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RoboSense wins CES 2020 Innovation Award for Autonomous Vehicle Technology

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RoboSense, the world’s leading autonomous driving LiDAR perception solution provider, announced that it has won the CES 2020 Innovation Award the second year in a row for autonomous vehicle technology.

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RoboSense won this year's CES 2020 Innovation Award for the first MEMS-based Smart LiDAR Sensor, the RoboSense RS-LiDAR-M1, winning in the Vehicle Intelligence and Self-Driving Technology category.

The RoboSense award-winning RS-LiDAR-M1 is the world's first and only MEMS-based smart LiDAR sensor for self-driving passenger vehicles with its own embedded AI algorithm technologies and SoC (System on a Chip). The RS-LiDAR-M1 goes beyond traditional LiDAR, providing full data collection and comprehension.

As a ground-breaking product, the RS-LiDAR-M1 not only collects and interprets high definition 3D point cloud data, but also processes road data in real-time, with a built-in AI algorithm and SoC, while at the same time synchronising high-precision positioning output; road traffic signage; lane markings, driving areas, road curbs, and obstacle detection; tracking; and classification. Also, it is the world's smallest MEMS-based solid-state LiDAR, providing a low cost, high stability, and superior manufacturability to meet automotive-grade and serial production requirements.

“Driving Safety is the most critical challenge for the Autonomous driving vehicles. Perception is the first sense for the AD vehicles to precept the surrounding environment. To ensure a completely safe perception working in all conditions, LiDAR with smart perception algorithm and supporting functions are indispensable. We are very proud that our smart LiDR sensor concept have been recognized by the customers and different application users, ” said Dr. Leilei Shinohara, RoboSense Vice President of R&D.

The RoboSenseRS-LiDAR-M1 Smart LiDAR Sensor will be on display in the Innovation Awards Showcase at the upcoming CES 2020.


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RoboSense wins CES 2020 Innovation Award for Autonomous Vehicle Technology
Modified on Friday 8th November 2019
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