Learning the References of Online Model Predictive Control for Urban Self-Driving
In this work, we propose a novel learning-based online model predictive control (MPC)
framework for motion synthesis of self-driving vehicles. In this framework, the decision …
framework for motion synthesis of self-driving vehicles. In this framework, the decision …
Graph-based Prediction and Planning Policy Network (GP3Net) for scalable self-driving in dynamic environments using Deep Reinforcement Learning
Recent advancements in motion planning for Autonomous Vehicles (AVs) show great
promise in using expert driver behaviors in non-stationary driving environments. However …
promise in using expert driver behaviors in non-stationary driving environments. However …
Interpretable end-to-end urban autonomous driving with latent deep reinforcement learning
Unlike popular modularized framework, end-to-end autonomous driving seeks to solve the
perception, decision and control problems in an integrated way, which can be more …
perception, decision and control problems in an integrated way, which can be more …
Integrated Decision and Control for High-Level Automated Vehicles by Mixed Policy Gradient and Its Experiment Verification
Self-evolution is indispensable to realize full autonomous driving. This paper presents a self-
evolving decision-making system based on the Integrated Decision and Control (IDC), an …
evolving decision-making system based on the Integrated Decision and Control (IDC), an …
A Pseudo-Hierarchical Planning Framework with Dynamic-Aware Reinforcement Learning for Autonomous Driving
Q Deng, Y Zhao, R Li, Q Hu, T Zhang… - 2024 IEEE Intelligent …, 2024 - ieeexplore.ieee.org
Reinforcement Learning (RL) over motion skill space has been verified to generate more
diverse behaviors than that over low-level control space, and has exhibited superior …
diverse behaviors than that over low-level control space, and has exhibited superior …
Integrating deep reinforcement learning with model-based path planners for automated driving
Automated driving in urban settings is challenging. Human participant behavior is difficult to
model, and conventional, rule-based Automated Driving Systems (ADSs) tend to fail when …
model, and conventional, rule-based Automated Driving Systems (ADSs) tend to fail when …
A fast integrated planning and control framework for autonomous driving via imitation learning
Safety and efficiency are two key elements for planning and control in autonomous driving.
Theoretically, model-based optimization methods, such as Model Predictive Control (MPC) …
Theoretically, model-based optimization methods, such as Model Predictive Control (MPC) …
Safe and computational efficient imitation learning for autonomous vehicle driving
FS Acerbo, H Van der Auweraer… - 2020 American Control …, 2020 - ieeexplore.ieee.org
Autonomous vehicle driving systems face the challenge of providing safe, feasible and
human-like driving policy quickly and efficiently. The traditional approach usually involves a …
human-like driving policy quickly and efficiently. The traditional approach usually involves a …
[PDF][PDF] CW-ERM: Improving Autonomous Driving Planning with Closed-loop Weighted Empirical Risk Minimization
The imitation learning of self-driving vehicle policies through behavioral cloning is often
carried out in an open-loop fashion, ignoring the effect of actions to future states. Training …
carried out in an open-loop fashion, ignoring the effect of actions to future states. Training …
Learning Residual Model of Model Predictive Control via Random Forests for Autonomous Driving
K Zhao, J Xue, X Meng, G Li, M Wu - arXiv preprint arXiv:2304.04366, 2023 - arxiv.org
One major issue in learning-based model predictive control (MPC) for autonomous driving is
the contradiction between the system model's prediction accuracy and computation …
the contradiction between the system model's prediction accuracy and computation …