[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 …
Proximal policy optimization through a deep reinforcement learning framework for multiple autonomous vehicles at a non-signalized intersection
D Quang Tran, SH Bae - Applied Sciences, 2020 - mdpi.com
Advanced deep reinforcement learning shows promise as an approach to addressing
continuous control tasks, especially in mixed-autonomy traffic. In this study, we present a …
continuous control tasks, especially in mixed-autonomy traffic. In this study, we present a …
Harmonious lane changing via deep reinforcement learning
In this paper, we study how to learn a harmonious deep reinforcement learning (DRL) based
lane-changing strategy for autonomous vehicles without Vehicle-to-Everything (V2X) …
lane-changing strategy for autonomous vehicles without Vehicle-to-Everything (V2X) …
Connected automated vehicle cooperative control with a deep reinforcement learning approach in a mixed traffic environment
This paper proposes a cooperative strategy of connected and automated vehicles (CAVs)
longitudinal control for a mixed connected and automated traffic environment based on deep …
longitudinal control for a mixed connected and automated traffic environment based on deep …
[HTML][HTML] COOR-PLT: A hierarchical control model for coordinating adaptive platoons of connected and autonomous vehicles at signal-free intersections based on deep …
Platooning and coordination are two implementation strategies that are frequently proposed
for traffic control of connected and autonomous vehicles (CAVs) at signal-free intersections …
for traffic control of connected and autonomous vehicles (CAVs) at signal-free intersections …
Day-to-day traffic control for networks mixed with regular human-piloted and connected autonomous vehicles
The emerging connected autonomous vehicle (CAV) technology offers a significant
opportunity to address the congestion problem caused by the selfish routing behavior of …
opportunity to address the congestion problem caused by the selfish routing behavior of …
Hybrid reinforcement learning-based eco-driving strategy for connected and automated vehicles at signalized intersections
Taking advantage of both vehicle-to-everything (V2X) communication and automated driving
technology, connected and automated vehicles are quickly becoming one of the …
technology, connected and automated vehicles are quickly becoming one of the …
Multi-agent deep reinforcement learning to manage connected autonomous vehicles at tomorrow's intersections
GP Antonio, C Maria-Dolores - IEEE Transactions on Vehicular …, 2022 - ieeexplore.ieee.org
In recent years, the growing development of Connected Autonomous Vehicles (CAV),
Intelligent Transport Systems (ITS), and 5G communication networks have led to the advent …
Intelligent Transport Systems (ITS), and 5G communication networks have led to the advent …
Traffic flow management of autonomous vehicles using deep reinforcement learning and smart rerouting
Autonomous Vehicles (AVs) promise to disrupt the traditional systems of transportation. An
autonomous driving environment requires an uninterrupted, continuous stream of data and …
autonomous driving environment requires an uninterrupted, continuous stream of data and …
Hybrid deep reinforcement learning based eco-driving for low-level connected and automated vehicles along signalized corridors
Q Guo, O Angah, Z Liu, XJ Ban - Transportation Research Part C …, 2021 - Elsevier
Eco-Driving has great potential in reducing the fuel consumption of road vehicles, especially
under the connected and automated vehicles (CAVs) environment. Traditional model-based …
under the connected and automated vehicles (CAVs) environment. Traditional model-based …