Tesla FSD Supervised Hits 8B Miles
🏠#autonomous-driving#fleet-data#long-tailFreshcollected in 24m

Tesla FSD Supervised Hits 8B Miles

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💡Tesla FSD's 8B miles nears 10B unsupervised threshold—vital data scale benchmark for AV AI researchers.

⚡ 30-Second TL;DR

What changed

FSD Supervised total driven by owners surpasses 8 billion miles (~12.875B km).

Why it matters

Boosts Tesla's lead in AV data scale, pressuring rivals to scale fleet data collection. Enables better handling of rare edge cases via real-world miles over simulation.

What to do next

Benchmark your AV dataset growth against Tesla's 1B-mile jump in months using fleet telemetry tools.

Who should care:Researchers & Academics

🧠 Deep Insight

Web-grounded analysis with 7 cited sources.

🔑 Key Takeaways

  • Tesla FSD Supervised has surpassed 8.2 billion miles driven by owners, with over 3 billion miles in city conditions, accelerating from 7 billion miles reached on December 27, 2025[1][2][3].
  • Safety data shows FSD Supervised achieves 7X fewer major and minor collisions and 5X fewer off-highway collisions compared to U.S. national averages, with 5.3 million miles per crash in North America[1][2][5].
  • The system processes over one million pixels of visual data every millisecond via eight external cameras for a 360-degree real-time view, improving road safety by over 80%[2].
📊 Competitor Analysis▸ Show
FeatureTesla FSD SupervisedWaymo
Miles Driven8.2B+ (supervised, customer fleet)Not specified in results; Tesla trails in some unsupervised benchmarks [5]
Miles per Major Collision5.3M (North America)U.S. avg: 0.66M [5]
Pricing$99/mo subscription; outright for some modelsRide-hailing service, no ownership pricing [5]
StatusSupervised (SAE Level 2)Unsupervised robotaxi in select areas

Note: Limited direct competitor benchmarks in results; Waymo mentioned as leading in unsupervised ops[5].

🛠️ Technical Deep Dive

  • FSD Supervised uses end-to-end AI models trained on real-world fleet data capturing long-tail scenarios (rare, complex events) beyond simulations[3].
  • Vision-based system with eight external cameras providing 360-degree coverage, processing >1 million pixels per millisecond for real-time environmental response[2].
  • Passive and active safety features avoid ~1.9 million potential injuries annually, with >80% road safety improvement claimed[6].
  • Cumulative data differentiates from total FSD miles; supervised miles heavily contribute to training for unsupervised capability[3].

🔮 Future ImplicationsAI analysis grounded in cited sources

The 8.2B mile milestone bolsters Tesla's data advantage for AI training, potentially enabling unsupervised FSD at 10B miles, accelerating robotaxi deployment like Cybercab and challenging competitors in scalable autonomy while highlighting supervised safety gains over human drivers.

⏳ Timeline

2025-12
FSD Supervised reaches 7 billion cumulative miles
2026-02
FSD Supervised surpasses 8 billion miles (confirmed 8.2B with safety stats)

📎 Sources (7)

Factual claims are grounded in the sources below. Forward-looking analysis is AI-generated interpretation.

  1. benzinga.com
  2. teslanorth.com
  3. teslarati.com
  4. longbridge.com
  5. stocktwits.com
  6. teslaoracle.com
  7. tesery.com

Tesla announced FSD Supervised cumulative mileage exceeds 8 billion miles, up from 7 billion in December 2024. This data accelerates training for unsupervised Full Self-Driving. Elon Musk states 10 billion miles needed to handle complex long-tail scenarios.

Key Points

  • 1.FSD Supervised total driven by owners surpasses 8 billion miles (~12.875B km).
  • 2.Milestone reached months after 7B miles on Dec 27, 2024, with accelerating pace.
  • 3.Data captures real-world long-tail scenarios for end-to-end AI model training.
  • 4.Musk: 10B miles threshold for safe unsupervised FSD.

Impact Analysis

Boosts Tesla's lead in AV data scale, pressuring rivals to scale fleet data collection. Enables better handling of rare edge cases via real-world miles over simulation.

Technical Details

Fleet data from supervised drives trains end-to-end AI models on long-tail events un-reproducible in simulation. Rapid accumulation supports iterative model optimization.

📰

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Original source: IT之家