Ding Peng | Autonomous Vehicles | Best Researcher Award

Assist. Prof. Dr. Ding Peng | Autonomous Vehicles | Best Researcher Award

Associate Professor | Wuxi University of Technology |China

Assist. Prof. Dr. Ding Peng is an accomplished academic and researcher currently serving as an Associate Professor at Wuxi University of Technology, formerly known as Wuxi Institute of Technology, China. He is also a key member of the Jiangsu Province Engineering Research Center for Energy Saving and Safety of New Energy Vehicles, where he plays a vital role in promoting innovation and sustainability in the automotive sector. Dr. Peng obtained his degree in Vehicle Engineering from Chongqing University which laid a strong foundation for his expertise in intelligent vehicle systems and new energy technologies. His professional experience includes working as a Design Engineer at King Long United Automotive Industry (Suzhou) Co., Ltd., where he participated in the design and development of commercial buses, followed by a distinguished academic career at Wuxi University of Technology . Over the years, Dr. Peng has demonstrated exceptional competence in teaching and research, covering courses such as Automobile Structure, Automobile Theory, Principles of Automatic Control, and Intelligent and Connected Vehicle Technologies. His research interests focus on thermal management technologies for new energy vehicles, autonomous vehicle control strategies, and intelligent connected vehicle (V2X) technologies. His research skills encompass modeling, simulation, data fusion, control algorithms, and system optimization, emphasizing practical integration between academia and industry. Dr. Peng has led numerous enterprise and government-funded projects, published several high-impact academic papers, and secured multiple national patents, showcasing his dedication to advancing innovation in smart and sustainable mobility. His awards and honors include recognition for his leadership in research excellence, academic innovation, and contributions to engineering education. Driven by a vision of merging scientific theory with real-world application, he continues to nurture the next generation of engineers while advancing intelligent vehicle technologies. He has achieved 29 citations , authored 7 scientific papers, and holds an h-index of 2 .

Profiles:  Scopus | ORCID

Featured Publications

  1. Ding, P. (2025). A Cooperative Control Strategy for Predicting Passing Capacity and Intelligent Obstacle Avoidance in Autonomous Vehicles Based on Multisensor Fusion. Journal of Energy Storage.
  2. Ding, P. (2024). A distributed multiple-heat source staged heating method in an electric vehicle. International Journal of Vehicle Performance and Energy Systems.

  3. Ding, P. (2024). Distributed multi-heat-source hybrid heating system based on waste heat recovery for electric vehicles. Journal of Thermal Science and Energy Engineering.

  4. Ding, P. (2023). Research on interactive coupled preheating method utilizing engine-motor cooling waste heat in hybrid powertrains. Applied Thermal Engineering

 

Mohammad Anis | Transportation Engineering | Best Researcher Award

Mr. Mohammad Anis | Transportation Engineering | Best Researcher Award

PhD Student | Texas A&M University| United States

Mr. Mohammad Anis is a dedicated Ph.D. candidate in Civil and Environmental Engineering at Texas A&M University, specializing in traffic safety, autonomous vehicle safety, crash risk modeling, pedestrian safety, and digital twin applications. He previously earned an M.S. in Civil Engineering from the University of Texas Rio Grande Valley (2021), where he conducted pioneering research on electrically heated rigid pavements, and a B.S. in Civil Engineering from Dhaka University of Engineering & Technology, Bangladesh (2018). With over four years of research experience, he has worked extensively on federally and state-funded projects with agencies such as FHWA, TxDOT, NCHRP, FMCSA, and ODOT, contributing to crash prediction models, pedestrian safety analysis, driver distraction studies, and systemic roadway design improvements. His dissertation integrates physics-informed near-miss data with hierarchical Bayesian frameworks for real-time crash occurrence risk estimation, pushing the boundaries of data-driven traffic safety planning. His professional experience includes roles as a doctoral researcher at Texas A&M University, a graduate research assistant at the Texas A&M Transportation Institute, and a graduate teaching assistant at both Texas A&M University and UTRGV, where he mentored students in transportation engineering and civil materials. His research interests lie in real-time safety modeling, AI and machine learning applications in transportation, spatiotemporal crash risk prediction, and sustainable roadway infrastructure. He is skilled in programming (Python, R, MATLAB), statistical modeling (MCMC, machine learning, time-series analysis), traffic simulation tools (SUMO, VISSIM, CARLA), and GIS platforms (ArcGIS, QGIS). He has published widely in high-impact journals such as Accident Analysis & Prevention and Transportation Research Record, along with multiple IEEE and Scopus-indexed conferences. Among his many accolades are the Keese-Wootan Transportation Fellowship (Top 5%), Zachry Excellence Fellowship, Terracon Foundation Scholarship, and Graduate Student Travel Awards. With a strong record of publications, collaborations, and peer-review service, Mr. Anis demonstrates outstanding potential to lead future research in traffic safety and intelligent mobility systems. He has achieved 30 citations across 27 documents, with 8 publications and an h-index of 2.

Profiles:  Scopus | ORCID

Featured Publications

Anis, M., Geedipally, S. R., & Lord, D. (2025). Pedestrian crash causation analysis near bus stops: Insights from random parameters Negative Binomial–Lindley model. Accident Analysis & Prevention, 220, 108137.

Zhang, H., Li, S., Li, Z., Anis, M., Lord, D., & Zhou, Y. (2025). Why anticipatory sensing matters in commercial ACC systems under cut-in scenarios: A perspective from stochastic safety analysis. Accident Analysis & Prevention, 218, 108064

Anis, M., Li, S., Geedipally, S. R., Zhou, Y., & Lord, D. (2025). Real-time risk estimation for active road safety: Leveraging Waymo AV sensor data with hierarchical Bayesian extreme value models. Accident Analysis & Prevention, 211, 107880.

Abdel-Raheem, M., & Anis, M. (2025). Toward sustainability: A new construction method for electrically heated rigid pavement systems. Transportation Research Record: Journal of the Transportation Research Board, 2679(3), 281–303.

Anis, M., & Abdel-Raheem, M. (2024). A review of electrically conductive cement concrete pavement for sustainable snow-removal and deicing: Road safety in cold regions. Transportation Research Record: Journal of the Transportation Research Board, 2678(9), 50–71.