Publication Type
Conference Paper
Book Title
Proceedings of the Genetic and Evolutionary Computation Conference Companion (GECCO '21)
Publication Date
Page Numbers
301 to 302
Publisher Location
Lille, France
Conference Name
Genetic and Evolutionary Computation Conference (GECCO) Conference
Conference Location
Lille, France
Conference Sponsor
Association for Computing Machinery
Conference Date
-
Abstract
We repurposed an adversarial evolutionary algorithm, Gremlin, from finding driving scenarios where a model of an autonomous vehicle drove poorly to troubleshooting driving quality evaluation criteria. We evaluated the driving performance of a "perfect driver" robot in a virtual town environment using the same fitness criteria intended for a deep learner (DL) trained driver. We found that the fitness evaluation criteria poorly handled turns, and used Gremlin to iteratively improve that criteria. We were confident that the same criteria could then be applied to the DL-based models as originally intended, and that this approach could be used as a general means of troubleshooting autonomous vehicle driving criteria.
