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FSD Beta 10.69
- Thread starter Buckminster
- Start date
Yep, there is a reason that I was held back on 2022.16.3 until I opted out, and was then immediately offered 2022.20.8.
2022.16.3.10 finally showed up on TeslaFi. Pervious version was FSD Beta 10.12.2.
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Release notes:
- Added a new "deep lane guidance" module to the Vector Lanes neural network which fuses features extracted from the video streams with coarse map data, i.e. lane counts and lane connectivities. This architecture achieves a 44% lower error rate on lane topology compared to the previous model, enabling smoother control before lanes and their connectivities becomes visually apparent. This provides a way to make every Autopilot drive as good as someone driving their own commute, yet in a sufficiently general way that adapts for road changes.
- Improved overall driving smoothness, without sacrificing latency, through better modeling of system and actuation latency in trajectory planning. Trajectory planner now independently accounts for latency from steering commands to actual steering actuation, as well as acceleration and brake commands to actuation. This results in a trajectory that is a more accurate model of how the vehicle would drive. This allows better downstream controller tracking and smoothness while also allowing a more accurate response during harsh maneuvers.
- Improved unprotected left turns with more appropriate speed profile when approaching and exiting median crossover regions, in the presence of high speed cross traffic ("Chuck Cook style" unprotected left turns). This was done by allowing optimizable initial jerk, to mimic the harsh pedal press by a human, when required to go in front of high speed objects. Also improved lateral profile approaching such safety regions to allow for better pose that aligns well for exiting the region. Finally, improved interaction with objects that are entering or waiting inside the median crossover region with better modeling of their future intent.
- Added control for arbitrary low-speed moving volumes from Occupancy Network. This also enables finer control for more precise object shapes that cannot be easily represented by a cuboid primitive. This required predicting velocity at every 3D voxel. We may now control for slow-moving UFOs.
- Upgraded Occupancy Network to use video instead of images from single time step. This temporal context allows the network to be robust to temporary occlusions and enables prediction of occupancy flow. Also, improved ground truth with semantics-driven outlier rejection, hard example mining, and increasing the dataset size by 2.4x.
- Upgraded to a new two-stage architecture to produce object kinematics (e.g. velocity, acceleration, yaw rate) where network compute is allocated O(objects) instead of O(space). This improved velocity estimates for far away crossing vehicles by 20%, while using one tenth of the compute.
- Increased smoothness for protected right turns by improving the association of traffic lights with slip lanes vs yield signs with slip lanes. This reduces false slowdowns when there are no relevant objects present and also improves yielding position when they are present.
- Reduced false slowdowns near crosswalks. This was done with improved understanding of pedestrian and bicyclist intent based on their motion.
- Improved geometry error of ego-relevant lanes by 34% and crossing lanes by 21% with a full Vector Lanes neural network update. Information bottlenecks in the network architecture were eliminated by increasing the size of the per-camera feature extractors, video modules, internals of the autoregressive decoder, and by adding a hard attention mechanism which greatly improved the fine position of lanes.
- Made speed profile more comfortable when creeping for visibility, to allow for smoother stops when protecting for potentially occluded objects.
- Improved recall of animals by 34% by doubling the size of the auto-labeled training set.
- Enabled creeping for visibility at any intersection where objects might cross ego's path, regardless of presence of traffic controls.
- Improved accuracy of stopping position in critical scenarios with crossing objects, by allowing dynamic resolution in trajectory optimization to focus more on areas where finer control is essential.
- Increased recall of forking lanes by 36% by having topological tokens participate in the attention operations of the autoregressive decoder and by increasing the loss applied to fork tokens during training.
- Improved velocity error for pedestrians and bicyclists by 17%, especially when ego is making a turn, by improving the onboard trajectory estimation used as input to the neural network.
- Improved recall of object detection, eliminating 26% of missing detections for far away crossing vehicles by tuning the loss function used during training and improving label quality.
- Improved object future path prediction in scenarios with high yaw rate by incorporating yaw rate and lateral motion into the likelihood estimation. This helps with objects turning into or away from ego's lane, especially in intersections or cut-in scenarios.
- Improved speed when entering highway by better handling of upcoming map speed changes, which increases the confidence of merging onto the highway.
- Reduced latency when starting from a stop by accounting for lead vehicle jerk. on
- Enabled faster identification of red light runners by evaluating their current kinematic state against their expected braking profile.
- Added a new "deep lane guidance" module to the Vector Lanes neural network which fuses features extracted from the video streams with coarse map data, i.e. lane counts and lane connectivities. This architecture achieves a 44% lower error rate on lane topology compared to the previous model, enabling smoother control before lanes and their connectivities becomes visually apparent. This provides a way to make every Autopilot drive as good as someone driving their own commute, yet in a sufficiently general way that adapts for road changes.
- Improved overall driving smoothness, without sacrificing latency, through better modeling of system and actuation latency in trajectory planning. Trajectory planner now independently accounts for latency from steering commands to actual steering actuation, as well as acceleration and brake commands to actuation. This results in a trajectory that is a more accurate model of how the vehicle would drive. This allows better downstream controller tracking and smoothness while also allowing a more accurate response during harsh maneuvers.
- Improved unprotected left turns with more appropriate speed profile when approaching and exiting median crossover regions, in the presence of high speed cross traffic ("Chuck Cook style" unprotected left turns). This was done by allowing optimizable initial jerk, to mimic the harsh pedal press by a human, when required to go in front of high speed objects. Also improved lateral profile approaching such safety regions to allow for better pose that aligns well for exiting the region. Finally, improved interaction with objects that are entering or waiting inside the median crossover region with better modeling of their future intent.
- Added control for arbitrary low-speed moving volumes from Occupancy Network. This also enables finer control for more precise object shapes that cannot be easily represented by a cuboid primitive. This required predicting velocity at every 3D voxel. We may now control for slow-moving UFOs.
- Upgraded Occupancy Network to use video instead of images from single time step. This temporal context allows the network to be robust to temporary occlusions and enables prediction of occupancy flow. Also, improved ground truth with semantics-driven outlier rejection, hard example mining, and increasing the dataset size by 2.4x.
- Upgraded to a new two-stage architecture to produce object kinematics (e.g. velocity, acceleration, yaw rate) where network compute is allocated O(objects) instead of O(space). This improved velocity estimates for far away crossing vehicles by 20%, while using one tenth of the compute.
- Increased smoothness for protected right turns by improving the association of traffic lights with slip lanes vs yield signs with slip lanes. This reduces false slowdowns when there are no relevant objects present and also improves yielding position when they are present.
- Reduced false slowdowns near crosswalks. This was done with improved understanding of pedestrian and bicyclist intent based on their motion.
- Improved geometry error of ego-relevant lanes by 34% and crossing lanes by 21% with a full Vector Lanes neural network update. Information bottlenecks in the network architecture were eliminated by increasing the size of the per-camera feature extractors, video modules, internals of the autoregressive decoder, and by adding a hard attention mechanism which greatly improved the fine position of lanes.
- Made speed profile more comfortable when creeping for visibility, to allow for smoother stops when protecting for potentially occluded objects.
- Improved recall of animals by 34% by doubling the size of the auto-labeled training set.
- Enabled creeping for visibility at any intersection where objects might cross ego's path, regardless of presence of traffic controls.
- Improved accuracy of stopping position in critical scenarios with crossing objects, by allowing dynamic resolution in trajectory optimization to focus more on areas where finer control is essential.
- Increased recall of forking lanes by 36% by having topological tokens participate in the attention operations of the autoregressive decoder and by increasing the loss applied to fork tokens during training.
- Improved velocity error for pedestrians and bicyclists by 17%, especially when ego is making a turn, by improving the onboard trajectory estimation used as input to the neural network.
- Improved recall of object detection, eliminating 26% of missing detections for far away crossing vehicles by tuning the loss function used during training and improving label quality.
- Improved object future path prediction in scenarios with high yaw rate by incorporating yaw rate and lateral motion into the likelihood estimation. This helps with objects turning into or away from ego's lane, especially in intersections or cut-in scenarios.
- Improved speed when entering highway by better handling of upcoming map speed changes, which increases the confidence of merging onto the highway.
- Reduced latency when starting from a stop by accounting for lead vehicle jerk. on
- Enabled faster identification of red light runners by evaluating their current kinematic state against their expected braking profile.
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Looks like maybe half of these 10.69 installs are coming from 10.13 (so half are from 10.12.2?):
teslascope.com
2022.16.3.10 Official Tesla Software Update - Teslascope
Release notes and statistics for 2022.16.3.10 with Full Self-Driving Beta v10.69 and more.
teslascope.com
I’ll be happy when the car stops frantically swerving around manhole covers and fresh asphalt patchwork thinking it’s a hole in the road. Keeping the sonar, especially repainted would have been great in avoiding false positives with potholes. Oh well. Since humans can’t always tell by vision alone I use the cameras will eventually that out.
;-)
;-)
This sounds like the update that might make the difference where it starts building confidence in drivers instead of scaring us to death.
The whole point of the single stack is to bring all the city street features to autopilot. It will make autopilot much, much, much more robust and safe.
Someone please translate the release notes to basic English 
AlanSubie4Life
Efficiency Obsessed Member
It sounds like they listened to our pleas to:
1) Drive more smoothly. (So glad all my moaning has been vindicated - it sounds like it was totally screwed up before…we’ll see whether this helps.)
2) Determine how to stop the car in the correct place.
3) Understand when the car can’t see.
4) See cross traffic.
5) Maybe pick the correct lanes sometimes.
6) Maybe have a little memory, unclear how much.
7) Detect and respond to UFOs (was not in the request list).
1) Drive more smoothly. (So glad all my moaning has been vindicated - it sounds like it was totally screwed up before…we’ll see whether this helps.)
2) Determine how to stop the car in the correct place.
3) Understand when the car can’t see.
4) See cross traffic.
5) Maybe pick the correct lanes sometimes.
6) Maybe have a little memory, unclear how much.
7) Detect and respond to UFOs (was not in the request list).
I skimmed and the notes are quite different. There are some similar line items but they’re described differently enough that it’s hard to tell if there’s a difference or it’s just wording. Here are the old notes: 2022.16.3.5 (FSD Beta 10.13) Official Tesla Release Notes - Software Updates
I really wish FSD would allow the option for lane change confirmation like on highway AP until it gets better at picking the correct lane, but hopefully 10.69 will do much better at it and then it won't matter
The biggest issue is it gets into the right lane so often and I. My area the right lane is sometimes parking only and sometimes driving only. It that people pay attention, or cate, one way or the other. You are car gets in the right lane and gets stuck behind a parked car and eventually goa around it only to go right back into the right lane and do it all over.
And why make left turns into left lanes when I almost immediately needs to make a right after the left you it try’s to cut access 3-4 lanes after completing the left to get ready for the right.
And why make left turns into left lanes when I almost immediately needs to make a right after the left you it try’s to cut access 3-4 lanes after completing the left to get ready for the right.
AlanSubie4Life
Efficiency Obsessed Member
I also hope that those people who argued that we just have to be happy with how FSD drives, and that it will never drive the way we would drive, and that we are expecting too much, appreciate the new (hopefully) smoother behavior.
We’ll see if it is smooth enough! I suspect strongly that I will still be griping, but I do hope for improvement - it was definitely low-hanging fruit, that made using FSD barely tolerable.
I hope Chuck doesn’t do the UPLs at night! Though Sunday morning doesn’t really bode that well for me either. Presumably everyone wants to see how it does with actual traffic. So hopefully that is what we see.
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