Map Matching for Improving Accuracy of Vehicle Navigation in Green IoV

In response to the call for energy conservation and emission reduction, the development of the Internet of Vehicles (IoV) has gradually shifted from capability improvement to green development, which has become a hot topic in current research. In this field, a large part of the research focuses on reducing road congestion through planning, such as Vehicle Routing Problem (VRP), intelligent traffic management, and collaborative driving. These research methods are all strongly related to the accuracy of vehicle position and navigation, which is an important precondition for algorithm design. At present, the common approach is to use highly accurate GPS positioning but still faces errors in the continuity and accuracy of GPS radio signals. The industry is attempting to avoid such errors through map matching but without considering the challenges brought by complex road conditions. Therefore, this paper proposes an algorithm based on Map Matching with Kalman-filtered Trajectory Slides (MMKTS) to solve the problems, which combines trajectory slides, Kalman Filtering, and map matching processes. Simulation results prove that the proposed algorithm performs well in GPS noise-added trajectory for map matching at different road levels, with a reasonable delay for application.