U.S. operator Verizon has deployed video nodes on existing street light infrastructures in two major cities, Boston and Sacramento. These smart lights, powered by technology from U.S. developer Nvidia, are intended to eventually be able to communicate with self-driving vehicles and to support street-light-to-car communication that could help reduce congestion and increase safety for pedestrians and drivers alike. Verizon’s video nodes leverage Nvidia’s Jetson TX1 “mobile supercomputer” to collect and analyze data.
Nvidia’s video nodes can accelerate deep learning up to the farthest edges of a city’s network, enabling real-time video analytics. This so-called edge computing can bring about more efficient, near real-time data analysis and less high-cost streaming and storing of video over LTE and Wi-Fi networks.
The video nodes capture and classify objects such as vehicles, cyclists and pedestrians and identify interactions nearly in real time, providing city officials with a data stream of everything from illegal right turns on red lights to pedestrian movement outside designated crosswalks to parking-lot metrics.
One of the liveliest new areas of opportunity for telecom operators to gain relevance and, eventually, revenue is the IoT and related applications, including, as we see here, urban traffic data analytics. Nvidia’s partnership with Verizon is an example of how an operator can make itself indispensible to the gather of data that will be leveraged by both governmental and private entities to enable functionalities that will have a serious impact on many aspects of everyday life for huge numbers of people.
Through this video node street-light initiative, Verizon hopes to make its network indispensible to the gathering and transmission of this kind of data. While no operator, no matter how large and dominant, can expect to control this sector completely, it may be possible to do so within municipalities. Verizon, therefore, will benefit directly from the data service involved, even though the system is designed to minimize cost to the city from data storage over LTE and Wi-Fi. It can also benefit indirectly in terms of brand strengthening, by being visibly allied to cutting-edge developments such as AI and deep learning. And while the future of self-driving or autonomous vehicles is still uncertain due to safety concerns, real-time data analytics will undoubtedly play a pivotal role in urban navigation.
With this partnership, speculative and future-oriented though it may be, Verizon is distinguishing itself as relevant to rapidly developing technological trends. How much return it will get on this investment will, of course, depend on how successful Nvidia’s technology is in producing the desired results, and on how many municipalities buy into it and how quickly they do so.