Self-Driving Electrical Car simulation using Mutation and DNN

The development of self-driving Electrical Cars has been one of the fascinating fields in the last decade, where machine learning algorithms and neural networks have shown impressive results in enabling autonomous vehicles to perceive and react to their surroundings. However, developing these technologies requires significant hardware, software, and infrastructure investments. This paper presents a self-driving Electrical Car simulation built using neural networks from scratch, without any libraries, using JavaScript. The simulation was developed as a proof of concept to demonstrate that creating a functioning self-driving Electrical Car model is possible using only essential tools and algorithms. The simulation comprises a feedforward neural network that controls the Electrical Car's acceleration and steering force. The Electrical Car is equipped with five sensors that serve as inputs to the neural network, allowing it to navigate through a course without going outside the track.