Self Driving Car Simulation
Neural Network-Based Autonomous Vehicle System
Project Overview
An advanced simulation environment that demonstrates the principles of autonomous driving using neural networks and computer vision. The system learns to navigate through complex scenarios while avoiding obstacles and following traffic rules.
Real-time Simulation
Dynamic environment with realistic physics and vehicle behavior
Neural Network
Multi-layer neural network for decision making and path planning
Computer Vision
Advanced sensor simulation for environment perception
Path Planning
Intelligent route calculation and obstacle avoidance
Technologies Used
Key Features
Sensor System
Ray-casting algorithms to simulate LIDAR and proximity sensors
Learning Algorithm
Genetic algorithm for efficient training and behavior optimization
Performance
Optimized rendering and computation for smooth 60 FPS operation
Technical Implementation
Neural Network Architecture
Multi-layer perceptron with customizable topology for optimal decision making
Physics Engine
Custom-built physics system for realistic vehicle dynamics
Visualization
HTML5 Canvas-based rendering with debug overlays for network visualization
Future Enhancements
Traffic System
Implementation of multiple vehicles and traffic scenarios
Weather Effects
Simulation of various weather conditions affecting vehicle behavior
Advanced Learning
Integration with reinforcement learning for improved performance