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) …
Digital twin empowered mobile edge computing for intelligent vehicular lane-changing
With automated driving forthcoming, lane-changing for Connected and Automated Vehicles
(CAVs) has received wide attention. The main challenge is that lane-changing requires not …
(CAVs) has received wide attention. The main challenge is that lane-changing requires not …
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 …
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 …
DRL-based low-latency content delivery for 6G massive vehicular IoT
Vehicle-to-everything communication is an indispensable component of 6G networks that
could help to facilitate future transportation systems. However, massive vehicles and …
could help to facilitate future transportation systems. However, massive vehicles and …
Connected automated vehicle cooperative control with a deep reinforcement learning approach in a mixed traffic environment
This paper proposes a cooperative strategy of connected and automated vehicles (CAVs)
longitudinal control for a mixed connected and automated traffic environment based on deep …
longitudinal control for a mixed connected and automated traffic environment based on deep …
Decision-making strategy on highway for autonomous vehicles using deep reinforcement learning
Autonomous driving is a promising technology to reduce traffic accidents and improve
driving efficiency. In this work, a deep reinforcement learning (DRL)-enabled decision …
driving efficiency. In this work, a deep reinforcement learning (DRL)-enabled decision …
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 …
Eco-vehicular edge networks for connected transportation: A distributed multi-agent reinforcement learning approach
This paper introduces an energy-efficient, software-defined vehicular edge network for the
growing intelligent connected transportation system. A joint user-centric virtual cell formation …
growing intelligent connected transportation system. A joint user-centric virtual cell formation …
CoTV: Cooperative control for traffic light signals and connected autonomous vehicles using deep reinforcement learning
The target of reducing travel time only is insufficient to support the development of future
smart transportation systems. To align with the United Nations Sustainable Development …
smart transportation systems. To align with the United Nations Sustainable Development …