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Abstract <p>The problem of modeling the movement of autopiloted vehicles in a mixed traffic stream of autopilots of various manufacturers, in which there are no collisions, is solved. A neural network model is proposed that implements reinforcement learning for an unmanned vehicle model integrated into a microscopic model of a mixed traffic flow. Computational experiments are being conducted with the proposed model. It was hypothesized that trained agents would work worse together than with the original automated objects in which they were trained. In the first experiment, for a closed multi-corridor circle, the percentage of implementation of trained agents gradually increased. In the second experiment, different trained agents were trained on a single-lane circle and then launched together. In the third experiment, agents are trained in different environments and run together on a track with multiple corridors. As a result of the experiments, the hypothesis was not confirmed. When using different learning environments, agents trained in these environments interact more effectively with each other in a common system than with other types of agents.</p>

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Reinforcement-Trained Neural Network for Various Manufacturers AV Mixed Traffic Flow Simulation

  • 期刊论文

  • 发表日期：

    2026年3月26日

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  • O. P. Bobrovskaya,
  • T. V. Gavrilenko,
  • V. A. Galkin

摘要

Abstract

The problem of modeling the movement of autopiloted vehicles in a mixed traffic stream of autopilots of various manufacturers, in which there are no collisions, is solved. A neural network model is proposed that implements reinforcement learning for an unmanned vehicle model integrated into a microscopic model of a mixed traffic flow. Computational experiments are being conducted with the proposed model. It was hypothesized that trained agents would work worse together than with the original automated objects in which they were trained. In the first experiment, for a closed multi-corridor circle, the percentage of implementation of trained agents gradually increased. In the second experiment, different trained agents were trained on a single-lane circle and then launched together. In the third experiment, agents are trained in different environments and run together on a track with multiple corridors. As a result of the experiments, the hypothesis was not confirmed. When using different learning environments, agents trained in these environments interact more effectively with each other in a common system than with other types of agents.