Trustworthy AI Autonomy

We aim to develop trustworthy (robust, safe, and generalizable) intelligent autonomy by bridging rigorous theories and practical technologies.

Safe reinforcement learning

Baiming Chen, Zuxin Liu, Jiacheng Zhu, Mengdi Xu, Wenhao Ding, Ding Zhao, ''Context-Aware Safe Reinforcement Learning for Non-Stationary Environments,'' IEEE International Conference on Robotics and Automation (ICRA), 2021.  |

Zuxin Liu, Zhepeng Cen, Vladislav Isenbaev, Wei Liu, Steven Wu, Bo Li and Ding Zhao, ''Constrained Variational Policy Optimization for Safe Reinforcement Learning,'' under review, 2022.​  |

Zuxin Liu, Hongyi Zhou, Baiming Chen, Sicheng Zhong, Martial Hebert and Ding Zhao, ''Constrained Model-based Reinforcement Learning with Robust Cross-Entropy Method,'' under review, 2022.​  |

Distributional robust reinforcement learning

Mengdi Xu, Wenhao Ding, Jiacheng Zhu, Zuxin Liu, Baiming Chen, Ding Zhao, ''Task-agnostic online reinforcement learning with an infinite mixture of gaussian processes,'' Advances in Neural Information Processing Systems (NeurIPS), 2020.  |

Peide Huang, Mengdi Xu, Fei Fang, Ding Zhao, ''Robust Reinforcement Learning as a Stackelberg Game via Adaptively-Regularized Adversarial Training,'' under review, 2022.​  |

Jiacheng Zhu, Aritra Guha, Dat Do, Mengdi Xu, XuanLong Nguyen, Ding Zhao, ''Functional optimal transport: map estimation and domain adaptation for functional data ,'' under review, 2022.​  |

Delay-aware model-based reinforcement learning

Chen B, Xu M, Li L, Zhao D., ''Delay-aware model-based reinforcement learning for continuous control,'' Neurocomputing, 2021.  |

Baiming Chen, Mengdi Xu, Zuxin Liu, Liang Li, Ding Zhao, ''Delay-aware multi-agent reinforcement learning for cooperative and competitive environments,'' Under view, 2022.  |

The influence of Lidar configuration to learning perception systems

Hanjiang Hu, Zuxin Liu, Sharad Chitlangia, Akhil Agnihotri, Ding Zhao, ''Investigating the Impact of Multi-LiDAR Placement on Object Detection for Autonomous Driving,'' Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2022.  |

Zuxin Liu, Mansur Arief, Ding Zhao, ''Where Should We Place LiDARs on the Autonomous Vehicle?-An Optimal Design Approach,'' International Conference on Robotics and Automation (ICRA), 2019.  |

Shenyu Mou, Yan Chang, Wenshuo Wang, Ding Zhao, ''An Optimal LiDAR Configuration Approach for Self-Driving Cars,'' 98th TRB Annual Meeting (TRB), Washington, USA, January 13–17, 2019.​  |

Robust automatic annotation tools for Lidar point clouds

Hasan Arief, Mansur Arief, Manoj Bhat, Ulf Indahl, Håvard Tveite, Ding Zhao, ''Density-Adaptive Sampling for Heterogeneous Point Cloud Object Segmentation in Autonomous Vehicle Applications,'' Workshops at the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Long Beach, USA, Jun 2019.  |

Abstract — Robust understanding of the driving scene is among the key steps for accurate object detection and reliable autonomous driving. Accomplishing these tasks with a high level of precision, however, is not trivial. One of the challenges comes from dealing with nature of point clouds data, e.g. heterogeneous density distribution, highly imbalanced class, sparsity, etc., making the crude adoption of deep learning architectures meaningless. This line of research focuses on the incorporation of self-driving domain knowledge, such as motion, scene, or driving models, in constructing an efficient, robust, and (semi- or fully-) automated annotation scheme to process the massive data recorded by self-driving cars.

The Impact of Road Configuration on V2V-based Cooperative Localization

Macheng Shen, Jing Sun, Ding Zhao ''The Impact of Road Configuration in V2V-based Cooperative Localization: Mathematical Analysis and Real-world Evaluation,'' IEEE Transactions on Intelligent Transportation Systems, 2017. |

Macheng Shen, Ding Zhao, Jing Sun, Huei Peng, ''Improving Localization Accuracy in Connected Vehicle Networks Using Rao-Blackwellized Particle Filters: Theory, Simulations, and Experiments,'' IEEE Transactions on Intelligent Transportation Systems, 2017. |