Sudip Dhakal

Papers

Total Citations

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About

Sudip Dhakal is a researcher focused on the frontier of autonomous vehicle technology, with a particular emphasis on making self-driving systems more accessible and robust. His primary research areas include open-source autonomous driving frameworks, real-time motion planning, and navigation in dynamic environments. Dhakal’s most influential work, "OASD: An Open Approach to Self-Driving Vehicle" (2021, 12 citations), addresses the critical challenge of democratizing autonomous vehicle development by providing a comprehensive, open-source blueprint that combines optimal hardware and software configurations. This contribution is especially valuable for researchers and students entering the field, as it lowers the barrier to entry for building and testing autonomous systems. Building on this foundation, his subsequent papers on "Real-Time Motion Planning for Autonomous Vehicles in Dynamic Environments" (2024–2025, 7 combined citations) tackle the pressing problem of trajectory planning amidst moving obstacles—a key hurdle for real-world deployment. Through his work, Dhakal demonstrates a clear trajectory from foundational framework development to advanced motion planning, offering practical solutions that enhance the safety and efficiency of self-driving cars in unpredictable settings.