Quix Webinar · Eng. Problem ·DWG-A / SCHEMATIC BLUEPRINT
SHEET 01 / 08· THE PROBLEM
The Engineering Problem sim-to-real correlation.
REV A · 2026-06-04
You tune against a model. Then reality disagrees. Closing that gap — and knowing where the model can't be trusted — is the job.
SHEET 02 / 08· DATA SOURCE
The data — real car data from CAN bus.
SOURCE · COMMA.AI OPENPILOT
Comma.ai openpilot logs. This is what the car's own controllers saw: the same signals openpilot drives on. Production hardware, real roads.
└─data/raw/segments/
└─PLATFORM/# Ford, Hyundai, Tesla, …
└─device/# comma 3 dongle ID
└─route/# one drive
└─segment/# 60 s window
└─rlog.zst# raw CAN log
One segment ≈ 60 seconds of driving · 3,000 samples per channel · steering, speed, yaw rate, lateral accel — sampled at 50 Hz.
SHEET 03 / 08· VEHICLE INVENTORY
Four cars — one model is about to treat all of these the same.
N = 4 PLATFORMS
PLAT-01 · TESLA
Tesla Model 3
wheelbase2.875 m
mass2,035 kg
PLAT-02 · FORD
Mustang Mach-E
wheelbase2.984 m
mass2,336 kg
PLAT-03 · HYUNDAI
Ioniq 5
wheelbase2.970 m
mass2,084 kg
PLAT-04 · FORD
F-150 Lightning
wheelbase3.70 m
mass3,084 kg
A 3-ton truck and a compact hatch. The baseline model is mass-independent — it predicts identical cornering for both.
SHEET 04 / 08· VIRTUAL MODEL
The virtual model — KS, the "driving-school" model.
KINEMATIC SINGLE-TRACK
A rigid rod of wheelbase L, no tyre, no slip — the car goes exactly where the wheels point. Yaw rate falls straight out of geometry.
ψ̇= (v / L) · tan(δ)
a_y=v·ψ̇// no forces computed
STATE x = (x, y, ψ, v, δ)
INPUT u = (δ̇, a)
SHEET 05 / 08· SCOPE OF MODEL
We give it speed and steering. It returns the lateral response.
CLAMPED · LATERAL ONLY
INPUT · MEASURED
What goes in
v — longitudinal speedm/s · clamped
δ — road-wheel anglerad · clamped
KS MODEL
PREDICTED · LATERAL
What comes out
x — positionm
y — positionm
ψ — headingrad
Measured v and δ are clamped at every integration step, so the longitudinal channel is an input, not a prediction.
What's left is purely lateral — and the model's lies are all lateral, so this isolates exactly the residual we want to measure.
SHEET 06 / 08· V0 · sim.csv SCHEMA
How V0 is built · the file — one row per 50 Hz sample, four kinds of column.
50 Hz · WIDE TABLE
01 · INPUTS
delta_road_rad
v_mps
a_long_mps2
accel_pedal_pct
From CAN — given to the model at inference.
02 · TRUTH
yaw_rate_meas_rads
a_lat_meas_mps2
·
·
Ford / Hyundai only. Tesla doesn't expose these.
03 · PREDICTION
yaw_rate_pred_rads
a_y_pred_mps2
x_m
y_m
psi_rad
04 · RESIDUAL
yaw_rate_resid_rads
a_y_resid_mps2
·
·
= pred − truth. The thing we want to shrink.
The truth columns exist in sim.csv — but not in what your model is given at inference.
SHEET 07 / 08· THE TASK
The task — predict the lateral response better than V0.
TWO METRICS · SCORED
METRIC · 01 · INSTANTANEOUS
Yaw-rate RMSE (rad/s)
Instantaneous fidelity — how close the predicted yaw rate is to measured, every sample.
METRIC · 02 · INTEGRATED
Cross-track-error RMSE (metres)
Where the integrated trajectory actually ends up, resampled at uniform distance.
Not redundant: a tiny persistent yaw bias is nearly invisible per-sample but compounds into hundreds of metres of drift.
SHEET 08 / 08· BASELINE V0
The start line — beat these two numbers.
N = 534 SEGMENTS · HELD-OUT
V0, scored on 534 held-out segments. Everything from here is measured against them.
YAW-RATE RMSE
0.0163
rad / s
CROSS-TRACK-ERROR RMSE
254
metres
The 254 m is the compounding-bias problem made concrete — integrate a slightly biased yaw rate over a minute of driving and the trajectory drifts off the map.