Controllability analysis and optimal control of mixed traffic flow with human-driven and autonomous vehicles
Connected and automated vehicles (CAVs) have a great potential to improve traffic
efficiency in mixed traffic systems, which has been demonstrated by multiple numerical …
efficiency in mixed traffic systems, which has been demonstrated by multiple numerical …
Merging and diverging impact on mixed traffic of regular and autonomous vehicles
J Guo, S Cheng, Y Liu - IEEE Transactions on Intelligent …, 2020 - ieeexplore.ieee.org
In the context of Connected and Autonomous Vehicles (CAVs), this paper aims to examine
the impacts of CAVs on mixed regular-automated traffic flow with the increase of the market …
the impacts of CAVs on mixed regular-automated traffic flow with the increase of the market …
Leveraging multiagent learning for automated vehicles scheduling at nonsignalized intersections
Recent advancements of Vehicle-to-Everything (V2X) communication combined with
artificial intelligence (AI) technologies have shown enormous potentials for improving traffic …
artificial intelligence (AI) technologies have shown enormous potentials for improving traffic …
Cooperative traffic signal control using multi-step return and off-policy asynchronous advantage actor-critic graph algorithm
Intelligent traffic signal control helps to reduce traffic congestion and thus has been studied
for a few decades. Multi-intersection cooperative traffic signal control (CTSC), which is more …
for a few decades. Multi-intersection cooperative traffic signal control (CTSC), which is more …
Integrated longitudinal and lateral hierarchical control of cooperative merging of connected and automated vehicles at on-ramps
S Jing, F Hui, X Zhao, J Rios-Torres… - IEEE Transactions on …, 2022 - ieeexplore.ieee.org
Connected and automated vehicles (CAVs) can improve traffic safety and transportation
network efficiency while also reducing environmental impacts. However, congestion and …
network efficiency while also reducing environmental impacts. However, congestion and …
Cooperative autonomous traffic organization method for connected automated vehicles in multi-intersection road networks
Y Wang, P Cai, G Lu - Transportation research part C: emerging …, 2020 - Elsevier
Connected automated vehicles (CAVs) have been currently considered as promising
solutions for realization of envisioned autonomous traffic management systems in the future …
solutions for realization of envisioned autonomous traffic management systems in the future …
GAN and multi-agent DRL based decentralized traffic light signal control
Adaptive traffic light signal control (ATSC) is a promising paradigm for alleviating traffic
congestion in intelligent transportation systems. Most of the existing methods require heavy …
congestion in intelligent transportation systems. Most of the existing methods require heavy …
IHG-MA: Inductive heterogeneous graph multi-agent reinforcement learning for multi-intersection traffic signal control
Multi-agent deep reinforcement learning (MDRL) has been widely applied in multi-
intersection traffic signal control. The MDRL algorithms produce the decentralized …
intersection traffic signal control. The MDRL algorithms produce the decentralized …
Distributed agent-based deep reinforcement learning for large scale traffic signal control
Traffic signal control (TSC) is an established yet challenging engineering solution that
alleviates traffic congestion by coordinating vehicles' movements at road intersections …
alleviates traffic congestion by coordinating vehicles' movements at road intersections …
Multi-agent deep reinforcement learning for urban traffic light control in vehicular networks
As urban traffic condition is diverse and complicated, applying reinforcement learning to
reduce traffic congestion becomes one of the hot and promising topics. Especially, how to …
reduce traffic congestion becomes one of the hot and promising topics. Especially, how to …