Side-fire lidar-based vehicle classification

Transportation Research Record

Volumn , Issue 2308, 2012, Pages 173-183

  Lee, Ho ^a   Coifman, Benjamin ^a  

^a Ohio State University   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

CLASSIFICATION (OF INFORMATION); CLUSTERING ALGORITHMS; MOTORCYCLES; PAVEMENTS; TRUCK TRAILERS;

CLASSIFICATION SYSTEM; CLASSIFIEDS; COMMUTER CARS; CONCEPT STUDIES; GROUND TRUTH DATA; PARTIAL OCCLUSIONS; PASSENGERS VEHICLES; PROOF OF CONCEPT; TRAFFIC CONDITIONS; VEHICLE CLASSIFICATION;

OPTICAL RADAR;

EID: 84874071537     PISSN: 03611981     EISSN: None     Source Type: Journal    
DOI: 10.3141/2308-19     Document Type: Article

References (15)

1. Traffic Monitoring Guide. FHWA, U.S. Department of Transportation, 2001.
2. Khattak, A. J., S. Hallmark, and R. Souleyrette. Application of Light Detection and Ranging Technology to Highway Safety. In Transportation Research Record: Journal of the Transportation Research Board, No. 1836, Transportation Research Board of the National Academies, Washington, D.C., 2003, pp. 7-15.
3. Tsai, Y., Q. Yang, and Y. Wu. Use of Light Detection and Ranging Data to Identify and Quantify Intersection Obstruction and Its Severity. In Transportation Research Record: Journal of the Transportation Research Board, No. 2241, Transportation Research Board of the National Academies, Washington, D.C., 2011, pp. 99-108.
4. Veneziano, D., R. Souleyrette, and S. Hallmark. Integration of Light Detection and Ranging Technology with Photogrammetry in Highway Location and Design. In Transportation Research Record: Journal of the Transportation Research Board, No. 1836, Transportation Research Board of the National Academies, Washington, D.C., 2003, pp. 1-6.
5. Souleyrette, R., S. Hallmark, S. Pattnaik, M. O'Brien, and D. Veneziano. Grade and Cross Slope Estimation from LIDAR-Based Surface Models. MTC Project 2001-02. FHWA, U.S. Department of Transportation, 2003.
6. Chenoweth, A. J., R. E. McConnell, and R. L. Gustavson. Overhead Optical Sensor for Vehicle Classification and Axle Counting. Mobile Robots XIV: Proceedings of SPIE, Vol. 3838, Boston, Mass., 1999.
7. Cunagin, W. D., and D. J. Vitello, Jr. Development of an Overhead Vehicle Sensor System. In Transportation Research Record 1200, TRB, National Research Council, Washington, D.C., 1988, pp. 15-23.
8. Yao, W., S. Hinz, and U. Stilla. Traffic Monitoring from Airborne Full-Waveform LIDAR: Feasibility, Simulation and Analysis. International Archives of the Photogrammetry, Remote Sensing and Spatial Geoinformation Sciences, Vol. 37(B3B), 2008, pp. 593-598.
9. Grejner-Brzezinska, D. A., C. Toth, and M. McCord. Airborne LiDAR: A New Source of Traffic Flow Data. FHWA/OH-2005/14. FHWA, U.S. Department of Transportation, 2005.
10. Yang, R. Vehicle Detection and Classification from a LIDAR-Equipped Probe Vehicle. MS thesis. Ohio State University, Columbus, 2009.
11. Redmill, K., B. Coifman, M. McCord, and R. Mishalani. Using Transit or Municipal Vehicles As Moving Observer Platforms for Large-Scale Collection of Traffic and Transportation System Information. Proc., 14th International IEEE Conference on Intelligent Transportation Systems, Washington, D.C., 2011.
12. Federal Size Regulations for Commercial Motor Vehicles. FHWA, U.S. Department of Transportation, 2000.
13. Highway Capacity Manual. TRB, National Research Council, Washington, D.C., 2000.
14. Coifman, B., and S. Kim. Speed Estimation and Length-Based Vehicle Classification from Freeway Single Loop Detectors. Transportation Research Part C, Vol. 17, No. 4, 2009, pp. 349-364.
15. Coifman, B., and H. Lee. Diagnosing Chronic Errors in Freeway Loop Detectors from Existing Field Hardware. TS-514 final report No. CA10-0978. Caltrans, Sacramento, Calif., 2011.