Adaptive Testing

Autonomous vehicles require sophisticated software controllers to maintain vehicle performance in the presence of vehicle faults. The test and evaluation of comples software controllers is a challenging task. The goal of this effort is to apply machine learning techniques to the general problem of evaluating an intelligent controller for an autonomous vehicle. The approach involves subjecting a controller to an adaptively chosen set of fault scenarios within a vehicle simulator, and searching for combinations of faults that produce noteworthy performance by the vehicle controller. The search employs a genetic algorithm. The evidence suggests that this approach is an effective supplement to manual and other forms of automated testing of sophisticated software controllers. Several intelligent controllers were tested in this project using several different genetic algorithm-based learning programs.

Learning to break things: adaptive testing of intelligent controllers,

In Handbook on Evolutionary Computation , G3.5, IOP Publishing Ltd. and Oxford Press, 1997.

Full publication list for Adaptive Testing

Contact: Alan C. Schultz, Principal Investigator (Click for contact information)