A Real-Time Context-Dependent Driver Attention AssessmentSystem Using Pupil Orientation and Head Posture

With the popularization of social media andInternet technology, people are more and more easilydistracted when driving, which has a serious adverseimpact on the safety of expected functions, and reducesthe efficiency of control transmission between humanand computer. This article proposes a real-time pupil re-construction (PURE) algorithm for real-time detectionof pupil direction, and combines it with a 3D eye modelto obtain the gaze direction of the pupil in real time.Subsequently, the Posit algorithm was used to obtain real-time head posture, and the head posture and pupil gaze direction were used as improved input features toestimate the driver’s gaze area in real-time using a random forest model. Finally, this article comprehensivelyconsiders the impact of multiple real-time indicatorson driving attention, such as the number of eye gazeareas, eye scanning areas, road conditions, brain latency,and driving tasks, and proposes a prototype framework for real-time driver multi indicator attention evaluation.Through various real-time evaluation experiments,we have demonstrated that: 1) these context relatedattention features are universal when tested in real-timeon 16 drivers in a driving simulator; 2) The proposeddriver attention evaluation model performs well underreal-time conditions and can produce reliable results invarious driving scenarios.