This document discusses a student project on object detection for autonomous vehicles using machine learning. The project aims to create a real-time object recognition application using a GPU device and self-driving algorithms that can gather information, locate and classify objects in camera images, and integrate input from other sensors. It discusses the importance of autonomous vehicles in reducing human errors in accidents and carbon emissions from transportation while increasing safety. The students will learn skills in programming, data analysis, embedded systems, artificial intelligence, machine learning, and computer vision.

1. Object Detection using Machine Learning in
Autonomous Driving
Student(s):
• Islam Mohamed
• Youssef Aser
• Sherif Hesham
• Ashraf Mahmoud
• Mina Nan
Supervisors:
• Dr. Ahmed Saeed
• Dr. Nermin Salem

2. Contents
Introduction
Why should we care ?
Project Goal
SAE Levels of Automation
Block Diagram
Project Goal
Components
What are we learning?
Outcomes
2

3. Introduction
3
• Transport is vital to promote connectivity, trade, economic growth, and
employment, but it is also a significant source of greenhouse gas emissions.
Resolving these balances is essential to achieve sustainable transport and,
through that, attain sustainable development.
Really, we need this product or is it a luxury?

4. Why should we care?
4
• Human Errors:
“Egypt has one of the highest rates of road accidents worldwide,
with more than 12,000 fatalities each year, according to the World
Health Organization. Human error contributes to more than 90
percent of crashes.”

5. Why should we care?
5
• carbon emissions:
“Emissions from cars increase the levels of carbon dioxide and
other greenhouse gases in the atmosphere. At normal levels,
greenhouse gases keep some of the sun's heat in the atmosphere
and help warm Earth. That said, many
scientists believe that burning fossil
fuels such as gasoline causes
greenhouse gas levels to spike,
leading to global warming..”

6. SAE Levels of Automation
6

7. Project Goal
7
The main objective of this project is to make a real-time object recognition application
and apply it to a Graphics Processing Unit (GPU) device using self-driving machine
learning algorithms. The system must be able to gather information in real time, locate
and classify multiple objects using camera images, and handle input from other
sensors.

8. Block Diagram
8

9. Components
9
• Nvidia Jetson Nano 4GB DEV Kit Microcontroller
• IMX219-83 8MP 3D Stereo Camera Module
• Sensors and Actuators
• Car-Kit

10. Basic Physical Ecosystem of an Autonomous Vehicle
10
• Global Positioning System
(GPS)
• Light Detection and Ranging
(LIDAR)
• Cameras (Video)
• Ultrasonic Sensors
• Central Computer
• Radar Sensors
• Dedicated Short-Range
Communications-Based
Receiver

11. What are we learning?
11
• Programming (Python)
• Data Analysis
• Embedded systems
• Artificial Intelligence (AI)
• Machine learning
• Computer Vision

12. How will AVs impact transport flows?
12

13. Companies Investing in Autonomous Vehicles
13
44+

14. Outcomes
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• The promises of self-driving car technology has always been tempting, especially since
it has the potential to transform our experience of commuting and long-distance
commutes, lift people out of high-stakes work environments and streamline our
industries. It also plays an essential role in helping us build the cities of the future, one
that will redefine our dependence on and relationship with cars, reduce carbon
emissions and pave the way towards more sustainable lifestyles. Moreover, this
technology can make our travel safer.
• Everyone will benefit from this technology, even those who do not own self-driving
cars.

15. 15
Thank you!