Vehicle Counting and Maneuver Classification with Support Vector Machines Using Low-Density Flash Lidar

Zhenming Xie, Rajesh Rajamani

Research output: Contribution to journalArticlepeer-review

4 Scopus citations

Abstract

This paper develops a machine-learning-based method for counting vehicles and classifying their maneuvers in a traffic intersection using inexpensive low-density Lidar sensors. First, each vehicle is automatically detected using hierarchical clustering and then its trajectory is tracked using a virtual point method that compensates for the low angular resolution of the sensor. Then, characteristic low-dimensional features of each trajectory are extracted for the classification task, so that use of the entire time-varying trajectory can be avoided. The novel features extracted include selected locations/velocities, and zero-mean singular vectors that describe the shape of the trajectory around the mean vehicle location. These features are found to provide excellent separation between various inlet-outlet maneuvers. Both simulation and extensive experimental results are presented. A single sensor that covers 2 out of 4 roads at a traffic intersection is found to work with high accuracy but has occlusion errors due to the limited coverage of the sensor. A two-sensor system that covers all 4 roads at an intersection needs an additional algorithm to merge trajectories from the two sensors to avoid double counting. High counting and classification accuracies are achieved with the developed systems.

Original languageEnglish (US)
Pages (from-to)86-97
Number of pages12
JournalIEEE Transactions on Vehicular Technology
Volume71
Issue number1
DOIs
StatePublished - Jan 1 2022

Bibliographical note

Publisher Copyright:
IEEE

Keywords

  • Lidar
  • Maneuver classification
  • Support vector machines
  • Traffic monitoring
  • Vehicle tracking

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