Tesla Begins Wide Rollout of FSD Beta v11 with Single Stack

Tesla has started a wide rollout of FSD Beta v11.3.2 for testers, which has a single stack for highway and city streets. The new version has a number of improvements that bring the best driving experience to date.

Over the weekend, Tesla began a wide rollout of FSD Beta v11.3.2 to all test program participants. Initially, it was rolled out to the company's employees at the end of 2022, and to select testers a few weeks ago. Earlier this week, Elon Musk revealed that FSD Beta v11 will be widely distributed over the coming weekend and the company has now started the rollout.

On Sunday early morning, some testers reported that they had begun installing the new version of FSD Beta. @Kristennetten/Twitter shared the release notes, to which Musk replied that Tesla is close to “using NNs all the way from image-space to vector-space to control.”

We’re close to using NNs all the way from image-space to vector-space to control

— Elon Musk (@elonmusk) March 19, 2023

v11.3.2 is the latest version of v11 FSD Beta, which uses a single stack for highway and city driving. According to the first reviews, it includes significant improvements bringing the best driving experience of all previously tested versions.

The full release notes:

• Enabled FSD Beta on highway. This unifies the vision and planning stack on and off-highway and replaces the legacy highway stack, which is over four years old. The legacy highway stack still relies on several single-camera and single-frame networks and was set up to handle simple lane-specific maneuvers. FSD Beta’s multi-camera video networks and next-gen planner, that allows for more complex agent interactions with less reliance on lanes, make way for adding more intelligent behaviors, smoother control and better decision making.
• Improved recall for close-by cut-in cases by 15%, particularly for large trucks and high-yaw rate scenarios, through an additional 30k auto-labeled clips, mined from the fleet. Additionally, expanded and tuned dedicated speed control for cut-in objects.
• Improved the position of ego in wide lanes, by biasing in the direction of the upcoming turn to allow other cars to maneuver around ego.
• Improved handling during scenarios with high curvature or large trucks by offsetting in lane to maintain safe distances to other vehicles on the road and increase comfort.
• Improved behavior for path blockage lane changes in dense traffic. Ego will now maintain more headway in blocked lanes to hedge for possible gaps in dense traffic.
• Improved lane changes in dense traffic scenarios by allowing higher acceleration during the alignment phase. This results in more natural gap selection to overtake adjacent lane vehicles very close to ego.
• Made turns smoother by improving the detection consistency between lanes, lines and road edge predictions. This was accomplished by integrating the latest version of the lane guidance module into the road edge and lines network.
• Improved accuracy for detecting other vehicles’ moving semantics. Improved precision by 23% for cases where other vehicles transition to driving and reduced error by 12% for cases where Autopilot incorrectly detects its lead vehicle as parked. These were achieved by increasing video context in the network, adding more data of these scenarios, and increasing the loss penalty for control-relevant vehicles.
• Extended maximum trajectory optimization horizon, resulting in smoother control for high curvature roads and far away vehicles when driving at highway speeds.
• Improved driving behavior next to row of parked cars in narrow lanes, preferring to offset and staying within lane instead of unnecessarily lane changing away or slowing down.
• Improved back-to-back lane change maneuvers through better fusion between vision-based localization and coarse map lane counts.
• Added text blurbs in the user interface to communicate upcoming maneuvers that FSD Beta plans to make. Also improved the visualization of upcoming slowdowns along the vehicle’s path. Chevrons render at varying opacity and speed to indicate the slowdown intensity, and a solid line appears at locations where the car will come to a stop.
• Improved the recall and precision of object detection, notably reducing the position error of semi-trucks by 10%, increasing the recall and precision of crossing vehicles over 100m away by 3% and 7%, respectively, and increasing the recall of motorbikes by 5%. This was accomplished by implementing additional quality checks in our two million video clip auto labeled dataset.
• Reduced false offsetting around objects in wide lanes and near intersections by improving object kinematics modeling in low-speed scenarios.

© 2023, Eva Fox | Tesmanian. All rights reserved.

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