City scale autonomy learning

Sydney Gibbs; Mitchell A. Thornton; Darrell L. Young

doi:10.1117/12.2668812

15 June 2023 City scale autonomy learning

Sydney Gibbs, Mitchell A. Thornton, Darrell L. Young

Proceedings Volume 12525, Geospatial Informatics XIII ; 125250M (2023) https://doi.org/10.1117/12.2668812
Event: SPIE Defense + Commercial Sensing, 2023, Orlando, Florida, United States

Conference Poster

Abstract

Reinforcement learning for agent autonomous actions requires many repetitive trials to succeed. The idea of this paper is to distribute the trials across a city-scale geospatial map. This has the advantage of providing rationale for the trial-totrial variance because each location is slightly different. The technique can simultaneously train the agent and deduce where difficult and potentially dangerous intersections exist in the city. The concept is illustrated using readily available open-source tools.

Citation Download Citation

Sydney Gibbs, Mitchell A. Thornton, and Darrell L. Young "City scale autonomy learning", Proc. SPIE 12525, Geospatial Informatics XIII , 125250M (15 June 2023); https://doi.org/10.1117/12.2668812

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