Good To Go

Status Quo

A third of the accidents caused while riding e-sccoters are estimated to be due to drunk driving, and the operators are searching for ways to solve this problem. Riding an e-scooter while being drunk is as illegal as driving in a car while being drunk but nevertheless, it is estimated that a third of the serious accidents are due to people driving while being drunk.
To combat this issue, some operators shut down the service of the e-scooters by night, either by collecting them to charge or by remotely shutting down the system. Collecting the e-scooters represent unwanted costs and the long periods of inactivity also results in sunk costs for the operators. It's also a disadvantage for the sober customers who wants to use the service at night.

Personal Experience

Unfortunately, I have to admit that I am also guilty. After an evening out with my colleagues I thought I would be still perfectly able to take a scooter and woke up the next day in the hospital with broken teeth and stitches on my chin.

Our solution

Good to go delivers an AI-based software plug-in for the e-scooter operators’ mobile applications. With our software in operation, in certain high risk periods of time such as Friday and Saturday nights, the e-scooter users will have to pass a drunk test to access the booking system of the operator. The test requires the users to record 5 seconds of audio and video, where they say a statement out loud. The software evaluates the voice and the video of the face to determine if the user is drunk.

Technology

In a paper proposed by Metha et al. 2019, known and open-source deep learning models trained on Youtube-videos can detect drunkenness through a combination of audio and video with an accuracy of almost 90 %. Thus, we aim to train the model on open-source data and then tune it with test data gathered from real-life situations to make the model fit our case.

Challenges

The implementation of Good to go presents a few dilemmas that arise in the core of the solution, that is, when a computer has to make the decision between drunk or sober. Depending on the stability of the model, the possible actions for the user after a finished test have to be tuned accordingly. For a more in-depth information on which scenarios unfold with our software solution and how we plan to tackle them, check out our blog-post.

Business Model

We offer e-scooter suppliers an integratable software to eliminate both real and sunk cost associated with accidents and the actions currently taken to prevent them. Furthermore, a better public image is valuable and crucial for the continuous growth of the e-scooter concept. This justifies our revenues, which are generated by a monthly license payment for the software including support and continuous development.

Five unique value propositions

1. Reducing the number of accidents on e-scooters related to drunkenness
2. Contributing to a better public image of e-scooter operators
3. Helping operators and municipalities to take a stand against drunk driving.
4. Creating awareness on the topic of drunk driving.
5. Making the streets safer for everyone.

Next Steps

With this solution we won the 2nd price of the Nordic Health Hackathon 2019. The next step is to develop and test the software in collaboration with Vejle Kommune and their e-scooter supplier, Voi. Jette Vindum is in charge of the deployment of e-scooters in the city of Vejle, and has agreed to make Vejle Kommune a testing platform for our solution.