Welcome to AutoMan Research Lab @ NTU
AutoMan members, Oct 2023
The Automated Driving and Human-Machine System (AutoMan) Lab @ NTU has a wide range of research activities covering design, control and optimization of human-centric robotics, human-machine systems, and cyber-physical systems, with particular applications to advanced road vehicles.
Our AutoMan group is exploring
Scientific automation mechanisms to better understand humans.
Smarter autonomous robotics to guide and assist humans.
Safer automated vehicles to better serve humans.
Smoother automation to interact with humans.
Sustainable automobiles for humans.
These are what AutoMan stands for!
Dr. Chen Lv (吕辰)
Associate Professor
Director, AutoMan Research Lab
School of Mechanical and Aerospace Engineering Cluster Director in Future Mobility Solutions, ERI@N
Thrust Lead in Smart Mobility and Delivery, Continental-NTU Corp Lab
Program Lead in Next Generation AMR, Schaeffler-NTU Joint Lab
E: lyuchen@ntu.edu.sg
Recent AutoMan News
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Chen is appointed as Senior Editor of IEEE T-ITS.
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Zhehao joined as a Research fellow in September. Welcome onboard!
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Chen is elected Vice Chair of IEEE VTS AdHoc Committee on Autonomous Vehicles.
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Runjia, Shanhe, and Yanxin's work on VLM-enabled robotic manipulation received the Best Paper Award in IEEE CIS-RAM 2024. Congrats!
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Hector, Lingxiao, and Hanlu joined as PhD and Master Research Student, respectively, in August. Welcome onboard!
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Haochen and Zhiyu secured 1st and 2nd Places at Waymo Open Data Challenge 2024, at Occupancy Flow Prediction and Sim Agent Tacks, respectively! Congrats!
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On behalf of NTU Singapore, We debuted at the inaugural Abu Dhabi Autonomous Racing League, applying our AI driving solution on an F1-type race car. Cheers!
Research Highlights
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Wenhui's work on Human-in-the-loop RL was accepted to T-ITS.
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Zhiyu's work (in collaboration with NVIDIA) on "DTPP" was accepted to ICRA'24.
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Yiran's work on interactive prediction and decision-making was accepted to IEEE T-ITS.
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Xiangkun's work on brain-inspired RL for safe AV was accepted to IEEE TPAMI.
Useful Resources
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CFP-SS: Learning-powered prediction and decision-making for AVs, (code: kaf7g), ITSC 2023
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CFP-SS: Trustworthy Learning-Enabled CPS (code: b9kq7), CDC 2023.
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CFP-SS: Advances in Decision, Control, and Testing for Autonomous Driving (code: 7dh27), CDC 2023.
