Deep LSTM with Dynamic Time Warping Processing Framework: A Novel Advanced Algorithm with Biosensor System for an Efficient Car-Driver Recognition

The latest generation of cars are increasingly equipped with driver assistance systems called ADAS (advanced driver assistance systems) which are able to assist the car driver in different driving scenarios, and in the most advanced automation levels, able to take over driving the car if required due to dangerous situations. Therefore, it is essential to adapt the ADAS specifically to the car-driver's identity in order to better customize the driving assistance. To this end, algorithms that allow correct recognition of the vehicle driver are fundamental and preparatory. In this context, an algorithm for car-driver identity recognition is proposed which allows, with an accuracy close to 99%, recognition of the driver by means of a properly designed pipeline based on the analysis of the car driver PhotoPlethysmoGraphic (PPG) signal. The proposed approach makes use of deep long short-term memory (LSTM) architecture for learning such PPG signal features needed to discriminate one car driver from another. The extended validation and testing of the proposed system confirm the reliability of the proposed pipeline.