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64 lines
2.4 KiB
64 lines
2.4 KiB
9 years ago
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import selfdrive.messaging as messaging
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import numpy as np
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X_PATH = np.arange(0.0, 50.0)
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def model_polyfit(points):
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return np.polyfit(X_PATH, map(float, points), 3)
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# lane width http://safety.fhwa.dot.gov/geometric/pubs/mitigationstrategies/chapter3/3_lanewidth.cfm
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_LANE_WIDTH_V = np.asarray([3., 3.8])
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# break points of speed
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_LANE_WIDTH_BP = np.asarray([0., 31.])
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def calc_desired_path(l_poly, r_poly, p_poly, l_prob, r_prob, p_prob, speed):
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#*** this function computes the poly for the center of the lane, averaging left and right polys
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lane_width = np.interp(speed, _LANE_WIDTH_BP, _LANE_WIDTH_V)
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# lanes in US are ~3.6m wide
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half_lane_poly = np.array([0., 0., 0., lane_width / 2.])
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if l_prob + r_prob > 0.01:
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c_poly = ((l_poly - half_lane_poly) * l_prob +
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(r_poly + half_lane_poly) * r_prob) / (l_prob + r_prob)
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c_prob = np.sqrt((l_prob**2 + r_prob**2) / 2.)
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else:
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c_poly = np.zeros(4)
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c_prob = 0.
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p_weight = 1. # predicted path weight relatively to the center of the lane
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d_poly = list((c_poly*c_prob + p_poly*p_prob*p_weight ) / (c_prob + p_prob*p_weight))
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return d_poly, c_poly, c_prob
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class PathPlanner(object):
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def __init__(self, model):
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self.model = model
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self.dead = True
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self.d_poly = [0., 0., 0., 0.]
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self.last_model = 0.
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self.logMonoTime = 0
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self.lead_dist, self.lead_prob, self.lead_var = 0, 0, 1
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def update(self, cur_time, v_ego):
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md = messaging.recv_sock(self.model)
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if md is not None:
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self.logMonoTime = md.logMonoTime
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p_poly = model_polyfit(md.model.path.points) # predicted path
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p_prob = 1. # model does not tell this probability yet, so set to 1 for now
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l_poly = model_polyfit(md.model.leftLane.points) # left line
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l_prob = md.model.leftLane.prob # left line prob
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r_poly = model_polyfit(md.model.rightLane.points) # right line
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r_prob = md.model.rightLane.prob # right line prob
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self.lead_dist = md.model.lead.dist
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self.lead_prob = md.model.lead.prob
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self.lead_var = md.model.lead.std**2
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#*** compute target path ***
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self.d_poly, _, _ = calc_desired_path(l_poly, r_poly, p_poly, l_prob, r_prob, p_prob, v_ego)
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self.last_model = cur_time
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self.dead = False
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elif cur_time - self.last_model > 0.5:
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self.dead = True
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