Drivers trust, acceptance, and takeover behaviors in fully automated vehicles: Effects of automated driving styles and driver's driving styles

被引:43
|
作者
Ma, Zheng [1 ]
Zhang, Yiqi [1 ]
机构
[1] Penn State Univ, Dept Ind & Mfg Engn, State Coll, PA 16801 USA
来源
基金
美国国家科学基金会;
关键词
Automated vehicles; Driving style; Trust; Takeover; E-GOVERNMENT SERVICES; USER ACCEPTANCE; INTEGRATING TRUST; TECHNOLOGY; RISKY; PERSONALITY; COMFORT; MODEL; AGE;
D O I
10.1016/j.aap.2021.106238
中图分类号
TB18 [人体工程学];
学科分类号
1201 ;
摘要
Automated Vehicle (AV) technology has the potential to significantly improve driver safety. Unfortunately, drivers could be reluctant to ride with AVs due to their lack of trust and acceptance of AVs' driving styles. The present study investigated the effects of the designed driving style of AV (aggressive/defensive) and driver's driving style (aggressive/defensive) on driver's trust, acceptance, and take-over behavior in a fully AV. Thirtytwo participants were classified into two groups based on their driving styles using the Aggressive Driving Scale and experienced twelve driving scenarios in either an aggressive AV or a defensive AV. Results revealed that driver's trust, acceptance, and takeover frequency were significantly influenced by the interaction effects between AV's driving style and driver's driving style. General estimating equations were conducted to analyze the relationships between driver's trust, acceptance, and take over frequency. The results showed that the effect of driver's trust in AVs on takeover frequency was mediated by driver's acceptance of AVs. These findings implied that driver's trust and acceptance of AVs could be enhanced when the designed AV's driving style aligned with driver's own driving style, which in turn, reduce undesired take over behavior. However, the "aggressive" AV driving style should be designed carefully considering driver safety.
引用
收藏
页数:14
相关论文
共 50 条
  • [41] Effectiveness and Driver Acceptance of Sharing Decision and Control in Automated Driving
    Muslim, Husam
    Liang, Cho Kiu
    Itoh, Makoto
    2020 IEEE INTERNATIONAL CONFERENCE ON SYSTEMS, MAN, AND CYBERNETICS (SMC), 2020, : 4447 - 4452
  • [42] Speech-based takeover requests in conditionally automated driving: Effects of different voices on the driver takeover performance
    Wang, Yi
    Zhang, Wei
    Zhou, Ronggang
    APPLIED ERGONOMICS, 2022, 101
  • [43] Drivers' gap acceptance during parking maneuvers as a basis for initiating driving actions in automated vehicles
    Hensch, Ann-Christin
    Beggiato, Matthias
    Krems, Josef F.
    TRANSPORTATION RESEARCH PART F-TRAFFIC PSYCHOLOGY AND BEHAVIOUR, 2023, 92 : 133 - 142
  • [44] Driver's Information Needs in Automated Driving
    Xing, Huining
    Qin, Hua
    Niu, Jianwei
    CROSS-CULTURAL DESIGN, 2017, 10281 : 736 - 744
  • [45] Driver Trust in Automated Driving Systems: The Case of Overtaking and Passing
    Abe, Genya
    Sato, Kenji
    Itoh, Makoto
    IEEE TRANSACTIONS ON HUMAN-MACHINE SYSTEMS, 2018, 48 (01) : 85 - 94
  • [46] THE INFLUENCE OF TRUST ON DRIVER COGNITIVE STATES DURING AUTOMATED DRIVING
    McDonnell, Amy
    Crabtree, Kaedyn
    Cooper, Joel
    Strayer, David
    PSYCHOPHYSIOLOGY, 2022, 59 : S104 - S105
  • [47] An Investigation of Drivers' Dynamic Situational Trust in Conditionally Automated Driving
    Ayoub, Jackie
    Avetisyan, Lilit
    Makki, Mustapha
    Zhou, Feng
    IEEE TRANSACTIONS ON HUMAN-MACHINE SYSTEMS, 2022, 52 (03) : 501 - 511
  • [48] Behavioral adaptation of drivers when driving among automated vehicles
    Aramrattana M.
    Fu J.
    Selpi J.
    Journal of Intelligent and Connected Vehicles, 2022, 5 (03): : 309 - 315
  • [49] An analysis of physiological responses as indicators of driver takeover readiness in conditionally automated driving
    Deng, Min
    Gluck, Aaron
    Zhao, Yijin
    Li, Da
    Menassa, Carol C.
    Kamat, Vineet R.
    Brinkley, Julian
    ACCIDENT ANALYSIS AND PREVENTION, 2024, 195
  • [50] Predicting the duration of reduced driver performance during the automated driving takeover process
    Wang, Changshuai
    Xu, Chengcheng
    Peng, Chang
    Tong, Hao
    Ren, Weilin
    Jiao, Yanli
    JOURNAL OF INTELLIGENT TRANSPORTATION SYSTEMS, 2024,