reviewIEEE Transactions on Intelligent VehiclesAug 22, 2023Closed access

Radar-Camera Fusion for Object Detection and Semantic Segmentation in Autonomous Driving: A Comprehensive Review

University of Liverpool · Xi’an Jiaotong-Liverpool University

Indexed incrossref

Abstract

Driven by deep learning techniques, perception technology in autonomous driving has developed rapidly in recent years, enabling vehicles to accurately detect and interpret surrounding environment for safe and efficient navigation. To achieve accurate and robust perception capabilities, autonomous vehicles are often equipped with multiple sensors, making sensor fusion a crucial part of the perception system. Among these fused sensors, radars and cameras enable a complementary and cost-effective perception of the surrounding environment regardless of lighting and weather conditions. This review aims to provide a comprehensive guideline for radar-camera fusion, particularly concentrating on perception tasks…

Citation impact

196
total citations
FWCI
22.55
Percentile
100%
References
348
Citations per year

Authors

11

Topics & keywords

Keywords
  • Computer vision
  • Artificial intelligence
  • Segmentation
  • Computer science
  • Fusion
  • Object (grammar)
  • Radar
  • Object detection
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