Tesla FSD Beta V10.5 Release Notes Leak Shows Significant Improvement on Road to Single Stack V11

Eva Fox by Eva Fox November 21, 2021

Tesla FSD Beta V10.5 Release Notes Leak Shows Significant Improvement on Road to Single Stack V11

Image: @DirtyTesla/Twitter

Tesla is preparing to soon roll out the FSD Beta V10.5 update, which contains noticeable and welcomed improvements. Release notes have recently leaked, detailing the major changes, some of which have been eagerly anticipated by owners.

Tesla is steadily approaching achieving Level 5 autonomy by improving its Full Self-Driving (FSD) Beta. A number of improvements that have come with updates indicate significant ongoing progress in this. At the moment, according to Elon Musk, the FSD Beta version is not so good, in part because it still uses a separate stack for city streets and highway driving. Moving to a single FSD stack requires a huge amount of neural network training, which is what Tesla's team is currently doing.

The leaked release note for the new FSD Beta V10.5 update indicates that the team has made great progress in merging stacks. While this is still not over at this point, it seems that the main effort will now be directed towards it. Here is the FSD Beta v10.5 Release Notes:

  • Improved VRU (pedestrians, bicyclists, motorcycles) crossing velocity error by 20% from improved quality in our auto-labeling.
  • Improved static world predictions (road lines, edges, and lane connectivity) by up to 13% using a new static world auto-labeler and adding 165K auto-labeled videos.
  • Improved cone and sign detections by upreving the generalized static object network with 15K more video clips and adjusting oversampling and overweighting strategies (+ 4.5% precision, + 10.4% recall).
  • Improved cut-in detection network by 5.5% to help reduce false slowdowns.
  • Enabled "emergency collision avoidance maneuvering" in shadow mode
  • Enabled behavior to lane change away from merges when safe to do so.
  • Improved merge object detection recall by using multi-modal object prediction at intersections.
  • Improved control for merges by increasing smoothness of arrival time constraints and considering possible merging objects beyond visibility.
  • Improved lane changes by allowing larger deceleration limit in short-deadline situations.
  • Improved lateral control for creeping forward to get more visibility.
  • Improved modeling of road boundaries on high curvature roads for finer maneuvers.
  • Improved logic to stay on-route and avoid unnecessary detours/rerouting.

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Article edited by @SmokeyShorts, you can follow him on Twitter






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