Project Title : Autonomous Vehicle Simulation

Objective:
To create a simulation of a self-driving car that can navigate roads, detect obstacles, follow traffic rules, and make driving decisions using artificial intelligence.

What It Does:
Simulates the behavior of an autonomous vehicle in a virtual environment using AI models for perception, decision-making, and control.

Key Concepts:

Computer Vision: For object and lane detection.

Reinforcement Learning / Deep Learning: For learning how to drive.

Path Planning & Control: To follow lanes, avoid obstacles, and navigate.

Steps Involved:

Simulation Environment Setup:

Use platforms like CARLA, Udacity Simulator, or Gazebo.

Simulate real-world driving scenarios (roads, traffic signs, pedestrians, etc.).

Perception System:

Use AI models to detect lanes, traffic lights, signs, vehicles, and pedestrians.

Use cameras, LiDAR (simulated), and sensors in the virtual environment.

Decision-Making:

Implement rule-based logic or AI (e.g., Reinforcement Learning) for driving behavior.

Decide when to stop, accelerate, turn, or change lanes.

Path Planning:

Calculate safe and efficient routes.

Use A*, Dijkstra’s algorithm, or neural networks for planning.

Control System:

Control steering, throttle, and braking.

Use PID controller or model-based control methods.

Testing & Evaluation:

Run simulation with different weather, traffic, and road conditions.

Measure performance based on safety, accuracy, and smoothness.

Applications:

Self-driving car R&D

Traffic and mobility simulation

Smart city planning

Robotics and AI research

Tools & Technologies:

Languages: Python, C++

Frameworks/Simulators: CARLA, ROS, OpenCV, TensorFlow/Keras, PyTorch

Extras: Jupyter Notebooks for logging & analysis