Effect of driving experience on collision avoidance braking: an experimental investigation and computational modelling

Abstract

Information technologies have been developed to facilitate driving performance and improve safety. However, there is a lack of computational methods that can take into account drivers’ adaptation to driving. That is, how behaviour changes with experience. Modelling the effect of driving experience on driver behaviour is important to the development of in-vehicle information technologies, because drivers at different skill levels may need different types or levels of assistance. Cognitive-architecture-based human performance modelling is a valuable method that can integrate different cognitive aspects underlying human behaviour such as skill levels and support quantitative simulation of behaviour. The study reported in this paper tested and examined computational models built in ACT-R (Adaptive Control of Thought-Rational) to account for the effect of driving experience on collision avoidance braking behaviour. The modelling results were compared with human data collected from a simulated driving experiment. The models produced braking behavioural results similar to the human results. Moreover, model predictions of three other emergent-braking scenarios were generally similar to and in the same order with the empirical results reported in previous studies. Future research can further integrate the method and results into intelligent driver assistance systems such as collision warning systems to better adjust the systems to the need of different drivers with different skill levels.