The Tesla Autonomous Driving Full Beta v10.11 release notes hint at a number of critical improvements to advanced driver assistance software. Tesla FSD Beta 10.11 is currently being distributed to Tesla employees. However, if the system works well, external users should receive the update in the coming days.
The FSD Beta v10.11 release notes describe several notable improvements. Tesla said V10.11 uses more accurate predictions of where other vehicles are turning or merging, reducing unnecessary slowdowns. The company also said version 10.11 should improve vehicle understanding of the right-of-way, which will be invaluable in scenarios where maps turn out to be inaccurate.
More importantly, certain improvements have been made to Vulnerable Road Users (VRUs) in FSD Beta V10.11. Tesla notes that the most recent version of FSD Beta should improve VRU detection by 44.9%, allowing the system to significantly reduce “pedestrian and cyclist false alarms.” The company has been able to achieve these VRU improvements by increasing the size of its next generation labeling machines.
Below are the beta versions of FSD v10.11. release notes.
Early Access Program | FSD Beta 10.11
– Upgraded track geometry modeling from dense rasters (“bag of dots”) to an autoregressive decoder that directly predicts and connects strips of “vector space” point by point using a transducer neural network. This allows us to predict band crossings, provides computationally cheaper and less error-prone post-processing, and paves the way for predicting many other signals and their relationships jointly and end-to-end.
– Use more accurate predictions of where vehicles turn or merge to reduce unnecessary deceleration for vehicles that don’t cross our path.
– Improved understanding of the right-of-way if the map is inaccurate or the vehicle cannot follow the navigator. In particular, intersection extent modeling is now entirely based on network predictions and no longer uses map-based heuristics.
– Improved VRU detection accuracy by 44.9%, significantly reducing false positives for pedestrians and cyclists (especially around bituminous seams, skid marks, and raindrops). This was achieved by increasing the data size of the next generation autolabeler, learning network parameters that were previously frozen, and modifying network loss functions. We found that this reduces the frequency of false slowdowns associated with VRU.
– Reduced speed prediction error for very close motorcycles, scooters, wheelchairs and pedestrians by 63.6%. To do this, we introduced a new dataset of simulated high-speed enemy interactions with the VRU. This update improves autopilot handling around fast moving and crashing VRUs.
– Improved creep profile with higher jerk at the start of creep.
– Improved control of nearby obstacles by predicting a continuous distance to static geometry using a common static obstacle network.
– 17% reduction in the error rate of the ‘parked’ vehicle attribute achieved by increasing the size of the data set by 14%.
– Reduced the speed error in the “clean path” scenario by 5% and the speed error in the “highway” scenario by 10% by tweaking the loss function to improve performance in complex scenarios.
– Improved detection and control of open car doors.
– Improved cornering smoothness by using an optimization-based approach to determine which road lines are irrelevant to control, taking into account lateral and longitudinal acceleration and jerk limits, as well as vehicle kinematics.
– Improved stability of FSD Ul visualizations by optimizing the Ethernet data transfer pipeline by 15%.
Tesla FSD Beta v10.11 is likely to be released as software version number 2022.4.5.15, according to reports from the online electric vehicle community. Benchmarks of v10.11 on real roads are usually posted by FSD beta program members within hours of a system’s mass release.
The Teslarati team would be grateful to hear from you. If you have any advice please contact me at [email protected] or via twitter @Writer_01001101.
Tesla FSD Beta 10.11 release notes tease critical improvements
