Near-field Perception for Low-Speed Vehicle Automation using Surround-view Fisheye Cameras

TL;DR

This paper surveys surround-view fisheye camera systems for low-speed vehicle automation, proposing a modular 4R architecture that integrates recognition, reconstruction, relocalization, and reorganization for enhanced near-field perception.

Contribution

It introduces the 4R architecture for low-speed vehicle perception and discusses how its components can be integrated for improved automated driving capabilities.

Findings

The 4R architecture effectively combines perception modules for low-speed automation.

Previous works support the feasibility of the 4R system components.

Qualitative results demonstrate the potential of the proposed architecture.

Abstract

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{\deg}+ field of view covering the entire 360{\deg} 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.