FlyEye – Assisted Driving Using a Drone and Deep Learning

FlyEye – Assisted Driving Using a Drone and Deep Learning
The Group:
Itamar Shenhar, Omer Licht, Yuval Salant

Overview:
Assisted Driving Systems (ADS) are becoming more and more prevalent in our day to day lives.
Today, almost all cars have integrated some form of ADS, whether it is a parking sensor or a full collision alert system in the likes of Mobileye.
The goal of this project is to create an ADS that resembles Mobileye, using a drone that hovers above the car as the only sensor.

In addition, we offer a mobile application as an interface between the driver and the ADS that presents to the drive an aerial view of his vehicle in real time and alerts him when a potential collision hazard is detected – both visually and audibly.

Using a Drone as a sensor has many potential advantages:
A drone is mobile, and so it offers a dynamic and virtually unlimited field of view.
Using a drone the ADS can detect traffic jams or collisions ahead in real time without relying on potentially distorted user data.
In addition, a drone requires no specific set-up for a car, and thus the ADS can be added or removed from the vehicle instantaneously.

Our ADS uses state-of-the-art Deep Learning algorithms to classify and detect the objects in the drone’s view.
These algorithms are lightweight enough to run in real-time on an ordinary laptop.

Demonstration:
[V_videoclip.mp4]
For Technical Details & Hardware Specifications, see the attached project’s book. For usage instructions, read the README.md file in the linked github repository.

Project’s book:
project_book
Link the project’s github repository:
FlyEye’s Github Repo
Future work:
This project focuses on the collision detection part of the ADS and does not include a system for making the drone hover above the car.
In future versions, GPS coordinates of the vehicle sent by the mobile app can be used by a more advanced drone model to track the car’s position.

Contact information:
Itamar Shenhar: itamar8910@gmail.com
Omer Licht: https://github.com/olicht
Yuval Salant: yusal1234@gmail.com

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