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

Mingming Zhang, Chao Chen, Tianyu Wo, Tao Xie, Md Zakirul Alam Bhuiyan, Xuelian Lin

Research output: Contribution to journalArticlepeer-review


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.

Original languageEnglish (US)
Article number7864432
Pages (from-to)2087-2096
Number of pages10
JournalIEEE Transactions on Industrial Informatics
Issue number4
StatePublished - Aug 2017


  • Anomaly
  • Internet-of-Vehicles
  • big data
  • data stream
  • driving behavior
  • on-board diagnostics (OBD)
  • state graph (SG)

ASJC Scopus subject areas

  • Control and Systems Engineering
  • Information Systems
  • Computer Science Applications
  • Electrical and Electronic Engineering


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