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Autonomous vehicles require that all processes be efficient in time, complexity and data storage. In fact, an ideal system employs multifunctional models where ever possible. This paper presents the region- and feature-based neural network (RFNN) as a viable pattern analysis process engine for solving a variety of problems with a single math model. The RFNN employs receptive fields and weight sharing which compensate for noise, minor phase shifts and occlusions. The RFNN also utilizes greedy adaptive learning rates and mature feature preservation to expedite the overall training process. A novel ad hoc approach called "shocking" is used to solve the instability problem inherent to greedy adaptive learning rates. The basic RFNN "feature" is grounded in computer vision morphology in that the neural network autonomously learns subpatterns unique to various problems. This paper comprehensively describes the flexible RFNN architecture and training process and presents two problems that can be solved by the RFNN: sensor pattern-recognition and traffic sign recognition.
Janet et al. (Mon,) studied this question.