Back to AI/MLTakes camera inputs and predicts which 3D voxels (volumetric pixels) are occupied Works even for objects not seen in training data Provides a dense 3D representation of the environment Helps with planning safe paths Handles unknown objects better than object detection Provides continuous spatial understanding Works well in complex scenarios Reduces reliance on object classification Requires significant computational resources Training on large-scale data Balancing accuracy with real-time performance
Occupancy Networks
Occupancy Networks are a key innovation in Tesla's FSD system. Instead of just detecting objects, occupancy networks predict the 3D occupancy of space around the vehicle.
How It Works
Advantages
Challenges
Tesla's occupancy networks are trained on millions of miles of driving data collected from the fleet, making them highly robust and capable.