Hostname: page-component-586b7cd67f-t7fkt Total loading time: 0 Render date: 2024-11-30T20:14:38.346Z Has data issue: false hasContentIssue false
  • English
  • Français

THE USE OF VEHICLE DATA IN ADAS DEVELOPMENT, VERIFICATION AND FOLLOW-UP ON THE SYSTEM

Part of: Engineering Design Practice

Published online by Cambridge University Press:  11 June 2020

J. Orlovska ,
C. Wickman  and
R. Soderberg
J. Orlovska*
Affiliation:
Chalmers University of Technology, Sweden
C. Wickman
Affiliation:
Chalmers University of Technology, Sweden Volvo Cars, Sweden
R. Soderberg
Affiliation:
Chalmers University of Technology, Sweden
*
*[email protected]

Abstract

Core share and HTML view are not available for this content. However, as you have access to this content, a full PDF is available via the ‘Save PDF’ action button.

Advanced Driver Assistance Systems (ADAS) require a high level of interaction between the driver and the system, depending on driving context at a particular moment. Context-aware ADAS evaluation based on vehicle data is the most prominent way to assess the complexity of ADAS interactions. In this study, we conducted interviews with the ADAS development team at Volvo Cars to understand the role of vehicle data in the ADAS development and evaluation. The interviews’ analysis reveals strategies for improvement of current practices for vehicle data-driven ADAS evaluation.

Keywords

data-driven designcomplex systemscase studydesign evaluationproduct development
Type
Article
Information
Proceedings of the Design Society: DESIGN Conference , Volume 1 , May 2020 , pp. 2551 - 2560
Creative Commons
Creative Common License - CCCreative Common License - BYCreative Common License - NCCreative Common License - ND
This is an Open Access article, distributed under the terms of the Creative Commons Attribution-NonCommercial-NoDerivatives licence (http://creativecommons.org/licenses/by-nc-nd/4.0/), which permits non-commercial re-use, distribution, and reproduction in any medium, provided the original work is unaltered and is properly cited. The written permission of Cambridge University Press must be obtained for commercial re-use or in order to create a derivative work.
Copyright
The Author(s), 2020. Published by Cambridge University Press

References

Ahlström, C. et al. (2018), “Effects of the road environment on the development of driver sleepiness in young male drivers”, Accident Analysis & Prevention, Vol. 112, pp. 127–134. https://doi.org/10.1016/j.aap.2018.01.012CrossRefGoogle ScholarPubMed
Ahmed, M.M. and Ghasemzadeh, A. (2018), “The impacts of heavy rain on speed and headway behaviors: an investigation using the SHRP2 naturalistic driving study data”, Transportation research part C: emerging technologies, Vol. 91, pp. 371–384. https://doi.org/10.1016/j.trc.2018.04.012CrossRefGoogle Scholar
Angelini, M. et al. (2018), STEIN: Speeding up Evaluation Activities With a Seamless Testing Environment INtegrator. In EuroVis.Google Scholar
Beggiato, M. and Krems, J.F. (2013), “The evolution of mental model, trust and acceptance of adaptive cruise control in relation to initial information”, Transportation research part F: traffic psychology and behaviour, Vol. 18, pp. 47–57. https://doi.org/10.1016/j.trf.2012.12.006CrossRefGoogle Scholar
Benmimoun, M. et al. (2013), “eurofot: Field operational test and impact assessment of advanced driver assistance systems: Final results”, Proceedings of the FISITA 2012 World Automotive Congress, Springer, Berlin, Heidelberg, pp. 537–547. https://doi.org/10.1007/978-3-642-33805-2_43CrossRefGoogle Scholar
Carta, T., Paternò, F. and de Santana, V.F. (2011), “Web Usability Probe: A Tool for Supporting Remote Usability Evaluation of Web Sites”, Lecture Notes in Computer Science, pp. 349–357, http://dx.doi.org/10.1007/978-3-642-23768-3_29.CrossRefGoogle Scholar
Fridman, L. et al. (2019), “MIT advanced vehicle technology study: Large-scale naturalistic driving study of driver behavior and interaction with automation”, IEEE Access, Vol. 7, pp. 102021–102038.10.1109/ACCESS.2019.2926040CrossRefGoogle Scholar
ISO/PAS 21448. (2019), Road vehicles – Safety of the intended functionality. Available at: https://www.iso.org/standard/70939.html.Google Scholar
Leakkaw, P. and Panichpapiboon, S. (2018, December), “Real-Time Lane Change Detection Through Steering Wheel Rotation”, 2018 IEEE Vehicular Networking Conference (VNC), IEEE, pp. 1–7. https://doi.org/10.1109/vnc.2018.8628461CrossRefGoogle Scholar
Liang, Y. et al. (2016, October), “Considering traffic and roadway context in driver behavior assessments: a preliminary analysis”, Adjunct Proceedings of the 8th International Conference on Automotive User Interfaces and Interactive Vehicular Applications, ACM, pp. 87–92. https://doi.org/10.1145/3004323.3004333CrossRefGoogle Scholar
Mitrovic, D. (2005), “Reliable method for driving events recognition”, IEEE transactions on intelligent transportation systems, Vol. 6 No. 2, pp. 198–205. https://doi.org/10.1109/tits.2005.848367CrossRefGoogle Scholar
Neale, V.L. et al. (2005), An overview of the 100-car naturalistic study and findings, National Highway Traffic Safety Administration, Paper, 5, p.0400.Google Scholar
NVivo 12. (2019), What is NVivo? Available at: https://www.alfasoft.com/en/products/statistics-and-analysis/nvivo.html (accessed 10.11.2019).Google Scholar
Orlovska, J. et al. (2019, July), Mixed-Method Design for User Behavior Evaluation of Automated Driver Assistance Systems: An Automotive Industry Case. Proceedings of the Design Society: International Conference on Engineering Design, Cambridge University Press, Vol. 1 No. 1, pp. 1803–1812. https://doi.org/10.1017/dsi.2019.186CrossRefGoogle Scholar
Orlovska, J. et al. (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, p. 100093.CrossRefGoogle Scholar
Papazikou, E., Quddus, M.A. and Thomas, P. (2017), “Detecting deviation from normal driving using SHRP2 NDS data”, Presented at the Transportation Research Board (TRB) 96th Annual Meeting, January 8th-12th, 2017, Washington, DC, US.Google Scholar
SAE International. (2018), J3016_201806 – Taxonomy and Definitions for Terms Related to Driving Automation Systems for On-Road Motor Vehicles.Google Scholar
Yin, R.K. (2013), “Validity and generalization in future case study evaluations”, Evaluation, Vol. 19 No. 3, pp. 321–332. https://doi.org/10.1177/1356389013497081CrossRefGoogle Scholar
Zhai, Y. et al. (2018, June), “A Context-Aware Evaluation Method of Driving Behavior” Pacific-Asia Conference on Knowledge Discovery and Data Mining, Springer, Cham, pp. 462–474. https://doi.org/10.1007/978-3-319-93034-3_37CrossRefGoogle Scholar