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

被引:4
作者
Peng, Haorong [1 ,2 ]
Chen, Feng [3 ]
Chen, Peiyan [3 ]
机构
[1] Tongji Architectural Design Grp Co Ltd, 1230 Siping Rd, Shanghai 200092, Peoples R China
[2] Shanghai Res Ctr Smart Mobil & Rd Safety, Shanghai 200092, Peoples R China
[3] Tongji Univ, Key Lab Rd & Traff Engn, Minist Educ, 4800 Caoan Rd, Shanghai 201804, Peoples R China
关键词
conditionally automated driving; takeover performance; driving simulator; fog condition; time headway; VEHICLE; PERFORMANCE; LOOP; BACK;
D O I
10.3390/ijerph192113904
中图分类号
X [环境科学、安全科学];
学科分类号
08 ; 0830 ;
摘要
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.
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页数:17
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共 27 条
  • [1] Behavioral changes to repeated takeovers in automated driving: The drivers' ability to transfer knowledge and the effects of takeover request process
    Brandenburg, Stefan
    Roche, Fabienne
    [J]. TRANSPORTATION RESEARCH PART F-TRAFFIC PSYCHOLOGY AND BEHAVIOUR, 2020, 73 : 15 - 28
  • [2] Effects of cognitive and visual loads on driving performance after take-over request (TOR) in automated driving
    Choi, Damee
    Sato, Toshihisa
    Ando, Takafumi
    Abe, Takashi
    Akamatsu, Motoyuki
    Kitazaki, Satoshi
    [J]. APPLIED ERGONOMICS, 2020, 85
  • [3] Takeover Time in Highly Automated Vehicles: Noncritical Transitions to and From Manual Control
    Eriksson, Alexander
    Stanton, Neville A.
    [J]. HUMAN FACTORS, 2017, 59 (04) : 689 - 705
  • [4] Examining the effect of road horizontal alignment on the speed of semi-automated vehicles
    Garcia, Alfredo
    Camacho-Torregrosa, Francisco Javier
    Baez, Pedro Vinicio Padovani
    [J]. ACCIDENT ANALYSIS AND PREVENTION, 2020, 146
  • [5] Modeling take-over performance in level 3 conditionally automated vehicles
    Gold, Christian
    Happee, Riender
    Bengler, Klaus
    [J]. ACCIDENT ANALYSIS AND PREVENTION, 2018, 116 : 3 - 13
  • [6] Huang Y, 2024, TRANSPORTMETRICA A, V20, P1, DOI [10.1007/978-981-16-7751-9_1, 10.1080/23249935.2022.2048917]
  • [7] How Does Approaching a Lead Vehicle and Monitoring Request Affect Drivers' Takeover Performance? A Simulated Driving Study with Functional MRI
    Li, Chimou
    Li, Xiaonan
    Lv, Ming
    Chen, Feng
    Ma, Xiaoxiang
    Zhang, Lin
    [J]. INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH, 2022, 19 (01)
  • [8] Investigation of older driver's takeover performance in highly automated vehicles in adverse weather conditions
    Li, Shuo
    Blythe, Phil
    Guo, Weihong
    Namdeo, Anil
    [J]. IET INTELLIGENT TRANSPORT SYSTEMS, 2018, 12 (09) : 1157 - 1165
  • [9] Understanding take-over performance of high crash risk drivers during conditionally automated driving
    Lin, Qingfeng
    Li, Shiqi
    Ma, Xiaowei
    Lu, Guangquan
    [J]. ACCIDENT ANALYSIS AND PREVENTION, 2020, 143
  • [10] [刘通 Liu Tong], 2020, [中国公路学报, China Journal of Highway and Transport], V33, P170