Contextual Data Rule Generation For Autonomous Vehicle Control

Autonomous vehicles are often called upon to deal with complex and varied situations. This requires analyzing input from sensor arrays to get as accurate a description of the environment as possible. These ad-hoc descriptions are then compared against existing rule sets generated from decision trees that decide upon a course of action. However, with so many environmental conditions it is often difficult to create decision trees that can account for every possible situation, so techniques to limit the size of the decision tree are used. Unfortunately, this can obscure data which is sparse, but also important to the decision process. This paper presents an algorithm to analyze a decision tree and develops a set of metrics to determine whether or not sparse data is relevant and should be include. An example demonstrating the use of this technique is shown.