TY - GEN
T1 - Inaccurate Prediction Is Not Always Bad: Open-World Driver Recognition via Error Analysis
AU - Li, Jianfeng
AU - Zhao, Kaifa
AU - Tang, Yajuan
AU - Luo, Xiapu
AU - Ma, Xiaobo
N1 - Funding Information:
This research was supported by Hong Kong ITF Project (No. ITS/197/17FP), National Natural Science Foundation (61972313), Postdoctoral Science Foundation (2019M663725), and the Fundamental Research Funds for the Central Universities, of China.
Publisher Copyright:
© 2021 IEEE.
PY - 2021/4
Y1 - 2021/4
N2 - Driver identification is of fundamental importance in many vehicle-related applications, such as fleet monitoring and anti-theft system. The vast majority of existing methods work under the closed-world assumption, which may be unrealistic in practice. In this paper, we consider a more practical but challenging scenario, i.e., open-world driver recognition, and propose a systematic method dubbed DRIVERPRINT. To recognize the driver of interest, DRIVERPRINT takes advantage of the behavioral predictability of the driver himself, thereby no need to collect data from other drivers for model training. Specifically, DRIVERPRINT predicts the behavior-related traveling speed with a driver-specific predictor, compares the prediction error with a pre-trained error model and finally recognizes drivers via error analysis. Besides open-world setting, our method is also compatible with closed-world driver classification. Real-world experiments demonstrate our method achieves reasonable accuracy. The average F1-score for open-world driver recognition is up to 0.91, while that for closed-world driver classification is up to 0.973.
AB - Driver identification is of fundamental importance in many vehicle-related applications, such as fleet monitoring and anti-theft system. The vast majority of existing methods work under the closed-world assumption, which may be unrealistic in practice. In this paper, we consider a more practical but challenging scenario, i.e., open-world driver recognition, and propose a systematic method dubbed DRIVERPRINT. To recognize the driver of interest, DRIVERPRINT takes advantage of the behavioral predictability of the driver himself, thereby no need to collect data from other drivers for model training. Specifically, DRIVERPRINT predicts the behavior-related traveling speed with a driver-specific predictor, compares the prediction error with a pre-trained error model and finally recognizes drivers via error analysis. Besides open-world setting, our method is also compatible with closed-world driver classification. Real-world experiments demonstrate our method achieves reasonable accuracy. The average F1-score for open-world driver recognition is up to 0.91, while that for closed-world driver classification is up to 0.973.
UR - http://www.scopus.com/inward/record.url?scp=85112447677&partnerID=8YFLogxK
U2 - 10.1109/VTC2021-Spring51267.2021.9448820
DO - 10.1109/VTC2021-Spring51267.2021.9448820
M3 - Conference article published in proceeding or book
AN - SCOPUS:85112447677
T3 - IEEE Vehicular Technology Conference
SP - 3104
EP - 3107
BT - 2021 IEEE 93rd Vehicular Technology Conference, VTC 2021-Spring - Proceedings
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 93rd IEEE Vehicular Technology Conference, VTC 2021-Spring
Y2 - 25 April 2021 through 28 April 2021
ER -