Nonlinear coupling of cognitive distraction and potential vehicle-pedestrian conflicts: Dynamic quantification of...

Cognitive distraction and potential vehicle-pedestrian conflicts are common crash risk factors, and their co-occurrence may compound the risk. Prior research has not fully characterized behavior changes, risk evolution over time, or interaction mechanisms in this combined setting. To address these gaps, we conducted a driving-simulator study with 38 licensed drivers, examining their behavioral responses to potential vehicle-pedestrian conflict scenarios under varying levels of cognitive distraction. We employed a sliding time-window approach combined with machine-learning methods to dynamically identify driving states. Multi-source indicators, including visual perception, vehicle kinematics, and response decision behavior, were integrated to develop a real-time quantitative model linking distraction level to driving risk. Results indicated that under baseline driving, drivers exhibited higher visual workload, broader environmental scanning, and more conservative control in the covert occlusion-zone scenario than in the overt noncompliant-crossing scenario. As secondary-task complexity increased, gaze became progressively more concentrated in the near-forward field, and fixation and braking response times lengthened, and control became more disordered. Consequently, behavioral differences between scenarios narrowed, and coping strategies converged under high-complexity tasks. Real-time estimates showed stepwise increases in both distraction level and driving risk as task complexity rose. Under high-complexity conditions, risk became more volatile, and peak-risk differences between scenarios nearly disappeared. These findings clarify the dynamic coupling between cognitive distraction and potential vehicle-pedestrian conflict risk, informing driver-state-aware assistance systems and context-adaptive intervention strategies.