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MMW Radar-Based Technologies in Autonomous Driving: A Review.

Taohua Zhou1, Mengmeng Yang1, Kun Jiang1

  • 1State Key Laboratory of Automotive Safety and Energy, School of Vehicle and Mobility, Tsinghua University, Beijing 100084, China.

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Summary
This summary is machine-generated.

This study surveys deep learning applications for millimeter-wave (MMW) radar data in automated vehicles (AVs). It covers MMW radar data models, AV applications from ADAS to high-level driving, and future challenges.

Keywords:
MMW radarautonomous drivingenvironmental perceptionself-localization

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Area of Science:

  • Robotics and Intelligent Systems
  • Sensor Fusion for Autonomous Systems
  • Machine Learning for Environmental Perception

Background:

  • Automated vehicles (AVs) require advanced environmental perception.
  • Millimeter-wave (MMW) radar is a key sensor due to its cost-effectiveness, all-weather adaptability, and motion detection capabilities.
  • Existing research lacks a comprehensive survey on deep learning applied to MMW radar data for AVs.

Purpose of the Study:

  • To provide an overview of state-of-the-art radar-based technologies in AVs.
  • To survey deep learning applications utilizing MMW radar data for autonomous driving.
  • To identify current challenges and future research directions.

Main Methods:

  • Introduction to MMW radar data models and representations.
  • Review of radar-based applications in AVs, including Advanced Driving-Assistance Systems (ADAS).
  • Analysis of deep learning techniques for object detection, tracking, motion prediction, and self-localization using radar data.

Main Results:

  • MMW radar data offers diverse types for various autonomous driving levels.
  • Radar is crucial for ADAS and high-level autonomous functions like object detection and localization.
  • Deep learning models are increasingly vital for extracting complex information from radar data.

Conclusions:

  • Deep learning applied to MMW radar data is essential for robust AV perception.
  • Further research is needed to address challenges in data representation and model generalization.
  • Future work should focus on enhancing sensor fusion and real-time processing for safety-critical applications.