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Koito and Quanergy Collaborate To…

Koito and Quanergy Collaborate To Design Automotive Headlight Concept With Built–In LiDAR Sensors

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Koito Manufacturing Co., Ltd. the largest global maker of automotive headlights, and Quanergy Systems, Inc., the leading provider of LiDAR sensors and smart sensing solutions, has announced a collaboration to design an automotive headlight concept with built-in Quanergy S3 solid state LiDAR sensors.

The Koito headlights, which will be located on the corners of a vehicle, each incorporates two compact Quanergy S3 solid state LiDARs that perform sensing forward and to the side, and provide real-time long-range 3D views of the environment around the vehicle and the ability to recognize and track objects. The sensors are seamlessly embedded inside the headlights and do not protrude or alter the exterior look of the headlights or the vehicle. Additionally, the headlight protects the sensors from dust, dirt and water, and headlight lens washers can be used to help ensure an unobstructed view for the sensors.

The LiDARs embedded in the headlights have the function of primary sensors in advanced driver assistance systems (ADAS) and autonomous vehicle (AV) systems. As primary sensors, the LiDARs are used for (a) perception, (b) mapping and localization, and (c) occupancy grid detection, path planning and navigation.

“We are excited to be working with Koito on this groundbreaking headlight,” said Dr. Louay Eldada, Quanergy CEO. “We believe that headlights with integrated LiDAR sensors will help accelerate the commercialization of cars with autonomous driving capability and will help reinvent the driving experience.”

The Quanergy S3 is the world’s first and only compact, low-cost, automotive-grade solid state LiDAR sensor with high reliability and superior capability. The S3 LiDAR sensor was recently recognized as the 2017 CES Best of Innovation Award grand winner in the vehicle intelligence category. The company’s sensors enable autonomous driving and perform real-time 3D mapping and object detection, classification and tracking.

 

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