Cooperation-aware lane change maneuver in dense traffic based on model predictive control with recurrent neural network
This paper presents a real-time lane change control framework of autonomous driving in
dense Traffic, which exploits cooperative behaviors of other drivers. This paper focuses on …
dense Traffic, which exploits cooperative behaviors of other drivers. This paper focuses on …
Leveraging multiple connected traffic light signals in an energy-efficient speed planner
Connecting automated vehicles to traffic lights can lead to significant energy savings by
enabling them to pass through intersections in an energy-efficient way without unnecessary …
enabling them to pass through intersections in an energy-efficient way without unnecessary …
Ecological velocity planning through signalized intersections: A deep reinforcement learning approach
The use of infrastructure-to-vehicle communication technologies can enable improved
energy efficient autonomous driving. Traditional ecological velocity planning methods have …
energy efficient autonomous driving. Traditional ecological velocity planning methods have …
An iterative and hierarchical approach to co-optimizing the velocity profile and power-split of plug-in hybrid electric vehicles
This paper investigates the additional fuel economy benefits with the direct fuel consumption
minimization by co-optimizing the vehicle-following and the hybrid powertrain subsystem in …
minimization by co-optimizing the vehicle-following and the hybrid powertrain subsystem in …
Multi-objective longitudinal decision-making for autonomous electric vehicle: a entropy-constrained reinforcement learning approach
X He, C Fei, Y Liu, K Yang, X Ji - 2020 IEEE 23rd International …, 2020 - ieeexplore.ieee.org
The challenging task of “autonomous electric vehicle” opens up a new frontier to improving
traffic, saving energy and reducing emission. However, many driving decision-making …
traffic, saving energy and reducing emission. However, many driving decision-making …