Professor, Wuhan University of Science and Technology, China
Chao Deng is a dedicated and innovative academic currently serving as an Associate Professor at the School of Automobile and Traffic Engineering, Wuhan University of Science and Technology. His work centers on traffic and transportation engineering, with a focus on integrating human factors into intelligent transport systems. In addition to his professorial duties, he holds several significant administrative and academic roles, including Director of the Intelligent Automobile Engineering Research Institute and Deputy Director of the Department of Transportation and Logistics Engineering. His research contributions span cognitive ergonomics, connected vehicle technology, and driving behavior modeling, establishing him as a forward-thinking contributor to transportation science in China and beyond.
📝 Profile
Orcid
🎓 Education
Chao Deng earned his Ph.D. in Traffic and Transportation Engineering from Wuhan University of Technology in June 2019, where he laid the foundation for his future in cognitive and intelligent transportation studies. During his doctoral training, he participated in a joint Ph.D. program in System Design at the University of Waterloo, Canada, in December 2018. This international academic exposure enriched his technical perspectives and helped him gain a more holistic understanding of global transportation system dynamics, particularly in human-computer interaction and automated vehicle systems.
💼 Experience
Since joining Wuhan University of Science and Technology in 2019, Chao Deng has advanced through various academic ranks, starting as a Lecturer and progressing to Associate Professor by December 2020. His influence expanded further as he took on leadership roles such as Director of the Intelligent Automobile Engineering Research Institute and Deputy Director of his department. His experience also includes serving on national academic committees, where he contributes to shaping the future of transportation research in China. His role as an Invited Reviewer and committee member highlights his recognition in scholarly circles and his commitment to academic excellence and mentorship.
🔬 Research Interest
Chao Deng’s research interests are deeply rooted in the development and refinement of intelligent transportation systems, with a specific focus on the human element in automated and connected environments. He investigates cognitive modeling, driver performance, and mental workload in vehicular contexts. His work often explores how human-computer interaction can be optimized in connected and automated vehicle (CAV) scenarios, aiming to improve safety, efficiency, and user experience. Traffic simulation, human factors, and cognitive ergonomics also form core pillars of his research, as he strives to bridge the gap between emerging technologies and practical roadway implementation.
📚 Publication Top Notes
Lee, S., Gong, Y., & Deng, C. (2025). “Counterfactual experience augmented off-policy reinforcement learning.” Neurocomputing, 541, Article 130017. [DOI: 10.1016/j.neucom.2025.130017]
Summary: This paper proposes a novel reinforcement learning framework—Counterfactual Experience Augmented (CEA) learning—designed to address the limitations of traditional off-policy learning by generating enriched experience through counterfactual inference. Using variational autoencoders and bisimulation metrics, the method improves performance in environments with limited exploration efficiency and non-stationary dynamics.
Cheng, M., Yan, Y., Han, Y., Lei, H., & Deng, C. (2024). “Adaptive machine learning algorithm for human target detection in IoT environment.” Computing, 106, 1061–1079. [DOI: 10.1007/s00607-022-01123-z]
Summary: This study develops an adaptive machine learning algorithm aimed at improving pedestrian detection in intelligent transportation systems. By fusing data from roadside and onboard sensors and applying Kalman filtering and fuzzy association techniques, the model significantly enhances trajectory prediction accuracy for collision avoidance in IoT-based traffic scenarios.
Yan, Y., Deng, C., Ma, J., Wang, Y., & Li, Y. (2023). “A traffic sign recognition method under complex illumination conditions.” IEEE Access, 11, 3266825. [DOI: 10.1109/ACCESS.2023.3266825]
Summary: This article introduces a robust traffic sign recognition method tailored for complex lighting environments. It combines an adaptive image enhancement algorithm with a lightweight attention mechanism—Feature Difference (FD) module—within CNNs, resulting in enhanced detection performance and computational efficiency for autonomous driving systems.
Deng, C., Cao, S., & Niu, J. (2021). “An experimental investigation of novice and experienced drivers’ car following task performance on snowy road.” Proceedings of the 6th International Conference on Transportation Information and Safety (ICTIS), 593–598. [DOI: 10.1109/ICTIS54573.2021.9798681]
Summary: This experimental research investigates how driving experience affects car-following behavior on snow-covered roads. Comparing novice and experienced drivers, the study highlights distinct differences in following distance and reaction patterns, providing foundational insights for adaptive safety systems and driver training under winter conditions.
✅ Conclusion
Chao Deng stands as a leading figure in transportation research, particularly where engineering intersects with human cognition and behavior. His multidisciplinary approach, combining rigorous technical expertise with an acute understanding of human dynamics, makes his work highly relevant in the age of automated and intelligent transportation systems. Through impactful research, influential publications, and dedicated service, he continues to shape the future of traffic engineering and transportation safety, both in China and internationally. His contributions not only advance academic discourse but also influence practical applications in real-world mobility systems.