Near-field Perception for Low-Speed Vehicle Automation using Surround-view Fisheye Cameras
This work addresses perception challenges for low-speed automated driving applications like parking and traffic jam assistance, but it is incremental as it synthesizes existing methods into a modular framework.
The paper surveys vision systems for low-speed vehicle automation using surround-view fisheye cameras, proposing a 4R Architecture (Recognition, Reconstruction, Relocalization, Reorganization) and argues it can form a complete perception system, supported by qualitative results from previous works.
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 https://youtu.be/ae8bCOF77uY.