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."
"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."
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!