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Division of Area of Fixation Interest for Real Vehicle Driving Tests

Author

Listed:
  • Qian Xu
  • Tang-yi Guo
  • Fei Shao
  • Xue-jiao Jiang

Abstract

The area of interest (AOI) reflects the degree of attention of a driver while driving. The division of AOI is visual characteristic analysis required in both real vehicle tests and simulated driving scenarios. Some key eye tracking parameters and their transformations can only be obtained after the division of AOI. In this study, 9 experienced and 7 novice drivers participated in real vehicle driving tests. They were asked to drive along a freeway section and a highway section, wearing the Dikablis eye tracking device. On average, 8132 fixation points for each driver were extracted. After coordinate conversion, the MSAP (Mean Shift Affinity Propagation) method is proposed to classify the distribution of fixation points into a circle type and a rectangle type. Experienced drivers’ fixation behavior falls into the circle type, in which fixation points are concentrated. Novice drivers’ fixation points, which are decentralized, are illustrated in the rectangle type. In the clustering algorithm, the damping coefficient determines the algorithm convergence, and the deviation parameter mainly affects the number of clusters, where larger values generate more clusters. This study not only provides the cluster type and cluster counts, but also presents the borderlines for each cluster. The findings provide significant contribution to eye tracking research.

Suggested Citation

  • Qian Xu & Tang-yi Guo & Fei Shao & Xue-jiao Jiang, 2017. "Division of Area of Fixation Interest for Real Vehicle Driving Tests," Mathematical Problems in Engineering, Hindawi, vol. 2017, pages 1-10, May.
  • Handle: RePEc:hin:jnlmpe:3674374
    DOI: 10.1155/2017/3674374
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    Cited by:

    1. Xu Ding & Haixiao Wang & Chutong Wang & Min Guo, 2023. "Analyzing Driving Safety on Prairie Highways: A Study of Drivers’ Visual Search Behavior in Varying Traffic Environments," Sustainability, MDPI, vol. 15(16), pages 1-29, August.
    2. Qin Zeng & Yun Chen & Xiazhong Zheng & Meng Zhang & Donghui Li & Qilin Hu, 2023. "Exploring the Visual Attention Mechanism of Long-Distance Driving in an Underground Construction Cavern: Eye-Tracking and Simulated Driving," Sustainability, MDPI, vol. 15(12), pages 1-25, June.

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