Anomaly Detection in Automated Vehicles Using Multistage Attention-based Convolutional Neural Network

Abdul Rehman Javed, MUHAMMAD USMAN, Saif ur Rehman, Mohib Ullah Khan, Mohammad Sayad Haghighi

Research output: Contribution to journalArticle (journal)peer-review

91 Citations (Scopus)


Connected and Automated Vehicles (CAVs), owing to their characteristics such as seamless and real-time transfer of data, are imperative infrastructural advancements to realize the emerging smart world. The sensor-generated data are, however, vulnerable to anomalies caused due to faults, errors, and/or cyberattacks, which may cause accidents resulting in fatal casualties. To help in avoiding such situations by timely detecting anomalies, this study proposes an anomaly detection method that incorporates a combination of a multi-stage attention mechanism with a Long Short-Term Memory (LSTM)-based Convolutional Neural Network (CNN), namely, MSALSTM-CNN. The data streams, in the proposed method, are converted into vectors and then processed for anomaly detection. We also designed a method, namely, weight-adjusted fine-tuned ensemble: WAVED, which works on the principle of average predicted probability of multiple classifiers to detect anomalies in CAVs and benchmark the performance of the MSALSTM-CNN method. The MSALSTM-CNN method effectively enhances the anomaly detection rate in both low and high magnitude cases of anomalous instances in the dataset with the gain of up to 2.54% in F-score for detecting different single anomaly types. The method achieves the gain of up to 3.24% in F-score in the case of detecting mixed anomaly types. The experiment results show that the MSALSTM-CNN method achieves promising performance gain for both single and mixed multi-source anomaly types as compared to the state-of-the-art and benchmark methods.
Original languageEnglish
Pages (from-to)4291 - 4300
JournalIEEE Transactions on Intelligent Transportation Systems
Early online date1 Oct 2020
Publication statusPublished - 1 Jul 2021


  • Anomaly detection
  • connected and automated vehicles (CAVs)
  • convolutional neural network (CNN)
  • intelligent transportation system (ITS)
  • multi-source anomaly detection


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