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 MSALSTMCNN 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.
|Journal||IEEE Transactions on Intelligent Transportation Systems|
|Publication status||Published - 1 Oct 2020|
- Anomaly Detection
- Connected and Automated Vehicles (CAVs)
- Convolutional Neural Network (CNN)
- Intelligent Transportation System (ITS)
- Multi-source Anomaly Detection