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53 lines
1.7 KiB
53 lines
1.7 KiB
#!/usr/bin/env python3
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import numpy as np
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import unittest
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from selfdrive.car.honda.interface import CarInterface
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from selfdrive.car.honda.values import CAR
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from selfdrive.controls.lib.vehicle_model import VehicleModel
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from selfdrive.locationd.liblocationd_py import liblocationd # pylint: disable=no-name-in-module, import-error
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class TestParamsLearner(unittest.TestCase):
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def setUp(self):
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self.CP = CarInterface.get_params(CAR.CIVIC)
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bts = self.CP.to_bytes()
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self.params_learner = liblocationd.params_learner_init(len(bts), bts, 0.0, 1.0, self.CP.steerRatio, 1.0)
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def test_convergence(self):
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# Setup vehicle model with wrong parameters
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VM_sim = VehicleModel(self.CP)
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x_target = 0.75
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sr_target = self.CP.steerRatio - 0.5
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ao_target = -1.0
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VM_sim.update_params(x_target, sr_target)
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# Run simulation
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times = np.arange(0, 15*3600, 0.01)
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angle_offset = np.radians(ao_target)
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steering_angles = np.radians(10 * np.sin(2 * np.pi * times / 100.)) + angle_offset
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speeds = 10 * np.sin(2 * np.pi * times / 1000.) + 25
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for i, t in enumerate(times):
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u = speeds[i]
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sa = steering_angles[i]
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psi = VM_sim.yaw_rate(sa - angle_offset, u)
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liblocationd.params_learner_update(self.params_learner, psi, u, sa)
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# Verify learned parameters
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sr = liblocationd.params_learner_get_sR(self.params_learner)
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ao_slow = np.degrees(liblocationd.params_learner_get_slow_ao(self.params_learner))
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x = liblocationd.params_learner_get_x(self.params_learner)
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self.assertAlmostEqual(x_target, x, places=1)
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self.assertAlmostEqual(ao_target, ao_slow, places=1)
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self.assertAlmostEqual(sr_target, sr, places=1)
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if __name__ == "__main__":
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unittest.main()
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