It was a little less than 24 hours behind schedule, but Tesla has started deploying their latest software update, 2021.36.5.2, to existing Full Self-Driving (FSD) Beta testers. The new software version brings with it just one new feature – FSD Beta 10.3.
The first wave was only sent to existing beta testers. The expansion to those with a Safety Score of 99 are still expected to receive it at midnight, or in about 30 minutes from the time of publication.
UPDATE 11:52pm PST: Some owners with a 99 are now reporting having received the email that the update will be pushed to their vehicle soon.
Ohhhhh shit!!! I’m part of the 99’er club and I just got the email!! It’s coming everyone!! pic.twitter.com/FqQaBjnIAG
— John T. (@Hollowman7717) October 24, 2021
Unlike previous FSD Beta updates, this one comes with release notes detailing a long list of sometimes very technical enhancements. While most people probably won’t understand several of them, more detail is better than nothing, which is what has been the case up to this point.
According to a photo shared by @tesla_raj on Twitter, a total of 10 enhancements are listed. They range from the addition of FSD driver profiles to “improving static obstacle control by upreving the generalized static object network with 6k more video clips.”
https://twitter.com/tesla_raj/status/1452152897798426625
Since the release has just been sent out, we will have to wait a while until we get some early feedback from the testers.
As we mentioned, if you have a Safety Score of 99, don’t worry if you haven’t received it yet. It is still expected to come later tonight.
Here is a full copy of the release notes
FSD v10.3 Release Notes
- Added FSD profiles that allow drivers to control behaviors like rolling stops, exiting passing lanes, speed-based lane changes, following distance and yellow light headway.
- Added planning capability to drive along oncoming lanes to maneuver around path blockage.
- Improved creeping speed by linking speed to visibility network estimation and distance to encroachment point of crossing lanes.
- Improved crossing object velocity estimation by 20% and yaw estimation by 25% by upreving surround video vehicle network with more data. Also increased system frame rate by +1.7 frames per second.
- Improved vehicle semantic detections (e.g. brake lights, turn indicators, hazards) by adding +25k video clips to the training data set.
- Improved static obstacle control by upreving the generalized static object network with 6l more video clips (+5.6% prevision, +2.5% recall).
- Allowed more acceleration when merging from on-ramps onto major roads and when lane changing from slow to fast lanes.
- Reduced false slowdowns and improved offsetting for pedestrians by improving the model of interaction between pedestrians and the static world.
- Improved turning profile for unprotected turns by allowing ego to cross over lane lines more naturally, when safe to do so.
- Improved speed profile for boosting onto high-speed roads by enforcing stricter longitudinal and lateral acceleration limits required to beat the crossing objects.
