Driver behaviour can be modelled in one of two approaches: ‘Descriptive’ models that describe the driving task in terms of what the driver does, and ‘Functional’ models that attempt to explain why the driver behaves the way he/she does, and how to predict drivers’ performance in demanding and routine situations. Demanding situations elicit peak performance capabilities, and routine situations elicit typical (not necessarily best) behaviour. It seems that the optimal approach might be a hybrid of several types of models, extracting the most useful features of each.
In recent years, a variety of driver support and information management systems have been designed and implemented with the objective of improving safety as well as performance of vehicles. To predict the impact of various assistance systems on driver behaviour predictive models of the interaction of the driver with the vehicle and the environment are necessary. The first step of the ITERATE project is to critically review existing Driver-Vehicle-Environment (DVE) models and identify the most relevant drivers’ parameters and variables that need to be included in such models: (a) in different surface transport modes (this paper deals with road vehicles only, other transport domains are detailed in D1.1 & D1.2 of the ITERATE project), and (b) in different safety critical situations. On the basis of this review, we propose here a Unified Model of Driver behaviour (UMD), that is a hybrid model of the two approaches. The model allows for individual differences on pre-specified dimensions and includes the vehicle and environmental parameters. Within the ITERATE project this model will be used to support safety assessment of innovative technologies (based on the abilities, needs, driving style and capacity of the individual drivers). In this brief paper we describe only the behaviour of a single test driver, while the environment and vehicle are defined as parameters with fixed values (and detailed in D1.2 of the ITERATE project). The selected driver characteristics (and variables used to measure them) are culture (Country), attitudes/personality (Sensation Seeking), experience (Hazard Perception Skills), driver state (Fatigue), and task demand (Subjective workload).