Deep reinforcement learning in transportation research: A review
Applying and adapting deep reinforcement learning (DRL) to tackle transportation problems
is an emerging interdisciplinary field. While rapidly growing, a comprehensive and synthetic …
is an emerging interdisciplinary field. While rapidly growing, a comprehensive and synthetic …
Deep reinforcement learning based energy management strategies for electrified vehicles: Recent advances and perspectives
H He, X Meng, Y Wang, A Khajepour, X An… - … and Sustainable Energy …, 2024 - Elsevier
Electrified vehicles provide an effective solution to address the unfavorable impacts of fossil
fuel use in the transportation sector. Energy management strategy (EMS) is the core …
fuel use in the transportation sector. Energy management strategy (EMS) is the core …
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 …
A survey of deep reinforcement learning algorithms for motion planning and control of autonomous vehicles
In this survey, we systematically summarize the current literature on studies that apply
reinforcement learning (RL) to the motion planning and control of autonomous vehicles …
reinforcement learning (RL) to the motion planning and control of autonomous vehicles …
Velocity control in car-following behavior with autonomous vehicles using reinforcement learning
Car-following behavior is a common driving behavior. It is necessary to consider the
following vehicle in the car-following model of autonomous vehicle (AV) under the …
following vehicle in the car-following model of autonomous vehicle (AV) under the …
Lateral control for autonomous wheeled vehicles: A technical review
Autonomous driving has the ability to reshape mobility and transportation by reducing road
accidents, traffic jams, and air pollution. This can yield energy efficiency, convenience, and …
accidents, traffic jams, and air pollution. This can yield energy efficiency, convenience, and …
Deep reinforcement learning aided platoon control relying on V2X information
The impact of Vehicle-to-Everything (V2X) communications on platoon control performance
is investigated. Platoon control is essentially a sequential stochastic decision problem …
is investigated. Platoon control is essentially a sequential stochastic decision problem …
Autonomous platoon control with integrated deep reinforcement learning and dynamic programming
Autonomous vehicles in a platoon determine the control inputs based on the system state
information collected and shared by the Internet of Things (IoT) devices. Deep reinforcement …
information collected and shared by the Internet of Things (IoT) devices. Deep reinforcement …
Collaborative optimization of energy management strategy and adaptive cruise control based on deep reinforcement learning
Hybrid electric vehicles (HEVs) have great prospects in reducing fossil fuel consumption,
and adaptive cruise control (ACC) technology provides safe and convenient travel for …
and adaptive cruise control (ACC) technology provides safe and convenient travel for …
Hybrid car-following strategy based on deep deterministic policy gradient and cooperative adaptive cruise control
R Yan, R Jiang, B Jia, J Huang… - IEEE Transactions on …, 2021 - ieeexplore.ieee.org
Deep deterministic policy gradient (DDPG)-based car-following strategy can break through
the constraints of the differential equation model due to the ability of exploration on complex …
the constraints of the differential equation model due to the ability of exploration on complex …