Physics-informed deep reinforcement learning-based integrated two-dimensional car-following control strategy for connected automated vehicles
Connected automated vehicles (CAVs) are broadly recognized as next-generation
transformative transportation technologies having great potential to improve traffic safety …
transformative transportation technologies having great potential to improve traffic safety …
Spatio-weighted information fusion and DRL-based control for connected autonomous vehicles
While on-board sensing equipment of CAVs can reasonably characterize the surrounding
traffic environment, their performance is limited by the range of the sensors. By integrating …
traffic environment, their performance is limited by the range of the sensors. By integrating …
Cooperative control of a platoon of connected autonomous vehicles and unconnected human‐driven vehicles
By using kinematic state information obtained through vehicle‐to‐vehicle communications,
connected autonomous vehicles (CAVs) can drive cooperatively to alleviate shockwave …
connected autonomous vehicles (CAVs) can drive cooperatively to alleviate shockwave …
Autonomous platoon control with integrated deep reinforcement learning and dynamic programming
Autonomous vehicles in a platoon determine the control inputs based on the system state
information collected and shared by the Internet of Things (IoT) devices. Deep reinforcement …
information collected and shared by the Internet of Things (IoT) devices. Deep reinforcement …
Ubiquitous control over heterogeneous vehicles: A digital twin empowered edge AI approach
The forthcoming of automated driving has led to vehicular heterogeneity, where vehicles
with different automation levels, including connected and automated vehicles (CAVs) …
with different automation levels, including connected and automated vehicles (CAVs) …
Reinforcement learning-based high-speed path following control for autonomous vehicles
Autonomous vehicles have received considerable attention, yet high-speed path following
control remains a critical and challenging issue. At high speeds, achieving perfect control …
control remains a critical and challenging issue. At high speeds, achieving perfect control …
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 …
Longitudinal control of connected and automated vehicles among signalized intersections in mixed traffic flow with deep reinforcement learning approach
Trajectory optimization for connected automated vehicles (CAVs) is an effective method to
improve the overall performance of urban traffic. At the same time, the emergence of deep …
improve the overall performance of urban traffic. At the same time, the emergence of deep …
Design of intelligent connected cruise control with vehicle-to-vehicle communication delays
Connected cruise control (CCC) refers to a type of advanced driver assistance system
combined with wireless vehicle-to-vehicle (V2V) communication technology to improve …
combined with wireless vehicle-to-vehicle (V2V) communication technology to improve …
A deep reinforcement learning based distributed control strategy for connected automated vehicles in mixed traffic platoon
This paper proposes an innovative distributed longitudinal control strategy for connected
automated vehicles (CAVs) in the mixed traffic environment of CAV and human-driven …
automated vehicles (CAVs) in the mixed traffic environment of CAV and human-driven …