I decided to start off developing the algorithmic part of the car collision project. After a few weeks of debating whether to use C++ or Python, I decided to use Python since there are around 10 weeks left to get this project up and running. I also decided to use OpenCV as it has many of the functions we need to perform and also its highly optimised. Although through my research into OpenCV shows that there is no built in way to get a Depth Map that returns exact distances for each pixel, I decided we can worry about that later after we get the Depth Map working. In a worst case scenario, we can map depth map values to distances or find an alternative. I just wanted to get our feet wet with the project.
After playing around with tinkerOS I decided to give the Raspberry Pi 3b a try and see if I liked it any better. For some reason I did and have decided to use it as the board for the rest of the project.
So mark the start of the car collision prevention project, I decided to start off by installing tinker OS, which is a Debian-based operating system, on the tinker board.
The final set of parts for the crash prevention project have arrived. These parts deal more with the mechanical side of things and we’ll use them near the last phase of our project where we have to trigger a brake leaver to stop the car.
After putting a lot of thought into what parts we need for the Crash Prevention Project, we decided on the following parts. Although we could have gotten better parts, we were restricted by budget and time which is the main factor that went into deciding which parts to get.
After deciding to go with the Raspberry Pi route rather than the Mojo FPGA board, I now have decided what peripherals I need in order to make the omputeollision prevention research project a reality. The most important thing was that I now knew I had to work around the Raspberry Pi.
My senior design project as an electrical engineering student is to build a system that prevents car collisions. It started off as a very simple project that alerts drivers if they’re approaching an object quickly but grew into a big project that involves advanced image processing and interviewing to prevent crashes.