Wenhao Ding

Wenhao Ding (丁文浩)

Email: wenhaod AT nvidia DOT com

I am a Research Scientist in the Autonomous Vehicle Group at NVIDIA Research. I received my Ph.D. from Carnegie Mellon University and my Bachelor's degree from Tsinghua University. I am interested in improving the safety of autonomous systems using simulation, reinforcement learning, and causal discovery.

             2024 -
I am a Research Scientist at the Autonomous Vehicle Group of NVIDIA Research. I work on the closed-loop simulation and safety for autonomous systems.
2019 - 2024
My Ph.D. research focused on safety-critical scenarios generation. My thesis title is ''Generative AI for Critical Digital Twins''.
2023 - 2023
I was a Research Intern in the Autonomous Vehicle Group led by Prof. Marco Pavone. I collaborated with Chaowei Xiao and Yulong Cao on a scenario generation project.
2022 - 2022
I was an Applied Scientist Intern of the Astro team at Lab126, where I collaborated with Nathalie Majcherczyk and Mohit Deshpande on the active perception project.
2021 - 2021
I was a Machine Learning Research Intern at Bosch Center for Artificial Intelligence, where I worked on traffic flow analysis and clustering.
2014 - 2018
I received my Bachelor degree from the Department of Electronic Engineering at Tsinghua University. I was a member of the Spark Program and a former leader of Skyworks.

2024/06 - We are organizing the 2024 IEEE International Automated Vehicle Validation Conference. Consider submitting your paper and special session proposals!

2024/06 - I join NVIDIA Research as a Research Scientist at the Autonomous Vehicle Group.

2024/03 - One paper using causal-aware representation for driving is accepted to RA-L.

2024/02 - One paper about privacy in robotics is accepted to ICRA 2024.

2023/09 - One paper combining causality and robustness is accepted to NeurIPS 2023.

2023/04 - One paper about reward-conditioned offline RL accepted to ICML 2023.

2022/12 - My research is covered by CMU Engineering News Driving autonomy into the metaverse!

2022/10 - Glad to present our work about causal discovery in Wayve!

2022/08 - My research proposal wins 2022 Qualcomm Innovation Fellowship, North America.

CaDRE: Controllable and Diverse Generation of Safety-Critical Driving Scenarios using Real-World Trajectories

Peide Huang, Wenhao Ding, Jonathan Francis, Bingqing Chen, Ding Zhao,
Preprint arXiv:2403.13208

arXiv / code / bibtex

RealGen: Retrieval Augmented Generation for Controllable Traffic Scenarios

Wenhao Ding*, Yulong Cao*, Ding Zhao, Chaowei Xiao, Marco Pavone
Preprint arXiv:2312.13303

website / arXiv / code / bibtex

Semantically Adversarial Driving Scenario Generation with Explicit Knowledge Integration

Wenhao Ding, Haohong Lin, Bo Li, Kim Ji Eun, Ding Zhao
Workshop on Environment Generation for Generalizable Robots (EGG) at RSS 2023
Workshop on Knowledge and Logical Reasoning in the Era of Data-driven Learning at ICML 2023

arXiv / code / bibtex

Safety-aware Causal Representation for Trustworthy Reinforcement Learning in Autonomous Driving

Haohong Lin, Wenhao Ding, Zuxin Liu, Yaru Niu, Jiacheng Zhu, Yuming Niu, Ding Zhao
IEEE Robotics and Automation Letters (RA-L) 2024

arXiv / code / bibtex

Seeing is not Believing: Robust Reinforcement Learning against Spurious Correlation

Wenhao Ding*, Laixi Shi*, Yuejie Chi, Ding Zhao
Conference on Neural Information Processing Systems (NeurIPS) 2023, New Orleans

website / arXiv / code / bibtex

Your Room is not Private: Gradient Inversion Attack for Deep Q-Learning

Miao Li, Wenhao Ding, Ding Zhao
International Conference on Robotics and Automation (ICRA) 2024, Japan

arXiv / code / bibtex

Bayesian Reparameterization of Reward-Conditioned Reinforcement Learning with Energy-based Models

Wenhao Ding*, Tong Che*, Ding Zhao, Marco Pavone
International Conference on Machine Learning (ICML) 2023, Hawaii

arXiv / code / bibtex

What Went Wrong? Closing the Sim-to-Real Gap via Differentiable Causal Discovery

Peide Huang, Xilun Zhang*, Ziang Cao*, Shiqi Liu*, Mengdi Xu, Wenhao Ding, Jonathan Francis, Bingqing Chen, Ding Zhao
Conference on Robot Learning (CoRL) 2023, Atlanta

arXiv / code / bibtex

Learning to View: Decision Transformers for Active Object Detection

Wenhao Ding, Nathalie Majcherczyk, Mohit Deshpande, Xuewei Qi, Ding Zhao, Rajasimman Madhivanan, Arnie Sen
IEEE International Conference on Robotics and Automation (ICRA) 2023, London

arXiv / code / bibtex

A Survey on Safety-Critical Driving Scenario Generation -- A Methodological Perspective

Wenhao Ding, Chejian Xu, Mansur Arief, Haohong Lin, Bo Li, Ding Zhao
IEEE Transactions on Intelligent Transportation Systems (T-ITS), March, 2023

arXiv / Xplore / code / bibtex

Generalizing Goal-Conditioned Reinforcement Learning with Variational Causal Reasoning

Wenhao Ding, Haohong Lin, Bo Li, Ding Zhao
Conference on Neural Information Processing Systems (NeurIPS) 2022, New Orleans

arXiv / code / bibtex

SafeBench: A Benchmarking Platform for Safety Evaluation of Autonomous Vehicles

Chejian Xu*, Wenhao Ding*, Weijie Lyu, Zuxin Liu, Shuai Wang, Yihan He, Hanjiang Hu, Ding Zhao, Bo Li
Conference on Neural Information Processing Systems (NeurIPS) 2022, New Orleans

website / arXiv / code / bibtex

CausalAF: Causal Autoregressive Flow for Safety-Critical Driving Scenario Generation

Wenhao Ding, Haohong Lin, Bo Li, Ding Zhao
Conference on Robot Learning (CoRL) 2022, New Zealand
Abridged in ICML 2022 Workshop on Safe Learning for Autonomous Driving

arXiv / code / bibtex

Multimodal Safety-Critical Scenarios Generation for Decision-Making Algorithms Evaluation

Wenhao Ding, Baiming Chen, Bo Li, Kim Ji Eun, Ding Zhao
IEEE Robotics and Automation Letters (RA-L) with ICRA 2021, Xi'an

arXiv / code / bibtex

Task-Agnostic Online Reinforcement Learning with an Infinite Mixture of Gaussian Processes

Mengdi Xu, Wenhao Ding, Jiacheng Zhu, Zuxin Liu, Baiming Chen, Ding Zhao
Neural Information Processing Systems (NeurIPS) 2020, Vancouver

arXiv / code / bibtex

Learning to Collide: An Adaptive Safety-Critical Scenarios Generating Method

Wenhao Ding, Baiming Chen, Minjun Xu, Ding Zhao
International Conference on Intelligent Robots and Systems (IROS) 2020, Las Vegas

arXiv / code / video / bibtex

CMTS: Conditional Multiple Trajectory Synthesizer for Generating Safety-critical Driving Scenarios

Wenhao Ding, Mengdi Xu, Ding Zhao
International Conference on Robotics and Automation (ICRA) 2020, Paris

arXiv / code / supplementary / bibtex

Conference Reviewer: ICML, ICLR, NeurIPS, AISTATS, CVPR, ECCV, ICCV, CoRL, ICRA, IROS, IJCAI, ICASSP, ITSC, ICME

Journal Reviewer: TMLR, IEEE RA-L, IEEE TNNLS, IEEE T-ITS, IEEE T-IV, IEEE Access, IEEE TII, IEEE MM, Scientific Report

Organizer: CVPR 2023 Secure and Safe Autonomous Driving Workshop and Challenge, ICRA 2022 SeasonDepth Challenge


© Copyright 2024 Wenhao Ding

Last Updated: April 28th, 2024