Article Preview
Top1. Introduction
Real-time positional data (Gupta & Shanker, 2020d) is used by navigational applications to give direction or location-relevant information. The major services include navigation assistance such as fleet management and path planning. Fleet management is a service that helps businesses that rely on transport to eliminate or reduce the costs associated with vehicle investment while increasing productivity, efficiency and lowering total transportation and other costs. The majority of other navigational services are classified as location-based services (LBS). The role of MM in intelligent vehicles systems and other context-aware services is depicted in Figure 1. Since1990, when the global positioning system first became available, MM seems to have been a hot topic of research (Buxton et al., 1991) (Alegiani et al., 1989). Initially, there was lot of interest in navigation aids, but as the mobile internet became available, a completely new world of applications emerged in the communication world. If the location and map data are precise enough, the problem can be solved quickly and MM may appear to be a simple task (Gupta, A. K., & Shanker, U., 2022a) (Gupta, A. K., & Shanker, U., 2021a).
Now-a-days, we are having highly précised satellite navigation systems with comprehensive maps with millimetre-scale resolutions. However, none of those is guaranteed yet and can contain complex flaws. Satellite-based location systems are unreliable particularly in urban regions wherein satellite observation is constrained, and signals are frequently deflected. When the satellite signal is poor, this results in missing samples and outliers in positioning data. This can make accurate MM difficult in some circumstances. The map, on the other hand, might be out of its current state (lack of certain roads), or the MM method itself could cause ambiguity in the matching process. Directed graphs, in which junctions are symbolized by their centre points and highways are symbolized by polygonal curves, are often used in any maps. Accurate matching can be a challenge with such a simplified depiction of an otherwise complicated road network. As a result, MM isn’t always straightforward. In the past two decades, hundreds of alternative approaches have been developed. These are based on a variety of methodologies and approaches ranging from basic topology and geometry to sophisticated ideas such as ant colony optimization, genetic algorithms, neural networks, Kalman filtering, conditional random fields, particle filtering, belief theory, and fuzzy logic.
Figure 1.
Relation of MM In Intelligent Vehicles Systems and Other Context-Aware Services
Due to the growing need for precision for autonomous vehicles and lane determination, MM methods has been studied a lot in recent decades and is still an active field (Gupta & Shanker, 2020e). The MM is the phase layer between incorrect positioning systems and software that uses the positioning system (Yeh et al., 2017).