Driver Modeling for Detection and Assessment of Driver Distraction: Examples from the UTDrive Test Bed
With the development of new in-vehicle technology, drivers are exposed to more sources of distraction, which can lead to an unintentional accident. Monitoring the driver attention level has become a relevant research problem. This is the precise aim of this study. A database containing 20 drivers was collected in real-driving scenarios. The drivers were asked to perform common secondary tasks such as operating the radio, phone and a navigation system. The collected database comprises of various noninvasive sensors including the controller area network-bus (CAN-Bus), video cameras and microphone arrays. The study analyzes the effects in driver behaviors induced by secondary tasks. The corpus is analyzed to identify multimodal features that can be used to discriminate between normal and task driving conditions. Separate binary classifiers are trained to distinguish between normal and each of the secondary tasks, achieving an average accuracy of 77.2%. When a joint, multi-class classifier is trained, the system achieved accuracies of 40.8%, which is significantly higher than chances (12.5%). We observed that the classifiers’ accuracy varies across secondary tasks, suggesting that certain tasks are more distracting than others. Motivated by these results, the study builds statistical models in the form of Gaussian Mixture Models (GMMs) to quantify the actual deviations in driver behaviors from the expected normal driving patterns. The study includes task independent and task dependent models. Building upon these results, a regression model is proposed to obtain a metric that characterizes the attention level of the driver. This metric can be used to signal alarms, preventing collision and improving the overall driving experience.