This AI Startup Tracks Your Cell Phone’s Location Data… For A Good Reason

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If you’ve indulged in the tantalizing concept of self-driving cars in the last year, you’ve likely read articles detailing their near arrival date on public roads and the trials and tribulations associated with the radar technology, or even Teslas in autonomous mode crashing. But did you know the self-driving cars need an internet connection to operate? All the edge-computing technology advances aside, autonomous vehicles still routinely send and receive data from their cloud servers. They’re enabled by low latency and high speed data. This begs the question: does this concept work if you don’t have a strong internet connection on the road while driving? To ensure robust internet connectivity while traveling in an Autonomous Vehicle (AV), Israeli startup Continual is attempting to solve this problematic situation impeding the adoption of AVs with its Mobility Experience Analytics solution.

Continual was originally founded by Greg Snipper, Jose Cohenca and Shmuel Morad in 2014: a team of Israeli executives with a complementary portfolio of technical skills and deep market expertise in the telecommunications market, where Israelis are particularly innovative. The Continual solution began as a project in the network department of mobile operator Cellcom Israel, before being spun out as a new start-up. The three founders were soon joined by serial AI entrepreneur Omer Geva – Continual is Geva’s second AI startup after eGlue, a robotic process automation startup sold to NICE Systems in 2010. With a shared view of the potential of AI, Geva and Snipper joined forces to tackle the newly coined market of emerging Intelligent Mobility ecosystem.

Continual revolutionizes network analytics with a unique and patented approach to tracking the quality of mobile phone connectivity and mapping it against user location—without the use of GPS, but rather the call records derived from the network while an individual or a Connected Car is on the move. It’s called Mobility Experience Analytics technology. It analyzes mobile experience data, like the quality and reliability of voice and data call connections while an individual is travelling in a car, and identifies usage patterns and correlates subscribers and Connected Cars to their position and direction of travel. With the data correlated, Continual’s machine learning algorithms map segments of highways and railways by multiple criteria to effectively rate the experience of fast-moving subscribers and Connected Cars. This serves as a map of the quality of cellular network connectivity for automotive manufacturers of connected and autonomous vehicles. With that map, the vehicle on-board software can instruct the AVs to drive on roads that have reliable and high cellular connectivity and to avoid adverse network connectivity routes.

Their disruptive technology looks at anonymized data-sets representing individual subscribers and vehicles rather than the aggregate usage from a mobile operator’s points of view. “Whenever you’re moving at a high speed, mobile operators cannot provide the living room experience” explained Geva. The key phrase here is the ‘living room experience’, described by industry folk as delivering high quality communication experience for driving- and passenger-subscribers. But with Continual’s revolutionary Mobility Experience Analytics, they can map and optimize mobile users travel routes without access to GPS signals. “Whenever any mobile activity takes place through mobile networks – the cellular towers and the communications backbone – the activity leaves a footprint in the network environment. We analyze that footprint to help optimize mobile networks for both subscribers and connected vehicles, allowing them to benefit from the best internet connectivity,” explained Geva.

With the work of its in-house algorithms team, Continual has developed unique machine learning algorithms that predict the geolocation of mobile users based on a series of communication events. This is accomplished through novel clustering techniques around the commuter’s routine patterns, like direction, concentration of the commute route, and relations to the group of commuters. With such information, they can predict the quality of communication between the automobile and cell towers, even in the future.

“We can show, based on three parameters – mobility, talking minutes at a certain time of day, and data consumer – prediction accuracy of communication two hours into the future ranging from 75% – 95%” explained Geva. “How much data is consumed and will be consumed by an individual mobile user is in fact important for the automotive segment. It gives them a warning of how much data they can effectively offload” added Geva.

A newly-announced collaboration with mapping and location specialist HERE Technologies has also led to the launch of a pioneering integrated solution, designed to continuously monitor and map the connected experience of users on the move across the entire road network, and analyzing performance across different mobile networks.

The startup offers their technology to car manufacturers and OEMS as well as mobile operators looking to increase their subscribers and improve their experience. They’ve already serviced clients like Cellcom Israel, Megafon Russia, OI Brazil, and Vodafone Ireland, with whom they collaborated with on a two-year research project for predicting subscriber communication patterns in mobility in preparation for the launch of 5G. With $5 million funding raised, Continual is truly a unique promise of innovation to bring autonomous vehicles on roads in the foreseeable future.

You can find Continual showcasing their technology at the Mobile World Congress conference on February 25th to 28th at booth 5D81 at the Israeli Pavilion.

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