During the previous phase of Fabulos, we defined an API which enables us to integrate and embed our on-demand technology within autonomous vehicles (AV), such as the GACHA, making them even more efficient and accessible for users. We’ve already demonstrated what such integration would look like during the lab-demos, but now we are going to turn it into a whole new reality.
In the final round, we’ll implement our system within AVs based on the already tried and tested API. We’ll deploy our solution under real-life conditions by driving in mixed-traffic during the two pilot schemes: The first pilot will be in Helsinki (Finland) from April to June and the second one in Gjesdal (Norway) from August to October. Throughout both pilots, any person who is interested in what the future of mobility might look like can use this service for free!
Users will be able to request an AV at any of the predefined stops along the route by using the Shotl-app. The app will also let users define where they want to be dropped off. Additionally, users can always check where the next available pickup spot is whilst viewing the vehicle’s current location in real-time. This creates a unique user-experience which makes the usage of AVs even more user-friendly.
By taking part in this ambitious task, we want to create a dispatching system that connects users with AVs in real-time. By delivering a system that will aid both passengers and operators, we will be able to provide more accessible and efficient service. If you would like to test demand-responsive AV, stop by Helsinki or Gjesdal on any of the aforementioned dates to find out for yourself how this unique project is going.
Machine Learning (ML) is a field of Artificial Intelligence (AI) which focuses on the development of techniques that allow computers to learn how to perform a specific task.