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<p>A machine learning technique for automating the traditional controller tests process that evaluates autonomous-vehicle software controllers is discussed. In the proposed technique, a controller is subjected to an adaptively chosen set of fault scenarios in a vehicle simulator, and then a genetic algorithm is used to search for fault combinations that produce noteworthy actions in the controller. This approach has been applied to find a minimal set of faults that produces degraded vehicle performance and a maximal set of faults that can be tolerated without significant performance loss.</p>
John J. Grefenstette, Kenneth A. De Jong, Alan C. Schultz, "Test and Evaluation by Genetic Algorithms", IEEE Intelligent Systems, vol. 8, no. , pp. 9-14, October 1993, doi:10.1109/64.236476
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