Driver Identification Via Hybrid Model and Few-Shot Technique Based CAN-BUS Data

Driver identification is vital in connected transport and has various benefits like usagebased insurance, personalized assisted driving, fleet management, etc.The data collected from behind the wheel makes it possible to identify unique driving styles as an alternative to adding extra costs or compromising drivers' biometric fingerprint privacy, such as facial recognition.The variable nature of drivers causes problems for traditional techniques because they become less accurate when faced with new drivers.This paper presents an innovative method of driver identification using few-shot learning techniques based 1D CNN-LSTM Attention model that can effectively solve the N-driver identification problem, given very few training examples on driving.Our findings reveal that this model can be generalized correctly from just a few examples, making it essential in real-life situations.We compare our proposed method with several baseline models such as LSTM Attention, LSTM, CNN, and ANN.Furthermore, applying our model to 3-way and 5-way classification problems using 1-shot and 5-shot methods further evidences its effectiveness in changing environments.Consequently, from this research, it is clear that knowledge based on the training dataset could be applied successfully to new drivers.Impressive results obtaining when trained on all raw databases but still getting correct identifications even with a small number of instances per driver label.