Tesla Improves FSD Software by Using Vector Space Lanes

Andrej Karpathy, Director of artificial intelligence and Autopilot Vision at Tesla, describes the improvements to Tesla FSD Beta 10.11 Tesla FSD 10.11 is using vecotr spaces lanes to reduce errors and have minimizes phantom breaking. FSD Beta 10.11 release notes. Fave item:”Upgraded modeling of lane geometry from dense rasters (“bag of points”) to an autoregressive…
Tesla Improves FSD Software by Using Vector Space Lanes


Andrej Karpathy, Director of artificial intelligence and Autopilot Vision at Tesla, describes the improvements to Tesla FSD Beta 10.11

Tesla FSD 10.11 is using vecotr spaces lanes to reduce errors and have minimizes phantom breaking.

FSD Beta 10.11 release notes. Fave item:

“Upgraded modeling of lane geometry from dense rasters (“bag of points”) to an autoregressive decoder that directly predicts and connects “vector space” lanes point by point using a transformer neural network.” https://t.co/Z6PpYrNiA1

— Andrej Karpathy (@karpathy) March 14, 2022

“This enables us to predict crossing lanes, allows 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.”

— Andrej Karpathy (@karpathy) March 14, 2022

TLDR a GPT-like Transformer is now predicting the lanes and their connectivity. This “direct to vector space” framework allows predictions to be jointly coherent (due to sequential conditioning) and v easily used by planner (due to sparsity). Excellent work from the team!

— Andrej Karpathy (@karpathy) March 14, 2022

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