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How generative adversarial networks promote the development of intelligent transportation systems: A survey
In current years, the improvement of deep learning has brought about tremendous changes:
As a type of unsupervised deep learning algorithm, generative adversarial networks (GANs) …
As a type of unsupervised deep learning algorithm, generative adversarial networks (GANs) …
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 …
Cooperative incident management in mixed traffic of CAVs and human-driven vehicles
Traffic incident management in metropolitan areas is crucial for the recovery of road systems
from accidents as well as the mobility and safety of the community. With the continuous …
from accidents as well as the mobility and safety of the community. With the continuous …
Formation control of multi-agent systems with actuator saturation via neural-based sliding mode estimators
In this paper, the formation control problem for second-order multi-agent systems with model
uncertainties and actuator saturation is investigated. An estimator-based robust formation …
uncertainties and actuator saturation is investigated. An estimator-based robust formation …
[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 …
Delay-throughput tradeoffs for signalized networks with finite queue capacity
Network-level adaptive signal control is an effective way to reduce delay and increase
network throughput. However, in the face of asymmetric exogenous demand, the increase of …
network throughput. However, in the face of asymmetric exogenous demand, the increase of …
[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 …
[HTML][HTML] Energy efficiency of connected autonomous vehicles: A review
Connected autonomous vehicles (CAVs) have emerged as a promising solution for
enhancing transportation efficiency. However, the increased adoption of CAVs is expected …
enhancing transportation efficiency. However, the increased adoption of CAVs is expected …
[HTML][HTML] Policy challenges for coordinated delivery of trucks and drones
The application of drone technology promises to revolutionize the transportation industry.
Particularly, the combination of drones with ground vehicles has tremendous advantages for …
Particularly, the combination of drones with ground vehicles has tremendous advantages for …
[HTML][HTML] Deep knowledge distillation: A self-mutual learning framework for traffic prediction
Traffic flow prediction in spatio-temporal networks is a crucial aspect of Intelligent
Transportation Systems (ITS). Existing traffic flow forecasting methods, particularly those …
Transportation Systems (ITS). Existing traffic flow forecasting methods, particularly those …