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A Vehicle Detection and Tracking Approach Using Probe Vehicle LIDAR Data

In: Traffic and Granular Flow’05

Author

Listed:
  • Bin Gao

    (The Ohio State University, Department of Electrical and Computer Engineering)

  • Benjamin Coifman

    (The Ohio State University, Department of Electrical and Computer Engineering
    The Ohio State University Columbus, Department of Civil and Environmental Engineering and Geodetic Science)

Abstract

Summary Detection, identification and tracking of multiple moving targets have important applications in transportation and vehicle control areas. In this paper we present our approach to detect, recognize and track the vehicles within the detection region of a moving probe vehicle, based on the data collected by multiple sensors, including LIDAR and GPS. This paper develops a methodology to group the LIDAR measurements into targets, classify the targets as vehicles or fixed objects, and track the vehicular targets within lanes using a Kalman-filter. One important feature of this approach is that we track all of the observations in world coordinates, allowing us to average over many samples and ideally many runs to differentiate between the fixed objects (road boundaries) and moving objects (vehicles).

Suggested Citation

  • Bin Gao & Benjamin Coifman, 2007. "A Vehicle Detection and Tracking Approach Using Probe Vehicle LIDAR Data," Springer Books, in: Andreas Schadschneider & Thorsten Pöschel & Reinhart Kühne & Michael Schreckenberg & Dietrich E. Wol (ed.), Traffic and Granular Flow’05, pages 675-685, Springer.
    • Handle: RePEc:spr:sprchp:978-3-540-47641-2_66
    DOI: 10.1007/978-3-540-47641-2_66
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