Unveiling Driver Workload Dynamics and Road Safety Risks in Assistant and Automated Driving Systems

Abstract

The progressive introduction of driving automation is profoundly transforming driver-vehicle interaction. Although automated driving systems are designed to improve vehicle stability and reduce driver workload, their influence on driver behavior especially during transitions between manual and automated control remains insufficiently understood.

The aim of this study is to investigate how different driving modes affect lateral vehicle control and visual behavior during curve negotiation, with specific attention to the transition from manual driving to full automation and the subsequent return to manual control. A simulator-based experiment was conducted in which drivers sequentially experienced three road segments: manual driving, fully automated driving, and manual driving resumed after automation.

A two-stage analytical approach was adopted. First, one-way analyses of variance were performed to assess the effect of driving mode on lateral trajectory (Lane Gap) and on two temporally defined visual indices related to curve negotiation. Subsequently, a fuzzy c-means clustering analysis was applied to the same variables to identify latent multivariate behavioral states, allowing the representation of uncertainty and gradual transitions in driver behavior.

The results revealed significant effects of driving mode on both vehicle trajectory and visual engagement. Automated driving was associated with reduced lateral variability and diminished anticipatory and guidance-related visual engagement. After automation, manual driving behavior showed partial recovery but did not fully return to pre-automation patterns.

These findings provide insights relevant for road safety and design of human–machine interfaces and demonstrate the value of studying driver behavior in automated driving contexts.

Keywords: driving automation, Curve negotiation, Lateral vehicle control, Visual behavior