TEL AVIV, Israel and TOKYO, Oct. 26, 2022 /PRNewswire/ -- Innoviz Technologies (Nasdaq: INVZ) (the "Company" or "Innoviz"), a technology leader of high-performance, automotive-grade LiDAR sensors and perception software, today announced its partnership with Kudan (Tokyo Stock Exchange securities code: 4425, hereafter "Kudan"). Kudan's perception software allows machines to generate 3D maps of surrounding environments and gain autonomous mobility based on Innoviz LiDARs.

The cooperation between Innoviz and Kudan addresses the growing need for 3D digital mapping solutions leveraging "Simultaneous Localization and Mapping" (SLAM) technology.  Since autonomous decisions depend on the quality of the information, perception of the environment is critical for machines.  Coupled with Innoviz's LiDAR scanning, the SLAM technology enables a dynamic mapping of the environment with accurate, high-density, colorized 3D point cloud generation.  With the global presence and strong networks of both companies, this partnership is a key enabler for a broad set of uses, including HD maps, robotics, construction, surveying, and "digital twinning" and "industrial metaverse" applications.

In addition to enabling non-automotive applications, Kudan's SLAM software also gives autonomous vehicles the ability to view and act upon changes to their environment in real-time by providing highly accurate, reliable results that allow vehicles to react to their surroundings. Innoviz's high-performing LiDARs deliver a rich 3D point cloud at distances up to 250 meters. Resilient to sunlight and weather conditions, Innoviz's LiDARs contribute to the accuracy and reliability of Kudan's SLAM software for safe automotive and non-automotive applications

"Innoviz is excited to promote our mapping collaboration both in automotive applications and beyond with the Kudan SLAM solution," said Omer Keilaf, Co-Founder and CEO of Innoviz. "Kudan's SLAM software and related tools are a great fit with Innoviz's products and we see great opportunity in a number of applications, including HD mapping for autonomous driving, robotics, geospatial mapping and surveying. We're also seeing traction in Asia that demonstrates high demand for our LiDAR technology for automotive and non-automotive applications"

"The need for LiDAR SLAM-based mapping for digital transformation in various industries has continued to grow and has never been more urgent," said Daiu Ko, CEO of Kudan. "Under this new partnership with Innoviz, we look forward to bringing the best mapping solution to the market in terms of use-case coverage and return over investment across different vertical domains."

Innoviz and Kudan will collaborate on digital mapping projects in Japan and globally. Innoviz will demonstrate its LiDAR solutions across various applications at Automotive World Nagoya, Oct 26-28, 2022.

About Kudan Inc.

Kudan is a deep tech research and development company specializing in algorithms for artificial perception (AP). As a complement to artificial intelligence (AI), AP functions allow machines to develop autonomy. Currently, Kudan is using its high-level technical innovation to explore business areas based on its own milestone models established for deep tech which provide wide-ranging impact on several major industrial fields.

For more information, please refer to Kudan's website at https://www.kudan.io/.

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About Innoviz Technologies

Innoviz is a global leader in LiDAR technology, working towards a future with safe autonomous vehicles on the world's roads. Innoviz's LiDAR and perception software "see" better than a human driver and reduce the possibility of error, meeting the automotive industry's strictest expectations for performance and safety. Operating across the U.S., Europe, and Asia, Innoviz has been selected by internationally recognized premium car brands for use in consumer vehicles as well as by other commercial and industrial leaders for a wide range of use cases. For more information, visit innoviz-tech.com.

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Forward Looking Statements

This announcement contains certain forward-looking statements within the meaning of the federal securities laws, including statements regarding the services offered by Innoviz, the anticipated technological capability of Innoviz's products, the markets in which Innoviz operates and Innoviz's projected future results. These forward-looking statements generally are identified by the words "believe," "project," "expect," "anticipate," "estimate," "intend," "strategy," "future," "opportunity," "plan," "may," "should," "will," "would," "will be," "will continue," "will likely result," and similar expressions. Forward-looking statements are predictions, projections and other statements about future events that are based on current expectations and assumptions and, as a result, are subject to risks and uncertainties. Many factors could cause actual future events to differ materially from the forward-looking statements in this announcement, including but not limited to, the ability to implement business plans, forecasts, and other expectations, the ability to identify and realize additional opportunities, and potential changes and developments in the highly competitive LiDAR technology and related industries. The foregoing list of factors is not exhaustive. You should carefully consider the foregoing factors and the other risks and uncertainties described in Innoviz's annual report on Form 20-F filed with the SEC on April 21, 2021, and other documents filed by Innoviz from time to time with the SEC. These filings identify and address other important risks and uncertainties that could cause actual events and results to differ materially from those contained in the forward-looking statements. Forward-looking statements speak only as of the date they are made. Readers are cautioned not to put undue reliance on forward-looking statements, and Innoviz assumes no obligation and does not intend to update or revise these forward-looking statements, whether as a result of new information, future events, or otherwise. Innoviz gives no assurance that it will achieve its expectations.

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