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Detection of cognitive distraction presents an indispensable function for driver distraction mitigation systems. In this study, the authors developed a layered algorithm that integrated two data mining methods—Dynamic Bayesian Network (DBN) and supervised clustering method—to identify cognitive distraction from drivers’ eye movements and driving performance measures. The authors used the data collected in a simulator study to compare the layered algorithm with the original DBN and found that the layered algorithm obtained comparable prediction performance as the original DBN. Meanwhile, the layered algorithm shortened training and prediction time and revealed rich information on the relationship between driver cognitive state and performance. This study demonstrates that data mining methods are suitable to identify human cognitive state from performance.

Using a Layered Algorithm to Detect Driver Cognitive Distraction