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82 lines
3.1 KiB
82 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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TRAJECTORY_SIZE = 33
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class LanePlanner:
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def __init__(self):
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self.ll_t = np.zeros((TRAJECTORY_SIZE,))
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self.ll_x = np.zeros((TRAJECTORY_SIZE,))
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self.lll_y = np.zeros((TRAJECTORY_SIZE,))
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self.rll_y = np.zeros((TRAJECTORY_SIZE,))
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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.lll_prob = 0.
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self.rll_prob = 0.
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self.lll_std = 0.
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self.rll_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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def parse_model(self, md):
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if len(md.laneLines) == 4 and len(md.laneLines[0].t) == TRAJECTORY_SIZE:
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self.ll_t = (np.array(md.laneLines[1].t) + np.array(md.laneLines[2].t))/2
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# left and right ll x is the same
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self.ll_x = md.laneLines[1].x
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# only offset left and right lane lines; offsetting path does not make sense
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self.lll_y = np.array(md.laneLines[1].y) - CAMERA_OFFSET
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self.rll_y = np.array(md.laneLines[2].y) - CAMERA_OFFSET
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self.lll_prob = md.laneLineProbs[1]
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self.rll_prob = md.laneLineProbs[2]
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self.lll_std = md.laneLineStds[1]
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self.rll_std = md.laneLineStds[2]
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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 get_d_path(self, v_ego, path_t, path_xyz):
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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.lll_prob, self.rll_prob
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width_pts = self.rll_y - self.lll_y
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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 = interp(t_check * (v_ego + 7), self.ll_x, width_pts)
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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.lll_std, [.15, .3], [1.0, 0.0])
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r_std_mod = interp(self.rll_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.rll_y[0] - self.lll_y[0])
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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.lll_y + clipped_lane_width / 2.0
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path_from_right_lane = self.rll_y - clipped_lane_width / 2.0
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lr_prob = l_prob + r_prob - l_prob * r_prob
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lane_path_y = (l_prob * path_from_left_lane + r_prob * path_from_right_lane) / (l_prob + r_prob + 0.0001)
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lane_path_y_interp = np.interp(path_t, self.ll_t, lane_path_y)
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path_xyz[:,1] = lr_prob * lane_path_y_interp + (1.0 - lr_prob) * path_xyz[:,1]
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return path_xyz
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