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Research on Pedestrian and Cyclist Classification Method Based on Micro-Doppler Effect.

Xinyu Chen1, Xiao Luo1, Zeyu Xie1

  • 1Intelligent Connected Vehicle Development Institute, China FAW Co., Ltd., Changchun 130011, China.

Sensors (Basel, Switzerland)
|October 16, 2024
PubMed
Summary

This study introduces a novel algorithm for distinguishing pedestrians and cyclists using the micro-Doppler effect in autonomous driving. The method enhances detection accuracy, improving vulnerable road user (VRU) safety.

Keywords:
SVMfractional polynomialleast squares methodmicro-dopplertime-frequency analysis

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Area of Science:

  • Autonomous Driving Systems
  • Radar Signal Processing
  • Vulnerable Road User (VRU) Detection

Background:

  • Effective detection of vulnerable road users (VRUs) like pedestrians and cyclists is crucial for autonomous driving safety.
  • Traditional methods struggle to differentiate pedestrians and cyclists due to their similar temporal characteristics, leading to suboptimal classification performance.

Purpose of the Study:

  • To propose a new algorithm for classifying pedestrian and cyclist targets based on the micro-Doppler effect.
  • To improve the accuracy of VRU detection in autonomous driving systems.

Main Methods:

  • A preprocessing module performs frequency-domain fitting on radar echo data, generating fitting coefficients.
  • Wavelet threshold processing, short-time Fourier transform, and periodogram methods are used for radar echo data processing.
  • A fractional polynomial enhances micro-Doppler characteristic extraction, followed by support vector machine (SVM) classification.

Main Results:

  • The proposed algorithm demonstrates superior performance in classifying pedestrian and cyclist targets.
  • Frequency-domain fitting and fractional polynomial introduction enhance classification precision.
  • Ablation experiments and comparative analyses validate the algorithm's effectiveness.

Conclusions:

  • The micro-Doppler effect-based algorithm offers a significant improvement for VRU classification in autonomous driving.
  • Enhanced feature extraction and precise classification contribute to safer autonomous vehicle perception systems.
  • This approach addresses the limitations of traditional methods in distinguishing between pedestrians and cyclists.