Human-inspired emotion and attention encoding for autonomous vehicles' decision-making: a lane-change timing...

In mixed traffic environments, autonomous vehicle (AV) lane-changing requires coordination with multiple surrounding vehicles, posing significant challenges. Improper lane-change timing can lead to traffic accidents, so current AVs tend to adopt conservative strategies, especially on busy highways. However, flexible and reliable lane-changing is often crucial and sometimes indispensable; otherwise, it not only reduces efficiency but may also cause traffic disruptions. To address this, we draw on research from cognitive psychology and neuroscience to propose an emotion and attention encoding framework that enables AVs to change lanes in a human-like manner. Specifically, based on Cognitive Energy Theory, Attenuator Theory, and Prospect Theory, we construct the neural encoding processes of drivers' physiological arousal, subjective experience, attention allocation, and emotional utility under multiple risk stimuli. We introduce an emotional utility model (EUM) and a human-like lane-changing decision (HLD) method to help AVs adaptively optimize lane-change timing. Finally, we evaluate our approach using a data-driven simulation built from 3,129 lane-change segments. Results show that under the 3-sigma rule, the HLD achieves a lane-change rate exceeding 99.8%, with lane-change timings closely matching real driver behavior and demonstrating even greater safety. This success is mainly attributed to the EUM's ability to adaptively adjust weights according to the relative urgency of risks, thereby better balancing overall utility and individual risk. Furthermore, the success of this case study will provide insights for AVs' decision-making in other complex tasks and scenarios.