This manuscript considers the problem of position and heading estimation of autonomous vehicles in situations where the vehicle’s global positioning system (GPS) is assumed to be not accessible. The position and heading estimation has been done with the use of inertial measurement unit (IMU) and vehicle dynamical models in a Raspberry Pi environment. Suitable Kalman filter algorithm has been developed to prepare IMU data for fusion, and data from the GPS module has been also effectively decoded for use in the algorithm. An extended Kalman filter algorithm has been developed and implemented for two separate cases, one of which is where the vehicle is traveling on a straight road at constant speed, and the other is a vehicle moving on a curved road at constant speed. Both cases have been tested with and without GPS outages. The sensors are first calibrated and modeled to use in the filtering process, and the model has been tested in the simulation environment created with CarMaker.