Multi-sensor information fusion for efficient smart transport vehicle tracking and positioning based on deep learning technique

A smart transportation system relies on connected environments and cloud systems for ease of operation and assisted routing. Smart vehicles understand the environment through multi-sensor, network, and pervasive systems for gaining useful information. The problem arises with the absence of useful information in explicit scenarios where heterogeneous information becomes mandatory. This article aims to improve transportation support's effectiveness using a discrete behavior information fusion (DBIF) based on the deep learning technique by considering the contradiction in information availability. This proposed technique observes the vehicles' behavior and response to the scene throughout the route displacement. The deep learning model achieves greater accuracy in target detection and classification. The learning output is the independent fusion of behavior (response output) and information (sensed). This sensed information is useful in categorizing further deviations and stipulations for the progressive displacements. The stipulated information and its deviations are recurrently categorized using support vector machine learning. The information provides accurate positioning and tracking of smart vehicles by reducing approximation errors and complexity. The simulation results illustrate the proposed technique's efficiency by improving the accuracy of 92.078% and fusion rate of 0.9741 and reducing error of 0.0662, complexity of 0.0717, and fusion time of 0.9938 compared to existing methods.