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Multi-agent reinforcement learning for connected and automated vehicles control: Recent advancements and future prospects
Connected and automated vehicles (CAVs) are considered a potential solution for future
transportation challenges, aiming to develop systems that are efficient, safe, and …
transportation challenges, aiming to develop systems that are efficient, safe, and …
[HTML][HTML] Graph reinforcement learning-based decision-making technology for connected and autonomous vehicles: Framework, review, and future trends
The proper functioning of connected and autonomous vehicles (CAVs) is crucial for the
safety and efficiency of future intelligent transport systems. Meanwhile, transitioning to fully …
safety and efficiency of future intelligent transport systems. Meanwhile, transitioning to fully …
Large-scale mixed traffic control using dynamic vehicle routing and privacy-preserving crowdsourcing
Controlling and coordinating urban traffic flow through robot vehicles (RVs) is emerging as a
novel transportation paradigm for the future. While this approach garners growing attention …
novel transportation paradigm for the future. While this approach garners growing attention …
Unified automatic control of vehicular systems with reinforcement learning
Emerging vehicular systems with increasing proportions of automated components present
opportunities for optimal control to mitigate congestion and increase efficiency. There has …
opportunities for optimal control to mitigate congestion and increase efficiency. There has …
Hierarchical reinforcement learning for dynamic autonomous vehicle navigation at intelligent intersections
Recent years have witnessed the rapid development of the Cooperative Vehicle
Infrastructure System (CVIS), where road infrastructures such as traffic lights (TL) and …
Infrastructure System (CVIS), where road infrastructures such as traffic lights (TL) and …
Can chatgpt enable its? the case of mixed traffic control via reinforcement learning
The surge in Reinforcement Learning (RL) applications in Intelligent Transportation Systems
(ITS) has contributed to its growth as well as highlighted key challenges. However, defining …
(ITS) has contributed to its growth as well as highlighted key challenges. However, defining …
Learning eco-driving strategies at signalized intersections
Signalized intersections in arterial roads result in persistent vehicle idling and excess
accelerations, contributing to fuel consumption and CO 2 emissions. There has thus been a …
accelerations, contributing to fuel consumption and CO 2 emissions. There has thus been a …
Learning to control and coordinate mixed traffic through robot vehicles at complex and unsignalized intersections
Intersections are essential road infrastructures for traffic in modern metropolises. However,
they can also be the bottleneck of traffic flows as a result of traffic incidents or the absence of …
they can also be the bottleneck of traffic flows as a result of traffic incidents or the absence of …
Stochastic time-optimal trajectory planning for connected and automated vehicles in mixed-traffic merging scenarios
Addressing safe and efficient interaction between connected and autonomous vehicles
(CAVs) and human-driven vehicles (HDVs) in a mixed-traffic environment has attracted …
(CAVs) and human-driven vehicles (HDVs) in a mixed-traffic environment has attracted …
[HTML][HTML] Continuum modeling of freeway traffic flows: State-of-the-art, challenges and future directions in the era of connected and automated vehicles
Connected and automated vehicles (CAVs) are expected to reshape traffic flow dynamics
and present new challenges and opportunities for traffic flow modeling. While numerous …
and present new challenges and opportunities for traffic flow modeling. While numerous …