3. Mitigation of Catastrophic Forgetting in Machine Learning
The recent 2026 findings by Capraru highlight a vital "Opportunity" framework for autonomous vehicle manufacturers. By exposing these highly specific weather-based exploits, his research helps engineers build smarter, weather-aware filtering algorithms capable of distinguishing between real atmospheric interference and deliberate laser spoofing. Industry Implications: The Road Ahead
A major bottleneck in fully autonomous vehicles is that core perception sensors (like LiDAR) struggle in environments like heavy rain or fog. Dr. Capraru has led multiple breakthroughs to fix this: Richard Capraru - Google Scholar
R) under Singapore's Agency for Science, Technology and Research (A*STAR). He also completed a specialized research attachment at Imperial College London alongside experts in computer systems security. Core Research Areas
When autonomous vehicles undergo sequential training to adapt to poor weather conditions, they often suffer from "catastrophic forgetting"—a phenomenon where the underlying neural network loses its initial ability to recognize objects in clear weather. richard capraru
Capraru's work primarily revolves around the intersection of radar hardware and advanced signal processing. Key areas of his research include: Low-Cost Radar Systems
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From a sustainability standpoint, the adaptive reuse approach championed by Capraru significantly reduces the carbon footprint of urban development. Concrete production is a major contributor to CO2 emissions; retaining the "bones" of industrial sites saves approximately 50-70% of the embodied carbon compared to new builds.
At UCL, he pursued a rigorous , demonstrating his foundational interest in complex systems. His talent and drive were recognized early when he was selected for the prestigious Laidlaw Research and Leadership Programme in 2019 . This competitive scholarship, awarded to first-year undergraduates, allowed him to undertake his own research project, providing an early platform for his investigative skills. His published paper from this period, "Exploring gesture recognition with low-cost CW radar modules," indicates his research focus was already forming in his undergraduate years. Industry Implications: The Road Ahead A major bottleneck
Conducted targeted sensory research at the Institute for Infocomm Research ( I2Rcap I squared cap R
: While at UCL, he co-developed the first and largest radar micro-Doppler database for data science challenges.
is an active cybersecurity and computer engineering researcher specialized in autonomous driving security, sensor vulnerabilities, and AI-driven vehicle perception. His pioneering work primarily focuses on exposing and mitigating physical threats to AI vision systems—specifically uncovering how environmental factors can be weaponized to exploit Light Detection and Ranging (LiDAR) hardware.
Richard Capraru: Advancing Security in Autonomous Driving Technology He also completed a specialized research attachment at
Presented at the prestigious IEEE/RSJ IROS Conference, detailing physical-grounded attacks in adverse environments.
, which tackles complex problems in 3D point cloud processing for automotive or robotics applications. Academic & Professional Standing Affiliation : He has been associated with the University College London (UCL)
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