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 cameras 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 sensor 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 describe our visual perception architecture on surround view cameras designed for a system deployed in commercial vehicles, provide a functional review of the different stages of such a computer vision system, and discuss some of the current technological challenges. We have designed our system 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 how they are synergized to form a complete system. Qualitative results are presented in the video at \url{this https URL}.
Abstract (translated)
URL
https://arxiv.org/abs/2103.17001
