Demystifying deep reinforcement learning-based autonomous vehicle decision-making
With the advent of universal function approximators in the domain of reinforcement learning,
the number of practical applications leveraging deep reinforcement learning (DRL) has …
the number of practical applications leveraging deep reinforcement learning (DRL) has …
Automated driving maneuvers under interactive environment based on deep reinforcement learning
Safe and efficient autonomous driving maneuvers in an interactive and complex
environment can be considerably challenging due to the unpredictable actions of other …
environment can be considerably challenging due to the unpredictable actions of other …
Complex Network Cognition-based Federated Reinforcement Learning for End-to-end Urban Autonomous Driving
Y Cai, S Lu, H Wang, Y Lian, L Chen… - IEEE Transactions on …, 2023 - ieeexplore.ieee.org
Compared to the modularized rule-based framework, end-to-end deep reinforcement
learning (DRL) algorithms have demonstrated greater adaptability in autonomous driving …
learning (DRL) algorithms have demonstrated greater adaptability in autonomous driving …
Multi-agent reinforcement learning for ecological car-following control in mixed traffic
The push towards sustainable transportation emphasizes vehicular energy efficiency in
mixed traffic scenarios. A research hotspot is the cooperative control of connected and …
mixed traffic scenarios. A research hotspot is the cooperative control of connected and …
Safe and robust multi-agent reinforcement learning for connected autonomous vehicles under state perturbations
Sensing and communication technologies have enhanced learning-based decision making
methodologies for multi-agent systems such as connected autonomous vehicles (CAV) …
methodologies for multi-agent systems such as connected autonomous vehicles (CAV) …
A multi-agent deep reinforcement learning coordination framework for connected and automated vehicles at merging roadways
The steady increase in the number of vehicles operating on the highways continues to
exacerbate congestion, accidents, energy consumption, and greenhouse gas emissions …
exacerbate congestion, accidents, energy consumption, and greenhouse gas emissions …
Confrontation and Obstacle-Avoidance of Unmanned Vehicles Based on Progressive Reinforcement Learning
C Ma, J Liu, S He, W Hong, J Shi - IEEE Access, 2023 - ieeexplore.ieee.org
The core technique of unmanned vehicle systems is the autonomous maneuvering decision,
which not only determines the applications of unmanned vehicles but also is the critical …
which not only determines the applications of unmanned vehicles but also is the critical …
A Two-Stage Based Social Preference Recognition in Multi-Agent Autonomous Driving System
Multi-Agent Reinforcement Learning (MARL) has become a promising solution for
constructing a multi-agent autonomous driving system (MADS) in complex and dense …
constructing a multi-agent autonomous driving system (MADS) in complex and dense …
[PDF][PDF] Centralized conflict-free cooperation for connected and automated vehicles at intersections by proximal policy optimization
Connected vehicles will change the modes of future transportation management and
organization, especially at intersections. There are mainly two categories coordination …
organization, especially at intersections. There are mainly two categories coordination …
Modeling Interaction-Aware Driving Behavior using Graph-Based Representations and Multi-Agent Reinforcement Learning
F Konstantinidis, M Sackmann… - 2023 IEEE 26th …, 2023 - ieeexplore.ieee.org
Modeling the driving behavior of traffic partici-pants in highly interactive traffic situations,
such as roundabouts, poses a significant challenge due to the complex interactions and the …
such as roundabouts, poses a significant challenge due to the complex interactions and the …