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Vehicle Detection Algorithms for Autonomous Driving: A Review.

Liang Liang1, Haihua Ma1,2, Le Zhao1,2

  • 1College of Information Science and Engineering, Henan University of Technology, Zhengzhou 450001, China.

Sensors (Basel, Switzerland)
|May 25, 2024
PubMed
Summary

This review comprehensively analyzes over 200 vehicle detection algorithms for autonomous driving, covering machine vision, LiDAR, radar, and sensor fusion methods. It highlights algorithm strengths, limitations, and future trends for improved vehicle perception.

Keywords:
autonomous drivingdeep learningsensor fusionsensorsvehicle detection

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Area of Science:

  • Computer Science
  • Robotics
  • Electrical Engineering

Background:

  • Autonomous driving is revolutionizing transportation.
  • Vehicle detection is crucial for autonomous systems, integrating sensor technology and computer vision.
  • Existing research lacks comprehensive reviews of vehicle detection algorithms.

Purpose of the Study:

  • To provide a thorough review and analysis of current vehicle detection algorithms.
  • To discuss the practical applications of these algorithms in autonomous driving.
  • To identify strengths, limitations, and future research directions.

Main Methods:

  • Summarizing over 200 classical and recent vehicle detection algorithms.
  • Categorizing algorithms based on sensor modalities: machine vision, LiDAR, millimeter-wave radar, and sensor fusion.
  • Analyzing algorithm performance, strengths, and limitations.

Main Results:

  • Detailed overview of diverse vehicle detection techniques.
  • Comparative analysis of different sensor-based and fusion approaches.
  • Identification of key challenges and opportunities in the field.

Conclusions:

  • Vehicle detection is a complex, multidisciplinary challenge.
  • Sensor fusion and advanced computer vision techniques show significant promise.
  • Further research is needed to address limitations and enhance real-world applicability.