Nexar – Business case


Making the road safer by using the crowd

Nexar was founded in 2016 by Eran Shir and Bruno Fernandez-Ruiz, two former employees of Yahoo (Tilly, 2016). To develop this application, they have raised 44,5 milion dollars in three founding rounds. The company provides a mobile application that turns your mobile phone in a dashcam that is connected in a vehicle-to-vehicle network (MacVie, 2018). The main focus of the application is making car driving safer and this can be seen as a real need as almost 1,3 million people worldwide die of car accidents (Kouwenhoven, 2018). 

How does it work

All Nexars users are connected in the Open Vehicle Network. By using AI dashcam functionalities Nexars application is able to record the surroundings and identify for example traffic signs, road signs, other drivers, pedestrians, etc. By analyzing this information with algorithms insights can be provided about traffic patterns and infrastructure. All this data is of course anonymized and other parties can subscribe for usage of this data (Nexar, 2019). Nexar also launched ‘City Stream’ in 2017 which by enough users can optimize driving routes, identifying driving infrastructure and finding available parking spots which all together makes a city and driving safer (Nexar, 2019). The Nexar application can therefore be seen as a two-sided platform at on the one hand the drivers that use the application and on the other hand 

Value for multiple parties 

The main focus of the application is to make driving safer by communicating real-time warnings about dangers to prevent accidents (MacVie, 2018). Moreover, when an accident happens Nexar provides her drivers with a detailed collision report including video and images about the accident. Not only for insurance can these recordings be useful, but also for example when somebody cannot communicate what happened to due to injuries. Besides these advantages for individual users it also provides valuable insights for city planners and managers to use for roadway maintenance and infrastructure- and traffic management. Especially, areas that are prone to incidents are important to be identified, so these areas can be redesigned and further accidents can be prevented. Furthermore, third party developers of ridesharing applications can take advantage of this real-time data to improve their applications. Besides the value of all the parties making cities safer benefits all citizen (Nexar, 2019). 

Crowdsourcing 

Besides these application Nexar also makes use of crowdsourcing for finding solutions for problems they face in which people can become co-creators. They have organized for example a competition to find a solution that the Nexar app can recognize the traffic light state in images (Brailovsky, 2017). Another challenge was detecting cars on diverse datasets around the world (Nexar, 2019). These crowdsourcing competitions could also form a new business model for them, as many of these challenges are interesting for companies that are developing autonomous driving as well. Besides the advantages for the company itself it challenges people to find solutions and motivates them to participate in these challenges. In the first challenge the participants were provided with a training and test data set and had two months to come up with a solution. The participant that won the competition got 5000 dollar (Brailovsky, 2017) and moreover the top 5 participants were invited to present their ideas and also invited for an interview that could lead in a job offer at Nexar’s Deep-Learning Team. So not only money was used as an incentive, but also acknowledgement plays a role in the success of the competitions. Also there is a leaderboard to motivate people to participate and the challenges are mentioned on the website of Nexar, so everybody can see who the winners of the challenges are. At this moment there is no competition, but people can subscribe to receive an email when a new competition starts (Nexar, 2019). 

Challenges for Nexar

Challenges that Nexar faces can be that also more accidents occur, because of the use of mobiles in the car. It could be said that instead of warning the drivers for dangerous events it can evoke dangerous situations as the drivers are paying more attention to the application then to the road, this also is an issue with the Uber drivers. Another challenge could be the many other applications that provide optimal routes, parking spot availability and information about traffic jams. Also it is a challenge to have enough users to collect enough data and create valuable insights. This also has to do with network effects, as there should be enough users to attract more users, but also to attract application developers and other parties that like to invest or buy the data. 

Future implications

At this moment Nexar has a very good working business model, but they could develop this business model further. By for example connecting their network to emergency services, so when an accident happens these services are immediately warned. Also their route could be optimized using Nexar traffic data. Another option to develop their smart city solution further is to expand it to public transport. By also collecting data of this area a complete picture of the traffic flows can be provided to city planners and managers, but also people can be provided with advice which transports is the best choice to a specific location. As I mentioned before it would be very valuable to become more of a crowdsourcing platform also for competitions like the two, they have organized before. Especially, for this area and thinking about autonomous driving there are a lot of challenges that we are still facing. Another area of interest could be working together with insurances companies, as with the recordings drivers can prove that they always drive the right speed and follow the traffic rules. All with all in my opinion there are enough possibilities to expand this application.

References:

Brailovsky, D. (2019). Recognizing Traffic Lights With Deep Learning – freeCodeCamp.org. Retrieved from https://medium.freecodecamp.org/recognizing-traffic-lights-with-deep-learning-23dae23287cc

Kouwenhoven, E. (2019). Wereldwijd sterven 1,3 miljoen mensen in het verkeer. Retrieved from https://www.ad.nl/auto/wereldwijd-sterven-jaarlijks-1-3-miljoen-mensen-in-het-verkeer~a9a15e46/

Macvie, C. (2019). 5 Cool Crowdsourcing Platforms You Should Check Out. Retrieved from https://www.fulcrumapp.com/blog/crowdsourcing-apps/

The Nexar Challenge. (2019). Retrieved from https://www.getnexar.com/challenges

Tilly, A. (2019). This App Turns Your iPhone Into A Dash Cam With Machine Vision. Retrieved from https://www.forbes.com/sites/aarontilley/2016/02/12/this-app-turns-your-iphone-into-a-dash-cam-with-machine-vision/#5d2206a41912

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