An unconventional approach to modifying data is cutting the amount of work required to train self-driving cars.

What’s new: Waymo unveiled a machine learning pipeline that varies lidar scans representing pedestrians and other objects. The company’s self-driving systems, in turn, learn from the augmented data, effectively giving the researchers a larger training dataset without collecting and labelling additional point clouds.

How it works: Waymo’s engineers started with two-dimensional data augmentation methods used to train object recognition models: flipping, rotating, color-shifting, and partially obscuring still images. Since lidar output generates three-dimensional points, Waymo developed ways to perform similar transformations while maintaining the geometrical relationships between points.

The researchers collected pre-labeled 3D representations of pedestrians, cyclists, cars, and road signs from the Waymo Open Dataset.
They developed eight modes of point-cloud distortion. For instance, one mode changed the object’s size, while another rotated it along its y-axis.
Then they trained neural networks to generate these variations, creating new versions of each object in the dataset.

Results: Called Progressive Population-Based Augmentation, the method improved object detection with both small and large datasets. It increased data efficiency between 3.3- and 10-fold compared with training on unaugmented datasets.
Behind the news: Released in March, Waymo’s latest self-driving platform is based on an electric Jaguar SUV. Its sensor suite includes a 360-degree lidar on top, a 95-degree lidar near each head and tail light, plus cameras and radars.

Why it matters: The new system breaks ground in 3D augmentation. Waymo is effectively giving its system more examples of obstacles while saving the time, expense, and labor it would take to collect real-world samples.

We’re thinking: As long as social distancing keeps real drivers and pedestrians off the street, self-driving cars will need all the augmented data they can get.

Progressive Population-Based Augmentation

Data Augmentation in 3D

Subscribe to The Batch