Cognitive load estimation using ocular parameters in automotive

Abstract

In automotive, usage of electronic devices increased visual inattention of drivers while driving and might lead to accidents. It is often challenging to detect if a driver experienced a change in cognitive state requiring new technology that can best estimate driver's cognitive load. In this paper, we investigated the efficacy of various ocular parameters to estimate cognitive load and detect cognitive state of driver. We derived gaze and pupil-based metrics and evaluated their efficacy in classifying different levels of cognitive states while performing psychometric tests in varying light conditions. We validated the performance of our metrics in simulation as well as in-car environments. We compared the accuracy (from confusion matrix) of detecting cognitive state while performing secondary task using our proposed metrics and Machine Learning models. It was found that a Neural Network model combining multiple ocular metrics showed better accuracy (75%) than individual ocular metrics. Finally, we demonstrated the potential of our system to alert drivers in real-time under critical distractions.

Keywords

• Automotive
• Distraction
• Cognitive load
• Pupil dilation
• N-back