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Found 4 papers, 2 papers with code
LLM-Assisted Light: Leveraging Large Language Model Capabilities for Human-Mimetic Traffic Signal Control in Complex Urban Environments
Specifically, a hybrid framework that augments LLMs with a suite of perception and decision-making tools is proposed, facilitating the interrogation of both the static and dynamic traffic information.
HoloVIC: Large-scale Dataset and Benchmark for Multi-Sensor Holographic Intersection and Vehicle-Infrastructure Cooperative
Based on HoloVIC, we formulated four tasks to facilitate the development of related research.
UniTSA: A Universal Reinforcement Learning Framework for V2X Traffic Signal Control
Traffic congestion is a persistent problem in urban areas, which calls for the development of effective traffic signal control (TSC) systems.
ADLight: A Universal Approach of Traffic Signal Control with Augmented Data Using Reinforcement Learning
In this paper, we propose a novel reinforcement learning approach with augmented data (ADLight) to train a universal model for intersections with different structures.
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