How self-driving cars learn to spot things they used to miss
We built a simple way to help cars see more in 3D scans from their sensors.
Instead of letting common objects like cars dominate the training, we made the system pay attention to rare ones too, so a bike or a stroller won't get missed.
The trick was to balance the examples used while training, and to group similar shapes so the model learns them better.
The result is a noticeable jump in detection, it finds more objects and makes fewer mistakes on small or uncommon things.
This method helped win a big challenge for autonomous driving, and yes we will share the code soon so others can try it.
If you care about safer streets, this means self-driving tech getting fairer at spotting every object, not just the frequent ones.
Hope this leads to fewer surprises on the road and better trust in driverless cars.
self-driving cars 3D objects class balance better accuracy
Read article comprehensive review in Paperium.net:
Class-balanced Grouping and Sampling for Point Cloud 3D Object Detection
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