Effects of Driving Experience and Traffic Density on the Performance of Takeover in Autonomous Driving

Abstract

This study examined the effects of driving experience and traffic density on driver performance in a Level 3 automated driving vehicle during takeover scenarios. The experiment was conducted using a six-degree-of-freedom driving simulator. A total of fifty-one driving volunteers were divided into two categories based on driving experience (twenty-six novices and twenty-five experienced). The volunteers were presented with two conflict scenarios while autonomous driving mode was engaged. Two different traffic densities (ten vehicles per km and thirty vehicles per km) were studied and driver responses were evaluated using metrics such as takeover time, vehicle control, and postural changes. The results demonstrated that driving experience significantly enhances driver performance, with experienced drivers exhibiting reduced lane offset and lower lateral accelerations. Experienced drivers outperform novice drivers in relation to lateral vehicle stability. The results also indicate the potentially protective effect of higher traffic flow density, namely significantly reducing takeover time, in contrast to previous findings. A better understanding of traffic and driver experience on takeover response provides important information for advanced driver-assistance system development. In addition, the differing results suggest that driving cultures should be further investigated in conjunction with automated driving and takeover procedures. The drivers’ physical reactions during conflict scenarios could be grouped in four posture categories. Of these four postures, sharp turning posture and single-handed takeover posture were identified as high-risk positions. These postures may increase the injury risk because of the driver’s position relative to the airbag module.