On generalizing driver gaze zone estimation using convolutional neural networks

Abstract

The knowledge of driver distraction will be important for self driving cars in the near future to determine the handoff time to the driver. Driver's gaze direction has been previously shown as an important cue in understanding distraction. While there has been a significant improvement in personalized driver gaze zone estimation systems, a generalized gaze zone estimation system which is invariant to different subjects, perspective and scale is still lagging behind. We take a step towards the generalized system using a Convolutional Neural Network (CNN). For evaluating our system, we collect large naturalistic driving data of 11 drives, driven by 10 subjects in two different cars and label gaze zones for 47515 frames. We train our CNN on 7 subjects and test on the other 3 subjects. Our best performing model achieves an accuracy of 93.36% showing good generalization capability.

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