A user-centered approach to road design : blending distributed situation awareness with self-explaining roads
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Driving is a complex dynamic task. As the car driver drives along a route they have to adjust their driving technique in accordance with the traffic level, infrastructure and environment around them. The amount of information in the environment would be overwhelming were it not for the presence of stored mental templates, accumulated through training and experience, which become active when certain features are encountered. Problems occur when the environment triggers the incorrect templates, or fails to trigger the correct templates. Problems like these can be overcome by adopting a “self-explaining” (SER) approach to road design. That is to say, purposefully designed roads which trigger correct behaviour. A concept which can help improve the theoretical robustness of the SER approach is Situation Awareness (SA). SA describes how the environment and mental templates work together to ensure drivers remain coupled to the dynamics of their situation. It is a widely researched concept in the field of Human Factors but not in the domain of Self-Explaining Roads (SER), despite the very obvious conceptual overlaps. This thesis, for the first time, blends the two approaches, SA and SER, together. From this the ability to extract cognitively salient features and ability to enhance driving behaviour and their effects on driving behaviour are sufficiently enhanced. After establishing SA as critical to driving through literature review the experiment phase started with determining the source of driver SA. Road environment was found to be of utmost importance for feeding into driver SA. This was also confirmed with the results of the on-road exploratory study. The success of the exploratory study led to large scale naturalistic study. It provided data on driver mental workload, subjective situation awareness, speed profile and endemic feature. Endemic features are unique characteristics of a road which make a road what it is. It was found that not all endemic features contribute to SA of a road system. Therefore through social network analysis list of cognitive salient features were derived. It is these cognitive salient features which hold compatible SA and facilitate SA transaction in a road system. These features were found to reduce speed variance among drivers on a road. The thesis ends by proposing a ‘road drivability tool’ which can predict potentially dangerous zones. Overall, the findings contribute to new imaginative ways road design in order to maximize safety and efficiency.