6 citations · 9 across the 10 of their papers we have counts for
4 papers · 1 filter
Multi-hop Upstream Anticipatory Traffic Signal Control with Deep Reinforcement Learning
Xiaocan Li, Xiaoyu Wang, Ilia Smirnov +2
Coordination in traffic signal control is crucial for managing congestion in urban networks. Existing pressure-based control methods focus only on immediate upstream links, leading…
Mitigating Partial Observability in Adaptive Traffic Signal Control with Transformers
Xiaoyu Wang, Ayal Taitler, Scott Sanner +1
Efficient traffic signal control is essential for managing urban transportation, minimizing congestion, and improving safety and sustainability. Reinforcement Learning (RL) has eme…
Generalized Multi-hop Traffic Pressure for Heterogeneous Traffic Perimeter Control
Xiaocan Li, Xiaoyu Wang, Ilia Smirnov +2
Perimeter control (PC) prevents loss of traffic network capacity due to congestion in urban areas. Homogeneous PC allows all access points to a protected region to have identical p…
SECRM-2D: RL-Based Efficient and Comfortable Route-Following Autonomous Driving with Analytic Safety Guarantees
Tianyu Shi, Ilia Smirnov, Omar ElSamadisy +1
Over the last decade, there has been increasing interest in autonomous driving systems. Reinforcement Learning (RL) shows great promise for training autonomous driving controllers,…