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[1] Guo Tangyi, Pan Shu, Shao Fei, Xu Qian, et al. Drivers’ fixation transfer characteristics in urban tunnels [J]. Journal of Southeast University (English Edition), 2021, 37 (3): 325-331. [doi:10.3969/j.issn.1003-7985.2021.03.013]
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Drivers’ fixation transfer characteristics in urban tunnels()
城市隧道驾驶员注视行为特征
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Journal of Southeast University (English Edition)[ISSN:1003-7985/CN:32-1325/N]

Volumn:
37
Issue:
2021 3
Page:
325-331
Research Field:
Traffic and Transportation Engineering
Publishing date:
2021-09-20

Info

Title:
Drivers’ fixation transfer characteristics in urban tunnels
城市隧道驾驶员注视行为特征
Author(s):
Guo Tangyi1;  Pan Shu2;  Shao Fei3;  Xu Qian3
1School of Automation, Nanjing University of Science and Technology, Nanjing 210094, China
2China Design Group, Nanjing 210014, China
3College of Field Engineering, Army Engineering University of PLA, Nanjing 210007, China
郭唐仪1;  潘姝2;  邵飞3;  徐倩3
1南京理工大学自动化学院, 南京 210094; 2华设设计集团, 南京 210014; 3中国人民解放军陆军工程大学野战工程学院, 南京 210007
Keywords:
traffic safety;  urban tunnel;  eye movement;  fixation transfer;  Markov theory
交通安全;  城市隧道;  眼动;  固定传递;  马尔可夫理论
PACS:
U49
DOI:
10.3969/j.issn.1003-7985.2021.03.013
Abstract:
To improve the safety performance of urban tunnels, the fixation transfer characteristics of drivers with different driving experience levels in urban tunnels were investigated. First, a real vehicle test was performed in an urban tunnel, and the eye movement data of 10 drivers with different driving experience levels were collected using a Dikablis eye-tracking system. Second, the driver fixation range was divided into eight areas of visual interest by using the K-means clustering method, and the fixations in different sections of the tunnel were comparatively analyzed. Finally, on the basis of the divided areas of visual interest, fixation transfer rules and the stationary distribution characteristics of drivers with different driving experience levels on different sections of the tunnel were discussed using Markov theory. Results indicate that drivers’ probability of repeated fixation is greater and that the efficiency of visual search is lower at internal sections of tunnels than in external sections. Drivers obtain information mainly from the straight upper front and straight lower front areas, and the probabilities of fixation points in these two areas at the threshold and exit sections are significantly higher than those in other sections. Relative to experienced drivers, novice drivers allocate little attention to the straight upper front area and rear-view mirrors. Hence, they have weak fixation when looking forward, and they lack experience in obtaining information on rear-approaching vehicles and controlling speed.
为了提高城市隧道的安全性能, 研究了不同驾驶经验的城市隧道驾驶员的固定传递特性.首先, 在某城市隧道进行了实车试验, 并利用Dikablis跟踪系统采集了10名不同驾驶经验驾驶员的眼动数据.其次, 采用K均值聚类方法将驾驶员固定范围划分为8个视觉兴趣区域, 并对隧道不同断面的固定效果进行了比较分析.最后, 基于视觉兴趣划分、固定传递规律和不同驾驶经验的驾驶员在隧道不同路段上的平稳分布特征, 运用马尔可夫理论进行了讨论.结果表明:隧道内段驾驶员重复固定的概率较大, 视觉搜索效率较低;驾驶员主要从直上前方和直下前方区域获取信息, 在这2个区域, 在门槛和出口段固定点的概率明显高于其他路段;与经验丰富的驾驶员相比, 新手驾驶员对直上前区和后视镜的关注度较低, 这意味着他们对前方的注视力较弱, 在获取后接近车辆信息和控制速度方面缺乏经验.

References:

[1] Hung Y N. “What are you looking at?”An eye movement exploration in science text reading[J]. International Journal of Science and Mathematics Education, 2014, 12(2): 241-260. DOI:10.1007/s10763-013-9406-z.
[2] Shi L N, Tu Y, Wang X J. Research of energy-saving design for tunnel lighting based on earlier and new specifications[J]. China Illuminating Engineering Journal, 2015, 26(1): 50-54.(in Chinese)
[3] Polus A, Mattar-Habib C. New consistency model for rural highways and its relationship to safety[J]. Journal of Transportation Engineering, 2004, 130(3): 286-293. DOI:10.1061/(asce)0733-947x(2004)130: 3(286).
[4] Ryu D, Cooke A, Bellomo E, et al. Watch out for the hazard! Blurring peripheral vision facilitates hazard perception in driving[J]. Accident Analysis & Prevention, 2020, 146: 105755. DOI:10.1016/j.aap.2020.105755.
[5] Ma Y L, Hu B Y, Chan C Y, et al. Distractions intervention strategies for in-vehicle secondary tasks: An on-road test assessment of driving task demand based on real-time traffic environment[J].Transportation Research Part D: Transport and Environment, 2018, 63: 747-754. DOI:10.1016/j.trd.2018.07.005.
[6] Vlakveld W, Romoser M R, Mehranian H, et al. Do crashes and near crashes in simulator-based training enhance novice drivers’ visual search for latent hazards?[J]. Transportation Research Record, 2011, 2265: 153-160. DOI:10.3141/2265-17.
[7] Meng Y, Wang J, Xia Y, et al. Effects of music’s emotional styles and tempo on driving behavior and eye movement: A driving simulation study[J]. Advances in Intelligent Systems and Computing, 2014, 215: 565-576.
[8] Hariyama T, Kato T. Rally driver’s eye movements when driving the corner on gravel road—differences between world rally championship and national championship drivers[M]//Advances in Intelligent Systems and Computing. Cham: Springer International Publishing, 2018: 174-178. DOI:10.1007/978-3-319-96074-6_18.
[9] Sun Q C, Xia J C, He J B, et al. Towards unpacking older drivers’ visual-motor coordination: A gaze-based integrated driving assessment[J].Accident Analysis & Prevention, 2018, 113: 85-96. DOI:10.1016/j.aap.2018.01.019.
[10] Ren H R, Ye Z X, Li Z W. Anomaly detection based on a dynamic Markov model[J]. Information Sciences, 2017, 411: 52-65. DOI:10.1016/j.ins.2017.05.021.

Memo

Memo:
Biographies: Guo Tangyi(1982—), male, doctor, associate professor, transtor@njust.edu.cn; Shao Fei(corresponding author), male, doctor, professor, shaofeiriver@foxmail.com.
Foundation items: The National Key Research and Development Program of China(No. 2019YFE0123800), the Fundamental Research Funds for the Central Universities(No.30919011290, 30920010010).
Citation: Guo Tangyi, Pan Shu, Shao Fei, et al. Drivers’ fixation transfer characteristics in urban tunnels[J].Journal of Southeast University(English Edition), 2021, 37(3):325-331.DOI:10.3969/j.issn.1003-7985.2021.03.013.
Last Update: 2021-09-20