Zirui Li, Ph.D.

Incoming Postdoc at Nanyang Technological University

About Me

Zirui Li, Ph.D.

I just successfully defensed my Ph.D. thesis on Interactive Motion Planning of Automated Vehicles based on Spatiotemporal Risk Continual Learning at Beijing Institute of Technology, Beijing, China, under the supervision of Prof. Jianwei Gong. Prior to this, I was a visiting researcher in TU Delft and TU Dresden from June, 2021 to June, 2024, working with Prof. Meng Wang, Prof. Victor Knoop. Now, I am an incoming postdoctoral research associate at Nanyang Technological University.

  • Title: Postdoctoral Research Associate
  • E-mail: ziruili.work.bit@gmail.com
  • Institution: Beijing Institute of Technology

 

Research Interests

My research interests include data-driven interactive behaviour modelling and motion planning in dense environment, continual/lifelong learning in robotics (e.g., automated vehicles), traffic safety assessment and collective intelligence emergence in edge (Postdoctoral research). Please refer to the Research Section for more details of my research.

I am open to research discussion and collaboration. Please feel free to reach out!

News

  • May. 2025: I just successfully defensed my Ph.D. thesis.
  • May. 2025: I was recognised as the IEEE ITSS Young Professionals Fellowship.
  • Jan. 2025: I was selected for China Association for Science and Technology Youth Talent Support.
  • Apr. 2025: Welcome to my homepage.

Biography

Education

Ph.D., Mechanical Engineering

Sep. 2019 - Jun. 2025

Beijing Institute of Technology (BIT), Beijing, China

Advisor: Prof. Jianwei Gong

Dissertation: Interactive Motion Planning of Automated Vehicles based on Spatiotemporal Risk Continual Learning

Awards: Excellent Doctoral Dissertation Seedling Fund

CSC Visiting Student

Jun. 2022 - Jun. 2023

TU Dresden, Germany

Advisor: Prof. Meng Wang

Topics: Interactive Behaviour modelling

CSC Visiting Student

Jun. 2021 - Jun. 2022

TU Delft, The Netherlands

Advisor: Prof. Meng Wang and Dr. Victor Knoop

Topics: Interactive Behaviour modelling

B.Eng., Xuteli Talent School (Mechanical Engineering)

Sep. 2015 - Jun. 2019

Beijing Institute of Technology (BIT), Beijing, China

Advisor: Prof. Jianwei Gong

Thesis: Analysis of influencing factors for driver decision-making at urban intersections

Awards: Outstanding Graduate and Outstanding Undergraduate Thesis

Work Experience

Part-time Researcher

Jun. 2023 - Jun. 2024

TU Dresden, Dresden, Germany

Advisor: Prof. Meng Wang

Topic: DNN-based Bus/tram arrival time prediction

Selected Research

Below are three selected interesting research I have done.

Interactive Motion Planning for Automated Vehicles

With the continuous advancements in information, sensing, and artificial intelligence technologies, unmanned vehicles as a new type of transport platform have made significant progress in fields such as autonomous delivery. Although current motion planning techniques can maintain a relatively high level of safety in closed environments, they still struggle to accurately represent the spatiotemporal risks induced by interactions among multiple traffic participants in dynamic and complex urban settings. This shortcoming leads to reduced efficiency in motion planning and undermines safety. To address these issues, this work focuses on modeling the interactive behaviors among multiple traffic participants, quantifying and integrating various types of risk, and designing motion planning algorithms. Accordingly, it proposes a spatiotemporal risk continual learning–based interactive motion planning method for unmanned vehicles. The “behavior prediction–risk assessment–motion planning” integrated approach aims to enhance driving safety in dynamically interactive environments through accurate spatiotemporal risk assessment.

Related Links:

IEEE T-ITS 1 IEEE T-ITS 2 IEEE T-ITS 3 IEEE/ASME T-Mech GEIT Nat. Sci. Data TR-Part C Demo


Large-scale Driver Perceived Risk analysis with 140K+ ratings

Driver's perceived risk in self-driving cars can create the very danger that automation is meant to prevent: a frightened rider may hesitate when seconds matter, misjudge hazards, or disengage. However, how this risk evolves dynamically in real world remains poorly understood. Understanding what sparks that fear is therefore critical to real-world safety. We conducted the largest controlled investigation of passenger emotion in automated driving to date. 2,164 volunteers viewed short, high-fidelity videos of four common highway situations and provided 141,628 second-by-second safety ratings. These data yielded continuous sense-of-safety curves spanning 236 hours of driving. From the kinematics of the ego and surrounding vehicles we trained interpretable AI models to predict each moment’s rating. The AI cut median error to smaller than 1 unit on a 0-10 scale, capturing rapid changes in human's feeling of risk. Explainable-AI analysis uncovered a robust signature of fear: sudden, unpredictable manoeuvres by nearby vehicles combined with very short headways. Smooth, predictable motion, even at high speed, rarely provoked the same response. These findings provide crucial insights for designing AV driving behaviours that adapt to users' changing risk perception, ultimately enhancing safety and fostering greater public acceptance and trust of AVs.

Related Links:

Questionnaire Project Demo Results Demo


Public Transport Arrival Time Prediction

Accurate arrival time prediction (ATP) of buses and trams plays a crucial role in public transport operations. Current methods only focused on modeling the time series along the temporal dimension by investigating the point-to-point relationship between different timesteps. This strategy overlooked the latent periodic information within time series. Moreover, most studies developed algorithms for ATP based on a single or a few routes of public transport, which reduces their applicability in public transport management systems. To this end, this work proposes ArrivalNet, a two-dimensional temporal variation-based multi-step ATP for buses and trams. 125 days of public transport data from Dresden were collected for model training and validation.

Related Links:

IEEE T-ITS Data Sci. in Trans. Traffic Monitor Platform

Selected Publications

 

Preprints

P2. Complementary Learning System Empowers Online Continual Learning of Vehicle Motion Forecasting in Smart Cities
Under Review, 2025.
Z. Li, Y. Lin, G. Du, X. Zhao, C. Gong, C. Lu, and J. Gong
ArXiv Coming Soon
P1. H2C: Hippocampal Circuit-inspired Continual Learning for Lifelong Trajectory Prediction in Autonomous Driving
Under Review at IEEE T-PAMI, 2025.
Y. Lin(#), Z. Li(#), G. Du, X. Zhao, C. Gong, X. Wang, C. Lu, and J. Gong
(# Equal Contribution)
ArXiv Coming Soon

 

Selected Journal Papers

J13. ArrivalNet: Predicting City-wide Bus/Tram Arrival Time with Two-dimensional Temporal Variation Modeling
IEEE Transactions on Intelligent Transportation Systems, 2025.
Z. Li, P. Wolf, M. Wang
IEEE
J12. Experience-Shared Variable-Step Predictive Control of Range-Extended Electric Vehicles Using Transferable Driver Model
IEEE Transactions on Intelligent Transportation Systems, 2025, 26(1): 1123-1133.
J Li, Z. Li*, C. Wen, Y. Wu, R. Dixon, X. Hu, H. Xu
(* Corresponding Author)
IEEE
J11. An Imputation-enhanced Hybrid Deep Learning Approach for Traffic Volume Prediction in Urban Networks: A Case Study in Dresden
Data Science for Transportation, 2024, 6(3): 22.
P Yan(#), Z. Li(#), J. Ijaradar, S. Pape, M. Körner, M. Wang
(# Equal Contribution)
PDF
J10. Interactive Trajectory Primitives Representation and Extraction Based on Naturalistic Driving Data (In Chinese)
Automotive Engineering, 46.8 (2024): 1382-1393.
Z. Li, H. Wang, J Gong, C. Lu, X. Zhao, M. Wang
PDF
J9. Interactive Behavior Modeling for Vulnerable Road Users With Risk-Taking Styles in Urban Scenarios: A Heterogeneous Graph Learning Approach
IEEE Transactions on Intelligent Transportation Systems, 2024, 25(8): 8538-8555.
Z. Li, J. Gong, Z. Zhang, C. Lu, VL. Knoop, M. Wang
IEEE
J8. Continual Driver Behaviour Learning for Connected Vehicles and Intelligent Transportation Systems: Framework, Survey and Challenges
Green Energy and Intelligent Transportation, 2023, 2(4): 100103.
Z. Li, C. Gong, Y. Lin, G. Li, X. Wang, C. Lu, M. Wang, S. Chen, J. Gong
Elsevier
J7. Leveraging Multi-Stream Information Fusion for Trajectory Prediction in Low-Illumination Scenarios: A Multi-Channel Graph Convolutional Approach
IEEE Transactions on Intelligent Transportation Systems, 2023, 25(5): 3854-3869.
H. Gong(#), Z. Li(#), C. Lu, G. Du, J. Gong
(# Equal Contribution)
IEEE Code
J6. Continual Interactive Behavior Learning with Traffic Divergence Measurement: A Dynamic Gradient Scenario Memory Approach
IEEE Transactions on Intelligent Transportation Systems, 2023, 25(3): 2355-2372.
Y. Lin(#), Z. Li(#), C. Gong, C. Lu, X. Wang, J. Gong
(# Equal Contribution)
IEEE Code
J5. Personalized driver braking behavior modeling in the car-following scenario: An importance-weight-based transfer learning approach
IEEE Transactions on Industrial Electronics, 2022, 69(10): 10704-10714.
Z. Li, J. Gong, C. Lu, J. Li
IEEE
J4. A Transferable Energy Management Strategy for Hybrid Electric Vehicles via Dueling Deep Deterministic Policy Gradient
Green Energy and Intelligent Transportation, 2022, 1(2): 100018.
J Xu(#), Z. Li(#), G. Du, Q. Liu, L. Gao, Y. Zhao
(# Equal Contribution)
Elsevier Code
J3. A Hierarchical Framework for Interactive Behaviour Prediction of Heterogeneous Traffic Participants based on Graph Neural Network
IEEE Transactions on Intelligent Transportation Systems, 2021, 23(7): 9102-9114.
Z. Li, C. Lu, Y. Yi, J. Gong
IEEE
J2. Interactive Behavior Prediction for Heterogeneous Traffic Participants in the Urban Road: A Graph-Neural-Network-based Multitask Learning Framework
IEEE/ASME Transactions on Mechatronics 26.3 (2021): 1339-1349.
Z. Li, J. Gong, C. Lu, Y. Yi
IEEE
J1. Importance Weighted Gaussian Process Regression for Transferable Driver Behaviour Learning in the Lane Change Scenario
IEEE Transactions on Vehicular Technology, 2020, 69(11): 12497-12509.
Z. Li, J. Gong, C. Lu, J. Xi
IEEE

 

Conference Papers

C3. An Ensemble Learning Framework for Vehicle Trajectory Prediction in Interactive Scenarios
2022 IEEE Intelligent Vehicles Symposium (IV). IEEE, 2022: 51-57.
Z. Li, Y. Lin, C. Gong, X. Wang, Q. Liu, J. Gong, C. Lu
IEEE Code
C2. Transferable Driver Behavior Learning via Distribution Adaption in the Lane Change Scenario
2019 IEEE Intelligent Vehicles Symposium (IV). IEEE, 2019: 193-200.
Z. Li, C. Gong, C. Lu, J. Gong, J. Lu, Y. Xu, F. Hu
IEEE
C1. Development and Evaluation of Two Learning-based Personalized Driver Models for Pure Pursuit Path-tracking Behaviors
2018 IEEE Intelligent Vehicles Symposium (IV). IEEE, 2018: 79-84.
Z. Li, B. Wang, J. Gong, T. Gao, C. Lu, G. Wang
IEEE

 

Books

B2. Scene Understanding and Behavior Prediction for Intelligent Vehicles (In Chinese)
Beijing Institute of Technology Press, 2025.
C. Lu, Z. Li
Link Coming Soon
B1. Interactive Behavior Prediction and Decision-making for Intelligent Vehicles (In Chinese)
Beijing Institute of Technology Press, 2021.
C. Lu, J. Gong, Z. Li
Link

Professional Services

Program Committee

  • Working group leader, intelligent vehicle risk assessment working group under IEEE Standard Association
  • Co-chair, Cooperative Decision-making for Connected and Automated Vehicles in ITS at IEEE ITSC 2023
  • Associate Editor for IEEE IV 2023
  • Chair, Social, interactive and safe behaviors for AVs: benchmarks, models and applications at IEEE IV 2023
  • Organising committee and Proceedings editor, MFTS 2022
  • Chair, Social and Interactive Behavior Modelling in ITS at IEEE ITSC 2021

Journal Reviewer

  • IEEE Transactions on Intelligent Transportation Systems
  • IEEE Transactions on Neural Networks and Learning Systems
  • IEEE Transactions on Pattern Analysis and Machine Intelligence
  • IEEE Transactions on Vehicular Technology
  • IEEE Transaction on Transportation Electrification
  • Accident Analysis & Prevention
  • IEEE Robotics and Automation Letters
  • IET Intelligent Transport Systems
  • Frontiers of Information Technology & Electronic Engineering
  • IEEE Transactions on Intelligent Vehicles
  • IEEE/CAA Journal of Automatica Sinica
  • IEEE Internet of Things Journal
  • Transportation Research Part C: Emerging Technologies
  • Transportation Research Part F: Traffic Psychology and Behaviour

Conference Reviewer

  • IEEE Conference on Intelligent Transportation Systems (ITSC)
  • IEEE Intelligent Vehicles Symposium (IVs)
  • Transportation Research Board (TRB) Annual Meeting
  • IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)
  • IEEE International Conference on Robotics and Automation (ICRA)

Supervision

  • Beijing Institute of Technology: Cheng Gong, Yunlong Lin, Chenxu Wen, Haowen Wang, Hailong Gong, Lianzhen Wei, Yangtian Yi, Xianqi He, Jingyi Xu, Yingqi Tan, Tairan Chen.
  • TU Dresden: Fanyi Wei.
© Zirui Li (2025)