Self Driving Car Simulation

Overview: Self-Driving Car Simulation

This self-driving car simulation is a visual and interactive tool designed to demonstrate how an artificial intelligence (AI) model—specifically a neural network—can learn to navigate a vehicle on a multi-lane road autonomously. The simulation integrates real-time car movement, obstacle detection, and neural network visualization.

Key Features

🛣️ Driving Environment

• The simulation includes a straight, multi-lane road with lane markers.
• Purple blocks represent traffic or obstacles that the AI car must avoid.
• The AI-controlled car is shown in blue with multiple faded overlays, indicating previous positions or trajectory prediction.

🧭 Sensor System

• The car uses sensor rays (yellow lines) to detect nearby objects and boundaries. These mimic technologies like LiDAR or ultrasonic sensors.
• The data collected from these sensors serves as input for the neural network.

🧠 Neural Network Visualization

• On the right side, a live representation of the neural network is displayed:
  • Input layer: Receives sensor data.
  • Hidden layers: Process the data using learned weights.
  • Output layer: Determines driving actions (e.g., accelerate, turn left/right).
• The lines connecting nodes show the weights and activation states in real-time, where color and intensity might indicate the strength and polarity of those weights.

💾 Model Controls

• Users can save the trained neural network for later use.
• A reset/delete option allows restarting the training process or clearing the current model.