Perceived risk and acceptance of automated vehicles users to unexpected hazard situations in real driving conditions

被引:0
|
作者
Perez-Moreno, Elisa [1 ]
Naranjo, Jose E. [2 ]
Hernandez, Maria J. [1 ]
Ruiz, Trinidad [1 ]
Valle, Alfredo [2 ]
Cruz, Alberto [2 ]
Serradilla, Francisco [2 ]
Jimenez, Felipe [2 ]
机构
[1] Univ Complutense Madrid, Fac Psychol, Madrid, Spain
[2] Univ Politecn Madrid, Univ Inst Automobile Res INSIA, Madrid, Spain
关键词
Automated vehicles; perceived risk; acceptance; pupil dilation; safety; unexpected hazards; INFORMATION-TECHNOLOGY; BEHAVIORAL ADAPTATION; DRIVERS; PERSONALITY; WORKLOAD; DILEMMA; TRUST; LOAD; CAR;
D O I
10.1080/0144929X.2025.2449982
中图分类号
TP3 [计算技术、计算机技术];
学科分类号
0812 ;
摘要
The decision-making systems in highly automated vehicles use artificial intelligence (AI) techniques to replicate human-like behaviour in driving. However, when studying the acceptance of such vehicles, analysing the human factor is essential if their deployment is to be a success. This paper presents an analysis, based on real driving conditions, of perceived risk and occupant acceptance of an SAE level 4 automated vehicle (where the user adopts only the role of a passenger) in two unforeseen situations involving risk, one in which the life of a pedestrian may be endangered and the other in which the life of the user himself may be endangered. To assess perceived risk, pupil diameter, a physiological variable closely related to emotional arousal, was measured. The study involved 40 participants. The study revealed that perceived risk is significantly higher when the user's personal safety is compromised, reinforcing the principle of prioritising passenger safety in autonomous vehicle design. Key findings include an inverse correlation between perceived risk and driving experience, and no evidence suggesting that prior automated vehicle experience modifies future usage expectations. The research emphasises the critical role of human-centered design in AI-driven decision-making systems, highlighting the importance of understanding psychological and physiological responses to autonomous technology.
引用
收藏
页数:18
相关论文
共 32 条
  • [21] Public Acceptance of Fully Automated Driving: Effects of Social Trust and Risk/Benefit Perceptions
    Liu, Peng
    Yang, Run
    Xu, Zhigang
    RISK ANALYSIS, 2019, 39 (02) : 326 - 341
  • [22] Risk Assessment in the Context of Dynamic Reconfiguration of Level of Driving Automation in Highly Automated Vehicles
    Panagiotopoulos, Ilias E.
    Karathanasopoulou, Konstantina N.
    Dimitrakopoulos, George J.
    2021 INTERNATIONAL CONFERENCE ON COMPUTATIONAL SCIENCE AND COMPUTATIONAL INTELLIGENCE (CSCI 2021), 2021, : 1868 - 1873
  • [23] How to Display Vehicle Information to Users of Automated Vehicles When Conducting Non-Driving-Related Activities
    Dandekar A.
    Mathis L.-A.
    Berger M.
    Pfleging B.
    Proceedings of the ACM on Human-Computer Interaction, 2022, 6 (MHCI)
  • [24] Autonomous Vehicles Acceptance: A Perceived Risk Extension of Unified Theory of Acceptance and Use of Technology and Diffusion of Innovation, Evidence from Tehran, Iran
    Farzin, Iman
    Mamdoohi, Amir Reza
    Ciari, Francesco
    INTERNATIONAL JOURNAL OF HUMAN-COMPUTER INTERACTION, 2023, 39 (13) : 2663 - 2672
  • [25] Underlying dimensions of benefit and risk perception and their effects on people’s acceptance of conditionally/fully automated vehicles
    Yukari Jessica Tham
    Takaaki Hashimoto
    Kaori Karasawa
    Transportation, 2022, 49 : 1715 - 1736
  • [26] Driver models for the definition of safety requirements of automated vehicles in international regulations. Application to motorway driving conditions
    Mattas, Konstantinos
    Albano, Giovanni
    Dona, Riccardo
    Galassi, Maria Christina
    Suarez-Bertoa, Ricardo
    Vass, Sandor
    Ciuffo, Biagio
    ACCIDENT ANALYSIS AND PREVENTION, 2022, 174
  • [27] Acceptance of Automated Shuttles-Application and Extension of the UTAUT-2 Model to Wizard-of-Oz Automated Driving in Real-Life Traffic
    Rybizki, Anne
    Ihme, Klas
    Nguyen, Hoai Phuong
    Onnasch, Linda
    Bosch, Esther
    FUTURE TRANSPORTATION, 2022, 2 (04): : 1010 - 1027
  • [28] Remote driving as the Failsafe: Qualitative investigation of Users' perceptions and requirements towards the 5G-enabled Level 4 automated vehicles
    Li, Shuo
    Zhang, Yanghanzi
    Blythe, Phil
    Edwards, Simon
    Ji, Yanjie
    TRANSPORTATION RESEARCH PART F-TRAFFIC PSYCHOLOGY AND BEHAVIOUR, 2024, 100 : 211 - 230
  • [29] Tactical Decisions for Lane Changes or Lane Following: Assessment of Automated Driving Styles Under Real-World Conditions
    Ossig, Johannes
    Cramer, Stephanie
    Eckl, Anna
    Bengler, Klaus
    IEEE TRANSACTIONS ON INTELLIGENT VEHICLES, 2023, 8 (01): : 502 - 511
  • [30] Dynamic Driving Risk Potential Field Model Under the Connected and Automated Vehicles Environment and Its Application in Car-Following Modeling
    Li, Linheng
    Gan, Jing
    Ji, Xinkai
    Qu, Xu
    Ran, Bin
    IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS, 2022, 23 (01) : 122 - 141