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This study enhances automotive ultrasonic sensors for autonomous driving by classifying objects and traversability using machine learning. A novel signal processing chain and convolutional neural network achieve high accuracy in diverse environments.

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Area of Science:

  • Robotics and Autonomous Systems
  • Sensor Signal Processing
  • Machine Learning for Automotive Applications

Background:

  • Current automotive ultrasonic sensors have limited range and classification capabilities.
  • Future autonomous driving and parking assist systems require enhanced sensor performance for object recognition and traversability assessment.
  • Processing raw ultrasonic sensor data is crucial for extracting meaningful information about the environment.

Purpose of the Study:

  • To investigate the classification of object classes and traversability using a single ultrasonic sensor.
  • To develop and evaluate a novel signal processing chain and machine learning model for ultrasonic sensor data.
  • To explore the impact of data augmentation techniques on model robustness and accuracy.

Main Methods:

  • Acquisition of raw time signals from ultrasonic sensors.
  • Transformation of time signals into time-frequency images (scalograms).
  • Classification using a convolutional neural network (CNN) and comparison with a LeNet-5 baseline.
  • Implementation of offline and online data augmentation strategies.
  • Conducting stationary and dynamic measurements in laboratory and outdoor settings.

Main Results:

  • The proposed scalogram-based CNN model significantly outperforms the LeNet-5 baseline.
  • Object classification achieved 90.1% accuracy in the lab and 66.4% outdoors for seven classes.
  • Traversability classification reached 96.4% accuracy in the lab and 91.5% outdoors.
  • Data augmentation techniques were shown to improve model robustness.

Conclusions:

  • A single ultrasonic sensor, coupled with advanced signal processing and machine learning, can effectively classify objects and assess traversability.
  • The developed method offers a promising approach for enhancing perception in autonomous driving systems.
  • Data augmentation is a critical component for building robust models for real-world automotive applications.