Human-machine cooperation strategies in game-theoretic scenarios within mixed traffic: A simulator study on driving styles

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. Our findings show that AV driving styles had a significantly stronger impact in parallel scenarios. 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. Regarding different drivers, conservative drivers showed greater maximum counter-steering rate and lateral deceleration to adjust their intentions and avoid risks. Scenario types significantly influenced drivers’ strategies. Drivers showed a higher tendency to defect in head-on scenarios by asserting their right of way. Trajectory clustering reflected differences in proactive versus reactive adjustments in specific scenarios. These findings highlight the need for adaptive AV strategies to foster safe and cooperative mixed-traffic interactions.