A new reinforcement learning-based variable speed limit control approach to improve traffic efficiency against freeway jam waves
Conventional reinforcement learning (RL) models of variable speed limit (VSL) control
systems (and traffic control systems in general) cannot be trained in real traffic process …
systems (and traffic control systems in general) cannot be trained in real traffic process …
Graph reinforcement learning application to co-operative decision-making in mixed autonomy traffic: Framework, survey, and challenges
Proper functioning of connected and automated vehicles (CAVs) is crucial for the safety and
efficiency of future intelligent transport systems. Meanwhile, transitioning to fully autonomous …
efficiency of future intelligent transport systems. Meanwhile, transitioning to fully autonomous …
[PDF][PDF] Multi-leader Adaptive Cruise Control Systems considering Sensor Measurement Uncertainties based on Deep Reinforcement Learning
YC Ni - 2022 - victorknoop.eu
You are reading my master thesis, which concludes my two-year study for the MSc degree at
TU Delft. Traffic flow, autonomous driving, and artificial intelligence are three fascinating …
TU Delft. Traffic flow, autonomous driving, and artificial intelligence are three fascinating …
[图书][B] Bias Estimation of Spatiotemporal Traffic Sensor Data with Physics-informed Deep Learning Techniques
H Yang - 2022 - search.proquest.com
Efficient operations of intelligent transportation systems rely on high-quality traffic data.
Infrastructure-based traffic sensors, though providing major data sources for ITS, are subject …
Infrastructure-based traffic sensors, though providing major data sources for ITS, are subject …
[引用][C] Intelligent Ramp Metering Control using Federated Reinforcement Learning
AH Maliakal - 2022