Towards a predictive model of driver acceptance of active collision avoidance systems

Large, David; Banks, Victoria; Burnett, Gary; Harvey, Catherine

Abstract

Drivers’ acceptance of advanced-driver-assistance-systems (ADAS), such as pedestrian alert systems (PAS), is vital if the full benefits are to be realised. However, the adoption and continued use of such technology is not only contingent on the system’s technical competence, but is also dependent upon drivers’ attitudes towards the system, and the impact that it has on their driving behaviour and performance. Understanding and integrating the factors that affect and define acceptance in a driving context is therefore important, but complex. We present an in-depth literature review, enriched by a driving simulator study (both conducted as part of the EU-Horizon2020 PROSPECT project), that together begin to collate these factors and explore their interrelationship. A preliminary, descriptive model of driver acceptance is subsequently presented. Further work will enhance and validate the model, with the aim of creating a predictive model that can be used to inform the design of future in-vehicle technologies.

Citation

Large, D., Banks, V., Burnett, G., & Harvey, C. (2018, April). Towards a predictive model of driver acceptance of active collision avoidance systems. Presented at 7th European Transport Research Arena TRA 2018, Vienna, Austria

• Presentation Conference Type: Conference Paper (published)
• Conference Name: 7th European Transport Research Arena TRA 2018
• Start Date: Apr 16, 2018
• End Date: Apr 19, 2018
• Acceptance Date: Apr 16, 2018
• Online Publication Date: Apr 16, 2018
• Publication Date: Apr 17, 2018
• Deposit Date: Nov 23, 2018
• Publicly Available Date: Nov 28, 2018
• Book Title: Proceedings of 7th Transport Research Arena TRA 2018, April 16 - 19, 2018, Vienna, Austria
• DOI: https://doi.org/10.5281/zenodo.1222174
• Public URL: https://nottingham-repository.worktribe.com/output/1217488
• Publisher URL: https://zenodo.org/record/1222174#.W_1pJ-j7SUk
• Contract Date: Nov 23, 2018