title: Predicting the acceptance of advanced rider assistance systems.
abstract: The strong prevalence of human error as a crash causation factor in motorcycle accidents calls for countermeasures that help tackling this issue. Advanced rider assistance systems pursue this goal, providing the riders with support and thus contributing to the prevention of crashes. However, the systems can only enhance riding safety if the riders use them. For this reason, acceptance is a decisive aspect to be considered in the development process of such systems. In order to be able to improve behavioural acceptance, the factors that influence the intention to use the system need to be identified. This paper examines the particularities of motorcycle riding and the characteristics of this user group that should be considered when predicting the acceptance of advanced rider assistance systems. Founded on theories predicting behavioural intention, the acceptance of technologies and the acceptance of driver support systems, a model on the acceptance of advanced rider assistance systems is proposed, including the perceived safety when riding without support, the interface design and the social norm as determinants of the usage intention. Since actual usage cannot be measured in the development stage of the systems, the willingness to have the system installed on the own motorcycle and the willingness to pay for the system are analyzed, constituting relevant conditions that allow for actual usage at a later stage. Its validation with the results from user tests on four advanced rider assistance systems allows confirming the social norm and the interface design as powerful predictors of the acceptance of ARAS, while the extent of perceived safety when riding without support did not have any predictive value in the present study.
authors.affiliation: Université de Lyon, IFSTTAR (LESCOT), 25 Avenue François Mitterrand, Case 24, 69675 Bron, France. veronique.huth@ifsttar.fr
authors.forename: Véronique
authors.initials: V
authors.lastname: Huth
authors.forename: Christhard
authors.initials: C
authors.lastname: Gelau
mesh_terms: D000056:Accident Prevention / Q000379:methods*; D000063:Accidents, Traffic / Q000517:prevention & control*; D000328:Adult; D005260:Female; D006801:Humans; D008297:Male; D008875:Middle Aged; D008962:Models, Theoretical; D009049:Motorcycles; D011795:Surveys and Questionnaires