Inferring Drivers’ Visual Focus Attention Through Head-Mounted Inertial Sensors

Abstract

Driver distraction is one of the major causes of accidents. Most methods for inferring distracted driving behaviors are vision-based systems that determine the head's orientation. One of the significant challenges of this approach is to develop robust algorithms that detect face and eye features under various lighting conditions. Our approach is based on inferring the vehicle's cabin spot drawing the driver's attention through head-mounted inertial sensors. To achieve this aim, we collected accelerometer, gyroscope, and magnetometer data from ten participants who drove under semi-naturalistic conditions. We generated classifiers by using the Support Vector Machine (SVM linear and RBF), k-nearest neighbor (k-NN), and Random Forest (RF) machine learning techniques. These techniques, except SVM linear, produced an accuracy, precision and recall higher than 96%. Our results demonstrate that raw signals collected from the inertial sensors provide enough information about the head posture associated with the car's cabin spot.

Keywords

• Machine learning
• vehicle driving
• sensor systems and applications
• distraction