mawei/sunnypilot
1
0
Fork 0
mirror of https://github.com/sunnypilot/sunnypilot.git synced 2026-07-16 00:02:07 +08:00
Code Issues Packages Projects Releases Wiki Activity

try this

Browse Source
This commit is contained in:
Jason Wen
2025-03-17 22:41:40 -04:00
parent 08eef47ce2
commit 73a83a204b
2 changed files with 22 additions and 19 deletions
Download Patch File Download Diff File Expand all files Collapse all files
sunnypilot/selfdrive/car/interfaces.py
+3 -2
View File
@@ -26,11 +26,12 @@ def log_fingerprint(CP: structs.CarParams) -> None:
def initialize_neural_network_lateral_control(CP: structs.CarParams, CP_SP: structs.CarParamsSP, params) -> None:
nnlc_model_path, nnlc_model_name, fuzzy_fingerprint = get_nn_model_path(CP)
nnlc_model_path, nnlc_model_name, exact_match = get_nn_model_path(CP)
if nnlc_model_path is None:
cloudlog.error({"nnlc event": "car doesn't match any Neural Network model"})
nnlc_model_path = "MOCK"
nnlc_model_name = "MOCK"
if nnlc_model_path != "MOCK" and CP.steerControlType != structs.CarParams.SteerControlType.angle:
CP_SP.neuralNetworkLateralControl.enabled = params.get_bool("NeuralNetworkLateralControl")
@@ -40,7 +41,7 @@ def initialize_neural_network_lateral_control(CP: structs.CarParams, CP_SP: stru
CP_SP.neuralNetworkLateralControl.modelPath = nnlc_model_path
CP_SP.neuralNetworkLateralControl.modelName = nnlc_model_name
CP_SP.neuralNetworkLateralControl.fuzzyFingerprint = fuzzy_fingerprint
CP_SP.neuralNetworkLateralControl.fuzzyFingerprint = not exact_match
def setup_car_interface_sp(CP: structs.CarParams, CP_SP: structs.CarParamsSP, params):
sunnypilot/selfdrive/controls/lib/nnlc/helpers.py
+19 -17
View File
@@ -21,35 +21,37 @@ def similarity(s1: str, s2: str) -> float:
def get_nn_model_path(CP: structs.CarParams) -> tuple[str | None, str, bool]:
_car = CP.carFingerprint
_eps_fw = str(next((fw.fwVersion for fw in CP.carFw if fw.ecu == "eps"), ""))
_model_name = ""
exact_match = True
car_fingerprint = CP.carFingerprint
eps_fw = str(next((fw.fwVersion for fw in CP.carFw if fw.ecu == "eps"), ""))
model_name = ""
def check_nn_path(_check_model):
def check_nn_path(nn_candidate):
_model_path = None
_max_similarity = -1.0
for f in os.listdir(TORQUE_NN_MODEL_PATH):
if f.endswith(".json"):
model = os.path.splitext(f)[0]
similarity_score = similarity(model, _check_model)
similarity_score = similarity(model, nn_candidate)
if similarity_score > _max_similarity:
_max_similarity = similarity_score
_model_path = os.path.join(TORQUE_NN_MODEL_PATH, f)
return _model_path, _max_similarity
if len(_eps_fw) > 3:
_eps_fw = _eps_fw.replace("\\", "")
check_model = f"{_car} {_eps_fw}"
if len(eps_fw) > 3:
eps_fw = eps_fw.replace("\\", "")
nn_candidate = f"{car_fingerprint} {eps_fw}"
else:
check_model = _car
model_path, max_similarity = check_nn_path(check_model)
if 0.0 <= max_similarity < 0.9:
check_model = _car
model_path, max_similarity = check_nn_path(check_model)
if 0.0 <= max_similarity < 0.9:
nn_candidate = car_fingerprint
model_path, max_similarity = check_nn_path(nn_candidate)
if car_fingerprint not in model_path or 0.0 <= max_similarity < 0.9:
nn_candidate = car_fingerprint
model_path, max_similarity = check_nn_path(nn_candidate)
if car_fingerprint not in model_path or 0.0 <= max_similarity < 0.9:
model_path = None
_model_name = os.path.splitext(os.path.basename(model_path))[0] if model_path else "MOCK"
if model_path is not None:
model_name = os.path.splitext(os.path.basename(model_path))[0]
exact_match = max_similarity >= 0.99
fuzzy_fingerprint = max_similarity < 0.99
return model_path, _model_name, fuzzy_fingerprint
return model_path, model_name, exact_match