Online Recognition of Driver-Activity Based on Visual Scanpath Classification

The next step towards the fully automated vehicle is the level of conditional automation, where the automated driving system can take over the control and responsibility for a limited time interval. Nevertheless, take-over situations may occur, forcing the driver to resume the driving task. Despite such situations, the driver is able to perform secondary tasks during conditionally automated driving, hence a low take-over quality must be expected. Methods for Driver-Activity Recognition (DAR) usually extract features for the classification within a moving time window. In this paper, the first DAR architecture based on the driver's scanpath, which is extracted by means of dynamic clustering and symbolic aggregate approximation patterns, is introduced. To demonstrate the potential of this approach, it is compared to a state-of-the-art method based on the data of a driving simulator study with 82 subjects. The classification performance of both DAR approaches was examined for decreasing window sizes with regard to the recognition of different secondary tasks and the separability of drivers using a handheld or hands-free device. Compared to the state-of-the-art approach, the proposed method shows a classification accuracy increase of nearly 20%, a significant improvement of the overall classification performance, and is able to classify the secondary tasks of the driver even for short windows of a duration of 5 s, i.e. with little information.