open source driving agent
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from common.numpy_fast import interp
import numpy as np
from selfdrive.controls.lib.latcontrol_helpers import model_polyfit, compute_path_pinv
CAMERA_OFFSET = 0.06 # m from center car to camera
def calc_d_poly(l_poly, r_poly, p_poly, l_prob, r_prob, lane_width):
# This will improve behaviour when lanes suddenly widen
lane_width = min(4.0, lane_width)
l_prob = l_prob * interp(abs(l_poly[3]), [2, 2.5], [1.0, 0.0])
r_prob = r_prob * interp(abs(r_poly[3]), [2, 2.5], [1.0, 0.0])
path_from_left_lane = l_poly.copy()
path_from_left_lane[3] -= lane_width / 2.0
path_from_right_lane = r_poly.copy()
path_from_right_lane[3] += lane_width / 2.0
lr_prob = l_prob + r_prob - l_prob * r_prob
d_poly_lane = (l_prob * path_from_left_lane + r_prob * path_from_right_lane) / (l_prob + r_prob + 0.0001)
return lr_prob * d_poly_lane + (1.0 - lr_prob) * p_poly
class LanePlanner(object):
def __init__(self):
self.l_poly = [0., 0., 0., 0.]
self.r_poly = [0., 0., 0., 0.]
self.p_poly = [0., 0., 0., 0.]
self.d_poly = [0., 0., 0., 0.]
self.lane_width_estimate = 3.7
self.lane_width_certainty = 1.0
self.lane_width = 3.7
self.l_prob = 0.
self.r_prob = 0.
self.lr_prob = 0.
self._path_pinv = compute_path_pinv()
self.x_points = np.arange(50)
def parse_model(self, md):
if len(md.leftLane.poly):
self.l_poly = np.array(md.leftLane.poly)
self.r_poly = np.array(md.rightLane.poly)
self.p_poly = np.array(md.path.poly)
else:
self.l_poly = model_polyfit(md.leftLane.points, self._path_pinv) # left line
self.r_poly = model_polyfit(md.rightLane.points, self._path_pinv) # right line
self.p_poly = model_polyfit(md.path.points, self._path_pinv) # predicted path
self.l_prob = md.leftLane.prob # left line prob
self.r_prob = md.rightLane.prob # right line prob
def update_lane(self, v_ego):
# only offset left and right lane lines; offsetting p_poly does not make sense
self.l_poly[3] += CAMERA_OFFSET
self.r_poly[3] += CAMERA_OFFSET
self.lr_prob = self.l_prob + self.r_prob - self.l_prob * self.r_prob
# Find current lanewidth
self.lane_width_certainty += 0.05 * (self.l_prob * self.r_prob - self.lane_width_certainty)
current_lane_width = abs(self.l_poly[3] - self.r_poly[3])
self.lane_width_estimate += 0.005 * (current_lane_width - self.lane_width_estimate)
speed_lane_width = interp(v_ego, [0., 31.], [2.8, 3.5])
self.lane_width = self.lane_width_certainty * self.lane_width_estimate + \
(1 - self.lane_width_certainty) * speed_lane_width
self.d_poly = calc_d_poly(self.l_poly, self.r_poly, self.p_poly, self.l_prob, self.r_prob, self.lane_width)
def update(self, v_ego, md):
self.parse_model(md)
self.update_lane(v_ego)