About

I am Yongqi Dong, a Research Group Leader (AI & Automated Mobility) at RWTH Aachen University, PhD researcher at TU Delft, and previously a visiting scholar at UC Berkeley. With a broad interdisciplinary background, my ultimate goal is to employ Artificial Intelligence (AI) and multi-disciplinary research as tools to shape a better world. To achieve this, I have focused on the transportation domain and explored various aspects of shared and automated mobility.

Our group is actively seeking PhD students, visiting (CSC-funded) PhDs/RAs/Scholars, and Master's students (thesis projects) in the wide domain of Transportation & Traffic Engineering, Intelligent Transportation Systems, AI, Data Science, and Digital Twins. Typical research areas include:

  • Connected Automated Vehicles
  • Mixed-Autonomy Traffic
  • Driving Risk Modelling
  • Transport & Traffic Safety
  • Socially Compliant Decision-making
  • Multi-agent Deep Reinforcement Learning
  • Shared Smart & Sustainable Mobility
  • Smart Logistics & Delivery Robots
  • Lane Detection, Object Detection & Tracking
  • Multi-modal Data Fusion & Data Mining in Transportation Research
  • Deep Learning/Reinforcement Learning/Large Language Models for Automated Mobility
  • Explainable AI Applications in Mobility & Interdisciplinary Problems

Feel free to email me at yongqi.dong@rwth-aachen.de for research discussion and possible collaboration!


Bio

From January 2020 to May 2024, I was a member of the Traffic and Transportation Safety Lab supervised by Dr. Haneen Farah and Prof. Bart van Arem. My Ph.D. project is Safe, Efficient, and Socially Compliant Automated Driving in Mixed Traffic: Sensing, Anomaly Detection, Planning and Control [dissertation to be released]. The objective is to expand the Operational Design Domain (ODD) of automated vehicles with Artificial Intelligence, data-driven, and simulation-based methods. It is within the Safe and efficient operation of AutoMated and human drivEN vehicles in mixed traffic (SAMEN) project, funded by the Dutch Research Council (NWO). From May 2023 to October 2023, I visited the Mechanical Systems Control Lab at UC Berkeley, supervised by Prof. Masayoshi Tomizuka. There, I cooperated with Dr. Chen Tang on topics related to socially compliant automated driving.

I obtained my Master’s degree in Control Science and Engineering from Tsinghua University and my Bachelor’s degree in Telecommunication Engineering from Beijing Jiaotong University (BJTU). I had also spent time at Singapore-MIT Alliance for Research and Technology (SMART) as a research intern.

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 Automated Driving. I have also delved into shared mobility employing big data analytics and machine learning techniques to reveal the 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), IEEE International Conference on Systems, Man, and Cybernetics (SMC), and Transportation Research Board annual meeting (TRB).

For more information, please refer to my CV.

News

Miscellany

I am a vegetarian and minimalist. I like Taichi, reading, and travelling.
I regularly practice meditation to pursue the Inner Peace.
I see travelling, reading, and meditation all as ways to explore the world, externally and internally, and they help stimulate my research inspirations.

Quotes:
“If anything is worth doing, do it with all your heart.”
“Not everything that counts can be counted, and not everything that can be counted really counts.”
“Live in the Present Moment. Irrespective of what happened yesterday or last year, and what may or may not happen tomorrow, the present moment is where you are—always.”