Vehicle Route Prediction through Multiple Sensors Data Fusion
This work addresses security and privacy issues in vehicle communication for route prediction, but it is incremental as it combines existing deep learning and supervised learning methods.
The authors tackled vehicle route prediction at crossroads by proposing a framework that fuses multiple sensor data, achieving an accuracy of 85% in experiments.
Vehicle route prediction is one of the significant tasks in vehicles mobility. It is one of the means to reduce the accidents and increase comfort in human life. The task of route prediction becomes simpler with the development of certain machine learning and deep learning libraries. Meanwhile, the security and privacy issues are always lying in the vehicle communication as well as in route prediction. Therefore, we proposed a framework which will reduce these issues in vehicle communication and predict the route of vehicles in crossroads. Specifically, our proposed framework consists of two modules and both are working in sequence. The first module of our framework using a deep learning for recognizing the vehicle license plate number. Then, the second module using supervised learning algorithm of machine learning for predicting the route of the vehicle by using velocity difference and previous mobility patterns as the features of machine learning algorithm. Experiment results shows that accuracy of our framework.