Cooperative Multi-Modal Localization in Connected and Autonomous Vehicles

TL;DR

This paper proposes a cooperative multi-modal localization method for connected autonomous vehicles using graph signal processing to leverage vehicle interconnections and motion patterns, enhancing localization accuracy.

Contribution

It introduces a novel multi-modal fusion approach based on graph Laplacian processing and temporal coherence for vehicle localization in CAV networks.

Findings

Effective fusion of GPS, LIDAR, and RADAR data improves localization accuracy.

Graph-based representation captures vehicle relationships better than traditional methods.

Temporal coherence enhances robustness against sensor noise.

Abstract

Cooperative Localization is expected to play a crucial role in various applications in the field of Connected and Autonomous vehicles (CAVs). Future 5G wireless systems are expected to enable cost-effective Vehicle-to-Everything (V2X)systems, allowing CAVs to share with the other entities of the network the data they collect and measure. Typical measurement models usually deployed for this problem, are absolute position from Global Positioning System (GPS), relative distance and azimuth angle to neighbouring vehicles, extracted from Light Detection and Ranging (LIDAR) or Radio Detection and Ranging(RADAR) sensors. In this paper, we provide a cooperative localization approach that performs multi modal-fusion between the interconnected vehicles, by representing a fleet of connected cars as an undirected graph, encoding each vehicle position relative to its neighbouring vehicles. This method is based on:i) the Laplacian Processing, a Graph Signal Processing tool that allows to capture intrinsic geometry of the undirected graph of vehicles rather than their absolute position on global coordinate system and ii) the temporal coherence due to motion patterns of the moving vehicles.