Table_1_Pedestrian Trust in Automated Vehicles: Role of Traffic Signal and AV Driving Behavior.docx
Pedestrians' acceptance of automated vehicles (AVs) depends on their trust in the AVs. We developed a model of pedestrians' trust in AVs based on AV driving behavior and traffic signal presence. To empirically verify this model, we conducted a human–subject study with 30 participants in a virtual reality environment. The study manipulated two factors: AV driving behavior (defensive, normal, and aggressive) and the crosswalk type (signalized and unsignalized crossing). Results indicate that pedestrians' trust in AVs was influenced by AV driving behavior as well as the presence of a signal light. In addition, the impact of the AV's driving behavior on trust in the AV depended on the presence of a signal light. There were also strong correlations between trust in AVs and certain observable trusting behaviors such as pedestrian gaze at certain areas/objects, pedestrian distance to collision, and pedestrian jaywalking time. We also present implications for design and future research.
History
References
- https://doi.org//10.1007/s40890-016-0018-5
- https://doi.org//10.1007/BF01322022
- https://doi.org//10.1145/3239092.3265950
- https://doi.org//10.1145/3122986.3122989
- https://doi.org//10.1080/10447318.2015.1070549
- https://doi.org//10.1037/a0025208
- https://doi.org//10.1177/0018720819836343
- https://doi.org//10.1016/j.apergo.2017.03.007
- https://doi.org//10.1109/THMS.2018.2799523
- https://doi.org//10.1145/3122986.3123009
- https://doi.org//10.1016/j.trc.2019.05.025
- https://doi.org//10.1177/002224378101800313
- https://doi.org//10.1109/ITSC.2018.8569486
- https://doi.org//10.1080/01621459.1969.10500976
- https://doi.org//10.1016/j.promfg.2015.07.847
- https://doi.org//10.1016/j.ssci.2015.01.018
- https://doi.org//10.3389/fpsyg.2018.01336
- https://doi.org//10.1016/j.aap.2006.07.001
- https://doi.org//10.1177/0018720816678714
- https://doi.org//10.1177/0018720815625744
- https://doi.org//10.1016/j.autcon.2015.03.020
- https://doi.org//10.3758/BF03192848
- https://doi.org//10.1016/j.ssci.2017.10.001
- https://doi.org//10.1145/3173386.3177073
- https://doi.org//10.1207/s15327108ijap0303_3
- https://doi.org//10.1177/0893318999132007
- https://doi.org//10.1006/ijhc.1994.1007
- https://doi.org//10.1518/hfes.46.1.50.30392
- https://doi.org//10.2307/2578601
- https://doi.org//10.1177/154193128903301812
- https://doi.org//10.1111/risa.13143
- https://doi.org//10.1145/3173574.3174003
- https://doi.org//10.1016/j.aap.2018.03.018
- https://doi.org//10.1177/0739456X16675674
- https://doi.org//10.1037/e366972004-001
- https://doi.org//10.1016/S0020-7373(87)80013-5
- https://doi.org//10.1177/1541931213601184
- https://doi.org//10.1109/IVS.2017.7995730
- https://doi.org//10.1109/TIV.2017.2788193
- https://doi.org//10.1109/TITS.2019.2901817
- https://doi.org//10.1145/3239060.3239064
- https://doi.org//10.2753/MIS0742-1222260210
- https://doi.org//10.1016/j.trf.2009.02.003
- https://doi.org//10.1109/ivs.2016.7535361
- https://doi.org//10.1109/ICCVE.2014.7297637
- https://doi.org//10.1016/j.trb.2008.01.007
- https://doi.org//10.1111/j.2517-6161.1979.tb01084.x
- https://doi.org//10.1016/j.aap.2017.02.018
- https://doi.org//10.1111/j.1468-2958.1986.tb00092.x
- https://doi.org//10.1016/j.aap.2011.04.012
- https://doi.org//10.1177/0018720812443825
- https://doi.org//10.1177/0018720815580749
- https://doi.org//10.1016/j.trc.2018.07.024
- https://doi.org//10.1016/j.trc.2018.11.018
- https://doi.org//10.1145/3122986.3122988
Usage metrics
Read the peer-reviewed publication
Categories
- Artificial Intelligence and Image Processing
- Adaptive Agents and Intelligent Robotics
- Artificial Life
- Computer Vision
- Image Processing
- Bioethics (human and animal)
- Artificial Intelligence and Image Processing not elsewhere classified
- Control Systems, Robotics and Automation
- Applied Ethics not elsewhere classified
CC BY 4.0