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Currently, the best quality LIDAR for driverless cars costs a minimum of $75,000. Add on another $25,000 for the rest of the systems that will make the car driverless plus the actual price of the physical car, driverless car ownership is definitely out of the reach for everyday motorists.
That is the bad news…the good news—over 50 different companies around the world are working to bring LIDAR costs down to a more agreeable level.
In this three part series on the Car of the Future Blog, I will explore the various challenges driverless automakers are facing in particular with LIDAR and other sensor technology.
What is LIDAR and why does a Driverless Car need it?
Many of us know LIDAR as one of the techniques law enforcement officers use to catch speeders. It can be used in either a hand-held device or as a measurement device in an automated speed camera. The word LIDAR is a combination of “light” and “radar” and is also an acronym for light (laser) imaging, detection and ranging.
Lidar is just of one of several components that allows a driverless car to “see” by shooting invisible beams of laser light off of objects and measuring how long it takes to beam back. The LIDAR also needs to then put all the millions of signals together creating 3-D image maps called “point clouds.”
Most driverless cars need a combination of technologies in order to see what is on the road ahead. These include visual sensors such as cameras and range-based sensors such as LIDAR (both complex and single beam) and radar. Automakers believe that all three components are critically necessary to the safety and performance of driverless vehicles.
In the past 10 years, range-based sensors have been commercially developed for use in robots and driverless cars. Currently, most range-based sensors can reliably tell the distance to objects for a range of around 100 meters or 329 feet. In order for driverless cars to work at highway speeds of 70 mph or more, the range-based sensors need to be able to detect objects by at least 200 meters or 656 feet—one of the many challenges LIDAR developers are finding.
Other challenges with range-based sensors include weather driving issues of snow, rain and fog plus snow banks on the side of the ride give another layer of problems. Lidar beams tend to bounce off of snowflakes and water droplets.
Also, range-based sensors can only give the car’s software a crude presentation of a pedestrian but cannot tell you the expression on the pedestrian’s face. Range-based sensors also have difficulty reading signs since signage is visual. Day-Night issues are also problematic as are oncoming cars with lights at night and street lights tend to distort the point clouds.
This is why range-based sensors are paired with video cameras which can provide details necessary for the bigger picture. Cameras can detect color and details that are necessary to compute while driving.
Human drivers, of course, can generally do this automatically and are flexible when road conditions would otherwise confound a driverless car. An example—a tropical downpour in the middle of the night. Humans can problem solve how to drive whereas a driverless car might be overwhelmed with input and cannot properly drive after a fashion.
Automakers are currently developing deep learning systems so that driverless cars can learn to drive on a scale with humans even though driverless cars need rigid diameters to operate and tend to be more cautious than human drivers.
Next week in Part 2 of this series on LIDAR, a look at just a few of the companies that are working on LIDAR and other sensors.
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