Low-cost, high-accuracy, state estimation for vehicle collision prevention system

Abstract

In this paper, a low-cost navigation system with high integrity and reliability is proposed for enhancing highway traffic safety in adverse weather situations, such as a foggy or rainy weather. A high-integrity, IS-state, estimation filter is proposed to obtain a high accuracy state estimate. The filter utilizes a vehicle velocity constraint measurement to enhance the accuracy of the estimate. Two estimations filters, the Kalman filter (KF) and the information filter (IF), are designed and compared to obtain the estimate of the vehicle state. An instrumentation system that consists of a microcontroller, a GPS receiver, an IMU, encoders, and a Zig bee transceiver is constructed. The microcontroller provides a vehicle navigation solution at 50 Hz by fusing the measurements of the IMU, the GPS receiver and the digital compass using the proposed filter design. Experimental tests, using the proposed system, are conducted and the obtained results are presented. These results are processed with and without the velocity constraints. The estimation accuracy improvement with the addition of the velocity constraints is demonstrated. Also, results of a static test are used to assess the accuracy of the low-cost navigation systems against a commercial solution.

Estimating Vehicle State by GPS/IMU Fusion with Vehicle Dynamics

“…The readings we are taking in this work are position and velocity from GPS and IMU, and velocity from encoders and nonholonomic constraints. Moreover, the EIF algorithm is not computationally heavy for a simple processor to do, so it is very suitable for real time applications [12,24].…”

“…The main components of the information filter [12,24] are the information state vector, y, and the information state matrix, Y, which is the inverse of the covariance matrix, that is…”