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A Machine Learning Approach to Pedestrian Detection for Autonomous Vehicles Using High-Definition 3D Range Data.

Pedro J Navarro1, Carlos Fernández2, Raúl Borraz3

  • 1División de Sistemas en Ingeniería Electrónica (DSIE), Universidad Politécnica de Cartagena, Campus Muralla del Mar, s/n, Cartagena 30202, Spain. pedroj.navarro@upct.es.

Sensors (Basel, Switzerland)
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Summary

This study presents an automated system using LIDAR sensors and machine learning to detect pedestrians for autonomous vehicles. The system achieved high accuracy in real-world traffic scenarios.

Keywords:
3D LIDAR sensormachine vision and machine learningpedestrian detection

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Area of Science:

  • Robotics
  • Computer Vision
  • Machine Learning

Background:

  • Autonomous vehicles require robust pedestrian detection systems.
  • LIDAR sensors provide rich 3D point cloud data for environmental perception.

Purpose of the Study:

  • To develop and evaluate an automated sensor-based system for pedestrian detection in autonomous vehicles.
  • To analyze the performance of machine learning algorithms using LIDAR data.

Main Methods:

  • Processing of Velodyne HDL-64E LIDAR sensor data.
  • Utilizing cubic shape selection and machine vision on point cloud projections (XY, XZ, YZ).
  • Comparing k-Nearest Neighbours (kNN), Naïve Bayes classifier (NBC), and Support Vector Machine (SVM) algorithms trained on 1931 samples.

Main Results:

  • The system demonstrated high performance in real traffic scenarios.
  • Sensitivity: 81.2%, Accuracy: 96.2%, Specificity: 96.8% with 16 pedestrians and 469 non-pedestrian samples.
  • Support Vector Machine (SVM) showed strong performance among the tested algorithms.

Conclusions:

  • The automated LIDAR-based system effectively detects pedestrians for autonomous driving.
  • Machine learning algorithms, particularly SVM, are viable for processing LIDAR data for pedestrian recognition.
  • The system shows promise for enhancing autonomous vehicle safety.