This study investigates how pedestrian trust, receptivity, and behavior evolve during interactions with Level-4 autonomous vehicles (AVs) at uncontrolled urban intersections in a naturalistic setting. While public acceptance is critical for AV adoption, most prior studies relied on simplified simulations or field tests. We conducted a real-world experiment in a commercial Robotaxi operation zone, where 33 participants repeatedly crossed an uncontrolled intersection with frequent Level-4 Robotaxi traffic. Participants completed the Pedestrian Behavior Questionnaire (PBQ), Pedestrian Receptivity Questionnaire for Fully AVs (PRQF), pre- and post-experiment Trust in AVs Scale, and Personal Innovativeness Scale (PIS). Results showed that trust in AVs significantly increased post-experiment, with the increase positively associated with the Interaction component of PRQF. Additionally, both the Positive and Error subscales of the PBQ significantly influenced trust change. This study reveals how trust forms in real-world pedestrian-AV encounters, offering insights beyond lab-based research by accounting for population heterogeneity.
AgarwalR.PrasadJ. (1998). A conceptual and operational definition of personal innovativeness in the domain of information technology. Information Systems Research, 9(2), 204–215.
2.
BeggiatoM.WitzlackC.SpringerS.KremsJ. (2018). The right moment for braking as informal communication signal between automated vehicles and pedestrians in crossing situations. In Advances in human aspects of transportation: Proceedings of the AHFE 2017 international conference on human factors in transportation, July 17− 21, 2017, The Westin Bonaventure Hotel, Los Angeles, CA (pp. 1072-1081). Springer International Publishing.
3.
ChangX.ChenZ.DongX.CaiY.YanT.CaiH.LiuQ.WangY.XuL.ZhangM.LiF.HuS.ZhaoT.LinR.FengJ.LuC.MaK.ZhouY.GongJ. (2024, May). “It must be gesturing towards me”: Gesture-based interaction between autonomous vehicles and pedestrians. In Proceedings of the CHI conference on human factors in computing systems (pp. 1–25). Association for Computing Machinery
4.
ClamannM.AubertM.CummingsM. L. (2017). Evaluation of vehicle-to-pedestrian communication displays for autonomous vehicles (No. 17-02119). Transportation Research Board.
5.
de MiguelM. Á.FuchshuberD.HusseinA.Olaverri-MonrealC. (2019, June). Perceived pedestrian safety: Public interaction with driverless vehicles. In 2019 IEEE intelligent vehicles symposium (IV) (pp. 90–95). IEEE.
6.
DebS.StrawdermanL.CarruthD. W.DuBienJ.SmithB.GarrisonT. M. (2017). Development and validation of a questionnaire to assess pedestrian receptivity toward fully autonomous vehicles. Transportation Research Part C: Emerging Technologies, 84, 178–195.
7.
DebS.StrawdermanL.DuBienJ.SmithB.CarruthD. W.GarrisonT. M. (2017). Evaluating pedestrian behavior at crosswalks: Validation of a pedestrian behavior questionnaire for the US population. Accident Analysis & Prevention, 106, 191–201.
8.
DommesA.DouffetB.PalaP.DebS.GraniéM. A. (2024). Pedestrians’ receptivity to fully automated vehicles: Assessing the psychometric properties of the PRQF and survey in France. Transportation Research Part F: Traffic Psychology and Behaviour, 105, 163–181.
9.
HoffK. A.BashirM. (2015). Trust in automation: Integrating empirical evidence on factors that influence trust. Human factors, 57(3), 407–434.
10.
HancockP. A.NourbakhshI.StewartJ. (2019). On the future of transportation in an era of automated and autonomous vehicles. Proceedings of the National Academy of Sciences, 116(16), 7684–7691
11.
HegnerS. M.BeldadA. D.BrunswickG. J. (2019). In automatic we trust: Investigating the impact of trust, control, personality characteristics, and extrinsic and intrinsic motivations on the acceptance of autonomous vehicles. International Journal of Human–Computer Interaction, 35(19), 1769–1780.
12.
HochmanM.ParmetY.Oron-GiladT. (2020). Pedestrians’ understanding of a fully autonomous vehicle’s intent to stop: A learning effect over time. Frontiers in Psychology, 11, 585280.
13.
KrausJ.ScholzD.BaumannM. (2021). What’s driving me? Exploration and validation of a hierarchical personality model for trust in automated driving. Human Factors, 63(6), 1076-1105.
14.
LeeE. R.NohH.ParkB. U. (2014). Model selection via Bayesian information criterion for quantile regression models. Journal of the American Statistical Association, 109(505), 216–229.
15.
LeeJ. D.SeeK. A. (2004). Trust in automation: Designing for appropriate reliance. Human Factors, 46(1), 50–80.
16.
MahadevanK.SanoubariE.SomanathS.YoungJ. E.SharlinE. (2019, June). AV-pedestrian interaction design using a pedestrian mixed traffic simulator. In Proceedings of the 2019 on Designing Interactive Systems Conference (pp. 475–486). Association for Computing Machinery.
17.
ManchonJ. B.BuenoM.NavarroJ. (2022). How the initial level of trust in automated driving impacts drivers’ behaviour and early trust construction. Transportation Research Part F: Traffic Psychology and Behaviour, 86, 281–295.
18.
MerrittS. M.IlgenD. R. (2008). Not all trust is created equal: Dispositional and history-based trust in human-automation interactions. Human Factors, 50(2), 194–210.
19.
MuirB. M.MorayN. (1996). Trust in automation. Part II. Experimental studies of trust and human intervention in a process control simulation. Ergonomics, 39(3), 429–460.
20.
NordhoffS.LehtonenE. (2025). Examining the effect of personality on user acceptance of conditionally automated vehicles. Scientific Reports, 15(1), 1091.
21.
On-Road Automated Driving (ORAD) Committee. (2021). Taxonomy and definitions for terms related to driving automation systems for on-road motor vehicles. SAE International.
22.
RasouliA.TsotsosJ. K. (2019). Autonomous vehicles that interact with pedestrians: A survey of theory and practice. IEEE Transactions on Intelligent Transportation Systems, 21(3), 900–918.
23.
RasouliA.KotserubaI.TsotsosJ. K. (2017). Understanding pedestrian behavior in complex traffic scenes. IEEE Transactions on Intelligent Vehicles, 3(1), 61–70.
24.
TrautmanP.KrauseA. (2010, October). Unfreezing the robot: Navigation in dense, interacting crowds. In 2010 IEEE/RSJ international conference on intelligent robots and systems (pp. 797–803). IEEE.
25.
VelascoJ. P. N.LeeY. M.UttleyJ.SolernouA.FarahH.van AremB.HagenziekerM.MeratN. (2021). Will pedestrians cross the road before an automated vehicle? The effect of drivers’ attentiveness and presence on pedestrians’ road crossing behavior. Transportation Research Interdisciplinary Perspectives, 12, 100466.
26.
WangZ.HuH.SunM.HeD. (2024, September). An Exploration of users’ trust in and willingness to pay for fully driverless vehicles after their first ride. In Proceedings of the human factors and ergonomics society annual meeting (Vol. 68, No. 1, pp. 861–865). SAGE Publications.
27.
WinkleT. (2016). Safety benefits of automated vehicles: Extended findings from accident research for development, validation and testing. In Autonomous driving: Technical, legal and social aspects (pp. 335–364). Springer Berlin Heidelberg.
28.
XieW.WangZ.HeD. (2025). Effect of riding experience and HMI on users’ trust and riding comfort in fully driverless autonomous vehicles: An on-road study. Applied Ergonomics, 129, 104580.
29.
ZhouS.SunX.LiuB.BurnettG. (2021). Factors affecting pedestrians’ trust in automated vehicles: Literature review and theoretical model. IEEE Transactions on Human-Machine Systems, 52(3), 490–500.
