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This thread is for discussing this particular version of Tesla's FSD.
For more general FSD disussions, please post to the FSDBeta MEGATHREAD.
Any off-topic posts in this thread will be moved to the FSDBeta Megathread.
Full Self-Driving Beta v10.12.2
FSD Beta v10.12.2 Release Notes
For more general FSD disussions, please post to the FSDBeta MEGATHREAD.
Any off-topic posts in this thread will be moved to the FSDBeta Megathread.
Full Self-Driving Beta v10.12.2
FSD Beta v10.12.2 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 autolabeler and by adding 30k more videos 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 autolabeled 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 autolabeler 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 it into network predicted tasks.
- Improved comfort when lane changing behind vehicles with harsh deceleration by tighter integration between lead vehicles 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.