pith:ZJBNQPUO
FutureSightDrive: Thinking Visually with Spatio-Temporal CoT for Autonomous Driving
Generating one future visual frame lets driving models plan trajectories by preserving spatial and temporal details that text chains of thought discard.
arxiv:2505.17685 v3 · 2025-05-23 · cs.CV
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\usepackage{pith}
\pithnumber{ZJBNQPUONLDWV5Z3AE6W474BSZ}
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Claims
This imagined scene serves as the visual spatio-temporal CoT, capturing both spatial structure and temporal evolution in a single representation. ... our visual spatio-temporal CoT bridges the perception-planning gap, enabling safer, more anticipatory autonomous driving.
The generated future frame is physically plausible and contains the exact spatio-temporal cues needed for accurate inverse-dynamics planning; if the predicted lanes or boxes are systematically wrong, the planning step will inherit those errors.
FSDrive uses a generated future scene frame as visual spatio-temporal CoT to improve VLA models for safer autonomous driving trajectory prediction.
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| First computed | 2026-05-17T23:38:50.443630Z |
|---|---|
| Builder | pith-number-builder-2026-05-17-v1 |
| Signature | Pith Ed25519
(pith-v1-2026-05) · public key |
| Schema | pith-number/v1.0 |
Canonical hash
ca42d83e8e6ac76af73b013d6e7f8196479dcf2303c0a3983b4480d1ef4374d8
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· · · · ·Agent API
Verify this Pith Number yourself
curl -sH 'Accept: application/ld+json' https://pith.science/pith/ZJBNQPUONLDWV5Z3AE6W474BSZ \
| jq -c '.canonical_record' \
| python3 -c "import sys,json,hashlib; b=json.dumps(json.loads(sys.stdin.read()), sort_keys=True, separators=(',',':'), ensure_ascii=False).encode(); print(hashlib.sha256(b).hexdigest())"
# expect: ca42d83e8e6ac76af73b013d6e7f8196479dcf2303c0a3983b4480d1ef4374d8
Canonical record JSON
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