The recent developments in the integration of Large Language Models (LLMs) into transportation systems highlight a significant shift towards enhancing situational awareness, decision-making, and operational efficiency across various modes of transport. These advancements are particularly notable in autonomous driving, public transportation, and intelligent transportation systems (ITS), where LLMs are being leveraged to process and analyze vast amounts of data in real-time. This enables more dynamic and context-aware responses to complex environments, improving safety, adaptability, and passenger satisfaction. Furthermore, the application of LLMs in military and high-speed train operations underscores their potential to revolutionize critical and high-stakes transportation scenarios through advanced decision-making models and fault handling systems.

Noteworthy Papers

• SenseRAG: Introduces a proactive Retrieval-Augmented Generation (RAG) framework for autonomous driving, significantly enhancing perception and prediction performance.
• Exploring the Potential of Large Language Models in Public Transportation: Demonstrates how LLMs can optimize public transit operations, with a focus on route planning and personalized travel assistance.
• Integrating LLMs with ITS: Offers a comprehensive review of LLM applications in ITS, highlighting their potential to improve traffic management and safety.
• Leveraging Edge Intelligence and LLMs to Advance 6G-Enabled Internet of Automated Defense Vehicles: Explores the integration of LLMs in military autonomous driving, emphasizing the importance of real-time data exchange and decision-making.
• A Driver Advisory System Based on Large Language Model for High-speed Train: Presents an LLM-based advisory system for high-speed trains, improving fault handling accuracy and explainability.