Multi-agent connected autonomous driving using deep reinforcement learning
P Palanisamy - 2020 International Joint Conference on Neural …, 2020 - ieeexplore.ieee.org
The capability to learn and adapt to changes in the driving environment is crucial for
developing autonomous driving systems that are scalable beyond geo-fenced operational …
developing autonomous driving systems that are scalable beyond geo-fenced operational …
A DRL-based multiagent cooperative control framework for CAV networks: A graphic convolution Q network
Connected Autonomous Vehicle (CAV) Network can be defined as a collection of CAVs
operating at different locations on a multilane corridor, which provides a platform to facilitate …
operating at different locations on a multilane corridor, which provides a platform to facilitate …
Reinforcement learning for autonomous driving with latent state inference and spatial-temporal relationships
Deep reinforcement learning (DRL) provides a promising way for learning navigation in
complex autonomous driving scenarios. However, identifying the subtle cues that can …
complex autonomous driving scenarios. However, identifying the subtle cues that can …
Model-free deep reinforcement learning for urban autonomous driving
Urban autonomous driving decision making is challenging due to complex road geometry
and multi-agent interactions. Current decision making methods are mostly manually …
and multi-agent interactions. Current decision making methods are mostly manually …
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 …
Autonomous highway driving using deep reinforcement learning
The operational space of an autonomous vehicle (AV) can be diverse and vary significantly.
Due to this, formulating a rule based decision maker for selecting driving maneuvers may …
Due to this, formulating a rule based decision maker for selecting driving maneuvers may …
Deep hierarchical reinforcement learning for autonomous driving with distinct behaviors
Deep reinforcement learning has achieved great progress recently in domains such as
learning to play Atari games from raw pixel input. The model-free characteristics of …
learning to play Atari games from raw pixel input. The model-free characteristics of …
Human-in-the-loop deep reinforcement learning with application to autonomous driving
Due to the limited smartness and abilities of machine intelligence, currently autonomous
vehicles are still unable to handle all kinds of situations and completely replace drivers …
vehicles are still unable to handle all kinds of situations and completely replace drivers …
Deep reinforcement learning for autonomous driving: A survey
With the development of deep representation learning, the domain of reinforcement learning
(RL) has become a powerful learning framework now capable of learning complex policies …
(RL) has become a powerful learning framework now capable of learning complex policies …
Driving in dense traffic with model-free reinforcement learning
Traditional planning and control methods could fail to find a feasible trajectory for an
autonomous vehicle to execute amongst dense traffic on roads. This is because the obstacle …
autonomous vehicle to execute amongst dense traffic on roads. This is because the obstacle …