This past week we solidified the overall structure and flow of our data pipeline. We plan on having two processes running on the RaspberryPi at the same time. One process will be continuously receiving, filtering, and decoding the CANbus data and writing it into a local SQL database. The other process will be reading and analyzing each new entry from that local database. After processing the data, we decided that the second process will send it to the AWS IoT console via MQTT protocol. Lastly, the web application will read all the data written to the AWS console and visualize it with charts and graphs. We also discussed how we are going to integrate the different software components. We decided on the format that the data would be in so that we could build our separate pieces of software knowing how the data will look when it is received. Lastly, we decided on the specific pieces of information we would need from the CANbus in order to perform a quality analysis of a person’s driving.
Ryan’s Status Report for 2/27/21
At the beginning of this week, I worked on creating our team’s slide deck for our presentation on Monday. We came up with the requirements for our project and the schedule to track our progress using a GANTT chart. This week I created a GitHub repository for our project where we will store all the analytics code that will run on the RaspberryPi as well as the code for our web application. I also researched how the RaspberryPi will communicate with its data. We decided to store our data using AWS IoT and send it via the MQTT protocol. I spend some time getting familiar with different APIs to find which one works best for our group, including a weather API and a google API for road data to access speed limit. I also read a few research papers that discussed data that teams had used from a vehicle to try and recognize good and bad driving.
Team Status Report for 2/20/21
This week the members of our team spent most of our time pivoting from our initial idea. After a brief discussion with Professor Fedder, we came to the conclusion that our first idea didn’t have the teeth to be considered for a full capstone project. We met a few times to reconsider the direction of our project and landed on the idea to make a virtual driver’s ed application. Since we decided on our new idea, we have researched how to gather the data from the car via the OBDII port and how to make a web application to display our data analytics. We are also considering adding cameras that record the environment around the individual’s car. This will be useful to add context to the analytics that we provide from the internal car data.