Pith Number

pith:EW4FUYWR

pith:2026:EW4FUYWRUEYBRX33LVRQ7PC7FP

not attested not anchored not stored refs resolved

Still Camouflage, Moving Illusion: View-Induced Trajectory Manipulation in Autonomous Driving

Feng Liu, Haotang Li, Huashan Chen, Kebin Peng, Qingzhao Zhang, Sen He, Shuo Ju, Wanqian Zhang, Xuheng Wang

A static camouflage on one vehicle can make passing autonomous cars see false cut-in trajectories and brake hard.

arxiv:2605.12743 v1 · 2026-05-12 · cs.CR · cs.CV

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\usepackage{pith}
\pithnumber{EW4FUYWRUEYBRX33LVRQ7PC7FP}

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Record completeness

1 Bitcoin timestamp

2 Internet Archive

3 Author claim open · sign in to claim

4 Citations open

5 Replications open

✓ Portable graph bundle live · download bundle · merged state

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

We demonstrate the novel attack on nuScenes dataset, showing the effectiveness with an end-to-end success rate of up to 87.5%, measured by hard-braking events, and robustness across different scene backgrounds, victim vehicle speeds, and perception models.

C2weakest assumption

The assumption that view-dependent feature drift from a static camouflage will reliably propagate through the full perception-to-planning pipeline in real-world lighting, sensor noise, and diverse perception models beyond the tested ones.

C3one line summary

Static adversarial camouflage exploits natural view-angle changes during relative motion to induce consistent feature drift in AV perception, leading to incorrect trajectory predictions and unnecessary braking.

References

56 extracted · 56 resolved · 2 Pith anchors

[1] Baidu Apollo.https://apollo.baidu.com/, 2022 2022

[2] Nvidia-alpamayo.https://www.nvidia.cn/solutions/autonomous-vehicles/alpamayo/, 2026 2026

[3] Tesla Full Self-Driving.https://www.tesla.com/fsd, 2026 2026

[4] Synthesizing robust adversarial examples 2018

[5] Transfusion: Robust lidar-camera fusion for 3d object detection with transformers 2022

Receipt and verification

First computed	2026-05-18T03:09:49.063552Z
Builder	pith-number-builder-2026-05-17-v1
Signature	Pith Ed25519 (`pith-v1-2026-05`) · public key
Schema	pith-number/v1.0

Canonical hash

25b85a62d1a13018df7b5d630fbc5f2bf4564fa696995fe08cc5b1da7cc3f1e2

Aliases

arxiv: 2605.12743 · arxiv_version: 2605.12743v1 · doi: 10.48550/arxiv.2605.12743 · pith_short_12: EW4FUYWRUEYB · pith_short_16: EW4FUYWRUEYBRX33 · pith_short_8: EW4FUYWR

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Resolver JSON Graph JSON Events JSON Schema Signing key

Verify this Pith Number yourself

curl -sH 'Accept: application/ld+json' https://pith.science/pith/EW4FUYWRUEYBRX33LVRQ7PC7FP \
  | 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: 25b85a62d1a13018df7b5d630fbc5f2bf4564fa696995fe08cc5b1da7cc3f1e2

Canonical record JSON

{
  "metadata": {
    "abstract_canon_sha256": "0d0dbd3cafb571e1c07477a5c276f4e7191f41c65f512564abeb35dbf972a6c0",
    "cross_cats_sorted": [
      "cs.CV"
    ],
    "license": "http://creativecommons.org/licenses/by/4.0/",
    "primary_cat": "cs.CR",
    "submitted_at": "2026-05-12T20:47:55Z",
    "title_canon_sha256": "2a0a7e80e142c1624a3e049ad34dcea7e05623475f177598fd68a0075a0bdf31"
  },
  "schema_version": "1.0",
  "source": {
    "id": "2605.12743",
    "kind": "arxiv",
    "version": 1
  }
}