## 2 Related Work  
Several anomaly detection methods have been proposed to enhance the information security of automated vehicles. Existing works on vehicle anomaly detection can be categorized into transmission characteristic-based and data content-based according to the objects of detection. Transmission characteristic-based methods analyze the information that describes the transmission state of the CAN message itself to detect anomaly behaviors, such as the ID field information, periodicity features, clock skew, and in-vehicle network channel features, etc. [PERSON] et al. [12] proposed an intrusion detection algorithm that observes the recurring patterns in the sequence of the CAN message ID and constructed the ID transformation matrix to detect intrusions. [PERSON] et al. [13] proposed a data-driven anomaly detection algorithm that exploits the frequency regularity of CAN bus messages and models inter-signal arrival times. [PERSON] et al. [14] designed a novel intrusion detection system called COIDS to detect anomaly behaviors. The authors utilized active learning to form the normal clock behavior of ECUs and used the cumulative sum method to monitor any anomalous deviation in the clock offset. [PERSON] et al. [15] designed a novel information entropy characteristic-based anomaly detection system. The authors used a fixed number of messages-based sliding window and improved the decision conditions to increase the detection performance. In fact, the methods mentioned above observe the changes in the transmission characteristics of CAN messages and raise alarms when the anomalous vehicle behaviors deviate from the normal baseline. However, these transmission characteristic-based methods may fail if the attacks are slight [16], the metadata or transmission features of it are imitated through proper crafting [17], or if they suffer from message alteration/falsification attacks [18]. Data content-based methods observe the information carried by the data field of CAN messages to detect vehicle anomalies. [PERSON] et al. [19] explored and verified the relationship between in-vehicle sensor signals and utilized the pairwise correlation between the messages of key vehicular sensors to design anomaly intrusion systems. [PERSON] et al. [7] proposed a novel intrusion detection system by using the time domain information of in-vehicle sensor signals to construct the vehicle movement states. In addition, with the rapid development of machine learning/deep learning technology and the data that are becoming more readily available, these technologies have been recently employed in many anomaly detection works. For instance, [PERSON] et al. [18] presented a regression learning-based intrusion detection system (IDS). The authors utilized pairwise raw in-vehicle sensor signals on the time domain to train detection models and detect anomalies by monitoring the deviations between the true and estimated values. [PERSON] et al. [20] used a deep neural network (DNN) to discriminate normal and attack packets based on feature vectors, which are constructed to convey the statistical behaviors from binary CAN messages. [PERSON] et al. [21] designed an unsupervised deep learning anomaly detection method. The authors used an artificial neural network and deep autoencoder to learn a behavior pattern from normal sensor messages and compare it with vehicle observations according to the constructed nominal behavior for vehicle anomaly detection. For data content-based methods, considering that the CAV is a typical cyber-physical system (CPS) [1] and that the data content of messages on the CAN bus always contains plentiful amounts of significant vehicle motion information, processing the sensor data well, e.g., sensor fusion, etc., is critical for enhancing CAV security in intelligent transportation systems. For instance, [PERSON] et al. [22] proposed an anomaly detection method by utilizing an edge-based sensor data fusion method named VeAnDe. [PERSON] et al.