Simulation to scaled city: zero-shot policy transfer for traffic control via autonomous vehicles
Using deep reinforcement learning, we successfully train a set of two autonomous vehicles
to lead a fleet of vehicles onto a round-about and then transfer this policy from simulation to …
to lead a fleet of vehicles onto a round-about and then transfer this policy from simulation to …
Hierarchically and cooperatively learning traffic signal control
Deep reinforcement learning (RL) has been applied to traffic signal control recently and
demonstrated superior performance to conventional control methods. However, there are …
demonstrated superior performance to conventional control methods. However, there are …
[PDF][PDF] Scalable multiagent driving policies for reducing traffic congestion
Traffic congestion is a major challenge in modern urban settings. The industry-wide
development of autonomous and automated vehicles (AVs) motivates the question of how …
development of autonomous and automated vehicles (AVs) motivates the question of how …
Hybrid traffic control and coordination from pixels
Traffic congestion is a persistent problem in our society. Existing methods for traffic control
have proven futile in alleviating current congestion levels leading researchers to explore …
have proven futile in alleviating current congestion levels leading researchers to explore …
Learning phase competition for traffic signal control
Increasingly available city data and advanced learning techniques have empowered people
to improve the efficiency of our city functions. Among them, improving urban transportation …
to improve the efficiency of our city functions. Among them, improving urban transportation …
FECO: an efficient deep reinforcement learning-based fuel-economic traffic signal control scheme
A Boukerche, D Zhong, P Sun - IEEE Transactions on …, 2021 - ieeexplore.ieee.org
Vehicle fuel efficiency (VFE) has a pivotal role in solving energy shortage issue due to the
increasing global demand for energy. The high frequency of go-stop movements and long …
increasing global demand for energy. The high frequency of go-stop movements and long …
A multiagent framework for learning dynamic traffic management strategies
There is strong commercial interest in the use of large scale automated transport robots in
industrial settings (eg warehouse robots) and we are beginning to see new applications …
industrial settings (eg warehouse robots) and we are beginning to see new applications …
TD3LVSL: A lane-level variable speed limit approach based on twin delayed deep deterministic policy gradient in a connected automated vehicle environment
Variable speed limit (VSL) control plays a vital role in the emerging connected automated
vehicle highway (CAVH) system, which can alleviate recurrent traffic congestion caused by …
vehicle highway (CAVH) system, which can alleviate recurrent traffic congestion caused by …
Image-based traffic signal control via world models
Traffic signal control is shifting from passive control to proactive control, which enables the
controller to direct current traffic flow to reach its expected destinations. To this end, an …
controller to direct current traffic flow to reach its expected destinations. To this end, an …
Large-scale traffic control using autonomous vehicles and decentralized deep reinforcement learning
In this work, we introduce a scalable, decentralized deep reinforcement learning (RL)
scheme for optimizing vehicle traffic consisting of both autonomous and human-driven …
scheme for optimizing vehicle traffic consisting of both autonomous and human-driven …