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RETRACTED: Zhang et al. A Novel Framework for Reconstruction and Imaging of Target Scattering Centers via Wide-Angle Incidence in Radar Networks. <i>Sensors</i> 2025, <i>25</i>, 6802.

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Towards Deep Radar Perception for Autonomous Driving: Datasets, Methods, and Challenges.

Yi Zhou^1,2,3,4, Lulu Liu^1,3,5,6, Haocheng Zhao^1,2,3,4

¹Institute of Deep Perception Technology (JITRI), Wuxi 214000, China.

Sensors (Basel, Switzerland)

|June 10, 2022

Summary

This summary is machine-generated.

The era of deep learning for automotive radar perception is here, with 4D radar enabling high-resolution imaging. This review provides a holistic overview of deep learning in radar perception, covering challenges and solutions.

Keywords:

automotive radars autonomous driving deep learning multi-sensor fusion object detection radar signal processing

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Area of Science:

Automotive Engineering
Computer Vision
Artificial Intelligence

Background:

Automotive radar performance has significantly advanced, with 4D radar offering high-resolution point cloud imaging capabilities.
The integration of deep learning into radar perception is becoming increasingly crucial for next-generation autonomous systems.

Purpose of the Study:

To provide a comprehensive overview of the deep learning radar perception stack.
To synthesize current research across various tasks, including signal processing, data handling, and downstream applications.
To highlight and address overlooked challenges in deep radar perception.

Main Methods:

Review of existing literature on deep learning for automotive radar perception.
Analysis of network structures adapted to radar domain knowledge for tasks like depth/velocity estimation, object detection, and sensor fusion.
Identification and summarization of challenges such as multi-path effects, uncertainty, and adverse weather.

Main Results:

A holistic view of the deep radar perception pipeline, from signal processing to sensor fusion.
Explanation of how network architectures are tailored for radar data.
Summary of key challenges and existing approaches to mitigate them.

Conclusions:

Deep learning is pivotal for advancing automotive radar perception, especially with 4D radar technology.
Addressing challenges like multi-path, uncertainty, and adverse weather is essential for robust radar perception systems.
This review serves as a foundational resource for future research in deep radar perception.