Zakhar Yagudin

Skolkovo Institute of Science and Technology

Papers

Total Citations

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About

Zakhar Yagudin is an emerging researcher at the intersection of artificial intelligence and autonomous systems, with a focused specialization in applying Vision-Language Models (VLMs) to real-world autonomous driving challenges. His most recognized work, "VLM-Auto: VLM-based Autonomous Driving Assistant with Human-like Behavior and Understanding for Complex Road Scenes" (2024), has garnered notable attention within the autonomous driving community, accumulating over 22 citations since its publication — a strong indicator of early-career impact in a highly competitive field. Yagudin's research tackles one of the most pressing tensions in modern autonomous driving: the gap between the adaptability of large language models and the reliability demands of safety-critical systems. His work directly addresses persistent challenges such as high computational overhead and hallucination-induced errors in trajectory prediction and control signal generation, proposing frameworks that aim to blend human-like scene understanding with robust, deterministic behavior. For students and researchers exploring AI-driven mobility, Yagudin represents a promising voice working to make autonomous vehicles not only smarter but genuinely context-aware — pushing the field toward systems that can navigate complex, unpredictable road environments with both intelligence and dependability.