# Comparison of KF-Based Vehicle Sideslip Estimation Logics with Increasing Complexity for a Passenger Car

## TL;DR

This paper compares different KF-based methods for estimating vehicle sideslip angle, focusing on balancing accuracy and computational cost for use in autonomous vehicles.

## Contribution

The novelty lies in the comparative analysis of KF-based estimation methods using identical models and real-world sensor data from low-end equipment.

## Key findings

- Different KF-based methods show varying levels of sideslip estimation accuracy.
- Low-end sensors can provide sufficient data for real-world virtual sensing techniques.
- Balancing computational burden and accuracy is critical for embedded vehicle systems.

## Abstract

Nowadays, control is pervasive in vehicles, and a full and accurate knowledge of vehicle states is crucial to guarantee safety levels and support the development of Advanced Driver-Assistance Systems (ADASs). In this scenario, real-time monitoring of the vehicle sideslip angle becomes fundamental, and various virtual sensing techniques based on both vehicle dynamics models and data-driven methods are widely presented in the literature. Given the need for on-board embedded device solutions in autonomous vehicles, it is mandatory to find the correct balance between estimation accuracy and the computational burden required. This work mainly presents different physical KF-based methodologies and proposes both mathematical and graphical analysis to explore the effectiveness of these solutions, all employing equal tire and vehicle simplified models. For this purpose, results are compared with accurate sensor acquisition provided by the on-track campaign on passenger vehicles; moreover, to truthfully represent the possibility of using such virtual sensing techniques in real-world scenarios, the vehicle is also equipped with low-end sensors that provide information to all the employed observers.