Tern AI’s low-cost GPS alternative actually works
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We’ve all experienced that moment of frustration when the GPS glitches and you miss an exit on the highway. The team at Tern AI, which is building a low-cost GPS alternative, says that’s because the current technology is limited by its reliance on satellite positioning.
Tern AI says it has figured out how to locate the position of a vehicle using only map information and a vehicle’s existing sensor data. The company’s pitch: It’s a cheap system that doesn’t require any additional expensive sensors.
At SXSW, the Austin-based startup demonstrated exclusively for TechCrunch that it could “derive a position from nothing.”
“No triangulation, no satellites, no Wi-Fi, nothing. We just figure out where we are as we drive,” Brett Harrison, co-founder and president, told TechCrunch while Cyrus Behroozi, senior software developer at Tern, loaded up the demo on his iPhone. “That’s really game changing because as we move away from triangulation-based, which limits technology, now we have the ability to be fully off that grid.”
Harrison says this breakthrough is important for a number of reasons. From a commercial standpoint, companies that rely on GPS — including ride-hail apps to delivery companies — lose time, money, and gas every time their drivers have to double back because of faulty GPS positioning.
More importantly, our most critical systems — from aviation to disaster response to precision farming — rely on GPS. Foreign adversaries have already demonstrated that they can spoof GPS signals, which could have catastrophic impacts both on the economy and national security.
The U.S. has signaled that it wants to prioritize alternatives to GPS. During his first term, President Donald Trump signed an executive order to reduce reliance on a single source of PNT (positioning, navigation, and timing) services, like GPS. There are also several other initiatives which direct agencies and bodies like the Department of Defense and the National Security Council to ensure resilient PNT by testing and integrating non-GPS technologies.
“DeepSeek came out and said it cost us $6 million to do what it took [OpenAI, Anthropic, and other AI companies] billions to do,” Harrison said. “To get that dot to move across a map in real time in a vehicle, it took the government billions of dollars and a few decades. We did it with less than $2 million.”
Tern came out of stealth in February 2024 and announced its $4.4 million seed round a few months later. That’s a quick turnaround to achieve the type of positioning I experienced this week at SXSW.
To start the demonstration, Behroozi connected his 2019 Honda Civic to his phone via Bluetooth, allowing the Tern application to pull in data from the vehicle’s existing sensors. He noted that Tern’s tech can be integrated directly into vehicles, beginning model years 2009 and up.
Usually, Tern sets the position manually to speed things up, but for our demo, the team wanted a “cold start.” Behroozi turned off his phone’s location services, so the Tern intelligent system had only a cached map of a 500-square-mile boundary around Austin and vehicle sensors to work with.
As the car drove, the system picked up road data to work toward “convergence.” It took roughly 10 minutes for the system to reach full convergence from a cold start because, according to Behroozi, there was traffic so our movements were limited. Harrison assured me convergence usually takes around one to two minutes without a start point, and is immediate with one.
Harrison noted that Tern’s system can also localize vehicles in parking garages, tunnels, and on mountains, which GPS struggles to do. Harrison wouldn’t explain exactly how, saying the information is “proprietary.”
We drove around for a few more minutes after the system reached full convergence, and I watched as it steadily tracked our precise movements in a way that appeared as good as, and in some cases better than, GPS. That became more apparent when we drove into downtown Austin, where my Google Maps regularly mislocated me throughout the week as I navigated urban streets dotted with towering buildings.
Harrison said that Tern’s system is also safer from a privacy perspective because with GPS, “if anyone knows your ID, they can find you at any time.”
“Our system is a total closed loop,” he said. “Right now, we’re not emitting anything. It’s independently deriving its own position [via on edge computing], so there are no external touchpoints.”
“We set up the company and the solution from the start to be scalable. If you look at that Waymo car and all of the hardware that’s embedded, we don’t see that going on a Nissan Sentra anytime soon. It’s just too expensive, ” Harrison said, pointing ahead of us to a Waymo-Uber robotaxi.
“At the manufacturer level, if [Tern] is implemented within the infotainment system, it’s just a software download, so extraordinarily scalable. All new vehicles have the sensor data we need. The map data already exists with all the providers today. So it’s quite simple.”
Tern’s potential future customers could be anyone from automakers to mobile phone manufacturers, from Google to Uber. Harrison said the startup is open to growing the company, but also an acquisition.
“The primary thing is getting this out into the economy’s hands, with the growing threats and the emergence of tech that’s not realizing its full potential because of the limits of triangulation,” Harrison said.
He noted that Tern is exploring possibilities with the government. The startup recently received a contract award from the U.S. Department of Transportation after a week of demonstrating its technology along with nine other companies from around the world.
“We’re hoping we did a good job of showing the government what’s possible now with American innovation,” Harrison said.
Clarification: This article was updated to clarify how long Tern claims its system usually takes to reach full convergence.
Topics
Senior Reporter, Transportation
Rebecca Bellan covers transportation for TechCrunch. She’s interested in all things micromobility, EVs, AVs, smart cities, AI, sustainability and more. Previously, she covered social media for Forbes.com, and her work has appeared in Bloomberg CityLab, The Atlantic, The Daily Beast, Mother Jones, i-D (Vice) and more. Rebecca studied journalism and history at Boston University. She has invested in Ethereum.
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