Tesla FSD phantom braking and obstacle hallucination — AI perception failures at highway speed
Date: 2024-11
Affected: Tesla FSD — 2.4 million vehicles under NHTSA investigation
Attack vector: rce
Category: real-world
Severity: Critical

NHTSA expanded its investigation into Tesla Full Self-Driving (FSD) after hundreds of reports of phantom braking events — the AI vision system hallucinating obstacles (shadows, overpasses, road markings) and applying emergency braking at highway speed. The investigation covered 2.4 million vehicles. Multiple rear-end collisions resulted from unexpected braking. The investigation questioned whether the pure-vision AI perception system (no LiDAR, no radar) could reliably distinguish real obstacles from visual artifacts.

Impact: NHTSA investigation; multiple collisions; regulatory scrutiny of pure-vision AI perception; recall risk

Mitigations:
Multi-sensor fusion (vision + radar + LiDAR) to validate perception outputs
Confidence thresholds for safety-critical actuations — require high certainty before emergency braking
Edge case testing with adversarial and ambiguous visual scenarios
Human override prominence and driver monitoring for attention

Architectural layers (MAESTRO):
label: Foundation Models
notes: Vision model hallucinating obstacles from ambiguous visual inputs (shadows, markings)
role: origin
label: Evaluation & Observability
notes: Edge cases in perception not caught by simulation-based testing
role: blind-spot
label: Deployment & Infrastructure
notes: Safety-critical actuation (emergency braking) triggered by hallucinated inputs
role: impact