FSD Beta 11.3.2 (2022.45.11) Official Tesla Release Notes

– Enabled FSD Beta on highway. This unifies the vision and planning stack on and off-highway and replaces the legacy freeway stack, which is over 4 decades aged. The legacy highway stack nevertheless depends on several single-camera and one-body networks, and was setup to handle uncomplicated lane-specific maneuvers. FSD Beta’s multi-camera online video networks and next-gen planner, that lets for extra advanced agent interactions with a lot less reliance on lanes, make way for including additional intelligent behaviors, smoother command and greater final decision creating.

– Included voice push-notes. After an intervention, you can now send Tesla an nameless voice information describing your encounter to enable boost Autopilot.

– Expanded Automated Emergency Braking (AEB) to cope with autos that cross ego’s path. This includes conditions in which other autos run their crimson light-weight or change across ego’s path, stealing the proper-of-way. Replay of previous collisions of this sort indicates that 49% of the events would be mitigated by the new habits. This enhancement is now lively in equally handbook driving and autopilot operation.

– Enhanced autopilot response time to purple light-weight runners and end sign runners by 500ms, by improved reliance on object’s instantaneous kinematics together with trajectory estimates.

– Included a prolonged-range freeway lanes community to permit before reaction to blocked lanes and high curvature.

– Diminished aim pose prediction error for prospect trajectory neural community by 40% and lowered runtime by 3X. This was realized by bettering the dataset utilizing heavier and additional robust offline optimization, escalating the dimensions of this enhanced dataset by 4X, and employing a much better architecture and feature area.

– Improved occupancy network detections by oversampling on 180K difficult films which include rain reflections, street debris, and higher curvature.

– Enhanced remember for close-by minimize-in scenarios by 20% by adding 40k autolabeled fleet clips of this state of affairs to the dataset. Also improved handling of slice-in instances by improved modeling of their movement into ego’s lane, leveraging the exact same for smoother lateral and longitudinal management for reduce-in objects.

– Added “lane direction module and perceptual loss to the Highway Edges and Lines network, improving upon the complete remember of strains by 6% and the complete remember of highway edges by 7%.

– Improved over-all geometry and balance of lane predictions by updating the “lane direction” module representation with info appropriate to predicting crossing and oncoming lanes.

– Improved dealing with by means of higher speed and high curvature scenarios by offsetting in direction of internal lane lines.

– Improved lane changes, including: earlier detection and handling for simultaneous lane alterations, better hole assortment when approaching deadlines, much better integration involving velocity-dependent and nav-based lane transform selections and much more differentiation between the FSD driving profiles with respect to velocity lane modifications.

– Enhanced longitudinal regulate reaction smoothness when adhering to direct autos by far better modeling the feasible influence of direct vehicles’ brake lights on their future pace profiles.

– Enhanced detection of exceptional objects by 18% and diminished the depth mistake to big trucks by 9%, principally from migrating to much more densely supervised autolabeled datasets.

– Enhanced semantic detections for university busses by 12% and autos transitioning from stationary-to-driving by 15%. This was obtained by enhancing dataset label accuracy and expanding dataset size by 5%.

– Enhanced conclusion building at crosswalks by leveraging neural network dependent ego trajectory estimation in location of approximated kinematic styles.

– Improved dependability and smoothness of merge manage, by deprecating legacy merge location tasks in favor of merge topologies derived from vector lanes.

– Unlocked extended fleet telemetry clips (by up to 26%) by balancing compressed IPC buffers and optimized write scheduling across twin SOCs.