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Tesla FSD Beta 10.12.1 upgrade notes:
1. Updated choice making structure for unprotected left turns with much better modeling of things' action to ego's actions by adding more functions that shape the go/no-go decision. This increases robustness to noisy measurements while being more sticky to decisions within a security margin. The framework likewise leverages median safe areas when necessary to maneuver across big turns and accelerating harder through maneuvers when needed to safely leave the intersection.
2. Improved sneaking for presence utilizing more precise lane geometry and higher resolution occlusion detection.
3. Lowered instances of trying unpleasant turns through better combination with item future predictions throughout lane choice.
4. Updated organizer to rely less on lanes to make it possible for navigating efficiently out of limited space.
5. Increased safety of turns with crossing traffic by enhancing the architecture of the lanes neural network which greatly increased recall and geometric accuracy of crossing lanes.
6. Enhanced the recall and geometric precision of all lane forecasts by including 180k video clips to the training set.
7. Lowered traffic control associated false slowdowns through much better combination with lane structure and enhanced behavior with respect to yellow lights.
8. Improved the geometric precision of road edge and line predictions by including a mixing/coupling layer with the generalized static barrier network.
9. Improved geometric accuracy and understanding of presence by re-training the generalized static challenge network with enhanced data from the autolabeler and by including 30k more videos clips.
10. Enhanced recall of bikes, decreased speed mistake of close-by pedestrians and bicyclists, and reduced heading mistake of pedestrians by adding brand-new sim and autolabeled data to the training set.
11. Enhanced precision of the "is parked" quality on lorries by adding 41k clips to the training set. Resolved 48% of failure cases captured by our telemetry of 10.11.
12. Improved detection recall of far-away crossing items by restoring the dataset with enhanced variations of the neural networks utilized in the autolabeler which increased information quality.
13. Enhanced balancing out habits when maneuvering around automobiles with open doors.
14. Enhanced angular velocity and lane-centric speed for non-VRU items by updating it into network forecasted jobs.
15. Enhanced convenience when lane altering behind cars with extreme deceleration by tighter combination between lead lorries future motion estimate and planned lane change profile.
16. Increased dependence on network-predicted velocity for all moving things, formerly only longitudinally relevant objects.
17. Upgraded close-by car properties with visualization showing when a car has a door open.
18. Better system frame rate +1.8 frames per second by getting rid of three legacy neural networks.
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