[HTML][HTML] Traffic expertise meets residual RL: Knowledge-informed model-based residual reinforcement learning for CAV trajectory control
Abstract Model-based reinforcement learning (RL) is anticipated to exhibit higher sample
efficiency than model-free RL by utilizing a virtual environment model. However, obtaining …
efficiency than model-free RL by utilizing a virtual environment model. However, obtaining …
A survey on reinforcement learning-based control for signalized intersections with connected automated vehicles
Recent advancements in connected automated vehicles (CAVs) and reinforcement learning
(RL) hold significant promise for enhancing intelligent traffic control systems. This paper …
(RL) hold significant promise for enhancing intelligent traffic control systems. This paper …
Dynamic urban traffic rerouting with fog‐cloud reinforcement learning
Dynamic rerouting has been touted as a solution for urban traffic congestion. However, its
implementation is stymied by the complexity of urban traffic. To address this, recent studies …
implementation is stymied by the complexity of urban traffic. To address this, recent studies …
Kinematics-aware multigraph attention network with residual learning for heterogeneous trajectory prediction
Trajectory prediction for heterogeneous traffic agents plays a crucial role in ensuring the
safety and efficiency of automated driving in highly interactive traffic environments …
safety and efficiency of automated driving in highly interactive traffic environments …
Ego‐planning‐guided multi‐graph convolutional network for heterogeneous agent trajectory prediction
Accurate prediction of the future trajectories of traffic agents is a critical aspect of
autonomous vehicle navigation. However, most existing approaches focus on predicting …
autonomous vehicle navigation. However, most existing approaches focus on predicting …
[HTML][HTML] 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 …
Dual-objective reinforcement learning-based adaptive traffic signal control for decarbonization and efficiency optimization
To improve traffic efficiency, adaptive traffic signal control (ATSC) systems have been widely
developed. However, few studies have proactively optimized the air environmental issues in …
developed. However, few studies have proactively optimized the air environmental issues in …
Entire route eco-driving method for electric bus based on rule-based reinforcement learning
Electric bus (EB) has gradually become one of the main ways of transportation in cities due
to the low energy consumption and low pollutant emissions. As battery endurance is easily …
to the low energy consumption and low pollutant emissions. As battery endurance is easily …
Synergizing Autonomous and Traditional Vehicles: A Systematic Review of Advances and Challenges in Traffic Flow Management With Signalized Intersections
The advent of technology has led to substantial progress in the field of autonomous vehicles
(AVs), indicating that AVs will become a generic mode of transport in the near future …
(AVs), indicating that AVs will become a generic mode of transport in the near future …
Intersection eco-driving strategies under mixed traffic environment: An novel cooperation of traffic signal and vehicle trajectory planning
H Ding, Y Sun, L Wang, X Zheng, W Huang… - Physica A: Statistical …, 2024 - Elsevier
In a mixed traffic environment where connected and autonomous vehicles (CAVs) coexist
with human-driven vehicles (HVs), the utilization of CAV information is important for …
with human-driven vehicles (HVs), the utilization of CAV information is important for …