- GitHub Accounts: @zalmanu, @saibot94, @ciprian12
- Category: IoT
- Table No.: 48B
We need a safer world. A world with zero car accidents. Traffic in our modern cities presents a high risk factor for its participants. One should only ask the question: “How do they handle huge volumes of traffic and identify trouble-zones in a large city?”
What it does
Speedmap comes with a solution. It enables local enforcement of speed-limiting measures in zone where traffic has gone out of hand. This is done by sampling live data from cars connected to the cloud and defining a heat-map based on trouble-points. It also provides an analytics solution with different useful aggregations.
How we built it
We’ve created an interface to Continental’s test car, pushing all the data recorded by sensors (GPS, RPM, Speed of the car and participants around the car) to Elasticsearch.
From Elasticsearch, we process the data with Python and prepare aggregations which are polled periodically through an API from the Web UI and show to the user.
Challenges we ran into
The main challenge was pulling the data out of the vehicle and impoving the performance such that we get a near real-time feed of the car data.
The Elasticsearch API is non-intuituve
Accomplishments that we're proud of
- It works
- It’s a noble cause, could help humanity
What we learned
- C++ can bite you.
- Google Maps has one awesome API.
- Python is great for doing stuff well and quickly
What's next for Atigeo SpeedMap
Connecting a large number of cars to the system. Adding big data storage mechanisms to handle the growing traffic. Adding security and role-based access.