Synthetic Data Generation for Minimum-Exposure Navigation in a Time-Varying Environment using Generative AI Models

• URL: http://arxiv.org/abs/2503.06619v1
• Date: Sun, 09 Mar 2025 13:45:15 GMT
• Title: Synthetic Data Generation for Minimum-Exposure Navigation in a Time-Varying Environment using Generative AI Models
• Authors: Nachiket U. Bapat, Randy C. Paffenroth, Raghvendra V. Cowlagi,
• Abstract summary: We study the problem of synthetic generation of samples of environmental features for autonomous vehicle navigation.<n>The proposed solution is a generative artificial intelligence model that we refer to as a split variational recurrent neural network (S-VRNN)<n>The S-VRNN merges the capabilities of a variational autoencoder, which is a widely used generative model, and a recurrent neural network, which is used to learn temporal dependencies in data.
• Score: 0.5499796332553707
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: We study the problem of synthetic generation of samples of environmental features for autonomous vehicle navigation. These features are described by a spatiotemporally varying scalar field that we refer to as a threat field. The threat field is known to have some underlying dynamics subject to process noise. Some "real-world" data of observations of various threat fields are also available. The assumption is that the volume of ``real-world'' data is relatively small. The objective is to synthesize samples that are statistically similar to the data. The proposed solution is a generative artificial intelligence model that we refer to as a split variational recurrent neural network (S-VRNN). The S-VRNN merges the capabilities of a variational autoencoder, which is a widely used generative model, and a recurrent neural network, which is used to learn temporal dependencies in data. The main innovation in this work is that we split the latent space of the S-VRNN into two subspaces. The latent variables in one subspace are learned using the ``real-world'' data, whereas those in the other subspace are learned using the data as well as the known underlying system dynamics. Through numerical experiments we demonstrate that the proposed S-VRNN can synthesize data that are statistically similar to the training data even in the case of very small volume of ``real-world'' training data.

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