Ankit R. Patel
Philipp Wintersberger
ABSTRACT
Over the past few years, there has been increased emphasis placed on the research and development of in-vehicle advanced driver assistance systems (ADAS) that can be used in both traditional and self-driving (so-called, autonomous) vehicles. This is a huge step toward providing better comfort and improving the driver experience coupled with improvements to safety concerns. Despite this, we have found that drivers do not use the ADAS to its full potential in everyday use. This is something that has come to our attention. There could be a number of factors at play here. The primary purpose of this workshop is to shed light to the reasons why participants are not activating their ADAS and other comfort functions. In addition, it will serve as a useful benchmark against which to measure the progress of future driver expectations and requirements for ADAS.
- Hanna Bellem, Barbara Thiel, Michael Schrauf, and Josef F. Krems. 2018. Comfort in automated driving: An analysis of preferences for different automated driving styles and their dependence on personality traits. Transportation Research Part F: Traffic Psychology and Behaviour 55 (May 2018), 90–100. https://doi.org/10.1016/j.trf.2018.02.036Google Scholar
- Alexander G. Mirnig, Philipp Wintersberger, Christine Sutter, and Jurgen Ziegler. 2016. A framework for analyzing and calibrating trust in automated vehicles. Adjunct Proceedings of the 8th International Conference on Automotive User Interfaces and Interactive Vehicular Applications (AutomotiveUI) (October 2016), 33–38. https://doi.org/10.1145/3004323.3004326Google ScholarDigital Library
- Eric J. E. Molin and V. A. W. J. Marchau. 2004. User perceptions and preferences of advanced driver assistance systems. Transportation Research Record: Journal of the Transportation Research Board 1886, 1 (January 2004), 119–125. https://doi.org/10.3141/1886-15Google ScholarCross Ref
- Julia Orlovska, Fjolle Novakazi, Bligard Lars-Ola, MariAnne Karlsson, Casper Wickman, and Rikard Soderberg. 2020. Effects of the driving context on the usage of automated driver assistance systems (ADAS) - Naturalistic driving study for ADAS evaluation. Transportation Research Interdisciplinary Perspectives 4 (March 2020), 100093. https://doi.org/10.1016/j.trip.2020.100093Google Scholar
- Sophie Le Page, Jason Millar, Kelly Bronson, Shalaleh Rismani, and Ajung Moon. 2021. Driver perceptions of advanced driver assistance systems and safety. arXiv (2021), 1–7. https://doi.org/10.48550/arXiv.1911.10920Google Scholar
- Sara Paiva, Xabiel Garcia Paneda, Victor Corcoba, Roberto Garcia, Prospero Moran, Laura Pozueco, Marina Valdes, and Covadonga del Camino. 2021. User preferences in the design of advanced driver assistance systems. Sustainability 13, 7 (April 2021), 3932. https://doi.org/10.3390/su13073932Google ScholarCross Ref
- Ankit R. Patel, Flora Ferreira, Sergio Monteiro, and Estela Bicho. 2020. Global implications of human tendencies towards automated driving and human driver availability in autonomous vehicles. In HCI International 2020 – Late Breaking Papers: Digital Human Modeling and Ergonomics, Mobility and Intelligent Environments(Lecture Notes in Computer Science, Vol. 12429), Duffy V.G. Streitz N. Konomi S. Krömker H. Stephanidis, C. (Ed.). Springer, Cham., Copenhagen, Denmark, 179–192. https://doi.org/10.1007/978-3-030-59987-4_13Google ScholarDigital Library
- Patrice Reilhac, Nick Millett, and Katharina Hottelart. 2015. Thinking intuitive driving automation. In Road Vehicle Automation - 2(Lecture Notes in Mobility), Beiker S. Meyer, G. (Ed.). Springer, Cham., 61–70. https://doi.org/10.1007/978-3-319-19078-5_6Google Scholar
- Dustin J. Souders, Kathryn Baringer, Savana L. King, and Alan Mintz. 2022. How do advanced driver assistance systems fit into level of automation frameworks?Proceedings of the 2022 HFES 66th International Annual Meeting 66, 1 (September 2022), 346–350. https://doi.org/10.1177/1071181322661323Google Scholar
- Nathan Tenhundfeld, Mustafa Demir, and Ewart J de Visser. 2021. An argument for trust assessment in human-machine interaction: Overview and call for integration. OSF Preprints (January 2021), 1–48. https://doi.org/10.31219/osf.io/j47dfGoogle Scholar
- Marcel Walch, Kristin Muhl, Johannes Kraus, Tanja Stoll, Martin Baumann, and Michael Weber. 2017. From car-driver-handovers to cooperative interfaces: Visions for driver–vehicle interaction in automated driving. In Automotive User Interfaces(Human–Computer Interaction Series), Müller C. Meixner, G. (Ed.). Springer, Cham., 273–294. https://doi.org/10.1007/978-3-319-49448-7_10Google Scholar
Index Terms
- 1st "International Workshop on Human And Technology" (i-WHAT) Theme: In the realm of ADAS
Recommendations
Effects of arousing attention on distracted driver's following behaviour under uncertainty
This paper proposes issuing alerts to arouse driver attention when the traffic situation ahead is uncertain. Here, "uncertain" means that there exist cues suggesting something is happening in traffic ahead, but it is unclear to the driver and the driver ...
Toward trustworthy haptic assistance system for emergency avoidance of collision with a pedestrian
Special Issue on Shared ControlThis paper proposes a haptic assistance system that provides momentary steering wheel torque to help the driver choose the steering direction for avoiding collision with a pedestrian. The results of an experiment with a driving simulator showed that ...
Research on the Influence of Vehicle Head-Up Display Warning Design on Driver Experience with Different Driving Styles
HCI in Mobility, Transport, and Automotive SystemsAbstractCompared with manual driving, the advanced driver assistance system reduces the pressure on the driver, and at the same time, the driver does not have to pay too much attention to driving safety issues. However, there are also potential conditions ...
Comments