The
paper by Yin Zhou and Oncel Tuzel, submitted on Nov. 17 to
independent online journal arXiv, is significant because Apple's
famed corporate secrecy around future products has been seen as
a drawback among artificial intelligence and machine learning
researchers.
The scientists proposed a new software approach called
"VoxelNet" for helping computers detect three-dimensional
objects. (https://arxiv.org/abs/1711.06396)
Apple declined to comment.
Academics are used to freely sharing their work with peers at
other organizations. Yielding to that dynamic, Apple in July
established the Apple Machine Learning Journal for its
researchers. Their work rarely appears outside the journal,
which so far has not published any research on self-driving
cars.
Self-driving cars often use a combination of normal
two-dimensional cameras and depth-sensing "LiDAR" units to
recognize the world around them. While the units supply depth
information, their low resolution makes it hard to detect small,
faraway objects without help from a normal camera linked to it
in real time.
But with new software, the Apple researchers said they were able
to get "highly encouraging results" in spotting pedestrians and
cyclists with just LiDAR data. They also wrote they were able to
beat other approaches for detecting three-dimensional objects
that use only LiDAR. The experiments were computer simulations
and did not involve road tests.
Though Chief Executive Tim Cook has called self-driving cars
"the mother of all AI projects," Apple has given few hints about
the nature of its self-driving car ambitious.
Last December, Apple told federal regulators it was excited
about the technology and asked regulators not to restrict
testing of the technology.
In April, Apple filed a self-driving car testing plan with
California regulators.
(Reporting by Stephen Nellis; Editing by Richard Chang)
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