Abstract
This paper addresses a new problem of automatic detection of visual attention in older adults based on their driving speed. All state-of-the-art methods try to understand the on-road performance of older adults by means of the Useful Field of View (UFOV) measure. Our method takes advantage of deep learning models such as Long-short Term Memory (LSTM) to automatically extract features from driving speed data for predicting drivers’ visual attention. We demonstrate, through extensive experiments on real dataset, that our method is able to predict the driver’s visual attention based on driving speed with high accuracy.
Highlights
Visual and cognitive abilities are important parameters for safe driving
In order to understand how to detect divided attention based on speed data, firstly we describe the Useful Field of View (UFOV) test in detail
The UFOV test lasts about 15 min and is highly recommended for drivers having an age of 55 years or older, those who suffer from health problems including cognitive deficits [18]
Summary
Visual and cognitive abilities are important parameters for safe driving. The decline in visual and cognitive abilities may lead to unsafe driving [1]. In the context of driving, drivers face multiple stimuli coming from the environment including the car, the road, other drivers, the weather, and driving time of day. These stimuli create a complex context, for older drivers, that requires from them to shift attention between all these stimuli in order to ensure safe driving
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