Cluster Regularized Extreme Learning Machine for Detecting Mixed-Type Distraction in Driving

Distraction was previously studied within each dimension separately, i.e., physical, cognitive and visual. However real-world activities usually involve multiple distraction dimensions in terms of brain resources that might conflict with the driving task. This brings difficulties for classifying dimension/type of distraction even for human experts. On the other hand, many subsequent functional blocks do not utilize distraction type information. For example, a pre-collision system usually makes decision based on distraction level rather than distraction type. Therefore this study aims to detect distraction in general regardless of its type, and proposes an effective machine learning algorithm, i.e., Cluster Regularized Extreme Learning Machine (CR-ELM), to detect mixed-type distraction in driving. Compared to traditional machine learning techniques, CR-ELM is designed to handle problems with multiple clusters per class, and provides more accurate detection performance, which could be used for advanced driver assistance systems.