Part 7/13:
Such capabilities are underpinned by training on enormous and diverse datasets, including rare edge cases. Tesla’s advantage lies in their ability to access real-world data from their extensive fleet—enabling them to train models that are both robust and safe.
Debugging and Interpreting End-to-End Neural Models
Despite the complexity of end-to-end neural systems, Tesla has devised methods for debugging and understanding their models:
Scene interpretation prompts: The models can generate predictions about scene components—traffic lights, road boundaries, occupancy—by querying the system directly.
Auxiliary outputs: The same neural network can be prompted to explain its decisions, providing transparency and aiding safety validation.