[HTML][HTML] Leveraging reinforcement learning for dynamic traffic control: A survey and challenges for field implementation
In recent years, the advancement of artificial intelligence techniques has led to significant
interest in reinforcement learning (RL) within the traffic and transportation community …
interest in reinforcement learning (RL) within the traffic and transportation community …
An overview on optimal flocking
LE Beaver, AA Malikopoulos - Annual Reviews in Control, 2021 - Elsevier
The decentralized aggregate motion of many individual robots is known as robotic flocking.
The study of robotic flocking has received considerable attention in the past twenty years. As …
The study of robotic flocking has received considerable attention in the past twenty years. As …
Deep reinforcement learning for intelligent transportation systems: A survey
Latest technological improvements increased the quality of transportation. New data-driven
approaches bring out a new research direction for all control-based systems, eg, in …
approaches bring out a new research direction for all control-based systems, eg, in …
A survey on autonomous vehicle control in the era of mixed-autonomy: From physics-based to AI-guided driving policy learning
This paper serves as an introduction and overview of the potentially useful models and
methodologies from artificial intelligence (AI) into the field of transportation engineering for …
methodologies from artificial intelligence (AI) into the field of transportation engineering for …
[HTML][HTML] Connected autonomous vehicles for improving mixed traffic efficiency in unsignalized intersections with deep reinforcement learning
Human driven vehicles (HDVs) with selfish objectives cause low traffic efficiency in an un-
signalized intersection. On the other hand, autonomous vehicles can overcome this …
signalized intersection. On the other hand, autonomous vehicles can overcome this …
Comparison of deep reinforcement learning and model predictive control for adaptive cruise control
This study compares Deep Reinforcement Learning (DRL) and Model Predictive Control
(MPC) for Adaptive Cruise Control (ACC) design in car-following scenarios. A first-order …
(MPC) for Adaptive Cruise Control (ACC) design in car-following scenarios. A first-order …
Flow: A modular learning framework for mixed autonomy traffic
The rapid development of autonomous vehicles (AVs) holds vast potential for transportation
systems through improved safety, efficiency, and access to mobility. However, the …
systems through improved safety, efficiency, and access to mobility. However, the …
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 …
Deploying traffic smoothing cruise controllers learned from trajectory data
Autonomous vehicle-based traffic smoothing con-trollers are often not transferred to real-
world use due to challenges in calibrating many-agent traffic simulators. We show a pipeline …
world use due to challenges in calibrating many-agent traffic simulators. We show a pipeline …
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 …