Paper Info
Title
Convolutional and Recurrent Neural Networks for Driver Identification: An Empirical Study
Abstract
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 networking 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 recurrent 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.
Year
DOI
Venue
2022
10.1109/NOMS54207.2022.9789929
PROCEEDINGS OF THE IEEE/IFIP NETWORK OPERATIONS AND MANAGEMENT SYMPOSIUM 2022
Keywords
DocType
ISSN
driver recognition, driver fingerprinting, DBA, sequence classification, driver embeddings
Conference
1542-1201
Citations 
PageRank 
References 
0
0.34
0
Authors
2
Name
Order
Citations
PageRank
Mozhgan Nasr Azadani102.37
Azzedine Boukerche200.34