Impact of Age on Takeover Behavior in Automated Driving in Complex Traffic Situations: A Case Study of Beijing, China

Jianguo Gong, Xiucheng Guo, Lingfeng Pan, Cong Qi and Ying Wang
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Jianguo Gong: School of Transportation, Southeast University, No.2 Dongnandaxue Road, Nanjing 211189, China
Xiucheng Guo: School of Transportation, Southeast University, No.2 Dongnandaxue Road, Nanjing 211189, China
Lingfeng Pan: School of Transportation, Southeast University, No.2 Dongnandaxue Road, Nanjing 211189, China
Cong Qi: School of Transportation, Southeast University, No.2 Dongnandaxue Road, Nanjing 211189, China
Ying Wang: Department of Road Traffic Management, Beijing Police College, Beijing 102202, China

Sustainability, 2022, vol. 14, issue 1, 1-11

Abstract: Research on the influence of age on various automated driving conditions will contribute to an understanding of driving behavior characteristics and the development of specific automated driving systems. This study aims to analyze the relationship between age and takeover behavior in automated driving, where 16 test conditions were taken into consideration, including two driving tasks, two warning times and four driving scenarios. Forty-two drivers in Beijing, China in 2020 were recruited to participate in a static driving simulator with Level 3 (L3) conditional automation to obtain detailed test information of the recorded takeover time, mean speed and mean lateral offset. An ANOVA test was proposed to examine the significance among different age groups and conditions. The results confirmed that reaction time increased significantly with age and the driving stability of the older group was worse than the young and middle groups. It was also indicated that the older group could not adapt to complex tasks well when driving due to their limited cognitive driving ability. Additionally, the higher urgency of a scenario explained the variance in the takeover quality. According to the obtained influencing mechanisms, policy implications for the development of vehicle automation, considering the various driving behaviors of drivers, were put forward, so as to correctly identify the high-risk driving conditions in different age groups. For further research, on-road validation will be necessary in order to check for driving simulation-related effects.

Keywords: automated driving; older driver; driving condition; takeover time; takeover quality (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2022
References: View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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