Modeling driver cognitive states for shared-control vehicles in intelligent transportation systems

Abstract

This study aims to model driver cognitive states for shared-control vehicles operating within intelligent transportation systems. We introduce driving activity level as an interpretable state variable that combines attention allocation, steering behavior, and perceived situational pressure for adaptive authority allocation. A driving simulator experiment with 12 licensed drivers was organized as a four-by-four factorial design involving distraction tasks, pressure tasks, and crossover conditions. Eye-movement measures, steering-derived variables, and a perceived-pressure model were integrated through principal component analysis to construct activity and performance indicators, and a feedforward neural network was trained to estimate activity level from gaze and pressure-related features. The results show an inverted U-shaped relationship between driving activity and performance: drivers performed best at moderate activity levels, whereas insufficient engagement and excessive pressure were associated with poorer control outcomes. The trained model produced consistent activity estimates in manual-driving and shared-control driving tests. These findings indicate that driving activity level can serve as a practical driver-state variable for real-time monitoring and adaptive authority allocation in intelligent transportation systems.