Traffic accidents in extra-long urban underwater tunnels are characterized by high numbers of causalities and severe traffic congestion. Analyzing drivers’ saccade characteristics under different curvature conditions in urban underwater tunnels can provide solutions to reduce the rates of such accidents and increase traffic safety. This paper reports real vehicle tests conducted in extra-long urban underwater tunnels, on curved sections of radii of 400, 680, 1,000, 1,500, and 3,000 m, and also on straight sections. Indicators of drivers’ saccade behavior, such as saccade angle, time, and frequency, and the saccade time ratio were evaluated. The coefficient of variation was used to analyze the discreteness of the saccade angle. The driver’s saccade characteristics, such as saccade time and frequency, were explored by combining the visual distances for different curved sections. The results demonstrated that (a) small angles in the range of [0, 10°] constituted the main distribution section of the driver’s saccade angle in extra-long urban underwater tunnels, and the saccade angle discreteness increased with increase in the radius, (b) the driver’s average saccade time increased while the average saccade frequency decreased with the increase of the radius, (c) the driver’s visual load was higher for long straight sections and small-radius curves, (d) the driver’s safety was generally higher on right-curving sections than on left-curving sections.
To study the influence of traffic signs information volume (TSIV) on drivers’ visual characteristics and driving safety, the simulation scenarios of different levels of TSIV were established, and 30 participants were recruited for simulated driving tests. The eye tracker was used to collect eye movement data under three-speed conditions (60 km/h, 80 km/h, and 100 km/h) and different levels of TSIV (0 bits/km, 10 bits/km, 20 bits/km, 30 bits/km, 40 bits/km, and 50 bits/km). Principal component analysis (PCA) was used to select indicators sensitive to the influence of TSIV on the drivers’ visual behavior and to analyze the influence of TSIV on the drivers’ visual characteristics and visual workload intensity under different speed conditions. The results show that the fixation duration, saccade duration, and saccade amplitude are the three eye movement indicators that are most responsive to changes in the TSIV. The driver’s visual characteristics perform best at the S3 TSIV level (30 bits/km), with the lowest visual workload intensity, indicating that drivers have the lowest psychological stress and lower driving workload when driving under this TSIV condition. Therefore, a density of 30 bits/km is suggested for the TSIV, in order to ensure the security and comfort of the drivers. The theoretical underpinnings for placing and optimizing traffic signs will be provided by this work.
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