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Near-field perception for low-speed vehicle automation using surround-view fisheye cameras

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conference contribution
posted on 2024-04-10, 11:24 authored by Ciarán EisingCiarán Eising, Jonathan Horgan, Senthil Yogamani

Cameras are the primary sensor in automated driving systems. They provide high information density and are optimal for detecting road infrastructure cues laid out for human vision. Surround-view camera systems typically comprise of four fisheye cameras with 190°+ field of view covering the entire 360° around the vehicle focused on near-field sensing. They are the principal sensors for low-speed, high accuracy, and close-range sensing applications, such as automated parking, traffic jam assistance, and low-speed emergency braking. In this work, we provide a detailed survey of such vision systems, setting up the survey in the context of an architecture that can be decomposed into four modular components namely Recognition, Reconstruction, Relocalization, and Reorganization. We jointly call this the 4R Architecture. We discuss how each component accomplishes a specific aspect and provide a positional argument that they can be synergized to form a complete perception system for low-speed automation. We support this argument by presenting results from previous works and by presenting architecture proposals for such a system. Qualitative results are presented in the video at



IEEE Transactions on Intelligent Transportation Systems, 2022, 23 (9) Sept. pp 13976–13993


Association for Computing Machinery


© ACM, 2022. This is the author's version of the work. It is posted here by permission of ACM for your personal use. Not for redistribution. The definitive version was published in IEEE Transactions on Intelligent Transportation Systems Volume 23 Issue 9 Sept. 2022pp 13976–13993.

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