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89 lines
3.1 KiB
89 lines
3.1 KiB
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(l=50):
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deg = 3
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x = np.arange(l*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 calc_d_poly(l_poly, r_poly, p_poly, l_prob, r_prob, lane_width):
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# This will improve behaviour when lanes suddenly widen
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lane_width = min(4.0, lane_width)
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l_prob = l_prob * interp(abs(l_poly[3]), [2, 2.5], [1.0, 0.0])
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r_prob = r_prob * interp(abs(r_poly[3]), [2, 2.5], [1.0, 0.0])
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path_from_left_lane = l_poly.copy()
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path_from_left_lane[3] -= lane_width / 2.0
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path_from_right_lane = r_poly.copy()
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path_from_right_lane[3] += 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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return lr_prob * d_poly_lane + (1.0 - lr_prob) * p_poly
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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_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.r_poly = np.array(md.rightLane.poly)
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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 - 1]
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self.r_lane_change_prob = md.meta.desireState[log.PathPlan.Desire.laneChangeRight - 1]
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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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# Find current lanewidth
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self.lane_width_certainty += 0.05 * (self.l_prob * self.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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self.d_poly = calc_d_poly(self.l_poly, self.r_poly, self.p_poly, self.l_prob, self.r_prob, self.lane_width)
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def update(self, v_ego, md):
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self.parse_model(md)
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self.update_d_poly(v_ego)
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