Vehicle Dynamics (Bicycle Model) L1-453
Unclaimed Principle — open for contribution
This Principle is declared in the catalog but has no reference solver, no pinned dataset, and is not registered on-chain. There is no reward pool. Submitting a cert against this Principle today will record the cert for reproducibility but pay zero PWM.
To claim it as a Bounty #7 contribution: open a PR adding (1) a reference solver, (2) ≥1 dataset pinned to IPFS, (3) updates to the L3 manifest with dataset CIDs. After verifier-agent triple-review, the founders' 3-of-5 multisig signs PWMRegistry.register() and the Principle becomes mineable.
Forward model E
Vehicle Dynamics (Bicycle Model): Vehicle dynamics: estimate vehicle state (yaw rate, side-slip) from IMU and wheel encoders using bicycle model. The forward operator produces the measurement through a 3-node primitive DAG (M.bicycle.lateral_dynamics…); recovery is posed as a parameter_estimation problem. Difficulty tier delta=3 with effective condition number kappa_eff~80; tire_force_saturation, road_friction_uncertainty set the accuracy floor at the Omega boundary. See the forward_model field for the closed-form equation.
L-DAG
Well-posedness W
- Existence:
- true
- Uniqueness:
- true
- Stability:
- conditional
- κ:
- 2000
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.
Solvability C
- Solver class:
- sequential-filter [EKF_vehicle_state or UKF_tire_model]
- Convergence rate q:
- 2
- Complexity:
- O(n_state * N_timesteps) for filter per iteration