BCTF自动驾驶-WriteUp
demo
Lane Detection: targeted attack
ADC Programming: interception
解题过程
planning.py
#!/usr/bin/env python3
import random
import cv2
import numpy as np
from collections import Counter
from simple_pid import PID
from sklearn.cluster import KMeans
from skimage.color import rgb2lab, deltaE_cie76
from PIL import Image
# Stanley controller parameters
k_e = 0.3
k_v = 0.01
delta_steer_per_iter = 0.1
#########################################################
# Some utility functions. Define new ones as needed.
#########################################################
def waypoint_distance(waypt1: list, waypt2: list) -> float:
return np.sqrt(np.sum((np.array(waypt1) - np.array(waypt2))**2))
def normalize_angle(angle: float) -> float:
while angle > np.pi:
angle -= 2.0*np.pi
while angle < -np.pi:
angle += 2.0*np.pi
return angle
def preprocess_image(cv_img: np.ndarray) -> np.ndarray:
# resize to have less pixels
cv_img = cv2.resize(cv_img, (416, 416), interpolation=cv2.INTER_AREA)
cv_img = cv2.cvtColor(cv_img, cv2.COLOR_BGR2RGB)
if cv_img.shape[0] == 3:
rgb_img = np.transpose(cv_img, (1, 2, 0))
else:
rgb_img = cv_img
return rgb_img
#########################################################
# Main control logic for the Autonomous Driving Car (ADC)
#########################################################
class Controller:
def __init__(self):
self.speed_pid = PID(1.0, 0.1, 0.01, setpoint=5)
self.speed_pid.sample_time = 0.01
self.speed_pid.output_limits = (-1, 1)
self.counter = 0
self.last_steering = 0
self.last_throttle = 0
self.plan_waypts = []
#########################################################
# TODO: Players should implement get_control() with their
# own control logic to achieve the attack goal
#########################################################
def get_control(self, adc_pose: list, adc_speed: float, adc_yaw: float, adc_frame: np.ndarray, npc_poses: list) -> (float, float):
"""
Control logic for ADC. This function will be invoked at each control iteration.
@param self
@param adc_pose list: ADC's current 2D position in east and north directions
@param adc_speed float: ADC's current speed
@param adc_yaw float: ADC's current heading in radians
@param adc_frame numpy.ndarray: ADC's front camera frame, shape=(1080, 1920, 3)
@param npc_poses list(list): The positions of NPCs as an array of 2D arrays
"""
# Note: Below is a sample implementation, you are free to modify it and
# add new functions as needed as long as this function will return
# and only returns steering and throttle
# sample usages of functions and inputs
adc_frame_preprocessed = preprocess_image(adc_frame)
for i in range(len(npc_poses)):
dist = waypoint_distance(adc_pose, npc_poses[i])
print("ADC distance to {}th NPC is {}m".format(i, dist))
if len(npc_poses) > 0:
self.plan_waypts = [adc_pose, npc_poses[0]]
steering = self.lateral_control(self.plan_waypts, adc_pose, adc_speed, adc_yaw, self.last_steering)
if dist > 150:
dist = 150
throttle = self.longitudinal_control(adc_speed, dist)
#steering = 0.0
#throttle = 0.05
self.counter += 1
self.last_steering = steering
self.last_throttle = throttle
# Note: steering: +: steer to right, -: steer to left
# throttle: +: speed up, -: slow down
# both are limited in the range (-1, 1)
return steering, throttle
def lateral_control(self, plan_waypts: list, curr_waypt: list, v: float, yaw: float, pre_steer: float, is_return: bool = False) -> float:
"""
create lateral controller.
@param self
@param plan_waypts list: planned trajectory, i.e., a list of waypoints, should contain at least two waypoints
@param curr_waypt list: ADC's current 2D position
@param v float: ADC speed
@param yaw float: ADC yaw in radians
@param pre_steer float: previous steering
@param is_return bool: if the ADC is returning the starting point
"""
# Ref: https://www.ri.cmu.edu/pub_files/2009/2/Automatic_Steering_Methods_for_Autonomous_Automobile_Path_Tracking.pdf
yaw_path = np.arctan2(plan_waypts[-1][1] - plan_waypts[0][1], plan_waypts[-1][0] - plan_waypts[0][0])
yaw_diff = normalize_angle(yaw_path - yaw)
crosstrack_error = np.sqrt(
np.min(np.sum((np.array(curr_waypt) - np.array(plan_waypts)) ** 2, axis=1)))
yaw_crosstrack = np.arctan2(curr_waypt[1] - plan_waypts[0][1], curr_waypt[0] - plan_waypts[0][0])
yaw_path2ct = normalize_angle(yaw_path - yaw_crosstrack)
if yaw_path2ct > 0:
crosstrack_error = abs(crosstrack_error)
else:
crosstrack_error = -abs(crosstrack_error)
yaw_diff_crosstrack = np.arctan(k_e * crosstrack_error / (k_v + v))
steer = normalize_angle(yaw_diff + yaw_diff_crosstrack)
steer = -np.clip(steer, -1., 1.)
if is_return:
steer_limited = np.clip(steer, pre_steer - 3*delta_steer_per_iter, pre_steer + 3*delta_steer_per_iter)
else:
steer_limited = np.clip(steer, pre_steer - delta_steer_per_iter, pre_steer + delta_steer_per_iter)
return steer_limited
def longitudinal_control(self, v: float, t_v: float) -> float:
"""
create longitudinal controller.
@param self
@param v float: input ADC speed
@param t_v float: target speed
"""
self.speed_pid.setpoint = t_v
throttle = self.speed_pid(v)
return throttle
Adversarial NPC: single box
解题过程
gps.py
f = open('npc_benign.json', 'r')
gps = f.read()
f.close()
gpslines = gps.split('\n')
pos = []
for line in gpslines:
i0 = line.find('timestamp')
if i0 >= 0:
ti = line[i0+12:-1]
i1 = line.find('latitude')
if i1 >= 0:
la = float(line[i1+11:-1])
i2 = line.find('longitude')
if i2 >= 0:
lo = float(line[i2+12:-1])
i3 = line.find('altitude')
if i3 >= 0:
al = float(line[i3+11:-1])
i3 = line.find('roll')
if i3 >= 0:
ro = float(line[i3+7:-1])
i3 = line.find('pitch')
if i3 >= 0:
pi = line[i3+8:-1]
i3 = line.find('yaw')
if i3 >= 0:
ya = line[i3+6:-1]
i3 = line.find('speed')
if i3 >= 0:
sp = line[i3+8:]
pos.append( [la, lo, al, ti, ro, pi, ya, sp] )
rate = 1.2
print rate
for i in range(len(pos) - 120):
pos[120 + i][0] = rate*(pos[120][0] - pos[399][0]) + pos[120 + i][0]
f = open('gps_new.json', 'w')
f.write('[\n')
for i in range(len(pos)):
f.write(' {\n')
f.write(' \"timestamp\": %s,\n' % pos[i][3])
f.write(' \"latitude\": %.12f,\n' % pos[i][0])
f.write(' \"longitude\": %s,\n' % pos[i][1])
f.write(' \"altitude\": %s,\n' % pos[i][2])
f.write(' \"roll\": %s,\n' % pos[i][4])
f.write(' \"pitch\": %s,\n' % pos[i][5])
f.write(' \"yaw\": %s,\n' % pos[i][6])
f.write(' \"speed\": %s\n' % pos[i][7])
if i == len(pos) - 1:
f.write(' }\n')
else:
f.write(' },\n')
f.write(']')
f.close()
gps_new.json
[
{
"timestamp": 0.0,
"latitude": 37.789961912203,
"longitude": -122.401553978,
"altitude": 10.2880001068,
"roll": 0.0,
"pitch": 0,
"yaw": 1.00179122455302e-05,
"speed": 20.0
},
{
"timestamp": 0.05,
"latitude": 37.789961854541,
"longitude": -122.401542626,
"altitude": 10.2728290558,
"roll": 0.00087853212608,
"pitch": 4.37385324403294e-06,
"yaw": 1.08999765870976e-05,
"speed": 19.986112009053592
},
{
"timestamp": 0.1,
"latitude": 37.789961797602,
"longitude": -122.401531416,
"altitude": 10.245757103,
"roll": 0.0404979214072,
"pitch": 0.000107885905890726,
"yaw": -0.000273296143859625,
"speed": 19.53953417980725
},
{
"timestamp": 0.15,
"latitude": 37.789961741532,
"longitude": -122.401520378,
"altitude": 10.2186584473,
"roll": 0.0877417102456,
"pitch": 0.000613974640145898,
"yaw": 0.0023149426560849,
"speed": 19.458443954458264
},
{
"timestamp": 0.2,
"latitude": 37.789961685130,
"longitude": -122.401509302,
"altitude": 10.1946020126,
"roll": 0.083763346076,
"pitch": 0.00211690412834287,
"yaw": 0.00415488658472896,
"speed": 19.555836055796547
},
{
"timestamp": 0.25,
"latitude": 37.789961628374,
"longitude": -122.401498156,
"altitude": 10.1744937897,
"roll": 0.0497044585645,
"pitch": 0.00419731345027685,
"yaw": 0.00471088895574212,
"speed": 19.678308862186125
},
{
"timestamp": 0.3,
"latitude": 37.789961571419,
"longitude": -122.401486944,
"altitude": 10.1585912704,
"roll": 0.00784304458648,
"pitch": 0.00621826434507966,
"yaw": 0.00434521911665797,
"speed": 19.779648523152936
},
{
"timestamp": 0.35,
"latitude": 37.789961513677,
"longitude": -122.401475686,
"altitude": 10.1464471817,
"roll": 359.972473145,
"pitch": 0.00776536809280515,
"yaw": 0.0035081971436739,
"speed": 19.849717576884554
},
{
"timestamp": 0.4,
"latitude": 37.789961455639,
"longitude": -122.401464397,
"altitude": 10.1374406815,
"roll": 359.948364258,
"pitch": 0.00868796184659004,
"yaw": 0.00251324544660747,
"speed": 19.89642120146336
},
{
"timestamp": 0.45,
"latitude": 37.789961397359,
"longitude": -122.401453087,
"altitude": 10.1309347153,
"roll": 359.935058594,
"pitch": 0.0090014636516571,
"yaw": 0.00152538309339434,
"speed": 19.927312974287453
},
{
"timestamp": 0.5,
"latitude": 37.789961339155,
"longitude": -122.401441766,
"altitude": 10.1262254715,
"roll": 359.930297852,
"pitch": 0.008848219178617,
"yaw": 0.000618399993982166,
"speed": 19.94533976993406
},
{
"timestamp": 0.55,
"latitude": 37.789961281179,
"longitude": -122.401430435,
"altitude": 10.1230268478,
"roll": 359.931060791,
"pitch": 0.00836139172315598,
"yaw": -0.000185332421096973,
"speed": 19.96002497668345
},
{
"timestamp": 0.6,
"latitude": 37.789961223581,
"longitude": -122.401419098,
"altitude": 10.1209049225,
"roll": 359.934997559,
"pitch": 0.00766501389443874,
"yaw": -0.000895189994480461,
"speed": 19.971443679571472
},
{
"timestamp": 0.65,
"latitude": 37.789961165952,
"longitude": -122.401407755,
"altitude": 10.1195383072,
"roll": 359.940490723,
"pitch": 0.006865537725389,
"yaw": -0.00152462569531053,
"speed": 19.980237728104957
},
{
"timestamp": 0.7,
"latitude": 37.789961108438,
"longitude": -122.401396408,
"altitude": 10.1186876297,
"roll": 359.946502686,
"pitch": 0.00605636136606336,
"yaw": -0.00208621425554156,
"speed": 19.98697477269317
},
{
"timestamp": 0.75,
"latitude": 37.789961050775,
"longitude": -122.401385058,
"altitude": 10.1180534363,
"roll": 359.952514648,
"pitch": 0.00531605770811439,
"yaw": -0.00259051029570401,
"speed": 19.989631499553205
},
{
"timestamp": 0.8,
"latitude": 37.789960993101,
"longitude": -122.401373707,
"altitude": 10.1177206039,
"roll": 359.95803833,
"pitch": 0.00465651554986835,
"yaw": -0.00304309162311256,
"speed": 19.992138222000087
},
{
"timestamp": 0.85,
"latitude": 37.789960935424,
"longitude": -122.401362355,
"altitude": 10.1175680161,
"roll": 359.963134766,
"pitch": 0.00408577406778932,
"yaw": -0.00344746187329292,
"speed": 19.992563261676434
},
{
"timestamp": 0.9,
"latitude": 37.789960877885,
"longitude": -122.401351004,
"altitude": 10.1175193787,
"roll": 359.96774292,
"pitch": 0.00361152645200491,
"yaw": -0.00380691210739315,
"speed": 19.991148027605753
},
{
"timestamp": 0.95,
"latitude": 37.789960820212,
"longitude": -122.401339654,
"altitude": 10.1175270081,
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"yaw": -0.00412555923685431,
"speed": 19.988266090103252
},
{
"timestamp": 1.0,
"latitude": 37.789960763237,
"longitude": -122.401328306,
"altitude": 10.117562294,
"roll": 359.975494385,
"pitch": 0.00291407643817365,
"yaw": -0.00440777745097876,
"speed": 19.984203483323423
},
{
"timestamp": 1.05,
"latitude": 37.789960706273,
"longitude": -122.40131696,
"altitude": 10.117606163,
"roll": 359.978637695,
"pitch": 0.00267567718401551,
"yaw": -0.00465807551518083,
"speed": 19.97910515475458
},
{
"timestamp": 1.1,
"latitude": 37.789960649325,
"longitude": -122.401305618,
"altitude": 10.1176509857,
"roll": 359.981292725,
"pitch": 0.00250738835893571,
"yaw": -0.00488132331520319,
"speed": 19.973785565312706
},
{
"timestamp": 1.15,
"latitude": 37.789960592389,
"longitude": -122.401294278,
"altitude": 10.1176919937,
"roll": 359.983337402,
"pitch": 0.00239476445131004,
"yaw": -0.00508645363152027,
"speed": 19.969312829104958
},
{
"timestamp": 1.2,
"latitude": 37.789960535463,
"longitude": -122.40128294,
"altitude": 10.117726326,
"roll": 359.984741211,
"pitch": 0.00231496570631862,
"yaw": -0.00528392428532243,
"speed": 19.965778502837836
},
{
"timestamp": 1.25,
"latitude": 37.789960478545,
"longitude": -122.401271604,
"altitude": 10.1177530289,
"roll": 359.985595703,
"pitch": 0.00226673320867121,
"yaw": -0.00548101216554642,
"speed": 19.963094857810283
},
{
"timestamp": 1.3,
"latitude": 37.789960421638,
"longitude": -122.401260271,
"altitude": 10.1176490784,
"roll": 359.986053467,
"pitch": 0.00226743193343282,
"yaw": -0.00568257411941886,
"speed": 19.958982534039922
},
{
"timestamp": 1.35,
"latitude": 37.789960364740,
"longitude": -122.401248939,
"altitude": 10.1174964905,
"roll": 359.986114502,
"pitch": 0.00230335025116801,
"yaw": 359.994110107422,
"speed": 19.95570381378949
},
{
"timestamp": 1.4,
"latitude": 37.789960307843,
"longitude": -122.401237608,
"altitude": 10.1174621582,
"roll": 359.985748291,
"pitch": 0.0023405384272337,
"yaw": 359.993896484375,
"speed": 19.95549023573878
},
{
"timestamp": 1.45,
"latitude": 37.789960250944,
"longitude": -122.401226277,
"altitude": 10.1174850464,
"roll": 359.985229492,
"pitch": 0.00236273277550936,
"yaw": 359.99365234375,
"speed": 19.95587559769856
},
{
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"latitude": 37.789960194042,
"longitude": -122.401214945,
"altitude": 10.1175336838,
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"speed": 19.956577547265994
},
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"speed": 19.957517881738802
},
{
"timestamp": 1.6,
"latitude": 37.789960080232,
"longitude": -122.401192281,
"altitude": 10.1175165176,
"roll": 359.983612061,
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"yaw": 359.992919921875,
"speed": 19.956472523193977
},
{
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"latitude": 37.789960023323,
"longitude": -122.401180949,
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"yaw": 359.992645263672,
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{
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{
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},
{
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"longitude": -122.401146953,
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"yaw": 359.991821289063,
"speed": 19.95961786464248
},
{
"timestamp": 1.85,
"latitude": 37.789959797876,
"longitude": -122.40113562,
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},
{
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},
{
"timestamp": 1.95,
"latitude": 37.789959685391,
"longitude": -122.401112949,
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"roll": 359.980682373,
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"yaw": 359.990936279297,
"speed": 19.965116928987015
},
{
"timestamp": 2.0,
"latitude": 37.789959629142,
"longitude": -122.401101613,
"altitude": 10.1173095703,
"roll": 359.980529785,
"pitch": 0.00243124924600124,
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},
{
"timestamp": 2.05,
"latitude": 37.789959572885,
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"roll": 359.98034668,
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"speed": 19.967977901361348
},
{
"timestamp": 2.1,
"latitude": 37.789959516619,
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"altitude": 10.1174201965,
"roll": 359.980194092,
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"speed": 19.97052236735932
},
{
"timestamp": 2.15,
"latitude": 37.789959460346,
"longitude": -122.401067595,
"altitude": 10.1174974442,
"roll": 359.980133057,
"pitch": 0.00234452867880464,
"yaw": 359.989715576172,
"speed": 19.972898943121464
},
{
"timestamp": 2.2,
"latitude": 37.789959404064,
"longitude": -122.401056253,
"altitude": 10.1175699234,
"roll": 359.980133057,
"pitch": 0.00230431067757308,
"yaw": 359.989410400391,
"speed": 19.975096200299582
},
{
"timestamp": 2.25,
"latitude": 37.789959347775,
"longitude": -122.40104491,
"altitude": 10.1176309586,
"roll": 359.980194092,
"pitch": 0.00226445542648435,
"yaw": 359.989105224609,
"speed": 19.977125605200175
},
{
"timestamp": 2.3,
"latitude": 37.789959291481,
"longitude": -122.401033566,
"altitude": 10.1176815033,
"roll": 359.980285645,
"pitch": 0.00223197950981557,
"yaw": 359.988830566406,
"speed": 19.979010055870532
},
{
"timestamp": 2.35,
"latitude": 37.789959235179,
"longitude": -122.401022221,
"altitude": 10.1177196503,
"roll": 359.980407715,
"pitch": 0.00220701354555786,
"yaw": 359.988525390625,
"speed": 19.980743832224416
},
{
"timestamp": 2.4,
"latitude": 37.789959178871,
"longitude": -122.401010875,
"altitude": 10.1177473068,
"roll": 359.980560303,
"pitch": 0.00218956684693694,
"yaw": 359.988220214844,
"speed": 19.98234600440188
},
{
"timestamp": 2.45,
"latitude": 37.789959122557,
"longitude": -122.400999528,
"altitude": 10.117767334,
"roll": 359.980712891,
"pitch": 0.00217794580385089,
"yaw": 359.987915039063,
"speed": 19.983822300586546
},
{
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]
GPS Spoofing: GPS is all you got
解题过程
gps.py
f = open('gps.json', 'r')
gps = f.read()
f.close()
gpslines = gps.split('\n')
pos = []
for line in gpslines:
i0 = line.find('timestamp')
if i0 >= 0:
ti = line[i0+12:-1]
i1 = line.find('latitude')
if i1 >= 0:
la = float(line[i1+11:-1])
i2 = line.find('longitude')
if i2 >= 0:
lo = float(line[i2+12:-1])
i3 = line.find('altitude')
if i3 >= 0:
al = float(line[i3+11:-1])
pos.append( [la, lo, al, ti] )
rate = (0.0107700551979-0.000017) / 0.0107700551979
print rate
for i in range(len(pos) - 1):
pos[1 + i][0] = rate*(pos[1+i][0] - pos[0][0]) + pos[i][0]
f = open('gps_new.json', 'w')
f.write('[\n')
for i in range(len(pos)):
f.write(' {\n')
f.write(' \"timestamp\": %s,\n' % pos[i][3])
f.write(' \"latitude\": %.13f,\n' % pos[i][0])
f.write(' \"longitude\": %.12f,\n' % pos[i][1])
f.write(' \"altitude\": %.13f\n' % pos[i][2])
if i == len(pos) - 1:
f.write(' }\n')
else:
f.write(' },\n')
f.write(']')
f.close()
gps_new.json
[
{
"timestamp": 0.0,
"latitude": 37.7899753693032,
"longitude": -122.397121279501,
"altitude": 10.1277933120720
},
{
"timestamp": 0.05,
"latitude": 37.7899754265434,
"longitude": -122.397132214792,
"altitude": 10.1244440078730
},
{
"timestamp": 0.1,
"latitude": 37.7899755414654,
"longitude": -122.397143236731,
"altitude": 10.1220254898070
},
{
"timestamp": 0.15,
"latitude": 37.7899757143859,
"longitude": -122.397154347742,
"altitude": 10.1203002929680
},
{
"timestamp": 0.2,
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"altitude": 10.1192083358760
},
{
"timestamp": 0.25,
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"altitude": 10.1185302734370
},
{
"timestamp": 0.3,
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"altitude": 10.1180019378660
},
{
"timestamp": 0.35,
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},
{
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},
{
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},
{
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},
{
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},
{
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},
{
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},
{
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},
{
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},
{
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},
{
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},
{
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},
{
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},
{
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},
{
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},
{
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},
{
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},
{
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},
{
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},
{
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},
{
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},
{
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},
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]
Adventure
Basic
解题思路
5
Expensive Sensor
解题思路
LIDAR
Google it
解题思路
330-320直接的某个数字，都尝试一下
Try it
解题思路
LGSVL
Lane Detection
Lane Detection: free drawing
解题思路
GPS Spoofing
GPS Spoofing: GPS is all you got
解题思路
gps.py
f = open('gps.json', 'r')
gps = f.read()
f.close()
gpslines = gps.split('\n')
pos = []
for line in gpslines:
i0 = line.find('timestamp')
if i0 >= 0:
ti = line[i0+12:-1]
i1 = line.find('latitude')
if i1 >= 0:
la = float(line[i1+11:-1])
i2 = line.find('longitude')
if i2 >= 0:
lo = float(line[i2+12:-1])
i3 = line.find('altitude')
if i3 >= 0:
al = float(line[i3+11:-1])
pos.append( [la, lo, al, ti] )
rate = (0.0107700551979-0.000017) / 0.0107700551979
print rate
for i in range(len(pos) - 1):
pos[1 + i][0] = rate*(pos[1+i][0] - pos[0][0]) + pos[i][0]
f = open('gps_new.json', 'w')
f.write('[\n')
for i in range(len(pos)):
f.write(' {\n')
f.write(' \"timestamp\": %s,\n' % pos[i][3])
f.write(' \"latitude\": %.13f,\n' % pos[i][0])
f.write(' \"longitude\": %.12f,\n' % pos[i][1])
f.write(' \"altitude\": %.13f\n' % pos[i][2])
if i == len(pos) - 1:
f.write(' }\n')
else:
f.write(' },\n')
f.write(']')
f.close()
gps_new.json
[
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]
Lane Detection: dirty road patch
解题思路
1111
Lane Detection: targeted attack
解题思路
答案和上一题一样
后面还有很多贴补下了，感兴趣的可以加入团队学习。
end
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