TeraSim: A City-Scale Agentic World Model Platform for Physical AI
Core developer at SaferDrive.AI. A city-scale simulation platform serving as foundational infrastructure for agentic world models under the Physical AI framework.
Core developer at SaferDrive.AI. A city-scale simulation platform serving as foundational infrastructure for agentic world models under the Physical AI framework.
Project leader on a CCAT-funded cooperative perception system connecting roadside LiDAR and CAV sensors for improved road safety.
Individual research on dynamic vehicle routing with cooperative perception for urban traffic monitoring. GitHub
Integrating Intelligent Driving Model (IDM) into deep learning for robust car-following trajectory prediction. GitHub
Multiple projects funded by US-DOT, Michigan DOT, and Leidos Inc. on V2X-based transit signal priority and co-simulation platforms.
Published in Tunneling and Underground Space Technology, 2023
This paper presents a data mining and machine learning approach for evaluating the serviceability of highway tunnels, using the continental United States as a case study.
Recommended citation: Ya-Dong Xue, Wei Zhang, Yi-Lin Wang*, et al. (2023). "Serviceability evaluation of highway tunnels based on data mining and machine learning: A case study of continental United States." Tunneling and Underground Space Technology, Volume 142, 105418.
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Published in Transportation Research Board Annual Meeting 2024, 2024
This paper formulates the CAV routing problem considering network coverage as an objective and proposes heuristic algorithms with greedy search to solve the multi-objective optimization.
Recommended citation: Yilin Wang, Yiheng Feng* (2024). "Dynamic Routing of Connected and Automated Vehicles for Improving Network Coverage." Transportation Research Board Annual Meeting 2024, Poster Presentation.
Published in IEEE Transactions on Intelligent Vehicles, 2024
We introduce a physics-informed neural network (PINN) model that integrates the Intelligent Driving Model (IDM) into a deep learning framework for car-following trajectory prediction. The proposed model exhibits robustness against real-time GPS noise.
Recommended citation: Y. Wang and Y. Feng* (2024). "IDM-Follower: A Model-Informed Deep Learning Method for Car-Following Trajectory Prediction." IEEE Transactions on Intelligent Vehicles, vol. 9, no. 6, pp. 5014-5020.
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Published in Transportation Research Record, 2024
This paper presents a cooperative perception system connected by V2X between roadside LiDAR and CAV sensors, demonstrating safety benefits in detecting occluded vulnerable road users (VRUs).
Recommended citation: Hanlin Chen, Vamsi K Bandaru, Yilin Wang, Mario A Romero, Andrew Tarko, Yiheng Feng* (2024). "Cooperative Perception System for Aiding Connected and Automated Vehicle Navigation and Improving Safety." Transportation Research Record, 2678(12), 1498-1510.
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Published in Transportation Research Board Annual Meeting 2026, 2025
We apply Cell Transmission Model (CTM) for traffic state prediction and propose a comprehensive MILP formulation for dynamic vehicle routing considering traffic monitoring performance. Also in submission to Transportation Research: Part B.
Recommended citation: Yilin Wang, Yiheng Feng* (2025). "A Cooperative Perception Based Dynamic Vehicle Routing Framework for Urban Traffic Monitoring." Transportation Research Board Annual Meeting 2026, Poster Presentation.
Published:
Poster presentation on formulating the CAV routing problem considering network coverage as an objective and proposing heuristic algorithms with greedy search to solve the multi-objective optimization.
Published:
Research presentation on integrating the Intelligent Driving Model (IDM) into a physics-informed neural network for car-following trajectory prediction.
Published:
Poster presentation on a cooperative perception based dynamic vehicle routing framework using Cell Transmission Model (CTM) and MILP formulation for urban traffic monitoring.