Toward learning human-like, safe and comfortable car-following policies with a novel deep reinforcement learning approach
In this paper, we present an advanced adaptive cruise control (ACC) concept powered by
Deep Reinforcement Learning (DRL) that generates safe, human-like, and comfortable car …
Deep Reinforcement Learning (DRL) that generates safe, human-like, and comfortable car …
Modified DDPG car-following model with a real-world human driving experience with CARLA simulator
In the autonomous driving field, fusion of human knowledge into Deep Reinforcement
Learning (DRL) is often based on the human demonstration recorded in a simulated …
Learning (DRL) is often based on the human demonstration recorded in a simulated …
[HTML][HTML] A decision-making strategy for car following based on naturalist driving data via deep reinforcement learning
W Li, Y Zhang, X Shi, F Qiu - Sensors, 2022 - mdpi.com
To improve the satisfaction and acceptance of automatic driving, we propose a deep
reinforcement learning (DRL)-based autonomous car-following (CF) decision-making …
reinforcement learning (DRL)-based autonomous car-following (CF) decision-making …
Improved deep reinforcement learning for car-following decision-making
Accuracy improvement of Car-following (CF) model has attracted much attention in recent
years. Although a few studies incorporate deep reinforcement learning (DRL) to describe CF …
years. Although a few studies incorporate deep reinforcement learning (DRL) to describe CF …
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 …
[HTML][HTML] Towards robust car-following based on deep reinforcement learning
One of the biggest challenges in the development of learning-driven automated driving
technologies remains the handling of uncommon, rare events that may have not been …
technologies remains the handling of uncommon, rare events that may have not been …
Ensemblefollower: A hybrid car-following framework based on reinforcement learning and hierarchical planning
Car-following models have made significant contributions to our understanding of
longitudinal driving behavior. However, they often exhibit limited accuracy and flexibility, as …
longitudinal driving behavior. However, they often exhibit limited accuracy and flexibility, as …
Adaptive cruise control based on safe deep reinforcement learning
R Zhao, K Wang, W Che, Y Li, Y Fan, F Gao - Sensors, 2024 - mdpi.com
Adaptive cruise control (ACC) enables efficient, safe, and intelligent vehicle control by
autonomously adjusting speed and ensuring a safe following distance from the vehicle in …
autonomously adjusting speed and ensuring a safe following distance from the vehicle in …
An Efficient Deep Reinforcement Learning-based Car-following Method via Rule-constrained Data Augmentation
X Wang, B Dai, Q Miao, Y Nie… - IEEE Transactions on …, 2024 - ieeexplore.ieee.org
This paper introduces a data augmentation method based on control theory and rule
constraints, aiming to address the insufficient coverage issue in existing vehicle trajectory …
constraints, aiming to address the insufficient coverage issue in existing vehicle trajectory …
Editfollower: Tunable car following models for customizable adaptive cruise control systems
In the realm of driving technologies, fully autonomous vehicles have not been widely
adopted yet, making advanced driver assistance systems (ADAS) crucial for enhancing …
adopted yet, making advanced driver assistance systems (ADAS) crucial for enhancing …