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DEC: refactor mode conditions with Experimental Mode
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@@ -160,7 +160,7 @@ class LongitudinalPlanner(LongitudinalPlannerSP):
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output_a_target_e2e = sm['modelV2'].action.desiredAcceleration
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output_should_stop_e2e = sm['modelV2'].action.shouldStop
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if sm['selfdriveState'].experimentalMode:
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if self.is_e2e(sm):
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output_a_target = min(output_a_target_e2e, output_a_target_mpc)
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self.output_should_stop = output_should_stop_e2e or output_should_stop_mpc
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if output_a_target < output_a_target_mpc:
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@@ -36,16 +36,12 @@ class LongitudinalPlannerSP:
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self.output_v_target = 0.
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self.output_a_target = 0.
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@property
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def mlsim(self) -> bool:
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# If we don't have a generation set, we assume it's default model. Which as of today are mlsim.
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return bool(self.generation is None or self.generation >= 11)
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def get_mpc_mode(self) -> str | None:
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def is_e2e(self, sm: messaging.SubMaster) -> bool:
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experimental_mode = sm['selfdriveState'].experimentalMode
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if not self.dec.active():
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return None
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return experimental_mode
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return self.dec.mode()
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return experimental_mode and self.dec.mode() == "blended"
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def update_targets(self, sm: messaging.SubMaster, v_ego: float, a_ego: float, v_cruise: float) -> tuple[float, float]:
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CS = sm['carState']
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