Analysis and Modeling of Driver's Gaze Trajectories in Curves Driving

Author:

Ren Yuan-Yuan1,Li Xian-Sheng1,Zheng Xue-Lian1,Li Zhe1,Zhao Qi-Chao2,Chen Xiao-Xia3

Affiliation:

1. College of Traffic, Jilin University, Changchun 130022, China

2. Kingfar International Inc. Technology Center, Beijing 10000, China

3. Mitsubishi Electric (China) CO., LTD., Shanghai Branch, Shanghai 200051, China

Abstract

To explore the relation between drivers' eye movements and vehicle steering performance, 25 participants were employed to drive a test vehicle on appointed tortuous routes. The drivers' visual behavior was measured while they were driving, and experiment data collected on motorway on-ramps and interchanges were used to analyze the drivers' visual characteristics. The distribution density of the fixation points showed that the drivers paid attention to the inner road edge when turning left and the outer road edge when turning right. Meanwhile, the heights of the gaze targets hardly changed. Therefore, a further investigation discovered that the gaze trajectory was parallel to the vehicle's driving trajectory on one-way curves. Then, six types of gaze trajectories were derived, and models for each type were established. Combining these models with the fact that gaze points are always distributed along the nearby road edge, it was deduced that the starting point of the gaze trajectory when driving on curves has a salient influence on the driver's gaze trajectory.

Funder

National Natural Science Foundation of China

Publisher

SAGE Publications

Subject

Mechanical Engineering

Cited by 4 articles. 订阅此论文施引文献 订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献

1. Reviews and prospects of human factors research on curve driving;Journal of Traffic and Transportation Engineering (English Edition);2023-10

2. Estimation of Vehicle State Based on Limited Memory Random Weighted Unscented Kalman Filter;2021 5th CAA International Conference on Vehicular Control and Intelligence (CVCI);2021-10-29

3. Towards unpacking older drivers’ visual-motor coordination: A gaze-based integrated driving assessment;Accident Analysis & Prevention;2018-04

4. Deep Learning-Based Gaze Detection System for Automobile Drivers Using a NIR Camera Sensor;Sensors;2018-02-03

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