behavior of 160 participants (56 females, age range 19-65) in a driving simulator experiment was analyzed using average time headway (THW) and standard deviation of relative speed (SDRS) as key metrics. Linear Mixed Models (LMMs) and Structural Equation Modelling (SEM) were applied. Results showed that compared to the baseline (no AVs), drivers' THW decreased when interacting with aggressive AVs by 11.1%, 8.6%, and 13.2% at MPRs of 25%, 50%, and 75%, respectively. Interactions with cautious AVs led to even larger reductions in THW by about 9.9%, 16.6%, and 17.3% at the same MPRs. SDRS improved across MPRs by 4.5%, 5.2%, and 6.2%, independent of AV driving style or appearance. SEM analysis indicated that AV driving style moderated relationships between independent, mediating, and dependent variables, while drivers' perceived stress and comfort mediated effects of AVs, trust propensity, and recognizability on THW and SDRS. These findings provide insights into human responses to AV behavior, highlighting implications for traffic flow and safety to ensure smoother and safer human-AV interactions.