Enhanced Full-State Estimation and Dynamic-Model-Based Prediction for Road-Vehicles

Abstract

In this paper, we address the enhanced state estimation and prediction system for automobile applications by fusing relatively low-cost and noisy Inertial Navigation System (INS) sensing with Global Positioning System (GPS) measurements. An unscented Kalman filter is used to merge multi-rate measurements from GPS and INS sensors together with a high-fidelity vehicle-dynamics model for state-predictions. The high-fidelity motion model (including suspension-effects) for the vehicle motion trajectory on uneven terrain is captured by a 20-state system of nonlinear differential equations. Computer simulation results illustrate the effectiveness of sensor-fusion (building upon the merger of an inexpensive INS sensing with GPS based measurements) to accurately estimate the full system-state. The relative ease of implementation, accuracy and predictive performance with low-cost sensing will facilitate its use in various electronic control and safety-systems, such as Electronic Stability Program, Anti-lock Brake Systems, and the Lateral Dynamic Stability Control.

Active Reconfiguration for Performance Enhancement in Articulated Wheeled Vehicles

“…While a vehicle is traversing on actual terrain, some quantities can be measured. For example, wheel-rolling rate, vehicle roll and pitch and some joint angles may be available by equipping the vehicle with an appropriate sensors such as accelerometer, GPS [21] or encoders. We developed an actual terrain topology in MATLAB, to easily extract the topology and altitude of the terrain Z ter at different (X C i ,Y C i ) positions of wheel-terrain contact.…”

“…This formulation helps to determine the contribution of each joint in articulated system, and wheel-terrain contact motion in the vehicle position and orientation. If we assume some quantities are measurable by sensors such as wheels-rolling angle (encoder), roll and pitch angles and rates (GPS [21] and accelerometer), therefore, we can separate measurable (known) and unknown quantities as…”

UKF‐based adaptive variable structure observer for vehicle sideslip with dynamic correction

“…Jia et al [10] proposed a switch model method for full vehicle dynamics states estimation; different models were used in different vehicle states to improve the model fitness. Alamdari et al [11] proposed another vehicle full state estimator which took many factors into consider including vehicle roll motion, road bank, road grade and so on. The complicated estimator improved the accuracy performance.…”