Press release

Ambarella and Longhorn Partner to Develop an Advanced Automatic Parking System (APS) Platform

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Ambarella, Inc. (NASDAQ: AMBA), a leading developer of high-resolution
video processing and computer vision semiconductors, and Longhorn Auto
Co., Ltd., a supplier of driver assistance products based on vision,
ultrasonic sensors, and microwave radar technology for global automotive
OEMs, today announced that they are partnering to develop an advanced
automatic parking system (APS) platform.

The new APS platform is based on Ambarella’s CV22AQ CVflow®
computer vision chip. Combined with Longhorn’s production-proven
ultrasonic sensors, 360° around view monitoring (AVM) technologies, and
visual perception algorithms, the platform delivers rich and highly
accurate computer vision features, including parking space detection,
parking line recognition, lane departure warning, automatic emergency
braking (AEB), vehicle identification, obstacle classification, and map
construction.

“Longhorn has focused on the global automotive electronics industry for
many years — paying attention to innovation and investment in advanced
intelligent driving technology while accelerating sales of AVM,
rearview, and ultrasonic sensors. Our intelligent driving products,
currently being developed for mass production, have received positive
feedback from leading OEMs,” said Xiaoping Luo, general manager of
Longhorn. “Longhorn appreciates Ambarella’s market strategy and their
investment in automotive visual perception, especially the introduction
of the CV22 series. Its CVflow architecture supports high convolutional
neural network (CNN) performance and meets the design requirements of
Longhorn’s APS hardware platform.”

“We are pleased to partner with Longhorn to deliver a robust hardware
platform for the next generation of automatic parking systems,” said
Fermi Wang, CEO of Ambarella. “Longhorn’s advanced visual perception
algorithms combined with our CV22AQ SoC offer a powerful and unique
solution that fuses visual perception and ultrasonic technologies.”

Manufactured using an advanced 10-nanometer process, the CV22AQ chip
enables the design of compact, high-performance automotive systems with
ultra-low power operation. The CV22AQ’s CVflow architecture delivers
computer vision processing at full 4K or 8-megapixel resolution at 30
frames per second (fps), while its image signal processor (ISP) provides
outstanding imaging in low light conditions and high-contrast scenes,
further enhancing the computer vision capabilities of the chip. The
CV22AQ also includes a suite of advanced security features to protect
against hacking, including secure boot, TrustZone™, I/O virtualization,
and support for online upgrades over the air (OTA).

The URL for this news release and the related image is: https://www.ambarella.com/news/ambarella-and-longhorn-aps-platform/

About Longhorn Auto Co., Ltd.

Longhorn provides automotive environment perception sensors and advanced
driver assistance systems (ADAS) products, focusing on the fusion of
vision, ultrasonic sensors, and microwave radar in the automotive
industry. In cooperation with automotive OEMs, Longhorn delivers
ultrasonic sensors, microwave sensors, dash cameras, around view
monitoring (AVM), eMirror, automatic parking, valet parking, pedestrian
protection, driver monitoring systems (DMS), and other products. For
more information, please visit: www.long-horn.com

About Ambarella

Ambarella’s products are used in a wide variety of human and computer
vision applications, including video security, advanced driver
assistance systems (ADAS), electronic mirror, drive recorder,
driver/cabin monitoring, autonomous driving, and robotic applications.
Ambarella’s low-power system-on-chips (SoCs) offer high-resolution video
compression, advanced image processing, and powerful deep neural network
processing to enable intelligent cameras to extract valuable data from
high-resolution video streams. For more information, please visit www.ambarella.com

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offerings, specifications, and pricing at any time without notice.
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