Mr. Haotong Tang | Chang’an University Xi’an | China
Haotong Tang is a dedicated researcher in the field of intelligent connected vehicles and autonomous driving systems. Currently pursuing a Ph.D. in Traffic Information Engineering and Control at Chang’an University, Xi’an, he has a solid academic background in Computer Science and Technology, holding both undergraduate and master’s degrees from the same institution. His research centers on intelligent decision-making, control optimization, and vehicle-infrastructure cooperation. Tang has contributed to high-impact publications and is focused on developing practical, efficient solutions to real-world transportation challenges. His work combines deep reinforcement learning, game theory, and collaborative control strategies to enhance the safety, efficiency, and intelligence of autonomous vehicle systems in complex traffic environments.
Author Profile
Education
Haotong Tang began his academic journey in 2019 at Chang’an University, Xi’an, where he earned his Bachelor’s degree in Computer Science and Technology in June 2023. Motivated by a passion for intelligent systems, he continued his studies at the same university, enrolling in a Master’s program in Computer Science and Technology in September 2023, with an expected graduation in June 2026. In parallel, he commenced a Ph.D. program in Traffic Information Engineering and Control in September 2025. This multidisciplinary educational path has equipped Tang with strong foundations in both computing and traffic systems, allowing him to integrate advanced technologies such as AI and control theory into the development of intelligent transportation systems.
Experience
Haotong Tang has actively participated in research projects focused on intelligent connected vehicle control and decision-making. His experience spans collaborative work in simulation-based optimization of traffic scenarios, particularly vehicle platooning and autonomous lane-changing strategies. He has contributed to publications in respected international journals and worked alongside academic mentors and interdisciplinary teams. His research involvement has included algorithm development, system modeling, and experimental validation. Tang is skilled in applying deep reinforcement learning, multi-agent systems, and game-theoretic approaches to real-world transportation problems, with hands-on experience in platforms such as CARLA and SUMO for traffic and vehicle simulations.
Research Focus
Haotong Tang’s research primarily explores the intersection of intelligent transportation systems and autonomous vehicle decision-making. His work emphasizes the development of advanced control strategies and decision algorithms for intelligent connected vehicles (ICVs), particularly in dynamic environments such as highways. His current studies address challenges in vehicle platooning, cooperative driving, and lane-changing maneuvers within mixed traffic flows. He applies deep reinforcement learning (DRL) to optimize decision-making processes and leverages game-theoretic models to handle interactions between autonomous and human-driven vehicles. Additionally, Tang is exploring cooperative vehicle-infrastructure control, aiming to create integrated systems that enhance traffic efficiency, safety, and scalability. By combining theoretical innovation with practical simulation tools, he seeks to contribute to the realization of next-generation transportation networks where autonomous vehicles operate harmoniously with traditional systems and infrastructure. His multidisciplinary approach positions him at the forefront of intelligent mobility research.
Publication
A time-efficient lane-changing strategy for connected and autonomous vehicle platoons in mixed traffic
- Authors: Fansheng Xing, Chenglin Liu, Zhigang Xu, Jiatong Xu, Haotong Tang, Ying Gao, Xiangmo Zhao, Xiaobo Qu, Xiaopeng Li
Journal: Expert Systems with Applications
Conclusion
Haotong Tang is a promising researcher advancing the future of autonomous driving through intelligent control and decision-making systems. With a strong academic foundation and active engagement in impactful research, he contributes innovative solutions to real-world transportation challenges. His work bridges theoretical insights and practical applications, supporting safer and more efficient traffic systems. As he progresses through his Ph.D. and Master’s studies, Tang remains committed to pushing the boundaries of intelligent transportation technologies through rigorous research and collaborative development.