Safety is the most important aspect of autonomous vehicle feature development, testing, and deployment. Predicting, generating, and obtaining real world ground truth accident scenarios for research and development is both dangerous and expensive. Simulation has become a popular method for test case generation, although current solutions do not always model vehicle movement realistically, and they model real world dynamic traffic scenarios poorly. A generalized algorithm for simulated vehicle control is needed. We will demonstrate generalized parameterization, training, and resulting vehicle control patterns obtained from using various machine learning and AI methods. The resulting vehicle behavior is realistic and improves simulation efforts.