106 lines
3.6 KiB
Python
106 lines
3.6 KiB
Python
from common.numpy_fast import interp
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import numpy as np
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from cereal import log
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CAMERA_OFFSET = 0.06 # m from center car to camera
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def compute_path_pinv(length=50):
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deg = 3
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x = np.arange(length*1.0)
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X = np.vstack(tuple(x**n for n in range(deg, -1, -1))).T
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pinv = np.linalg.pinv(X)
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return pinv
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def model_polyfit(points, path_pinv):
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return np.dot(path_pinv, [float(x) for x in points])
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def eval_poly(poly, x):
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return poly[3] + poly[2]*x + poly[1]*x**2 + poly[0]*x**3
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class LanePlanner:
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def __init__(self):
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self.l_poly = [0., 0., 0., 0.]
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self.r_poly = [0., 0., 0., 0.]
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self.p_poly = [0., 0., 0., 0.]
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self.d_poly = [0., 0., 0., 0.]
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self.lane_width_estimate = 3.7
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self.lane_width_certainty = 1.0
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self.lane_width = 3.7
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self.l_prob = 0.
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self.r_prob = 0.
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self.l_std = 0.
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self.r_std = 0.
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self.l_lane_change_prob = 0.
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self.r_lane_change_prob = 0.
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self._path_pinv = compute_path_pinv()
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self.x_points = np.arange(50)
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def parse_model(self, md):
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if len(md.leftLane.poly):
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self.l_poly = np.array(md.leftLane.poly)
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self.l_std = float(md.leftLane.std)
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self.r_poly = np.array(md.rightLane.poly)
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self.r_std = float(md.rightLane.std)
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self.p_poly = np.array(md.path.poly)
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else:
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self.l_poly = model_polyfit(md.leftLane.points, self._path_pinv) # left line
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self.r_poly = model_polyfit(md.rightLane.points, self._path_pinv) # right line
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self.p_poly = model_polyfit(md.path.points, self._path_pinv) # predicted path
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self.l_prob = md.leftLane.prob # left line prob
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self.r_prob = md.rightLane.prob # right line prob
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if len(md.meta.desireState):
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self.l_lane_change_prob = md.meta.desireState[log.PathPlan.Desire.laneChangeLeft]
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self.r_lane_change_prob = md.meta.desireState[log.PathPlan.Desire.laneChangeRight]
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def update_d_poly(self, v_ego):
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# only offset left and right lane lines; offsetting p_poly does not make sense
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self.l_poly[3] += CAMERA_OFFSET
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self.r_poly[3] += CAMERA_OFFSET
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# Reduce reliance on lanelines that are too far apart or
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# will be in a few seconds
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l_prob, r_prob = self.l_prob, self.r_prob
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width_poly = self.l_poly - self.r_poly
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prob_mods = []
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for t_check in [0.0, 1.5, 3.0]:
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width_at_t = eval_poly(width_poly, t_check * (v_ego + 7))
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prob_mods.append(interp(width_at_t, [4.0, 5.0], [1.0, 0.0]))
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mod = min(prob_mods)
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l_prob *= mod
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r_prob *= mod
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# Reduce reliance on uncertain lanelines
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l_std_mod = interp(self.l_std, [.15, .3], [1.0, 0.0])
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r_std_mod = interp(self.r_std, [.15, .3], [1.0, 0.0])
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l_prob *= l_std_mod
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r_prob *= r_std_mod
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# Find current lanewidth
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self.lane_width_certainty += 0.05 * (l_prob * r_prob - self.lane_width_certainty)
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current_lane_width = abs(self.l_poly[3] - self.r_poly[3])
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self.lane_width_estimate += 0.005 * (current_lane_width - self.lane_width_estimate)
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speed_lane_width = interp(v_ego, [0., 31.], [2.8, 3.5])
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self.lane_width = self.lane_width_certainty * self.lane_width_estimate + \
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(1 - self.lane_width_certainty) * speed_lane_width
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clipped_lane_width = min(4.0, self.lane_width)
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path_from_left_lane = self.l_poly.copy()
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path_from_left_lane[3] -= clipped_lane_width / 2.0
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path_from_right_lane = self.r_poly.copy()
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path_from_right_lane[3] += clipped_lane_width / 2.0
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lr_prob = l_prob + r_prob - l_prob * r_prob
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d_poly_lane = (l_prob * path_from_left_lane + r_prob * path_from_right_lane) / (l_prob + r_prob + 0.0001)
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self.d_poly = lr_prob * d_poly_lane + (1.0 - lr_prob) * self.p_poly.copy()
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