Zhiyu Huang

Zhiyu Huang

Nanyang Technological University

I am currently a visiting student researcher at UC Berkeley, working in the Mechanical Systems Control (MSC) Lab under the guidance of Prof. Masayoshi Tomizuka and Dr. Wei Zhan. I am pursuing a Ph.D. in the School of Mechanical and Aerospace Engineering at Nanyang Technological University. My research endeavors are based in the Automated Driving and Human-Machine System (AutoMan) Lab, which is led by Prof. Chen Lyu. I am actively seeking postdoctoral positions to propel my research career further.

My research primarily centers around the intersection of autonomous driving and machine learning. My goal is to develop algorithms and techniques that enable machines to interact with humans naturally, make intelligent decisions, and drive as skillfully as experienced human drivers. Specifically, my research interests encompass deep learning and reinforcement learning, applied to areas such as autonomous driving decision-making, prediction and planning, simulation, and human-machine interaction. My contributions have resulted in the publication of over 20 papers in top AI/ITS/Robotics journals and conferences.

🔥 News

  • 2024.01:  🎉🎉 Our paper on joint prediction and planning for tree policy has been accepted by ICRA! See you in Yokohama, Japan!
  • 2023.11:   I was invited by zdjszx.com to give a public lecture on “Scalable, Learnable, and Interactive Decision-making for Autonomous Driving”. The recorded version of the lecture (in Chinese) is available for viewing on bilibili.
  • 2023.10:  🎉🎉 Our paper on brain-inspired reinforcement learning for safe autonomous driving has been accepted by TPAMI!
  • 2023.09:  🎉🎉 We won the best paper runner-up award in ITSC 2023!
  • 2023.09:  🎉🎉 Our paper on human-guided reinforcement learning for robot navigation has been accepted by TPAMI!
  • 2023.08:  🎉🎉 Our GameFormer paper has been accepted by ICCV as Oral presentation!
  • 2023.07:  🎉🎉 Our ITSC special session on learning-powered prediction and decision-making has received 17 paper submissions, all of which were accepted. Congratulations to the authors!
  • 2023.06:  🎉🎉 Our team won the innovation award in the nuPlan Planning Challenge! Check out our report and presentation on our GameFormer Planner.
  • 2023.06:  🎉🎉 Our team secured third place in the Waymo Open Dataset Motion Prediction Challenge! Our report is available on CVPR 2023 Workshop on Autonomous Driving.

📝 Publications

Highlights

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DTPP: Differentiable Joint Conditional Prediction and Cost Evaluation for Tree Policy Planning in Autonomous Driving

Zhiyu Huang, Peter Karkus, Boris Ivanovic, Yuxiao Chen, Marco Pavone, Chen Lv

IEEE International Conference on Robotics and Automation (ICRA), 2024

Paper |

  • We employ a tree-structured policy planner and propose a differentiable joint training framework for both ego-conditioned prediction and cost evaluation models, resulting in a direct improvement of the final planning performance.
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Learning-enabled Decision-making for Autonomous Driving: Framework and Methodology

PhD Thesis, 2023

Thesis

  • This thesis presents a comprehensive framework and a series of learning-based methodologies for decision-making in AVs, with the objective of improving the scalability, adaptability, and alignment of their decision-making systems.
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GameFormer: Game-theoretic Modeling and Learning of Transformer-based Interactive Prediction and Planning for Autonomous Driving

Zhiyu Huang, Haochen Liu, Chen Lv

IEEE/CVF International Conference on Computer Vision (ICCV), 2023

Oral presentation (top 3%)

Paper | Project | | GameFormer Planner

  • We address the interaction prediction problem by formulating it with hierarchical game theory and implementing it with TransFormer networks.
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Learning Interaction-aware Motion Prediction Model for Decision-making in Autonomous Driving

Zhiyu Huang, Haochen Liu, Jingda Wu, Wenhui Huang, Chen Lv

IEEE International Conference on Intelligent Transportation Systems (ITSC), 2023

Best Paper Runner-up Award

Paper |

  • We propose an interaction-aware motion prediction model that is able to predict other agents’ future trajectories according to the ego agent’s future plans, i.e., their reactions to the ego’s actions.
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Conditional Predictive Behavior Planning with Inverse Reinforcement Learning for Human-like Autonomous Driving

Zhiyu Huang, Haochen Liu, Jingda Wu, Chen Lv

IEEE Transactions on Intelligent Transportation Systems, 2023

Paper

  • Distinguished from existing learning-based methods that directly output decisions, we introduce a predictive behavior planning framework that learns to predict and evaluate from human driving data.
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Differentiable Integrated Motion Prediction and Planning with Learnable Cost Function for Autonomous Driving

Zhiyu Huang, Haochen Liu, Jingda Wu, Chen Lv

IEEE Transactions on Neural Networks and Learning Systems, 2023

Paper | Project |

  • We propose an end-to-end differentiable framework that integrates prediction and planning modules and is able to learn the cost function from data.
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Multi-modal Motion Prediction with Transformer-based Neural Network for Autonomous Driving

Zhiyu Huang, Xiaoyu Mo, Chen Lv

IEEE International Conference on Robotics and Automation (ICRA), 2022

Paper

  • We propose a neural prediction framework based on the Transformer structure to model the relationship among the interacting agents and extract the attention of the target agent on the map waypoints.
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Efficient Deep Reinforcement Learning with Imitative Expert Priors for Autonomous Driving

Zhiyu Huang, Jingda Wu, Chen Lv

IEEE Transactions on Neural Networks and Learning Systems, 2022

Paper | Project |

  • We propose a novel framework to incorporate human prior knowledge in DRL, in order to improve the sample efficiency and save the effort of designing sophisticated reward functions.
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Driving Behavior Modeling using Naturalistic Human Driving Data with Inverse Reinforcement Learning

Zhiyu Huang, Jingda Wu, Chen Lv

IEEE Transactions on Intelligent Transportation Systems, 2021

Paper |

  • We propose a structural assumption about internal reward function-based human driving behavior and employ sampling-based maximum entropy inverse reinforcement learning (IRL) algorithm to infer the reward function parameters from naturalistic human driving data.
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Multi-modal sensor fusion-based deep neural network for end-to-end autonomous driving with scene understanding

Zhiyu Huang, Chen Lv, Yang Xing, Jingda Wu

IEEE Sensors Journal, 2020

Paper

  • We propose a novel deep neural network-based system for end-to-end autonomous driving, consisting of multimodal sensor fusion, scene understanding, and conditional driving policy modules.

All Publications

Journal

Conference

Preprints

🎖 Honors and Awards

📖 Education

  • 2019.08 - 2024.01, Doctor of Philosophy, Robotics and Intelligent Systems, Nanyang Technological University, Singapore
  • 2015.09 - 2019.06, Bachelor of Engineering, Vehicle Engineering, Chongqing University, Chongqing, China

📚 Academic Services

Program Committee

  • Lead organizer of Special Session on learning-powered prediction and decision-making at ITSC, 2023

Journal Reviewer

  • IEEE Transactions on Intelligent Transportation Systems
  • IEEE Transactions on Neural Networks and Learning Systems
  • IEEE Transactions on Intelligent Vehicles
  • IEEE Transactions on Cybernetics
  • IEEE Robotics and Automation Letters
  • Transportation Research Part C: Emerging Technologies
  • Engineering Applications of Artificial Intelligence
  • Artificial Intelligence Review

Conference Reviewer

  • IEEE International Conference on Robotics and Automation (ICRA) 2022 – 2024
  • IEEE Intelligent Vehicles Symposium (IV) 2022 – 2023
  • IEEE Intelligent Transportation Systems Conference (ITSC) 2022
  • IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) 2023
  • European Conference on Computer Vision (ECCV) 2024