bAIcycle: mapping the state of cycle paths while cycling

Regular cyclists are well aware of the problem. There are more and more good cycle paths, but many are still in a deplorable state. This is not only annoying, but also dangerous. Mapping the condition of cycle paths is a first step towards improvement. This is already happening, but it is a labour of love. Our research centre aims to accelerate this with the help of cycling citizens and Artificial Intelligence in bAIcycle.

bAIcycle research project

For this, we use a smart measurement box that we install on an electric bike. In the future, you could also have one on your bike. This measurement box reads the data from all kinds of sensors on the bike, such as the incline, speed and power sensors, combining them with your GPS location and accelerometer data from a smartphone. Cyclists 'label' the data while en route, reporting potholes or the type of road surface, for example. This data is then used to teach an AI system to automatically classify cycle paths by layout and condition. The results are incorporated into an interactive map of Flanders' cycling routes. That way, users of cycle paths, like you and me, help improve their quality. By using them.

bAIcycle

Amai!

The bAIcycle project came about thanks to amai!, an initiative of the Flemish government to raise awareness of AI among companies and society. Citizens can suggest topics that can be resolved with AI. The projects chosen by the public and a professional jury are then awarded grants. Last year, for instance, three out of the seven applications submitted by the KdG University of Applied Sciences and Arts were retained.

Want to know more, collaborate or have a press question?

You can collaborate with our research centre Sustainable Industries on topics such as:

  • Greening chemical processes and reusing waste streams.
  • AI-based tools developed in-house, to optimise your processes and products.
  • Developing alternative propulsion systems, alternative fuel systems, and emission measurements of internal combustion engines.
  • Vehicle data analyses using simulations and reverse engineering on CAN bus systems.

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