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FSD Beta V11 partial release notes mention single stack for highways only, Smart Summon to come next month, says Musk

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It has been almost 24 hours since Tesla started releasing the much-anticipated single-stack FSD Beta V11 software update. Immediately after the update rolled out, Tesla owners and enthusiasts began looking for the release notes. But until now the community could get their hands on partial notes for the FSD Beta V11 (2022.40.5).

However, the first item of the list of improvements that Tesla has made in FSD Beta 11 confirms that this is actually the single-stack software update that everyone was waiting for. But there is a catch, Tesla only mentions single stack for highways not city streets (release notes below).

Although Elon Musk just answered on Twitter that Actually Smart Summon will probably be deployed next month, he did not talk about single stack for navigating city streets on Autopilot.

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FSD Beta V11 (2022.40.5) 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 setup 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 Occupancy Network’s recall for close-by obstacles and precision in severe weather conditions with 4x increase in transformer spatial resolution, 20% increase in image featurizer capacity, improved side camera calibration, and 260k more video training clips (real-world and simulation).
  • Reduced the predicted velocity error of very close-by motorcycles, scooters, wheelchairs, and pedestrians by 63.6%. To do this, we introduced a new dataset of simulated adversarial high-speed VRU interactions. This update improves autopilot control around fast-moving and cutting-in VRUs.
  • Improved creeping profile with higher jerk when creeping starts and ends.
  • Improved control for nearby obstacles by predicting continuous distance to static geometry with the general static obstacle network.
  • Reduced vehicle “parked” attribute error rate by 17%, achieved by increasing the dataset size by 14%. Also improved brake light accuracy.
  • Improved clear-to-go scenario velocity error by 5% and highway scenario velocity error by 10%, achieved by tuning loss function targeted at improving performance in difficult scenarios.
  • Improved detection and control for open car doors.
  • Improved smoothness through turns by using an optimization-based approach to decide which road lines are irrelevant for calculations given lateral and longitudinal acceleration and jerk limits as well as vehicle kinematics.
  • Improved stability of the FSD UI visualizations by optimizing ethernet data transfer pipeline by 15%.
  • Improved recall for vehicles directly behind ego, and improved precision for vehicle detection network.

What is Occupancy Network

Elon Musk and his team mentioned the term Occupancy Network a lot during the AI Day 2022 Full Self-Driving presentation.

The above FSD Beta V11 release notes also mention occupancy networks, YouTuber Dr. Know-it-all Knows it all explained what an occupancy network is in the following video — a must-watch for the ones who want a deeper technical understanding of Tesla’s Full Self-Driving algorithms.

Stay tuned as complete release notes become available. Let us know your thoughts in the comments section below.

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Video: Detailed explanation of what a Tesla Full Self-Driving (FSD) occupancy network is.
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By Iqtidar Ali

Iqtidar has been writing about Tesla, Elon Musk, and EVs for more than 3 years on XAutoWorld.com, many of his articles have been republished on CleanTechnica and InsideEVs, maintains a healthy relationship with the Tesla community across the Social Media sphere. You can reach him on Twitter @IqtidarAlii

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