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Comment by sohkamyung | Hacker News Reader

That's answered in the Velodyne press release [1] linked from the article: "The new Velarray LiDAR sensor uses Velodyne’s proprietary ASICs (Application Specific Integrated Circuits) to achieve superior performance metrics in a small package size of 125mm x 50mm x 55mm that can be embedded into the front, sides, and corners of vehicles. It provides up to a 120-degree horizontal and 35-degree vertical field-of-view, with a 200-meter range even for low-reflectivity objects. With an automotive integrity safety level rating of ASIL B, Velarray will not only ensure safe operation in L4 and L5 autonomous vehicles but also in ADAS-enabled cars. It has a target price in the hundreds of dollars when produced in mass volumes."

[1] [ http://www.businesswire.com/news/home/20170419005516/en/Velo... ]

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Then why do you think will the current technology still be useful, given an order of magnitude higher price?

I'm not the writer of that piece. But my guess would be that automotive technology tends to change slowly and gets gradually replaced.

Car companies are getting used to reading data from standard Lidar. Getting them to suddenly dump it for Solid-State Lidar may be a step too fast and they would rather go through a transition period first (standard + solid-state) until they are happy with the performance of solid-state lidar.

You are referring to this in the article, right? “I don’t necessarily believe that [the solid-state lidar] will obviate or replace the 360-degree units—it will be a complement,” Marty Neese, chief operating officer of Velodyne, told IEEE Spectrum earlier this month. “There’s a lot of learning yet to go by carmakers to incorporate lidar in a thoughtful way.”

is it hard to put 3 x 120 degree units to get 360 coverage if the unit is only 55mm wide?

It's probably not hard to put the units in, but I imagine combining the data from them wouldn't be trivial.

are seams really that big a problem? you could go to 4 and have overlap, interleaving and soft rollover.

Most autonomous vehicles already do this to some extent or another. Tools like ROS (which Uber at least uses for its vehicles) practically handle this for you.

The ros tf2 (tf for "transformations", not Tensorflow) library allows you to basically input a 3d model of your vehicle, like you might get from CAD, add the pose and location of your various sensors, and it will automatically handle the spacial transforms required to build a singular world model for you.

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