Autonomous vehicle lane-change maneuver accounting for emotion-induced driving behavior in other vehicles

Abstract

Lane-change maneuvers are a critical aspect of autonomous vehicles operation, but executing them efficiently and safely in the presence of other vehicles with varying driving behaviors, influenced by drivers’ emotions, poses a significant challenge. This paper presents a novel decision-making framework with trajectory generation and control algorithm, which considers the emotion-induced driving behavior of other vehicles’ drivers to perform safe and efficient lane-change maneuvers. The algorithm generates smooth trajectory candidates based on the position and velocity of other vehicles, selecting the most efficient and safest option. The control system tracks the generated lane-change trajectory, allowing the autonomous vehicle to pass the other vehicle if the driver is in a “happy,” “calm,” or “neutral” emotional state, exhibiting cautious behavior such as maintaining or reducing speed. Conversely, if the other vehicle’s driver is in an “angry” or “unpleasant” emotional state, causing aggressive behavior like accelerating and not allowing the autonomous vehicle to pass, the control system ensures the autonomous vehicle stays on its previous lane. Simulation and experimental results demonstrate that the proposed algorithm enables autonomous vehicles to perform lane-change maneuvers safely and efficiently in the presence of the other vehicle’s driver’s emotions, mitigating collisions. This proposed algorithm represents a significant step toward enabling autonomous vehicles to navigate complex traffic scenarios involving other vehicles with varying driving emotions.