Pith Number

pith:3H5SWNI2

pith:2025:3H5SWNI2YTDKLUXUJXSHHLPR3K

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System Identification for Dynamic Modeling of Large Steering Angle Vehicles

Giancarlo Ferrari Trecate, Simone Baratto, Tobias Petri

Physics-informed neural networks model large-steering-angle vehicle dynamics more accurately than pure physics baselines at lower computational cost.

arxiv:2512.02803 v2 · 2025-12-02 · eess.SY · cs.SY

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\pithnumber{3H5SWNI2YTDKLUXUJXSHHLPR3K}

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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

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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

physics-informed neural network models surpass the purely physical baseline in accuracy at lower computational cost.

C2weakest assumption

That the modified planar bicycle models combined with the chosen identification techniques adequately capture the dynamics of large steering angles without unstated limitations in the experimental data.

C3one line summary

Physics-informed neural network models for large-steering-angle vehicle dynamics outperform purely physical baselines in accuracy while using less computation.

References

22 extracted · 22 resolved · 0 Pith anchors

[1] Chen, J., Yu, C., and Wang, Y. (2024). Hybrid modeling for vehicle lateral dynamics via agru with a dual-attention mechanism under limited data. Control Engineering Practice, 151, 106015 2024

[2] Chrosniak, J., Ning, J., and Behl, M. (2024). Deep dynamics: Vehicle dynamics modeling with a physics-constrained neural network for autonomous racing. IEEE Robotics and Automation Letters, 9(6), 5292 2024

[3] de Silva, B., Champion, K., and Quade, M. (2020). Pysindy: A python package for the sparse identification of nonlinear dynamical systems from data. Journal of Open Source Software, 5(49), 2104 2020

[4] Frogerais, P., Bellanger, J.J., and Senhadji, L. (2012). Various ways to compute the continuous-discrete extended kalman filter. IEEE Transactions on Automatic Control, 57(4), 1000--1004 2012

[5] Ghosn, A.B., Polack, P., and de La Fortelle, A. (2024). The hybrid extended bicycle: A simple model for high dynamic vehicle trajectory planning 2024

Formal links

2 machine-checked theorem links

Receipt and verification

First computed	2026-05-17T23:39:04.595014Z
Builder	pith-number-builder-2026-05-17-v1
Signature	Pith Ed25519 (`pith-v1-2026-05`) · public key
Schema	pith-number/v1.0

Canonical hash

d9fb2b351ac4c6a5d2f44de473adf1da907205e6fe52068fec1175183083ba53

Aliases

arxiv: 2512.02803 · arxiv_version: 2512.02803v2 · doi: 10.48550/arxiv.2512.02803 · pith_short_12: 3H5SWNI2YTDK · pith_short_16: 3H5SWNI2YTDKLUXU · pith_short_8: 3H5SWNI2

Agent API

Resolver JSON Graph JSON Events JSON Schema Signing key

Verify this Pith Number yourself

curl -sH 'Accept: application/ld+json' https://pith.science/pith/3H5SWNI2YTDKLUXUJXSHHLPR3K \
  | 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: d9fb2b351ac4c6a5d2f44de473adf1da907205e6fe52068fec1175183083ba53

Canonical record JSON

{
  "metadata": {
    "abstract_canon_sha256": "606925dbc657189e46d1c94ed1656bacd4cac7d659b9ed3a00ccfd7be7ed6032",
    "cross_cats_sorted": [
      "cs.SY"
    ],
    "license": "http://creativecommons.org/licenses/by/4.0/",
    "primary_cat": "eess.SY",
    "submitted_at": "2025-12-02T14:19:34Z",
    "title_canon_sha256": "aeb3ae8223c4749b6fe2ac700a891c62b3dd4c74309eab8449cc20bd616ed181"
  },
  "schema_version": "1.0",
  "source": {
    "id": "2512.02803",
    "kind": "arxiv",
    "version": 2
  }
}