Abstract

This project aims to develop a depth extraction system using stereo vision technology for self-driving cars. Stereo vision uses two cameras to capture images from slightly different perspectives, allowing the system to estimate the depth of objects in the environment. By integrating this technology into autonomous vehicles, the project seeks to enhance the car’s ability to perceive and navigate its surroundings, improving safety and efficiency. The system will be designed, implemented, and tested to demonstrate its feasibility and effectiveness in real-world driving scenarios.

Introduction

Self-driving cars rely on advanced sensor systems to perceive their environment and make driving decisions. Depth perception is crucial for tasks such as obstacle detection, navigation, and path planning. While various technologies like LiDAR and radar are commonly used, stereo vision offers a cost-effective and robust alternative for depth extraction. This project proposes the development of a stereo vision-based depth extraction system for autonomous vehicles, aiming to enhance their environmental awareness and decision-making capabilities.

Problem

Accurate depth perception is essential for self-driving cars to detect obstacles, navigate complex environments, and ensure passenger safety. Current solutions, such as LiDAR, are effective but often expensive and can be affected by environmental conditions like rain or fog. Stereo vision, which mimics human binocular vision, provides a more affordable solution but presents challenges in terms of image processing and depth calculation accuracy. Developing a reliable and efficient stereo vision system for depth extraction in autonomous vehicles is a significant challenge that this project seeks to address.

Aim

The primary aim of this project is to design, develop, and test a depth extraction system using stereo vision technology for self-driving cars. The system will leverage stereo camera pairs to capture images, process these images to compute depth information, and integrate this data into the vehicle’s navigation and obstacle detection systems. The goal is to enhance the vehicle’s ability to perceive and respond to its environment, improving overall safety and performance.

Objectives

1. Research existing stereo vision technologies, depth extraction algorithms, and their applications in autonomous vehicles.

2. Design the hardware setup, including the selection and placement of stereo cameras on the vehicle.

3. Develop image processing algorithms to rectify stereo images, compute disparity maps, and extract depth information.

4. Integrate the depth extraction system with the vehicle’s existing navigation and obstacle detection systems.

5. Test and validate the system in controlled environments and real-world driving scenarios to evaluate its performance, accuracy, and reliability.

6. Optimize the system for real-time processing and ensure it meets the computational constraints of an autonomous vehicle.

7. Collaborate with industry partners and experts to ensure the system aligns with current standards and best practices in autonomous vehicle technology.

Research

The project involves extensive research in computer vision, image processing, and autonomous vehicle technology. Initial research will focus on reviewing existing literature on stereo vision and depth extraction techniques, including algorithms such as block matching, semi-global matching, and deep learning-based methods. The design phase will involve selecting appropriate stereo cameras and determining their optimal placement on the vehicle. Development will include implementing image rectification, disparity computation, and depth calculation algorithms, as well as integrating these components with the vehicle’s control systems. Testing will be conducted in both simulated and real-world environments to assess the system’s accuracy, robustness, and real-time performance. Ethical considerations, such as data privacy and safety standards, will be addressed throughout the project lifecycle.

By developing a reliable and efficient depth extraction system using stereo vision, this project aims to contribute to the advancement of autonomous vehicle technology, making self-driving cars safer and more accessible.