Fear-neuro-inspired reinforcement learning for safe autonomous driving
Ensuring safety and achieving human-level driving performance remain challenges for
autonomous vehicles, especially in safety-critical situations. As a key component of artificial …
autonomous vehicles, especially in safety-critical situations. As a key component of artificial …
Last-iterate convergent policy gradient primal-dual methods for constrained mdps
We study the problem of computing an optimal policy of an infinite-horizon discounted
constrained Markov decision process (constrained MDP). Despite the popularity of …
constrained Markov decision process (constrained MDP). Despite the popularity of …
Recent advances in reinforcement learning-based autonomous driving behavior planning: A survey
Autonomous driving (AD) holds the potential to revolutionize transportation efficiency, but its
success hinges on robust behavior planning (BP) mechanisms. Reinforcement learning (RL) …
success hinges on robust behavior planning (BP) mechanisms. Reinforcement learning (RL) …
Deep Reinforcement Learning in Autonomous Car Path Planning and Control: A Survey
Y Chen, C Ji, Y Cai, T Yan, B Su - arXiv preprint arXiv:2404.00340, 2024 - arxiv.org
Combining data-driven applications with control systems plays a key role in recent
Autonomous Car research. This thesis offers a structured review of the latest literature on …
Autonomous Car research. This thesis offers a structured review of the latest literature on …
A novel deep ensemble reinforcement learning based control method for strip flatness in cold rolling steel industry
W Peng, J Lei, C Ding, C Yue, G Ma, J Sun… - … Applications of Artificial …, 2024 - Elsevier
The flatness control system of cold-rolled strip is characterized by nonlinearity, strong
coupling, and multivariable features. Theoretical models of flatness derived from physical …
coupling, and multivariable features. Theoretical models of flatness derived from physical …
A methodology of cooperative driving based on microscopic traffic prediction
We present a methodology of cooperative driving in vehicular traffic, in which for short-time
traffic prediction rather than one of the statistical approaches of artificial intelligence (AI), we …
traffic prediction rather than one of the statistical approaches of artificial intelligence (AI), we …
A Risk-Based Decision-Making Process for Autonomous Trains Using POMDP: Case of the Anti-Collision Function
As the railway domain progresses towards autonomy, maintaining safety at levels
comparable to human-operated systems is a crucial challenge. Autonomous trains require …
comparable to human-operated systems is a crucial challenge. Autonomous trains require …
A Reinforcement Learning-Boosted Motion Planning Framework: Comprehensive Generalization Performance in Autonomous Driving
This study introduces a novel approach to autonomous motion planning, informing an
analytical algorithm with a reinforcement learning (RL) agent within a Frenet coordinate …
analytical algorithm with a reinforcement learning (RL) agent within a Frenet coordinate …
Shared-unique Features and Task-aware Prioritized Sampling on Multi-task Reinforcement Learning
We observe that current state-of-the-art (SOTA) methods suffer from the performance
imbalance issue when performing multi-task reinforcement learning (MTRL) tasks. While …
imbalance issue when performing multi-task reinforcement learning (MTRL) tasks. While …
Imagination-augmented Hierarchical Reinforcement Learning for Safe and Interactive Autonomous Driving in Urban Environments
SH Lee, Y Jung, SW Seo - arXiv preprint arXiv:2311.10309, 2023 - arxiv.org
Hierarchical reinforcement learning (HRL) has led to remarkable achievements in diverse
fields. However, existing HRL algorithms still cannot be applied to real-world navigation …
fields. However, existing HRL algorithms still cannot be applied to real-world navigation …