Sensor Data Based Anomaly Detection in Autonomous Vehicles using Modified Convolutional Neural Network

Automated Vehicles (AVs) reform the automotive industry by enabling real-time and efficient data exchange between the vehicles. While connectivity and automation of the vehicles deliver a slew of benefits, they may also introduce new safety, security, and privacy risks. Further, AVs rely entirely on the sensor data and the data from other vehicles too. On the other hand, the sensor data is susceptible to anomalies caused by cyber-attacks, errors, and faults, resulting in accidents and fatalities. Hence, it is essential to create techniques for detecting anomalies and identifying their sources before the wide adoption of AVs. This paper proposes an anomaly detection model using a Modified-Convolutional Neural Network (M-CNN) with Safety Pilot Model Deployment (SPMD) dataset. The M-CNN model comprises specifically trained layers involving the ReLU activation function for feature extraction and detection of AV anomalies. Furthermore, the Adam is used as the optimization algorithm to train the model. The detection accuracy of the proposed model is compared with Isolation Forest (IF) and Support Vector Machine (SVM). The experimental result reveals that the proposed model outperforms the other models with an accuracy of 99.40% in AV anomaly detection.