Simulation forms the backbone of modern self-driving development. Simulators
help develop, test, and improve driving systems without putting humans,
vehicles, or their environment at risk. However, simulators face a major
challenge: They rely on realistic, scalable, yet interesting content. While
recent advances in rendering and scene reconstruction make great strides in
creating static scene assets, modeling their layout, dynamics, and behaviors
remains challenging. In this work, we turn to language as a source of
supervision for dynamic traffic scene generation. Our model, LCTGen, combines a
large language model with a transformer-based decoder architecture that selects
likely map locations from a dataset of maps, and produces an initial traffic
distribution, as well as the dynamics of each vehicle. LCTGen outperforms prior
work in both unconditional and conditional traffic scene generation in terms of
realism and fidelity. Code and video will be available at
this https URL

该研究论文介绍了一种名为 LCTGen 的模型，它结合了大型语言模型和基于变压器的解码器架构，利用语言作为动态交通场景生成的监督来源，其在无条件和条件下的交通场景生成方面表现优于之前的研究，并且在现实感和保真度方面更好。

语言条件下的交通生成

Language Conditioned Traffic Generation

In this paper we describe a learned method of traffic scene generation
designed to simulate the output of the perception system of a self-driving car.
In our "Scene Diffusion" system, inspired by latent diffusion, we use a novel
combination of diffusion and object detection to directly create realistic and
physically plausible arrangements of discrete bounding boxes for agents. We
show that our scene generation model is able to adapt to different regions in
the US, producing scenarios that capture the intricacies of each region.

本文描述了一种基于学习的交通场景生成方法，旨在模拟自动驾驶汽车的感知系统输出。通过在传输中聚合物体检测，我们的 “场景扩散” 系统直接创建真实而物理上可行的代理离散边界框组合。我们展示了我们的场景生成模型能够适应美国不同地区，从而产生捕捉每个地区细节的场景。