Pith Number

pith:P4KL45MZ

pith:2024:P4KL45MZHTYCWF6VQIQW3PQDWV

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Senna: Bridging Large Vision-Language Models and End-to-End Autonomous Driving

Bencheng Liao, Bo Jiang, Chang Huang, Qian Zhang, Shaoyu Chen, Wei Yin, Wenyu Liu, Xinggang Wang, Xingyu Zhang

Senna uses a large vision-language model for natural language driving plans that an end-to-end model converts into precise trajectories.

arxiv:2410.22313 v1 · 2024-10-29 · cs.CV · cs.RO

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Record completeness

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2 Internet Archive

3 Author claim open · sign in to claim

4 Citations open

5 Replications open

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The bundle contains the canonical record plus signed events. A mirror can host it anywhere and recompute the same current state with the deterministic merge algorithm.

Claims

C1strongest claim

Senna achieves state-of-the-art planning performance. Notably, with pre-training on a large-scale dataset DriveX and fine-tuning on nuScenes, Senna significantly reduces average planning error by 27.12% and collision rate by 33.33% over model without pre-training.

C2weakest assumption

That natural-language planning outputs from the LVLM can be translated into low-level trajectories by the E2E model without introducing critical errors or losing necessary detail in complex or rare scenarios.

C3one line summary

Senna decouples language-based high-level planning from an LVLM with low-level trajectory prediction from an E2E model, reporting 27% lower planning error and 33% lower collisions after pre-training on DriveX and fine-tuning on nuScenes.

References

73 extracted · 73 resolved · 12 Pith anchors

[1] Detr3d: 3d object detection from multi-view images via 3d-to-2d queries, 2022

[2] Y . Hu, J. Yang, L. Chen, K. Li, C. Sima, X. Zhu, S. Chai, S. Du, T. Lin, W. Wang et al., “Planning-oriented autonomous driving,” in CVPR, 2023 2023

[3] Vad: Vectorized scene representation for efficient autonomous driving, 2023

[4] Lift, splat, shoot: Encoding images from arbitrary camera rigs by implicitly unprojecting to 3d, 2020

[5] arXiv preprint arXiv:2203.17270 (2022) 2022

Formal links

3 machine-checked theorem links

Cited by

36 papers in Pith

Distill to Think, Foresee to Act: Cognitive-Physical Reinforcement Learning for Autonomous Driving

SpaceDrive: Infusing Spatial Awareness into VLM-based Autonomous Driving

LVDrive: Latent Visual Representation Enhanced Vision-Language-Action Autonomous Driving Model

Structured Labeling Enables Faster Vision-Language Models for End-to-End Autonomous Driving

MAPLE: Latent Multi-Agent Play for End-to-End Autonomous Driving

Receipt and verification

First computed	2026-05-17T23:38:51.101947Z
Builder	pith-number-builder-2026-05-17-v1
Signature	Pith Ed25519 (`pith-v1-2026-05`) · public key
Schema	pith-number/v1.0

Canonical hash

7f14be75993cf02b17d582216dbe03b5642e447c32af17337aa20574e8bcd085

Aliases

arxiv: 2410.22313 · arxiv_version: 2410.22313v1 · doi: 10.48550/arxiv.2410.22313 · pith_short_12: P4KL45MZHTYC · pith_short_16: P4KL45MZHTYCWF6V · pith_short_8: P4KL45MZ

Agent API

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Verify this Pith Number yourself

curl -sH 'Accept: application/ld+json' https://pith.science/pith/P4KL45MZHTYCWF6VQIQW3PQDWV \
  | 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: 7f14be75993cf02b17d582216dbe03b5642e447c32af17337aa20574e8bcd085

Canonical record JSON

{
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    "abstract_canon_sha256": "4db2bef8e97b5fbee9ad57a0aa57ecbe04b66fad10f3907cac3add794caeb3b9",
    "cross_cats_sorted": [
      "cs.RO"
    ],
    "license": "http://creativecommons.org/licenses/by-nc-sa/4.0/",
    "primary_cat": "cs.CV",
    "submitted_at": "2024-10-29T17:53:56Z",
    "title_canon_sha256": "ae2cf11612e74b8675dfee265a59fcc34206c63f3a805027932443606d76fa4d"
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  "source": {
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    "kind": "arxiv",
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