Integrating Inertial Sensors With Global Positioning System (GPS) for Vehicle Dynamics Control

Abstract

This paper demonstrates a method of estimating several key vehicle states—sideslip angle, longitudinal velocity, roll and grade—by combining automotive grade inertial sensors with a Global Positioning System (GPS) receiver. Kinematic Kalman filters that are independent of uncertain vehicle parameters integrate the inertial sensors with GPS to provide high update estimates of the vehicle states and the sensor biases. Using a two-antenna GPS system, the effects of pitch and roll on the measurements can be quantified and are demonstrated to be quite significant in sideslip angle estimation. Employing the same GPS system as an input to the estimator, this paper develops a method that compensates for roll and pitch effects to improve the accuracy of the vehicle state and sensor bias estimates. In addition, calibration procedures for the sensitivity and cross-coupling of inertial sensors are provided to further reduce measurement error. The resulting state estimates compare well to the results from calibrated models and Kalman filter predictions and are clean enough to use in vehicle dynamics control systems without additional filtering.

Estimation of longitudinal speed robust to road conditions for ground vehicles

“…The bearing viscous damping forces are negligible compared to other terms in (1). This approach provides a virtual wheel speed using a so-called Proportional, Integral,…”

“…Linear, Kalman, or nonlinear observers are used in such kinematic-based methods [1], [5], [6] without using a tire model. Kinematic-based estimation structure uses transformed kinematic equations at each corner with the following dynamics:…”

“…The first one uses vehicle's planar kinematics, to develop observers [1], [2] that use standard measurements from an inertial measurement unit (IMU) such as acceleration, the yaw, the pitch, and the roll rates, without implementing a tire model. This approach is independent from tire parameters, but instead the sensors bias and noise should be identified precisely to have a reliable estimation.…”

Advanced Driver Assistance Systems from Autonomous to Cooperative Approach

“…For vehicle control, a trajectory corresponding to the desired path can be defined in the global coordinate reference frame and stored on board the vehicle. An approach to integrate an Inertial Navigation System (INS) and a DGPS has been studied on several occasions (Farrell et al, 2003;Ryu and Gerdes, 2004). Inertial navigation has been widely used in air, land and sea application systems (Parkinson and Spilker, 1996;Farrell and Barth, 1999).…”

Distributed robust vehicle state estimation

“…A model-based vehicle lateral state estimator is developed in [9] using a yaw rate gyroscope, a forward-looking monocular camera, an a priori map of road superelevation and temporally previewed lane geometry. On the other hand, the kinematic method uses acceleration and the yaw rate measurements from an inertial measurement unit (IMU) and estimates the vehicle velocities employing Kalman-based [10], [11], or nonlinear [12] observers. This method does not employ a tire model, but instead the sensors bias and noise should be identified precisely to have a reliable estimation.…”