Tesla releases a new Full Self-Driving Beta update with performance improvements

Tesla has started to release a new Full Self-Driving Beta update with some performance improvements – although it doesn’t look like a huge update from the release notes.

FSD Beta enables Tesla vehicles to drive autonomously to a destination entered in the car’s navigation system, but the driver needs to remain vigilant and ready to take control at all times.

Since the responsibility rests with the driver and not Tesla’s system, it is still considered a level-two driver-assist system, despite its name. It has been sort of a “two steps forward, one step back” type of program, as some updates have seen regressions in terms of driving capabilities.

Tesla has frequently been releasing new software updates to the FSD Beta program and adding more owners to it.

Since the wider release of the beta last year, there are currently over 400,000 Tesla owners in the program in North America – virtually every Tesla owner who bought the FSD package on their vehicles.

Earlier this year, Tesla started to push the FSD Beta v11 update, which was an important update that merged Tesla’s FSD Beta software stack primarily used on roads and city streets with Tesla’s Autopilot software stack, which is used as a level-two driver-assist system on highways.

Now Tesla is starting to internally push a new FSD Beta v11.4 update, which Elon Musk claimed included “major improvements.”

Today, we have the release notes for Tesla FSD Beta v11.4.1 (via Not a Tesla App), and it includes the details of the update:

• Improved control through turns, and smoothness in general, by improving geometry, curvature, position, type and topology of lanes, lines, road edges, and restricted space. Among other improvements, the perception of lanes in city streets improved by 36%, forks improved by 44%, merges improved by 27% and turns improved by 16%, due to a bigger and cleaner training set and updated lane-guidance module.
• Added lane-guidance inputs to the Occupancy Network to improve detections of long-range roadway features, resulting in a 16% reduction in false negative median detections.
• Improved ego’s assertiveness for crossing pedestrians in cases where ego can easily and safely cross before the pedestrian.
• Improved motorbike recall by 8% and increased vehicle detection precision to reduce false positive detections. These models also add more robustness to variance in vision frame-rate.
• Reduced interventions caused by other vehicles cutting into ego’s lane by 43%. This was accomplished by creating a framework to probabilistically anticipate objects that may cut into ego’s lane and proactively offset and/or adjust speed to position ego optimally for these futures.
• Improved cut-in control by reducing lane-centric velocity error by 40-50% for close-by vehicles.
• Improved recall for object partial lane encroachment by 20%, high yaw-rate cut-in by 40%, and cut-out by 26% by using additional features of the lane-change trajectory to improve supervision.
• Reduced highway false slowdowns related to underestimated velocities for faraway objects by adding 68K videos to the training set with improved auto-labeled ground truth.
• Smoothed in-lane offsetting for large vehicles by tuning the amount of lateral jerk allowed for the maneuver.
• Improved lateral control for upcoming high-curvature merges to bias away from the merging lane.