ADAS Project

This project is designed for Advanced Driver Assistance Systems (ADAS) and focuses on visualizing vehicle trajectories based on Ackermann steering.

Table of Contents

• Requirements
• Project Structure
• Running the Code

Requirements

Make sure you have the following installed:

• Python 3.x
• OpenCV (pip install opencv-contrib-python)
• NumPy (pip install numpy)
• Matplotlib (pip install matplotlib)

Project Structure

• Variables.py: Contains project-specific variables.

  • Adjust vehicle-specific values such as wheelbase, track width, and steering ratio in this file.
  • Modify the steering angle for visualization within SteeringTrajectory.py.
  • Update simulation parameters like vehicle speed for trajectory generation.
  • Adjust camera intrinsic and extrinsic parameters to customize the imaging setup.

• Functions.py: Includes functions for calculating wheel angles, trajectories, and visualization.

• SteeringTrajectory.py: The code snippet performs a trajectory visualization using predefined parameters and functions for calculating vehicle trajectories and visualizing them. The process involves the following steps:

1. Calculate The Ackermann Angle:

   • Compute the Ackermann steering angle based on predefined values for wheelbase, steering angle, and steering ratio.

2. Compute The Trajectories:

   • Determine trajectories for the center of the rear axle, considering the turning radius from the Instantaneous Center of Curvature (ICC) to the center of the rear axle.
   • Simulate the vehicle's movement by calculating the heading angle, incorporating the vehicle speed in discrete time steps over a specified duration.
   • Update the positions of the vehicle during the simulation.

3. Convert Points to 3D:

   • Transform 2D trajectory points representing the center, inner rear wheel, and outer rear wheel positions to their corresponding 3D representations.
   • Utilize numpy arrays to perform the conversion, adjusting the coordinates and data types accordingly.
   • Introduce a constant depth value to convert 2D points into 3D space, maintaining a consistent depth throughout the trajectories.

4. Project 3D to 2D:

   • Utilize the OpenCV library to project 3D trajectory points, including the center, inner rear wheel, and outer rear wheel positions, onto a 2D image plane.
   • Employ the camera parameters such as rotation vector, translation vector, camera matrix, and distortion coefficients to perform the projection.
   • Return the resulting 2D trajectory points for further visualization and analysis.

• ReversingTrajectoryInteractive.py: The code snippet makes an interactive trajectory visualization tool that can update the trajectories of the vehicle in real time based on the steering value that can be changed using a slider.

1. Interactive Steering Visualization:
   • Create an interactive visualization using OpenCV to demonstrate the impact of steering angle on vehicle trajectories.
   • Utilize a video file (ReversingCamera.mp4) as input to simulate real-time scenarios.
   • Enable user interaction through a trackbar, adjusting the steering angle from -360 to 360 for dynamic trajectory changes.
   • Display the processed frame with overlaid trajectories and real-time steering angle information.

Running the Code

1. Clone the repository:

git clone https://github.com/Zaid-Waghoo/ADAS.git

pip install -r requirements.txt

python ReversingTrajectoryInteractive.py
python SteeringTrajectory.py