comma-controls-challenge

A real-time controller for the comma controls challenge: keep the simulated car on its target lateral-acceleration trajectory without sawing the wheel.

total_cost 72.3 vs the pid baseline's 110.3 — 34% lower, and better on both axes. Scored on the full 5,000-segment set.

controller	total_cost	lataccel_cost	jerk_cost
karti	72.3	0.97	24.1
pid (baseline)	110.3	1.70	25.5

How it works

Feedforward does the driving. PID just trims the residual.

Most of the steer command is a static inverse-model — desired lateral accel → steering:

steer = k1·net + k2·net·v + k3·net·v²        net = target_lataccel − road_roll_lataccel

Three things, in the order I learned them:

1. Fit the feedforward to the sim, not the car. First I fit that map on the real comma-steering-control logs. Clean fit (R² ≈ 0.86) — and it made things worse. TinyPhysics' steer→lataccel gain isn't the real car's. So I dropped the logs, ran pid, recorded what lataccel the simulator actually returns for each steer, and fit on that. Gap closed. (analysis/fit_ff_sim.py)

2. The jerk came from the feedforward, not the feedback. A raw inverse-model tracks well but pipes step-to-step state noise (roll, v) straight to the wheel — lataccel_cost drops, jerk_cost jumps. The fix isn't a low-pass on the output (that just adds lag). It's to feed the model the target averaged over the next 8 steps of the future plan: a zero-lag smoother. It previews the trajectory (kills tracking lag) and smooths the command (kills the injected jerk) at the same time. Both costs fall.

3. Don't reach for more feedback. With a 50× weight on lataccel_cost, the temptation is to crank the gains. Don't — feedback is the source of the jerk here; stronger PID makes it explode. The stock baseline gains (0.195 / 0.10 / −0.053) turned out optimal once the feedforward was carrying the load.

Run it

karti.py is a drop-in controller. Put it in controllers/ inside a checkout of commaai/controls_challenge:

python eval.py --model_path ./models/tinyphysics.onnx --data_path ./data \
  --num_segs 5000 --test_controller karti --baseline_controller pid

analysis/ is the offline work, meant to run from inside that same checkout: fit_ff_sim.py (the sim-calibrated fit), tune.py (coordinate descent), diag*.py (the cost-breakdown sweeps that found the jerk source), validate.py (held-out check).

What didn't work

• Backward EMA on roll or on the output — phase lag, worse tracking. The future plan hands you a zero-lag smoother; use that instead.
• Stronger feedback — jerk_cost blows up.
• The fitted constant offset — a steady steering bias that hurt tracking. Dropped.

Where this sits

72 is the honest middle. The top of the leaderboard (~7) is per-segment offline action optimization — not a real-time controller. The clear path lower from here is receding-horizon MPC over a learned 1-step dynamics surrogate, targeting sub-20 while staying causal. The feedforward + PID story was the one worth shipping first.

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Solution to the comma controls challenge. MIT.