pith:EDZVNKCX
Accelerating Time-Optimal Trajectory Planning for Connected and Automated Vehicles with Graph Neural Networks
A graph neural network provides warm starts that let numerical optimizers compute time-optimal trajectories for connected automated vehicles much faster.
arxiv:2511.20383 v2 · 2025-11-25 · eess.SY · cs.SY
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Record completeness
Claims
The trained model produces online predictions that serve as warm-starts for numerical optimization, thereby enabling rapid computation of minimal exit times and the associated feasible trajectories.
That the graph neural network trained on offline-generated data will generalize to produce effective warm starts for previously unseen online traffic configurations without degrading the quality or feasibility of the resulting optimized trajectories.
A graph isomorphism network with edge features is trained on offline data to provide warm starts for numerical optimization of time-optimal multi-agent trajectories in connected automated vehicles, reducing computation time while preserving performance through replanning.
Formal links
Receipt and verification
| First computed | 2026-05-18T02:44:32.334919Z |
|---|---|
| Builder | pith-number-builder-2026-05-17-v1 |
| Signature | Pith Ed25519
(pith-v1-2026-05) · public key |
| Schema | pith-number/v1.0 |
Canonical hash
20f356a857e65aece28c04879ebff6272f68c601c4494f65b2aa93953989c634
Aliases
· · · · ·Agent API
Verify this Pith Number yourself
curl -sH 'Accept: application/ld+json' https://pith.science/pith/EDZVNKCX4ZNOZYUMASDZ5P7WE4 \
| 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: 20f356a857e65aece28c04879ebff6272f68c601c4494f65b2aa93953989c634
Canonical record JSON
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