Title: Non-Contact Sensing and Multi-modal Imaging for Driver Drowsiness Estimation
Description: Drowsiness, i.e., the unintentional and uncontrollable need to sleep, is responsible for 20-30% of road traffic accidents. A recent WHO statistic shows that road accidents are ranked eighth as primary cause of death in the world, resulting in more than 1.35 million deaths annually. As a result, driver monitoring systems containing drowsiness detection capabilities are becoming more common and will be mandatory in new vehicles from 2025 onwards. But there remain significant challenges and unexplored areas, particularly surrounding multimodal imaging (NIR, LWIR and neuromorphic) techniques for drowsiness estimation. Therefore, the overall aim of this research is to improve and implement novel neural AI algorithms for non-contact drowsiness detection that can be used in unconstrained driving environments. In detail, this research will examine SoA non-contact driver drowsiness techniques, evaluate existing and research new drowsiness indictors, and build / validate innovative complex machine learning models that are trained with both public and specialized industry datasets.