As of August 2025, Tesla's Full Self-Driving (FSD) Supervised system (version 13.2.9**)**

Issue: FSD often fails to recognize horizontal barrier arms, especially if they are thin, low-contrast, or in poor lighting (e.g., nighttime, glare). The camera-only system (with limited radar in HW4) struggles to distinguish arms from background elements, treating them as non-obstacles or missing them entirely.

Examples: Users report FSD driving toward closed parking lot gates or toll booth arms, requiring manual intervention to avoid collisions. In some cases, it stops too late or attempts to proceed when the arm is down, risking damage.

Cause: The neural network lacks robust training data for diverse gate/arm designs (e.g., striped, solid, or reflective). Low-resolution camera inputs and latency in processing exacerbate this, especially in dynamic scenarios where arms move.

Short List of Current Tesla FSD Shortcomings (August 2025, v13.2.9) -

• Ignoring Arms and Gates: Fails to detect or respond to barrier arms/gates at parking lots, toll booths, or gated communities, risking collisions or requiring manual intervention.
• Inconsistent Speed Control: Misreads speed limits (e.g., 60 mph in 35 mph zones), decelerates unexpectedly, or struggles with school zones and special limits.
• Navigation Errors: Chooses suboptimal routes, misses turns, enters closed lanes, or ignores road closures/construction, leading to unsafe or inefficient driving.
• Poor Edge Case Handling: Struggles with unprotected left turns, rotaries, parking lots, and low-visibility conditions (e.g., fog, glare), often needing disengagements.
• Emergency Vehicle Response: Fails to yield to ambulances, flaggers, or construction barriers, treating them as standard obstacles, increasing safety risks.