# Cloud-Aided State Estimation of A Full-Car Semi-Active Suspension System

## TL;DR

This paper presents a cloud-assisted state estimation method for a full-car semi-active suspension system using moving horizon estimation and remote computation, demonstrating effectiveness through simulations.

## Contribution

It introduces a vehicle-to-cloud-to-vehicle scheme with remote optimization for complex suspension state estimation, enhancing computational efficiency.

## Key findings

- Effective state estimation demonstrated in simulations
- Utilizes remote optimization to handle complex calculations
- Shows potential for real-time vehicle suspension management

## Abstract

In this work, we investigate a state estimation problem for a full-car semi-active suspension system. To account for the complex calculation and optimization problems, a vehicle-to- cloud-to-vehicle (V2C2V) scheme is utilized. Moving horizon estimation is introduced for the state estimation system design. All the optimization problems are solved in a remotely-embedded agent with high computational ability. Measurements and state estimates are transmitted between the vehicle and the remote agent via networked communication channels. The effectiveness of the proposed method is illustrated via a set of simulations.