Part 13/13:
During Tesla’s presentation, questions were asked about the specifics of their end-to-end neural systems, sensor-specific encoders, and evaluation methodologies. Tesla emphasized that while sensor-specific encoding can be more efficient, the choice depends on empirical performance and latency considerations. They also clarified that their models are flexible enough to generate auxiliary information—such as scene semantics—that help interpret decisions and improve safety.
Tesla’s commitment to transparency, interpretability, and rigorous validation continues to guide its development of autonomous systems—paving the way toward a fully autonomous and robotic future.