Wenchao Ding

Wenchao Ding | 丁文超

I am a tenure-track associate professor at the Academy for Engineering and Technology (FAET), Fudan University. I am looking for self-motivated students who are interested in perception, prediction, decision-making and motion planning for autonomous driving.

From Sept. 2020 to Jan. 2023, I was a research scientist and technical lead at Autonomous Driving Solution (ADS), Huawei, where I led a team working on cutting-edge prediction and decision-making problems for autonomous vehicles. At Huawei, I am honored to be one of the Huawei TopMinds (天才少年). I was one of the core contributors to the comprehensive product-level prediction solution (incl. urban and highway env.) in ADS.

Before joining ADS, I received my PhD from the Hong Kong University of Science and Technology in 2020, where I was supervised by Prof. Shaojie Shen, and worked in HKUST Aerial Robotics Group.

Email / Google Scholar / Youtube

Research

I'm interested in AI for robotics, especially the application in autonomous driving. Much of my research is about prediction, decision-making, and motion planning for autonomous vehicles. Unlike extensive literature which remains in simulation or datasets, many of my methods are deployed on real autonomous vehicles and tested in dense city traffic.

Journal papers

	Traffic Flow-Based Crowdsourced Mapping in Complex Urban Scenario Tong Qin, Haihui Huang, Ziqiang Wang, Tongqing Chen, Wenchao Ding^⁎ IEEE Robotics and Automation Letters (RA-L), 2023 paper / video Crowdsourced semantic mapping for mapless autonomous driving.
	EPSILON: An Efficient Planning System for Automated Vehicles in Highly Interactive Environments Wenchao Ding^⁎, Lu Zhang^⁎, Jing Chen, Shaojie Shen (^⁎ equal contribution) IEEE Transactions on Robotics (TRO), 2021 paper / video / code EPSILON enables human-like autonomous driving in real-world dense city traffic by effectively modeling social interaction with other human drivers.
	An Efficient B-spline-Based Kinodynamic Replanning Framework for Quadrotors Wenchao Ding, Wenliang Gao, Kaixuan Wang, Shaojie Shen IEEE Transactions on Robotics (TRO), 2019 paper / video Enabling efficient replanning for quadrotors in real-world cluttered environments by properly using B-spline properties.
	Safe Trajectory Generation For Complex Urban Environments Using Spatio-temporal Semantic Corridor Wenchao Ding^⁎, Lu Zhang^⁎, Jing Chen, Shaojie Shen (^⁎ equal contribution) IEEE Robotics and Automation Letters (RA-L), 2019 paper / video / code Safe trajectory generation in convex corridors with safety guarantee.
	Autonomous Aerial Robot Using Dual-fisheye Cameras Wenliang Gao, Kaixuan Wang, Wenchao Ding^⁎ (Corresponding author), Fei Gao, Tong Qin, Shaojie Shen Journal of Field Robotics (JFR), 2020 paper Achieving spherical omnidirectional sensing coverage with a minimum sensor suite (two ultrawide field-of-view (FOV) fisheye cameras and a low-cost IMU).

Conference papers

	OpenAnnotate3D: Open-Vocabulary Auto-Labeling System for Multi-modal 3D Data Yijie Zhou, Likun Cai, Xianhui Cheng, Zhongxue Gan, Xiangyang Xue^⁎ Wenchao Ding^⁎, IEEE International Conference on Robotics and Automation (ICRA), 2024 Code comming soon / paper Open-vocabulary auto-Labeling system for multi-modal 3D data
	DeepPointMap: Advancing LiDAR SLAM with Unified Neural Descriptors Xiaze Zhang, Ziheng Ding, Qi Jing, Yuejie Zhang, Wenchao Ding^⁎, Rui Feng^⁎ AAAI Conference on Artificial Intelligence (AAAI), 2024 Code comming soon / paper Neural descriptors for next-gen SLAM.
	Swift-Mapping: Online Neural Implicit Dense Mapping in Urban Scenes Ke Wu, Kaizhao Zhang, Mingzhe Gao, Jieru Zhao, Zhongxue Gan^⁎, Wenchao Ding^⁎ AAAI Conference on Artificial Intelligence (AAAI), 2024 Code comming soon / Paper comming soon Neural implicit representation for next-gen SLAM.
	FlowMap: Path Generation for Automated Vehicles in Open Space Using Traffic Flow Wenchao Ding, Jieru Zhao, Yubin Chu, Tong Qin, Chunjing Xu, Yuxiang Guan, Zhongxue Gan IEEE International Conference on Robotics and Automation (ICRA), 2023 video / paper Online path planning for automated vehicles in open space for mapless autonomous driving.
	Learning to Predict Vehicle Trajectories with Model-based Planning Haoran Song, Di Luan, Wenchao Ding, Michael Yu Wang, Qifeng Chen Conference on Robot Learning (CoRL), 2021 project page / paper Make accurate, feasible, and robust trajectory prediction by a novel architecture with a model-based generator and a learning-based evaluator.
	PiP: Planning-informed Trajectory Prediction for Autonomous Driving Haoran Song, Wenchao Ding, Yuxuan Chen, Shaojie Shen, Michael Yu Wang, Qifeng Chen European Conference on Computer Vision (ECCV), 2020 project page / paper Inform the multi-agent future prediction with ego vehicle's planning in a novel planning-prediction-coupled pipeline.
	Efficient Uncertainty-aware Decision-making for Autonomous Vehicles Using Guided Branching Lu Zhang^⁎, Wenchao Ding^⁎, Jing Chen, Shaojie Shen (^⁎ equal contribution) IEEE International Conference on Robotics and Automation (ICRA), 2020 paper / video / code Capable of modeling uncertainties while being much more efficient than POMDP.
	Predicting Vehicle Behaviors Over an Extended Horizon Using Behavior Interaction Network Wenchao Ding, Jing Chen, Shaojie Shen IEEE International Conference on Robotics and Automation (ICRA), 2019 paper / video Learning social interaction among vehicles for long-term intention prediction.
	Online Vehicle Trajectory Prediction using Policy Anticipation Network and Optimization-based Context Reasoning Wenchao Ding, Shaojie Shen IEEE International Conference on Robotics and Automation (ICRA), 2019 paper / video Combining learning and model-based optimization in one trajectory prediction framework.
	Trajectory Replanning for Quadrotors Using Kinodynamic Search and Elastic Optimization Wenchao Ding, Wenliang Gao, Kaixuan Wang, Shaojie Shen IEEE International Conference on Robotics and Automation (ICRA), 2018 paper / video Fast kinodynamic replanning for quadrotors.