LCV2I: Communication-Efficient and High-Performance Collaborative Perception Framework with Low-Resolution LiDAR

TL;DR

This paper introduces LCV2I, a collaborative perception framework that uses low-resolution LiDAR and cameras to reduce costs while maintaining high detection accuracy through feature enhancement and efficient communication.

Contribution

The paper proposes a novel framework combining low-resolution LiDAR, feature correction, and regional assessment to improve perception accuracy and communication efficiency in V2I systems.

Findings

Outperforms existing algorithms in 3D object detection tasks

Reduces sensor costs without sacrificing detection performance

Enhances feature representation with correction modules

Abstract

Vehicle-to-Infrastructure (V2I) collaborative perception leverages data collected by infrastructure's sensors to enhance vehicle perceptual capabilities. LiDAR, as a commonly used sensor in cooperative perception, is widely equipped in intelligent vehicles and infrastructure. However, its superior performance comes with a correspondingly high cost. To achieve low-cost V2I, reducing the cost of LiDAR is crucial. Therefore, we study adopting low-resolution LiDAR on the vehicle to minimize cost as much as possible. However, simply reducing the resolution of vehicle's LiDAR results in sparse point clouds, making distant small objects even more blurred. Additionally, traditional communication methods have relatively low bandwidth utilization efficiency. These factors pose challenges for us. To balance cost and perceptual accuracy, we propose a new collaborative perception framework, namely LCV2I. LCV2I uses data collected from cameras and low-resolution LiDAR as input. It also employs feature offset correction modules and regional feature enhancement algorithms to improve feature representation. Finally, we use regional difference map and regional score map to assess the value of collaboration content, thereby improving communication bandwidth efficiency. In summary, our approach achieves high perceptual performance while substantially reducing the demand for high-resolution sensors on the vehicle. To evaluate this algorithm, we conduct 3D object detection in the real-world scenario of DAIR-V2X, demonstrating that the performance of LCV2I consistently surpasses currently existing algorithms.