
Wenhao Ding
丁文浩
Ph.D. Candidate, Carnegie Mellon University.
M.S. in Machine Learning, Carnegie Mellon University.
Github / Google Scholar / LinkedIn / CV (4/2023) / EmailI am a fourth-year Ph.D. in Safe AI Lab, co-advised by Prof. Ding Zhao and Prof. Bo Li. I received my Bachelor degree from Electronic Engineering, Tsinghua University.
I am an FPV drone flyer. Check my gallery.
Research Highlights
My research goal is to build robust, generalizable, and interpretable autonomous systems that can truly understand the physical world. Specifically, I am interested in the following topics:
News & Updates
2023/04 - One paper about reward-conditioned offline RL accepted to ICML 2023.
2023/03 - We are hosting the Secure and Safe Autonomous Driving (SSAD) Workshop and Challenge at CVPR 2023!
2023/01 - One paper about embodied AI accepted to ICRA 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!
2022/09 - Two papers got accepted to NeurIPS 2022. See you in New Orleans!
2022/09 - One paper about using causal graph to generate safety-critical scenarios got accepted to CoRL 2022.
2022/08 - Our proposal "Safety-critical Scenarios Generation and Generalization for Autonomous Driving" wins 2022 Qualcomm Innovation Fellowship, North America.
2022/03 - I will start my Applied Scientist Intern in Amazon Lab126 working on Astro.
Selected Publications
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
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
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
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
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
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
Context-Aware Safe Reinforcement Learning for Non-Stationary Environments
Baiming Chen,
Zuxin Liu,
Jiacheng Zhu,
Mengdi Xu,
Wenhao Ding,
Liang Li,
Ding Zhao
IEEE International Conference on Robotics and Automation (ICRA) 2021, Xi'an
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
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
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
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
A New Multi-vehicle Trajectory Generator to Simulate Vehicle-to-Vehicle Encounters
Wenhao Ding,
Wenshuo Wang,
Ding Zhao
International Conference on Robotics and Automation (ICRA) 2019, Montreal
arXiv / code / supplementary / bibtex
Preprints
Trustworthy Reinforcement Learning Against Intrinsic Vulnerabilities: Robustness, Safety, and Generalizability
*Mengdi Xu,
*Zuxin Liu,
*Peide Huang,
Wenhao Ding,
Zhepeng Cen,
Bo Li,
Ding Zhao
Preprint arXiv:2209.08025
arXiv / code / bibtex
Semantically Adversarial Driving Scenario Generation with Explicit Knowledge Integration
Wenhao Ding,
Haohong Lin,
Bo Li,
Kim Ji Eun,
Ding Zhao
Preprint arXiv:2106.04066
arXiv / code / bibtex
Academic Services
Conference Reviewer: ICML, ICLR, NeurIPS (top reviewer), CVPR, ECCV, ICCV, ICRA, IROS, ICASSP, ITSC, ICME
Journal Reviewer: TMLR, IEEE RA-L, IEEE TNNLS, IEEE Access, IEEE T-ITS, IEEE TII, IEEE MM
Organizer: CVPR 2023 Secure and Safe Autonomous Driving Workshop and Challenge
Program Committee: ICRA 2022 SeasonDepth Challenge, NeurIPS 2022 ML4AD Workshop, NeurIPS 2022 TSRML Workshop, IJCAI 2022 AI4AD Workshop and Challenge