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IQ.Pilot Release Commit @ aedea0e

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IQ.Lvbs CI [bot]
2026-07-23 21:59:32 -05:00
parent 38b686ee2f
commit ade24fb921
131 changed files with 111 additions and 184 deletions
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6
iqpilot/selfdrive/controls/lib/helpers/lane_change.py
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@@ -50,7 +50,7 @@ class LaneSwapEngine:
"sec": 0.0,
"tick": 0,
"gate": 0.0,
"preset": self._kv.get("AutoLaneChangeTimer", return_default=True),
"preset": self._kv.get("IQLaneChangeTimer", return_default=True),
"bsm_hold": False,
"braked": False,
"ready": False,
@@ -59,8 +59,8 @@ class LaneSwapEngine:
self.reload_setup()
def _pull_setup(self) -> None:
self._mem["bsm_hold"] = self._kv.get_bool("AutoLaneChangeBsmDelay")
self._mem["preset"] = self._kv.get("AutoLaneChangeTimer", return_default=True)
self._mem["bsm_hold"] = self._kv.get_bool("IQLaneChangeBsmDelay")
self._mem["preset"] = self._kv.get("IQLaneChangeTimer", return_default=True)
def _idle_phase(self) -> bool:
return (

32
iqpilot/selfdrive/controls/lib/neural_network_feed_forward/tests/test_network.py
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@@ -12,16 +12,40 @@ import pytest
from openpilot.selfdrive.controls.lib.latcontrol_torque import NNTorqueModel
from openpilot.selfdrive.controls.lib.latcontrol_torque import TORQUE_NN_MODEL_PATH
# A minimal valid NNFF model (Twilsonco format: column-vector mean/std, dense_N_W/b
# layers). Used as a fallback so the loader logic is still exercised when no trained
# models are shipped (they are removed pending retraining and re-added over time).
_SYNTHETIC_MODEL = {
"input_size": 4,
"output_size": 1,
"input_mean": [[0.0], [0.0], [0.0], [0.0]],
"input_std": [[1.0], [1.0], [1.0], [1.0]],
"layers": [
{"dense_1_W": [[0.5, 0.5, 0.5, 0.5], [0.5, 0.5, 0.5, 0.5]], "dense_1_b": [[0.0], [0.0]], "activation": "sigmoid"},
{"dense_2_W": [[2.0, 2.0]], "dense_2_b": [[-1.0]], "activation": "identity"},
],
}
MODEL_FILES = sorted(f for f in os.listdir(TORQUE_NN_MODEL_PATH) if f.endswith(".json"))
SAMPLE = [f for f in ("HYUNDAI_IONIQ_5.json", "TOYOTA_RAV4_TSS2_2022.json", "MOCK.json") if f in MODEL_FILES] \
or MODEL_FILES[:3]
if MODEL_FILES:
_MODEL_DIR = TORQUE_NN_MODEL_PATH
_NAMES = MODEL_FILES
else:
import tempfile
_MODEL_DIR = tempfile.mkdtemp(prefix="nnff_synthetic_")
with open(os.path.join(_MODEL_DIR, "SYNTHETIC.json"), "w") as _f:
json.dump(_SYNTHETIC_MODEL, _f)
_NAMES = ["SYNTHETIC.json"]
SAMPLE = [f for f in ("HYUNDAI_IONIQ_5.json", "TOYOTA_RAV4_TSS2_2022.json", "MOCK.json") if f in _NAMES] \
or _NAMES[:3]
def _path(name):
return os.path.join(TORQUE_NN_MODEL_PATH, name)
return os.path.join(_MODEL_DIR, name)
@pytest.mark.parametrize("name", MODEL_FILES, ids=[n[:-5] for n in MODEL_FILES])
@pytest.mark.parametrize("name", _NAMES, ids=[n[:-5] for n in _NAMES])
def test_every_model_loads_and_is_finite(name):
m = NNTorqueModel(_path(name))
assert m.input_size >= 2

7
iqpilot/selfdrive/controls/lib/neural_network_feed_forward/tests/test_nnff_assembly.py
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@@ -18,7 +18,12 @@ from openpilot.selfdrive.controls.lib.latcontrol_torque import NeuralNetworkFeed
from openpilot.selfdrive.controls.lib.latcontrol_torque import TORQUE_NN_MODEL_PATH
_REAL_MODEL = next((f for f in sorted(os.listdir(TORQUE_NN_MODEL_PATH))
if f.endswith(".json") and f != "MOCK.json"))
if f.endswith(".json") and f != "MOCK.json"), None)
# Models are shipped separately and re-added as retrained; with none present,
# NNFF is a no-op (falls back to stock torque FF), so the assembly tests skip.
pytestmark = pytest.mark.skipif(_REAL_MODEL is None,
reason="no NNFF models present (nuked pending retraining)")
def _torque_fn():