A Novel Model Driven Approach for Trajectory-Based Technique of Autonomous Vehicle Route Prediction

The autonomous vehicle is one of the significant efforts toward the luxurious life. The process involves several technical difficulties like synchronized communication between different sensors, efficient response time etc. In this regard, route prediction is one of the critical tasks which requires the extensive knowledge of complex concepts due to its real time nature. Although there exist several studies in this area, most of them are focusing on accuracy while ignoring the importance of simplicity. Therefore, the research community is likely to be benefited through a route prediction approach that focuses on simplicity while preserving good level of accuracy. For this purpose, a model-driven approach is proposed in this paper for automating the complex process of trajectory-based route prediction of a fully autonomous vehicle. Particularly, a metamodel which is M2 level Ecore Model of standard meta-object facility is proposed for trajectory- based route prediction. Subsequently, a complete modeling tool is developed using Sirius platform. Finally, Model-to-Text transformations are applied to generate low level Java implementations with simplicity. The validation is performed through real world case study where autonomous vehicle gain data from different sensors and store that data in the cloud for route prediction by applying an autonomous multiple model algorithm. The results are highly encouraging for the efficient route prediction in autonomous vehicle with simplicity.