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A Multi-Feature Fusion Approach for Road Surface Recognition Leveraging Millimeter-Wave Radar.

Zhimin Qiu1, Jinju Shao1,2, Dong Guo1,2

  • 1School of Transportation and Vehicle Engineering, Shandong University of Technology, Zibo 255049, China.

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|June 27, 2025
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Summary
This summary is machine-generated.

This study introduces a new method using millimeter-wave radar and feature fusion to identify road surfaces for autonomous driving. The approach achieves high accuracy, enhancing vehicle safety and perception.

Keywords:
machine learningmillimeter-wave radarroad surface recognitionstatistical featureswavelet transform

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Area of Science:

  • Intelligent Transportation Systems
  • Sensor Fusion
  • Machine Learning for Autonomous Driving

Background:

  • Accurate road surface recognition is vital for autonomous driving safety and comfort.
  • Existing methods may lack robustness in diverse road conditions.

Purpose of the Study:

  • To develop a multi-feature fusion approach for road surface identification using millimeter-wave radar.
  • To enhance the perception capabilities of intelligent vehicles.

Main Methods:

  • Utilized 24 GHz millimeter-wave radar for data acquisition.
  • Extracted six-dimensional statistical features and wavelet transform features.
  • Fused features into a 56-dimensional vector for classification.
  • Employed Wide Neural Network, KNN, SVM, and Kernel methods as classifiers.

Main Results:

  • Achieved a road surface type identification accuracy of 94.2% with 8865 real-world samples.
  • Demonstrated the effectiveness of fusing statistical and wavelet features.
  • Validated the method across 12 typical road surface types and conditions.

Conclusions:

  • The proposed multi-feature fusion method provides an efficient and cost-effective road perception solution.
  • Millimeter-wave radar shows significant potential for road environment sensing in autonomous driving.
  • This research supports the advancement of autonomous driving technology through improved road surface identification.