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 …
Multi-Agent Reinforcement Learning for Connected and Automated Vehicles Control: Recent Advancements and Future Prospects
Connected and automated vehicles (CAVs) have emerged as a potential solution to the
future challenges of developing safe, efficient, and eco-friendly transportation systems …
future challenges of developing safe, efficient, and eco-friendly transportation systems …
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 …
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 …
Unleashing the two-dimensional benefits of connected and automated vehicles via dedicated intersections in mixed traffic
The management of mixed traffic systems is critical to realize the benefits of connected and
automated vehicles (CAVs). Generally, the benefits of CAVs can be categorized into the one …
automated vehicles (CAVs). Generally, the benefits of CAVs can be categorized into the one …
Multiagent deep reinforcement learning for automated truck platooning control
Human-leading automated truck platooning has been an effective technique to improve
traffic capacity and fuel economy and eliminate uncertainties of the traffic environment …
traffic capacity and fuel economy and eliminate uncertainties of the traffic environment …
Enhancing System-Level Safety in Mixed-Autonomy Platoon via Safe Reinforcement Learning
Connected and automated vehicles (CAVs) have recently gained prominence in traffic
research due to advances in communication technology and autonomous driving. Various …
research due to advances in communication technology and autonomous driving. Various …
Enhancing Car-Following Performance in Traffic Oscillations Using Expert Demonstration Reinforcement Learning
Deep reinforcement learning (DRL) algorithms often face challenges in achieving stability
and efficiency due to significant policy gradient variance and inaccurate reward function …
and efficiency due to significant policy gradient variance and inaccurate reward function …
Multi-agent reinforcement learning for ecological car-following control in mixed traffic
The push towards sustainable transportation emphasizes vehicular energy efficiency in
mixed traffic scenarios. A research hotspot is the cooperative control of connected and …
mixed traffic scenarios. A research hotspot is the cooperative control of connected and …