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GETTING MORE MEANING FROM LESS DATA



Articles
February 21, 2023

Training perception systems often requires a lot of data. To teach a system to
identify an object as a vehicle, pedestrian or something else, an engineer
typically has to show it sensor readings of that object from many different
angles and in many different environments. Vehicles, trees and even people come
in many shapes and sizes, and exposing the system to more models of those
objects enables it to identify them more accurately and extensively.

However, in a challenge leading up to the recent European Conference on Computer
Vision, Aptiv intern Frederik Hasecke proved that it is possible to train a
neural network well even when the data available is limited.

The competition was sponsored by Innoviz, one of Aptiv’s technology partners,
and Nvidia. Four participating teams looked for ways to use Innoviz’s InnovizTwo
lidar system and correctly perceive 3D images in situations where the system had
collected and annotated only a limited number of lidar frames. Hasecke and his
professor Anton Kummert won first place for their innovative approach. Hasecke
is a doctoral student at the University of Wuppertal in Germany in the field of
artificial intelligence and computer vision, and was working under a grant
provided by Aptiv at the time of the challenge.

Participants were given a dataset with 1,200 lidar frames from several driving
scenarios — but only 100 of those frames were annotated, meaning that objects
had not been identified in the other 1,100 frames. In the annotated frames, the
sponsors had identified only 790 cars, 30 pedestrians, eight bicycles, 17
motorcycles and 77 trucks, and teams had the task of training their systems to
identify as many objects as they could in the unannotated frames.

The typical lidar frame in the set of 1,200 frames that were provided showed
traffic scenes with the rough size and outline of objects picked up by the
lidar. The lidar did not show the color of the objects. With only the
three-dimensional point clouds, the teams could get a general sense of the shape
of objects, but without much context or detail.

Given this limited dataset, Hasecke used techniques he has been working on while
pursuing his Ph.D. He took scans of objects like cars, bicycles and trees both
from the annotated frames supplied in the competition and from an outside 3D
mesh source to match against objects in the frames that had not been annotated.
By resizing, flipping and otherwise manipulating these imported objects, he and
Kummert trained the underlying neural network to recognize more of the objects.

While radar and cameras are the primary external sensors for vehicles today,
lidar is often combined with other data from test vehicles to establish ground
truth. That is, a test vehicle can be equipped with a highly sensitive lidar to
establish exactly what objects are around the test vehicle, their size, their
distance and other factors. Perceptions from radars and cameras under
development for production vehicles can then be compared to that ground truth to
see how well they are performing. Radar will continue to provide fundamental
sensing for all levels of driving automation, with lidar being added for Level 4
automation and autonomous mobility on demand.

The competition showed that lidar can be used to identify objects even with
limited data, Hasecke says. That can lead to better object detection in
self-driving cars sold to the public — and, ultimately, a safer automated
driving experience.

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