This book was prepared to give students and traffic management professionals a concise description of the benefits that sensor and data fusion bring to the transportation community, especially for Intelligent Transportation System, connected and cooperative vehicle, and autonomous vehicle operations. The book highlights data fusion processes that enhance the interpretation of information gathered from a diverse mixture of sensors and other data sources, and a complex environment characterized by the presence of different types of vehicles, unexpected objects such as pedestrians darting across a roadway, inclement weather, vehicles changing lanes, and roadside structures or weather effects that interfere with the normal observation of traffic patterns and the gathering of needed data. Several data fusion algorithms that combine information from infrastructure-based sensors and other sources of information, such as cellular phones, probe vehicles, global positioning system location devices, and connected and automated vehicles, are described with illustrative examples of their application to traffic management.
Readers desiring additional information concerning sensor and data fusion are referred to Sensor and Data Fusion: A Tool for Information Assessment and Decision Making, Second Edition, SPIE Press, Bellingham, Washington (2012) [doi: 10.1117/3.928035], which is also written by the author.
Lawrence A. Klein