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Neuroergonomics provides a multidisciplinary translational approach that merges elements of neuroscience, human factors, cognitive psychology, and ergonomics to study brain structure and function in everyday environments. Driving safety, particularly that of older drivers with cognitive impairments, is a fruitful application domain for neuroergonomics. Driving makes demands on multiple cognitive processes that are often studied in isolation and so presents a useful challenge in generalizing findings from controlled laboratory tasks to predict safety outcomes. Neurology and the cognitive sciences help explain the mechanisms of cognitive breakdowns that undermine driving safety. Ergonomics complements this explanation with the tools for systematically exploring the various layers of complexity that define the activity of driving. A variety of tools, such as part task simulators, driving simulators, and instrumented vehicles provide a window into cognition in the natural settings needed to assess the generalizability of laboratory findings and can provide an array of potential interventions to increase safety.

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Summary:A questionnaire study was conducted in order to identify relevant driver motives and assistance options for environmentally-friendly driving. Eighteen participants rated items belonging to the three factors time, environment, and consumption, and subsequently evaluated three types of driver assistance systems. Results concerning the motives showed that older drivers (above the age of 65) focused significantly less on the time motive, whereas high annual mileage drivers tended to focus more on the time motive compared to low annual mileage drivers. Small vehicle drivers emphasized more on the environment motive compared to middle-class/van drivers. In terms of the driver assistance options for eco-friendly driving, a display of the current consumption rate through colorcoding received the highest number of first place rankings in the category intelligent vehicle information systems. A function that automatically shuts off the engine after a certain stopping time, as well as optimal gear choice and time to shift, were ranked high in the category intelligent advanced driver assistance systems. In the category intelligent navigation systems, a traffic-and situational adaptive navigation system was ranked the highest most often. Assessment of drivers' preferences additionally showed that most participants preferred the assistance functions to provide visual information as opposed to direct intervention. The results concerning the driver groups and their underlying driving motives as well as the preference statements will be used to inform the design and development of assistance functions for promoting eco-driving within the European eCo Move project.

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