The evolution of Tesla’s FSD suite has been consistently defined by a singular engineering philosophy. The physical world can be navigated and interacted with best through pure vision and generalized artificial intelligence. In previous installments of our How FSD Works Series, we explored how FSD’s underlying neural networks construct three-dimensional spaces out of two-dimensional pixels and track moving objects through time. While that’s all very complex, one of the most persistent hurdles in autonomy doesn’t involve dynamic hazards; rather, it involves understanding the invisible, shifting, and human-oriented logic of road architecture. When an autonomous vehicle approaches a complex, unmapped, multi-lane intersection, it must determine which lane connects to which exit vector across the intersection. Competitors like Waymo and Zoox attempt to solve this by anchoring themselves to brittle, high-definition (HD) localized maps. Tesla, conversely, treats the problem as a living puzzle. According to a recent patent application, US 2026/0170852 A1, titled "Vision-Based Machine Learning Model for Lane Connectivity in Autonomous or Semi-Autonomous Driving," Tesla has unlocked this capability by repurposing an architecture typically reserved for generative AI and LLMs: the autoregressive transformer. Tokenizing Asphalt To understand how Tesla’s lane connectivity engine operates, it helps to look at how Large Language Models (LLMs) compose sentences. When a system like Grok generates text, it does not produce a complete paragraph at once. Instead, it breaks language down into discrete fragments called tokens, which are syllables or words, and predicts the most statistically logical next token based on the context of the tokens that preceded it. Tesla is applying this exact logic to road geometry. As the vehicle traverses a real-world environment, the vision suite streams raw imagery into FSD’s neural networks. These raw pixels are filtered through backbone networks and fused via a cross-attention transformer into a three-dimensional, top-down vector space known