Comfortable and energy-efficient speed control of autonomous vehicles on rough pavements using deep reinforcement learning
Rough pavements cause ride discomfort and energy inefficiency for road vehicles. Existing
methods to address these problems are time-consuming and not adaptive to changing …
methods to address these problems are time-consuming and not adaptive to changing …
Safe, efficient, and comfortable autonomous driving based on cooperative vehicle infrastructure system
J Chen, C Zhao, S Jiang, X Zhang, Z Li… - International journal of …, 2023 - mdpi.com
Traffic crashes, heavy congestion, and discomfort often occur on rough pavements due to
human drivers' imperfect decision-making for vehicle control. Autonomous vehicles (AVs) …
human drivers' imperfect decision-making for vehicle control. Autonomous vehicles (AVs) …
A hierarchical framework for improving ride comfort of autonomous vehicles via deep reinforcement learning with external knowledge
Ride comfort plays an important role in determining the public acceptance of autonomous
vehicles (AVs). Many factors, such as road profile, driving speed, and suspension system …
vehicles (AVs). Many factors, such as road profile, driving speed, and suspension system …
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 …
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 …
[HTML][HTML] Impact of deep reinforcement learning on variable speed limit strategies in connected vehicles environments
Abstract The Variable Speed Limit (VSL) control is considered in the context of connected
vehicles acting as moving sensors, while their obedience to speed limit is enforced by a …
vehicles acting as moving sensors, while their obedience to speed limit is enforced by a …
Hybrid deep reinforcement learning based eco-driving for low-level connected and automated vehicles along signalized corridors
Q Guo, O Angah, Z Liu, XJ Ban - Transportation Research Part C …, 2021 - Elsevier
Eco-Driving has great potential in reducing the fuel consumption of road vehicles, especially
under the connected and automated vehicles (CAVs) environment. Traditional model-based …
under the connected and automated vehicles (CAVs) environment. Traditional model-based …
An integrated model for autonomous speed and lane change decision-making based on deep reinforcement learning
The implementation of autonomous driving is inseparable from developing intelligent driving
decision-making models, which are facing high scene complexity, poor decision-making …
decision-making models, which are facing high scene complexity, poor decision-making …
Pavement performance monitoring and anomaly recognition based on crowdsourcing spatiotemporal data
Pavement performance is a critical factor toward riding comfort experience and drastically
affect traffic management and the safety of road users. Since road quality declines over time …
affect traffic management and the safety of road users. Since road quality declines over time …
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 …
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