用生成模型合成稀少实测数据，模拟动态威胁环境。
Synthetic Data Generation for Minimum-Exposure Navigation in a Time-Varying Environment using Generative AI Models
- 分拆潜在空间，融合真实数据与系统动态建模
- 小样本下生成数据与真实分布高度一致
- 适合自动驾驶在数据稀缺场景下的环境仿真
我们研究自动驾驶导航中环境特征的合成数据生成问题。这些特征由随时空变化的标量场描述，称为威胁场，其具有受过程噪声影响的内在动力学。虽有部分真实威胁场观测数据，但数量有限。目标是生成统计上与真实数据相似的样本。本文提出一种名为分裂变分循环神经网络（S-VRNN）的生成式AI模型，结合变分自编码器与循环神经网络的优势，分别捕捉空间分布与时间依赖性。主要创新在于将潜在空间分为两个子空间：一个基于真实数据学习，另一个同时利用真实数据与已知系统动力学学习。数值实验表明，即使真实训练数据量极小，S-VRNN仍能生成与训练数据统计特性相近的样本。
原文摘要 · Abstract (English)
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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