Distributed multiagent coordinated learning for autonomous driving in highways based on dynamic coordination graphs
C Yu, X Wang, X Xu, M Zhang, H Ge… - Ieee transactions on …, 2019 - ieeexplore.ieee.org
Autonomous driving is one of the most important AI applications and has attracted extensive
interest in recent years. A large number of studies have successfully applied reinforcement …
interest in recent years. A large number of studies have successfully applied reinforcement …
A selective federated reinforcement learning strategy for autonomous driving
Currently, the complex traffic environment challenges the fast and accurate response of a
connected autonomous vehicle (CAV). More importantly, it is difficult for different CAVs to …
connected autonomous vehicle (CAV). More importantly, it is difficult for different CAVs to …
Graph neural network and reinforcement learning for multi‐agent cooperative control of connected autonomous vehicles
A connected autonomous vehicle (CAV) network can be defined as a set of connected
vehicles including CAVs that operate on a specific spatial scope that may be a road network …
vehicles including CAVs that operate on a specific spatial scope that may be a road network …
Federated transfer reinforcement learning for autonomous driving
Reinforcement learning (RL) is widely used in autonomous driving tasks and training RL
models typically involves in a multi-step process: pre-training RL models on simulators …
models typically involves in a multi-step process: pre-training RL models on simulators …
Multi-agent reinforcement learning for cooperative lane changing of connected and autonomous vehicles in mixed traffic
Autonomous driving has attracted significant research interests in the past two decades as it
offers many potential benefits, including releasing drivers from exhausting driving and …
offers many potential benefits, including releasing drivers from exhausting driving and …
Robustness and adaptability of reinforcement learning-based cooperative autonomous driving in mixed-autonomy traffic
Building autonomous vehicles (AVs) is a complex problem, but enabling them to operate in
the real world where they will be surrounded by human-driven vehicles (HVs) is extremely …
the real world where they will be surrounded by human-driven vehicles (HVs) is extremely …
RACE: Reinforced cooperative autonomous vehicle collision avoidance
With the rapid development of autonomous driving, collision avoidance has attracted
attention from both academia and industry. Many collision avoidance strategies have …
attention from both academia and industry. Many collision avoidance strategies have …
Towards socially responsive autonomous vehicles: A reinforcement learning framework with driving priors and coordination awareness
The advent of autonomous vehicles (AVs) alongside human-driven vehicles (HVs) has
ushered in an era of mixed traffic flow, presenting a significant challenge: the intricate …
ushered in an era of mixed traffic flow, presenting a significant challenge: the intricate …
Hierarchical reinforcement learning for dynamic autonomous vehicle navigation at intelligent intersections
Recent years have witnessed the rapid development of the Cooperative Vehicle
Infrastructure System (CVIS), where road infrastructures such as traffic lights (TL) and …
Infrastructure System (CVIS), where road infrastructures such as traffic lights (TL) and …
Communication-efficient decentralized multi-agent reinforcement learning for cooperative adaptive cruise control
Connected and autonomous vehicles (CAVs) promise next-gen transportation systems with
enhanced safety, energy efficiency, and sustainability. One typical control strategy for CAVs …
enhanced safety, energy efficiency, and sustainability. One typical control strategy for CAVs …