663 citations · 1.3k across the 13 of their papers we have counts for
13 papers
CarPlanner: Consistent Auto-regressive Trajectory Planning for Large-scale Reinforcement Learning in Autonomous Driving
Dongkun Zhang, Jiaming Liang, Ke Guo +5
Trajectory planning is vital for autonomous driving, ensuring safe and efficient navigation in complex environments. While recent learning-based methods, particularly reinforcement…
RING#: PR-by-PE Global Localization with Roto-translation Equivariant Gram Learning
Sha Lu, Xuecheng Xu, Yuxuan Wu +4
Global localization using onboard perception sensors, such as cameras and LiDARs, is crucial in autonomous driving and robotics applications when GPS signals are unreliable. Most a…
A Two-stage Based Social Preference Recognition in Multi-Agent Autonomous Driving System
Jintao Xue, Dongkun Zhang, Rong Xiong +2
Multi-Agent Reinforcement Learning (MARL) has become a promising solution for constructing a multi-agent autonomous driving system (MADS) in complex and dense scenarios. But most m…
Zero-shot Transfer Learning of Driving Policy via Socially Adversarial Traffic Flow
Dongkun Zhang, Jintao Xue, Yuxiang Cui +6
Acquiring driving policies that can transfer to unseen environments is challenging when driving in dense traffic flows. The design of traffic flow is essential and previous studies…
Domain Generalization for Vision-based Driving Trajectory Generation
Yunkai Wang, Dongkun Zhang, Yuxiang Cui +5
One of the challenges in vision-based driving trajectory generation is dealing with out-of-distribution scenarios. In this paper, we propose a domain generalization method for visi…
Learning Observation-Based Certifiable Safe Policy for Decentralized Multi-Robot Navigation
Yuxiang Cui, Longzhong Lin, Xiaolong Huang +3
Safety is of great importance in multi-robot navigation problems. In this paper, we propose a control barrier function (CBF) based optimizer that ensures robot safety with both hig…
Imitation Learning of Hierarchical Driving Model: from Continuous Intention to Continuous Trajectory
Yunkai Wang, Dongkun Zhang, Jingke Wang +3
One of the challenges to reduce the gap between the machine and the human level driving is how to endow the system with the learning capacity to deal with the coupled complexity of…
Learning hierarchical behavior and motion planning for autonomous driving
Jingke Wang, Yue Wang, Dongkun Zhang +2
Learning-based driving solution, a new branch for autonomous driving, is expected to simplify the modeling of driving by learning the underlying mechanisms from data. To improve th…
PPINN: Parareal Physics-Informed Neural Network for time-dependent PDEs
Xuhui Meng, Zhen Li, Dongkun Zhang +1
Physics-informed neural networks (PINNs) encode physical conservation laws and prior physical knowledge into the neural networks, ensuring the correct physics is represented accura…
Learning in Modal Space: Solving Time-Dependent Stochastic PDEs Using Physics-Informed Neural Networks
Dongkun Zhang, Ling Guo, George Em Karniadakis
One of the open problems in scientific computing is the long-time integration of nonlinear stochastic partial differential equations (SPDEs). We address this problem by taking adva…
Physics-Informed Generative Adversarial Networks for Stochastic Differential Equations
Liu Yang, Dongkun Zhang, George Em Karniadakis
We developed a new class of physics-informed generative adversarial networks (PI-GANs) to solve in a unified manner forward, inverse and mixed stochastic problems based on a limite…
Quantifying total uncertainty in physics-informed neural networks for solving forward and inverse stochastic problems
Dongkun Zhang, Lu Lu, Ling Guo +1
Physics-informed neural networks (PINNs) have recently emerged as an alternative way of solving partial differential equations (PDEs) without the need of building elaborate grids,…
An Atomistic Fingerprint Algorithm for Learning Ab Initio Molecular Force Fields
Yu-Hang Tang, Dongkun Zhang, George Em Karniadakis
Molecular fingerprints, i.e. feature vectors describing atomistic neighborhood configurations, is an important abstraction and a key ingredient for data-driven modeling of potentia…