Driver's Preview Modeling Based on Visual Characteristics through Actual Vehicle Tests

This paper proposes a method for obtaining driver's fixation points and establishing a preview model based on actual vehicle tests. Firstly, eight drivers were recruited to carry out the actual vehicle test on the actual straight and curved roads. The curvature radii of test curved roads were selected to be 200, 800, and 1500 m. Subjects were required to drive at a speed of 50, 70 and 90 km/h, respectively. During the driving process, eye movement data of drivers were collected using a head-mounted eye tracker, and road front scene images and vehicle statuses were collected simultaneously. An image-world coordinate mapping model of the visual information of drivers was constructed by performing an image distortion correction and matching the images from the driving recorder. Then, fixation point data for drivers were accordingly obtained using the Identification-Deviation Threshold (I-DT) algorithm. In addition, the Jarque-Bera test was used to verify the normal distribution characteristics of these data and to fit the distribution parameters of the normal function. Furthermore, the preview points were extracted accordingly and projected into the world coordinate. At last, the preview data obtained under these conditions are fit to build general preview time probability density maps for different driving speeds and road curvatures. This study extracts the preview characteristics of drivers through actual vehicle tests, which provides a visual behavior reference for the humanized vehicle control of an intelligent vehicle.