Richard Capraru Jun 2026

A major cornerstone of Capraru’s research is understanding how bad weather changes the baseline physics of sensor data, and how malicious actors can use those changes to trick self-driving cars.

Dop-NET is a comprehensive micro-Doppler radar data framework designed to push the boundaries of gesture recognition and human-machine interaction. This dataset enabled global artificial intelligence labs to benchmark low-cost Continuous Wave (CW) and Frequency-Modulated Continuous Wave (FMCW) architectures against complex biological motion, laying critical framework code for in-cabin driver monitoring systems. Technical Portfolio Overview

Completed a research attachment at Imperial College London (2023) focusing on computer science and autonomous driving.

Proved ghost object insertion using 8.8x fewer points by leveraging rainy atmospheric degradation. richard capraru

Beyond interaction, his work addresses critical security and reliability challenges in the automotive sector. Richard Capraru | Laidlaw Scholars Network

Dr. Capraru’s research has heavily influenced the ways automotive manufacturers approach multi-sensor fusion. Below is a summary of his most influential contributions:

Dr. Capraru’s research investigates how deep learning object detection pipelines degrade under rainy conditions. At the , he introduced an object-aware upsampling framework designed to restore degraded point cloud data, allowing self-driving algorithms to accurately isolate targets despite heavy rain distortion. 2. Cybersecurity and Adversarial Sensor Attacks A major cornerstone of Capraru’s research is understanding

| Year | Title | Publication/Conference | Key Focus | | :--- | :--- | :--- | :--- | | 2020 | Dop‐NET: a micro‐Doppler radar data challenge | Electronics Letters | Creating a shared database for radar-based classification. | | 2020 | Exploring gesture recognition with low-cost CW radar modules | IEEE International Radar Conference (RADAR) | Developing low-cost radar for human-computer interaction. | | 2024 | Rain-Reaper: Unmasking LiDAR-based Detector Vulnerabilities in Rain | IEEE/RSJ IROS | Novel attack exploiting rain to fool LiDAR systems. | | 2025 | Overcoming Catastrophic Forgetting in Radar and Lidar Object Detection in Rain | IEEE Radar Conference | Using ML techniques to maintain detection performance in rain. | | 2025 | GhostLite: Data Minimization with Applications to Real-Time LiDAR Attacks | IEEE Vehicular Technology Conference | Creating efficient and stealthy "ghost" objects. | | 2026 | Leveraging Adverse Weather for Enhanced LiDAR Spoofing in Autonomous Driving | IEEE Vehicular Technology Magazine | Broader analysis of weather's role in sensor spoofing. |

: He earned his Bachelor of Engineering (B.Eng.) in Electrical and Electronic Engineering in 2021. During his tenure at UCL, he was recognized as a Laidlaw Scholar and laid the foundation for his signal processing expertise by developing open-source benchmark datasets.

reflect a deep interest in making autonomous systems more resilient against environmental interference and security threats: Adverse Weather Performance Richard Capraru | Laidlaw Scholars Network Dr

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To further expand his cross-cultural and academic perspectives, he studied as a visiting scholar at: The University of Tokyo Peking University Hong Kong University of Science and Technology (HKUST) Korea University

Capraru, a principal at the acclaimed design firm MGroup, has carved out a niche that defies the ephemeral trends of the industry. To understand his approach, one must first understand that he treats space not as a container for objects, but as a medium for living.

In addition to his leadership philosophy, Richard Capraru is frequently recognized for his foresight regarding digital transformation. He was an early adopter of data-driven decision-making, integrating advanced analytics into core business operations long before it became a standard industry practice. By leveraging data to predict consumer behavior and streamline supply chains, Capraru helped his organizations stay ahead of the curve, minimizing risk and maximizing operational efficiency.