Digital Twins for Personalized and Safer Automated Driving

By bridging the gap between the physical and cyber domains, the Digital Twin (DT) technology will contribute to revolutionize mobility. So far, the research focus has been on pairing connected and automated vehicles with their digital counterparts in order to monitor them in real-time and to predict and optimize their operation. On the contrary, capturing the driver behaviour through the associated DT is still at an early research stage. The benefits of the interplay between the vehicle DT and the driver DT (DDT) to achieve personalized and safe automated driving have to be practically unveiled. In this work, we fill this gap by showcasing the advantages derived from the interaction of the two types of DTs, with the aim of personalizing vision-based models to detect the driver drowsiness. Fine tuning is applied to the Machine Learning (ML) algorithm available on board, based on the data collected by the DDT. This results in an accuracy improvement up to 3%, at the cost of the ML model exchange between DTs.