Convolutional and Recurrent Neural Networks for Driver Identification: An Empirical Study

As a powerful non-intrusive method, driver identification based on driving data analysis has recently gained attention as it is beneficial for providing security, privacy, and personalization for driver assistance systems. Fortunately, the considerable variety of available in-vehicle sensors and net-working technologies has contributed to collecting high-quality data for driver identification purposes. Nevertheless, the main challenge in this task is extracting and capturing unique driving-related features and behavior of each individual. In this study, we analyze and compare the effectiveness of benchmark deep learning-based approaches in terms of driver identification accuracy. More specifically, we design an encoder-based framework to compare the performance of temporal convolutional and recur-rent neural networks in capturing the underlying features within the driving sequence data. We also provide insights on their strengths and limitations. Our qualitative and quantitative results demonstrate that a temporal convolution-based network can outperform recurrent architectures while reducing computational complexity by a factor of 5.6.