Intelligent control of the 3D autonomous cars: From theory to application

The rapid increase in the world population and the inadequacy of the existing transportation system have pushed scientists and researchers to develop another option. Consequently, development of the autonomous vehicles (AVs) has begun, which is an energy-efficient and intelligent technology that will not disrupt the existing transportation system, but rather enhance it. One of the essential purposes of this dissertation is to examine the components of the AVs. Among these are the vehicle's battery, Battery Management System (BMS), the mathematical model of the vehicle, and the controller of the vehicle. In order to construct the simulation for the Electrical Vehicle (EV), these components must be examined step by step. MATLAB/Simulink and MapleSim have been used to simulate the EV. The driving range (DR) estimate is based on physical parameters such as vehicle speed, motor angular speed, motor voltage, State of Charge (SoC), and the position of the constructed EV model. The DR estimate has been performed using the Multi-Layer Perceptron with Back-Propagation (MLP-BP) technique. Finally, the dissertation focuses on intelligent control of AV. Python and OpenCV have been used while constructing the lane line/vehicle detection and tracking algorithms. The method for recognizing lane lines in road images or videos utilizes the Canny Function and the Hough Transform. Feature extraction, Support Vector Machines (SVMs), and sliding windows techniques have been used to detect and track moving vehicles on the road.