Patrol: A velocity control framework for autonomous vehicle via spatial-temporal reinforcement learning
The largest portion of urban congestion is caused by'phantom'traffic jams, causing
significant delay travel time, fuel waste, and air pollution. It frequently occurs in high-density …
significant delay travel time, fuel waste, and air pollution. It frequently occurs in high-density …
Rise: A velocity control framework with minimal impacts based on reinforcement learning
Velocity control in autonomous driving is an emerging technology that has achieved rapid
progress over the last decade. However, existing velocity control models are developed in …
progress over the last decade. However, existing velocity control models are developed in …
Deep adaptive control: Deep reinforcement learning-based adaptive vehicle trajectory control algorithms for different risk levels
In this study, we explore the problem of adaptive vehicle trajectory control for different risk
levels. Firstly, we introduce a sliding window-based car-following scenario extraction …
levels. Firstly, we introduce a sliding window-based car-following scenario extraction …
EMVLight: A decentralized reinforcement learning framework for efficient passage of emergency vehicles
Emergency vehicles (EMVs) play a crucial role in responding to time-critical events such as
medical emergencies and fire outbreaks in an urban area. The less time EMVs spend …
medical emergencies and fire outbreaks in an urban area. The less time EMVs spend …
Traffic Smoothing Controllers for Autonomous Vehicles Using Deep Reinforcement Learning and Real-World Trajectory Data
Designing traffic-smoothing cruise controllers that can be deployed onto autonomous
vehicles is a key step towards improving traffic flow, reducing congestion, and enhancing …
vehicles is a key step towards improving traffic flow, reducing congestion, and enhancing …
Human as AI Mentor: Enhanced Human-in-the-loop Reinforcement Learning for Safe and Efficient Autonomous Driving
Despite significant progress in autonomous vehicles (AVs), the development of driving
policies that ensure both the safety of AVs and traffic flow efficiency has not yet been fully …
policies that ensure both the safety of AVs and traffic flow efficiency has not yet been fully …
Joint optimization of sensing, decision-making and motion-controlling for autonomous vehicles: A deep reinforcement learning approach
The three main modules of autonomous vehicles, ie, sensing, decision making, and motion
controlling, have been studied separately in most existing works on autonomous driving …
controlling, have been studied separately in most existing works on autonomous driving …
Safe, efficient, and comfortable velocity control based on reinforcement learning for autonomous driving
A model used for velocity control during car following is proposed based on reinforcement
learning (RL). To optimize driving performance, a reward function is developed by …
learning (RL). To optimize driving performance, a reward function is developed by …
DeepGAL: Intelligent Vehicle Control for Traffic Congestion Alleviation at Intersections
Intersections are prone to congestion in urban areas and making competent speed plans for
vehicles to efficiently utilize green time resources is significant for congestion alleviation and …
vehicles to efficiently utilize green time resources is significant for congestion alleviation and …
Cityflow: A multi-agent reinforcement learning environment for large scale city traffic scenario
Traffic signal control is an emerging application scenario for reinforcement learning. Besides
being as an important problem that affects people's daily life in commuting, traffic signal …
being as an important problem that affects people's daily life in commuting, traffic signal …