2 citations · 2 across the 2 of their papers we have counts for
7 papers
CARL: Congestion-Aware Reinforcement Learning for Imitation-based Perturbations in Mixed Traffic Control
Bibek Poudel, Weizi Li, Shuai Li
Human-driven vehicles (HVs) exhibit complex and diverse behaviors. Accurately modeling such behavior is crucial for validating Robot Vehicles (RVs) in simulation and realizing the…
Learning to Change: Choreographing Mixed Traffic Through Lateral Control and Hierarchical Reinforcement Learning
Dawei Wang, Weizi Li, Lei Zhu +1
The management of mixed traffic that consists of robot vehicles (RVs) and human-driven vehicles (HVs) at complex intersections presents a multifaceted challenge. Traditional signal…
LASIL: Learner-Aware Supervised Imitation Learning For Long-term Microscopic Traffic Simulation
Ke Guo, Zhenwei Miao, Wei Jing +4
Microscopic traffic simulation plays a crucial role in transportation engineering by providing insights into individual vehicle behavior and overall traffic flow. However, creating…
Traffic Reconstruction and Analysis of Natural Driving Behaviors at Unsignalized Intersections
Supriya Sarker, Bibek Poudel, Michael Villarreal +1
This paper explores the intricacies of traffic behavior at unsignalized intersections through the lens of a novel dataset, combining manual video data labeling and advanced traffic…
Large-scale Mixed Traffic Control Using Dynamic Vehicle Routing and Privacy-Preserving Crowdsourcing
Dawei Wang, Weizi Li, Jia Pan
Controlling and coordinating urban traffic flow through robot vehicles is emerging as a novel transportation paradigm for the future. While this approach garners growing attention…
EnduRL: Enhancing Safety, Stability, and Efficiency of Mixed Traffic Under Real-World Perturbations Via Reinforcement Learning
Bibek Poudel, Weizi Li, Kevin Heaslip
Human-driven vehicles (HVs) amplify naturally occurring perturbations in traffic, leading to congestion--a major contributor to increased fuel consumption, higher collision risks,…