Intellifusion - A System for Augmenting Inductive Loop Vehicle Sensor Data with SPAT and GrID (MAP) via Data Fusion

Our goal is to create a demonstrable prototype system at the end of Phase I which fuses IntelliDrive (SM)data with data from traditional inductive loop detectors and uses this data in a modified NEMATS2 traffic signal controller within eTEXAS Model for Intersection Traffic to reduce traffic delay at intersections. Our work will improve the safety of intersections and improve the mobility of traffic through adaptive traffic control which uses data produced by IntelliFusion, the product of our research. The process of data fusion involves merging data obtained from traditional inductive loop detectors or similar presence detectors and data obtained from a combination of IntelliDrive (SM) Signal Phasing and timing (SPAT), IntelliDrive (SM)Geographic Reference Information Data (GRIS)(also known as MAP data), and individual vehicle data from vehicles equipped with IntelliDrive (SM) Dedicated Short Range Communications (DSRC) On-Board Units (OBUs). It is assumed that the IntelliDrive (SM) Road Side Equipment (RSE) will have as a minimum the vehicle reference number, vehicle latitude, and vehicle lognitude of each operational DSRC equipped vehicle within range of an intersection.