The traffic environment of an extra-long expressway tunnel is more complex than that of a long tunnel, which increases the driving risk. The visual load of drivers can be used to evaluate driving safety and comfort. To reveal drivers’ visual load characteristics at the entrance and exit of extra-long tunnels on mountainous expressways, this study conducted vehicle tests with 12 drivers at Gonghe extra-long tunnel on the Yu-Xiang expressway in the Wulong District. An eye tracker, non-contact multifunctional velocimetry, illuminometer, and other test equipment were used to record drivers’ pupil areas, velocity, and illuminance when entering and leaving the tunnel. The change characteristics of drivers’ pupil areas were studied. The maximum transient velocity value (MTPA) of the pupil area was selected as an index to evaluate the visual load degree. Based on velocity and illuminance coupling, a visual load model was constructed using the optimized support vector machine (GA-SVM). The influence of velocity and illuminance on the MTPA in the tunnel’s approach, entrance, exit, and departure section was analyzed. The results show that drivers’ psychological tension order at the entrance and exit is entrance section ≈ exit section > departure section > approach section. In the approach section, the visual load is mainly affected by environmental illumination. In the entrance and exit sections, the visual load is positively correlated with velocity and negatively correlated with illuminance, and velocity has a greater impact on visual load. In the tunnel departure section, the two variables synergistically influence the driving visual load. The research results provide theoretical support for the safety design and management of extra-long tunnel entrances and exits.
To study the variation law of driver's sight distance at tunnel entrance and exit, an actual vehicle test was conducted on an expressway in Chongqing (a total length of 160 km, 36 tunnels, including 4 short tunnels, 8 middle tunnels, 14 long tunnels, and 10 extra-long tunnels) in non-peak hours. Eye-tracking data, illuminance, and velocity were collected by eye-tracker, illuminance meter, and non-contact velocimetry with multi-function respectively. Based on the image illuminance extraction method, the illuminance of tunnel entrance and exit were obtained through Matlab. According to the tunnel parameters in reality, the scale model test was designed, and the driving simulation test was carried out to obtain the driver's sight distance at the tunnel entrance and exit. The support vector regression sight distance calculation model was constructed to analyze the relationship among the driver's driving sight distance, velocity, and illuminance to inspect the driver's sight distance at the entrance and exit of the tunnel and put forward some improvement measures.
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