Autonomous Automobile Behavior through Context-Based Reasoning

Fernando G. Gonzalez, Patrick Grejs, and Avelino J. Gonzalez, University of Central Florida, USA

Today’s driving simulators are used in vehicle research and design as well as in training. However, most simulators are not convincing because the degree of realism is not adequate. To achieve greater realism, a simulator must include autonomous vehicles in the environment. To accomplish this the autonomous vehicles must incorporate some form of reasoning. One traditional approach is to implement the decision model with a large finite state machine (FSM) that given the current state and the input it yields an appropriate response. Context Based Reasoning (CxBR) is a new method of reasoning that reduces the size and response time of the traditional method by limiting the decision model to be appropriate only in a particular context. One will then use the appropriate model based on the current context. The intent of this research is to use the CxBR approach to develop a system that produces an interactive traffic model that behaves autonomously and intelligently and to show that this model is effective, computationally efficient and developed with relative ease.

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