Abstract
Intelligent tire systems enable real-time measurement of tire forces, offering new opportunities for improving vehicle state estimation. This paper proposes a state estimation approach that integrates longitudinal, lateral, and vertical tire force information obtained from a PVDF-based intelligent tire system. An optimal in-tire sensor layout is determined using global sensitivity analysis, and tire forces are estimated from in-tire voltage signals via Gaussian Process Regression. To effectively utilize the estimated forces, a tire force update frequency compensation strategy and a vehicle parameter estimation method are incorporated into an adaptive cubature Kalman filter framework. Simulation and experimental results demonstrate that the proposed approach significantly improves the accuracy of vehicle speed and sideslip angle estimation and enhances robustness against variations in vehicle and tire parameters, including tire-road friction.