Project Description/project implementation overview:
Deep Learning Techniques for Obstacle Detection and Avoidance in Driverless Cars
Obstacle Detection and Avoidance in Driverless Cars project aims to develop a driverless car system that utilizes deep learning techniques for obstacle detection and avoidance. The system utilizes a Convolutional Neural Network (CNN) for real-time video and image analysis using an IoT device, specifically a Raspberry Pi. The Raspberry Pi is responsible for controlling the car and performing inference using the trained CNN model. The model was trained and achieved an accuracy of 88.6%, providing a reliable and efficient solution for obstacle detection and avoidance in autonomous vehicles, as part of the smart city transportation systems.
Hardware and software requirements for project implementation
Hardware requirements for this project include:
- A Raspberry Pi, which serves as the main computer for the driverless car system
- A camera for capturing video and image data
- An IoT device for communicating with the Raspberry Pi and other components of the system
- A motor driver for controlling the car’s movement
- A power source for the Raspberry Pi and other components
Software requirements for this project include:
- An operating system for the Raspberry Pi, such as Raspbian
- Python programming language and relevant libraries, such as TensorFlow and OpenCV, for implementing the deep learning models and performing image analysis
- A software for interfacing with the motor driver and controlling the car’s movement
- A software for monitoring and debugging the system’s performance.
Overall this project aims to develop a driverless car system that utilizes deep learning techniques for obstacle detection and avoidance. The system will use a Convolutional Neural Network (CNN) to analyze video and image data captured by a camera, with the goal of achieving a high level of accuracy in detecting and avoiding obstacles. The system will be built using a Raspberry Pi and other hardware components, and will be controlled using Python programming language and relevant libraries.
What you will learn?
- How to implement a deep learning model for obstacle detection and avoidance in driverless cars using a CNN.
- How to use IoT devices such as the Raspberry Pi for real-time video and image analysis.
- How to train and evaluate a deep learning model using a dataset of obstacle images and videos.
- How to integrate the trained model into an autonomous vehicle control system.
- How to implement the project as part of smart city transportation systems.
- Understanding the challenges and limitations of implementing deep learning techniques in driverless cars.
- Applying the acquired knowledge to work on real-world projects, and to build a portfolio that showcases your skills to potential employers.
Curriculum
- 6 Sections
- 14 Lessons
- 15 Hours
- Module 6These modules will provide students with a comprehensive understanding of the technical aspects of the project as well as the practical skills needed to implement such a system. The capstone project will give students the opportunity to apply their knowledge and skills to a real-world scenario and develop a complete solution for obstacle detection and avoidance in driverless cars.0
- Module 1: Introduction to autonomous vehicles and IoT2
- Module 2: Deep learning techniques for obstacle detection and avoidance3
- Module 3: Implementation of the obstacle detection and avoidance system3
- Module 4: Testing and evaluation3
- Module 5: Capstone Project:3
