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Vehicle Detection and Tracking with Roadside LiDAR Using Improved ResNet18 and the Hungarian Algorithm.

Ciyun Lin1,2, Ganghao Sun1, Dayong Wu3

  • 1Department of Traffic Information and Control Engineering, Jilin University No. 5988, Renmin Street, Changchun 130022, China.

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PubMed
Summary
This summary is machine-generated.

A new vehicle tracking algorithm using roadside LiDAR infrastructure achieves 100 ms latency for autonomous driving. This system enhances detection accuracy and tracking performance, crucial for Level 5 autonomy and real-time transportation applications.

Keywords:
KITTIMATLAB/SimulinkResNet18autonomous drivingroadside LiDAR sensorvehicle detectionvehicle tracking

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Area of Science:

  • Computer Vision
  • Robotics
  • Transportation Engineering

Background:

  • Achieving Level 5 autonomous driving necessitates vehicle-infrastructure collaboration, requiring low-latency, high-speed data processing.
  • Current systems face challenges in maintaining detection accuracy and reducing latency for real-time applications.

Purpose of the Study:

  • To introduce a vehicle tracking algorithm utilizing roadside LiDAR infrastructure to achieve low latency (100 ms) without compromising detection accuracy.
  • To enhance vehicle detection and tracking for improved performance in autonomous driving scenarios.

Main Methods:

  • Developed a vehicle detection architecture using ResNet18, optimizing Bird's-Eye View (BEV) mapping and loss functions for full frame rate detection.
  • Proposed a three-stage vehicle tracking algorithm enhancing the Hungarian algorithm with time-space logicality and trajectory similarity to handle occlusions.
  • Tested the framework on the KITTI dataset and a MATLAB/Simulink simulation dataset.

Main Results:

  • Achieved F1-scores of 96.97% (KITTI) and 98.58% (MATLAB/Simulink) for vehicle detection.
  • Obtained MOTA scores of 88.12% (KITTI) and 90.56% (MATLAB/Simulink), and ID-F1 scores of 95.16% (KITTI) and 96.43% (MATLAB/Simulink) for vehicle tracking.
  • Demonstrated significant improvements in calculation speed compared to traditional methods.

Conclusions:

  • The proposed LiDAR-based vehicle tracking algorithm effectively reduces latency to 100 ms while maintaining high detection and tracking accuracy.
  • The enhanced tracking algorithm outperforms traditional methods, particularly in handling occlusions and improving computational speed.
  • This framework is a promising solution for enabling real-time performance required for Level 5 autonomous driving.