Offline reinforcement learning: Tutorial, review, and perspectives on open problems
In this tutorial article, we aim to provide the reader with the conceptual tools needed to get
started on research on offline reinforcement learning algorithms: reinforcement learning …
started on research on offline reinforcement learning algorithms: reinforcement learning …
Integrating machine learning and model predictive control for automotive applications: A review and future directions
In this review paper, the integration of Machine Learning (ML) and Model Predictive Control
(MPC) in Automotive Control System (ACS) applications are discussed. ACS can be divided …
(MPC) in Automotive Control System (ACS) applications are discussed. ACS can be divided …
A survey of deep learning techniques for autonomous driving
The last decade witnessed increasingly rapid progress in self‐driving vehicle technology,
mainly backed up by advances in the area of deep learning and artificial intelligence (AI) …
mainly backed up by advances in the area of deep learning and artificial intelligence (AI) …
Interaction dataset: An international, adversarial and cooperative motion dataset in interactive driving scenarios with semantic maps
Behavior-related research areas such as motion prediction/planning, representation/
imitation learning, behavior modeling/generation, and algorithm testing, require support from …
imitation learning, behavior modeling/generation, and algorithm testing, require support from …
Differentiable mpc for end-to-end planning and control
We present foundations for using Model Predictive Control (MPC) as a differentiable policy
class for reinforcement learning. This provides one way of leveraging and combining the …
class for reinforcement learning. This provides one way of leveraging and combining the …
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 …
Path tracking and direct yaw moment coordinated control based on robust MPC with the finite time horizon for autonomous independent-drive vehicles
H Peng, W Wang, Q An, C Xiang… - IEEE Transactions on …, 2020 - ieeexplore.ieee.org
It is a striking fact that the characteristics of parametric uncertainties, external disturbance,
time-varying and nonlinearities are available in the constructed model of autonomous …
time-varying and nonlinearities are available in the constructed model of autonomous …
Deep imitative models for flexible inference, planning, and control
Imitation Learning (IL) is an appealing approach to learn desirable autonomous behavior.
However, directing IL to achieve arbitrary goals is difficult. In contrast, planning-based …
However, directing IL to achieve arbitrary goals is difficult. In contrast, planning-based …
Autonomous driving motion planning with constrained iterative LQR
Motion planning is a core technique for autonomous driving. Nowadays, there still exists a
lot of challenges in motion planning for autonomous driving in complicated environments …
lot of challenges in motion planning for autonomous driving in complicated environments …
A survey of deep reinforcement learning algorithms for motion planning and control of autonomous vehicles
In this survey, we systematically summarize the current literature on studies that apply
reinforcement learning (RL) to the motion planning and control of autonomous vehicles …
reinforcement learning (RL) to the motion planning and control of autonomous vehicles …