*4.3. Vehicle Speed Identification*

Acquisition of vehicle speed information is vital for control of vehicle overspeed. Therefore, this paper carries out a vehicle speed identification task to verify the performance of the proposed method in vehicle speed identification. Similar to vehicle weight identification, speed is divided into five grades from slow to fast (A, B, C, D, E). The speed classification table is shown in Table 4.


**Table 4.** Classification of vehicle speed.

Similarly, 500 speed samples are randomly generated, including 100 samples per grade. Then, based on the VBI dynamic equations, the responses of each speed sample are obtained in turn under the condition that the vehicle weight is class B, and the unevenness of the road surface is class B. Thus, the sample images are constructed according to the aforementioned strategy to generate a sample library for the vehicle speed identification task. Then, the samples are used as input to the network model for model training and evaluation of vehicle speed identification, respectively.

The accuracy and loss curves of the type training to convergence process are shown in Figure 10. Accuracy and loss at the end of training are shown in Table 5. The identification results and the confusion matrix are shown in Table 6 and Figure 11. After a detailed analysis of Figures 10 and 11 and Tables 5 and 6, the following conclusions can be drawn:

**Figure 10.** Results of vehicle speed identification: (**a**) Training accuracy curves of response samples; (**b**) Training loss curves of the response samples; (**c**) Validation accuracy curves of the response samples; (**d**) Validation loss curves of response samples.

**Table 5.** Train results of vehicle speed identification after 50 epochs.


**Table 6.** Identification results for the vehicle weight.


**Figure 11.** Confusion matrix for vehicle speed identification results: (**a**) Test results of acceleration samples; (**b**) Test results of speed sample; (**c**) Test results of displacement sample.

