This project simulates a self-driving car using a Convolutional Neural Network (CNN). It uses images collected from a Unity-based driving simulator where the car is manually driven by a user. The collected dataset is then preprocessed and used to train a deep learning model that can drive the car autonomously based on road images.
📌 Project Overview
- User drives a car in a Unity simulation to collect road data.
- Captured frames and corresponding steering angles form the dataset.
- Data analysis and preprocessing done using Pandas, NumPy, Matplotlib, and Seaborn.
- CNN model trained to predict steering direction based on input images.
- Trained model used to autonomously control the vehicle in the simulation.
| Tool/Library | Purpose |
|---|---|
| Python | Core programming language |
| TensorFlow/Keras | CNN model building and training |
| OpenCV | Image preprocessing and manipulation |
| Unity | Driving simulation and data collection |
| Pandas, NumPy | Data handling and manipulation |
| Matplotlib, Seaborn | Data visualization and analytics |
