Surround-View Fisheye Camera Perception for Automated Driving: Overview, Survey & Challenges

Varun Ravi Kumar, Ciaran Eising, Christian Witt, Senthil Kumar Yogamani

Research output: Contribution to journalArticlepeer-review

Abstract

Surround-view fisheye cameras are commonly used for near-field sensing in automated driving. Four fisheye cameras on four sides of the vehicle are sufficient to cover 360° around the vehicle capturing the entire near-field region. Some primary use cases are automated parking, traffic jam assist, and urban driving. There are limited datasets and very little work on near-field perception tasks as the focus in automotive perception is on far-field perception. In contrast to far-field, surround-view perception poses additional challenges due to high precision object detection requirements of 10cm and partial visibility of objects. Due to the large radial distortion of fisheye cameras, standard algorithms cannot be extended easily to the surround-view use case. Thus, we are motivated to provide a self-contained reference for automotive fisheye camera perception for researchers and practitioners. Firstly, we provide a unified and taxonomic treatment of commonly used fisheye camera models. Secondly, we discuss various perception tasks and existing literature. Finally, we discuss the challenges and future direction.

Original languageEnglish
Pages (from-to)3638-3659
Number of pages22
JournalIEEE Transactions on Intelligent Transportation Systems
Volume24
Issue number4
DOIs
Publication statusPublished - 1 Apr 2023

Keywords

  • Automated driving
  • bird-eye's view perception
  • fisheye camera
  • multi-task learning
  • omnidirectional camera
  • surround view perception

Fingerprint

Dive into the research topics of 'Surround-View Fisheye Camera Perception for Automated Driving: Overview, Survey & Challenges'. Together they form a unique fingerprint.

Cite this