Sensor and Data Fusion for Intelligent Transportation Systems
Sensor and Data Fusion for Intelligent Transportation Systems introduces readers to the roles of the data fusion processes defined by the Joint Directors of Laboratories (JDL) data fusion model and the Data Fusion Information Group (DFIG) enhancements, data fusion algorithms, and noteworthy applications of data fusion to intelligent transportation systems (ITS). Additionally, the monograph offers detailed descriptions of three of the widely applied data fusion techniques and their relevance to ITS (namely, Bayesian inference, Dempster?Shafer evidential reasoning, and Kalman filtering), and indicates directions for future research in the area of data fusion. The focus is on data fusion algorithms rather than on sensor and data fusion architectures, although the book does summarize factors that influence the selection of a fusion architecture and several architecture frameworks.
2 Sensor and Data Fusion in Traffic Management
3 Bayesian Inference for Traffic Management
4 Dempster–Shafer Evidential Reasoning for Traffic Management
5 Kalman Filtering for Traffic Management
6 State of the Practice and Research Gaps
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