Intel's self-driving car unit plans to step up use of its own radar tech by 2025By  Stephen Nellis January 12, 20215:27 PMUpdated 11 hours ago By  Stephen Nellis
  SAN  FRANCISCO (Reuters) - The head of Intel Corp’s self-driving car  subsidiary said on Tuesday the company wants to shift toward using its  own radar-based technology and use a single lidar sensor per vehicle by  2025 in a bid to lower the cost of autonomous driving.
  Mobileye  has taken a different strategy from many of its self-driving car  competitors, with a current camera-based system that helps cars with  adaptive cruise control and lane change assistance. Those systems are on  the road today and are gathering data to help Mobileye map the roads in  new cities.
  For  more advanced systems, the company plans to add both radar sensors,  which use radio waves to detect distance from objects, and lidar, a  laser-based system that helps self-driving vehicles gain a  three-dimensional view of the road. For a planned fleet of so-called  robotaxis, which are commercial vehicles meant to ferry around  passengers, the company is tapping sensors from Luminar Technologies  Inc.
  In a  presentation a the Consumer Electronics Show, Chief Executive Amnon  Shashua said on Tuesday that Mobileye’s robotaxis will use multiple  Luminar units to gain 360-degree lidar, radar and camera coverage all  around the vehicle. The robotaxis rolling out in at least eight cities  starting in 2022 will each have four Luminar units, Shashua said in a  subsequent question and answer session.
  But Mobileye is also developing its own lidar sensor that it plans to start using in 2025 for cars aimed at consumers.
  That  2025 consumer system will feature a single lidar unit facing the front  of the vehicle, while cameras and a new radar-based system that Mobileye  is also developing will cover the entire vehicle. Shashua said Mobileye  is developing new ways to process radar data with software that will  make radar more powerful. Radar sensors are cheaper than lidar but give a  less detailed image.
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