The Influence of Incentives on Drivers’ Aggressive Behaviors Toward Automated Vehicles

Abstract

In the process of merging into the mixed traffic flow composed of human driven vehicles, automated vehicles may be attacked by human drivers. The purpose of this study was to compare the inhibitory effect of four incentive measures, including reward, punishment, education, and warning, on aggressive behaviors of drivers in automated vehicles and to explore their impact mechanism. In this study, 410 participants were recruited and divided into five groups, one of which was the control group, and the other four groups were given an incentive measure. The study found that reward and punishment significantly inhibit aggressive behaviors, although their influence mechanisms are different, while education and warning have no significant inhibition effect. Our research expands the theory of human-vehicle interaction, enriches the specific measures to inhibit the aggressive behavior of humans to autonomous driving, and provides some empirical suggestions for the future hybrid traffic situation of humans and vehicles. In the face of the upcoming mixed traffic of people and vehicles in the future, in order to ensure the good order of the road, it is necessary to take effective measures against human drivers to reduce their attacks on automated vehicles. On the one hand, relevant government departments need to play a guiding role in educating human drivers; on the other hand, manufacturers in related industries need to consider the above factors in the design of automated vehicles functions to make human-vehicle hybrid traffic safe and efficient.