A methodology for intelligent sensor validation and fusion used in tracking and avoidance of objects for automated vehicles

For longitudinal control the automated vehicles in intelligent vehicle highway system (IVHS) require sensors to estimate the relative distance and velocity between vehicles. High data fidelity of these sensors is required to maintain the reliability and safety of the IVHS. In this paper, the authors develop a methodology for validation and fusion of sensory readings obtained from multiple sensors used for tracking automated vehicles and for avoiding objects in its path. The authors introduce tracking models for the various operating states of the automated vehicle, namely vehicle following, maneuvering, i.e. split, merge, lane change, emergencies, and for the lead vehicle in a platoon. The Kalman filtering approach is proposed for the formation of real time validation gates. This along with the algorithmic sensor validation filter is used for sensory data validation. The validated data are then fused by using a Bayesian method called the probabilistic data association filter. The procedure is demonstrated by two examples using simulated data, data obtained from a platooning test set-up