**3. Experiments and discussion**

In this section we present the dataset and the details of the experiments performed in this study. Experiments were performed to select the best number of folds, the best number of layers and the best optimization's function. The final proposed model was implemented using Matlab 2018 with an Intel Core i7 1.8 GHz CPU working in a windows 10 environment. The simulink model of the proposed algorithm is presented by **Figure 13**.

#### **3.1 Used database**

In order to validate the proposed method, we used the German traffic sign recognition benchmark (GTSRB). To solve the traffic sign recognition problem, this database has been made with different visual indications. The image qualities vary depending on the illumination, the contrast and the color. **Table 3** shows some details of the data-set used in our work.

#### **3.2 Speed sign recognition result**

The partitioning of the input data was performed randomly according to 10 folds cross-validation procedure: we divided the training database into ten folders.

**Figure 13.** *Simulink model of our algorithm.*


#### **Table 3.**

*Characteristics of the used database.*

Each time, we used one folder as a validation set and we trained the nine remaining folders. The process will continue until the validation error starts increasing. At this moment, the training procedure will be stopped, and then saved. After running the 10-folds, we have selected the best neural network obtained with the best validation performances. An example can be shown in **Figure 14**. Concerning the final recognition rate, it will be calculated after trying the test database with the selected network that assigns the value of 1 for exact speed limit sign and 0 for all other signs (**Figure 15**). The average speed limit sign recognition rate obtained after several tries is 90.8% with a validation accuracy 0f 94.75% (**Table 4**).

As can be seen from **Figure 16**, 100 and 60 are two of the most difficult signs to classify, whereas 30 and 50 are the easiest to recognize. **Table 4** summarizes the recognition rates obtained with different folds: 3, 5, 8 and 10 as well as the Area-Under-Curve of the ROC curve. The best recognition rates were obtained with 10-folds. Besides, we show in **Table 5** that the *adam* function is a best choice for the optimization of the deep learning network.

*Advanced Driving Assistance System for an Electric Vehicle Based on Deep Learning DOI: http://dx.doi.org/10.5772/intechopen.98870*

**Figure 14.** *Example of the best validation performance. The training stops when the validation error is increasing.*


**Figure 15.** *Confusion matrix of the deep learning process.*

#### *New Perspectives on Electric Vehicles*


**Table 4.**

*Speed limit recognition accuracy with different k-folds.*

#### **Figure 16.**

*Average recognition rates per speed limit sign.*


**Table 5.**

*Speed limit recognition accuracy with different optimization function, K = 10 folds.*
