# A Survey on the Applications of Frontier AI, Foundation Models, and Large Language Models to Intelligent Transportation Systems

arXiv: https://arxiv.org/abs/2401.06831 · HTML: https://mlsanity.com/paper/2401.06831/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 13 of its 73 references that are in the map.
- First posted on arXiv 12 Jan 2024.

## Builds on (most cited first, 13 of 13)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [On the Opportunities and Risks of Foundation Models](https://mlsanity.com/paper/2108.07258/) (2021)
- [Frontier AI Regulation: Managing Emerging Risks to Public Safety](https://mlsanity.com/paper/2307.03718/) (2023)
- [Privacy-preserving Traffic Flow Prediction: A Federated Learning Approach](https://mlsanity.com/paper/2003.08725/) (2020)
- [On the Power of Foundation Models](https://mlsanity.com/paper/2211.16327/) (2022)
- [A Smart, Efficient, and Reliable Parking Surveillance System with Edge Artificial Intelligence on IoT Devices](https://mlsanity.com/paper/2001.00269/) (2020)
- [Characterizing Driving Styles with Deep Learning](https://mlsanity.com/paper/1607.03611/) (2016)
- [FedParking: A Federated Learning based Parking Space Estimation with Parked Vehicle assisted Edge Computing](https://mlsanity.com/paper/2110.12876/) (2021)
- [Autoencoder Regularized Network For Driving Style Representation Learning](https://mlsanity.com/paper/1701.01272/) (2017)
- [Know Your Master: Driver Profiling-based Anti-theft Method](https://mlsanity.com/paper/1704.05223/) (2017)
- [Crossfire Attack Detection using Deep Learning in Software Defined ITS Networks](https://mlsanity.com/paper/1812.03639/) (2018)
- [Differentially Private Collaborative Intrusion Detection Systems For VANETs](https://mlsanity.com/paper/2005.00703/) (2020)
- [Machine Learning and Location Verification in Vehicular Networks](https://mlsanity.com/paper/1904.05610/) (2019)

## Built on by (most cited first, 4 of 4)

- [A Survey on Large Language Models for Communication, Network, and Service Management: Application Insights, Challenges, and Future Directions](https://mlsanity.com/paper/2412.19823/) (2024)
- [Integrating LLMs with ITS: Recent Advances, Potentials, Challenges, and Future Directions](https://mlsanity.com/paper/2501.04437/) (2025)
- [Large Language Models for Mobility Analysis in Transportation Systems: A Survey on Forecasting Tasks](https://mlsanity.com/paper/2405.02357/) (2024)
- [Privacy-Preserving Offloading for Large Language Models in 6G Vehicular Networks](https://mlsanity.com/paper/2509.05320/) (2025)

## Abstract (arXiv)

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.