A survey on offline reinforcement learning: Taxonomy, review, and open problems
RF Prudencio, MROA Maximo… - IEEE Transactions on …, 2023 - ieeexplore.ieee.org
With the widespread adoption of deep learning, reinforcement learning (RL) has
experienced a dramatic increase in popularity, scaling to previously intractable problems …
experienced a dramatic increase in popularity, scaling to previously intractable problems …
[HTML][HTML] Path planning algorithms in the autonomous driving system: A comprehensive review
This comprehensive review focuses on the Autonomous Driving System (ADS), which aims
to reduce human errors that are the reason for about 95% of car accidents. The ADS …
to reduce human errors that are the reason for about 95% of car accidents. The ADS …
A survey of deep RL and IL for autonomous driving policy learning
Z Zhu, H Zhao - IEEE Transactions on Intelligent Transportation …, 2021 - ieeexplore.ieee.org
Autonomous driving (AD) agents generate driving policies based on online perception
results, which are obtained at multiple levels of abstraction, eg, behavior planning, motion …
results, which are obtained at multiple levels of abstraction, eg, behavior planning, motion …
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 …
Safe reinforcement learning for model-reference trajectory tracking of uncertain autonomous vehicles with model-based acceleration
Applying reinforcement learning (RL) algorithms to control systems design remains a
challenging task due to the potential unsafe exploration and the low sample efficiency. In …
challenging task due to the potential unsafe exploration and the low sample efficiency. In …
Fault-tolerant predictive control with deep-reinforcement-learning-based torque distribution for four in-wheel motor drive electric vehicles
This article proposes a fault-tolerant control (FTC) method for four in-wheel motor drive
electric vehicles considering both vehicle stability and motor power consumption. First, a …
electric vehicles considering both vehicle stability and motor power consumption. First, a …
Receding-horizon reinforcement learning approach for kinodynamic motion planning of autonomous vehicles
X Zhang, Y Jiang, Y Lu, X Xu - IEEE Transactions on Intelligent …, 2022 - ieeexplore.ieee.org
Kinodynamic motion planning is critical for autonomous vehicles with high maneuverability
in dynamic environments. However, obtaining near-optimal motion planning solutions with …
in dynamic environments. However, obtaining near-optimal motion planning solutions with …
Convolutional neural network-based lane-change strategy via motion image representation for automated and connected vehicles
The lane-change decision-making module of automated and connected vehicles (ACVs) is
one of the most crucial and challenging issues to be addressed. Motivated by human beings' …
one of the most crucial and challenging issues to be addressed. Motivated by human beings' …
[HTML][HTML] Constrained reinforcement learning for vehicle motion planning with topological reachability analysis
Rule-based traditional motion planning methods usually perform well with prior knowledge
of the macro-scale environments but encounter challenges in unknown and uncertain …
of the macro-scale environments but encounter challenges in unknown and uncertain …
[HTML][HTML] Verifying learning-based robotic navigation systems
Deep reinforcement learning (DRL) has become a dominant deep-learning paradigm for
tasks where complex policies are learned within reactive systems. Unfortunately, these …
tasks where complex policies are learned within reactive systems. Unfortunately, these …