Differed Risk Perception in Manual and Automated Driving: An Empirical Study of Varied Conditions

Abstract

The interface of L3 automated driving systems needs to take both automated and manual driving into consideration. This calls for a comprehension of drivers’ differed risk perception under manual and automated driving conditions, thus supporting description of contextual information and appropriate risk warnings. Existing studies have reported drivers’ impaired ability during automated driving, however, a quantified measurement is still lacking. This study tried to measure the difference in drivers’ ability to perceive risks during automated and manual driving. Specifically, a simulated driving experiment in car-following scenarios was conducted to collect drivers’ perceived risk under multiple manual and automated driving conditions, including varied motion directions, speed, and distance among vehicles. Then, the influences of driving mode, motion directions, speed, and distance on drivers’ risk perceptions were described using a linear mixed model. The result demonstrated a complicated interaction effect. Automated driving impaired drivers’ risk perception, and this effect was less severe in highly risky events. In both manual and automated driving, drivers were less sensitive to risk only when risky events happened backwards. These results indicated drivers’ varied ability under multiple conditions, and supported warning design and interface refinement under automated and manual driving conditions.