Automatic intersection map generation task 10 report.

Details

• Alternative Title:
  Automatic intersection map generation : V2I safety applications project ; task 10 report.
• Creators:
  Zheng, Jianfeng ; Liu, Henry X. ; Parikh, Jay
• Corporate Creators:
  Crash Avoidance Metrics Partners
• Corporate Contributors:
  United States. Federal Highway Administration ; United States. Joint Program Office for Intelligent Transportation Systems
• Subject/TRT Terms:
  • Accuracy
  • Automatic vehicle detection and identification systems
  • Center lines
  • Data collection
  • Feasibility analysis
  • Intersections
  • Laser radar
  • Mapping
  • Maps
  • Traffic lanes
  More +
• Publication/ Report Number:
  FHWA-JPO-16-416
• Resource Type:
  Tech Report
• Geographical Coverage:
  United States
• Edition:
  Final report
• Corporate Publisher:
  United States. Federal Highway Administration ; United States. Joint Program Office for Intelligent Transportation Systems
• Abstract:
  This report describes the work conducted in Task 10 of the V2I Safety Applications Development Project. The work was performed by the University of ; Michigan Transportation Research Institute (UMTRI) under contract to the Crash Avoidance Metrics Partners LLC (CAMP) Vehicle to Infrastructure ; (V2I) Consortium. Participating companies in the V2I Consortium were FCA US LLC, Ford, General Motors, Hyundai-Kia, Honda, Mazda, Nissan, ; Subaru, Volvo Truck, and VW/Audi. This project investigated the feasibility of automatically generating intersection maps in SAE J2735 MAP format ; using Basic Safety Messages (BSMs) received by Roadside Units (RSUs). A procedure for classifying vehicle trips through the intersection and ; subsequently estimating lane centerlines was developed. In addition, a method for associating vehicle movements to the green signal phase was also ; developed using Signal Phase and Timing (SPaT) messages. BSM data from five intersections used in the Safety Pilot Model Deployment Project ; were analyzed. The estimated maps were compared to reference maps produced from LIDAR surveys of the intersections. With 48 traffic lanes in ; total, 43 lanes and associated vehicle movements were correctly identified using the estimation approach. Five lanes were not successfully identified ; due mainly to a lack of BSM data. For the identified lanes, two measurements of accuracy were calculated: the mean distance of the estimated ; geometry node points to closest points in the surveyed lane centerline geometry and the maximum distance of estimated points and surveyed lane ; geometry. On average, the mean distance was found to be 0.5 meter and maximum distance was found to be 1.2 meters between the estimated maps ; and reference maps. Overall, this project demonstrated the significant potential of using BSM data for estimation of an intersection map as well as ; association of the vehicle lanes (vehicle movements) to the traffic signal phases.
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• Format:
  PDF
• Funding:
  DTFH6114H00002
• Download URL:
  https://rosap.ntl.bts.gov/view/dot/3601/dot_3601_DS1.pdf
• File Type:
  [PDF - 11.95 MB]
• Collection(s):
  ITSJPO Digital Library ; Federal Highway Administration
• Main Document Checksum:
  urn:sha-512:3f1cbad8095ad58cf03f1c09eab50ea46b05bbe10541060badf10e4637710cda03d21a5d1ebe60ffde24b27bb39621c6aebd8114a3f6f56eda1c04a286c56b81