Tesla FSD Beta 10.12 Release Notes and test videos — much is discovered during testing, says Musk

“Lot of code updates means much is discovered during testing,” Tesla CEO Elon Musk posted on Twitter about two weeks ago when answering for a possible rollout date of the FSD Beta version 10.12. He also said that the Release Notes for this version will be a long list of upgrades and improvements.

According to Elon Musk, FSD Beta 10.12 had to be released the previous weekend (May 14 – 15) but it only started rolling out late last week. Since this is a large release and Tesla employees are the first ones to get these updates, Tesla owners registered in the beta testing program are still receiving it.

The previous FSD Beta version 10.11.1 got some good reviews from the users, it seems like Tesla has really pushed forward with this update as well.

In addition to upgrades, Tesla also seems to have re-written a lot of code and algorithms to address self-driving challenges from new angles and a different approach. Looking at FSD Beta 10.12 release notes also reveals that Tesla is deprecating its neural nets as the automaker is constantly improving its infrastructure and machine learning for Autopilot/Full Self-Driving.

Probably this weekend. Lot of code updates means much is discovered during testing.
— Elon Musk (@elonmusk) May 11, 2022

Release notes on this version will be long
— Elon Musk (@elonmusk) May 11, 2022

FSD Beta 10.12 (2022.12.3.10) 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 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 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 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 (vulnerable road user) 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.

FSD Beta 10.12 driving visualization improvement showing the door of the front vehicle as open with red color marking. Credit: u/110110 via Reddit.

FSD Beta 10.12 Test Video

Rob Maurer, the host of the Tesla Daily podcast has also become an FSD Beta testing community member. A good and calm driver himself, he is convinced that the problem of autonomous driving should be solved to remove human error and make vehicles safer.

He allowed FSD Beta 10.12 to drive his Tesla Model 3 on the busy streets of Milwaukee, Wisconsin. The car drove itself through construction zones, roundabouts, and other areas of the city’s downtown area.

The previous FSD Beta version 10.11.1 performed well at a roundabout in Canada, so, this version should be even better at handling roundabouts.

Video: Testing FSD Beta 10.12.1 in Milwaukee, Wisconsin’s busy downtown area (roundabouts, construction zones, and more).

Another test of the FSD Beta 10.12 comes from James Locke, he is one of the oldest FSD Beta testers and due to his reputation as a safe driver, Tesla never revoked his access to FSD Beta.

“I had 1 intervention due to beta not committing to an uncontrolled left turn and needing to intervene before cross traffic became an issue,” James stated in the video description of his FSD Beta 10.12 test drive, “I also intervened for efficiency sake to do a u-turn vs a longer drive to make it to my destination,” he added. James performed this test in Santa Clara, California.