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Evaluation of 3D Vulnerable Objects' Detection Using a Multi-Sensors System for Autonomous Vehicles.

Esraa Khatab1,2, Ahmed Onsy2, Ahmed Abouelfarag1

  • 1Arab Academy for Science, Technology and Maritime Transport, Alexandria 1029, Egypt.

Sensors (Basel, Switzerland)
|February 26, 2022
PubMed
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This study introduces a low-cost system for detecting vulnerable road users like pedestrians and cyclists using a monocular camera and LiDAR. It achieves real-time 3D object detection, improving autonomous vehicle safety.

Area of Science:

  • Robotics and Artificial Intelligence
  • Computer Vision
  • Sensor Fusion

Background:

  • Autonomous vehicles (AVs) rely heavily on object detection for safe operation.
  • Detecting vulnerable road users (VRUs) like pedestrians and cyclists is challenging due to their unpredictable movements.
  • Current AV systems often use expensive sensors, limiting research and accessibility.

Purpose of the Study:

  • To develop a real-time, cost-effective 3D object detection system for vulnerable road users.
  • To integrate data from a monocular camera and a single-beam LiDAR sensor.
  • To address challenges like occlusion, truncation, and scale variations in object detection.

Main Methods:

  • Utilized a deep learning detector (YOLOv3) for video processing.
Keywords:
2D LiDARautonomous drivingmultiple object detectionsensor fusion

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  • Pre-processed and clustered LiDAR measurements.
  • Combined camera and LiDAR data for 3D localization and classification.
  • Focused on real driving scenarios with complex object interactions.

    • Main Results:

    • Successfully achieved real-time detection of multiple 3D vulnerable objects.
    • Provided accurate object classification and localization with depth information.
    • Demonstrated effectiveness in challenging scenarios with occlusion and scale changes.

    Conclusions:

    • The proposed system offers an efficient and affordable solution for 3D vulnerable object detection in AVs.
    • Sensor fusion of low-cost LiDAR and monocular cameras enhances detection reliability.
    • This approach can advance research and deployment of safer autonomous driving systems.