Safe, efficient, and comfortable velocity control based on reinforcement learning for autonomous driving
A model used for velocity control during car following is proposed based on reinforcement
learning (RL). To optimize driving performance, a reward function is developed by …
learning (RL). To optimize driving performance, a reward function is developed by …
Velocity control in car-following behavior with autonomous vehicles using reinforcement learning
Car-following behavior is a common driving behavior. It is necessary to consider the
following vehicle in the car-following model of autonomous vehicle (AV) under the …
following vehicle in the car-following model of autonomous vehicle (AV) under the …
Human-like autonomous car-following model with deep reinforcement learning
This study proposes a framework for human-like autonomous car-following planning based
on deep reinforcement learning (deep RL). Historical driving data are fed into a simulation …
on deep reinforcement learning (deep RL). Historical driving data are fed into a simulation …
Driver behavior modeling using game engine and real vehicle: A learning-based approach
As a good example of Advanced Driver-Assistance Systems (ADAS), Advisory Speed
Assistance (ASA) helps improve driving safety and possibly energy efficiency by showing …
Assistance (ASA) helps improve driving safety and possibly energy efficiency by showing …
Deep adaptive control: Deep reinforcement learning-based adaptive vehicle trajectory control algorithms for different risk levels
In this study, we explore the problem of adaptive vehicle trajectory control for different risk
levels. Firstly, we introduce a sliding window-based car-following scenario extraction …
levels. Firstly, we introduce a sliding window-based car-following scenario extraction …
Lane-change intention estimation for car-following control in autonomous driving
Car-following is the most general behavior in highway driving. It is crucial to recognize the
cut-in intention of vehicles from an adjacent lane for safe and cooperative driving. In this …
cut-in intention of vehicles from an adjacent lane for safe and cooperative driving. In this …
A novel car-following control model combining machine learning and kinematics models for automated vehicles
D Yang, L Zhu, Y Liu, D Wu… - IEEE Transactions on …, 2018 - ieeexplore.ieee.org
The machine learning-based car-following models are widely adopted to control the
longitudinal movements of automated vehicles, such as Google Car and Apple Car, by …
longitudinal movements of automated vehicles, such as Google Car and Apple Car, by …
Personalized car-following control based on a hybrid of reinforcement learning and supervised learning
D Song, B Zhu, J Zhao, J Han… - IEEE Transactions on …, 2023 - ieeexplore.ieee.org
With the development of intelligent vehicles, more research has focused on achieving
human-like driving. As an important component of intelligent vehicle control, car-following …
human-like driving. As an important component of intelligent vehicle control, car-following …
Automated vehicle's behavior decision making using deep reinforcement learning and high-fidelity simulation environment
Automated vehicles (AVs) are deemed to be the key element for the intelligent transportation
system in the future. Many studies have been made to improve AVs' ability of environment …
system in the future. Many studies have been made to improve AVs' ability of environment …
Personalized car following for autonomous driving with inverse reinforcement learning
Driving automation is gradually replacing human driving maneuvers in different applications
such as adaptive cruise control and lane keeping. However, contemporary driving …
such as adaptive cruise control and lane keeping. However, contemporary driving …
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