# Distributed Complementary Fusion for Connected Vehicles

**Authors:** James Klupacs, Amirali Khodadadian Gostar, Alireza Bab-Hadiashar, Jennifer Palmer, Reza Hosseinezhad  
**arXiv:** [2209.04104](https://arxiv.org/abs/2209.04104)  
**Date:** 2022-09-09  
**Summary page:** https://www.summarizepaper.com/en/paper/2209.04104v1/

## Key points

- Paper titled "Distributed Complementary Fusion for Connected Vehicles"
- Method for achieving comprehensive situation awareness in a distributed vehicle network
- Random finite set-based solution for labeled multi-Bernoulli filters in each vehicle
- Complementary fusion of sensor information through consensus iterations
- Improved accuracy and reliability of situation awareness in each vehicle
- Novel label merging algorithm to eliminate duplicate labels and address double counting
- Extension of label space to incorporate sensor identities and overcome label inconsistencies
- Outperforms standard LMB filter when implemented with limited fields of view
- Improved accuracy and reliability compared to existing approaches
- Valuable for applications such as autonomous driving and intelligent transportation systems

## Summary

In their paper titled "Distributed Complementary Fusion for Connected Vehicles," James Klupacs, Amirali Khodadadian Gostar, Alireza Bab-Hadiashar, Jennifer Palmer, and Reza Hosseinezhad present a method for achieving comprehensive situation awareness in a distributed vehicle network. The authors propose a random finite set-based solution that is specifically designed for labeled multi-Bernoulli filters running in each vehicle. The key feature of their approach is the complementary fusion of sensor information, which occurs locally through consensus iterations. This fusion process helps to improve the accuracy and reliability of the situation awareness achieved by each vehicle. To address the issue of double counting, the authors introduce a novel label merging algorithm that effectively eliminates duplicate labels. Furthermore, the authors extend the label space to incorporate sensor identities. By doing so, they are able to overcome label inconsistencies that may arise due to variations in sensor performance or environmental conditions. Through extensive experimentation and analysis, the authors demonstrate that their proposed algorithm outperforms the standard LMB filter when implemented using a distributed complementary approach with limited fields of view. The proposed algorithm offers improved accuracy and reliability compared to existing approaches making it highly valuable for applications such as autonomous driving and intelligent transportation systems. Overall, this research contributes to enhancing situation awareness in connected vehicles by providing an effective and efficient method for fusing sensor information.