AUTOMOTIVE: A case study on AUTOmatic multiMOdal drowsiness detecTIon for smart VEhicles

As technology and artificial intelligence conquer a place under the spotlight in automotive world, driver drowsiness monitoring systems have sparked much interest as way to increase safety avoid sleepiness-related accidents. Such technologies, however, stumble upon observation that each presents distinct set of behavioral physiological manifestations drowsiness, thus rendering its objective assessment non-trivial process. The AUTOMOTIVE project studied application signal processing machine learning techniques for driver-specific detection smart vehicles, enabled by immersive driving simulators. More broadly, comprehensive research on biometrics using electrocardiogram (ECG) face enables continuous subject-specific models more efficient monitoring. This paper aims offer holistic but view development work conducted across various addressed topics how it ultimately brings us closer target improved