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 …
An introduction to multi-agent reinforcement learning and review of its application to autonomous mobility
Many scenarios in mobility and traffic involve multiple different agents that need to cooperate
to find a joint solution. Recent advances in behavioral planning use Reinforcement Learning …
to find a joint solution. Recent advances in behavioral planning use Reinforcement Learning …
Robust Driving Policy Learning with Guided Meta Reinforcement Learning
Although deep reinforcement learning (DRL) has shown promising results for autonomous
navigation in interactive traffic scenarios, existing work typically adopts a fixed behavior …
navigation in interactive traffic scenarios, existing work typically adopts a fixed behavior …
Multi-agent driving behavior prediction across different scenarios with self-supervised domain knowledge
How to make precise multi-agent trajectory prediction is a crucial problem in the context of
autonomous driving. It is significant to have the ability to predict surrounding road …
autonomous driving. It is significant to have the ability to predict surrounding road …
Safelight: A reinforcement learning method toward collision-free traffic signal control
Traffic signal control is safety-critical for our daily life. Roughly one-quarter of road accidents
in the US happen at intersections due to problematic signal timing, urging the development …
in the US happen at intersections due to problematic signal timing, urging the development …
Mix Q-learning for Lane Changing: A Collaborative Decision-Making Method in Multi-Agent Deep Reinforcement Learning
X Bi, M He, Y Sun - arXiv preprint arXiv:2406.09755, 2024 - arxiv.org
Lane-changing decisions, which are crucial for autonomous vehicle path planning, face
practical challenges due to rule-based constraints and limited data. Deep reinforcement …
practical challenges due to rule-based constraints and limited data. Deep reinforcement …
Mixlight: Mixed-agent cooperative reinforcement learning for traffic light control
Optimizing traffic light configuration is viewed as a method to increase the traffic throughput
in urban cities. Recent studies have employed reinforcement learning to optimize the traffic …
in urban cities. Recent studies have employed reinforcement learning to optimize the traffic …
Multi-agent reinforcement learning based on two-step neighborhood experience for traffic light control
YC Luo, CW Tsai - Proceedings of the 2021 ACM International …, 2021 - dl.acm.org
Several recent studies pointed out that an effective traffic signal/light control strategy will be
able to mitigate the traffic congestion problem, and therefore variants of solutions have been …
able to mitigate the traffic congestion problem, and therefore variants of solutions have been …
A multi-agent reinforcement learning approach for safe and efficient behavior planning of connected autonomous vehicles
The recent advancements in wireless technology enable connected autonomous vehicles
(CAVs) to gather information about their environment by vehicle-to-vehicle (V2V) …
(CAVs) to gather information about their environment by vehicle-to-vehicle (V2V) …
Multi-level objective control of AVs at a saturated signalized intersection with multi-agent deep reinforcement learning approach
W Lin, X Hu, J Wang - Journal of Intelligent and Connected …, 2023 - ieeexplore.ieee.org
Reinforcement learning (RL) can free automated vehicles (AVs) from the car-following
constraints and provide more possible explorations for mixed behavior. This study uses …
constraints and provide more possible explorations for mixed behavior. This study uses …