Shayan Sharifi

Till Beemelmanns, Shayan Sharifi, Manas Mehrotra et al.

Deep Neural Networks have become the dominant solution for Autonomous Driving perception, but their opacity conflicts with emerging Trustworthy AI guidelines and complicates safety assurance, debugging, and human oversight. While theoretical frameworks for safe and Explainable AI (XAI) exist, concrete implementations of Trustworthy AI for 3D scene understanding remain scarce. We address this gap by proposing a Trustworthy AI perception module that is remarkably robust, integrates faithful explainability, and calibrated uncertainty estimates. Building on a transformer-based detector, we derive explanation from the attention mechanism at inference time and validate their faithfulness using perturbation-based consistency tests. We further integrate an uncertainty estimation and calibration module, and apply robustness-enhancing training methods. Experiments show faithful saliency behavior, improved robustness, and well-calibrated uncertainty estimates. Finally, we deploy these Trustworthy AI elements in a prototype vehicle and provide an XAI Interface that visualizes documentation artifacts, model uncertainty state, and saliency maps, demonstrating the feasibility of trustworthy perception monitoring in real time. Supplementary materials are available at https://tillbeemelmanns.github.io/trustworthy_ai/ .

Shayan Sharifi

This paper presents a comprehensive review of the last two decades of research on Kidney Exchange Programs (KEPs), systematically categorizing and classifying key contributions to provide readers with a structured understanding of advancements in the field. The review highlights the evolution of KEP methodologies and lays the foundation for our contribution. We propose three mathematical models aimed at improving both the quantity and quality of kidney transplants. Model 1 maximizes the number of transplants by focusing on compatibility based on blood type and PRA, without additional constraints. Model 2 introduces a minimum Human Leukocyte Antigen (HLA) compatibility threshold to enhance transplant quality, though this leads to fewer matches. Model 3 extends the problem to a Multi-Agent Kidney Exchange Program (MKEP), pooling incompatible donor-recipient pairs across multiple agents, resulting in a higher number of successful transplants while ensuring fairness across agents. Sensitivity analyses demonstrate trade-offs between transplant quantity and quality, with Model 3 striking the optimal balance by leveraging multi-agent collaboration to improve both the number and quality of transplants. These findings underscore the potential benefits of more integrated kidney exchange systems.