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A list of all the posts and pages found on the site. For you robots out there, there is an XML version available for digesting as well.
Pages
Posts
Upcoming: RoboSense at the 2026 INFORMS Annual Meeting
Published:
I will present RoboSense at the 2026 INFORMS Annual Meeting on Nov 3, 2026.
FHWA Visit to UMTRI and Mcity
Published:
UMTRI covered a visit by FHWA delegates to AACE and Mcity, including a live demonstration of a near-miss alert system.
Live V2X Transit Signal Priority Demo at UMTRI 60th Anniversary Symposium
Published:
Our CART Lab at Purdue University demonstrated a live Transit Signal Priority (TSP) system at the University of Michigan Transportation Research Institute (UMTRI) 60th Anniversary Symposium in Ann Arbor, MI.
portfolio
AgentSim: An Agentic Test Engineer for Autonomous-Driving Safety Validation
Initiated and developed at Inchor (SaferDrive AI). LLM/VLM agents mutate real logged drives into safety-critical corner cases, replay them in closed loop, and a video world model renders the result.
RoboSense: Leveraging Robotaxi Fleets as Drive-by Sensors for Urban Traffic Monitoring
A dynamic robotaxi routing framework that turns centrally controlled fleets into cooperative drive-by sensors for urban traffic monitoring.
Developing A Cooperative Perception System
Project leader on a CCAT-funded cooperative perception system connecting roadside LiDAR and CAV sensors for improved road safety.
IDM-Follower: A Physics-Informed Neural Network Model for Trajectory Prediction
Integrating Intelligent Driving Model (IDM) into deep learning for robust car-following trajectory prediction.
Developing V2X System for Traffic Signal Control
Multiple projects funded by US-DOT, Michigan DOT, and Leidos Inc. on V2X-based transit signal priority and co-simulation platforms.
publications
Serviceability evaluation of highway tunnels based on data mining and machine learning: A case study of continental United States
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, Yilin 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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Dynamic Routing of Connected and Automated Vehicles for Improving Network Coverage
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.
IDM-Follower: A Model-Informed Deep Learning Method for Car-Following Trajectory Prediction
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: Yilin Wang and Yiheng 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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Cooperative Perception System for Aiding Connected and Automated Vehicle Navigation and Improving Safety
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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A Cooperative Perception Based Dynamic Vehicle Routing Framework for Urban Traffic Monitoring
Published in Transportation Research Board Annual Meeting 2026, 2026
We apply Cell Transmission Model (CTM) for traffic state prediction and propose a comprehensive MILP formulation for dynamic vehicle routing considering traffic monitoring performance. An extended version, RoboSense: Leveraging Robotaxi Fleets as Drive-by Sensors for Urban Traffic Monitoring (Yilin Wang, Yiheng Feng), is currently under review — see the open-source code and arXiv preprint.
Recommended citation: Yilin Wang, Yiheng Feng* (2026). "A Cooperative Perception Based Dynamic Vehicle Routing Framework for Urban Traffic Monitoring." Transportation Research Board Annual Meeting 2026, Poster Presentation.
talks
Dynamic Routing of Connected and Automated Vehicles for Improving Network Coverage
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.
IDM-Follower: A Model-Informed Deep Learning Method for Car-Following Trajectory Prediction
Published:
Research presentation on integrating the Intelligent Driving Model (IDM) into a physics-informed neural network for car-following trajectory prediction.
A Cooperative Perception Based Dynamic Vehicle Routing Framework for Urban Traffic Monitoring
Published:
Poster presentation on a cooperative perception based dynamic vehicle routing framework using Cell Transmission Model (CTM) and MILP formulation for urban traffic monitoring.
RoboSense: Leveraging Robotaxi Fleets as Drive-by Sensors for Urban Traffic Monitoring
Published:
Scheduled presentation of RoboSense, a dynamic routing framework that turns centrally controlled robotaxi fleets into cooperative drive-by sensors for urban traffic monitoring. The preprint is on arXiv and the code is on GitHub.