Popular scientific abstract
Automated vehicles (AV) will be applied on urban transport and share the space with traditional vehicles with the benefit of reducing the car crashes caused by the human error, harmful emissions and congestion. Similar to all the technology’s emerging period, the miscellaneous considerations from the users are consequent unwound to evaluate and improve the technology based on their user experience, behaviors adaptation, receptivity and trust. As the increase of AV on road, the vulnerable road users (VRU), including pedestrian, cyclist, motorcyclist and the user of scooter are exposed to the unfamiliar traffic environment than ever when they have to adapt the increase of AV on road, will this adaptation developing as time goes or what facilitates their intentions to adapt? Are they truly trust the AV technology or will the trust enhanced with the repeated exposure or improved technology? Will their overall experience change as the repetitive interactions with AV or what influence that experience? Are these results are diverse based on the personality, social status, demographic and other factors? Conversely, by figuring out these questions, the engineers will have the cues to improve the AV with practical feedbacks on road with safety and better performance.
Meanwhile, in conventional traffic scenarios, determinations such “yield or not ”or “who go first” are operated by implicit negotiations between road users and drivers, when pedestrians can refer to the eye contacts and gestures from the drivers. However, with the control taken over from driver to automation functions, the pedestrians are deprived of these cues to communicate with the vehicles leading to an ambiguous even risky crossing experience. Therefore, studying how the interactions between AV/VRU contribute to the design of more functionable, reliable and acceptable external AV interfaces to communicate their intent to road users thus ensuring safety in the mixed urban traffic environment of tomorrow.
Overall, I will investigate the interactions between VRU and AV and the long-term effects such as experience, adoption and trust as well as its relation to the AV eHMI design strategies to accelerate the future AV technology and extensive use.
My affiliation
My host university is University of Leeds.
Contacts of supervisors:
Prof Natasha Merat (Leeds) : N.Merat@its.leeds.ac.uk
Prof Klaus Bengler (TUM) : bengler@tum.de
Dr Yee Mun Lee (Leeds) : Y.M.Lee@leeds.ac.uk
Background
Existing research on new technology focuses on the behavioral intention to use a technology (BI) as a measure of acceptance and seeks to identify influencing factors as the predictors of BI. Researchers have proposed various conceptual modal of technology acceptance such as Theory of Reasoned Action (TRA) model (Fisbein and Ajzen, 1975), Theory of Planned Behavior (TPB) model (Ajzen,1991), Unified Theory of Acceptance and Use of Technology (UTAUT) model (Venkatesh et al. ,2003), with the influential factors of effort expectancy, performance expectancy, social influence and facilitating conditions to predict the intention (Fisbein and Ajzen, 1975; Ajzen,1991; Venkatesh et al. ,2003). With the introduction of AV system, the specific acceptance models are introduced with additional factors of anxiety, perceived safety, attitude towards new technology and fun in the Car Technology Acceptance Model (CTAM) (Osswald et al.,2012) and Automation Acceptance Model (AAM)( Ghazizadeh et al.,2012). Most factors and studies are based on surveys or interviews based on text other than the real world experience when their perceptions and responses are guided by the description provided by the text sources (Rahman et al.,2017). To better investigate the acceptance of VRU towards AV, my research will conduct field experiments make use of the facility in HIKER lab in University of Leeds to validate these models and evaluate the most functioning factors in real case. Meanwhile, the current studies provide the static overview of the results from the observation or studies, my study will focus more on a dynamic and longer-terms effects.
Similarly, the possible effects that can be researched within the interactions between VRU/AV are receptivity and trust. Following the same research process of conducting literature reviews, comparing the existing conceptual modal, evaluating relevant factors and designing empire experiments to validate the results.
Aims and objectives
Aims:
The aim of this PhD projects is a) to train the Marie Curie PhD the complementary skills in collaboration and methods regarding the human factors in AV area as well as in industrial field study, and b)systematical outcomes of interactions between AV/VRU with the overall experience, adoption and trust modal in long-term exposure.
Objectives:
- Understand how VRUs’ experience, trust, and acceptance of AVs change with long-term/repeated exposure to AVs in urban traffic.
- Determine whether these effects are influenced by user characteristics such as gender, age, and personality.
- Evaluate the influence of implicit and explicit AV design strategies on VRUs’ experience, trust, and acceptance after their long-term/repeated exposure to AV.
Results
The following is an overview of Yue’s work in relation to the objectives and expected results.
We investigated patterns of learning strategies of VRUs exposed to AVs in a range of road-crossing scenarios using different metrics. In Yang et al. (2024), we evaluated how a zebra crossing, AV’s yielding behaviour and eHMI affect head movements across exposures, using a CAVE-based pedestrian simulator. The metrics that we investigated include absolute head-turning rate and head-turning frequency. In relation to learning strategies, a negative relationship was found between the number of exposures to the AVs and head-turning frequency, implying the lesser demand for information acquisition with the increased exposures. In this study, we also learned that pedestrians exhibit a significantly increased head-turning rate around 1s before the crossing initiation, indicating a ‘last-second check’ before crossing. The increase in head-turning rate could be a good indicator of pedestrians’ intention to cross, which AVs’ sensors could use as a cue to recognise intention.
In Yang et al. (2024), we found that a yielding AV resulted in a lower head-turning rate by pedestrians, during a crossing. The presence of eHMI also decreased pedestrians’ head-turning rate and head-turning frequency. The absolute head-turning frequency before crossing initiation was also lower for zebra than for no-zebra crossings. This implies that pedestrians had greater confidence and less uncertainty about their decisions in these conditions, showing the value of such road infrastructure and explicit communication methods. Using a distributed simulation set-up, we also investigated drivers’ behaviour by studying how drivers and pedestrians interacted with each other in real time, during a road crossing scenario (Yang et al., 2023). This study found that drivers decelerated less and deviated away from pedestrians more during the no zebra crossings trials, compared to the zebra crossing trials. These observed lateral deviations were particularly true when the vehicle approached the pedestrian at 3- and 4-second time gaps (Yang et al., in prep (a)). Pedestrians showed a higher likelihood of crossing when the driver exhibited a higher deceleration rate and lower lateral deviation away from them. They were also more likely to cross with an increased number of encounters, showing a learning effect over time. Understanding how these driving behaviours affect pedestrians’ crossing is essential in AV design. Previous studies in our lab have found that VRUs over-trust eHMIs if the message from the eHMI is in conflict with the implicit behaviour of the vehicle. This led to a crossing by pedestrians, when the AVs were not actually yielding (Kaleefathullah et al., 2022). Our findings in Yang et al. (2024) aligned with this. eHMI decreased pedestrians’ head-turning rate and head-turning frequency, associated with less information acquisition demand. Our latest work investigated how familiarity and intuitiveness of different Augmented Reality Human-Machine Interfaces affect information acquisition using pedestrians’ eye-tracking behaviour. We found that higher familiarity led to shorter fixation duration on the AR concepts before crossing (Yang et al., in prep (b)). Again, this shows a potential risk of over-trusting AR concepts, especially those they are familiar with.
My publications
Journals
Yang, Y., Lee, Y. M., Madigan, R., Solernou, A., & Merat, N. (2024). Interpreting pedestrians’ head movements when encountering automated vehicles at a virtual crossroad. Transportation Research Part F: Traffic Psychology and Behaviour, 103, 340-352.
Kalantari, A. H., Yang, Y., de Pedro, J. G., Lee, Y. M., Horrobin, A., Solernou, A., … & Markkula, G. (2023). Who goes first? A distributed simulator study of vehicle–pedestrian interaction. Accident Analysis & Prevention, 186, 107050. https://doi.org/10.1016/j.aap.2023.107050
Markkula, G., Lin, Y. S., Srinivasan, A. R., Billington, J., Leonetti, M., Kalantari, A. H., Yang, Y.… & Merat, N. (2023). Explaining human interactions on the road by large-scale integration of computational psychological theory. PNAS nexus, 2(6), pgad163.
Kalantari, A. H., Yang, Y., Lee, Y. M., Merat, N., & Markkula, G. (2023). Driver-pedestrian interactions at unsignalized crossings are not in line with the Nash equilibrium. IEEE Access.
Conference / Posters
Yang, Y., Lee, Y. M., Merat, N. (2022). Pedestrians’ head movements when encountering automated cars.In 7th International Conference on Traffic and Transport Psychology, Gothenburg, Sweden.
Yang, Y., Lee, Y. M., Kalantari, A. H.,de Pedro, J. G., Horrobin, A., Daly, M., Solernou, A., Markkula, G., Merat, N. (2023). The effect of different infrastructures on driver response to a crossing pedestrian: a distributed simulation study. In Proceedings of the driving simulation conference. Antibes, France.
Yang, Y., Kalantari, A. H., Lee, Y. M., Solernou, A., Markkula, G., & Merat, N. (2023, September). A Distributed Simulation Study to Examine Vehicle–Pedestrian Interactions. In Adjunct Proceedings of the 15th International Conference on Automotive User Interfaces and Interactive Vehicular Applications (pp. 327-329).
Li, Y., Lee, Y. M., Yang, Y., Tian, K., Daly, M., Horrobin, A., … & Merat, N. (2023, September). Do Drivers have Preconceived Ideas about an Automated Vehicle’s Driving Behaviour?. In Proceedings of the 15th International Conference on Automotive User Interfaces and Interactive Vehicular Applications (pp. 291-299).
Kalantari, A. H., Yang, Y., de Pedro, J. G., Lee, Y. M., Horrobin, A., Solernou, A., … & Markkula, G. (August, 2023). A distributed simulator study of car-pedestrian interaction. In 7th International Conference on Traffic and Transport Psychology, Gothenburg, Sweden.
Zhang, C., Kalantari, A.H., Yang, Y., Ni, Z., Markkula, G., Merat, N. (2023). Cross or Wait? Predicting Pedestrian Interaction Outcomes at Unsignalized Crossings. https://doi.org/10.48550/arXiv.2304.08260
SHAPE-IT Deliverables
Figalová, N., Mbelekani, N.Y., Zhang, C., Yang, Y., Peng, C., Nasser, M., Yuan-Cheng, L., Pir Muhammad, A., Tabone, W., Berge, S. H., Jokhio, S., He, X., Hossein Kalantari, A., Mohammadi, A., & Yang, X. (2021). SHAPE-IT Deliverable 1.1: Methodological framework for modelling and empirical approaches.https://doi.org/10.17196/shape-it/2021/02/D1.1
Mbelekani, N., Yue, Y. (2023). Long-Term Behavioural Adaptation and Learning Curve of Humans Interacting with AVs. (Deliverable D1.3 in the EC ITN project SHAPE-IT). SHAPE-IT Consortium. http://doi.org/10.17196/shape-it/2023/D1.3
Figalova, N., Mbelekani, N.Y., Yang, Y., Cheng, Y.C., Tabone, W., Kalantari, A.H., Mohammadi, A., Yang, X. (2023). An extension of the human-factors methodological toolbox for human-AV interaction design research. (Deliverable D1.4 in the EC ITN project SHAPE- IT). SHAPE-IT Consortium. http://doi.org/10.17196/shape-it/2023/D1.4
Merat, N., Yang, Y., Lee, Y. M., Berge, S. H., Figalová, N., Jokhio, S., Peng, C., Mbelekani, N.Y., Nasser, M., Pir Muhammad, A., Tabone, W., Yuan-Cheng, L., & Bärgman, J. (2021). SHAPE-IT Deliverable 2.2: An overview of interfaces for automated vehicles (inside/outside). https://doi.org/10.17196/shape-it/2021/02/D2.1
de Winter, J., Berge, S. H., Tabone, W., Yang, Y., Muhammad, A. P., Jokhio, S., & Hagenzieker, M. (2023). Design strategies and prototype HMI designs for pedestrians, cyclists, and non-automated cars: Deliverable D2. 5 in the EC ITN project SHAPE-IT.
References and links
Ajzen, I., 1991. The theory of planned behavior. Organ. Behav. Hum. Decis. Process. 50 (2), 179–211.
Kaleefathullah, A. A., Merat, N., Lee, Y. M., Eisma, Y. B., Madigan, R., Garcia, J., & Winter, J. D. (2022). External human–machine interfaces can be misleading: An examination of trust development and misuse in a CAVE-based pedestrian simulation environment. Human factors, 64(6), 1070-1085.
Ghazizadeh, M., Peng, Y., Lee, J.D., Boyle, L.N., 2012. Augmenting the technology acceptance model with trust: commercial drivers’ attitudes towards monitoring and feedback. Proceedings of the Human Factors and Ergonomics Society Annual Meeting, vol. 56, no. 1. Sage Publications, Sage CA: Los Angeles, CA, pp. 2286–2290.
Osswald, S., Wurhofer, D., Trösterer, S., Beck, E., Tscheligi, M., 2012. Predicting information technology usage in the car: towards a car technology acceptance model. In: Proceedings of the 4th International Conference on Automotive User Interfaces and Interactive Vehicular Applications. ACM, pp. 51–58.
Rahman, M.M., Lesch, M.F., Horrey, W.J., Strawderman, L., 2017. Assessing the utility of TAM, TPB, and UTAUT for advanced driver assistance systems. Accid. Anal. Prev. 108, 361–373.
Venkatesh, V., Morris, M.G., Davis, G.B., Davis, F.D., 2003. User acceptance of information technology: toward a unified view. MIS Quart. 27 (3), 425–478.
Who am I ?
I am Yue Yang, and as the ESR 4 in the SHAPE-IT project, I work at the Institute for Transport Studies, University of Leeds.
My primary research interests lie in the human – vehicle interactions, with a specific focus on the development of proper kinematics and eHMI for future automated vehicles to enhance safe and smooth interaction with the external road participants.
I received the BEng. in Automation and Control Science, Harbin Institute of Technology (HIT), China. After it, I read for an MSc and obtained the double degree in Human Computer Interaction and Design from Kungliga Tekniska Högskolan (KTH), Sweden and Aalto University, Finland, during this period, I also completed a minor track in the Innovation & Entrepreneurship from EIT Digital Master and Business School.