UnstrPrompt: Large Language Model Prompt for Driving in Unstructured Scenarios

IF 2.3 Q2 ENGINEERING, ELECTRICAL & ELECTRONIC IEEE journal of radio frequency identification Pub Date : 2024-02-20 DOI:10.1109/JRFID.2024.3367975
Yuchen Li;Luxi Li;Zizhang Wu;Zhenshan Bing;Zhe Xuanyuan;Alois Christian Knoll;Long Chen
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Abstract

The integration of language descriptions or prompts with Large Language Models (LLMs) into visual tasks is currently a focal point in the advancement of autonomous driving. This study has showcased notable advancements across various standard datasets. Nevertheless, the progress in integrating language prompts faces challenges in unstructured scenarios, primarily due to the limited availability of paired data. To address this challenge, we introduce a groundbreaking language prompt set called “UnstrPrompt.” This prompt set is derived from three prominent unstructured autonomous driving datasets: IDD, ORFD, and AutoMine, collectively comprising a total of 6K language descriptions. In response to the distinctive features of unstructured scenarios, we have developed a structured approach for prompt generation, encompassing three key components: scene, road, and instance. Additionally, we provide a detailed overview of the language generation process and the validation procedures. We conduct tests on segmentation tasks, and our experiments have demonstrated that text-image fusion can improve accuracy by more than 3% on unstructured data. Additionally, our description architecture outperforms the generic urban architecture by more than 0.1%. This work holds the potential to advance various aspects such as interaction and foundational models in this scenario.
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UnstrPrompt:用于非结构化场景驾驶的大型语言模型提示
将语言描述或提示与大型语言模型(LLM)整合到视觉任务中,是目前自动驾驶技术发展的一个焦点。这项研究在各种标准数据集方面取得了显著进展。然而,在非结构化场景中,整合语言提示的进展面临挑战,这主要是由于配对数据的可用性有限。为了应对这一挑战,我们推出了一个突破性的语言提示集,名为 "UnstrPrompt"。该提示集来自三个著名的非结构化自动驾驶数据集:IDD、ORFD 和 AutoMine,总共包含 6K 条语言描述。针对非结构化场景的显著特点,我们开发了一种结构化的提示生成方法,包括三个关键组成部分:场景、道路和实例。此外,我们还详细介绍了语言生成过程和验证程序。我们对分割任务进行了测试,实验证明,文本-图像融合可将非结构化数据的准确率提高 3% 以上。此外,我们的描述架构比一般的城市架构高出 0.1% 以上。这项工作有望推动该场景中交互和基础模型等各个方面的发展。
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