Exhibit D - Research Project Requirement: PDF Link

Project Outcome Brief: PDF Link

Final Report: TBD

Project Summary: The potential of CAVs to revolutionize transportation is undeniable. However, unlocking this potential requires addressing the unique challenges rural roads pose. Rigorous testing and innovative solutions are key to enhancing CAV safety in these challenging environments. The proposed project will provide safe mobility and accessibility to populations in rural road areas. Given the operational challenges of CAVs in such settings, the project will 1) develop a conceptual framework by synthesizing the literature on CAV algorithms, 2) develop a CAV testing plan in rural settings utilizing a unique CAV-in-the-loop simulation setup, 3) engage in software development for simulation using CARLA and OpenPilot software, and 4) analyze the results and provide innovative solutions to address challenges, such as adverse weather conditions, complex terrain, hazard classification, and limited sensor range, to improve CAV performance on rural roads. 

• Perception Error Modeling (PEM) for Reproducing Realistic Sensor Errors for AVs on Rural Roads

Beck, J., Huff, S., & Chakraborty, S. (2024). Diagnosing and predicting autonomous vehicle operational safety using multiple simulation modalities and a virtual environment. Accident Analysis and Prevention. https://doi.org/10.2139/ssrn.4734319

Beck, J., & Chakraborty, S. (2024). Fully embedded time-series generative adversarial networks. Neural Computing and Applications, 36(24), 14885–14894. https://doi.org/10.1007/s00521-024-09825-5

Yang, H. J., Beck, J., Hasan, M. Z., Beyazit, E., Chakraborty, S., Wongpiromsarn, T., & Sarkar, S. (2024). GENESIS-RL: Generating natural edge-cases with systematic integration of safety considerations and reinforcement learning. IEEE International Automated Vehicle Validation Conference (IAVVC), Pittsburgh, PA, USA, 2024, 1–8. https://doi.org/10.1109/IAVVC63304.2024.10786471

Beck, J. W. (2025). Advancing safety validation and testing of autonomous vehicles using advanced simulation techniques (Doctoral dissertation, University of Tennessee).

Beck, J., Huff, S., & Chakraborty, S. Diagnosing and predicting autonomous vehicle operational safety using multiple simulation modalities and a virtual environment (under review).

Beck, J., & Chakraborty, S. Modeling and propagation of perception errors in autonomous vehicle control: A generative time-series framework for safety-critical evaluation (under review).

Adeel, M., Khattak, A., Usman, S. M., & Moradloo, N. (2025). Are we ready for highly automated vehicles? Evaluating automated driving systems’ safety and reliability using inverse probability weighted regression adjustment. Journal of Intelligent Transportation Systems (under review).

Beck, J., & Chakraborty, S. Modeling and propagation of perception errors in autonomous vehicle control: A generative time-series framework for safety-critical evaluation (under preparation for submission).

Beck, J., & Chakraborty, S. (2024). “Enhancing Automated Vehicle Operation and Safety on Rural Roads: Challenges and Innovative Solutions,” Future of Transportation Summit, DOT Headquarters, Washington, D.C., Aug. 13–15, 2024.

Yang, H. J., Beck, J., Hasan, M. Z., Beyazit, E., Chakraborty, S., Wongpiromsarn, T., & Sarkar, S. (2024). “GENESIS-RL: Generating Natural Edge-Cases with Systematic Integration of Safety Considerations and Reinforcement Learning,” IEEE International Automated Vehicle Validation Conference (IAVVC), Pittsburgh, PA, 2024.

Enhancing Automated Vehicle Operation and Safety on Rural Roads - Challenges and Innovative Solutions,  Subhadeep Chakraborty (UTK), Southeast Annual Symposium  on Freight and Mobility, April 14-16, 2026, University of Tennessee, Knoxville.

Enhancing Automated Vehicle Operation and Safety on Rural Roads: Challenges and Innovative Solutions, Subhadeep Chakraborty (MABE, UTK), Asad Khattak (CEE, UTK) , Joint CATM and CR2C2 Symposium, April 17-18, 2024.