Car-following models for human-driven vehicles and autonomous vehicles: A systematic review
The focus of car-following models is to analyze the microscopic characteristics of traffic
flows, with particular attention given to the interaction between adjacent vehicles. This paper …
flows, with particular attention given to the interaction between adjacent vehicles. This paper …
[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 Recent Advancements in Autonomous Driving Using Deep Reinforcement Learning: Applications, Challenges, and Solutions
R Zhao, Y Li, Y Fan, F Gao… - IEEE Transactions on …, 2024 - ieeexplore.ieee.org
Autonomous driving (AD) endows vehicles with the capability to drive partly or entirely
without human intervention. AD agents generate driving policies based on online perception …
without human intervention. AD agents generate driving policies based on online perception …
Hybrid car following control for CAVs: Integrating linear feedback and deep reinforcement learning to stabilize mixed traffic
This paper introduces a novel hybrid car-following strategy for connected automated
vehicles (CAVs) to mitigate traffic oscillations while simultaneously improving CAV car …
vehicles (CAVs) to mitigate traffic oscillations while simultaneously improving CAV car …
Analyzing the impact of mixed vehicle platoon formations on vehicle energy and traffic efficiencies
Connected and automated vehicles (CAVs) offer promising prospects for the future of
transportation. However, the longstanding dominance of human-driven vehicles (HDVs) in …
transportation. However, the longstanding dominance of human-driven vehicles (HDVs) in …
Attentive hybrid reinforcement learning-based eco-driving strategy for connected vehicles with hybrid action spaces and surrounding vehicles attention
In environments characterized by complex multi-source traffic information, the interaction
between the ego vehicle and surrounding vehicles, along with behavioral interference …
between the ego vehicle and surrounding vehicles, along with behavioral interference …
Deep reinforcement learning-based eco-driving control for connected electric vehicles at signalized intersections considering traffic uncertainties
Eco-driving control poses great energy-saving potential at multiple signalized intersection
scenarios. However, traffic uncertainties can often lead to errors in ecological velocity …
scenarios. However, traffic uncertainties can often lead to errors in ecological velocity …
MARP: A Cooperative Multi-Agent DRL System for Connected Autonomous Vehicle Platooning
S Dai, S Li, H Tang, X Ning, F Fang, Y Fu… - IEEE Internet of …, 2024 - ieeexplore.ieee.org
In modern urban areas, inefficiency traffic management is one of the main causes of road
congestion, leading to reduced fuel efficiency and increased traffic safety hazards …
congestion, leading to reduced fuel efficiency and increased traffic safety hazards …
Graph-based interaction-aware multimodal 2D vehicle trajectory prediction using diffusion graph convolutional networks
Predicting vehicle trajectories is crucial to ensuring automated vehicle operation efficiency
and safety, particularly on congested multi-lane highways. In such dynamic environments, a …
and safety, particularly on congested multi-lane highways. In such dynamic environments, a …
[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 …