## 2 Related Work  
When driving on an expressway, it is crucial to perceive the road conditions behind you for driving safety. PTV detection in the over-the-horizon range not only plays a significant role in driving safety, but also in avoiding traffic accidents. At present, vehicle safety detection for the over-the-horizon range is a challenging emerging research field,and research related to the over-the-horizon range mainly focuses on the air and sea. For example, [PERSON] et al. [16] proposed an over-the-horizon air combat situation assessment method that combines air-to-air missile tactical attack areas, reflecting the true situation characteristics of over-the-horizon air combat; [PERSON] et al. [17] proposed a perception and guidance system based on LiDAR, which enables unmanned helicopters to independently complete obstacle detection and avoidance, terrain tracking, and close-range inspection tasks beyond the line of sight; [PERSON] et al. [18] proposed a clutter cancellation method based on FFT phase analysis using over-the-horizon radar to detect ships under the condition of short accumulation. However, there is relatively little research on the detection of threatening vehicles on expressways. At present, there are many related studies on abnormal driving detection, which mainly use video surveillance data, which is data collected by sensors and communication devices at vehicle terminals. For example, [PERSON] et al. [19] used a lightweight gradient elevator algorithm based on video taken by UAV to detect abnormal driving. However, in the case of imperfect video monitoring or bad weather on the expressway, video data is difficult to accurately detect driving, and detection of PTVs on the expressway is proportional to hardware requirements. Therefore, it is difficult to achieve the expected detection outcome. [PERSON] et al. [20] used the kernel method and an extreme learning machine to detect the abnormal driving of vehicles by acquiring acceleration sensor data, gyroscope sensor data, and magnetic field sensor data in the vehicle's associated smart phone. However, it only detects abnormal driving of individual vehicles and does not upload information to the cloud, making it impossible to transmit information to other vehicles around the road network and achieve information sharing. [PERSON] et al. [21] used the extended neural network detection model of bidirectional long short-term memory network (BiLSTM) and fully connected neural network (FC) to detect abnormal driving using vehicle driving data collected from vehicle terminals. However, the operation of sensors and communication devices will be affected by various factors, which leads to various problems in the quality of data. Moreover, there are also disadvantages such as incomplete sample data and high sparsity in this method. ETC data, on the other hand, has the advantages of full samples, accuracy, time series, and good data quality. The use of ETC data to detect PTVs is of great significance for future expressway research. In order to enhance the precision of PTV detection and provide more accurate traffic information, it is important to predict the speed of vehicles on the road in real time. At present, many domestic and international researchers have conducted research in the field of traffic prediction. The main traffic prediction methods include statistical models, deep learning models, and traditional machine learning models. Statistical learning models predict future values based on historical data by analyzing historical time series. [PERSON] et al. [22] used the autoregressive integrated moving average model to predict railway passenger flow. The statistical model is suitable for simple and stable data, while the changes in expressway traffic conditions are significant and require too much calculation. In addition, it can only capture linear relationships, while traffic speed data has nonlinear characteristics. The deep learning model demonstrates its high prediction accuracy and extensive prediction ability.