Building an Autonomous Driving Car Simulator from Scratch: A Neural Network-Powered Intelligent Vehicle Implemented in Pure JavaScript

This project was developed by Neelamsahu24 and released on GitHub (project name: SelfDrivingCar, link: "https://github.com/Neelamsahu24/SelfDrivingCar", release date: June 13, 2026). It is an autonomous driving car simulation system built entirely using native JavaScript and HTML5 Canvas without relying on any external machine learning libraries. Core components include a custom neural network, sensor system, collision detection mechanism, and AI decision-making logic. By optimizing network weights through genetic algorithms, the vehicle gradually evolves intelligent driving strategies, providing an excellent learning material for understanding the underlying principles of autonomous driving.

Autonomous driving technology involves multiple fields such as computer vision, sensor fusion, and path planning, which is highly complex and deters many developers. This project demonstrates a feasible path to build an autonomous driving simulation environment from scratch. Its implementation without external dependencies allows learners to directly access the original implementation of algorithms, deeply understand the basic cycle of "perception-decision-control", and lower the learning threshold.

The core innovation of the project lies in the fully custom neural network framework, which is responsible for receiving sensor inputs and outputting control commands such as steering and acceleration. Network training uses genetic algorithms: vehicles with the best performance (long driving distance, few collisions) in each generation are retained, and their network parameters are inherited by the next generation. After hundreds of iterations, the vehicle gradually learns to drive safely under complex road conditions. This evolutionary training simulates the natural selection process and effectively optimizes driving strategies.

The vehicle's perception system is based on ray casting distance sensors, which emit rays at different angles to detect the distance to road boundaries and obstacles. The results are normalized and input into the neural network, simulating real LiDAR/ultrasonic sensors and considering occlusion issues. Collision detection uses the Separating Axis Theorem (SAT) to detect polygon intersections. The physics engine includes basic kinematic models (position, speed, steering angle, etc.) to ensure the authenticity of the simulation.

The project implements a configurable road system (supporting Bezier curves, straight segments, multi-lanes) and dynamic traffic flow. Roads are randomly generated with different curvature degrees to ensure the generalization ability of driving strategies. Traffic simulation introduces other AI vehicles as dynamic obstacles, forcing the trained vehicle to learn complex behaviors such as following, overtaking, and avoiding, thereby enhancing the authenticity of training.

1. Dependency-free Pure Implementation: No external libraries are used, demonstrating solid programming skills and increasing learning value; 2. Visual Debugging: Real-time display of neural network structure, sensor rays, etc., making the decision-making process transparent; 3. Real-time Training in Browser: Runs entirely in the browser without the need for back-end or GPU, allowing users to directly observe the evolution of the vehicle from random behavior to intelligent driving.

This project has reference significance for real autonomous driving development: the modular architecture (separation of perception, decision-making, and control) is consistent with the industrial software stack; the simulation-first development process reduces the cost of real-world testing; the potential of genetic algorithms in sparse reward problems is worth exploring. For learners, it is an ideal introductory material that strips away complex details and retains the essence of core concepts.

The SelfDrivingCar project proves that a basic technology stack can also build excellent AI demonstrations, and tool selection is not as important as a deep understanding of the problem. It provides developers with a modifiable and experimental platform, where adjusting parameters can directly see the impact on the vehicle's learning effect. In today's era of AI black-boxing, this transparent and controllable implementation method is particularly precious.