A Factor Graph-based approach to vehicle sideslip angle estimation

TL;DR

This paper introduces a novel factor graph-based method for vehicle sideslip angle estimation, offering a flexible and potentially more versatile alternative to traditional filtering techniques, validated through real vehicle data.

Contribution

It models sideslip angle estimation as a graphical model, enabling both offline and online optimization, which is a new approach compared to existing filtering methods.

Findings

Estimates closely match actual sideslip angles in real vehicle tests.

Performs comparably to state-of-the-art filtering methods.

Flexible framework allows future extensions.

Abstract

Sideslip angle is an important variable for understanding and monitoring vehicle dynamics but it lacks an inexpensive method for direct measurement. Therefore, it is typically estimated from inertial and other proprioceptive sensors onboard using filtering methods from the family of the Kalman Filter. As a novel alternative, this work proposes modelling the problem directly as a graphical model (factor graph), which can then be optimized using a variety of methods, such as whole dataset batch optimization for offline processing or fixed-lag smoother for on-line operation. Experimental results on real vehicle datasets validate the proposal with a good agreement between estimated and actual sideslip angle, showing similar performance than the state-of-the-art with a great potential for future extensions due to the flexible mathematical framework.