Driver-Automated Vehicle Interaction in Mixed Traffic: Types of Interaction and Drivers’ Driving Styles

Abstract

Objective This study investigated drivers’ subjective feelings and decision making in mixed traffic by quantifying driver’s driving style and type of interaction. Background Human-driven vehicles (HVs) will share the road with automated vehicles (AVs) in mixed traffic. Previous studies focused on simulating the impacts of AVs on traffic flow, investigating car-following situations, and using simulation analysis lacking experimental tests of human drivers. Method Thirty-six drivers were classified into three driver groups (aggressive, moderate, and defensive drivers) and experienced HV-AV interaction and HV-HV interaction in a supervised web-based experiment. Drivers’ subjective feelings and decision making were collected via questionnaires. Results Results revealed that aggressive and moderate drivers felt significantly more anxious, less comfortable, and were more likely to behave aggressively in HV-AV interaction than in HV-HV interaction. Aggressive drivers were also more likely to take advantage of AVs on the road. In contrast, no such differences were found for defensive drivers indicating they were not significantly influenced by the type of vehicles with which they were interacting. Conclusion Driving style and type of interaction significantly influenced drivers’ subjective feelings and decision making in mixed traffic. This study brought insights into how human drivers perceive and interact with AVs and HVs on the road and how human drivers take advantage of AVs. Application This study provided a foundation for developing guidelines for mixed transportation systems to improve driver safety and user experience.

Machines meet humans on the social road: Risk implications

“…We surveyed comparative empirical works from the fields of human factors, behavioral science, and traffic safety. These works employed various methods, including survey experiments (e.g., Lee et al., 2021; Liu et al., 2020, 2022; Ma & Zhang, in press), driving simulators (e.g., Chityala et al., 2020; de Zwart et al., 2023; Gouy et al., 2014; Lee et al., 2018; Razmi Rad et al., 2021; Schoenmakers et al., 2021), test‐tracks (e.g., Soni et al., 2022; Zhao et al., 2020), on‐road observations (e.g., Li et al., 2023; Wen et al., 2022), and AV accident analysis (e.g., Goodall, 2021; Liu et al., 2021; McCarthy, 2022).…”

“…(2020) documented that drivers showed a greater intention to be aggressive toward AVs versus HVs, even without knowledge of AVs’ defensive style. Ma and Zhang (in press) informed participants about AVs’ defensive style and found that aggressive and moderate drivers were more likely to behave aggressively when following AVs compared to HVs.…”

A dilemma study of the traffic flow system emerged due to the lane-change by follower’s tailgating effect

“…In the proposed traffic model, an appropriate strategy would be quantified for the cooperative (C) vehicles and defective (D) vehicles to ensure a maximum social benefit from under dilemma environment. To maximize the social payoff, several kinds of CA models [1][2][3][4][5] have been studied considering various phenomena such as cluster or platoons car following model [6][7][8][9][10][11][12][13], human-automated mixed traffic model [14][15][16][17][18], route section traffic model following the traffic forecasting [19][20][21][22][23][24][25], and some models introduced advanced lane changing protocol based on the preceding vehicle or vehicle cluster [26][27][28]. These models didn't pay attention to the following car or vehicle cluster, which is an influential factor in enhancing the social benefits.…”

Driving In Mixed Traffic: Will Increased Penetration Rates of Automated Vehicles Change Driver Behaviors?

“…Despite the growing prevalence of AVs, few studies have focused on driver performance during HV-AV interactions. A major concern of the HV-AV interaction is that human drivers may “bully” AVs and behave more aggressively if they perceive AVs to minimize their driving risks (Ma & Zhang, 2022). Prior research on mixed traffic concentrated on the simulation of mixed-traffic characteristics and the development of automated vehicle algorithms (e.g., Arvin et al, 2020; Sinha et al, 2020; Ye & Yamamoto, 2019).…”