# Track-to-Track Fusion for Cooperative Perception Using Collective Perception Messages

## TL;DR

This paper compares different methods for combining sensor data from connected vehicles to improve cooperative perception in automated driving.

## Contribution

The novel contribution is a comparative analysis of five track-to-track fusion methods using a simulation framework for cooperative perception.

## Key findings

- Five track-to-track fusion methods were evaluated in a simulated cooperative perception framework.
- The study highlights trade-offs between fusion strategies in asynchronous multi-agent scenarios.
- The architecture is designed to be extensible for future integration of classification and confidence fusion modules.

## Abstract

Vehicle-to-everything communication grants connected and automated road vehicles the opportunity to share their sensor information such as detected road objects for collective awareness. This paper compares various state fusion strategies within a high-level cooperative perception architecture, focusing on the fusion of object-level information provided in standard Collective Perception Messages. This work compares five track-to-track fusion methods, namely Covariance Intersection, Inverse Covariance Intersection, Adapted Extended Kalman Filter, Adapted Unscented Kalman Filter and Information Matrix Fusion, using a simulation framework built with CARLA and Autoware. The methods are analyzed in a case study to assess their performance under different vehicle maneuvers and varying input information accuracy. The case study highlights trade-offs between fusion strategies and illustrate their behavior in asynchronous multi-agent scenarios. While the analysis is conducted in simulation, the architecture is designed to be extensible, and directions for future development are outlined, including the integration of classification and object confidence fusion modules.