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Multi-Target Tracking with Collaborative Roadside Units Under Foggy Conditions.

Tao Shi1,2, Xuan Wang2,3, Wei Jiang2

  • 1State Key Laboratory of Intelligent Vehicle Safety Technology, Chongqing 400023, China.

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|February 13, 2026
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Summary
This summary is machine-generated.

This study introduces a new method for intelligent roadside units (RSUs) to improve multi-target tracking in foggy conditions. The approach enhances detection accuracy and reliability for safer intelligent transportation systems (ITSs).

Keywords:
LiDAR denoisingcollaborative RSUfoggy conditionsmulti-target trackingparticle PHD filterroadside LiDAR

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Area of Science:

  • Transportation Engineering
  • Computer Vision
  • Sensor Fusion

Background:

  • Intelligent Road Side Units (RSUs) are vital for Intelligent Transportation Systems (ITSs).
  • Roadside LiDAR sensors offer high precision but are challenged by fog, which degrades performance.
  • Fog-induced scattering and attenuation of LiDAR beams hinder multi-target tracking and compromise ITS safety.

Purpose of the Study:

  • To develop an enhanced collaborative RSU method for accurate multi-target tracking in foggy environments.
  • To improve the reliability and safety of ITSs by addressing LiDAR performance degradation in fog.
  • To integrate denoising and tracking capabilities for robust RSU-based perception.

Main Methods:

  • A modified bilateral filter dynamically adjusts kernel scale for effective point cloud denoising.
  • A multi-RSU cooperative tracking framework utilizes a particle Probability Hypothesis Density (PHD) filter for measurement fusion.
  • Implementation of a multi-target tracking system on an intelligent roadside platform for real-world testing.

Main Results:

  • The proposed method significantly improves target detection accuracy by 8% (thin fog) and 29% (thick fog) compared to statistical filtering after fog noise removal.
  • The system demonstrates strong performance in tracking multi-class targets.
  • Superiority over state-of-the-art methods is shown, particularly in high-order metrics like HOTA, MOTA, and IDs.

Conclusions:

  • The developed collaborative RSU method effectively enhances multi-target tracking accuracy and reliability in foggy conditions.
  • This approach contributes to improved safety and performance of Intelligent Transportation Systems.
  • The algorithm shows excellent real-time performance and robustness for practical ITS applications.