Full Self Driving (FSD) Beta Version 10.12

Release Date: 20th May 2022

Models: All models with FSD Beta

Regions: USA Only

Notable Changes

This beta includes more detailed vehicle models that have doors, windows, and wheels.

In addition to the improved display, this update will also display if a surrounding vehicle has an open door or an active turn signal.

Another notable improvement Tesla has mentioned is in the car’s confidence. This is seen in areas such as left turns, overtaking parked vehicles and other cases where it is not clear what the vehicle should do.

Such changes in confidence are crucial to the acceptance of FSD by the greater public as they would not accept or trust a hesitant vehicle, just as you would be unsure if your taxi driver was not confident.

Release Notes

  • Upgraded decision-making framework for unprotected left turns with better modeling of objects’ response to ego’s actions by adding more features that shape the go/no-go decision. This increases robustness to noisy measurements while being more sticky to decisions within a safety margin. The framework also leverages median safe regions when necessary to maneuver across large turns and accelerating harder through maneuvers when required to safely exit the intersection.
  • Improved creeping for visibility using more accurate lane geometry and higher resolution occlusion detection.
  • Reduced instances of attempting uncomfortable turns through better integration with object future predictions during lane selection.
  • Upgraded planner to rely less on lanes to enable maneuvering smoothly out of restricted space.
  • Increased safety of turns with crossing traffic by improving the architecture of the lanes neural network which greatly boosted recall and geometric accuracy of crossing lanes.
  • Improved the recall and geometric accuracy of all lane predictions by adding 180k video clips to the training set.
  • Reduced traffic control-related false slowdowns through better integration with lane structure and improved behavior with respect to yellow lights.
  • Improved the geometric accuracy of road edge and line predictions by adding a mixing/coupling layer with the generalized static obstacle network.
  • Improved geometric accuracy and understanding of visibility by retraining the generalized static obstacle network with improved data from the auto labeler and by adding 30k more video clips.
  • Improved recall of motorcycles, reduced velocity error of close-by pedestrians and bicyclists, and reduced heading error of pedestrians by adding new sim and auto labeled data to the training set.
  • Improved precision of the “is parked” attribute on vehicles by adding 41k clips to the training set. Solved 48% of failure cases captured by our telemetry of 10.11.
  • Improved detection recall of far-away crossing objects by regenerating the dataset with improved versions of the neural networks used in the auto labeler which increased data quality.
  • Improved offsetting behavior when maneuvering around cars with open doors.
  • Improved angular velocity and lane-centric velocity for non-VRU objects by upgrading them into network-predicted tasks.
  • Improved comfort when lane changing behind vehicles with harsh deceleration by tighter integration between the lead vehicle’s future motion estimate and planned lane change profile.
  • Increased reliance on network-predicted acceleration for all moving objects, previously only longitudinally relevant objects.
  • Updated nearby vehicle assets with visualization indicating when a vehicle has a door open.
  • Improved system frame rate +1.8 frames per second by removing three legacy neural networks.
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