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AI-based competition of autonomous vehicle fleets with application to fleet modularity

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  • Li, Xingyu
  • Epureanu, Bogdan I.

Abstract

Because operational environments change over time and technology upgrades are common in fleets of ground vehicles, a large number of vehicles quickly become obsolete. A possible solution is to develop fleets of modular vehicles, which are built with interchangeable components, i.e. modules. This paper evaluates the performance of a future reconfigurable and autonomous vehicle fleet in a high fidelity military operation scenario. The military fleet operates in a hostile environment under a high risk of damage and needs to react to adversarial actions in real-time. The operation decisions are numerous including vehicle reconfiguration, relocation, damage recovery, and dispatch decisions. Given the limited resources and time delays in the operation, decision effectiveness and foresightedness are necessities, which require a good understanding of the adversary, close collaboration among commanders, and breaking of the equilibrium between adversaries. To capture these characteristics, we formulate an intelligent agent-based model for the decision-making process during fleet operations by combining real-time optimization with artificial intelligence. With continuous updating of learning models, the intelligent agents refine their decisions during interactions with the environment and other agents, and evolve their competition strategies according to adversarial historical behaviors. With the same level of resources, the conventional fleet wins when the dispatch decisions are stochastic. However, once each fleets start to learn from each other’s behavior, the modular fleet outperforms the conventional fleet. The strategic and operational benefits of fleet modularity are revealed and discussed in terms of win rate, adaptability, unpredictability and damage recovery.

Suggested Citation

  • Li, Xingyu & Epureanu, Bogdan I., 2020. "AI-based competition of autonomous vehicle fleets with application to fleet modularity," European Journal of Operational Research, Elsevier, vol. 287(3), pages 856-874.
    • Handle: RePEc:eee:ejores:v:287:y:2020:i:3:p:856-874
    DOI: 10.1016/j.ejor.2020.05.020
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    1. Li, Xingyu & Krivtsov, Vasiliy & Arora, Karunesh, 2022. "Attention-based deep survival model for time series data," Reliability Engineering and System Safety, Elsevier, vol. 217(C).

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