Verification of Identification Accuracy of Eye-Gaze Data on Driving Video
It is said that the most cause of traffic accidents is the lack of confirming the safety. Visual information from both eyes is one of the important factors for safe driving. In this paper, we collect eye-gaze data of drivers who watch a driving video, and try to develop a model of their eye movements to identify factors to enhance their safety. For the purpose of modeling, we adopted a recurrent neural network and Long Short-Term Memory (LSTM) to the collected eye-gaze data because the LSTM is able to deal with a time-series data such as the eye-gaze data. Moreover, we performed an experiment to evaluate the identification accuracy of drivers. The results indicated that the driver’s intention and habit can be approximated partially by the trained network, but it was insufficient to identify a personal driver for practical use.
This work is supported by the Research Project of Agent Mediated Driving Support of Nagoya University. We are truly thankful for the members of the group.
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