The correlation between drivers' road familiarity and glance behavior using real vehicle experimental data and mathematical models.

OBJECTIVE

Road familiarity is an important factor affecting drivers' visual features. Analyzing the quantitative correlation between drivers' road familiarity and visual features in complex environment is of great help to improve driving safety. However, there are few relevant studies. This paper takes urban plane intersection as the environmental object to explore the correlation between drivers' glance behavior and road familiarity, and conducts research on the quantitative evaluation model of road familiarity based on this correlation.

METHOD

First, a real vehicle experiment was carried out to record the eye movement data of 24 drivers with different road familiarity. The driver's visual field plane was divided into 10 areas of interest (AOIs) based on the driver's perspective. Three measures, including average glance duration, number of glances, and fixation transition probabilities between AOIs at urban plane intersections, were extracted. Finally, based on the experimental results, the driver road familiarity evaluation model was constructed using the factor analysis method.

RESULTS

There are significant differences between unfamiliar and familiar drivers regarding the average glance duration toward the forward (FW) area, the left window (LW) area, the left rearview mirror (LVM) area and the left forward (LF) area, the number of glances toward the other (OT) area, and the fixation transition probabilities of LW→RF (right forward), LF→LF, LF→FW, FW→LW, FW→FW, FW→RVM (right rearview mirror). The comprehensive evaluation results show that the accuracy rate of the driver road familiarity evaluation model reached 83%.

CONCLUSIONS

This paper revealed that there is a strong correlation between drivers' road familiarity and drivers' glance behavior. Based on this correlation, we can include road familiarity as a part of drivers' working status and establish a high accuracy evaluation model of driver road familiarity. The conclusion of this paper can provide some reference for the humanized design and improvement of advanced driving assistance system, which is of great significance for reducing the driving workload of drivers and improving the driving safety.