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Tesla FSD 12 won’t be BETA, says Elon Musk, stable FSD coming?



Tesla FSD 12

June 27, 2023, Tuesday, Tesla is a world leader in autonomous driving and there’s some breaking information that just coming from the company’s CEO, Elon Musk himself, revealing that the FSD 12 won’t be beta. This is quite a shocking hint given by the Tesla chief and it may mean that Tesla could launch a stable version soon enough.

Elon’s latest reply came during a conversation on Twitter, WholeMarsCatalog asked “FSD Beta 12 Maybe later this year”, and Elon replied, “Version 12 won’t be beta”. WholeMarsBlog also asked about the rollout date but Elon didn’t respond.

Started in October 2023, Full Self-Driving (FSD) is Tesla’s advanced driver assistance system (ADAS) and uses deep neural networks but it is technically still in the beta stage. Since its launch, Tesla keeps on testing driving models and collecting data to achieve autonomous driving.

The new response from Elon indicates that Tesla is ready to announce something big in the autonomous driving industry. However, we’ll have to wait for more official information.

FSD Beta V 11.4.4:

Previously, Tesla released FSD Beta v11.4.4, which comes with various improvements, you can check the changelog.

  • Improved short-deadline lane changes, to avoid going off-route, through better modeling of target lane vehicles to improve gap selection assertiveness.
  • Improved offset consistency when controlling for static obstacles. Also improved smoothness when changing offset direction by adjusting speed more comfortably.
  • Improved handling of oncoming cars on narrow unmarked roads by improved prediction of oncoming car’s trajectory and leaving enough room for them to pass before re-centering.
  • Improved Occupancy Flow prediction from the Occupancy Network for arbitrarily moving obstacles by 8%.
  • Expanded usage of the new object ground truth auto labeler for the NonVRU detection model, improving distant vehicle recall and geometry precision for semi-trucks, trailers, and exotic vehicles.
  • Improved VRU control by expanding planning scope to control gently for low-confidence detections that may interfere with the ego’s path.
  • Improved handling for VURs near crosswalks by predicting their future intent more accurately. This was done by leveraging more kinematic data to improve the association between crosswalks and VRUs.
  • Improved ego behavior near VRUs by turning their assumed kinematic properties and utilizing available semantic information to classify more accurately their probability of intersecting ego’s path.
  • Improved Automatic Emergency Braking recall in response to cut-in vehicles and vehicles behind ego while reversing.