Imagine hopping into a ride-share car, glancing at your smartphone, and telling the driver that the car’s left front tire needs air, its air filter should be replaced next week, and its engine needs two new spark plugs.
Within the next year or two, people may be able to get that kind of diagnostic information in just a few minutes, in their own cars or any car they happen to be in. They wouldn’t need to know anything about the car’s history or to connect to it in any way; the information would be derived from analyzing the car’s sounds and vibrations, as measured by a phone’s microphone and accelerometers.
The technology began as the doctoral thesis of MIT scientist Joshua Siegel, PhD ’16, who worked with mechanical engineering professor Sanjay Sarma. A smartphone app combining the various diagnostic systems the team developed could save the average driver $125 a year and slightly improve overall gas mileage, Siegel says.
With today’s smartphones, Siegel explains, “the sensitivity is so high, you can do a good job [of detecting the relevant signals] without needing any special connection.”
The basic idea is to provide diagnostic information that can warn the driver of upcoming issues or needed routine maintenance, before these conditions lead to breakdowns or blowouts. For example, an engine’s sounds alone can indicate how clogged the air filter is and when to change it. “We’re listening to the car’s breathing, and listening for when it starts to snore,” Siegel says. “As it starts to get clogged, it makes a whistling noise as air is drawn in. Listening to it, you can’t differentiate it from the other engine noise, but your phone can.”
Many of the diagnostics are derived by using machine learning to compare many recordings of sound and vibration from well-tuned cars with similar ones from cars that have a specific problem. The systems can then extract even very subtle differences. For example, algorithms designed to detect wheel balance problems did so more successfully than expert drivers from a major car company, Siegel says.
A prototype smartphone app that incorporates all these diagnostic tools is being developed and should be ready for field testing in about six months, Siegel says. He has founded a startup company called Data Driven to commercialize it.
The hype around DeepMind’s new AI model misses what’s actually cool about it
Some worry that the chatter about these tools is doing the whole field a disservice.
The walls are closing in on Clearview AI
The controversial face recognition company was just fined $10 million for scraping UK faces from the web. That might not be the end of it.
A quick guide to the most important AI law you’ve never heard of
The European Union is planning new legislation aimed at curbing the worst harms associated with artificial intelligence.
These materials were meant to revolutionize the solar industry. Why hasn’t it happened?
Perovskites are promising, but real-world conditions have held them back.
Get the latest updates from
MIT Technology Review
Discover special offers, top stories, upcoming events, and more.