5 papers
Thinking While Driving: A Concurrent Framework for Real-Time, LLM-Based Adaptive Routing
Xiaopei Tan, Muyang Fan
We present Thinking While Driving, a concurrent routing framework that integrates LLMs into a graph-based traffic environment. Unlike approaches that require agents to stop and del…
Analyzing Collision Rates in Large-Scale Mixed Traffic Control via Multi-Agent Reinforcement Learning
Muyang Fan
Vehicle collisions remain a major challenge in large-scale mixed traffic systems, especially when human-driven vehicles (HVs) and robotic vehicles (RVs) interact under dynamic and…
Origin-Destination Pattern Effects on Large-Scale Mixed Traffic Control via Multi-Agent Reinforcement Learning
Muyang Fan, Songyang Liu, Shuai Li +1
Traffic congestion remains a major challenge for modern urban transportation, diminishing both efficiency and quality of life. While autonomous driving technologies and reinforceme…
Large-Scale Mixed-Traffic and Intersection Control using Multi-agent Reinforcement Learning
Songyang Liu, Muyang Fan, Weizi Li +2
Traffic congestion remains a significant challenge in modern urban networks. Autonomous driving technologies have emerged as a potential solution. Among traffic control methods, re…
A Comprehensive Review on Traffic Datasets and Simulators for Autonomous Vehicles
Supriya Sarker, Brent Maples, Iftekharul Islam +3
Autonomous driving has rapidly evolved through synergistic developments in hardware and artificial intelligence. This comprehensive review investigates traffic datasets and simulat…