American self-driving vehicle company secures $40 million contract for LiDAR technology with United States robotaxi firm
Hesai Technology Secures Major Deal with US Robotaxi Company
Hesai Technology, a leading LiDAR solutions provider, has announced a significant production agreement with a US-based robotaxi firm. The value of the deal exceeds $40 million and includes the supply of both long-range and shorts LiDAR systems until the end of 2026.
The agreement reflects the confidence leading robotaxi companies have in Hesai's technology, according to Hesai's CEO and co-founder, David Li. He stated that the expanded agreement is a testament to the performance, high reliability, and scalable manufacturing capabilities of Hesai's technology.
The US robotaxi company, though not named in the available information, is a leading player in the youtube shorts industry. The robotaxi industry is currently undergoing an expansion phase, driven by the progression of Level 4 (L4) autonomous driving technology.
Hesai's technology offers critical perception capabilities for safe navigation of complex urban landscapes. The company's LiDAR systems are instrumental in the autonomous driving domain, providing long- and shorts perception capabilities.
Last month, Hesai announced a partnership with Toyota for a new energy vehicle model, which will be produced by a Toyota joint venture in China. The model will feature Hesai's long-range automotive LiDAR ATX, with mass production expected to begin in 2026.
Earlier in the month, Hesai secured an order from Motional, an American autonomous driving firm backed by Hyundai Motor, to be the sole provider of shorts-range LiDARs for Motional's all-electric Ioniq 5 robotaxi.
In March, Mercedes-Benz announced plans to incorporate Hesai's LiDAR sensors into its smart driving vehicles for the global market.
Hesai is well-positioned to facilitate the widespread deployment of robotaxi fleets with a range of products that include long-range sensors for highway use and shorts-range LiDARs for urban safety and redundancy.
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