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Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications

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MmWave Radar and Vision Fusion for Object Detection in Autonomous Driving: A Review.

Zhiqing Wei¹, Fengkai Zhang¹, Shuo Chang¹

¹Key Laboratory of Universal Wireless Communications, Ministry of Education, School of Information and Communication Engineering, Beijing University of Posts and Telecommunications, Beijing 100876, China.

Sensors (Basel, Switzerland)

|April 12, 2022

Summary

This summary is machine-generated.

This survey reviews millimeter wave (mmWave) radar and vision fusion for obstacle detection in autonomous driving. It details sensor fusion methods and future directions for enhanced safety.

Keywords:

autonomous driving data level fusion decision level fusion feature level fusion lidar object detection radar and camera fusion radar and vision fusion review survey

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Area of Science:

Robotics and Artificial Intelligence
Computer Vision and Sensor Fusion
Automotive Engineering

Background:

Autonomous driving technology is rapidly advancing, necessitating robust object detection for safety.
Millimeter wave (mmWave) radar and vision fusion is a key technology for accurate obstacle detection in complex environments.

Purpose of the Study:

To provide a comprehensive survey of mmWave radar and vision fusion methods for obstacle detection in autonomous driving.
To categorize and analyze different sensor fusion techniques.
To highlight future research directions in multimodal sensor fusion.

Main Methods:

Review of object detection tasks, evaluation criteria, and datasets for autonomous driving.
Detailed examination of sensor deployment, calibration, and fusion processes.
Classification of fusion methods into data-level, decision-level, and feature-level approaches.

Main Results:

The survey comprehensively reviews existing mmWave radar and vision fusion techniques.
It categorizes fusion methods and discusses their respective advantages and applications.
Emerging areas like 3D object detection and lidar-vision fusion are also introduced.

Conclusions:

mmWave radar and vision fusion is crucial for safe and accurate autonomous driving.
Understanding different fusion levels and future multimodal approaches is vital for continued development.
This survey serves as a foundational resource for researchers and engineers in the field.