**Abstract**

This chapter deals with a design of a new speed control method using artificial intelligence techniques applied to an autonomous electric vehicle. In this research, we develop an Advanced Driver Assistance System (ADAS) which aims to enhance the driving manner and the safety, especially when traveling too fast. The proposed model is a complete end-to-end vehicle speed system controller that proceeds from a detected speed limit sign to the regulation of the motor's speed. It recognizes the speed limit signs before extracting from them, a speed information that will be sent, as reference, to a NARMA-L2 based controller. The study is developped specially for electric vehicle using Brushless Direct Current (BLDC) motor. The simulation results, implemented using Matlab-Simulink, show that the speed of the electric vehicle is controlled successfully with different speed references coming from the image processing unit.

**Keywords:** Brushless DC, Deep learning, Intelligent electrical vehicle, NARMA-L2 controller, Speed control, Traffic sign recognition

#### **1. Introduction**

Nowadays, internal combustion engine vehicles are the major source of air pollution and damage to our health. To solve these problems, electric vehicles are one of the most encouraging energy saving and environmental protection solutions. However, such solutions show luck of autonomy. The current trend in vehicle transportation is to implement solution where the driver and an intelligent system coexist and communicate to improve road safety and security. Advanced driver assistance systems (ADAS) are bridging the gap between traditional electric vehicles and the vehicles of tomorrow which are intelligent, autonomous and safe for the drivers, the passengers and everyone else on the road.

Generally, an autonomous electric vehicle consists of 4 main modules: the energy source module, the auxiliary module, the intelligent module composed of computers, sensors and actuators, and an electrical propulsion module [1], which consists of an electronic controller, a power converter, a mechanical transmission and an electric motor of either AC or DC, as shown in **Figure 1**. The motors convert the electrical energy that comes from the battery into a mechanical energy that allows the vehicle to move. They can also be considered as generators when sending energy back to the source of energy.

**Figure 1.** *Block of diagram of an Autonomous electric vehicle.*

According to the specific requirements needed for autonomous electric vehicle's applications, such as, robustness, power, speed range and level of noise, the choice of motors can vary depending on their types. DC motors have been widely used since they fulfill some of the requirements mentioned before [2]. However, in recent years, in order to compensate for their electrical losses and mechanical friction, they have been replaced by Brushless DC Motors (BLDC), given their high efficiency and low noise [3–5]. Besides, BLDC are known to have a big initial torque with small size and low weight. They also can be built in the tires to reduce the complexity and weight of the driving mechanism.

Speed regulation is an important control challenge for any BLDC motor. In [6], the authors proposed an implementation of Space Vector Pulse Width Modulation (SVPWM) for the control of the power converter and the BLDC motor. They showed that using SVPWM methodology, offers the minimum switching losses, reduces harmonics compared with the other Pulse Width Modulation (PWM) methods.

In order to enhance and perfect the speed adaptation of the autonomous electric vehicle, while assisting at the same time the driver, and increasing its safety and security, ADAS has been developed, relying on inputs from multiple data sources, including automotive imaging, image processing, and in-vehicle networking.

In fact, It has been said that 80–90% of the driver's performance depends on visual information [2]. This is why, a huge number of collisions occur when the driver is not looking forward or unable to stop at an intersection due to excessive speed. It will be very important to automatically detect the speed limit sign and control the vehicle's speed when it is traveling too fast. Various ADAS are now designed with a large number of sensors and actuators to analyze the environment and take the appropriate actions. Among the existing ADAS systems, the traffic sign recognition holds a lot of potential as they enhance the driver's safety by notifying him about possible dangers related to speed.

The automatic recognition of these signs, however, is not easy to carry due to the weather's conditions, the blur resulting from moving vehicles and the lighting conditions. **Figure 2** shows some of these factors that make it difficult to identify the road signs.

To handle these challenges, researchers recommended the use of image processing and machine learning techniques. The automatic recognition of traffic signs includes, mainly, the traffic sign detection and the traffic sign classification.

Traffic signs have several distinctive features like colors, shapes and symbols. In the detection stage, the input images are preprocessed, enhanced and then, segmented according to their color or geometry. Color-based methods usually use

#### **Figure 2.**

*Difficulties that affect the traffic sign recognition systems.*

normalized RGB space [7–10] or HSV space [11–16] or YUV space [17] to distinguish between traffic and non traffic signs. However, these methods are generally affected by the weather conditions and the illumination variations.

Geometry-based methods are, on the contrary, robust to the illumination changes since they characterize the shape of the traffic sign. Mainly, authors used corner detection [11, 18], distance transform [19], Hough transform [20, 21] or radial geometry voting [22–24].

Recently, many researchers combined the color-based methods with the shapebased methods to achieve better results [10, 13, 16, 25, 26]. For instance, authors in [16], used color segmentation to roughly identify the sign before applying a matching technique based on the logical XOR operator using the shape extracted details. Similarly, authors in [10] used color segmentation to roughly locate the signs and then, used the shape information to eliminate the false candidates.

As for the classification stage, many methods have been used to identify the class of the traffic signs, such as, Support Vector Machine (SVM) [8, 13, 16], Viola-Jones detector [27, 28], neural networks [2, 15] and random forest [29, 30].

As we can see, all the presented researches focused only on recognizing traffic signs without trying to integrate and test them into a complete system controlling mechanical and electrical part. In [2], the authors designed an end-to-end system, where they proposed a NARMA-L2 neuro controller for speed regulation based on steerable decomposition and Bayesian neural networks. Despite the acceptable accuracy rate (about 0.975), it failed, unfortunately, to accurately recognize noisy signs.

In this chapter, we continue to design an end-to-end system, based on deep learning approaches, that enhances autonomous vehicle speed control and presents better performances in the presence of noisy inputs.

The rest of this chapter is organized as follows: in Section 2, we describe the proposed model. Section 3 will be reserved to the experimental results and finally, Section 4 will conclude the paper.
