A System of Networked Car-Mounted Sensors for Measuring Road Surfaces

In this article, we propose a system of networked car-mounted sensors that measures road surfaces. The system consists of cars each of which carries a GPS, an accelerometer, a torque meter, and a rotating meter of a wheel. The proposed system estimates angles of inclination of road surfaces and their friction coefficients based on measurements obtained by the sensors. Each car obtains the measurements at each location while moving, and uploads those measurements to a server. The server estimates the angles and the friction coefficients based on the uploaded measurements. We model the dynamics of moving cars, and derive constraint conditions that should be satisfied by the measurements. We compute the maximum likelihood estimates of the coefficients with the measurements under the constraint conditions. The ML solution, however, can contain indeterminacies and have two degrees of freedom. Hence, we need two more constraints for removing the indeterminacies. For this purpose, one set of car-mounted sensors that is calibrated in advance is incorporated into the car-mounted sensor network. We call the calibrated sensor a reference sensor. If the reference sensor removes the indeterminacies we can obtain unique set of ML estimates, but the set of estimates is perturbed by the measurement noise. Defining the distance between two sensors based on a measurement graph, in this article, we analyze the relationship between the variance of the estimates and the distance between a sensor and the reference sensor