Analyzing the Influencing Factors and Workload Variation of Takeover Behavior in Semi-Autonomous Vehicles

Hui Zhang, Yijun Zhang, Yiying Xiao and Chaozhong Wu
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Hui Zhang: Intelligent Transportation Systems Research Center, Wuhan University of Technology, Wuhan 430063, China
Yijun Zhang: Intelligent Transportation Systems Research Center, Wuhan University of Technology, Wuhan 430063, China
Yiying Xiao: Intelligent Transportation Systems Research Center, Wuhan University of Technology, Wuhan 430063, China
Chaozhong Wu: Intelligent Transportation Systems Research Center, Wuhan University of Technology, Wuhan 430063, China

IJERPH, 2022, vol. 19, issue 3, 1-22

Abstract: There are many factors that will influence the workload of drivers during autonomous driving. To examine the correlation between different factors and the workload of drivers, the influence of different factors on the workload variations is investigated from subjective and objective viewpoints. Thirty-seven drivers were recruited to participant the semi-autonomous driving experiments, and the drivers were required to complete different NDRTs (Non-Driving-Related Tasks): mistake finding, chatting, texting, and monitoring when the vehicle is in autonomous mode. Then, we introduced collision warning to signal there is risk ahead, and the warning signal was triggered at different TB (Time Budget)s before the risk, at which time the driver had to take over the driving task. During driving, the NASA-TLX-scale data were obtained to analyze the variation of the driver’s subjective workload. The driver’s pupil-diameter data acquired by the eye tracker from 100 s before the TOR (Take-Over Request) to 19 s after the takeover were obtained as well. The sliding time window was set to process the pupil-diameter data, and the 119-s normalized average pupil-diameter data under different NDRTs were fitted and modeled to analyze the variation of the driver’s objective workload. The results show that the total subjective workload score under the influence of different factors is as follows: obstacle-avoidance scene > lane-keeping scene; TB = 7 s and TB = 3 s have no significant difference; and mistake finding > chatting > texting > monitoring. The results of pupil-diameter data under different factors are as follows: obstacle-avoidance scene > lane-keeping scene; TB = 7 s > TB = 3 s; and monitoring type (chatting and monitoring) > texting type (mistake finding and texting). The research results can provide a reference for takeover safety prediction modeling based on workload.

Keywords: shared driving; workload; influencing factors; NASA-TLX scale (search for similar items in EconPapers)
JEL-codes: I I1 I3 Q Q5 (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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