Adaptive driver information the way forward?

Abstract: Driver information is information that a driver needs to fulfil his or her goals of driving. Previously, information supported reliability in order to make the car work safely but, today, other attributes have become equally or more important. Driver information also needs to support safe, efficient, legal, environmentally friendly and enjoyable transportation. Functional growth is expected due to new technology, new purposes of driving and customers' desires. One way to meet functional growth and at the same time improve drivers' situation awareness and optimize workload may be to make driver information adaptive. The information presented, the output factors, could change salience governed by different input factors such as driver state, context, situation etc. However, changing information automatically may cause new types of errors, such as mode error, over-trust, under-trust, vigilance problems or change of locus of control. These types of errors belong to the category automation induced errors. The aim of this thesis was therefore to investigate whether adaptive driver information has a potential to improve driver performance, support goals of driving, improve situation awareness (SA) and optimize workload. This question was decomposed into four more specific research questions. (1) What are the purposes of future driver information and their relations to different functions? (2) What are the potential benefits and negative effects of adaptive driver information? (3) What information do drivers need and want throughout the driving task? (4) How can the negative aspects of adaptivity be avoided by design? In paper A, the purposes of driver information were identified and linked together with future driver information components and, as a result, several new functions were identified. The different benefits and negative effects were identified by literature studies. However, the scope was extended to the aviation and power industry, which has greater experience of automation. In paper B, functions were mapped to different contexts. The results can be used as a guideline for future design. The different negative effects of automation were handled in paper C by applying the "team player" approach to car design. The results showed a potential in the "team player" approach, but it was also clear that the visual impact on driving must be solved. It seems that adaptive driver information has a potential to improve driver performance, support goals of driving, improve situation awareness and optimize workload.