Drivers’ Eye Movement Characteristics in a Combined Bridge-Tunnel Scenario on a Mountainous Urban Expressway: A Realistic Study

Author:

Bai Jingrong1ORCID,Lin Qinghai2ORCID,Bi Huiyun1,Tang Boming1

Affiliation:

1. School of Civil Engineering, Chongqing Jiaotong University, Chongqing 400074, China

2. School of Intelligent Systems Engineering, Sun Yat-Sen University, Guangzhou 510006, China

Abstract

Combined bridge-tunnel scenarios of driving on mountainous city expressways occur when bridges and tunnels frequently alternate during driving. The complex nature of these driving scenarios imposes crucial requirements on the drivers’ eye movement characteristics. This paper attempts to clarify these characteristics using descriptive statistics and the box graph method, registering the pupil diameter, blink duration, fixation, saccades, and fixation loci at different tunnel locations, bridges, and ramps. Realistic driving experiments were performed on the road segment spanning from the Nanchang tunnel to the Liujiatai tunnel freeway in Chongqing, China. Eye movement data were collected for 21 drivers. The experimental results showed that, while driving in the tunnel, the maximal pupil diameter of the participating drivers was approximately 4.0 mm as the driving mileage and the number of tunnels increased, and the maximal visual load on the drivers in the tunnel tended to be stable. At the second tunnel exit, the ramp, the middle section of the first bridge, and the third tunnel exit, the driving load was the highest, while the fixation duration was shorter for nighttime driving. The fixation duration was the longest for the diversion road of bridge B1 to the ramp during the day, and the fixation times were the longest at the beginning and end of the test road. The drivers more often paid attention to the speed dashboard while entering tunnels during daytime driving (compared with nighttime driving).

Funder

National Natural Science Foundation of China

Publisher

Hindawi Limited

Subject

Strategy and Management,Computer Science Applications,Mechanical Engineering,Economics and Econometrics,Automotive Engineering

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