Analysis and Estimation of Driver Visual Attention Using Head Position and Orientation in Naturalistic Driving Conditions

Download or read book Analysis and Estimation of Driver Visual Attention Using Head Position and Orientation in Naturalistic Driving Conditions written by Sumit Jha and published by . This book was released on 2016 with total page 106 pages. Available in PDF, EPUB and Kindle. Book excerpt: Monitoring driver behaviors is crucial in the design of assistance systems that can detect drivers' actions, providing necessary warnings when needed. The visual attention of a driver is an important aspect to consider, as most driving tasks require visual resources. Studying the behaviors of the driver can help in understanding the focus of attention and his/her capability to perform driving-related tasks. Estimating gaze on vehicle environments is a challenging problem due to changes in illumination, head rotations, and occlusions. This thesis explores the relationship between head pose and gaze during natural driving conditions. We collect a corpus in naturalistic driving environment, where 16 subjects were asked to look at predetermined marker locations. The task was conducted while the participants were driving, and when the vehicle was parked. We use a headband with multiple AprilTags to determine the ground truth head pose, which were compared with the results of a state-of-the-art head pose estimation algorithm. We design regression models to study the predictability of the gaze when the orientation and position of the driver's head is known. We study the amount of eye movement bias during glance actions when looking in different directions. Finally, we propose a probabilistic model to predict a visual salient map providing confidence scores about potential gaze directions. Using this model, we project the saliency map into the road scene to identify potential objects that the driver is focusing his/her attention. This thesis, therefore, contributes to an improved understanding of head pose and eye gaze and how characterizing this knowledge could improve driver safety systems.