L1 ⚛ L1-453 ⊙ Testnet

Vehicle Dynamics (Bicycle Model)

Robotics · Autonomous vehicles

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⬇ principle.md ⬇ principle.json

Fork L1-453

⬇ L1-453.md ⬇ L1-453.json

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You are helping me submit a MODIFICATION of L1-453 (Vehicle Dynamics (Bicycle Model)) to the PWM Protocol — a Principle (L1) artifact.

I will paste a Markdown template (or the current L1-453.md).
1. Rewrite the Markdown so the science is correct and clearly explained for my change.
2. Regenerate the sibling L1-453.json so EVERY field matches the Markdown.
3. Keep the schema in the "File Mapping" footer at the bottom of the MD.
4. Keep the parent reference unchanged unless I ask otherwise.
Rules: the Markdown is the source of truth; use SI units; do NOT invent benchmark scores.
Output each file in its own fenced code block tagged with the filename.

Here is my template:
[PASTE THE .md HERE]

⚙ Forward Model

y = `O.ekf.vehicle_state` `S.tire.pacejka_model` ∂_t x + n,    n ~ 𝒩(0, σ²)

world state x (vehicle state vector)

→

Time derivative ∂_t

→

S · tire · pacejka model `S.tire.pacejka_model`

→

O · ekf · vehicle state `O.ekf.vehicle_state`

→

observation y (N/A detector)

Noise: additive Gaussian noise

Markdown — human-readable source of truth

⚙ auto-generated

⬇ L1-453.md

# ⚛ L1 Principle — Vehicle Dynamics (Bicycle Model) **ID:** `L1-453` · **Status:** ⊙ Testnet (genesis catalog) > **🌐 Domain:** Robotics — *Autonomous vehicles* > **🎯 Problem class:** parameter estimation · **🧮 Solution space:** vehicle state vector > **📡 Carrier:** N/A · **🌫 Noise:** gaussian > **⚖ Difficulty (δ):** 3 · **⛓ Block:** 41555278 --- ## 🧠 1. Introduction **Vehicle Dynamics (Bicycle Model)** is a **parameter-estimation problem** whose unknown lives in **vehicle state vector** space, within the **Autonomous vehicles** sub-domain of **Robotics**. Measurements consist of N/A via a **vehicle state estimation** sensing mechanism. The forward operator applies, in order: time evolution of the state; S · tire · pacejka model operator; O · ekf · vehicle state operator. Observations are corrupted by additive Gaussian noise. Existence of the recovered vehicle_state_vector is guaranteed within the declared Omega bounds. Uniqueness holds on the measurement-supported subspace; out-of-support modes are controlled by declared priors. Stability is conditionally stable (kappa_eff ~= 80); tire_force_saturation dominates the stability cliff; the remaining mismatch parameters contribute higher-order bias terms. Gaussian sets the irreducible data-fidelity floor. ## ⚙ 2. Forward Model Physical chain: **x** → Time derivative → S · tire · pacejka model → O · ekf · vehicle state → **y** (detector). ``` y = `O.ekf.vehicle_state` `S.tire.pacejka_model` ∂_t x + n, n ~ 𝒩(0, σ²) ``` **Measurement DAG:** | Primitive | What it does | |---|---| | `D.time` | Time evolution of the state | | `S.tire.pacejka_model` | S · tire · pacejka model operator | | `O.ekf.vehicle_state` | O · ekf · vehicle state operator | ## 🔬 3. Physics Fingerprint | Property | Value | |---|---| | Domain | Robotics | | Sub domain | Autonomous vehicles | | Carrier | N/A | | Problem class | parameter_estimation | | Solution space | vehicle_state_vector | | Noise model | gaussian | | Integration axis | time | | Difficulty delta | 3 | | L dag | 3 | ## 📡 4. Measurement Model Existence of the recovered vehicle_state_vector is guaranteed within the declared Omega bounds. Uniqueness holds on the measurement-supported subspace; out-of-support modes are controlled by declared priors. Stability is conditionally stable (kappa_eff ~= 80); tire_force_saturation dominates the stability cliff; the remaining mismatch parameters contribute higher-order bias terms. Gaussian sets the irreducible data-fidelity floor. | Metric | Value | |---|---| | Metric | yaw_rate_RMSE_deg_s | | Secondary | side_slip_angle_RMSE_deg | ## 📏 5. Operating Range (Ω) **Center problem class:** `parameter_estimation` · **Forward operator:** `vehicle_state_estimation` **Center point:** | Parameter | Unit | Value | |---|---|---| | Road mu | — | 0.8 | | Speed m s | s | 20 | | Vehicle mass kg | kg | 1500 | | Lateral acceleration m s2 | — | 3 | **Allowed bounds:** | Parameter | Unit | Range | |---|---|---| | Road mu | — | 0.3 – 1.2 | | Speed m s | s | 5 – 50 | | Vehicle mass kg | kg | 500 – 5000 | | Lateral acceleration m s2 | — | 0.5 – 10.0 | ## 🎯 6. Tolerance (ε) **Center tolerance:** 1.0 yaw_rate_RMSE_deg_s | Metric | Range | |---|---| | Yaw rate rmse deg s | 0.1 – 10.0 | ## ⚖ 7. Hardness Function Hardness scales as **`epsilon_fn`** on **yaw_rate_RMSE_deg_s**, with κ = `2000` and δ = `3`. ## 💾 8. Reference Dataset - **primary** · weight 1.0 · IPFS _(not pinned yet)_ ## 9. On-chain Registration - **Chain hash:** `0xc079116e269d4400b1e08b23e98cfacd962ca5f3d799db830a3c0c80b5339c60` - **Chain tx hash:** `0x96bc7f9c7d1a5b61c977fc9baccc0d68aaa3c34c9fccdfc81b6e2b8d132e9333` - **Chain block:** `41555278` --- ## File Mapping This bundle consists of: `L1-453.md`, `L1-453.json`. | File | Role | How to regenerate | |------|------|-------------------| | `L1-453.md` | Source of truth — edit this | Human or LLM | | `L1-453.json` | Structured metadata for the registry | LLM regenerates from the sections above | **Prompt for your LLM after editing this Markdown:** > Read the attached Markdown. Regenerate the sibling `.json` so every field matches. > Preserve the schema documented in the rows above. > Output each file in its own fenced code block tagged with the filename. > Output only the JSON object. _This Markdown was auto-synthesized from the catalog row for `L1-453`._ _Edit it, regenerate the JSON, and submit at [/submit](/submit) to claim the artifact._

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L2-453-001 Vehicle Dynamics (Bicycle Model) — Nominal + Mismatch Specs

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📋 JSON metadata

{
  "artifact_id": "L1-453",
  "chain_block": 41555278,
  "chain_hash": "0xc079116e269d4400b1e08b23e98cfacd962ca5f3d799db830a3c0c80b5339c60",
  "chain_tx_hash": "0x96bc7f9c7d1a5b61c977fc9baccc0d68aaa3c34c9fccdfc81b6e2b8d132e9333",
  "domain": "Robotics",
  "hardness_fn": {
    "delta": 3,
    "kappa": 2000,
    "metric": "yaw_rate_RMSE_deg_s",
    "type": "epsilon_fn"
  },
  "initiator_dataset": [
    {
      "ipfs_cid": null,
      "license_hash": null,
      "name": "primary",
      "weight": 1.0
    }
  ],
  "layer": "L1",
  "observable_profile": {
    "metric": "yaw_rate_RMSE_deg_s",
    "regime": "Existence of the recovered vehicle_state_vector is guaranteed within the declared Omega bounds. Uniqueness holds on the measurement-supported subspace; out-of-support modes are controlled by declared priors. Stability is conditionally stable (kappa_eff ~= 80); tire_force_saturation dominates the stability cliff; the remaining mismatch parameters contribute higher-order bias terms. Gaussian sets the irreducible data-fidelity floor.",
    "secondary": "side_slip_angle_RMSE_deg"
  },
  "physics_fingerprint": {
    "L_DAG": 3.0,
    "carrier": "N/A",
    "difficulty_delta": 3,
    "domain": "Robotics",
    "integration_axis": "time",
    "noise_model": "gaussian",
    "primitives": [
      "D.time",
      "S.tire.pacejka_model",
      "O.ekf.vehicle_state"
    ],
    "problem_class": "parameter_estimation",
    "sensing_mechanism": "vehicle_state_estimation",
    "solution_space": "vehicle_state_vector",
    "sub_domain": "Autonomous vehicles",
    "title": "Vehicle Dynamics (Bicycle Model)"
  },
  "size_tiers": {
    "allowed_forward_operators": [
      "vehicle_state_estimation"
    ],
    "allowed_omega_dimensions": [
      "vehicle_mass_kg",
      "speed_m_s",
      "lateral_acceleration_m_s2",
      "road_mu"
    ],
    "allowed_problem_classes": [
      "parameter_estimation"
    ],
    "center_spec": {
      "epsilon_fn_center": "1.0 yaw_rate_RMSE_deg_s",
      "forward_operator": "vehicle_state_estimation",
      "input_format": "measurement_only",
      "omega": {
        "lateral_acceleration_m_s2": 3.0,
        "road_mu": 0.8,
        "speed_m_s": 20,
        "vehicle_mass_kg": 1500
      },
      "problem_class": "parameter_estimation"
    },
    "epsilon_bounds": {
      "yaw_rate_RMSE_deg_s": [
        0.1,
        10.0
      ]
    },
    "omega_bounds": {
      "lateral_acceleration_m_s2": [
        0.5,
        10.0
      ],
      "road_mu": [
        0.3,
        1.2
      ],
      "speed_m_s": [
        5,
        50
      ],
      "vehicle_mass_kg": [
        500,
        5000
      ]
    }
  },
  "staked_pwm": 0.0,
  "status": "testnet",
  "sub_domain": "Autonomous vehicles",
  "title": "Vehicle Dynamics (Bicycle Model)"
}

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