SafeDrive: Online Driving Anomaly Detection From Large-Scale Vehicle Data

Identifying driving anomalies is of great significance for improving driving safety. The development of the Internet-of-Vehicle (IoV) technology has made it feasible to acquire big data from multiple vehicle sensors, and such big data play a fundamental role in identifying driving anomalies. Existin...

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Bibliographic Details
Published in:IEEE transactions on industrial informatics 2017-08, Vol.13 (4), p.2087-2096
Main Authors: Mingming Zhang, Chao Chen, Tianyu Wo, Tao Xie, Bhuiyan, Md Zakirul Alam, Xuelian Lin
Format: Article
Language:English
Subjects:
Acceleration
Algorithm design and analysis
Anomalies
Anomaly
Big Data
Clustering algorithms
Data management
Data models
data stream
driving behavior
Informatics
Internet-of-Vehicles
on-board diagnostics (OBD)
Sensors
state graph (SG)
Vehicle safety
Vehicles
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Summary:Identifying driving anomalies is of great significance for improving driving safety. The development of the Internet-of-Vehicle (IoV) technology has made it feasible to acquire big data from multiple vehicle sensors, and such big data play a fundamental role in identifying driving anomalies. Existing approaches are mainly based on either rules or supervised learning. However, such approaches often require labeled data, which are typically not available in big data scenarios. In addition, because driving behaviors differ under vehicle statuses (e.g., speed and gear position), to precisely model driving behaviors needs to fuse multiple sources of sensor data. To address these issues, in this paper, we propose SafeDrive, an online and status-aware approach, which does not require labeled data. From a historical dataset, SafeDrive statistically offline derives a state graph (SG) as a behavior model. Then, SafeDrive splits the online data stream into segments and compares each segment with the SG. SafeDrive identifies a segment that significantly deviates from the SG as an anomaly. We evaluate SafeDrive on a cloud-based IoV platform with over 29 000 real connected vehicles. The evaluation results demonstrate that SafeDrive is capable of identifying a variety of driving anomalies effectively from a large-scale vehicle data stream with an overall accuracy of 93%; such identified driving anomalies can be used to timely alert drivers to correct their driving behaviors.
ISSN:1551-3203
1941-0050
DOI:10.1109/TII.2017.2674661