Senior Engineer at Chang’an University, China.
Dr. Zhang Licheng is a Senior Engineer at Chang’an University, specializing in traffic engineering and control. He has a profound interest in sustainable driving behavior, fuel consumption modeling, and autonomous vehicle efficiency. With 33 publications and 10 patents to his name, his pioneering work in fuel consumption prediction models has advanced the understanding of vehicular dynamics. Dr. Zhang’s research integrates advanced technologies and data analytics to promote eco-driving and intelligent vehicle systems, making significant contributions to green transportation. He is a recognized thought leader in the domain, blending academic rigor with practical applications to impact the automotive industry globally.
Profile Verification
Education
Experience
Dr. Zhang brings over a decade of experience in automotive engineering and intelligent vehicle research. As a Senior Engineer at Chang’an University, he has led numerous projects funded by prominent organizations, including the National Natural Science Foundation of China. His work emphasizes fuel-efficient driving strategies, autonomous vehicle simulations, and hybrid data modeling for energy optimization. Dr. Zhang has collaborated with global institutions, contributed to 33 journal publications, and mentored young researchers, shaping the future of green transportation technologies.
Research Interests
Dr. Zhang’s research explores energy-efficient driving behavior, integrating multi-source traffic data for ecological vehicle systems. He specializes in developing fuel consumption prediction models, autonomous driving strategies, and motion planning methods for lane-changing scenarios. His studies bridge the gap between driving behavior and environmental sustainability, contributing significantly to the design of energy-efficient autonomous vehicles. Dr. Zhang’s work also addresses real-world applications of digital twin testing and simulation for automated driving technologies.
Awards and Honors
Publications
Ma, S., Chen, C., Zhang, L., Zhang, J., Zhao, X.
Title: AMTrack: Transformer tracking via action information and mix-frequency features
Journal: Expert Systems with Applications
Year: 2025
Citations: 0
Zhang, L., Ya, J., Khattak, A.J., Peng, K., Guo, Y.
Title: Novel fuel consumption models integrating vehicular speed, acceleration, and jerk
Journal: Journal of Intelligent Transportation Systems: Technology, Planning, and Operations
Year: 2024
Citations: 0
Ma, S., Zhao, B., Zhang, L., Hou, Z., Zhao, X.
Title: Correlation Filter based on Trajectory Correction and Context Interference Suppression for Real-Time UAV Tracking
Journal: IEEE Transactions on Intelligent Vehicles
Year: 2024
Citations: 2
Zhang, L., Ya, J., Xu, Z., Xing, Y., Yang, R.
Title: Novel Neural-Network-Based Fuel Consumption Prediction Models Considering Vehicular Jerk
Journal: Electronics (Switzerland)
Year: 2023
Volume: 12
Issue: 17
Citations: 0
Wang, G., Zhang, L., Xu, Z., Qu, X.
Title: Predictability of Vehicle Fuel Consumption Using LSTM: Findings from Field Experiments
Journal: Journal of Transportation Engineering Part A: Systems
Year: 2023
Volume: 149
Issue: 5
Citations: 4
Peng, K., Xing, Y., Zhang, L., Song, Y., Ya, J.
Title: Quantitative Evaluation of Energy-saving Driving Based on Wavelet Transform
Conference: 7th IEEE International Conference on Transportation Information and Safety, ICTIS 2023
Year: 2023
Citations: 0
Zhang, L., Peng, K., Zhao, X., Khattak, A.J.
Title: New fuel consumption model considering vehicular speed, acceleration, and jerk
Journal: Journal of Intelligent Transportation Systems: Technology, Planning, and Operations
Year: 2023
Volume: 27
Citations: 12
Zhang, L., Zhang, T., Peng, K., Zhao, X., Xu, Z.
Title: Can Autonomous Vehicles Save Fuel? Findings from Field Experiments
Journal: Journal of Advanced Transportation
Year: 2022
Citations: 9
Wang, G., Zhang, L., Xu, Z., Wei, T., Qu, X.
Title: FuelNet: A precise fuel consumption prediction model using long short-term memory deep network for eco-driving
Conference: Energy Proceedings
Year: 2020
Citations: 0
Min, H., Zhao, X., Xu, Z., Zhang, L., Wang, R.
Title: Stereo Visual Odometry Based on Robust Features
Journal: Shanghai Jiaotong Daxue Xuebao/Journal of Shanghai Jiaotong University
Year: 2017
Citations: 2
Conclusion
Zhang Licheng is a strong candidate for the Best Researcher Award, owing to his innovative contributions to traffic engineering, eco-driving, and energy-efficient vehicular technologies. His well-rounded portfolio of research projects, patents, and publications underscores his dedication to advancing his field. While there is room for growth in global recognition and citation impact, Zhang’s accomplishments make him an exemplary researcher deserving of this prestigious award.