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This study introduces an improved 1D-CNN algorithm for road surface recognition in connected and automated vehicles (CAVs). The novel method achieves high accuracy, enhancing driving safety and reducing maintenance costs.

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Area of Science:

  • Engineering
  • Computer Science
  • Artificial Intelligence

Background:

  • Road surface conditions critically affect driving safety and vehicle maintenance costs.
  • Accurate road surface recognition is essential for environmental perception in connected and automated vehicles (CAVs).
  • Traditional methods struggle with effective feature extraction for road surface identification.

Purpose of the Study:

  • To develop an improved 1D-CNN algorithm for enhanced road surface recognition.
  • To reduce computational costs and training time while maintaining high accuracy.
  • To improve the generalization capabilities and suppress overfitting in road surface recognition models.

Main Methods:

  • Developed a vibration signal acquisition system for high-quality data collection.
  • Proposed an optimized 1D-CNN algorithm based on the VGG16 architecture.
  • Integrated data augmentation, Adam optimization, and L2 regularization techniques.

Main Results:

  • The optimized 1D-CNN model has a reduced parameter count (101.6k), lowering computational demands.
  • Achieved high recognition accuracy rates of 99.3% on public datasets and 99.4% on actual vehicle tests.
  • Demonstrated strong adaptability across different data sources, outperforming conventional methods.

Conclusions:

  • The proposed 1D-CNN algorithm offers a significant advancement in accurate and efficient road surface identification.
  • This technology has practical implications for improving the safety and efficiency of connected and automated vehicles.
  • The method provides a robust solution for environmental perception challenges in autonomous driving systems.