2022.20.7 Official Tesla Launch 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
– 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 optimisable 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
– Reduced false slowdowns near crosswalks. This was done with
improved understanding of pedestrian and bicyclist intent based on
– 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
– 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
– 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
– Enabled faster identification of red light runners by evaluating
their current kinematic state against their expected braking profile.
Press the “Video Record” button on the top bar UI to share your feedback. When pressed, your vehicle’s external cameras will share a short VIN-associated Autopilot Snapshot with the Tesla engineering team to help make improvements to FSD. You will not be able to view the clip.