Abstract

This paper investigates the use of pupil dilation, head movement and EEG for detecting distraction and cognitive load of drivers while performing secondary tasks in an automotive environment. We tracked pupil dilation from Tobii Pro Glasses 2, head movement from Kinect and EEG from Emotive Insight system. We have analyzed data using Fast Fourier Transform, Continuous Wavelet Transform, and Discrete Wavelet Transform for the full-length signal as well as in windows of 1 second for real-time implementation. We investigated detection of distraction and cognitive load from three different conditions - free driving, driving with lane change, driving with lane change and operating secondary task for each participant in a driving simulator. Our results show that the pupil dilation, head yaw, and EEG can detect the increase in cognitive load due to operation of secondary task within a time buffer of 1 second which can be adapted for real-time implementation. We have also found that FFT of Pupil dilation shows significant categorization of normal and distracted states than the categorization by DWT which contrasts with state of the art methods. Finally, we have proposed an expert system to alert drivers utilizing the signal processing analysis.

Highlights

  • In recent time, distraction of drivers increases with increase in number of sophisticated interactive systems inside car which may lead to road accidents

  • There was no significant difference between the pair C2vsC3

  • A KruskalWallis test did not find any significant difference between the groups for the SMSS in windows of 1 second for pupil dilation of the right eye, head yaw, and EEG

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Summary

Introduction

Distraction of drivers increases with increase in number of sophisticated interactive systems inside car which may lead to road accidents. Detecting distraction may not be enough to as the driver can be driving and thinking about his/her personal stress in life. In such situations, measuring cognitive load or affective state of driver becomes a necessity. Cognitive load is detected by invasive as well as non-invasive methods. Invasive methods include invasive EEG tracker, heart-rate tracker and so on. Non-invasive methods include non-invasive EEG, eye tracker, head movement tracker, face tracker, voice pattern tracker, questionnaire (NASA TLX) and so on

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