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Wenshuo Wang

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''All models are wrong, but some are useful.'' --- George E. P. Box
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About Me

Hi! I am Wenshuo Wang, a Postdoctoral Fellow supported by IVADO Awards at the Smart Transportation Lab, McGill University, collaborating with Prof. Lijun Sun. I received my Ph.D. degree in Mechanical Engineering from Beijing Institute of Technology, Beijing, China in 2018. Prior to joining McGill, I was a Postdoctoral Fellow with UC Berkeley, DeepDrive and Carnegie Mellon University. From 2015-2017, I was a Research Assistant working with Prof. J. Karl Hedrick at Vehicle Dynamics & Control Lab (VDL), UC Berkeley and with Prof. Ding Zhao and Prof. Xuanlong Nguyen at University of Michigan, Ann Arbor. My research interests are Bayesian nonparametric learning and reinforcement learning, and their applications to multi-agent interaction behavior modeling and prediction in common-but-challenging situations for smart mobility.


Research

My research goal is to develop analytically computable tools to semantically understand and predict human-involved interactions using machine learning techniques with support of data science toward eco-safe deployment of AI-based agents (e.g., autonomous cars) in the human cyber-physical systems (H-CPS) such as Smart Cities.

My research interests mainly focus on Cognitive Autonomy & Interactive Learning (CAIL) — combining human interactive behavior analysis and prediction from insights of computational cognition with machine learning & AI techniques to enable mobile robotic systems (e.g. intelligent vehicles) to reason competently about their own safety, thus making H-CPS more safe and smarter, including:


Publications

JOURNALS (*Corresponding Author)

  1. Chao Lu, Chen Lv, Jianwei Gong, Wenshuo Wang, Dongpu Cao, Fei-Yue Wang. Instance-level knowledge transfer for data-driven driver model adaptation with homogeneous domains IEEE Transactions on Intelligent Transportation Systems, DOI: 10.1109/TITS.2022.3161939, 2022 (In Press).

  2. Huanjie Wang, Wenshuo Wang*, Shihua Yuan, Xueyuan Li, Lijun Sun. On social interactions of merging behaviors at highway on-ramps in congested traffic, IEEE Transactions on Intelligent Transportation Systems, DOI: 10.1109/TITS.2021.3102407, 2021 (In Press).

  3. Huanjie Wang, Wenshuo Wang*, Shihua Yuan, Xueyuan Li. Uncovering interpretable internal states of merging tasks at highway on-ramps for autonomous driving decision-making, IEEE Transactions on Automation Science and Engineering, DOI: 10.1109/TASE.2021.3103179, 2021 (In Press).

  4. Sen Yang, Wenshuo Wang*, Junqiang Xi. Leveraging human driving preferences to predict vehicle speed, IEEE Transactions on Intelligent Transportation Systems, DOI: 10.1109/TITS.2021.3101000, 2021 (In Press).

  5. Chengyuan Zhang, Jiacheng Zhu, Wenshuo Wang*, and Junqiang Xi. Spatiotemporal learning of multivehicle interaction patterns in lane-change scenarios, IEEE Transactions on Intelligent Transportation Systems, DOI: 10.1109/TITS.2021.3057645, 2021. (In Press)

  6. Wenshuo Wang, Weiyang Zhang, Jiacheng Zhu, Ding Zhao. Understanding V2V driving scenarios through traffic primitives, IEEE Transactions on Intelligent Transportation Systems. DOI: 10.1109/TITS.2020.3014612, 2020. (In Press).

  7. Wenshuo Wang, Xiaoxiang Na, Dongpu Cao, Jianwei Gong, Junqiang Xi, Yang Xing, Fei-Yue Wang. Decision-making in driver-automation shared control: A review and perspectives, IEEE/CAA Journal of Automatica Sinica, vol. 5, no. 7, pp. 1289–1307, 2020.

  8. Wenshuo Wang, Aditya Ramesh, Jiacheng Zhu, Jie Li, Ding Zhao. Clustering driving encounter scenarios using connected vehicle trajectories, IEEE Transactions on Intelligent Vehicles, vol. 5, no. 3, pp. 485–496, 2020.

  9. Wenshuo Wang, Wei Han, Xiaoxiang Na, Jianwei Gong, Junqiang Xi. A probabilistic approach to measuring driving behavior similarity with driving primitives, IEEE Transactions on Intelligent Vehicles, vol. 5, no. 1, pp. 127–138, 2020.

  10. Chunqing Zhao, Wenshuo Wang*, Shaopeng Li, Jianwei Gong. Influence of cut-in maneuvers for an autonomous car on surrounding drivers: Experiment and analysis, IEEE Transactions on Intelligent Transportation Systems, vol. 21, no. 06, pp. 2266 – 2276, 2020.

  11. Wenshuo Wang, Junqiang Xi, J Karl Hedrick. A learning-based personalized driver model using bounded generalized Gaussian mixture models, IEEE Transactions on Vehicular Technology, vol. 68, no. 12, pp. 11679–11690, 2019.

  12. Weiyang Zhang and Wenshuo Wang*. Learning V2V interactive driving patterns at signalized intersections, Transportation Research Part C: Emerging Technologies, vol. 108, pp. 151–166, 2019.

  13. Shun Yang, Wenshuo Wang, Yuande Jiang, Sumin Zhang, Weiwen Deng. What contributes to driving behavior prediction at unsignalized intersections?, Transportation Research Part C: Emerging Technologies, vol. 108, pp. 100–114, 2019.

  14. Sen Yang, Wenshuo Wang*, Chao Lu, Jianwei Gong, Junqiang Xi. A time-efficient approach for decision-making style recognition in lane-change behavior, IEEE Transactions on Human-Machine Systems, vol. 49, no. 06, pp. 579–588, 2019.

  15. Xiaohan Li, Wenshuo Wang*, Matthias Roetting. Estimating driver’s lane-change intent considering driving style and contextual traffic, IEEE Transactions on Intelligent Transportation Systems, vol. 20, no. 09, pp. 3258–3271, 2019.

  16. Weihan, Wenshuo Wang*, Xiaohan Li, and Junqiang Xi. Statistical-based approach for driving style recognition using Bayesian probability with kernel density estimation, IET Intelligent Transportation Systems, vol. 13, no. 01, pp. 22–30, 2019.

  17. Shun Yang, Wenshuo Wang, Chang Liu, Weiwen Deng. Scene understanding in deep learning-based end-to-end controllers for autonomous vehicles, IEEE Transactions on Systems, Man, and Cybernetics: Systems, vol. 49, no. 01, pp. 53–63, 2019.

  18. Wenshuo Wang, Junqiang Xi, and Ding Zhao. Driving style analysis using primitive driving patterns with Bayesian nonparametric approaches, IEEE Transactions on Intelligent Transportation Systems, vol. 20, no. 08, pp. 2986–2998, 2019.

  19. Sen Yang, Wenshuo Wang, Fengqi Zhang, Yuhui Hu, Junqiang Xi. Driving style-oriented adaptive equivalent consumption minimization strategies for HEVs, IEEE Transactions on Vehicular Technology, vol. 67, no. 10, pp. 9249–9261, 2018.

  20. Wenshuo Wang, Ding Zhao, Junqiang Xi, Wei Han. A learning-based approach for lane departure warning systems with a personalized driver model, IEEE Transactions on Vehicular Technology, vol. 67, no. 10, pp. 9145–9157, 2018.

  21. Wenshuo Wang and Ding Zhao. Extracting traffic primitives directly from naturalistically logged data for self-driving applications, IEEE Robotics and Automation Letters, vol. 03, no. 02, pp. 1223–1229, 2018.

  22. Wenshuo Wang, Junqiang Xi, and Ding Zhao. Learning and inferring a driver’s braking action in car-following scenarios, IEEE Transactions on Vehicular Technology, vol. 67, no. 05, pp. 3887–3899, 2018.

  23. Wenshuo Wang, Junqiang Xi, Alexandre Chong, Lin Li. Driving style classification using a semisupervised support vector machine, IEEE Transactions on Human-Machine Systems, vol. 47, no. 05, pp. 650–660, 2017.

  24. Wenshuo Wang and Ding Zhao. Evaluation of lane departure correction systems using a regenerative stochastic driver model, IEEE Transactions on Intelligent Vehicles, vol. 02, no. 03, pp. 221–232, 2017.

  25. Wenshuo Wang, Chang Liu, and Ding Zhao. How much data are enough? A statistical approach with case study on longitudinal driving behavior, IEEE Transactions on Intelligent Vehicles, vol. 02, no. 02, pp. 85–98, 2017.

  26. Wenshuo Wang, Junqiang Xi, Chang Liu, and Xiaohan Li. Human-centered feed-forward control of a vehicle steering system based on a driver’s path-following characteristics, IEEE Transactions on Intelligent Transportation Systems, vol. 18, no. 06, pp. 1440–1453, 2017.

  27. Min Zhou, Hui Jin, Wenshuo Wang. A review of vehicle fuel consumption models to evaluate eco-driving and eco-routing, Transportation Research Part D: Transport and Environment, vol. 49, pp. 203–218, 2016.

  28. Wenshuo Wang and Junqiang Xi. Study of semi-active suspension control strategy based on driving behaviour characteristics, International Journal of Vehicle Design, vol. 68, no. 1-3, pp. 141–161, 2015.

  29. Ying Zong, Junqiang Xi, and Wenshuo Wang. Research on virtual experimental teaching platform of vehicle electronic control, Laboratory Research and Exploration, vol. 34, no. 1, 2015.

  30. Wenshuo Wang, Junqiang Xi, Huiyan Chen. Modeling and recognizing driver behavior based on driving data: A survey, Mathematical Problems in Engineering, vol. 2014, pp. 1-19, 2014.


CONFERENCES

  1. W. Zhang, Wenshuo Wang, J. Zhu, D. Zhao. Multi-vehicle interaction scenarios generation with interpretable traffic primitives and gaussian process regression, In Proceedings of the 2020 IEEE Intelligent Vehicles Symposium (IV2020), October 19 - November 13, 2020. (Virtual) Las Vegas, NV, United States.

  2. Wenshuo Wang, C. Zhang, P. Wang, C.-Y. Chan. Learning representations for multi-vehicle spatiotemporal interactions with semi-stochastic potential fields, In Proceedings of the 2020 IEEE Intelligent Vehicles Symposium (IV2020), October 19 - November 13, 2020. (Virtual) Las Vegas, NV, United States.

  3. Q. Lin, Wenshuo Wang, Y. Zhang, J. Dolan. Measuring similarity of interactive driving behaviors using matrix profile, In Proceedings of the 2020 IEEE American Control Conference (2020ACC), 1-3 July 2020. Denver, CO, USA.

  4. S. Qin, J. Zhu, J. Qin, Wenshuo Wang, D. Zhao. Recurrent attentive neural process for sequential data, In the Workshop on Proceedings of the 2019 Conference on Neural Information Processing Systems (2019NeurIPS).

  5. Y. Guo, V. V. Kalidindi, M. Arief, Wenshuo Wang, J. Zhu, H. Peng, D. Zhao. Modeling multi-vehicle interaction scenarios using Gaussian random fields, In Proceedings of the 2019 IEEE Intelligent Transportation Systems Conference (ITSC), 27-30 Oct. 2019. Auckland, New Zealand, New Zealand.

  6. C. Zhang, J. Zhu, Wenshuo Wang, D. Zhao. A general framework of learning multi-vehicle interaction patterns from video, In Proceedings of the 2019 IEEE Intelligent Transportation Systems Conference (ITSC), 27-30 Oct. 2019. Auckland, New Zealand, New Zealand.

  7. S. Yang, J. Xi, Wenshuo Wang. Driver drowsiness detection through a vehicle’s active probe action, In Proceedings of the 2019 IEEE 2nd Connected and Automated Vehicles Symposium (CAVS), 22-23 Sept. 2019, Honolulu, HI, USA.

  8. W. Ding, Wenshuo Wang, D. Zhao. A multi-vehicle trajectories generator to simulate vehicle-to-vehicle encountering scenarios, In Proceedings of the 2019 International Conference on Robotics and Automation (ICRA), 20-24 May 2019, Montreal, QC, Canada.

  9. J. Zhu, Wenshuo Wang, D. Zhao. A tempt to unify heterogeneous driving databases using traffic primitives, In Proceedings of the 2018 IEEE International Intelligent Transportation Systems Conference (ITSC), 4-7 Nov. 2018, Maui, HI, USA.

  10. C. Lu, F. Hu, Wenshuo Wang, J. Gong, Z. Ding. Transfer learning for driver model adaptation via modified local procrustes analysis, In Proceedings of the 2018 IEEE Intelligent Vehicles Symposium (IV), 26-30 June 2018, Changshu, China.

  11. C. Zhao, S. Li, F. Liu, Wenshuo Wang, J. Gong. Influence analysis of autonomous cars’ cut-in behavior on human drivers in a driving simulator, In Proceedings of the 2018 IEEE Intelligent Vehicles Symposium (IV), 26-30 June 2018, Changshu, China.

  12. S. Li, Wenshuo Wang, Z. Mo, D. Zhao. Clustering of naturalistic driving encounters using unsupervised learning, In Proceedings of the 2018 IEEE Intelligent Vehicles Symposium (IV), 26-30 June 2018, Changshu, China.

  13. S. Yang, Wenshuo Wang, C. Liu, W. Deng, J K. Hedrick. Feature analysis and selection for training an end-to-end autonomous vehicle controller using deep learning approach, In Proceedings of the 2017 IEEE Intelligent Vehicles Symposium (IV), 11-14 June 2017, Los Angeles, CA, USA, pp. 1033–1038.

  14. Wenshuo Wang, D. Zhao, J. Xi, D. J. LeBlanc, J. K. Hedrick. Development and evaluation of two learning-based personalized driver models for car-following behaviors, In Proceedings of the 2017 IEEE American Control Conference, May 24?26, 2017, Seattle, USA, pp. 1133–1138.

  15. D. Zhao, Wenshuo Wang, and D. J. LeBlanc. Evaluation of a semi-autonomous lane departure correction system using naturalistic driving data, In Proceedings of the 2017 IEEE Intelligent Vehicles Symposium (IV). 11-14 June 2017, Los Angeles, CA, USA.

  16. X. Li, M. Rotting, and Wenshuo Wang. Bayesian network-based identification of driver lane-changing intents using eye tracking and vehicle-based data, In Proceedings of the 13th International Symposium on Advanced Vehicle Control. 2016.

  17. Wenshuo Wang and J. Xi. A rapid pattern-recognition method for driving styles using clustering-based support vector machines, In Proceedings of the 2016 IEEE American Control Conference. 6–8 July 2016, Boston, MA, USA, pp. 5270–5275.

  18. Wenshuo Wang, J. Xi, and J. Wang. Human-centered feed-forward control of a vehicle steering system based on a driver’s steering model, In Proceedings of the 2015 IEEE American Control Conference. July 1-3, Chicago, IL, USA, pp. 3361–3366.


PATENTS

  1. Wenshuo Wang, Aditya Ramesh, Ding Zhao. Unsurpervised classification of encountering scenarios using connected vehicle datasets. US20200133269A1, USA, 2020

  2. Ding Zhao, Jiacheng Zhu, Wenshuo Wang. System and method for unifying heterogenous datasets using primitives. US20200193324A1, USA, 2020

  3. Ding Zhao, Senyu Mou, Yan Chang, Wenshuo Wang. System and method for determining optimal LiDAR placement on autonomous vehicles. US20200191972A1, USA, 2020

  4. Junqiang Xi and Wenshuo Wang. Intelligent driving systems with an embedded driver model. US20170297564A1, USA, 2019


Services

Editors/Associate Editors

Reviewers

Chairs/Co-chairs


Talks & Awards

Talks

  1. 2021/09, ‘Spatiotemporal learning of multivehicle interaction patterns with nonparametric Bayesian statistics’, @ 24th IEEE International Conference on Intelligent Transportation, Indianapolis, IN, United States.

  2. 2021/09, ‘Variational Inference for Multivaritate Gaussian Mixtures’, @ McGill University

  3. 2021/07, ‘Interpretable Learning of multi-vehicle interactions for autonomous driving decision-making’, @ McGill University

  4. 2021/05, ‘How does human make sequential decisions during interactions?’, @ Beijing Institute of Technology (BIT), and @ Tongji University, China

  5. 2021/04, ‘Human-level learning of multi-vehicle interactions toward autonomous driving decision-making’, @ Jilin University, China

  6. 2021/01, ‘Driving primitives – Human-level learning for autonomous vehicles’, @ Beijing Jiaotong University, China

  7. 2020/11, ‘Adaptive path tracking for agricultural vehicles with sliding consideration’, @ Beijing AIForce Technoloy Co. Ltd., China

  8. 2019/08, ‘Human-level learning of driving primitives through Bayesian nonparametric statistics’, @ Vision and Automation System Lab at Robotics Institute, Carnegie Mellon University (CMU)

  9. 2019/07, ‘Traffic primitives: Concept, theory, and applications’, @ Mechanical Systems Control (MSC) Lab, UC Berkeley

  10. 2019/05, ‘Traffic primitives: concept, learning, and applications’, @ Smart Transportation Lab, McGill University

  11. 2018/09, ‘Traffic primitives: learning-based extraction and its applications’, @ Series Talks of Safe AI Lab, Carnegie Mellon University (CMU).

  12. 2018/07, ‘Extracting traffic primitives through Bayesian nonparametric learning and its applications’, @ Workshop on Safe AI in Autonomous Vehicles, Carnegie Mellon University (CMU).

  13. 2017/06, ‘A learning-based method for modeling driver behavior: theory and applications’, @ 2017 IEEE Intelligent Vehicles Symposium, CA, USA.

Awards