ZHU Meixin, Young Chair Professor, Xiaomi Scholar
School of Transportation, Southeast University
No.2, Dongnandaxue Road, Jiangning, Nanjing 211189, P.R.China
Email:meixin@seu.edu.cn
Google Scholar:https://scholar.google.com/citations?user=5Ysgg7AAAAAJ
RESEARCH INTERESTS
Autonomous Driving, Driving Behavior, AI for Transportation, Robotics
EDUCATION
2018–2022 Ph.D. in Civil Engineering,University of Washington, Seattle, USA
Advisor: Prof. Yinhai Wang
Dissertation: Behavior Modeling and Motion Planning for Autonomous Driving using Artificial Intelligence
2021–2023 M.S. in Computer Science (Machine Learning),Georgia Institute of Technology, Atlanta, USA
2015–2018 M.S. in Communication and Transportation Engineering,Tongji University, Shanghai, China
2011–2015 B.S. in Traffic Engineering,Tongji University, Shanghai, China
WORKING EXPERIENCE
2025–present Professor, School of Transportation,Southeast University
2022–2025 Tenure-track Assistant Professor, Intelligent Transportation Thrust,HKUST(GZ)
2022–2025 Affiliated Assistant Professor, Dept. of Civil and Environmental Engineering,HKUST
RESEARCH EXPERIENCE
Jan–Jul 2022 Software Research Intern,Motional
Behavioral planning for autonomous driving.
Jun–Sep 2021 Applied Scientist Intern,Amazon
Last Mile ML Science Team. Developed a new model for last mile delivery optimization.
Jun–Dec 2019 Research Intern,Oak Ridge National Laboratory (ORNL)
Signal Timing Control for Large-Scale Networked Intersections.
TEACHING EXPERIENCE
Spring 2023, 2024 Instructor,INTR 5230 Data-driven Methods in Transportation, HKUST(GZ)
Fall 2022, 2023 Instructor,INTR 5130 Traffic Control and Simulation, HKUST(GZ)
Fall 2021 Instructor,CET590 Traffic Systems Operations, University of Washington
Fall 2020 Teaching Assistant,CET590 Traffic Systems Operations, University of Washington
Fall 2017 Teaching Assistant,Statistical Analysis in Transportation Engineering, Tongji University
AWARDS & SCHOLARSHIPS
• Xiaomi Young Scholar, Xiaomi Foundation, Nov 2025
• IEEE ITSS Best Dissertation Award (Finalist), IEEE Intelligent Transportation Systems Society (ITSS), Aug 2024
• Top 3 Cited Paper, Transportation Research Part C: Emerging Technologies, Mar 2023
• Best Dissertation Award, TRB Standing Committee on Artificial Intelligence and Advanced Computing Applications (AED50), Jan 2023
• 2022 Transportation Statistics Interest Group (TSIG) Student Paper Award, Jan 2022
• 2nd Place, Transportation Forecasting Competition, TRB AI Committee AED50, Jan 2022
• Most Cited Paper, Transportation Research Part C: Emerging Technologies, Apr 2020
• Winning Award, 2021 Digital China Innovation Contest (Top 4 of 1332 teams), Apr 2021
• Outstanding Graduates of Shanghai (Top 5%), Shanghai Education Commission, Mar 2018
• National Graduate Scholarship (twice, Top 0.2%), Ministry of Education, China, Oct 2017, Oct 2016
SELECTED PUBLICATIONS (* CORRESPONDING AUTHOR)
Journal Articles
[1] H. Lu,M. Zhu*, C. Lu, S. Feng, X. Wang, Y. Wang, and H. Yang, "Empowering safer socially sensitive autonomous vehicles using human-plausible cognitive encoding," Proceedings of the National Academy of Sciences (PNAS), vol. 122, no. 21, 2025.
[2] H. Lu,M. Zhu*, and H. Yang, "Human-like driving technology for autonomous electric vehicles," Nature Reviews Electrical Engineering, pp. 1–2, 2025.
[3] H. Zhong, D. Chen, P. Wang, W. Wang, S. Shen, Y. Liu, andM. Zhu*, "Predicting On-Road Air Pollution Coupling Street View Images and Machine Learning," Environmental Science & Technology, vol. 59, no. 7, pp. 3582–3591, 2025.
[4] R. Shi, X. Wang, Y. Zhou, andM. Zhu*, "RuleNet: rule-priority-aware multi-agent trajectory prediction in ambiguous traffic scenarios," Transportation Research Part C: Emerging Technologies, vol. 180, 2025.
[5] X. Guo, X. Yang, M. Peng, H. Lu,M. Zhu*, and H. Yang, "Automating traffic model enhancement with AI research agent," Transportation Research Part C: Emerging Technologies, vol. 178, 2025.
[6] H. Lu, J. Yang,M. Zhu*, C. Lu, X. Chen, X. Zheng, and H. Yang, "A knowledge-driven, generalizable decision-making framework for autonomous driving via cognitive representation alignment," Transportation Research Part C: Emerging Technologies, vol. 172, 2025.
[7] X. Chen, X. Han,M. Zhu*, X. Chu, P. Tiu, X. Zheng, and Y. Wang, "EditFollower: Tunable Car Following Models for Customizable Driving Behavior," IEEE Transactions on Intelligent Transportation Systems, 2025.
[8] K. Chen, Y. Luo,M. Zhu*, X. Wang, H. Wang, and H. Yang, "Score-based spatial-temporal point process for traffic accident prediction," IEEE Transactions on Intelligent Transportation Systems, 2025.
[9] M. Peng, K. Chen, X. Guo, Q. Zhang, H. Zhong,M. Zhu*, and H. Yang, "Diffusion models for intelligent transportation systems: A survey," IEEE Transactions on Intelligent Transportation Systems, 2025.
[10] D. Chen, R. Zhong, K. Chen, Z. Shang,M. Zhu*, and E. Chung, "Dynamic High-Order Control Barrier Functions With Diffuser for Safety-Critical Trajectory Planning," IEEE Transactions on Intelligent Transportation Systems, 2025.
[11] X. Han, X. Chen,M. Zhu*, P. Cai, J. Zhou, and X. Chu, "EnsembleFollower: A Hybrid Car-Following Framework Based on Hierarchical Planning and Reinforcement Learning," IEEE Transactions on Vehicular Technology, 2025.
[12] M. Peng, X. Guo, X. Chen, K. Chen,M. Zhu*, L. Chen, and F-Y. Wang, "Lc-llm: Explainable lane-change intention and trajectory predictions with large language models," Communications in Transportation Research, vol. 5, 2025.
[13] X. Chen, K. Chen,M. Zhu*, H. F. Yang, S. Shen, X. Wang, and Y. Wang, "MetaFollower: Adaptable personalized autonomous car following," Transportation Research Part C: Emerging Technologies, vol. 169, 2024.
[14] H. Lu, C. Lu, H. Wang, J. Gong,M. Zhu*, and H. Yang, "Scenario-level knowledge transfer for motion planning of autonomous driving via successor representation," Transportation Research Part C: Emerging Technologies, vol. 169, 2024.
[15] K. Chen,M. Zhu*, L. Sun, and H. Yang, "Combining time dependency and behavioral game: A Deep Markov Cognitive Hierarchy Model for human-like discretionary lane changing modeling," Transportation Research Part B: Methodological, vol. 189, 2024.
[16] K. Chen, Y. Liang, J. Han, S. Feng, M. Zhu, and H. Yang, "Semantic-fused multi-granularity cross-city traffic prediction," Transportation Research Part C: Emerging Technologies, vol. 162, 2024.
[17] X. Guo, Q. Zhang, J. Jiang, M. Peng,M. Zhu*, and H. F. Yang, "Towards explainable traffic flow prediction with large language models," Communications in Transportation Research, vol. 4, 2024.
[18] P. Wang,M. Zhu*, X. Zheng, H. Lu, H. Zhong, X. Chen, S. Shen, X. Wang, Y. Wang, and F-Y. Wang, "Bevgpt: Generative pre-trained foundation model for autonomous driving prediction, decision-making, and planning," IEEE Transactions on Intelligent Vehicles, 2024.
[19] K. Chen, Y. Luo,M. Zhu*, and H. Yang, "Human-Like Interactive Lane-Change Modeling Based on Reward-Guided Diffusive Predictor and Planner," IEEE Transactions on Intelligent Transportation Systems, 2024.
[20] H. Zhong, K. Chen, C. Liu,M. Zhu*, and R. Ke, "Models for predicting vehicle emissions: A comprehensive review," Science of The Total Environment, vol. 923, 2024.
[21] Q. Wang, F. Ju, H. Wang, Y. Qian,M. Zhu*, W. Zhuang, and L. Wang, "Multiagent Reinforcement Learning for Ecological Car-Following Control in Mixed Traffic," IEEE Transactions on Transportation Electrification, vol. 10, no. 4, 2024.
[22] D. Chen,M. Zhu*, H. Yang, X. Wang, and Y. Wang, "Data-driven Traffic Simulation: A Comprehensive Review," IEEE Transactions on Intelligent Vehicles, 2024.
[23] X. Chen, X. Yuan,M. Zhu*, X. Zheng, S. Shen, X. Wang, Y. Wang, and F-Y. Wang, "Aggfollower: Aggressiveness informed car-following modeling," IEEE Transactions on Intelligent Vehicles, 2024.
[24] X. Chen, M. Peng, P. Tiu, Y. Wu, J. Chen,M. Zhu*, and X. Zheng, "Genfollower: Enhancing car-following prediction with large language models," IEEE Transactions on Intelligent Vehicles, 2024.
[25] H. Lu, Y. Liu,M. Zhu*, C. Lu, H. Yang, and Y. Wang, "Enhancing interpretability of autonomous driving via human-like cognitive maps," IEEE Transactions on Intelligent Vehicles, 2024.
[26] D. Chen, H. Li, Z. Jin, H. Tu, andM. Zhu*, "Risk-anticipatory autonomous driving strategies considering vehicles’ weights based on hierarchical deep reinforcement learning," IEEE Transactions on Intelligent Transportation Systems, 2024.
[27] Z. Yu,M. Zhu*, and X. Chu, "Risk-Aware Net: An Explicit Collision-Constrained Framework for Enhanced Safety Autonomous Driving," IEEE Robotics and Automation Letters, 2024.
[28] X. Han, Q. Yang, X. Chen, Z. Cai, X. Chu, andM. Zhu*, "Autoreward: Closed-loop reward design with large language models for autonomous driving," IEEE Transactions on Intelligent Vehicles, 2024.
[29] Z. Cui, M. Tsai, M. Zhu, H. Yang, C. Liu, S. Yin, and Y. Wang, "Traffic Performance Score: Measuring Urban Mobility and Online Predicting of Near-Term Traffic," Transportation Research Record, vol. 2678, no. 8, 2024.
[30] K. Chen, J. Han, S. Feng, M. Zhu, and H. Yang, "Region-aware hierarchical graph contrastive learning for ride-hailing driver profiling," Transportation Research Part C: Emerging Technologies, vol. 156, 2023.
[31] X. Chen,M. Zhu*, K. Chen, P. Wang, H. Lu, H. Zhong, X. Han, X. Wang, and Y. Wang, "Follownet: A comprehensive benchmark for car-following behavior modeling," Scientific Data, vol. 10, no. 1, 2023.
[32] Y. Du, J. Chen, C. Zhao, F. Liao, and M. Zhu, "A hierarchical framework for improving ride comfort of autonomous vehicles via deep reinforcement learning with external knowledge," Computer-Aided Civil and Infrastructure Engineering, vol. 38, no. 8, 2023.
[33] H. Zhong, R. Xu, H. Lu, Y. Liu, andM. Zhu*, "Dynamic assessment of population exposure to traffic-originated PM2.5 based on multisource geo-spatial data," Transportation Research Part D: Transport and Environment, vol. 124, 2023.
[34] H. Yu, P. Wang, J. Wang, J. Ji, Z. Zheng, J. Tu, G. Lu, J. Meng,M. Zhu*, S. Shen, and others, "Catch Planner: Catching High-Speed Targets in the Flight," IEEE/ASME Transactions on Mechatronics, vol. 28, no. 4, 2023.
[35] R. Ke, Z. Cui, Y. Chen, M. Zhu, H. Yang, Y. Zhuang, and Y. Wang, "Lightweight edge intelligence empowered near-crash detection towards real-time vehicle event logging," IEEE Transactions on Intelligent Vehicles, vol. 8, no. 4, 2023.
[36] Z. Yu, M. Zhu, K. Chen, X. Chu, and X. Wang, "LF-Net: A learning-based Frenet planning approach for urban autonomous driving," IEEE Transactions on Intelligent Vehicles, vol. 9, no. 1, 2023.
[37] C. Liu, H. Yang, M. Zhu, F. Wang, T. Vaa, and Y. Wang, "Real-time multi-task environmental perception system for traffic safety empowered by edge artificial intelligence," IEEE Transactions on Intelligent Transportation Systems, vol. 25, no. 1, 2023.
[38] M. Zhu, H. F. Yang, C. Liu, Z. Pu, and Y. Wang, "Real-time crash identification using connected electric vehicle operation data," Accident Analysis & Prevention, vol. 173, 2022.
[39] H. Wang, M. Zhu, W. Hong, C. Wang, W. Li, G. Tao, and Y. Wang, "Network-wide traffic signal control using bilinear system modeling and adaptive optimization," IEEE Transactions on Intelligent Transportation Systems, vol. 24, no. 1, 2022.
[40] H. Yang, J. Cai, M. Zhu, C. Liu, and Y. Wang, "Traffic-informed multi-camera sensing (TIMS) system based on vehicle re-identification," IEEE Transactions on Intelligent Transportation Systems, vol. 23, no. 10, 2022.
[41] H. Yang, C. Liu, M. Zhu, X. Ban, and Y. Wang, "How fast you will drive? Predicting speed of customized paths by deep neural network," IEEE Transactions on Intelligent Transportation Systems, vol. 23, no. 3, 2021.
[42] M. Zhu, W. Zhu, J. M. Lutin, Z. Cui, and Y. Wang, "Developing a practical method to compute state-level bus occupancy rate," Journal of Transportation Engineering, Part A: Systems, vol. 147, no. 6, 2021.
[43] P. Sun, X. Wang, and M. Zhu, "Modeling car-following behavior on freeways considering driving style," Journal of Transportation Engineering, Part A: Systems, vol. 147, no. 12, 2021.
[44] M. Zhu, Y. Wang, Z. Pu, J. Hu, X. Wang, and R. Ke, "Safe, efficient, and comfortable velocity control based on reinforcement learning for autonomous driving," Transportation Research Part C: Emerging Technologies, vol. 117, 2020.
[45] M. Zhu, X. Wang, and J. Hu, "Impact on car following behavior of a forward collision warning system with headway monitoring," Transportation Research Part C: Emerging Technologies, vol. 111, 2020.
[46] H. Wang, M. Zhu, W. Hong, C. Wang, G. Tao, and Y. Wang, "Optimizing signal timing control for large urban traffic networks using an adaptive linear quadratic regulator control strategy," IEEE Transactions on Intelligent Transportation Systems, vol. 23, no. 1, 2020.
[47] Z. Pu, M. Zhu, W. Li, Z. Cui, X. Guo, and Y. Wang, "Monitoring public transit ridership flow by passively sensing Wi-Fi and Bluetooth mobile devices," IEEE Internet of Things Journal, vol. 8, no. 1, 2020.
[48] M. Zhu, X. Wang, A. Tarko, and S. Fang, "Modeling car-following behavior on urban expressways in Shanghai: A naturalistic driving study," Transportation Research Part C: Emerging Technologies, vol. 93, 2018.
[49] M. Zhu, X. Wang, and Y. Wang, "Human-like autonomous car-following model with deep reinforcement learning," Transportation Research Part C: Emerging Technologies, vol. 97, 2018.
[50] X. Wang and M. Zhu, "Calibration and Validation of Car-following Models on Urban Expressways for Chinese Drivers Using Naturalistic Driving Data," China Journal of Highway and Transport, vol. 31, no. 9, 2018.
[51] X. Wang, M. Zhu, and M. Chen, "Dimensionality Reduction and Multivariate Analysis of Driver’s Forward Collision Avoidance Behavior Features," Journal of Tongji University (Natural Science), vol. 44, no. 12, 2017.
[52] X. Wang, M. Chen, M. Zhu, and P. Tremont, "Development of a kinematic-based forward collision warning algorithm using an advanced driving simulator," IEEE Transactions on Intelligent Transportation Systems, vol. 17, no. 9, 2016.
[53] X. Wang, M. Zhu, M. Chen, and P. Tremont, "Drivers’ rear end collision avoidance behaviors under different levels of situational urgency," Transportation Research Part C: Emerging Technologies, vol. 71, 2016.
[54] X. Wang, M. Zhu, and Y. Xing, "Impacts of collision warning system on car following behavior based on naturalistic driving data," Journal of Tongji University (Natural Science), vol. 44, no. 7, 2016.
[55] X. Wang, M. Zhu, and M. Chen, "Impacts of Situational Urgency on Drivers’ Collision Avoidance Behaviors," Journal of Tongji University (Natural Science), vol. 44, no. 6, 2016.
Selected Conference Articles
[1] K. Chen,M. Zhu*, L. Sun, and H. Yang, "Combining time dependency and behavioral game: a deep markov cognitive hierarchy model for human-like discretionary lane changing modeling," 25th ISTTT, Ann Arbor, Michigan, USA, Jul 2024.
[2] X. Chen,M. Zhu*, P. Tiu, and Y. Wang, "Personalized context-aware multi-modal transportation recommendation," 35th IEEE IV, Jeju Island, Korea, Jun 2024.
[3] M. Zhu*, D. Chen, X. Yuan, Z. Shang, and C. Liu, "Learning realistic and reactive traffic agents," 35th IEEE IV, Jeju Island, Korea, Jun 2024.
[4] Y. Luo, K. Chen, andM. Zhu*, "GRANP: a graph recurrent attentive neural process model for vehicle trajectory prediction," 35th IEEE IV, Jeju Island, Korea, Jun 2024.
[5] M. Zhu, H. Yang, and C. Liu, and Y. Wang, "Multi-Agent Deep Reinforcement Learning for Network-Wide Traffic Signal Control," 102nd TRB Annual Meeting, Washington D.C., USA, Jan 2023.
[6] K. Chen, X. Chen, Z. Yu, M. Zhu, H. Yang, "EquiDiff: A Conditional Equivariant Diffusion Model For Trajectory Prediction," IEEE ITSC 2023, Bilbao, Spain, Sep 2023.
[7] M. Zhu, X. Wang, and Y. Wang, "Human-like autonomous car-following model by deep deterministic policy gradient reinforcement learning," ASCE ICTD, Pittsburgh, Pennsylvania, Jul 2018.
PROFESSIONAL ACTIVITIES
Editorial Board
• Associate Editor,IEEE Transactions on Intelligent Vehicles
• Associate Editor,Scientific Data
Selected Reviewer
• IEEE Transactions on Robotics
• IEEE Transactions on Intelligent Transportation Systems
• Transportation Research Part B: Methodological
• Transportation Research Part C: Emerging Technologies
• International Conference on Machine Learning (ICML)
• Conference on Neural Information Processing Systems (NeurIPS)
• International Conference on Learning Representations (ICLR)
• IEEE International Conference on Robotics and Automation (ICRA)
Committee Memberships
• Committee Member, Subcommittee on Connected and Automated Traffic Flow (CAT-Flow), TRB ACP50, Oct 2021 – Present
• Younger Committee Member, Connected & Autonomous Vehicles (CAV) Impacts Committee, ASCE T&DI, Sep 2019 – Present
• Associate Committee Member, Artificial Intelligence Committee, ASCE T&DI, 2020 – Present
• Associate Committee Member, Street and Highway Operations Committee, ASCE T&DI, 2019 – Present
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