Intersection and Stop Bar Position Extraction from Crowdsourced GPS Trajectories

Detailed road features (e.g., lane marks and stop bars) are crucial for many recent intelligent transportation system applications, especially for automated or autonomous driving systems. In this paper, a crowdsourcing based method is proposed to mark intersection areas and map stop bar positions without prior knowledge of road information. The proposed method includes an efficient approach for marking intersection areas by analyzing the entropy of moving direction, as well as a statistical model of stop positions for estimating the number and coordinates of stop bars. The proposed method is applied to the real-world dataset collected for the Safety Pilot Model Deployment Program (SPMDP). The numerical analysis results prove its applicability and robustness in processing GPS trajectories of an urban region (a 1.2 km by 2 km rectangular area). For the intersections covered well by trajectories, the accuracy of marking intersections is 95.7%. For stop bar positioning, the mean and standard deviation of the errors are 0.25 m and 0.32 m.

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• Supplemental Notes:
  • This paper was sponsored by TRB committee ABJ30 Standing Committee on Urban Transportation Data and Information Systems.
• Corporate Authors:

  Transportation Research Board

  500 Fifth Street, NW
  Washington, DC  United States  20001
• Authors:
  • Wang, Chao
  • Hao, Peng
  • Wu, Guoyuan
  • Qi, Xuewei
  • Lyu, Tingxu
  • Barth, Matthew
• Conference:
  • Transportation Research Board 96th Annual Meeting
  • Location: Washington DC, United States
  • Date: 2017-1-8 to 2017-1-12
• Date: 2017

Language

• English

Media Info

• Media Type: Digital/other
• Features: Figures; References; Tables;
• Pagination: 18p
• Monograph Title: TRB 96th Annual Meeting Compendium of Papers

Subject/Index Terms

• TRT Terms: Accuracy; Crowdsourcing; Data collection; Detection and identification systems; Floating car data; Intersections; Vehicle trajectories
• Uncontrolled Terms: Stop lines
• Subject Areas: Data and Information Technology; Highways;

Filing Info

• Accession Number: 01628206
• Record Type: Publication
• Report/Paper Numbers: 17-06670
• Files: PRP, TRIS, TRB, ATRI
• Created Date: Mar 7 2017 10:25AM