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Radar Transformer: An Object Classification Network Based on 4D MMW Imaging Radar.

Jie Bai¹, Lianqing Zheng¹, Sen Li¹

¹Institute of Intelligent Vehicles, School of Automotive Studies, Tongji University, Shanghai 201804, China.

Sensors (Basel, Switzerland)

|July 2, 2021

Summary

This summary is machine-generated.

A new Radar Transformer model enhances object classification for autonomous vehicles using 4D imaging radar. This advanced deep learning approach achieves 94.9% accuracy, improving perception in all weather conditions.

Keywords:

MMW imaging radar autonomous driving deep learning object classification self-attention

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Area of Science:

Computer Vision
Artificial Intelligence
Automotive Engineering

Background:

Automotive millimeter-wave (MMW) radar is crucial for autonomous vehicles, offering all-weather robustness.
Traditional radar systems face limitations in object classification due to insufficient resolution.

Purpose of the Study:

To introduce an advanced object classification network, Radar Transformer, for 4D imaging radar point clouds.
To improve the accuracy and efficiency of object detection and classification in autonomous driving systems.

Main Methods:

Development of the Radar Transformer network, utilizing a combination of vector and scalar attention mechanisms.
Deep fusion of local and global attention features to leverage spatial, Doppler, and reflection intensity information.
Creation and manual annotation of a dedicated imaging radar classification dataset.

Main Results:

The Radar Transformer achieved an overall classification accuracy of 94.9% on the generated dataset.
Demonstrated superior performance in processing radar point clouds compared to existing deep learning frameworks.
Validated the effectiveness of fusing multi-modal radar data through attention mechanisms.

Conclusions:

The proposed Radar Transformer network offers a significant advancement in 4D imaging radar-based object classification.
This method shows promising potential for enhancing the perception capabilities of autonomous vehicles.
The attention-based deep fusion approach effectively utilizes rich radar data for robust classification.