Hybrid driver monitoring system based on Internet of Things and machine learning

With the rapid development of intelligent mobile terminal equipment, more and more intelligent mobile terminal platforms are constantly emerging, and the types are gradually diversified. This process has also slowly promoted the spring up of the Internet of things (LOT). which can collect more information from more edge devices. Thanks to the increasing amount of data that can be collected, machine learning (ML) technology can make existing applications more intelligent and analyze more complex situations. This article reviews the existing popular methods developed in vehicles, consumer electronics products and smart transportation to assess driver state, detect the driver's environment, as well as vehicle performance, and propose a hybrid driver state monitoring system model. This model is designed to use IoT to collect all-round data from each edge devices to ensure the reliability and validity of the data, and then analyze it through ML technology, and finally give the driver appropriate instructions to help the driver in the safest driving conditions.