Exhibit D - Research Project Requirement: PDF Link

Project Summary: Sporadic transportation demand over large, geographically dispersed rural areas makes conventional fixed-schedule, fixed-route solutions inefficient. This project will develop technology-driven solutions for the development and deployment of microtransit transportation vehicles to address distributed and low-demand transportation needs in rural areas by providing more efficient, customer-focused autonomous transit services via flexible routing and scheduling while interacting with and integrating into other transportation modes. This integrated system of systems and technology-driven approach will lead to efficient transportation solutions by reducing transportation costs due to smaller vehicle sizes and the adoption of ridesharing strategies for transit services in rural and disadvantaged communities.

Realtime Semantic segmentation-based lane detection model for rural roads [GitHub link]

Vision-based perception and control tool for lane keeping of autonomous vehicles validated in  rural roads [GitHub Link]

Robust sliding mode control algorithm for path tracking of autonomous vehicles in challenging terrains [GitHub Link]

1. 1. Pang, Y., Ni, Z., & Zhong, X. (2026). Decoupling shared and personalized knowledge: A dual-branch federated learning framework for multi-domain with non-IID data. In Proceedings of the AAAI Conference on Artificial Intelligence, 40(29), 24745–24754.

   2. Zhong, X., & Ni, Z. (2025). Intelligent control in asymmetric decision-making: An event-triggered RL approach for mismatched uncertainties. IEEE Transactions on Systems, Man, and Cybernetics: Systems, 55(10), 7288–7301. https://doi.org/10.1109/TSMC.2025.3583066

   3. Ni, Z., Pang, Y., Zhong, X. (2025). A Fast Federated Reinforcement Learning Approach with Phased Weight-Adjustment Technique. Neurocomputing. https://doi.org/10.1016/j.neucom.2025.129550

   4. Lin, Y., Ni, Z., Zhong, X. (2025). Learning From Demonstrations: A Computationally Efficient Inverse Reinforcement Learning Approach With Simplified Implementation. IEEE Transactions on Emerging Topics in Computational Intelligence. https://doi.org/10.1109/TETCI.2025.3526502

   5. Lin, Y., Ni, Z. (2025). A Robust Multi-Virtual-Agent Inverse Reinforcement Learning Approach With Data Aggregation for Perturbed Environments. IEEE Transactions on Neural Networks and Learning Systems. https://doi.org/10.1109/TNNLS.2025.3531839

   6. Lin, Y.; Ni, Z. (2025). Adaptive Inverse Reinforcement Learning in Continuous-Time Systems Using CNN-Based Feature Extraction. IJCNN.

   7. Pang, Y.; Ni, Z.; Zhong, X. (2025). Personalized Observation Normalization for Federated Reinforcement Learning in Heterogeneous Environments. IJCNN. https://doi.org/10.1109/IJCNN64981.2025.11229364

   8. Pang, Y.; Ni, Z.; Zhong, X. (2025). Integration of Layer Normalization into Federated Reinforcement Learning for Heterogeneous Environments. SPIE Defense + Commercial Sensing. https://doi.org/10.1117/12.3053916

   9. Matute, J., Searcy, S., & Karimoddini, A. (2025). The Aggie Auto Shuttles: Technical insights from the first public road demonstration. Transportation Engineering, 20. https://doi.org/10.1016/j.treng.2025.100335

   10. Arab, A., Khaleghi, M., Partovi, A., Abbaspour, A., Shinde, C., Mousavi, Y., Azimi, V., Karimoddini, A. (2025). High-Resolution Safety Verification for Evasive Obstacle Avoidance in Autonomous Vehicles. IEEE Open Journal of Vehicular Technology, 6, 276–287. https://doi.org/10.1109/OJVT.2024.3519951

   11. Dejene, M.; Matute, J.; Karimoddini, A. (2025). Development and Implementation of Safe, Human-like Autonomous Vehicle Control on Horizontal Curves. IEEE ICM. https://doi.org/10.1109/ICM62621.2025.10934884

   12. Mengistu, T.; Getahun, T.; Karimoddini, A. (2025). A Robust Radar-Camera Fusion for 2D Object Detection for Autonomous Driving. IEEE ITSC. https://doi.org/10.1109/ITSC60802.2025.11423247

   13. Martha, S. R. P.; Javadi, A. M. M.; Ghorai, P.; Khorsandroo, S.; Karimoddini, A. (2025). Analyzing Driver Behavior Distraction During Animal Crossing on Rural Roads. IISE Annual Conference.

   14. Getahun, T.; Karimoddini, A. (2025). From City Streets to Country Roads: Can Automated Vehicles Generalize Lane Detection? International Conference on Intelligent Driving and Smart Transportation.

   15. Getahun, T.; Karimoddini, A. (2025). Assessing Lane Marking Visibility Range in Night-Time Driving. International Conference on Intelligent Driving and Smart Transportation.

   16. Zakaria, M.; Getahun, T.; Karimoddini, A. (2025). AI-Enabled Traffic Sign Detection for Rural Operations of Connected and Automated Vehicles. International Conference on Intelligent Driving and Smart Transportation.

   17. Matute, Jose; Khaleghi, Milad; Karimoddini, Ali (2024). Decentralized Most Permissive Observer Architecture for Brake Leakage Diagnosis in Automotive Systems. IEEE ACCESS, 12, 110457–110468. https://doi.org/10.1109/ACCESS.2024.10630519

   18. Matute, Jose; Diaz, Sergio; Karimoddini, Ali (2024). Sliding Mode Control for Robust Path Tracking of Automated Vehicles. IEEE Open Journal of Vehicular Technology, 5, 1314–1325. https://doi.org/10.1109/OJVT.2024.3456035

   19. Azage, Michael; Matute, Jose; Karimoddini, Ali (2024). Learning from Human Driver Demonstration for Speed Control of Ground Autonomous Vehicles. IEEE ICIEA. https://doi.org/10.1109/ICIEA.2024.10664716

   20. Getahun, T.; Karimoddini, A. (2024). An Integrated Vision-Based Perception and Control for Lane Keeping of Autonomous Vehicles. IEEE Transactions on Intelligent Transportation Systems, 25(8), 9001–9015. https://doi.org/10.1109/TITS.2024.3376516

   21. Benjamin Lartey; Wendwosen Bedada; Xuyang Yan; Abdollah Homaifar; Ali Karimoddini; Edward Tunstel (2024). An Efficient Profit-Aware Scalable Vehicle Dispatch Framework for On-Demand Ridesharing. IEEE Transactions on Industrial Cyber-Physical Systems, 2, 542–555. https://doi.org/10.1109/TICPS.2024.3446957

   22. Islam, Muhammad Mobaidul; Abdullah Al Redwan Newaz; Li Song; Benjamin Lartey; Shih-Chun Lin; Wei Fan; Ali Hajbabaie; Abdullah Homaifar; Ali Karimoddini (2023). Connected autonomous vehicles: State of practice. Applied Stochastic Models in Business and Industry. https://doi.org/10.1002/asmb.2772

   23. Yan, X.; Sarkar, M.; Lartey, B.; Gebru, B.; Homaifar, A.; Karimoddini, A. (2023). An Online Learning Framework for Sensor Fault Diagnosis Analysis in Autonomous Cars. IEEE Transactions on Intelligent Transportation Systems. https://doi.org/10.1109/TITS.2023.3305620

   24. Jose Matute; Mario Rodriguez-Arozamena; Joshué Pérez Rastelli; Ali Karimoddini (2023). Sensor Fusion-Based Localization Framework for Autonomous Vehicles in Rural Forested Environments. IEEE ITSC. https://doi.org/10.1109/ITSC57777.2023.10422436

   25. Tesfamichael Getahun; Ali Karimoddini (2023). GPS-Guided Vision-Based Lane Detection for Autonomous Vehicles. IEEE ITSC. https://doi.org/10.1109/ITSC57777.2023.10422633

   26. McBride, M.; Kelly, S. Impact of Dispositional and Situational Factors on Willingness to Use Shared Autonomous Shuttles. (under review)

1. A. Karimoddini and C. Spencer, “Aggie Autonomous Shuttle,” NCSITE Safety and SimCap User Group Meeting, Feb 2024, Greensboro, NC.

2. M. McBride, “Are we ready? Attitudes and Perceptions of Shared Autonomous Shuttles across College Communities” Institute of Industrial and Systems Engineers Annual Conference & Expo, Atlanta, GA, May 31–Jun 1, 2025.

3. M. McBride, “Impact of Exposure on Shared Autonomous Shuttle Perceptions.” Applied Human Factors and Ergonomics International Conference (AHFE 2024), Nice, Italy, Jul 24–28, 2024.

4. A. Karimoddini, “Autonomous Vehicles: From Sensing to Decision-Making and Control”, keynote speaker, 2024 North Carolina Autonomous Vehicle Symposium.

5. Tienake Phuapaiboon, Jose Matute, Daniel Tobias, Tesfamichael Getahun, Ali Karimoddini, “Development of a Testbed of Connected Autonomous Vehicles,” TRB 104th Annual Meeting, 2024.

6. Michael Azage, Jose Matute, and Ali Karimoddini, “Learning from Human Driver Demonstration for Speed Control of Ground Autonomous Vehicles,” IEEE 19th Conference on Industrial Electronics and Applications (ICIEA), Kristiansand, Norway, 05–08 August 2024.

7. Ali Karimoddini, “Autonomy and AI for Advanced Transportation Technologies,” NCDOT, CLEAR Program, Sep 26, 2024.

8. Ali Karimoddini, “AI-Enabled Development and Deployment of Microtransit Autonomous Vehicles in Rural Environments,” US DOT Future of Transportation Summit, Aug 13–15, 2024.

9. Ali Karimoddini, Keynote talk: "Autonomous Vehicles: From Sensing to Decision-Making and Control,” Autonomous Vehicle Symposium, Raleigh, April 1, 2024.

10. Ali Karimoddini, “Lessons Learned From Aggie Auto Pilot Program,” AAMVA Automated Vehicles Information Sharing, June 3, 2024.

11. Jose Matute, Mario Rodriguez-Arozamena, Joshue Perez, and Ali Karimoddini, “Sensor Fusion-Based Localization Framework for Autonomous Vehicles in Rural Forested Environments,” 26th IEEE International Conference on Intelligent Transportation Systems (ITSC 2023), Spain, September 2023.

12. Tesfamchael Getahun, Ali Karimoddini, “GPS-guided Vision-based Lane Detection for Autonomous Vehicles,” 26th IEEE International Conference on Intelligent Transportation Systems (ITSC 2023), Spain, September 2023.

13. Maranda McBride, “Shared Autonomous Shuttle Perceptions,” ULTIMO Webinar: Supply & Demand in AV Markets – Insights from Europe, USA and Asia, March 20, 2025.

14. Yanbin Lin, Zhen Ni, “Adaptive Inverse Reinforcement Learning in Continuous-Time Systems Using CNN-Driven Visual Feature Extraction,” IEEE International Joint Conference on Neural Networks (IJCNN), 2025.

15. Yiran Pang, Zhen Ni, Xiangnan Zhong, “Personalized Observation Normalization for Federated Reinforcement Learning in Simulation Environments with Heterogeneity,” IEEE IJCNN, 2025.

16. Yiran Pang, Zhen Ni, Xiangnan Zhong, “Integration of a New Layer Normalization Process into Federated Reinforcement Learning for Environments with Heterogeneous Attribute Spaces,” SPIE Defense & Commercial Sensing, 2025.

17. Zhen Ni, “Learning From Demonstrations: A Computationally Efficient Inverse Reinforcement Learning Approach with Simplified Implementation,” FAU PACE Club, Boca Raton, FL, January 2025.

What Happens When Travel Needs Are Not Met and How Well Could They Be Satisfied with New Microtransit Service? Pamela Murray-Tuite (Clemson), April 15, 2026, Southeast Annual Symposium  on Freight and Mobility, University of Tennessee, Knoxville

Development of a testbed of connected automated driving shuttles for rural environments, Jose Matute (NCAT), Joint CATM and CR2C2 Symposium, April 17, 2024

Development and Deployment of a Risk-Aware Decision-Making Approach for Navigation of Autonomous Vehicles at Rural Intersections,  Miressa Mamo (NCAT), Tesfamichael Getahun (NCAT), Ali Karimoddini (NCAT), Southeast Annual Symposium  on Freight and Mobility, April 14-16, 2026, University of Tennessee, Knoxville.

A Shared Microtransit Optimization Model for Balancing Passenger and Provider Perspectives,  Tahmina Sultana (Clemson), Southeast Annual Symposium  on Freight and Mobility, April 14-16, 2026, University of Tennessee, Knoxville.

A Lightweight Camera and Lidar Fusion for Rural Scene Understanding, Tekalign Tesfaye (NCAT), Tesfamichael Getahun (NCAT), Ali Karimoddini (NCAT), Joint CATM and CR2C2 Symposium, April 17-18, 2024.   

Development of testbed of Connected Autonomous Vehicles for Deployment on Rural Roads, Tienake Phuapaiboon (NCAT), Jose Matute (NCAT), Ali Karimoddini (NCAT), Joint CATM and CR2C2 Symposium, April 17-18, 2024.   

Interaction-aware Decision Making for Control of Autonomous Vehicles in Rural Environments, Miressa Fikadu (NCAT), Tesfamichael Getahun (NCAT), Ali Karimoddini (NCAT), Joint CATM and CR2C2 Symposium, April 17-18, 2024.