Big autonomous vehicular data classifications: Towards procuring intelligence in ITS

Abstract

For effectively utilization of acquired resources in Autonomous Vehicle (AV), big data analysis in real time will be a reliable way to produce valuable information from sensor data. With the combined ability of telematics and real-time analysis, big data analytics forming the key drivers of autonomous cars. To emphasize the significant of data fusion or knowledge discovery, an efficient architecture has been proposed for real-time big data analysis in an autonomous vehicle, which indeed will keep pace with the latest trends and development with respect to emerging big data paradigm. The proposed architecture comprises distributed data storage mechanism for a streaming process for real-time analysis and the vehicular cloud server tool for batch processing the offline data. Furthermore, a workflow model has also been designed for big data architecture to examine streaming data in near real time process. Furthermore, an algorithm is developed for data classification in distributed storage unit, and mathematical modeling is carried to analysis the data classification functionality in AV. The proposed system model using Hadoop framework which is for the optimal utilization of the massive data set, meant for data classification in distributed environment for streaming data in real time, which is intended for intelligent transportation of the autonomous vehicle.