Optimization for life quality: we saw many people constantly looking at their phones to get public transport information and directions, since the voice instructions are either non-existing or not really like natural conversations. Statistics show that this has increased the cases of accidents. Additionally, there is no public facility for such voice assistants in the streets.

What it does

It is a natural language AI in combination with the city public transport info. In addition, the bigger concept is to implement such assistants in many locations in the city so that one may not necessarily need to use the phone to get the directions in the city

How we built it

Using NLP capabilities of Dialogflow to define intents and getting the transport information from Deutsche Bahn API. The intents then query through a webhook to python script to get conversational permutations.

Challenges we ran into

API connections, sometimes lack of API documentation or time limitation constraints due to document complexity

Accomplishments that we're proud of

Teamwork and that we managed to quickly assimilate new information from the workshops to implement in the product

What we learned

Lots of stuff about Dialogflow and speech recognition that we were unfamiliar with before the Hackathon

What's next for

Improve the code and define the concept for the implementation in the pilot city

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