Nexar Announces Automotive AI Challenge with Global Dataset

Challenge dataset includes 55,000 tagged street images from 80 countries

Nexar, the provider of the world's first vehicle-to-vehicle (V2V) communication network for road safety, issued a challenge to researchers to develop a geography-adaptive autonomous driving perception model. For the challenge, the company released its NEXET image dataset that includes over 55,000 street-level images from over 80 countries.

The goal of the challenge is to initiate a collaborative effort to address the problem of building a driving perception that performs consistently over different geographies. The key element for developing an all-weather, all-road, all-country driving perception is the ability to obtain data from an large and diverse training dataset. NEXET was carefully curated to contain scenarios of varying lighting, weather, and topographical conditions, as well as varying driving cultures in different countries to offer a comprehensive dataset.

"The robustness of learning driving policy models depends critically on having access to the largest possible training dataset exposing the true diversity of the 10 trillion miles that humans drive every year in the real world. Current approaches are trained using homogenous data from a small number of vehicles running in controlled environments, or in simulation, which fail to perform adequately in the true diversity of real-world dangerous corner cases," said Bruno Fernandez Ruiz, co-founder and CTO of Nexar. "Safe driving requires continuously resolving a long tail of those corner cases. The only way to ensure safety in ADAS is to continuously capture as many of these cases as possible. By releasing this diverse dataset, we are opening our challenge to researchers to help us develop these algorithms and together create more robust ADAS models - essential to a safe autonomous future."