Abstract
In virtual stop (vStop) pick-up scenarios the shared automated vehicle’s (SAV) approaching behavior as implicit communication to the awaiting customer is an important factor to build trust in the vehicle. Identifying the SAV timely could also help users getting ready for boarding and foster a positive perception of the automated service. An online study was conducted to identify trust building, information enhancing and collaboration fostering vehicle braking dynamics from the perspective of waiting customers. 102 participants viewed videos with three different SAV longitudinal braking dynamics in combination with three different conventional light signals. Results showed a user preference for defensive braking to approach flexible pick-up locations curbside. This complements the vehicle passengers’ desires for smooth driving dynamics. Additionally, turn indicator light signals as explicit communication received significantly higher ratings than only implicit communication in the pick-up scenario. Findings add value to understanding SAV behavior when approaching vStops and help designing coherent explicit and implicit communication of SAVs when interacting with surrounding traffic participants in pick-up scenarios.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Martinez, L.M., Viegas, J.M.: Assessing the impacts of deploying a shared self-driving urban mobility system: an agent-based model applied to the city of Lisbon, Portugal. Int. J. Transp. Sci. Technol. 6(1), 13–27 (2017). https://doi.org/10.1016/j.ijtst.2017.05.005
Herminghaus, S.: Mean field theory of demand responsive ride pooling systems. Transp. Res. Part A: Pol. Pract. 119, 15–28 (2019). https://doi.org/10.1016/j.tra.2018.10.028
Niculescu, A.I., Dix, A., Yeo, K.H.: Are you ready for a drive? In: Mark, G., et al. (eds.) Proceedings of the 2017 CHI Conference Extended Abstracts on Human Factors in Computing Systems. CHI ‘17: CHI Conference on Human Factors in Computing Systems, Denver Colorado USA, 06 05 2017 11 05 2017, New York, NY, USA, pp. 2810–2817. ACM (2017). doi: https://doi.org/10.1145/3027063.3053182
Kemper, A., Bubb, I., Kriebel, E., Müller, A.: Identifikation von kritischen Interaktionen des bedarfsgerechten ÖPNV mit autonom betriebenen Fahrzeugen [Identification of Critical Interactions in Demand Responsive Public Transport with Autonomously Operated Vehicles]. In: 66. Frühjahrskongress der Gesellschaft für Arbeitswissenschaft 2020 (2020)
Al-Turki, M., Ratrout, N.T., Rahman, S.M., Reza, I.: Impacts of autonomous vehicles on traffic flow characteristics under mixed traffic environment: future perspectives. Sustainability 13(19), 11052 (2021). https://doi.org/10.3390/su131911052
Hub, F., Wilbrink, M., Kettwich, C., Oehl, M.: Designing ride access points for shared automated vehicles - an early stage prototype evaluation. In: Stephanidis, C., Antona, M., Ntoa, S. (eds.) HCII 2020. CCIS, vol. 1294, pp. 560–567. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-60703-6_72
Risto, M., Emmenegger, C., Vinkhuyzen, E., Cefkin, M., Hollan, J.: Human-vehicle interfaces: the power of vehicle movement gestures in human road user coordination. In: Proceedings of the 9th International Driving Symposium on Human Factors in Driver Assessment, Training, and Vehicle Design: driving assessment 2017. Driving Assessment Conference, Manchester Village, Vermont, USA, 26 June 2017–29 June 2017, pp. 186–192. University of Iowa, Iowa City, Iowa (2017). https://doi.org/10.17077/drivingassessment.1633
Bazilinskyy, P., Sakuma, T., de Winter, J.: What driving style makes pedestrians think a passing vehicle is driving automatically? Appl. Ergon. 95, 103428 (2021). https://doi.org/10.1016/j.apergo.2021.103428
Jayaraman, S.K., et al.: Pedestrian trust in automated vehicles: role of traffic signal and AV driving behavior. Front. Robot. AI 6, 117 (2019). https://doi.org/10.3389/frobt.2019.00117
Ekman, F., Johansson, M., Bligård, L.-O., Karlsson, M., Strömberg, H.: Exploring automated vehicle driving styles as a source of trust information. Transport. Res. F: Traffic Psychol. Behav. 65, 268–279 (2019). https://doi.org/10.1016/j.trf.2019.07.026
Lee, Y.M., et al.: Understanding the messages conveyed by automated vehicles. In: Proceedings of the 11th International Conference on Automotive User Interfaces and Interactive Vehicular Applications. AutomotiveUI 2019: 11th International Conference on Automotive User Interfaces and Interactive Vehicular Applications, Utrecht Netherlands, 21 09 2019 25 09 2019, New York, NY, USA, pp. 134–143. ACM (2019). https://doi.org/10.1145/3342197.3344546
Franke, T., Attig, C., Wessel, D.: A personal resource for technology interaction: development and validation of the affinity for technology interaction (ATI) scale. Int. J. Hum.-Comput. Interact. 35(6), 456–467 (2019). https://doi.org/10.1080/10447318.2018.1456150
Dietrich, A., Maruhn, P., Schwarze, L., Bengler, K.: Implicit communication of automated vehicles in urban scenarios: effects of pitch and deceleration on pedestrian crossing behavior. In: Ahram, T., Karwowski, W., Pickl, S., Taiar, R. (eds.) IHSED 2019. AISC, vol. 1026, pp. 176–181. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-27928-8_27
Ackermann, C., Beggiato, M., Bluhm, L.-F., Löw, A., Krems, J.F.: Deceleration parameters and their applicability as informal communication signal between pedestrians and automated vehicles. Transport. Res. F: Traffic Psychol. Behav. 62, 757–768 (2019). https://doi.org/10.1016/j.trf.2019.03.006
Scherer, S., Schubert, D., Dettmann, A., Hartwich, F., Bullinger, A.C.: Wie will der” Fahrer” automatisiert gefahren werden? Überprüfung verschiedener Fahrstile hinsichtlich des Komforterlebens [How Does the Driver Want to Be Driven? An Evaluation of Different Driving Styles Regarding the Experienced Driving Comfort]. Tagungsband 32. VDI/VW-Gemeinschaftstagung Fahrerassistenzsysteme und automatisiertes Fahren 8, 2016 (2016)
Körber, M.: Theoretical considerations and development of a questionnaire to measure trust in automation. In: Bagnara, S., Tartaglia, R., Albolino, S., Alexander, T., Fujita, Y. (eds.) IEA 2018. AISC, vol. 823, pp. 13–30. Springer, Cham (2019). https://doi.org/10.1007/978-3-319-96074-6_2
Cohen, J.: Statistical Power Analysis for the Behavioral Sciences. Routledge (2013)
Pagano, R.R.: Understanding Statistics in the Behavioral Sciences, 10th edn. Wadsworth Cengage, Australia (2013)
Acknowledgements
This research was funded by the German Federal Ministry for Digital and Transport within the research project “ViVre” (Grant no.: 01MM19014A).
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2022 The Author(s), under exclusive license to Springer Nature Switzerland AG
About this paper
Cite this paper
Hub, F., Heß, S., Wilbrink, M., Oehl, M. (2022). Is This My Ride? AV Braking Behavior from the Perspective of Waiting Ride Hailing Customers. In: Stephanidis, C., Antona, M., Ntoa, S., Salvendy, G. (eds) HCI International 2022 – Late Breaking Posters. HCII 2022. Communications in Computer and Information Science, vol 1655. Springer, Cham. https://doi.org/10.1007/978-3-031-19682-9_48
Download citation
DOI: https://doi.org/10.1007/978-3-031-19682-9_48
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-031-19681-2
Online ISBN: 978-3-031-19682-9
eBook Packages: Computer ScienceComputer Science (R0)