Estimation of Driver Behavior for Autonomous Vehicle Applications

Cyber-physical systems (CPS) refer to the co-joining of environmental and computational elements of a system. One CPS application area is in autonomous vehicles. Autonomous (or self-driving) vehicles are likely to be an upcoming revolution in personal and commercial transportation. While there are many outstanding public policy questions, this technology promises to improve our quality of life by providing transportation that is safe and efficient. A likely technology adoption path includes a period in which human driven and autonomous vehicles will need to coexist. In such an environment, referred to as a Mixed Urban Environment, autonomous vehicles may only be able to obtain information from human driven vehicles through on board sensors or vehicle-to-vehicle communication. From this information, an autonomous vehicle will need to determine the likely behavior of the human driven vehicle, a task which is referred to as driver behavior estimation. This task requires a qualitative-quantitative architecture capable of explaining the driver/vehicle coupling being observed. A vehicle's ability to determine other vehicle's likely behavior also has applications to driver safety and collision avoidance systems. In essence, a vehicle must be able to estimate the behavior of another vehicle, and determine its course of action.;This thesis proposes an architecture for driver behavior estimation through the unified development of two theoretical concepts, namely: Graphical models, and Hybrid State Systems. Hybrid State Systems (HSS) provide the qualitative relationship between driver/vehicle couplings through a two layer model. Pattern recognition techniques in conjunction with Hidden Markov Models (HMMs), a type of graphical model, provide the quantitative relation between HSS layers. The estimation of current driver state is based on easy-to-measure continuous observations. The proposed system uses machine-learning concepts and requires extensive data collection, which is discussed. This thesis further provides an extension of the proposed system that includes external factors such as roadway type conditions in the decision making process. Results are provided for driver behavior estimation and system extension. A discussion of some of the public policy questions behind autonomous vehicles is also provided.