| Publication Title | Focus Area | Key Contribution | | :--- | :--- | :--- | | (2020) | Radar-based Gesture Recognition | Proved that low-cost Continuous Wave (CW) radar can match the gesture recognition accuracy of more complex systems. | | Dop-NET: a micro-Doppler radar data challenge (2020) | Radar Data & Machine Learning | Introduced a standard dataset to train machine learning algorithms for specific radar data. | | Exploring deep transfer learning interference classification... (2022) | Synthetic Data & SAR | Demonstrated that AI-generated synthetic radar data could be used to train other AI models effectively. | | Upsampling Data Challenge: Object-Aware Approach for 3D Object Detection in Rain (2023) | LiDAR & 3D Detection | Proposed a new data processing method to improve object detection for autonomous vehicles in rainy conditions. | | Rain-Reaper: Unmasking LiDAR-based Detector Vulnerabilities in Rain (2024, IROS) | LiDAR Security & Weather | Developed an attack that exploits rain’s physical properties to trick a LiDAR system into ignoring real obstacles. | | Leveraging Adverse Weather for Enhanced LiDAR Spoofing... (2026, IEEE Vehicular Technology Magazine ) | Autonomous Vehicle Security | Argued that weather isn't just a hindrance but can be strategically leveraged to design more sophisticated attacks on self-driving car sensors. |
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Beyond environmental interference, autonomous sensors face targeted adversarial disruptions. Dr. Capraru analyzes where bad actors strategically manipulate environmental reflections or transmit spoofed signals to trick machine learning models. His doctoral research addresses how easily standard 3D object detection pipelines can be blinded or deceived, laying the groundwork for cryptographic and algorithmic defenses within Advanced Driver-Assistance Systems (ADAS).
Richard Capraru is a photographer and visual artist known for portraiture and documentary-style work that emphasizes natural light, intimacy, and cultural storytelling. His projects often explore personal identity, community, and contemporary life through candid and composed images. richard capraru
This philosophy drives his operational strategies. He argues that traditional business structures are obsolete. In the digital age, the marketing department cannot work independently of the IT department, and finance cannot be detached from customer experience. Capraru’s methodology involves "silo dismantling"—creating cross-functional teams that operate with shared KPIs. His strategic frameworks often include:
Dr. Capraru’s research has built significant momentum across major IEEE conferences and specialized engineering journals. His highly cited contributions include:
: He frequently collaborates with established figures in the field such as Matthew Ritchie Francesco Fioranelli | Publication Title | Focus Area | Key
Richard Capraru’s research is crucial for the automotive and AI industries, which are under pressure to ensure that self-driving cars can operate safely in all environments, including those with adverse weather and potential cybersecurity threats. By identifying how attackers can leverage weather to mask their efforts, this research helps shape the development of more robust, secure sensory technology.
Doctoral thesis mapping out the physical realities of multi-sensor security degradation.
: Investigating how sensors like LiDAR perform in adverse weather, such as heavy rain, and how these conditions affect the reliability of autonomous navigation. (2022) | Synthetic Data & SAR | Demonstrated
Dr. Capraru is a highly cited and active IEEE member whose collaborative projects have laid foundational benchmarks in radar and autonomous perception. Publication / Dataset Title Core Innovation / Focus
Co-author of "Dop-NET: a micro-Doppler radar data challenge" published in IET Electronics Letters , providing the robotics community with standard open-source datasets to train advanced gesture-recognition systems.