Enhancing road roughness identification and system observability through measurement strategies in vehicle dynamics

Vehicle systems dynamics often involve unknown inputs and unmeasured states, posing challenges for estimation and control. This paper addresses the estimation of these variables in reconstructing road profiles and identifying vehicle dynamics, crucial for applications like vehicle control, autonomous driving, and road condition monitoring. The study focuses on developing estimation methods for unknown inputs and states using various vehicle models under single or multiple road excitations, including multi-input scenarios like the wheelbase filtering effect. A joint estimation approach augments the state vector with the unknown road profile input, maintaining linear time-invariance and enabling efficient estimation. Practical challenges arise due to unavailable measurements, affecting system observability and estimation accuracy. Numerical simulations and experiments identify critical measurements and evaluate the impact of correlated inputs on estimation performance. A comprehensive comparison assesses the Augmented Kalman filtering-based approach for accuracy, robustness, and computational efficiency.