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Apple’s work on self-driving cars has been more secretive than just about every other project in the autonomous car space, but now, two of the company’s scientists have published some of their auto-focused research for the first time.
The paper, authored by Apple engineers Yin Zhou and Oncel Tuzel and published in the independent journal arXiv, details a new computer imaging software technique called VoxelNet that could improve a driverless car system’s ability to detect pedestrians and cyclists.
Moreover, the scientists claim their new method could be even more effective than the two-tiered LiDAR and camera systems that have become the industry standard for object detection in self-driving cars. Those expensive systems depend on cameras to help determine the small or faraway objects (like pedestrians or cyclists) detected by LiDAR sensors, which use light beams to detect and map 3D obstacles in the world around the vehicle.
The VoxelNet system which was named after the voxel unit of value for a point in a three-dimensional grid eliminates the need for a camera to help identify the objects detected by LiDAR sensors, allowing the autonomous platform to work on LiDAR alone. The scientists tested the software using models that showed pedestrians, cyclists, and other faraway objects.
The new technique was only tested in computer simulations, so Apple will still need to put VoxelNet to the test on the streets IRL before it can actually remove the cameras from its self-driving platform. The initial results were called highly encouraging, however, so it might not be too long before the system described in the public research is put into use, by Apple or some other self-driving projects.
Apple officials couldn’t be reached for comment.