Car-related accidents remain a major source of fatalities all over the world.
Car manufacturers, suppliers and AI companies are looking to build computer vision systems to monitor driver state and help improve safety. Recent EU regulations have catalyzed the development of more advanced solutions, but high-qualty in-car data needed to train AI models is lacking and expensive to obtain. Synthetic data can robustly model diverse drivers, key behaviors, and the in-cabin environment to enable the cost effective and efficient development of more capable models.
Model Camera Systems
Model multi-modal camera systems (RGB, NIR) across variable positions within the car environment.
Monitor Driver State
Vary driver demographics, head pose, emotion, and gestures to accurately model real-world scenarios.
Pixel-perfect annotation of gaze angle to build more accurate models. Model confounding elements like head pose, presence of accessories, and driver behavior to ensure model work across a wide range of use-cases.
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