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Domain: Interior sensing
Service: Methodology preparation, dataset collection and analysis
Our Tier 2 Partner needed ground truth dataset for the development of novel driver drowsiness detection algorithms as part of their driver monitoring product. Our team had to combine multiple measures including: driving performance, detailed eye-tracking and physiological data (EEG and ECG) to reference drowsiness levels. As driver drowsiness is a safety-critical state, our team proposed a safe and standardised solution that also enabled to generate relevant dataset.
The Robotec.ai team developed proprietary methodology for collecting and referencing drivers’ drowsiness data while driving using a high-end driving simulator as well as a dedicated EEG and ECG setup. The study was conducted with human drivers from multiple ethnicities and age groups to meet requirements of international automotive standards.
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.
Read moreWe 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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