Review and perspectives on driver digital twin and its enabling technologies for intelligent vehicles
Digital Twin (DT) is an emerging technology and has been introduced into intelligent driving
and transportation systems to digitize and synergize connected automated vehicles …
and transportation systems to digitize and synergize connected automated vehicles …
Transferring policy of deep reinforcement learning from simulation to reality for robotics
H Ju, R Juan, R Gomez, K Nakamura… - Nature Machine …, 2022 - nature.com
Deep reinforcement learning has achieved great success in many fields and has shown
promise in learning robust skills for robot control in recent years. However, sampling …
promise in learning robust skills for robot control in recent years. However, sampling …
End-to-end autonomous driving: Challenges and frontiers
The autonomous driving community has witnessed a rapid growth in approaches that
embrace an end-to-end algorithm framework, utilizing raw sensor input to generate vehicle …
embrace an end-to-end algorithm framework, utilizing raw sensor input to generate vehicle …
HiVeGPT: Human-machine-augmented intelligent vehicles with generative pre-trained transformer
Recently, a chat generative pre-trained transformer (ChatGPT) attracts widespread attention
in the academies and industries because of its powerful conversational ability with human …
in the academies and industries because of its powerful conversational ability with human …
Society-centered and DAO-powered sustainability in transportation 5.0: An intelligent vehicles perspective
As economic and social activities continue to increase, transportation is increasingly
contributing to climate change, air pollution and other environmental damage. The growing …
contributing to climate change, air pollution and other environmental damage. The growing …
Conditional predictive behavior planning with inverse reinforcement learning for human-like autonomous driving
Making safe and human-like decisions is an essential capability of autonomous driving
systems, and learning-based behavior planning presents a promising pathway toward …
systems, and learning-based behavior planning presents a promising pathway toward …
Human-guided reinforcement learning with sim-to-real transfer for autonomous navigation
Reinforcement learning (RL) is a promising approach in unmanned ground vehicles (UGVs)
applications, but limited computing resource makes it challenging to deploy a well-behaved …
applications, but limited computing resource makes it challenging to deploy a well-behaved …
Enhance sample efficiency and robustness of end-to-end urban autonomous driving via semantic masked world model
End-to-end autonomous driving provides a feasible way to automatically maximize overall
driving system performance by directly mapping the raw pixels from a front-facing camera to …
driving system performance by directly mapping the raw pixels from a front-facing camera to …
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 …
Augmenting reinforcement learning with transformer-based scene representation learning for decision-making of autonomous driving
Decision-making for urban autonomous driving is challenging due to the stochastic nature of
interactive traffic participants and the complexity of road structures. Although reinforcement …
interactive traffic participants and the complexity of road structures. Although reinforcement …