In dense urban environments, GPS is highly inaccurate as satellite signals are often blocked or reflected by high-rise buildings (urban canyons). This poses a critical challenge for vehicle-to-vehNexar announced a scalable method that improves GPS location accuracy in urban areasicle (v2v) safety applications.
In this line, the mobility company, Nexar has announced a scalable method that greatly improves GPS location accuracy in urban areas. Nexar’s new AI-powered image retrieval algorithm claims to dramatically improve localization in cities, solving a problem that has long-plagued both rideshare operators and navigation apps, as well as autonomous vehicle manufacturers.
As a benchmark to evaluate this new visual localization approach, the company is also releasing a dataset and benchmark based on anonymized dash cam and GPS information from its connected vehicle network to advance the research of visual localization for safety applications.
Nexar’s research of crowd-sourced data of over 250,000 driving hours in New York City found that at least 40% of rides suffered GPS errors of 10 meters or more due to the urban canyon effect. Nexar’s method for localization is based on its continuously growing database of fresh road imagery observed by its network.
Technically speaking, to solve for the failings of satellite GPS, Nexar has developed a hybrid coarse-to-fine approach that leverages visual and GPS location cues. The company has trained a deep learning model to identify a driver’s accurate location using its massive archive of anonymized images. The archive includes billions of these images from more than 400 million miles driven on the Nexar network.
According to Nexar, its experiments confirm that this localization approach is highly effective in challenging urban environments, reducing the distribution of localization errors by an order of magnitude. This method will be used to deliver alerts to Nexar users, including dangerous intersections and collisions on the road ahead.
Source: Press Release