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Examining the Effects of Visibility and Time Headway on the Takeover Risk during Conditionally Automated Driving

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  • Haorong Peng

    (Tongji Architectural Design (Group) Co., Ltd., 1230 Siping Road, Yangpu, Shanghai 200092, China
    Shanghai Research Center for Smart Mobility and Road Safety, Shanghai 200092, China)

  • Feng Chen

    (The Key Laboratory of Road and Traffic Engineering, Ministry of Education, Tongji University, 4800 Cao’an Road, Jiading, Shanghai 201804, China)

  • Peiyan Chen

    (The Key Laboratory of Road and Traffic Engineering, Ministry of Education, Tongji University, 4800 Cao’an Road, Jiading, Shanghai 201804, China)

Abstract

The objective of this study is to examine the effects of visibility and time headway on the takeover performance in L3 automated driving. Both non-critical and critical driving scenarios were considered by changing the acceleration value of the leading vehicle. A driving simulator experiment with 18 driving scenarios was conducted and 30 participants complete the experiment. Based on the data obtained from the experiment, the takeover reaction time, takeover control time, and takeover responses were analyzed. The minimum Time-To-Collision (Min TTC) was used to measure the takeover risk level and a binary logit model for takeover risk levels was estimated. The results indicate that the visibility distance (VD) has no significant effects on the takeover control time, while the time headway (THW) and the acceleration of the leading vehicle (ALV) could affect the takeover control time significantly; most of the participants would push the gas pedal to accelerate the ego vehicle as the takeover response under non-critical scenarios, while braking was the dominant takeover response for participants in critical driving scenarios; decreasing the TCT and taking the appropriate takeover response would reduce the takeover risk significantly, so it is suggested that the automation system should provide the driver with the urgency of the situation ahead and the tips for takeover responses by audio prompts or the head-up display. This study is expected to facilitate the overall understanding of the effects of visibility and time headway on the takeover performance in conditionally automated driving.

Suggested Citation

  • Haorong Peng & Feng Chen & Peiyan Chen, 2022. "Examining the Effects of Visibility and Time Headway on the Takeover Risk during Conditionally Automated Driving," IJERPH, MDPI, vol. 19(21), pages 1-17, October.
    • Handle: RePEc:gam:jijerp:v:19:y:2022:i:21:p:13904-:d:953510
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    References listed on IDEAS

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    1. Hui Zhang & Yijun Zhang & Yiying Xiao & Chaozhong Wu, 2022. "Analyzing the Influencing Factors and Workload Variation of Takeover Behavior in Semi-Autonomous Vehicles," IJERPH, MDPI, vol. 19(3), pages 1-22, February.
    2. Chimou Li & Xiaonan Li & Ming Lv & Feng Chen & Xiaoxiang Ma & Lin Zhang, 2021. "How Does Approaching a Lead Vehicle and Monitoring Request Affect Drivers’ Takeover Performance? A Simulated Driving Study with Functional MRI," IJERPH, MDPI, vol. 19(1), pages 1-20, December.
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