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Haomo Links with ByteDance’s Volcano Engine to Build China’s Largest Autonomous Driving Computing Center

Haomo Links with ByteDance’s Volcano Engine to Build China's Largest Autonomous Driving Computing Center

Haomo.AI Technology Co., Ltd. launched MANA OASIS, the largest autonomous driving computing center in China, together with Volcano Engine, a cloud service platform owned by ByteDance.

MANA OASIS has a total computing power of 670 PFLOPS, higher than any other similar facilities in China.

“With abundant data and computing power enabled by MANA OASIS, Haomo’s product capability will be even stronger, steering the company into the era of autonomous driving 3.0,” said Gu Weihao, CEO of Haomo. He considers the autonomous driving 1.0 era as hardware-driven, the 2.0 era software-driven and the 3.0 era data-driven.

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“We are honored to reach in-depth cooperation with Haomo in the field of intelligent driving computing center,” said Tan Dai, president of Volcano Engine, referring this as a manifestation of his company’s confidence in building blocks in the autonomous driving industry.

“We will jointly promote the leap-forward development of intelligent training platform in the autonomous driving industry, and accelerate the commercialization of autonomous driving technology,” he added.

With MANA OASIS, Haomo has updated its five models key for developing autonomous driving, namely the visual self-monitoring model, the 3D rebuilding model, the multi-modal mutual supervision model, the dynamic environment model, and the driver self-supervision model.

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The visual self-monitoring model makes 100% automatic labeling of 4D video clips a reality, reducing the manual labeling cost by 98%. The 3D rebuilding model leverages on the NeRF technology to generate highly realistic data by changing the angle of view, illumination and texture materials, allowing Haomo to easily gain data on corner cases, which could be very expensive to acquire otherwise and reducing the rate of wrong perception by 30%.

The multi-modal mutual supervision model helps a vehicle to recognize barriers with abnormal sizes, while the dynamic environment model keeps the vehicle always on the right path and the driver self-supervision model learns from experienced and skillful drivers and allows the vehicle to make smarter decisions about how to drive.

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