Y. (Yongqi) Dong
Y. (Yongqi) Dong
Profiel
Academic Homepage: https://yongqidong.github.io/
I got my B.Sc. in Telecommunication from Beijing Jiaotong University, and my M.Sc. in Control Science and Engineering from Tsinghua University, where I also minored in Data Science. During the very past few years, I had trained myself as a researcher in various universities, research institutions and companies, adopting Machine Learning and Data Science methods to transportation research and smart mobility. In January 2020, I started my Ph.D. research career within the SAMEN project, under the supervision of Dr. ir. Haneen Farah and Prof. dr. Bart van Arem. My research focuses on developing data-driven models to expand Automated Vehicles' Operational Design Domain in mixed traffic.
My current research centres around the areas of Automated Vehicles, Smart & Shared Mobility, and Artificial Intelligence. I aim to develop innovative Deep Learning models for Automated Vehicles' sensing and Deep Reinforcement Learning models for Automated Vehicles' controlling, and thus realize Safe, Efficient, and Socially Compliant Autonomous Driving. I have also delved into shared mobility employing big data analytics and machine learning techniques to reveal unique spatial-temporal patterns. My previous works have been published in high-quality top journals and conferences, including Transportation Research Part C, IEEE Transactions on Intelligent Transportation Systems, and Computer-Aided Civil and Infrastructure Engineering, as well as IEEE International Conference on Intelligent Transportation Systems (ITSC) and Transportation Research Board annual meeting (TRB).
Profile
Onderzoeksinteresses
My ultimate goal is to employ artificial intelligence and interdisciplinary research as tools to shape a better world. For that, I have delved into the transportation domain as the use case. The essence of transportation is to reconcile the spatio-temporal imbalance in the distribution of matter, information and energy, which is all about time and space. Thus, I had attached the utmost importance to the spatial-temporal correlations in my research.
My current research centres around three main pillars:
Deep Learning for sensing and anomaly detecting;
Deep Reinforcement Learning for controlling and decision-making;
Big Data Analytics for spatial-temporal pattern mining.
Publications
-
2024
-
2024
Intelligent Anomaly Detection for Lane Rendering Using Transformer with Self-Supervised Pre-Training and Customized Fine-Tuning
Yongqi Dong / Xingmin Lu / Ruohan Li / Wei Song / Bart van Arem / Haneen Farah
-
2024
Understanding cyclists’ perception of driverless vehicles through eye-tracking and interviews
Siri Berge / Joost de Winter / Dimitra Dodou / Amir Pooyan Afghari / Nagarjun Reddy / Yongqi Dong / Haneen Farah
-
2024
eHMI on the Vehicle or on the Infrastructure?
A Driving Simulator Study
Shiva Nischal Lingam / Joost de Winter / Yongqi Dong / Anastasia Tsapi / Bart van Arem / Haneen Farah -
2023
Comparative Study on Semi-Supervised Learning Applied for Anomaly Detection in Hydraulic Condition Monitoring System
Yongqi Dong / Kejia Chen / Zhiyuan Ma
-