基于语言条件的交通生成

摘要

模拟技术是现代自动驾驶开发的支柱。模拟器帮助开发、测试和改进驾驶系统，而无需让人类、车辆或其环境面临风险。然而，模拟器面临一个重大挑战：它们依赖于逼真、可扩展且有趣的内容。尽管最近在渲染和场景重建方面取得了巨大进展，创造静态场景资产，对其布局、动态和行为进行建模仍然具有挑战性。在这项工作中，我们将语言作为动态交通场景生成的监督来源。我们的模型LCTGen结合了一个大型语言模型和基于Transformer的解码器架构，从地图数据集中选择可能的地图位置，并生成初始的交通分布，以及每辆车的动态。在逼真度和保真度方面，LCTGen在无条件和有条件的交通场景生成方面均优于先前的工作。代码和视频将在https://ariostgx.github.io/lctgen 上提供。

English

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 https://ariostgx.github.io/lctgen.

基于语言条件的交通生成

Language Conditioned Traffic Generation

摘要

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