Cooperative Infrastructure Perception

TL;DR

This paper introduces Cooperative Infrastructure Perception (CIP), a system that fuses multiple infrastructure sensors to enhance perception speed and accuracy, thereby improving safety and traffic flow in autonomous vehicle environments.

Contribution

It presents a novel system combining algorithms and optimizations to enable rapid, accurate perception from infrastructure sensors, a new approach in the field.

Findings

Perception outputs generated within 100 ms

Achieves accuracy comparable to state-of-the-art methods

Enhances safety and traffic throughput when integrated with vehicle systems

Abstract

Recent works have considered two qualitatively different approaches to overcome line-of-sight limitations of 3D sensors used for perception: cooperative perception and infrastructure-augmented perception. In this paper, motivated by increasing deployments of infrastructure LiDARs, we explore a third approach, cooperative infrastructure perception. This approach generates perception outputs by fusing outputs of multiple infrastructure sensors, but, to be useful, must do so quickly and accurately. We describe the design, implementation and evaluation of Cooperative Infrastructure Perception (CIP), which uses a combination of novel algorithms and systems optimizations. It produces perception outputs within 100 ms using modest computing resources and with accuracy comparable to the state-of-the-art. CIP, when used to augment vehicle perception, can improve safety. When used in conjunction with offloaded planning, CIP can increase traffic throughput at intersections.