Grant: Research Tools and Instruments - Category 1 (<$150,000)

The first driving-related casualty was recorded in London, England in 1896. Since then, the World Health Organization (WHO) estimated that 25 million people died in vehicle-related accidents. World-wide deaths from injuries are projected to rise from 5.1 million in 1990 to 8.4 million in 2020, with traffic-related injuries representing the major cause for this increase. This research aims at reducing these fatalities by first developing a deeper understanding of the cognitive task of driving and then developing a model integrating these findings into a predictive model. The other components of this model will be information from the car itself (onboard instrumentation) and information about the environment obtained from sensors on the automobile itself, such as GPS, cameras, radar, etc.). These diverse sources of information will be used to build predictive models of driver intentionality. The primary goal of the research is to determine whether driver intentionality and driving-related actions can be predicted from quantitative and qualitative analyses of driver behavior and information from the car and its environment. This implies, of course, that these analyses can be done in a few fractions of a second in order to have effective results. This requires the development of clever algorithms for processing data and for prediction as well as the use of specialized high performance processors; a prototype of the latter has already been developed. Our main research goal is the timely prediction of driver intentionality in order to warn the driver or, potentially, to correct driver behavior through automated driving intervention. The long term goal of this program is to address core problems leading to the development of intelligent, advanced driving assistance systems.