A Multi-Modal Warning–Monitoring System Acceptance Study: What Findings Are Transferable?

Christelle Al Haddad (), Mohamed Abouelela, Graham Hancox, Fran Pilkington-Cheney, Tom Brijs and Constantinos Antoniou
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Christelle Al Haddad: Chair of Transportation System Engineering, Technical University of Munich, 80333 Munich, Germany
Mohamed Abouelela: Chair of Transportation System Engineering, Technical University of Munich, 80333 Munich, Germany
Graham Hancox: Digital and Technology Services, University of Nottingham, Nottingham NG7 2RD, UK
Fran Pilkington-Cheney: NTU Psychology, School of Social Sciences, Nottingham Trent University, Nottingham NG1 4FQ, UK
Tom Brijs: School for Transportation Sciences, Transportation Research Institute, UHasselt, Wetenschapspark 5, 3590 Diepenbeek, Belgium
Constantinos Antoniou: Chair of Transportation System Engineering, Technical University of Munich, 80333 Munich, Germany

Sustainability, 2022, vol. 14, issue 19, 1-19

Abstract: Advanced driving-assistance systems (ADAS) have been recently used to assist drivers in safety-critical situations, preventing them from reaching boundaries of unsafe driving. While previous studies have focused on ADAS use and acceptance for passenger cars, fewer have assessed the topic for professional modes, including trucks and trams. Moreover, there is still a gap in transferring knowledge across modes, mostly with regards to road safety, driver acceptance, and ADAS acceptance. This research therefore aims to fill this gap by investigating the user acceptance of a novel warning–monitoring system, based on experiments conducted in a driving simulator in three modes. The experiments, conducted in a car, truck, and tram simulator, focused on different risk factors, namely forward collision, over-speeding, vulnerable road user interactions, and special conditions including distraction and fatigue. The conducted experiments resulted in a multi-modal dataset of over 122 drivers. The analysis of drivers’ perceptions obtained through the different questionnaires revealed that drivers’ acceptance is impacted by the system‘s perceived ease of use and perceived usefulness, for all investigated modes. A multi-modal technology acceptance model also revealed that some findings can be transferable between the different modes, but also that some others are more mode-specific.

Keywords: driving simulator; warning system; technology acceptance model; multi-modal; professional drivers; road transportation (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2022
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