Driver impairment detection and safety enhancement through comprehensive volatility analysis [R23]

This report documents the activities undertaken by the research team during the first year of the project. Combining the team’s earlier work with new efforts, we have developed a framework for obtaining, processing, and analyzing high-frequency multi-dimensional large-scale data using sensors that monitor the driver, vehicle, and roadways. The framework harnesses the data by exploring volatility. Detailed naturalistic driving study data from the NDS SHRP-2 program was analyzed for obtaining insights on impairment and distracted driving. The risks associated with engagement in non-driving tasks in terms of safety critical events are quantified and discussed. A real-time artificial intelligence method is applied to harness the data and quantify instantaneous crash risk by monitoring driver biometrics (in terms of distraction), vehicular movements, and volatility in driving. The analysis presented can detect anomalies in driving, which can lead to crashes and near-crashes. Finally, the use of experimentation in simulated and naturalistic settings is demonstrated. The entails collection and processing of driver biometric, vehicle, and roadway surroundings data. This effort further includes a review the literature on driver monitoring, as well as setting up the experimentation procedures, which will contribute to future research in driver biometric monitoring and impairment detection.