Introduced by Nicholas Hirons, Julian Kudszus, Soham Kudtarkar and Spencer Lee

Mechanics spend a lot of time diagnosing car issues and performing tests, but oftentimes customers aren’t sure if their cars are getting the service they need.

But a new web app created by UC Berkeley students could change this. The algorithm takes standard information about the type of car a user owns and takes error codes from car telematics data, or wireless information transmitted from your car. From this information, the algorithm is able to predict what issues need to addressed and what services need to be performed.

The algorithm takes into account a variety of vehicle data, such as make, mileage and engine control units to give the most precise prediction possible.

Because it takes information from vehicle telematics, the tool could mean that dealerships and auto repair shops could preemptively reach out to customers to let them know they need service, to stop a potential breakdown on the road.

A feature of the app that suggests likely causes and solutions for car issues also means that customers could avoid paying for services they don’t need once they arrive at an auto repair shop.

While initial results are mixed, the team was able to predict when certain service operations were required fairly well. For example, the model accurately predicted the need for tire inflation about 70 percent of the time. With more data, the team believes that they could significantly improve prediction accuracy by building models specific to each car brand.

The students hope their project can help members of the vehicle service industry understand the benefit of using connected car telematics to improve efficiency and customer service.