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cleanup planner (#2519)

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* no reason for external function

* dont need d_poly for this test

* dont copy float

* typo
This commit is contained in:
HaraldSchafer
2020-11-10 14:42:43 -08:00
committed by GitHub
parent 72a62c3bff
commit 39f27f9d6a
2 changed files with 32 additions and 36 deletions
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65
selfdrive/controls/lib/lane_planner.py
View File

@@ -5,9 +5,9 @@ from cereal import log
CAMERA_OFFSET = 0.06 # m from center car to camera
def compute_path_pinv(l=50):
def compute_path_pinv(length=50):
deg = 3
x = np.arange(l*1.0)
x = np.arange(length*1.0)
X = np.vstack(tuple(x**n for n in range(deg, -1, -1))).T
pinv = np.linalg.pinv(X)
return pinv
@@ -21,37 +21,6 @@ def eval_poly(poly, x):
return poly[3] + poly[2]*x + poly[1]*x**2 + poly[0]*x**3
def calc_d_poly(l_poly, r_poly, p_poly, l_prob, r_prob, lane_width, v_ego, l_std=0.05, r_std=0.05):
# This will improve behaviour when lanes suddenly widen
# these numbers were tested on 2000 segments and found to work well
lane_width = min(4.0, lane_width)
width_poly = l_poly - r_poly
prob_mods = []
for t_check in [0.0, 1.5, 3.0]:
width_at_t = eval_poly(width_poly, t_check * (v_ego + 7))
prob_mods.append(interp(width_at_t, [4.0, 5.0], [1.0, 0.0]))
mod = min(prob_mods)
l_prob = mod * l_prob
r_prob = mod * r_prob
# Remove reliance on uncertain lanelines
# these numbers were tested on 2000 segments and found to work well
l_std_mod = interp(l_std, [.15, .3], [1.0, 0.0])
l_prob = l_std_mod * l_prob
r_std_mod = interp(r_std, [.15, .3], [1.0, 0.0])
r_prob = r_std_mod * r_prob
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:
def __init__(self):
self.l_poly = [0., 0., 0., 0.]
@@ -106,4 +75,32 @@ class LanePlanner:
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, v_ego, self.l_std, self.r_std)
# This will improve behaviour when lanes suddenly widen
# these numbers were tested on 2000 segments and found to work well
l_prob, r_prob = self.l_prob, self.r_prob
width_poly = self.l_poly - self.r_poly
prob_mods = []
for t_check in [0.0, 1.5, 3.0]:
width_at_t = eval_poly(width_poly, t_check * (v_ego + 7))
prob_mods.append(interp(width_at_t, [4.0, 5.0], [1.0, 0.0]))
mod = min(prob_mods)
l_prob *= mod
r_prob *= mod
# Remove reliance on uncertain lanelines
# these numbers were tested on 2000 segments and found to work well
l_std_mod = interp(self.l_std, [.15, .3], [1.0, 0.0])
r_std_mod = interp(self.r_std, [.15, .3], [1.0, 0.0])
l_prob *= l_std_mod
r_prob *= r_std_mod
clipped_lane_width = min(4.0, self.lane_width)
path_from_left_lane = self.l_poly.copy()
path_from_left_lane[3] -= clipped_lane_width / 2.0
path_from_right_lane = self.r_poly.copy()
path_from_right_lane[3] += clipped_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)
self.d_poly = lr_prob * d_poly_lane + (1.0 - lr_prob) * self.p_poly.copy()

3
selfdrive/controls/tests/test_lateral_mpc.py
View File

@@ -3,7 +3,6 @@ import numpy as np
from selfdrive.car.honda.interface import CarInterface
from selfdrive.controls.lib.lateral_mpc import libmpc_py
from selfdrive.controls.lib.vehicle_model import VehicleModel
from selfdrive.controls.lib.lane_planner import calc_d_poly
def run_mpc(v_ref=30., x_init=0., y_init=0., psi_init=0., delta_init=0.,
@@ -25,7 +24,7 @@ def run_mpc(v_ref=30., x_init=0., y_init=0., psi_init=0., delta_init=0.,
p_p = poly_p.copy()
p_p[3] += poly_shift
d_poly = calc_d_poly(p_l, p_r, p_p, l_prob, r_prob, lane_width, v_ref)
d_poly = p_p
CP = CarInterface.get_params("HONDA CIVIC 2016 TOURING")
VM = VehicleModel(CP)