French Adaptation and Validation of the Pedestrian Receptivity Questionnaire for Fully Autonomous Vehicles (F-PRQF)

References

• Abele, J., Kerlen, C., Kruger, S., Baum, H., Giessler, T., Grawenhoff, S., & Schulz, W. H. (2005). Exploratory Study on the potential socio-economic impact of the introduction of Intelligent Safety Systems in Road Vehicles. SEISS Final report. http://ec.europa.eu/information_society/activities/esafety/doc/call_4/final_seiss.pdf
   Google Scholar
• Ahmed, H. U., Huang, Y., Lu, P., & Bridgelall, R. (2022). Technology developments and impacts of connected and autonomous vehicles: An overview. Smart Cities, 5(1), 382–404. https://doi.org/10.3390/smartcities5010022
   Google Scholar
• Alessandrini, A., Campagna, A., Site, P. D., Filippi, F., & Persia, L. (2015). Automated vehicles and the rethinking of mobility and cities. Transportation Research Procedia, 5(1), 145–160. https://doi.org/10.1016/j.trpro.2015.01.002
   Google Scholar
• Al-Gahtani, S. S., Hubona, G. S., & Wang, J. (2007). Information technology (IT) in Saudi Arabia: Culture and the acceptance and use of IT. Information & Management, 44(8), 681–691. https://doi.org/10.1016/j.im.2007.09.002
   Web of Science ®Google Scholar
• Alsalman, A., Assi, L. N., Ghotbi, S., Ghahari, S., & Shubbar, A. (2021). Users, planners, and governments perspectives: A public survey on autonomous vehicles future advancements. Transportation Engineering, 3(October 2020), 100044. https://doi.org/10.1016/j.treng.2020.100044
   Google Scholar
• Alvarez León, L. F., & Aoyama, Y. (2022). Industry emergence and market capture: The rise of autonomous vehicles. Technological Forecasting and Social Change, 180(August 2021), 121661. https://doi.org/10.1016/j.techfore.2022.121661
   Google Scholar
• Bagloee, S. A., Tavana, M., Asadi, M., & Oliver, T. (2016). Autonomous vehicles: Challenges, opportunities, and future implications for transportation policies. Journal of Modern Transportation, 24(4), 284–303. https://doi.org/10.1007/s40534-016-0117-3
   Web of Science ®Google Scholar
• Bazilinskyy, P., Dodou, D., & de Winter, J. (2019). Survey on eHMI concepts: The effect of text, color, and perspective. Transportation Research Part F: Traffic Psychology and Behaviour, 67(1), 175–194. https://doi.org/10.1016/j.trf.2019.10.013
   Web of Science ®Google Scholar
• Bella, F., & Silvestri, M. (2015). Effects of safety measures on driver’s speed behavior at pedestrian crossings. Accident; Analysis and Prevention, 83(1), 111–124. https://doi.org/10.1016/j.aap.2015.07.016
   PubMed Web of Science ®Google Scholar
• Benz, M. (2015). The F 015 Luxury in motion. Retrieved August 15, 2021, from https://www.mercedes-benz.com/en/innovation/autonomous/research-vehicle-f-015-luxury-in-motion/
   Google Scholar
• Bissell, D., Birtchnell, T., Elliott, A., & Hsu, E. L. (2020). Autonomous automobilities: The social impacts of driverless vehicles. Current Sociology, 68(1), 116–134. https://doi.org/10.1177/0011392118816743
   Web of Science ®Google Scholar
• Buehler, R. (2018). Can public transportation compete with automated and connected cars? Journal of Public Transportation, 21(1), 7–18. https://doi.org/10.5038/2375-0901.21.1.2
   Web of Science ®Google Scholar
• Burton-Jones, A., & Hubona, G. S. (2005). Individual differences and usage behavior : Revisiting a technology acceptance model assumption. ACM SIGMIS Database: The DATABASE for Advances in Information Systems, 36(2), 58–77. https://doi.org/10.1145/1066149.1066155
   Google Scholar
• Cattell, R. B. (1966). The scree test for the number of factors. Multivariate Behavioral Research, 1(2), 245–276. https://doi.org/10.1207/s15327906mbr0102_10
   PubMed Web of Science ®Google Scholar
• Chang, C.-M., Toda, K., Sakamoto, D., & Igarashi, T. (2017). Eyes on a car: An interface design for communication between an autonomous car and a pedestrian [Paper presentation]. In Proceedings of the 9th International Conference on Automotive User Interfaces and Interactive Vehicular Applications - AutomotiveUI ’17. ACM. https://doi.org/10.1145/3122986.3122989
   Google Scholar
• Chen, Y., Gonder, J., Young, S., & Wood, E. (2019). Quantifying autonomous vehicles national fuel consumption impacts: A data-rich approach. Transportation Research Part A: Policy and Practice, 122(November 2017), 134–145. https://doi.org/10.1016/j.tra.2017.10.012
   Google Scholar
• Crayton, T. J., & Meier, B. M. (2017). Autonomous vehicles: Developing a public health research agenda to frame the future of transportation policy. Journal of Transport & Health, 6(February), 245–252. https://doi.org/10.1016/j.jth.2017.04.004
   Google Scholar
• Davis, F. D. (1989). Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS Quarterly, 13(3), 319. https://doi.org/10.2307/249008
   Web of Science ®Google Scholar
• de Clercq, K., Dietrich, A., Núñez Velasco, J. P., de Winter, J., & Happee, R. (2019). External human-machine interfaces on automated vehicles: Effects on pedestrian crossing decisions. Human Factors, 61(8), 1353–1370. https://doi.org/10.1177/0018720819836343
   PubMed Web of Science ®Google Scholar
• Deb, S., Strawderman, L., Carruth, D. W., DuBien, J., Smith, B., & Garrison, T. M. (2017). Development and validation of a questionnaire to assess pedestrian receptivity toward fully autonomous vehicles. Transportation Research Part C: Emerging Technologies, 84(1), 178–195. https://doi.org/10.1016/j.trc.2017.08.029
   Web of Science ®Google Scholar
• Deb, S., Strawderman, L., DuBien, J., Smith, B., Carruth, D. W., & Garrison, T. M. (2017). Evaluating pedestrian behavior at crosswalks: Validation of a pedestrian behavior questionnaire for the U.S. population. Accident Analysis & Prevention, 106(January), 191–201. https://doi.org/10.1016/j.aap.2017.05.020
   PubMedGoogle Scholar
• Dey, D., Habibovic, A., Löcken, A., Wintersberger, P., Pfleging, B., Riener, A., Martens, M., & Terken, J. (2020). Taming the eHMI jungle: A classification taxonomy to guide, compare, and assess the design principles of automated vehicles’ external human-machine interfaces. Transportation Research Interdisciplinary Perspectives, 7, 100174. https://doi.org/10.1016/j.trip.2020.100174
   Google Scholar
• Dey, D., & Terken, J. (2017). Pedestrian interaction with vehicles: Roles of explicit and implicit communication [Paper presentation]. In AutomotiveUI 2017 - 9th International ACM Conference on Automotive User Interfaces and Interactive Vehicular Applications, Proceedings (pp. 109–113). ACM. https://doi.org/10.1145/3122986.3123009
   Google Scholar
• Dey, D., Holländer, K., Berger, M., Eggen, B., Martens, M., Pfleging, B., & Terken, J. (2020). Distance-dependent eHMIs for the interaction between automated vehicles and pedestrians. In Proceedings - 12th International ACM Conference on Automotive User Interfaces and Interactive Vehicular Applications, AutomotiveUI 2020 (pp. 192–204). ACM. https://doi.org/10.1145/3409120.3410642
   Google Scholar
• Dey, D., Martens, M., Wang, C., Ros, F., & Terken, J. (2018). Interface concepts for intent communication from autonomous vehicles to vulnerable road users [Paper presentation].Adjunct Proceedings - 10th International ACM Conference on Automotive User Interfaces and Interactive Vehicular Applications, In, AutomotiveUI 2018 (pp. 82–86). https://doi.org/10.1145/3239092.3265946
   Google Scholar
• Dommes, A., Olivier, A.-H., Merlhiot, G., & Lobjois, R. (2020). Projet AUTOMA-PIED: Quelles interactions véhicules AUTOMAtisés - PIEtons pour Demain ?. Délégation à la Sécurité Routiére.
   Google Scholar
• Gana, K., Boudouda, N. E., Ben Youssef, S., Calcagni, N., & Broc, G. (2021). Adaptation transculturelle de tests et échelles de mesure psychologiques: guide pratique basé sur les Recommandations de la Commission Internationale des Tests et les Standards de pratique du testing de l’APA. Pratiques Psychologiques, 27(3), 223–240. https://doi.org/10.1016/j.prps.2021.02.001
   Google Scholar
• Gholamhosseinian, A., & Seitz, J. (2022). A comprehensive survey on cooperative intersection management for heterogeneous connected vehicles. IEEE Access, 10, 7937–7972. https://doi.org/10.1109/ACCESS.2022.3142450
   Web of Science ®Google Scholar
• Golbabaei, F., Yigitcanlar, T., & Bunker, J. (2021). The role of shared autonomous vehicle systems in delivering smart urban mobility: A systematic review of the literature. International Journal of Sustainable Transportation, 15(10), 731–748. https://doi.org/10.1080/15568318.2020.1798571
   Web of Science ®Google Scholar
• Greenblatt, J. B., & Saxena, S. (2015). Autonomous taxis could greatly reduce greenhouse-gas emissions of US light-duty vehicles. Nature Climate Change, 5(9), 860–863. https://doi.org/10.1038/nclimate2685
   Web of Science ®Google Scholar
• Gronier, G. (2022). Psychometric analyses in the transcultural adaptation of psychological scales. In S. Misciagna (Ed.), Psychometrics - new insights (pp. 1–13). IntechOpen.
   Google Scholar
• Hawes, M. (2017). Connected and Autonomous Vehicles: Revolutionising Mobility in Society. International Automotive Summit. Retrieved from https://www.smmt.co.uk/wp-content/uploads/sites/2/Connected-and-Autonomous-Vehicles-Revolutionising-Mobility-in-Society.pdf.
   Google Scholar
• Hayes, A. F., & Coutts, J. J. (2020). Use omega rather than Cronbach’s alpha for estimating reliability. But…. Communication Methods and Measures, 14(1), 1–24. https://doi.org/10.1080/19312458.2020.1718629
   Web of Science ®Google Scholar
• Herrero-Fernández, D., MacÍa-Guerrero, P., Silvano-Chaparro, L., Merino, L., & Jenchura, E. C. (2016). Risky behavior in young adult pedestrians: Personality determinants, correlates with risk perception, and gender differences. Transportation Research Part F: Traffic Psychology and Behaviour, 36(1), 14–24. https://doi.org/10.1016/j.trf.2015.11.007
   Web of Science ®Google Scholar
• Hsu, H. (2012). The acceptance of moodle: An empirical study based on UTAUT. Creative Education, 03(08), 44–46. https://doi.org/10.4236/ce.2012.38B010
   Google Scholar
• Hu, L. T., & Bentler, P. M. (1999). Cutoff criteria for fit indexes in covariance structure analysis: Conventional criteria versus new alternatives. Structural Equation Modeling: A Multidisciplinary Journal, 6(1), 1–55. https://doi.org/10.1080/10705519909540118
   Web of Science ®Google Scholar
• Imprialou, M., & Quddus, M. (2019). Crash data quality for road safety research: Current state and future directions. Accident; Analysis and Prevention, 130(1), 84–90. https://doi.org/10.1016/j.aap.2017.02.022
   PubMed Web of Science ®Google Scholar
• ISO 9241-210:2010 (2010). Ergonomics of human-system interaction - Part 210: Human-centred design for interactive systems. International Organization for Standardization.
   Google Scholar
• Jian, J.-Y., Bisantz, A. M., Drury, C. G., & Llinas, J. (1998). United States Air Force research laboratory foundations for an empirically determined scale of trust in automated systems. Air Force Research Laboratory. https://apps.dtic.mil/sti/pdfs/ADA395339.pdf
   Google Scholar
• Jones, E. C., & Leibowicz, B. D. (2019). Contributions of shared autonomous vehicles to climate change mitigation. Transportation Research Part D: Transport and Environment, 72(May), 279–298. https://doi.org/10.1016/j.trd.2019.05.005
   Google Scholar
• Kline, R. B. (2005). Principles and practice of structural equation modeling (2nd ed.). New York: The Guilfo.
   Google Scholar
• Landauer, T. K. (1997). Chapter 9 - behavioral research methods in human-computer interaction. In Handbook of human-computer interaction (2nd ed., pp. 203–227). Elsevier Science.
   Google Scholar
• Lee, Y. M., Madigan, R., Giles, O., Garach-Morcillo, L., Markkula, G., Fox, C., Camara, F., Rothmueller, M., Vendelbo-Larsen, S. A., Rasmussen, P. H., Dietrich, A., Nathanael, D., Portouli, V., Schieben, A., & Merat, N. (2021). Road users rarely use explicit communication when interacting in today’s traffic: Implications for automated vehicles. Cognition, Technology & Work, 23(2), 367–380. https://doi.org/10.1007/s10111-020-00635-y
   Web of Science ®Google Scholar
• Lewis, J. R., & Sauro, J. (2017). Revisiting the factor structure of the system usability scale. Journal of Usability Studies, 12(4), 183–192. https://uxpajournal.org/revisit-factor-structure-system-usability-scale/
   Web of Science ®Google Scholar
• Liu, Y. C., & Tung, Y. C. (2014). Risk analysis of pedestrians’ road-crossing decisions: Effects of age, time gap, time of day, and vehicle speed. Safety Science, 63(1), 77–82. https://doi.org/10.1016/j.ssci.2013.11.002
   Web of Science ®Google Scholar
• Liu, H., Hirayama, T., & Watanabe, M. (2021). Importance of Instruction for Pedestrian-Automated Driving Vehicle Interaction with an External Human Machine Interface: Effects on Pedestrians’ Situation Awareness, Trust, Perceived Risks and Decision Making. In 2021 IEEE Intelligent Vehicles Symposium (IV). (pp. 748–754). Nagoya, Japan: IEEE.
   Google Scholar
• Lu, Q., Tettamanti, T., Hörcher, D., & Varga, I. (2020). The impact of autonomous vehicles on urban traffic network capacity: An experimental analysis by microscopic traffic simulation. Transportation Letters, 12(8), 540–549. https://doi.org/10.1080/19427867.2019.1662561
   Web of Science ®Google Scholar
• Masello, L., Castignani, G., Sheehan, B., Murphy, F., & McDonnell, K. (2022). On the road safety benefits of advanced driver assistance systems in different driving contexts. Transportation Research Interdisciplinary Perspectives, 15(July), 100670. https://doi.org/10.1016/j.trip.2022.100670
   Google Scholar
• Metz, D. (2018). Developing policy for urban autonomous vehicles: Impact on congestion. Urban Science, 2(2), 33. https://doi.org/10.3390/urbansci2020033
   Google Scholar
• Minelli, S., Izadpanah, P., & Razavi, S. (2015). Evaluation of connected vehicle impact on mobility and mode choice. Journal of Traffic and Transportation Engineering (English Edition), 2(5), 301–312. https://doi.org/10.1016/j.jtte.2015.08.002
   Google Scholar
• Nieuwenhuijsen, J., Correia, G. H. D. A., Milakis, D., van Arem, B., & van Daalen, E. (2018). Towards a quantitative method to analyze the long-term innovation diffusion of automated vehicles technology using system dynamics. Transportation Research Part C: Emerging Technologies, 86(May 2017), 300–327. https://doi.org/10.1016/j.trc.2017.11.016
   Google Scholar
• Nunnally, J. C. (1978). Psychometric theory (2d ed.). McGraw-Hill New York.
   Google Scholar
• 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 AutomotiveUI 2012 - 4th International Conference on Automotive User Interfaces and Interactive Vehicular Applications, in-Cooperation with ACM SIGCHI - Proceedings, (October 2012) (pp. 51–58). ACM. https://doi.org/10.1145/2390256.2390264
   Google Scholar
• Palmer, K. (2018). Jaguar Land Rover’s virtual eyes look at trust in self-driving cars. https://www.jaguarlandrover.com/news/2018/08/jaguar-land-rovers-virtual-eyes-look-trust-self-driving-cars
   Google Scholar
• Pavone, M. (2015). Autonomous mobility-on-demand systems for future urban mobility. In M. Maurer, J. Gerdes, B. Lenz, & H. Winner (Ed.), Autonomes Fahren: technische, rechtliche und gesellschaftliche Aspekte. Springer. https://doi.org/10.1007/978-3-662-45854-9
   Google Scholar
• Pettigrew, S. (2017). Why public health should embrace the autonomous car. Australian and New Zealand Journal of Public Health, 41(1), 5–7. https://doi.org/10.1111/1753-6405.12588
   PubMed Web of Science ®Google Scholar
• Pettigrew, S., & Cronin, S. L. (2019). Stakeholder views on the social issues relating to the introduction of autonomous vehicles. Transport Policy, 81(June), 64–67. https://doi.org/10.1016/j.tranpol.2019.06.004
   Google Scholar
• Pettigrew, S., Talati, Z., & Norman, R. (2018). The health benefits of autonomous vehicles: Public awareness and receptivity in Australia. Australian and New Zealand Journal of Public Health, 42(5), 480–483. https://doi.org/10.1111/1753-6405.12805
   PubMed Web of Science ®Google Scholar
• Rabardel, P., & Beguin, P. (2005). Instrument mediated activity: From subject development to anthropocentric design. Theoretical Issues in Ergonomics Science, 6(5), 429–461. https://doi.org/10.1080/14639220500078179
   Google Scholar
• Ren, Z., Jiang, X., & Wang, W. (2016). Analysis of the influence of pedestrians’ eye contact on drivers’ comfort boundary during the crossing conflict. Procedia Engineering, 137(1), 399–406. https://doi.org/10.1016/j.proeng.2016.01.274
   Google Scholar
• Sanguesa, J. A., Torres-Sanz, V., Garrido, P., Martinez, F. J., & Marquez-Barja, J. M. (2021). A review on electric vehicles: Technologies and challenges. Smart Cities, 4(1), 372–404. https://doi.org/10.3390/smartcities4010022
   Google Scholar
• Sauro, J., & Lewis, J. R. (2011). When designing usability questionnaires, does it hurt to be positive? In Conference on Human Factors in Computing Systems - Proceedings (pp. 2215–2223). ACM. https://doi.org/10.1145/1978942.1979266
   Google Scholar
• Schoettle, B., & Sivak, M. (2014). Public opinion about self-driving vehicles in China, India, Japan, the U.S., the U.K., and Australia. The University of Michigan.
   Google Scholar
• Schweizer, K. (2010). Some guidelines concerning the modeling of traits and abilities in test construction. European Journal of Psychological Assessment, 26(1), 1–2. https://doi.org/10.1027/1015-5759/a000001
   Web of Science ®Google Scholar
• Shiwakoti, N., Stasinopoulos, P., & Fedele, F. (2020). Investigating the state of connected and autonomous vehicles: A literature review. Transportation Research Procedia, 48(2018), 870–882. https://doi.org/10.1016/j.trpro.2020.08.101
   Google Scholar
• Sindi, S., & Woodman, R. (2021). Implementing commercial autonomous road haulage in freight operations: An industry perspective. Transportation Research Part A: Policy and Practice, 152(August), 235–253. https://doi.org/10.1016/j.tra.2021.08.003
   Google Scholar
• Steiger, J. H. (2007). Understanding the limitations of global fit assessment in structural equation modeling. Personality and Individual Differences, 42(5), 893–898. https://doi.org/10.1016/j.paid.2006.09.017
   Web of Science ®Google Scholar
• Sucha, M., Dostal, D., & Risser, R. (2017). Pedestrian-driver communication and decision strategies at marked crossings. Accident; Analysis and Prevention, 102(1), 41–50. https://doi.org/10.1016/j.aap.2017.02.018
   PubMed Web of Science ®Google Scholar
• Thomopoulos, N., & Givoni, M. (2015). The autonomous car—a blessing or a curse for the future of low carbon mobility? An exploration of likely vs. desirable outcomes. European Journal of Futures Research, 3(14), 1–14. https://doi.org/10.1007/s40309-015-0071-z
   Google Scholar
• Tu, R., Alfaseeh, L., Djavadian, S., Farooq, B., & Hatzopoulou, M. (2019). Quantifying the impacts of dynamic control in connected and automated vehicles on greenhouse gas emissions and urban NO2 concentrations. Transportation Research Part D: Transport and Environment, 73(July), 142–151. https://doi.org/10.1016/j.trd.2019.06.008
   Google Scholar
• Vallerand, R. J. (1989). Vers une méthodologie de validation trans-culturelle de questionnaires psychologiques : Implications pour la recherche en langue française. Canadian Psychology / Psychologie canadienne, 30(4), 662–680. https://doi.org/10.1037/h0079856
   Web of Science ®Google Scholar
• Várhelyi, A. (1998). Drivers’ speed behaviour at a zebra crossing: A case study. Accident Analysis & Prevention, 30(6), 731–743. https://doi.org/10.1016/S0001-4575(98)00026-8
   PubMed Web of Science ®Google Scholar
• Venkatesh, V., Morris, M. G., Davis, G. B., & Davis, F. D. (2003). User acceptance of information technology: Toward a unified view. MIS Quarterly, 27(3), 425–478. https://doi.org/10.2307/30036540
   Web of Science ®Google Scholar
• Vorsteher, D. (2017). Autonomous concept car smart vision EQ fortwo: Welcome to the future of car sharing. Retrieved August 15, 2021, from https://media.daimler.com/marsMediaSite/en/instance/ko/Autonomous-concept-car-smart-vision-EQ-fortwo-Welcome-to-the-future-of-car-sharing.xhtml?oid=29042725
   Google Scholar
• Wang, S., Li, Z., & Levin, M. W. (2022). Optimal policy for integration of autonomous vehicles into the auto market. Transportation Research Part C: Emerging Technologies, 143(July), 103821–103836. https://doi.org/10.1016/j.trc.2022.103821
   Google Scholar
• Williams, M. D., Rana, N. P., & Dwivedi, Y. K. (2015). The unified theory of acceptance and use of technology (UTAUT): A literature review. Journal of Enterprise Information Management, 28(3), 443–488. https://doi.org/10.1108/JEIM-09-2014-0088
   Web of Science ®Google Scholar