Daniel Watzenig
Papers
8
Total Citations
76
H-Index
4
About
Daniel Watzenig is a prominent researcher whose work sits at the intersection of autonomous systems, robotics, and intelligent transportation. His research spans LiDAR-based perception, path planning, autonomous driving, and search-and-rescue robotics — areas of growing urgency as automation becomes increasingly embedded in both civilian and safety-critical applications. Among his most influential contributions is his 2020 work on correcting distorted point clouds from fast-moving LiDAR sensors, which has earned 23 citations and addresses a fundamental challenge in automotive-grade perception systems. His 2021 paper on mixed-integer optimization for autonomous racing trajectory planning — garnering 17 citations — demonstrates his ability to bridge theoretical rigor with real-world engineering constraints. A 2024 literature review on search-and-rescue robotics in harsh environments (20 citations) reflects his broader commitment to life-saving applications of autonomous technology. Watzenig's portfolio also includes work on cost-effective A* path planning for non-holonomic vehicles, Kalman filter-based angular sensing, thermal-LiDAR fusion for GNSS-denied localization, and quantifying automated vehicles' impact on mixed traffic. Collectively, his research makes him a versatile and impactful voice shaping the future of safe, intelligent autonomous systems.
Research Focus
Key Achievements
Top Papers
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- 6Quantifying the Impact of Automated Vehicles on Traffic2 citations · 2023
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