Using Veniam’s software APIs, the company says it will be able to better collect the images it needs to create an AI-digital twin of US roads, enabling it to make the most of all available wireless networks (4G/5G, Wi-Fi, mesh, etc.) and transfer massive amounts of data to the cloud in a secure and cost-effective way. This cooperation, says the company, will also help to further expand the wealth of collected vision data to build a real time twin of the road, improve safety, train autonomous vehicles (AVs) and inform city decisions, as well as “see” changes in the road.

“Veniam’s capabilities are a great fit for Nexar and will help it realize its vision of seeing what’s ahead on the road,” says Eran Shir, Co-founder and CEO of Nexar. “Given the huge volumes of vision data coming from the road and the AI it requires for solving connectivity/bandwidth problems, as well as addressing the issue of compute costs, Veniam’s technology helps us with the connectivity, allowing us to transfer more data, making the real time digital twin of the road better and more robust.”

João Barros, founder and CEO of Veniam adds, “Both Veniam and Nexar look at vehicles not just as transportation machines but as critical data sources to make our cities healthier, safer and most sustainable. We are delighted to complement Nexar´s system with Veniam’s intelligent networking software thereby expanding and accelerating Nexar’s access to valuable city data.”

Using Veniam’s software, some Nexar dash cam models will be able to make smart decisions on what data to send and when, as well as through which networks. This dramatically reduces the overall cost per GB, while increasing the total amount of data available.

As more and more auto OEMs install cameras and advanced sensor systems onboard their vehicles, says the company, it will be able to leverage Veniam’s communications technology at the edge to split and share the data flows, making it easier for different cars to collaborate in understanding the world and driving safely to their destinations.


Related articles:
Dashcam network map offered as ‘real-time Google Street View’
AI dashcam image retrieval method improves urban localization accuracy
Street-level analytics startup accelerates 3D reality capture plans
Digital twins for urban planning to save $280B in costs by 2030


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