Project Title:Self-Driving Car Simulation Using Machine Learning

Objective:

To develop and train a machine learning model that can drive a simulated car autonomously by recognizing road features, making driving decisions, and avoiding obstacles.

Summary:

This project focuses on simulating a self-driving car that can navigate roads, follow lanes, avoid obstacles, and make driving decisions using machine learning. Instead of testing on real roads, a virtual environment is used (like CARLA, Udacity Simulator, or DonkeyCar) to train and evaluate the car safely.

Computer vision techniques (like convolutional neural networks) are used to process images from the car’s front camera to detect lanes and traffic signs. For decision-making, reinforcement learning or behavioral cloning (learning from human driving data) can be used.

This project introduces students to key components of autonomous vehicles such as perception, control, and planning, all within a simulated and safe environment.

Key Steps:

Set Up Simulator – Use a driving simulator (CARLA, Udacity, or DonkeyCar).

Collect Data – Capture images and control signals (steering, throttle, brake).

Train ML Model – Use CNNs or reinforcement learning to control the car.

Evaluate – Test the model on different tracks and conditions.

Technologies Used:

Python

TensorFlow / PyTorch

OpenCV for image processing

Driving simulators: CARLA, Udacity Simulator, or DonkeyCar

Applications:

Autonomous vehicle development

Smart traffic systems

Robotics and AI navigation

Real-time vision-based control systems

Expected Outcomes:

A trained model that can drive a car autonomously in simulation

Lane following, obstacle avoidance, and smooth driving behavior

Visual demonstration and performance metrics (e.g., time on track, collisions)