pith:Q55FZGYV
LIDSA: Cognitive Arbitration for Signal-Free Autonomous Intersection Management
An LLM can manage autonomous intersections without traffic signals by arbitrating vehicle intents and priorities.
arxiv:2605.12321 v2 · 2026-05-12 · cs.AI · cs.CY · cs.ET
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\pithnumber{Q55FZGYVNOM2AY4FYUXFAMSEUO}
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Record completeness
Claims
Results show that LISA reduces mean control delay by up to 89.1% and maintains Level of Service C while all non-LLM baselines degrade to Level of Service F. Under near-saturated demand, LISA reduces mean waiting time by 93% and peak queue length by 60.6% relative to fixed-cycle control. It also lowers fuel consumption by up to 48.8% and achieves 86.2% intent satisfaction.
LLM inference can be executed with low enough latency to support real-time sub-second speed advisory decisions despite the acknowledged limits of current LLM inference speeds for sub-second control.
LISA applies LLMs as primary decision-makers for signal-free intersection management, cutting mean control delay by up to 89.1% and maintaining better service levels than fixed-cycle, SCATS, AIM, or GLOSA baselines.
Formal links
Receipt and verification
| First computed | 2026-05-21T01:05:21.082183Z |
|---|---|
| Builder | pith-number-builder-2026-05-17-v1 |
| Signature | Pith Ed25519
(pith-v1-2026-05) · public key |
| Schema | pith-number/v1.0 |
Canonical hash
877a5c9b156b99a06385c52e503244a39aabd9b1657d0a9ac9095ce5f9e128be
Aliases
· · · · ·Agent API
Verify this Pith Number yourself
curl -sH 'Accept: application/ld+json' https://pith.science/pith/Q55FZGYVNOM2AY4FYUXFAMSEUO \
| 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: 877a5c9b156b99a06385c52e503244a39aabd9b1657d0a9ac9095ce5f9e128be
Canonical record JSON
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"license": "http://creativecommons.org/licenses/by/4.0/",
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