Dedicated driving simulator dataset collection campaign for generating ground truth datasets drowsiness detection. Our team applied combination of eye closure metrics, driving performance and physiological data analysis (ECG and EEG).
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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 more
We co-developed ROS 2 gem for Open 3D Engine to enable performant and scalable simulation of agriculture, mining and warehouse robots. Tailored for ROS, it gives full access to the ROS 2 ecosystem.Read more