rdsx.dev

Sun Jun 15 2025

Autonomous Car Simulation

Autonomous Car Simulation

Next.js

JavaScript

AI

Neural Networks

Genetic Algorithm

Simulation

Canvas

An advanced autonomous car simulation that uses Next.js 13 framework to visualize the behavior of self-driving cars on a road, featuring neural networks, genetic algorithms, and realistic physics simulation.

What is Autonomous Car Simulation?

Self-Driving Car Simulation is an interactive web application that demonstrates the power of artificial intelligence in autonomous vehicle navigation. The simulation combines realistic physics, neural networks, and genetic algorithms to create self-driving cars that learn and evolve their driving capabilities over time. Users can observe AI-controlled vehicles navigate complex traffic scenarios while also manually controlling a car using keyboard inputs.


Tech Stack

  • Frontend Framework: Next.js 13
  • Programming Language: JavaScript
  • Rendering Engine: HTML5 Canvas
  • AI Implementation: Custom Neural Networks
  • Evolution Algorithm: Genetic Algorithm
  • Physics Engine: Custom collision detection and vehicle dynamics
  • State Management: React hooks and context
  • Styling: CSS modules with responsive design

Features

1. Neural Network-Controlled Vehicles

Self-driving cars are powered by sophisticated neural networks that process sensor data and make real-time driving decisions. Each car's "brain" can be customized with different activation functions and network architectures.

2. Genetic Algorithm Evolution

Watch as AI cars evolve and improve their driving skills across generations. The genetic algorithm breeds better drivers by combining successful traits and introducing beneficial mutations.

3. Realistic Physics and Collision Detection

Experience authentic vehicle dynamics with accurate acceleration, braking, steering mechanics, and sophisticated collision detection with road boundaries and other traffic.

4. Advanced Sensor Visualization

See exactly how autonomous vehicles perceive their environment through visual representation of sensor arrays and distance readings.

User Workflow

  1. Launch the simulation and observe AI cars navigating the road autonomously.
  2. Use WASD keys to manually control the main car and interact with AI traffic.
  3. Adjust neural network parameters and training configurations in the settings panel.
  4. Watch multiple generations evolve as cars learn from successful behaviors.
  5. Experiment with different road layouts and traffic density configurations.

How It Works

  1. Neural Network Processing: Each car's neural network receives sensor inputs (distances to obstacles, current speed, direction) and outputs driving commands (steering angle, acceleration, braking).

  2. Genetic Algorithm Evolution: After each generation, the best-performing cars (based on distance traveled and collisions avoided) are selected as parents for the next generation.

  3. Physics Simulation: Real-time physics calculations handle vehicle movement, collision detection, and environmental interactions using custom algorithms optimized for performance.

  4. Sensor System: Cars use multiple distance sensors to detect obstacles, road boundaries, and other vehicles, providing crucial input data for decision-making.

Features of Autonomous Car Simulation

  1. Real-time AI Learning

    • Neural networks adapt and learn optimal driving strategies in real-time.

  2. Customizable Parameters

    • Adjust network architecture, learning rates, mutation rates, and population sizes.

  3. Interactive Manual Control

    • Take control of a car using WASD keys to interact with AI traffic.

  4. Visual Sensor Feedback

    • See sensor readings and neural network decision-making processes visualized on screen.

  5. Dynamic Traffic Generation

    • AI-controlled traffic creates realistic and challenging driving scenarios.

  6. Performance Analytics

    • Track AI improvement metrics across generations with detailed statistics.

  7. Flexible Road Layouts

    • Configure different road designs, lane counts, and complexity levels.

  8. Evolution Visualization

    • Watch the genetic algorithm process unfold with generation-by-generation improvements.

Development Highlights

  • Built a custom neural network implementation optimized for real-time vehicle control.
  • Implemented efficient genetic algorithm with configurable selection, crossover, and mutation strategies.
  • Developed high-performance collision detection system using spatial partitioning techniques.
  • Created intuitive visual feedback system showing AI decision-making processes.
  • Optimized Canvas rendering for smooth 60fps simulation with multiple vehicles.
  • Designed modular architecture allowing easy experimentation with different AI configurations.

Controls

  • W: Accelerate the manual car
  • S: Brake or reverse the manual car
  • A: Steer left
  • D: Steer right
  • Space: Reset the current simulation
  • R: Evolve to the next AI generation

Configuration Options

The simulation offers extensive customization options for experimenting with different AI and physics parameters:

  • Neural Network Architecture: Modify layer counts, neuron numbers, and activation functions
  • Genetic Algorithm Settings: Adjust population size, mutation rates, and selection pressure
  • Physics Parameters: Customize vehicle acceleration, friction, and collision sensitivity
  • Environment Settings: Configure road layout, traffic density, and sensor ranges