pith:HCABSCBY
Enhancing End-to-End Autonomous Driving with Latent World Model
LAW uses self-supervised prediction of future scene features to strengthen end-to-end autonomous driving planners.
arxiv:2406.08481 v2 · 2024-06-12 · cs.CV
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Claims
LAW achieves state-of-the-art performance across multiple benchmarks, including real-world open-loop benchmark nuScenes, NAVSIM, and simulator-based closed-loop benchmark CARLA.
That the self-supervised future-feature prediction task will reliably improve downstream trajectory prediction quality in both open-loop and closed-loop settings without introducing new failure modes.
LAW introduces a self-supervised prediction task on latent scene features that boosts end-to-end driving performance on nuScenes, NAVSIM, and CARLA benchmarks.
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| First computed | 2026-05-17T23:38:14.696860Z |
|---|---|
| Builder | pith-number-builder-2026-05-17-v1 |
| Signature | Pith Ed25519
(pith-v1-2026-05) · public key |
| Schema | pith-number/v1.0 |
Canonical hash
38801908384b2c1a814b2185035bcd215ec3e578788fa9efbb72e76077949627
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· · · · ·Agent API
Verify this Pith Number yourself
curl -sH 'Accept: application/ld+json' https://pith.science/pith/HCABSCBYJMWBVAKLEGCQGW6NEF \
| 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: 38801908384b2c1a814b2185035bcd215ec3e578788fa9efbb72e76077949627
Canonical record JSON
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