Parking Guidance System Based on Geomagnetic Sensors and Recurrent Neural Networks

Abstract

The increase of motor vehicles year by year has led to a number of parking difficulties and traffic congestion problems. The intelligent parking system can effectively alleviate the parking difficulties and has received wide attention. Geomagnetic sensors are widely used due to their low cost and easy deployment. However, traditional geomagnetic parking detection algorithms cannot cope with complex parking behaviors and have low vehicle detection performance. Therefore, in this paper, a new parking guidance system is proposed by integrating related technologies such as ZigBee, geomagnetic sensor, and RNN. With our limited knowledge, in the research branch of the parking guidance system, RNN is applied to geomagnetic vehicle detection for the first time to detect the status of parking spaces and obtain more accurate identification results of geomagnetic signals. The training data is obtained from real scenarios. It is experimentally demonstrated that our method receives 96.6% accuracy in the detection of vehicle status, which is 9% higher than the state-of-the-art method. Finally, a robust parking guidance system gets 97% accuracy.