Research on Visual Cognition About Sharp Turn Sign Based on Driver’s Eye Movement Characteristic

Abstract

As a traffic language, sharp turn sign is a kind of important road infrastructure that indicates a sharp turning will show up at the coming road, warning drivers to slow down to ensure the driving safety. In this paper, real vehicle test was carried out on mountain road with an eye tracker equipment. At different driving speeds, parameters of eye movement characteristics in the visual cognition process of sharp turn signs were collected, including distribution of gaze points, fixation and saccade. Simultaneously, driver’s scan paths of recognizing sharp turn signs with different supporting forms were gathered. The results of the analysis of testing data showed that the dispersion of distribution of gaze points would increase with driving velocity. Saccade was the main method for driver to capture information of sharp turn signs. While driving speed was lower than 60[Formula: see text]km/h, fixation was also one of the methods. For the visual cognition process of sharp turn sign with cantilever, compared to post, the searching scope was wider both in horizontal and vertical directions. This study is beneficial to evaluate the rationality of sharp turn signs, promoting the using efficiency of signs and improving the driving safety on mountain road.

Automated Visual Recognizability Evaluation of Traffic Sign Based on 3D LiDAR Point Clouds

“…Motamedi et al [9] used BIM enabled Virtual Reality (VR) environments to analyze traffic sign visibility. Some researchers [10,18] used eye tracker equipment to determine the visual cognition of traffic signs under simulated driving conditions.…”

“…Our model, as well as other models [8][9][10], also relies on human vision. The advantage of our method is it calculates, from the 3D point cloud, an image area of the traffic sign on the retina.…”

“…Existing research on the evaluation of traffic sign visibility and recognizability is based mainly on simulator and image methods and naturalistic driving experimentation. Simulator based methods [8][9][10] cannot evaluate the visibility and recognizability of real roads. Image based methods [11][12][13] are limited by fixed viewpoints and cannot evaluate the visibility and recognizability over the whole road surface.…”

Automated Visibility Field Evaluation of Traffic Sign Based on 3d Lidar Point Clouds

“…Motamedi et al (Motamedi et al, 2017) used BIM enabled Virtual Reality (VR) environments to analyze traffic sign visibility. Some researchers (Bohua et al, 2011) (Li , Zhang, 2017) used eye tracker equipment to determine the visual cognition of the traffic signs under simulated driving conditions. These methods do not focus on the quantitative value of visibility.…”

“…Existing research on the evaluation of traffic signs' visibility is based mainly on simulator and image methods. Simulator based methods (Lyu et al, 2017, Motamedi et al, 2017, Li , Zhang, 2017 evaluate the visibility or recognizability by observing the simulated traffic environment in the screen. Those methods * Corresponding author can cover all of factors affected visibility.…”