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Multitarget-Tracking Method Based on the Fusion of Millimeter-Wave Radar and LiDAR Sensor Information for Autonomous

Junren Shi1, Yingjie Tang1, Jun Gao2

  • 1School of Automation, Chongqing University of Posts and Telecommunications, Chongqing 400065, China.

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This study introduces an advanced multitarget tracking method for autonomous vehicles, fusing millimeter-wave radar and lidar data for enhanced accuracy in complex driving scenarios. The new approach significantly reduces estimation errors and improves overall tracking performance compared to single-sensor systems.

Keywords:
autonomous vehiclesdata fusionlidarmillimeter-wave radarmultitarget tracking

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Area of Science:

  • Robotics and Autonomous Systems
  • Sensor Fusion and Perception
  • Intelligent Transportation Systems

Background:

  • Multitarget tracking is crucial for autonomous vehicle intelligence.
  • Existing methods struggle with data accuracy, reliability, and complex scenes.
  • Fusion of millimeter-wave radar and lidar data presents challenges.

Purpose of the Study:

  • To propose a novel multitarget tracking method for autonomous vehicles.
  • To enhance tracking accuracy and reliability by fusing millimeter-wave radar and lidar data.
  • To address limitations of current tracking methods in complex driving environments.

Main Methods:

  • Developed a distributed multisensor multitarget tracking (DMMT) system.
  • Implemented single-sensor tracking for radar and lidar, followed by Kalman filter and residual bias estimation for temporal and spatial registration.
  • Utilized sequential m-best method for track association and IF heterogeneous sensor fusion for optimal track combination.

Main Results:

  • The proposed method successfully tracks multiple targets in high-speed driving scenarios.
  • Significant reductions in position (85.5%), velocity (64.6%), size (75.3%), and direction (9.5%) estimation errors compared to single-radar trackers.
  • A 19.8% reduction in the average GOSPA (Grounded Optimal Subpattern Assignment) metric, indicating improved tracking accuracy.

Conclusions:

  • The proposed sensor fusion method provides stable and high-precision global tracks for autonomous vehicles.
  • This approach overcomes limitations of single-sensor tracking and improves performance in complex scenarios.
  • The method yields more accurate target state information than traditional single-radar trackers.