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.
| Original language | English |
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| Title of host publication | GECCO 2021 Companion - Proceedings of the 2021 Genetic and Evolutionary Computation Conference Companion |
| Publisher | Association for Computing Machinery, Inc |
| Pages | 301-302 |
| Number of pages | 2 |
| ISBN (Electronic) | 9781450383516 |
| DOIs | |
| State | Published - Jul 7 2021 |
| Event | 2021 Genetic and Evolutionary Computation Conference, GECCO 2021 - Virtual, Online, France Duration: Jul 10 2021 → Jul 14 2021 |
Publication series
| Name | GECCO 2021 Companion - Proceedings of the 2021 Genetic and Evolutionary Computation Conference Companion |
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Conference
| Conference | 2021 Genetic and Evolutionary Computation Conference, GECCO 2021 |
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| Country/Territory | France |
| City | Virtual, Online |
| Period | 07/10/21 → 07/14/21 |
Funding
This research used resources of the Oak Ridge Leadership Computing Facility, which is a DOE Office of Science User Facility supported under Contract DE-AC05-00OR22725. This manuscript has been authored by UT-Battelle, LLC under Contract No. DE-AC05-00OR22725 with the U.S. Department of Energy (DOE). Funding provided by the U.S. Department of Energy Office of Energy Efficiency and Renewable Energy Vehicle Technologies Office. The views expressed in the article do not necessarily represent the views of the DOE or the U.S. Government. The United States Government retains and the publisher, by accepting the article for publication, acknowledges that the United States Government retains a non-exclusive, paid-up, irrevocable, world-wide license to publish or reproduce the published form of this manuscript, or allow others to do so, for United States Government purposes. The Department of Energy will provide public access to these results of federally sponsored research in accordance with the DOE Public Access Plan (http://energy.gov/downloads/doe-public-access-plan).
Keywords
- adversarial algorithms
- autonomous vehicles
- evolutionary algorithms