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pith:2024:HCABSCBYJMWBVAKLEGCQGW6NEF
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Enhancing End-to-End Autonomous Driving with Latent World Model

Jiawei He, Lue Fan, Tieniu Tan, Yingyan Li, Yuntao Chen, Yuqi Wang, Zhaoxiang Zhang

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

C1strongest claim

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.

C2weakest assumption

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.

C3one line summary

LAW introduces a self-supervised prediction task on latent scene features that boosts end-to-end driving performance on nuScenes, NAVSIM, and CARLA benchmarks.

References

21 extracted · 21 resolved · 7 Pith anchors

[1] NuPlan: A closed-loop ML-based planning benchmark for autonomous vehicles · arXiv:2106.11810
[2] Navsim: Data-driven non-reactive autonomous vehicle simulation and benchmarking
[3] BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding · arXiv:1810.04805
[4] GAIA-1: A Generative World Model for Autonomous Driving 2025 · arXiv:2309.17080
[5] Planning-oriented autonomous driving

Formal links

2 machine-checked theorem links

Cited by

25 papers in Pith

Receipt and verification
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

Aliases

arxiv: 2406.08481 · arxiv_version: 2406.08481v2 · doi: 10.48550/arxiv.2406.08481 · pith_short_12: HCABSCBYJMWB · pith_short_16: HCABSCBYJMWBVAKL · pith_short_8: HCABSCBY
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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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    "license": "http://creativecommons.org/licenses/by/4.0/",
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    "submitted_at": "2024-06-12T17:59:21Z",
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