Continual Learning for Behavior-based Driver Identification

被引:0
作者
Fanan, Mattia [1 ]
Pezze, Davide Dalle [1 ]
Efatinasab, Emad [1 ]
Carli, Ruggero [1 ]
Rampazzo, Mirco [1 ]
Susto, Gian Antonio [1 ]
机构
[1] Univ Padua, Padua, Italy
关键词
Continual Learning; Deep Learning; Driver Identification; AUTHENTICATION;
D O I
10.1016/j.engappai.2025.110459
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
Behavior-based Driver Identification is an emerging technology that recognizes drivers based on their unique driving behaviors, offering important applications such as vehicle theft prevention and personalized driving experiences. However, most studies fail to account for the real-world challenges of deploying Deep Learning models within vehicles. These challenges include operating under limited computational resources, adapting to new drivers, and changes in driving behavior over time. The objective of this study is to evaluate if Continual Learning (CL) is well-suited to address these challenges, as it enables models to retain previously learned knowledge while continually adapting with minimal computational overhead and resource requirements. We tested several CL techniques across three scenarios of increasing complexity based on a well-known dataset for the Driver Identification problem. This work provides an important step forward in scalable driver identification solutions, demonstrating that CL approaches, such as Dark Experience Replay (DER), can obtain strong performance with only an 11% reduction in accuracy compared to the static scenario. Furthermore, to enhance the performance, we propose two new methods, Smooth Experience Replay (SmooER) and Smooth Dark Experience Replay (SmooDER), that leverage the temporal continuity of driver identity over time to enhance classification accuracy. Our novel method, SmooDER, achieves optimal results with only a 2% accuracy reduction compared to the 11% of the DER approach. In conclusion, this study proves the feasibility of CL approaches to address the challenges of Driver Identification in dynamic environments, making them suitable for deployment on cloud infrastructure or directly within vehicles.
引用
收藏
页数:11
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