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Prepared and applied full laboratory setup for generating highly accurate ground truth datasets for vehicle interior sensing including driver monitoring in reference to distraction detection. We used a high-end motion capture system in the manufactured vehicle mock-up.
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Dedicated driving simulator dataset collection campaign for generating ground truth datasets drowsiness detection. Our team applied a combination of eye closure metrics, driving performance and physiological data analysis (ECG and EEG).
Read moreWe selected and integrated LIDAR-based sensors setup in a truck. We worked on perception algorithms for obstacles detection using also novel machine-learning methods like point pillars.
Based on real world 3D scans and space shuttle radar topography mission information we developed detailed terrain model. We applied our own automatic meshing algorithms. We were also able to develop road logic layer based on OpenDrive standard.
Our team developed proprietary machine learning solution for detecting anomaly behavior in the automotive application processor. Our work included preparation of the full AI pipeline including automated dataset generation and proprietary neural network architecture.
We developed a ROS2 module that allows to integrate the Robot Operating System 2 with Unity3D for performance-oriented AV/ADAS simulation.
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