Game-theoretic Strategies in Mixed Traffic: A Simulator Study on the Impacts of Automated and Human Driving Styles

Abstract

As human-driven vehicles (HVs) and automated vehicles (AVs) increasingly share roadways, understanding their interactions is essential for traffic safety and efficiency. This driving simulator study using game-theoretic scenarios investigates how AV and human driving styles influence decision-making in mixed traffic. The findings showed that conservative AVs were more likely to be exploited, while aggressive human drivers acted less cooperatively. AV driving styles had a stronger impact in parallel interactions: aggressive AVs led to passive yet riskier human maneuvers, with shorter time-to-collision and higher lateral deceleration. Conservative drivers showed greater maximum counter-steering rate and lateral deceleration to adjust their intentions and avoid risks. In head-on interactions, drivers more often insisted on their right of way. Additionally, trajectory clustering demonstrated the differences in driver strategies in specific scenarios. These findings highlight the need for adaptive AV strategies to foster safe and cooperative mixed-traffic interactions.

Keywords

automated vehicles game theory mixed traffic human-machine cooperation

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