Tesla FSD to Learn Driver Habits for Personalized Autopilot

Elon Musk confirmed that Tesla's Full Self-Driving (FSD) software is shifting from a uniform driving logic to a personalized system. In the future, FSD will use neural networks to learn each owner’s driving habits and preferences, adjusting its behavior accordingly. Previously, FSD employed the same strategy across all vehicles; the new approach will enable the system to support multiple response options for the same scenario. A user recently reported on social media that FSD often unnecessarily exits high-occupancy vehicle (HOV) lanes into congested traffic. Musk responded that the system will begin logging manual interventions and optimizing decisions based on individual preferences. Tesla already offers HOV lane usage settings with three options: “Auto,” “Yes,” and “No.” In “Auto” mode, the cabin camera determines whether occupancy requirements are met; “Yes” forces HOV lane usage, while “No” disables it entirely. Additionally, after FSD takes over, a feedback menu appears to collect reasons for driver takeovers, which helps improve system performance. Parking remains one of the primary causes of driver intervention today. Musk stated that FSD will eventually learn users’ preferred parking methods—such as backing in or parking nose-first—at frequently visited locations like home, the office, or school. This capability is expected to debut in parking scenarios first and gradually expand to other driving situations. The next-generation consumer version, FSD v15, is anticipated to launch later this year or early next year, featuring a model with approximately 10 times more parameters.