A Survey on the Applications of Frontier AI, Foundation Models, and
Large Language Models to Intelligent Transportation Systems
- URL: http://arxiv.org/abs/2401.06831v1
- Date: Fri, 12 Jan 2024 10:29:48 GMT
- Title: A Survey on the Applications of Frontier AI, Foundation Models, and
Large Language Models to Intelligent Transportation Systems
- Authors: Mohamed R. Shoaib, Heba M. Emara, Jun Zhao
- Abstract summary: This survey paper explores the transformative influence of frontier AI, foundation models, and Large Language Models (LLMs) in the realm of Intelligent Transportation Systems (ITS)
It emphasizes their integral role in advancing transportation intelligence, optimizing traffic management, and contributing to the realization of smart cities.
- Score: 8.017557640367938
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: This survey paper explores the transformative influence of frontier AI,
foundation models, and Large Language Models (LLMs) in the realm of Intelligent
Transportation Systems (ITS), emphasizing their integral role in advancing
transportation intelligence, optimizing traffic management, and contributing to
the realization of smart cities. Frontier AI refers to the forefront of AI
technology, encompassing the latest advancements, innovations, and experimental
techniques in the field, especially AI foundation models and LLMs. Foundation
models, like GPT-4, are large, general-purpose AI models that provide a base
for a wide range of applications. They are characterized by their versatility
and scalability. LLMs are obtained from finetuning foundation models with a
specific focus on processing and generating natural language. They excel in
tasks like language understanding, text generation, translation, and
summarization. By leveraging vast textual data, including traffic reports and
social media interactions, LLMs extract critical insights, fostering the
evolution of ITS. The survey navigates the dynamic synergy between LLMs and
ITS, delving into applications in traffic management, integration into
autonomous vehicles, and their role in shaping smart cities. It provides
insights into ongoing research, innovations, and emerging trends, aiming to
inspire collaboration at the intersection of language, intelligence, and
mobility for safer, more efficient, and sustainable transportation systems. The
paper further surveys interactions between LLMs and various aspects of ITS,
exploring roles in traffic management, facilitating autonomous vehicles, and
contributing to smart city development, while addressing challenges brought by
frontier AI and foundation models. This paper offers valuable inspiration for
future research and innovation in the transformative domain of intelligent
transportation.
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