Ziyan Wang

Ziyan Wang

Beihang University

Hi! I am a postgraduate student with the School of Transportation Science and Engineering, Key Laboratory of Autonomous Transportation Technology for Special Vehicles, Ministry of Industry and Information Technology, Beihang University and is supervised by Professor Peng Chen. Before that, I got my Bachelor degree at Beihang University.

My research interests cover multi-agent interaction modeling in traffic flow and integration of prediction and planning for intelligent vehicles. I am actively looking for PhD positions in autonomous vehicle and embodied AI, with a particular interest in generative network world model, multimodal learning.

Status: Seeking PhD positions in Fall 2026

📝 Publications

arxiv
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Ziyan Wang

arxiv, 2025

[Paper]| video

  • We propose R1Sim, a tokenized traffic simulation policy that enhances human-aligned behavior generation through reinforcement learning guided by motion token entropy. By introducing entropy-based adaptive sampling and optimizing via group relative policy optimization with human-preferred rewards, R1Sim enables diverse and meaningful exploration of traffic behaviors.
ITSC
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Dynamic Game-Informed Lane Changing: Leveraging Stochastic Search for Autonomous Vehicle Decision-Making

Tan Xiang1, Ziyan Wang1, Ding Li, Peng Chen

The IEEE International Conference on Intelligent Transportation Systems (ITSC), 2025

[Paper]| video

  • We propose Dynamic Game Informed Stochastic Search (DGSS), a novel decision-making framework that models recursive inter-vehicle interactions using multi-tree Monte Carlo Tree Search and game-theoretic principles. By simulating turn-based strategies for each surrounding vehicle and evaluating actions with a multi-objective reward, DGSS improves safety, efficiency, and interaction awareness in autonomous lane-changing.
TITS
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Dynamic Origin-Destination Flow Imputation Using Feature-based Transfer Learning

Peng Chen, Ziyan Wang, Bin Zhou, Guizhen Yu

IEEE Transactions on Intelligent Transportation Systems (TITS), 2024

Paper

  • We developed an innovative framework utilizing an autoencoder network with feature transfer to estimate urban dynamic origin-destination flows, leveraging both connected vehicle trajectories and automatic vehicle identification data. Tested on a real-world road network, our model demonstrated superior performance, and showed robust estimation capabilities under varying observation conditions and data quality.

🎖 Honors and Awards

  • 2025.07, Our team won the 3rd place in the Scenario generation track of the Third Onsite Autonomous Driving Algorithm Challenge. [Link]
  • 2024.11, I won the National Scholarship for Graduate Students.
  • 2024.05, Our team secured the 2nd place and innovation solution (Honorable Mention) in the track 4: Robust Depth Estimation of Robodrive Challenge. | [report] | [video]
  • 2023.06, Our team won the 1st place in the Combined track and Intersection track of the First Onsite Autonomous Driving Algorithm Challenge. | [Link]
  • 2022.06, Our team “A dynamic OD estimation method for urban road network based on mobile crowdsourcing and AVI data” project won the second prize in the main track of the 32nd Beihang “Feng Ru Cup”.

📖 Educations

  • 2023.09 - 2026.01 (now), Master of Engineering, Transportation Engineering, Beihang University, Beijing, China.
  • 2019.09 - 2023.06, Bachelor of Engineering, Transportation Engineering, Beihang University, Beijing, China.

📚 Academic Services

Journal Reviewer

  • Journal of Intelligent Transportation Systems

Conference Reviewer

  • The 2025 IEEE 28th International Conference on Intelligent Transportation Systems