# An adaptive approach to real-time estimation of vehicle sideslip, road bank angles and sensor bias

## TL;DR

This paper introduces a real-time vehicle sideslip and bank angle estimation method using only standard sensors, combining vehicle models with extended Kalman filters for accurate and stable results.

## Contribution

It presents a novel adaptive estimation algorithm that integrates kinematic and dynamic models with EKF observers, improving estimation accuracy without specialized sensors.

## Key findings

- Provides smooth and accurate sideslip angle estimates
- Offers reliable bank angle and sensor bias estimations
- Proven stability of the estimation algorithm

## Abstract

Robust estimation of vehicle sideslip angle is essential for stability control applications. However, the direct measurement of sideslip angle is expensive for production vehicles. This paper presents a novel sideslip estimation algorithm which relies only on sensors available on passenger and commercial vehicles. The proposed method uses both kinematics and dynamics vehicle models to construct extended Kalman filter observers. The estimate relies on the results provided from the dynamics model observer where the tire cornering stiffness parameters are updated using the information provided from the kinematics model observer. The stability property of the proposed algorithm is discussed and proven. Finally, multiple experimental tests are conducted to verify its performance in practice. The results show that the proposed approach provides smooth and accurate sideslip angle estimation. In addition, our novel algorithm provides reliable estimates of bank angles and lateral acceleration sensor bias.