Tesla FSD V13 — Occupancy + 4D World
https://www.thinkautonomous.ai/blog/occupancy-networks/
Single end-to-end neural network ingesting multi-camera video + navigation + ego-motion. Maintains evolving 3D occupancy volume (voxel-level) + 3D Gaussian rendering as internal world state — spatial memory persisting across frames; tracks objects that temporarily leave the camera FOV.
At a glance
- Type
- 3D occupancy network + temporal multi-modal fusion
- Tier
- T1
- Created
- 2024 (FSD v13 first released December 2024 to early access HW4 vehicles; wide release mid-December 2024)
- Latest release
- not applicable — not OSS
- License
- not applicable — not OSS
- GitHub
- not applicable — no GitHub repo
- Pricing
- $99/month subscription (as of Apr 2024); purchase price $8000 (discontinued Feb 2026 — subscription only)
- Funding
- Public company (TSLA)
Taxonomy
- storage
- vector
- retrieval
- attention
- persistence
- session
- update
- overwrite
- unit
- episode
- governance
- opaque
- conflict
- overwrite
When to use
Optimised for: real-time perception + spatial reasoning + multi-modal sensor fusion
Anti-fit: not for non-embodied / non-physical use cases
Pros & cons
Pros
Largest fleet-driven memory in any robotic system — millions of vehicles producing data daily; 4D occupancy is the memory format.
Cons
Tesla-only; closed system; safety / regulatory track record is contested.
Claims & capabilities
70k GPU-hours per training cycle. 1.5 PB fleet data from 4M+ vehicles.
Technical surface
- API surface
- searched not found
- Backend storage
- searched not found
- Deployment
- HW4/AI4 Tesla vehicles only (on-device)
- Embedding model
- searched not found
- Multi-tenancy
- searched not found
- MCP
- no MCP support advertised — vertical product, no MCP server / client integration documented
- A2A
- no A2A protocol support advertised — vertical product, no A2A integration documented
- OpenTelemetry
- no OpenTelemetry integration advertised — vendor logs/observability not publicly documented
Similar systems
Other vertical / domain-specific ai memory in the catalog, ranked by inbound references.
- NVIDIA ReMEmbR T3
Builds long-horizon memory by captioning video segments with VILA, storing captions with timestamps + 3D position coordinates in MilvusDB. At query time, LLM iterates retrieval across text, time, and position modalities. Deployed on Nova Carter robot (Jetson Orin).
- Abridge T1
Clinician-assist ambient documentation. Source mapping: every AI-generated summary element traced back to the source utterance. Audit-and-trust layer over episodic memory. Built on proprietary 1.5M+ medical-encounter dataset.
- ASAPP GenerativeAgent T1
Treats memory as first-class architecture. Captures the digital footprint of every interaction; retrieves preference and history at engagement time. Public example: airline knowing a frequent flyer wants aisle seats with her son — preference-aware, not just history-lookup.
- BenevolentAI T1
Target identification / drug repurposing / mechanism tracing. 85+ data sources, petabyte-scale, rebuilt every few weeks. Wet-lab results re-enter the graph and shift downstream predictions — institutional experimental memory closing a feedback loop.
- Causaly T1
Drug discovery / target identification / causal mechanism tracing. The graph is the memory: 7 years of curated biomedical cause-effect relationships compounding with each new ingestion. Scientific RAG retrieves from a versioned causal substrate.
- Character.ai T1
Chat Memories (user-defined facts), auto-memories for c.ai+ subscribers, pinned memories, in-context retention. PipSqueak 2 model (April 2026) reduces in-conversation drift. Memory Visualization meter forthcoming.