Raspberry Pi Car Black Box
This project is an open sourced car black box using a Raspberry Pi and other cheap COTS (Commercial Off The Shelf) hardware. The Pi acts as a hub to gather car analytics in the background any time the car is running. The data is stored in the cloud and accessible from any device.
For less than $100 in hardware and a few hours of setup you can have your own black box. All the data it generates can sit on your own server, so you have control over the data. Not tech savvy and don't have a server? No problem. We've got a cloud storage service you can use for free that's on by default.
The database and Big Data lover in me wants data, lots of it. So I've
gone with building a black box for my car that runs all the time the
car is on, and logs as much data as I can capture. This includes:
- Compass and azimuth heading
- and more
Once you've got a daemon running, and the inputs are being saved
then the rest is all just inputs. Doesn't matter what it is. It's just
My initial goal is to build a blackbox that constantly logs OBD2
data and stores it to a database. Looking around at what's out there
for OBD2 software, I don't see anything that's built for long term
logging. All the software out there is meant for 2 use cases: 1)live
monitoring 2)tuning the ECU to get more power out of the car. What I
want is a 3rd use case: long term logging of all available OBD2 data to a
database for analysis.
In order to store all this data I decided to build an OBD2 storage architecture that's comprised of
- MySQL database
- JSON + REST web services API
- SDK that existing OBD2 software would use to store the data it's capturing
- Wrapping up existing open source OBD2 capture data so it runs as a daemon on the Pi
- Logging data to a local storage buffer, which then gets synced to
the aforementioned cloud storage service when there's an internet
Right now I'm just doing this for myself. But I'm also reaching
out to developers of OBD2 software to gauge interest in adding this
storage service to their work. In addition to the storage, an API can be
added for reading back the data such as pulling DTS (error) codes,
getting trends and summary data, and more.
The first SDK I wrote was in Python. It's available
on GitHub. It includes API calls to register an email address to get an
API key. After that, there are some simple logging functions to save a
single PID (OBD2 data point such as RPM or engine temp). Since this has
to run without an internet connection I've implemented a buffer. The SDK
writes to a buffer in local storage and when there's any internet
connection a background sync daemon pulls data off the buffer, sends it
to the API and removes the item from the buffer. Since this is all JSON
data and very simple key:value data I've gone with a NoSQL approach and
used MongoDB for the buffer.
The API is built in PHP and runs on a standard Linux VPS in apache.
At this point the entire stack has been built. The code's nowhere near
production-ready and is missing some features, but it works enough to
demo. I've built a test utility that simulates a client car logging 10
times/second. Each time it's logging 10 different PIDs. This all builds
up in the local buffer and the sync script then clears it out and
uploads it to the API. With this estimate, the client generates 100 data
points per second. For a car being driven an average of 101 minutes per day, that's 606,000 data points per day.
The volume of data will add up fast. For starters, the main database
table I'm using stores all the PIDs as strings and stores each one as a
separate record. In the future, I'll evaluate pivoting this data so that
each PID has it's own field (and appropriate data type) in a table.
We'll see which method proves more efficient and easier to query. The OBD2 spec
lists all the possible PIDs. Car manufacturers aren't required to use
them all, and they can add in their own proprietary ones too. Hence my
ambivalence for now about creating a logging table that contains a field
for each PID. If most of the fields are empty, that's a lot of wasted
Systems integration is much more of a factor in this project than
coding each underlying piece. Each underlying piece, from what I've
found, has already been coded somewhere by some enthusiast. The open
source Python code already exists for reading OBD2 data. That solves a
major coding headache and makes it easier to plug my SDK into it.
There are some useful smartphone apps that can connect to a Bluetooth OBD2 reader
to pull the data. Even if they were to use my SDK, it's still not an
ideal solution for logging. In order to log this data, you need a
dedicated device that's always on when the car's on and always logging.
Using a smartphone can get you most of the way there, but there'll be
gaps. That's why I'm focusing on using my Pi as a blackbox for this
All project details will be moved to BlackBoxPi.com.