Abstract
In recent years, the development of intelligent transportation systems (ITS) has involved the input of various kinds of heterogeneous data in real time and from multiple sources, which presents several additional challenges. Studies on Data Fusion (DF) have delivered significant enhancements in ITS and demonstrated a substantial impact on its evolution. This paper introduces a systematic literature review on recent data fusion methods and extracts the main issues and challenges of using these techniques in intelligent transportation systems (ITS). It endeavors to identify and discuss the multi-sensor data sources and properties used for various traffic domains, including autonomous vehicles, detection models, driving assistance, traffic prediction, Vehicular communication, Localization, and management systems. Moreover, it attempts to associate abstractions of observation-level fusion, feature-level fusion, and decision-level fusion with different methods to better understand how DF is used in ITS applications. Consequently, the main objective of this paper is to review DF methods used for ITS studies to extract its trendy challenges. The review outcomes are (i) a description of the current Data fusion methods that adopt multi-sensor sources of heterogeneous data under different evaluation strategies, (ii) identifying several research gaps, current challenges, and new research trends.
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