SAVN is a simulation platform (in development) allowing researchers to visualize and benchmark traffic control algorithms for autonomous vehicle fleets.
The platform allows to test algorithms against real-world data such as popular origin/destination pairs and realistic traffic data (car, pedestrian, road hazards, etc.). It is also possible to use multiple algorithms together, in the same simulation.
SAVN is separated into three components:
- Website: Create, configure, visualize and benchmark algorithms
- Python framework: Use to connect an algorithm to SAVN, providing fleet information in real-time
- Server: Stores data and handles communication between the website and the algorithm
First, install the libraries required for the webserver by executing the following command within the webserver directory:
npm install
If desired, install the dummy data (including default cities):
node backend/insert-dummy-data.js
The MongoDB data will be stored in the folder webserver/backend/data/db, so make sure that it is writable.
If you want to use our sample algorithm, you also need to install the python dependencies by executing the following command within the sample_algorithms directory:
python3 setup.py install
First, start up mongod and load the database:
mongod --dbpath webserver/backend/data/db
In order to start the webserver, run the following command within the webserver directory:
npm run dev
If you want to attach our sample algorithm to a simulation, make sure that it is marked active in the frontend, and then run the following command within the sample_algorithms directory:
python3 non_colliding.py
You should then start seeing your specified objects like cars on the map in the frontend.