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match.py
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# info_car [[ch,p,scope,t, maxValue, carType]]
# -record_v = [[ch, p,scope,t,v]]
# scope = [min,max]
import sys
import threading
import time
import uuid
import copy, scipy
from scipy import optimize
import numpy as np
import pandas as pd
import random
from ZMQServer import MessageServer
from ZMQPublish import MessagePublisher
from Coodingdata import RoadEncoder
def getClean(y):
d = []
y.sort(reverse=True)
for v in y:
d.append(v[1])
d = np.unique(d)
maxV = []
maxK = []
for value in y:
if value[1] in d and len(maxV) < len(d) and value[1] not in maxK:
# 存放最大概率
maxV.append(value[0])
# 存放最大概率car索引
maxK.append(value[1])
return maxK, maxV
def is_cross_match(record_k, record_v, info_car, k_type):
def judge_condition_cross(x, y):
flag = False
if len(x) == 0:
return False
# if x[_car_type]!=y[_car_type]:
# return False
if 2000 > x[_t] - y[_t] >= _min_time_limited * 1000 and (
x[_scope][0] <= y[_scope][1] <=
x[_scope][1] or x[_scope][0] <=
y[_scope][0] < x[_scope][1] or y[_scope][0] < x[_scope][1] <= y[_scope][1]):
flag = True # 判断预测条件
return flag
def judge_condition_small(x, y):
if len(x) == 0:
return False
# if x[_car_type]!=y[_car_type]:
# return False
# if x[_car_type]==0:
# if y[_car_type]!=0:
# return False
if y[_car_type] == 0 and (
x[_scope][0] <= y[_scope][1] <=
x[_scope][1] or x[_scope][0] <=
y[_scope][0] < x[_scope][1] or y[_scope][0] < x[_scope][1] <= y[_scope][1]):
return False
if 2000 > x[_t] - y[_t] >= _min_time_limited * 1000 and (
(x[_p] - y[_p]) / (x[_t] - y[_t])) * y[_v] > 0:
# 判断预测条件
return True
def is_info_cross(x, y):
if len(x) == 0 or len(y) == 0:
return False
if x == y or (x[_p] < start and y[_p] < start):
return False
if x[_ch] == y[_ch] and (x[_scope][0] < y[_scope][1] <
x[_scope][1] or x[_scope][0] <
y[_scope][0] < x[_scope][1] or y[_scope][0] < x[_scope][1] < y[_scope][1]):
return True
return False
# 去点大车旁边车道的定位点
# for i in range(len(k_type)):
# if k_type[i] == 1:
# for j in range(len(info_car)):
# if judge_condition_cross(info_car[j], record_v[i]) and abs(info_car[j][_ch] - record_v[i][_ch])==1:
# info_car[j] = []
# 匹配同车道
# for i in range(len(info_car)):
# for j in range(len(info_car)):
# if is_info_cross(info_car[i], info_car[j]):
# print("范围有重叠", info_car[i], info_car[j])
# info_car[j] = []
for i in range(len(info_car)):
tmp_cross_car = []
for j in range(len(record_v)):
if judge_condition_cross(info_car[i], record_v[j]) and record_v[j][_ch] == info_car[i][_ch]:
tmp_cross_car.append(j)
latest_cross_time_index = -1
if len(tmp_cross_car) == 1:
latest_cross_time_index = tmp_cross_car[0]
elif len(tmp_cross_car) > 1:
latest_cross_time = 2000
for j in tmp_cross_car:
if record_v[j][_t] - info_car[i][_t] < latest_cross_time:
latest_cross_time = info_car[i][_t] - record_v[j][_t]
latest_cross_time_index = j
is_single_cross[latest_cross_time_index] = False
print("多辆车可以交叉匹配，匹配最近时间的历史定位,最小时间差为", latest_cross_time)
j = latest_cross_time_index
if j != -1:
if ((info_car[i][_p] - record_v[j][_p]) / (info_car[i][_t] - record_v[j][_t])) * record_v[j][
_v] >= 0:
if info_car[i][_p] - record_v[j][_p] != 0:
print("交叉匹配同车道" + "车辆" + str(record_k[j]) + "位置从" + str(record_v[j][_p]) + '变为' + str(
info_car[i][_p]), info_car[i])
info_car[i].append(record_v[j][_v])
record_v[j] = info_car[i]
else:
print("位置重复出现,只更新时间", info_car[i], record_v[j])
record_v[j][_t] = info_car[i][_t]
else:
if record_v[j][_p] < start:
print("在起始区域出现位置后退", info_car[i], record_v[j])
else:
print("交叉匹配同车道出现位置倒退，只更新时间", record_v[j], info_car[i])
record_v[j][_t] = info_car[i][_t]
info_car[i] = []
for i in range(len(info_car)):
small_distance = 40
small_index = -1
for j in range(len(record_v)):
if judge_condition_small(info_car[i], record_v[j]) and record_v[j][_ch] == info_car[i][_ch]:
if abs(info_car[i][_p] - record_v[j][_p]) < small_distance * ((
info_car[i][_t] - record_v[j][
_t] + 1000) // 1000):
small_distance = abs(info_car[i][_p] - record_v[j][_p])
small_index = j
if small_index != -1:
print(
"相邻匹配同车道" + "车辆" + str(record_k[small_index]) + "位置从" + str(record_v[small_index][_p]) + '变为' + str(
info_car[i][_p]), info_car[i])
info_car[i].append(record_v[small_index][_v])
record_v[small_index] = info_car[i]
info_car[i] = []
for i in range(len(info_car)):
small_distance = 40
small_index = -1
for j in range(len(record_v)):
if judge_condition_small(info_car[i], record_v[j]):
if info_car[i][_car_type] == 1 and record_v[j][_car_type] == 0:
print("车型判断不一致", info_car[i], record_v[j])
continue
elif info_car[i][_t] - record_v[j][_t] < 320 and record_v[j][_car_type] == 0:
continue
else:
if abs(record_v[j][_ch] - info_car[i][_ch]) == 1 and record_v[j][_p] > start:
if abs(info_car[i][_p] - record_v[j][_p]) < small_distance:
small_distance = abs(info_car[i][_p] - record_v[j][_p])
small_index = j
if small_index != -1:
distance_flag = True
for n in range(len(record_v)):
if small_index != n:
if (info_car[i][_p] - record_v[n][_p]) * (record_v[small_index][_p] - record_v[n][_p]) < 0:
print("相邻匹配出现位置重叠", info_car[i], record_v[small_index], record_v[n])
info_car[i] = []
distance_flag = False
break
#
if distance_flag and road_change[small_index] >= 1:
print("相邻匹配相邻车道" + '通道' + str(record_v[small_index][_ch]) + "变为" + str(info_car[i][_ch]),
"车辆" + "位置从" + str(record_v[small_index][_p]) + '变为' + str(info_car[i][_p]), info_car[i])
info_car[i].append(record_v[small_index][_v])
record_v[small_index] = info_car[i]
road_change[small_index] = 0
info_car[i] = []
else:
info_car[i] = []
road_change[small_index] += 1
return record_k, record_v, info_car
def is_path_judge(former_record_v, new_record_v):
def is_average_position(y, y_time):
tmp_y = 0
for i in y:
tmp_y += i - y[0]
if tmp_y == 0:
return 15
x = (np.array(y_time) - y_time[0]) / 1000
n = scipy.optimize.curve_fit(lambda t, a, b: a + b * t, x, y)
predict_value = []
for i in x:
predict_value.extend([abs(n[0][0] + n[0][1] * i)])
ave_pos = []
ave_time = []
for i in range(len(predict_value) - 1):
ave_pos.append(predict_value[i + 1] - predict_value[i])
ave_time.append(abs(x[i + 1] - x[i]))
ave_speed = 0
for i in range(len(ave_pos)):
if ave_pos[i] != 0 and ave_time[i] != 0:
ave_speed += ave_pos[i] / ave_time[i]
return ave_speed / len(ave_pos)
new_index = []
for new_location in range(len(new_record_v)):
if former_record_v[new_location] != new_record_v[new_location] and former_record_v[new_location][_ch] == \
new_record_v[new_location][_ch]:
new_index.append(new_location)
if len(new_index) > 0:
print("#####", former_record_v, new_index)
for i in new_index:
for j in range(len(new_record_v)):
if i != j and is_single_cross[i] and former_record_v[j][_ch] == new_record_v[j][_ch] == new_record_v[i][
_ch] == \
former_record_v[i][_ch]:
if (former_record_v[i][_p] < former_record_v[j][_p] <= new_record_v[j][_p] < new_record_v[i][_p] and
new_record_v[i][_t] - new_record_v[j][_t] < 2000) or \
new_record_v[i][_p] < new_record_v[j][_p] <= former_record_v[j][_p] < former_record_v[i][
_p]:
tmp = new_record_v[j]
new_record_v[j] = new_record_v[i]
new_record_v[i] = tmp
print("位置匹配错误，已更改", tmp, "改为", new_record_v[j])
for i in range(len(new_record_v)):
print("&&", car_his_pos[i])
if new_record_v[i] != former_record_v[i]:
vDeleteTime[i] = time.time() * 1000
car_his_pos[i].append(copy.deepcopy(new_record_v[i][_p]))
car_his_pos_time[i].append(copy.deepcopy(new_record_v[i][_t]))
if len(car_his_pos[i]) >= 10:
new_record_v[i][_v] = is_average_position(np.array(car_his_pos[i][-10:]), car_his_pos_time[i][-10:])
else:
print('没有存够位置进行速度更新', car_his_pos[i], car_his_pos_time[i])
print("^^^^", line_change)
if new_record_v[i][_ch] != former_record_v[i][_ch]: # 变道
if len(line_change[i]) != 0:
print("历史变道信息未被清除", line_change, new_record_v[i])
line_change[i] = []
line_change[i].append(copy.deepcopy(former_record_v)) # 存入变道之前所有车辆0位置
line_change[i].append(copy.deepcopy(former_record_v[i])) # 存入变道之前位置
line_change[i].append(i) # 变道车索引
line_change[i].append(kType[i]) # 变道车的车型
line_change[i].append(copy.deepcopy(k)) # 存入变道点所有车辆信息
line_change[i].append(copy.deepcopy(kType)) # 存入变道点所有车辆车型
line_change[i].append(copy.deepcopy(new_record_v[i])) # 存入第一个点进行更新
if len(line_change[i]) > 0:
latest_index = -1
latest_value = 50
if len(line_change[i]) > 10:
car_position_count = 0
for history_car_position in line_change[i][0]:
if line_change[i][3] == 0 and history_car_position[_ch] == \
line_change[i][1][_ch] and line_change[i][5][car_position_count] == 1: # 小车变成大车
if abs(history_car_position[_p] - line_change[i][1][_p]) < latest_value:
latest_value = abs(history_car_position[_p] - line_change[i][1][_p])
latest_index = car_position_count
elif line_change[i][3] == 1 and abs(
history_car_position[_ch] - line_change[i][1][_ch]) == 1 and line_change[i][5][
car_position_count] == 0: # 找不同道上是否有车为小车(变道出现异常为小车占了大车的道)
if abs(history_car_position[_p] - line_change[i][1][_p]) < latest_value:
latest_value = abs(history_car_position[_p] - line_change[i][1][_p])
latest_index = car_position_count
car_position_count += 1 # 自增点确定车辆
car_info_flag = False
record_latest_index = latest_index # 被纠正的车的历史索引
for car_info in range(len(k)):
if line_change[i][4][latest_index] == k[car_info]:
latest_index = car_info # 被纠正的车被更新为现在的索引
if car_info_flag:
print("出现错误，多次匹配！！！")
car_info_flag = True
if car_info_flag:
print("被初始匹配到的车辆仍然在轨迹中", k, k[latest_index], line_change[i][4])
else:
print("被匹配到的车辆已经被移除了，没有轨迹信息", k, line_change[i][4][latest_index], line_change[i][4])
if line_change[i][3] == 0 and car_info_flag:
if latest_value != 50:
print("小车变道出现错误", "小车变道点为", line_change[i][1], "小车已经行驶到",
line_change[i][-1]
, "最近的大车历史位置为", line_change[i][0][record_latest_index], '最近大车已经行驶到',
new_record_v[latest_index])
car_position_tmp = new_record_v[i] # 错误车辆定位
new_record_v[i] = new_record_v[latest_index]
new_record_v[latest_index] = car_position_tmp
print("位置纠正，清空变道信息")
# print("最近的大车已经行驶出范围，被移除", "历史记录定位点为", line_change[i][0], "最新的定位点为", new_record_v)
line_change[i] = []
break
else:
print("小车变道点出现问题，没有找到合适的点进行纠正", line_change[i][1], "小车已经行驶到",
line_change[i][-1],
"变道点所有车辆信息为", line_change[i][0])
if line_change[i][3] == 1 and len(
line_change[i]) > 13 and car_info_flag: # 大车变小车, 条件较严格一点
if latest_value != 50:
print("大车变道出现错误", "大车变道点为", line_change[i][1], "大车已经行驶到",
line_change[i][-1]
, "最近的小车历史位置为", line_change[i][0][record_latest_index], '最近小车已经行驶到',
new_record_v[latest_index])
car_position_tmp = new_record_v[i] # 错误车辆定位
new_record_v[i] = new_record_v[latest_index]
new_record_v[latest_index] = car_position_tmp
print("位置纠正，清空变道信息")
# print("最近的小车已经行驶出范围，被移除", "历史记录定位点为", line_change[i][0], "最新的定位点为", new_record_v)
line_change[i] = []
break
else:
print("大车变道点出现问题，没有找到合适的点进行纠正", line_change[i][1], "大车已经行驶到",
line_change[i][-1], "变道点所有车辆信息为", line_change[i][0])
if len(line_change[i]) > 0 and new_record_v[i] != line_change[i][-1] and new_record_v[i][_car_type] != \
line_change[i][3]: # 车型不一致
time_remove_list = []
for time_index in range(len(line_change[i])):
if time_index > 5 and new_record_v[i][_t] - line_change[i][time_index][_t] > 2000: # 大于2s移掉
time_remove_list.append(line_change[i][time_index])
print("$$$", line_change)
print("$$$", i)
print("$$$", new_record_v)
for _remove_position in time_remove_list:
line_change[i].remove(_remove_position)
line_change[i].append(copy.deepcopy(new_record_v[i]))
if len(line_change[i]) > 0 and new_record_v[i][_t] - line_change[i][1][_t] > 5000:
print("变道时间超过了5s仍符合条件，清空此次变道信息")
line_change[i] = []
def Iou(newMessage, oldMessage):
if newMessage[0] <= oldMessage[0] <= newMessage[1] <= oldMessage[1]:
return True
if oldMessage[0] <= newMessage[0] <= oldMessage[1] <= newMessage[1]:
return True
if oldMessage[0] <= newMessage[0] <= newMessage[1] <= oldMessage[1]:
return True
if newMessage[0] < oldMessage[0] < oldMessage[1] <= newMessage[1]:
return True
if newMessage[1] <= oldMessage[1] <= newMessage[0] <= oldMessage[0]:
return True
if oldMessage[1] <= newMessage[1] <= oldMessage[0] <= newMessage[0]:
return True
if newMessage[1] <= oldMessage[1] <= oldMessage[0] <= newMessage[0]:
return True
if newMessage[1] <= oldMessage[1] <= oldMessage[0] <= newMessage[0]:
return True
return False
def Distance(a, b, threshold):
return abs(a - b) < threshold if True else False
def isDuplicated(a, b, c1, c2):
liCheng = []
for i in range(len(b)):
liCheng.append(b[i][cPotition])
# print('licheng = ', liCheng, ", ceshi = ", a, ', c1= ', c1, ', c2 = ', c2)
# 不允许攒相同的点
if a not in liCheng and c1 == c2:
return False
# 允许攒相同的点
# if a == b[-1][cPotition] and c1 == c2:
# return False
elif a not in liCheng and c1 != c2:
return True
elif a in liCheng and c1 == c2:
return True
elif a in liCheng and c1 != c2:
return True
def initialCars(car):
global car_num
global usedRecord
global vt
global entrance
length = len(carStack)
for messageNo in car:
if len(messageNo) == 0:
continue
if messageNo[cPotition] in entrance and messageNo[cTongDao] != 0 and car_num < 8:
print('开始检测车辆：', '通道=', messageNo[cTongDao], ',测区 = ', messageNo[cPotition])
pushFlag = False
for i in range(len(carStack) - 1, -1, -1):
if messageNo not in usedRecord and isDuplicated(messageNo[cPotition], carStack[i], messageNo[cTongDao],
carStack[i][-1][cTongDao]):
usedRecord.append(messageNo)
continue
if messageNo not in usedRecord and (
(Iou(messageNo[cScope], carStack[i][-1][cScope]) and not isDuplicated(messageNo[cPotition],
carStack[i],
messageNo[cTongDao],
carStack[i][-1][
cTongDao]) and
messageNo[
cTongDao] == carStack[i][-1][cTongDao]
and messageNo[cPotition] - carStack[i][-1][cPotition] >= 0)
or
(Distance(messageNo[cPotition], carStack[i][-1][cPotition], threshold) and not isDuplicated(
messageNo[cPotition], carStack[i], messageNo[cTongDao], carStack[i][-1][cTongDao]) and
messageNo[cTongDao] == carStack[i][-1][
cTongDao] and messageNo[cPotition] -
carStack[i][-1][cPotition] >= 0)
):
if len(carStack[i]) >= initialNum:
carStack[i].append(messageNo)
vt = getSpeed(carStack[i])
# k.append(str(uuid.uuid1()))
k.append(car_num + 1)
temp = copy.deepcopy(messageNo)
ty = getCarType(carStack[i])
kType.append(ty)
line_change.append([])
push_v_predict.append(temp[_p])
road_change.append(0)
temp[cCartype] = ty
temp.append(vt)
car_his_pos.append([temp[_p]])
car_his_pos_time.append([temp[_t]])
v.append(temp)
carState.append(1)
vDeleteTime.append(time.time() * 1000)
is_single_cross.append(True)
pushFlag = True
car_num += 1
print("攒点删除:", carStack[i])
carStack.remove(carStack[i])
cStackDeleteTime.remove(cStackDeleteTime[i])
usedRecord.append(messageNo)
else:
carStack[i].append(messageNo)
pushFlag = True
usedRecord.append(messageNo)
# 匹配失败则新建一条记录
if not pushFlag and messageNo not in usedRecord:
new = [messageNo]
carStack.append(new)
cStackDeleteTime.append(time.time() * 1000)
usedRecord.append(messageNo)
usedRecord = []
# 超时删除车
def deleteCars(_car):
global k, car_num
global v
global carStack
for i in range(len(foreCastAndBianDaoStack) - 1, -1, -1):
# print("时间差2 = ", time.time() - ""cStackDeleteTime[i])
if time.time() * 1000 - foreCastAndBianDaoStack[i][-1][cTime] > deleteZanDian:
print('攒点超时删除', foreCastAndBianDaoStack[i])
foreCastAndBianDaoStack.remove(foreCastAndBianDaoStack[i])
for i in range(len(v) - 1, -1, -1):
if chuKou < v[i][vScope][0]:
v.remove(v[i])
k.remove(k[i])
carState.pop(i)
kType.pop(i)
line_change.remove(line_change[i])
road_change.remove(road_change[i])
vDeleteTime.pop(i)
is_single_cross.pop(i)
push_v_predict.remove(push_v_predict[i])
car_his_pos_time.remove(car_his_pos_time[i])
car_his_pos.remove(car_his_pos[i])
# car_num = car_num - 1
for i in range(len(carStack) - 1, -1, -1):
# print("时间差2 = ", time.time() - ""cStackDeleteTime[i])
if time.time() * 1000 - cStackDeleteTime[i] > stackTime:
print('stack超时删除', carStack[i])
carStack.remove(carStack[i])
cStackDeleteTime.remove(cStackDeleteTime[i])
# 矩形匹配
def getMaxtrixLocation():
global car
maxtrixs = []
channels = []
for carInfor in car:
if len(carInfor) == 0:
continue
# 通过大车记录矩形
if carInfor[cCartype] == 1:
maxtrixs.append(carInfor[cScope])
# 记录大车所在通道
channels.append(carInfor[cTongDao])
print('maxtrix = ', maxtrixs)
for i in range(len(car)):
if len(car[i]) != 0:
# 如果是大车则不需要判断
if car[i][cCartype] == 1:
continue
for j in range(len(maxtrixs)):
# 如果当前车辆影响范围在矩形内，并且这个车是小车或者伴随，则将这个记录抹除
if isIn(car[i][cPotition], maxtrixs[j]) and car[i][cCartype] == 0 and abs(
car[i][cTongDao] - channels[j]) == 1:
print("排除：", car[i])
car[i] = []
break
# 返回车型
def getCarType(carInfor):
global cCartype
numZero = []
numOne = []
for infor in carInfor:
if infor[cCartype] == 0:
numZero.append(0)
else:
numOne.append(1)
if len(numZero) >= len(numOne):
return 0
else:
return 1
# 返回车辆影响范围
def getCarScope(carInfor):
return carInfor[2]
# 返回车辆定位
def getCarLocation(carInfor):
return carInfor[1]
# 判断小车是否在矩形范围内
def isIn(carPosition, scope):
if scope[0] <= carPosition <= scope[1]:
return True
return False
# 计算平均速度d
def averageSpeed(speed):
return np.average(speed)
# 计算测量速度(m/s)
def ceLiangSpeed(lastP, curP, lastTime, curTime):
print('diff = ', (curTime - lastTime + 1) / 1000)
a = (curTime - lastTime + 1) / 1000
if a < 0:
print('出错！！！！！！！！！！！！！！！！！！！！！')
return (curP - lastP) / a
# 预测匹配函数(不赞点)
# def forecastDistance(zhiXing):
# # 寻找全局变量
# global car
# global p
# global vPosition
# global vTime
# global vSpeed
# # 预测距离
# forecastS = []
# # 概率分布矩阵
# forestcastArray = []
# print('当前的car是：', car)
# # 对每一辆历史记录的车计算一个预测距离
# for i in range(len(k)):
# # 上一次记录的时间
# lastTime = vDeleteTime[i]
# # 预测行驶距离 = 时间（需要将毫秒转成秒） * 速度
# timeDiff = (time.time() * 1000 - lastTime) / 1000
# if timeDiff > 0:
# distance = timeDiff * abs(v[i][vSpeed])
# # distance = ((time.time() * 1000 - lastTime) / 1000) * 22
# forecastS.append(abs(distance))
# else:
# forecastS.append(0)
# # 计算每一个历史记录的预测距离与当前所有定位的距离比值,如果有m个车在v中，car中有n条记录，那么将有m*n个记录
# print('每辆车的预测距离：', forecastS)
# for m in range(len(k)):
# distribution = []
# for n in range(len(car)):
# if len(car[n]) == 0:
# distribution.append(0)
# continue
# # 两者距离差值除预测距离
# if forecastS[m] != 0 and v[m][vPosition] != car[n][cPotition]:
# # 历史记录与车的位置差值 / 预测距离
# xDistance = pow(abs(car[n][cTongDao] - v[m][vTongDao]) * 3.75, 2)
# yDistance = pow(abs(v[m][vPosition] - car[n][cPotition]), 2)
# trueDistance = pow(xDistance + yDistance, 1 / 2)
# temp1 = (trueDistance / abs(forecastS[m]))
# temp2 = (abs(forecastS[m]) / trueDistance)
# temp = min(temp1, temp2)
# # 存放到1的距离，越接近1表示可能性越大
# distribution.append(temp)
# else:
# distribution.append(0)
# # if forecastS[m] != 0 and v[m][vPosition] != car[n][cPotition]:
# # # 历史记录与车的位置差值 / 预测距离
# # temp1 = (abs(v[m][vPosition] - car[n][cPotition]) / abs(forecastS[m]))
# # temp2 = (abs(forecastS[m]) / abs(v[m][vPosition] - car[n][cPotition]))
# # temp = min(temp1, temp2)
# # # 存放到1的距离，越接近1表示可能性越大
# # distribution.append(temp)
# # else:
# # distribution.append(0)
# # 存放概率矩阵(形状是车的个数*当前定位个数)
# forestcastArray.append(distribution)
# # 遍历概率矩阵选出最合理的定位
# maxK = []
# maxV = []
# # [概率， car索引]
# yArray = []
# print('预测行驶概率矩阵 = ', forestcastArray)
# for m in range(len(v)):
# # 找出每辆车最大概率的索引
# maxIndex = np.argmax(forestcastArray[m])
# # 保存这个概率值
# maxK.append(forestcastArray[m][maxIndex])
# # 保存这个概率对应的car索引
# maxV.append(maxIndex)
# # [概率，car索引]
# for i in range(len(maxK)):
# yArray.append([maxK[i], maxV[i]])
# print('操作矩阵 = ', yArray)
# # 去除定位点一样的概率更低的点
# cleanData = getClean(yArray)
# gaiLv = cleanData[1]
# carIndex = cleanData[0]
# # 存放筛选出的最有可能的车
# vIndex = []
# if len(gaiLv) == 0 or (len(gaiLv) == 0 and gaiLv[0] == 0):
# return
# for g in gaiLv:
# vIndex.append(np.where(np.array(maxK) == g)[0])
# for i in range(len(gaiLv)):
# if len(vIndex[i]) > 1:
# vNo = int(vIndex[i][0])
# else:
# vNo = int(vIndex[i])
# carNo = carIndex[i]
# lastSpeed = v[vNo][vSpeed]
# if len(car[carNo]) == 0:
# continue
# realSpeed = ceLiangSpeed(v[vNo][vPosition], car[carNo][cPotition], v[vNo][vTime], car[carNo][cTime])
# # newSpeed = (realSpeed + lastSpeed) / 2
# print('第' + str(vNo) + '个车以概率' + str(gaiLv[i]) + '到达' + str(car[carNo]) + "他的速度为：", v[vNo][vSpeed])
# if gaiLv[i] > zhiXing and realSpeed * lastSpeed >= 0 and v[vNo][vCartype] == car[carNo][cCartype] and abs(
# v[vNo][vTongDao] - car[carNo][cTongDao]) <= 1:
# if v[vNo][_ch] != car[carNo][_ch]:
# count = 0
# for car_no in v:
# if car[carNo][_ch] == car_no[_ch] and (car[carNo][_p] - car_no[_p]) * (v[vNo][_p] - car_no[_p]) < 0:
# count += 1
# if count > 2:
# continue
# print('预测匹配:', v[vNo], '匹配到', car[carNo], '概率为:', gaiLv[i], ' , 它的速度为：', v[vNo][vSpeed])
# v[vNo][vPosition] = car[carNo][cPotition]
# v[vNo][vScope] = car[carNo][cScope]
# v[vNo][vCartype] = car[carNo][cCartype]
# v[vNo][vTime] = car[carNo][cTime]
# v[vNo][vTongDao] = car[carNo][cTongDao]
# vDeleteTime[vNo] = time.time() * 1000
# car[carNo] = []
# 攒点预测函数
def forecastDistance(zhiXing):
car2 = []
foreList = []
# 加入攒够的轨迹
for index in range(len(foreCastAndBianDaoStack)):
if len(foreCastAndBianDaoStack[index]) == zanDianNeedNum + 1:
car2.append(foreCastAndBianDaoStack[index][-1])
foreList.append(index)
# 预测距离
forecastS = []
# 概率分布矩阵
forestcastArray = []
if len(car2) > 0:
print('当前的car是：', car2)
# 对每一辆历史记录的车计算一个预测距离
for i in range(len(k)):
# 上一次记录的时间
lastTime = vDeleteTime[i]
# 预测行驶距离 = 时间（需要将毫秒转成秒） * 速度
timeDiff = (time.time() * 1000 - lastTime) / 1000
if timeDiff > 0:
distance = timeDiff * abs(v[i][vSpeed])
# distance = ((time.time() * 1000 - lastTime) / 1000) * 22
forecastS.append(abs(distance))
else:
forecastS.append(0)
# 计算每一个历史记录的预测距离与当前所有定位的距离比值,如果有m个车在v中，car中有n条记录，那么将有m*n个记录
print('每辆车的预测距离：', forecastS)
for m in range(len(k)):
distribution = []
for n in range(len(car2)):
if len(car2[n]) == 0:
distribution.append(0)
continue
# 两者距离差值除预测距离
if forecastS[m] != 0 and v[m][vPosition] != car2[n][cPotition]:
# 历史记录与车的位置差值 / 预测距离
xDistance = pow(abs(car2[n][cTongDao] - v[m][vTongDao]) * 3.75, 2)
yDistance = pow(abs(v[m][vPosition] - car2[n][cPotition]), 2)
trueDistance = pow(xDistance + yDistance, 1 / 2)
temp1 = (trueDistance / abs(forecastS[m]))
temp2 = (abs(forecastS[m]) / trueDistance)
temp = min(temp1, temp2)
# 存放到1的距离，越接近1表示可能性越大
distribution.append(temp)
else:
distribution.append(0)
# 存放概率矩阵(形状是车的个数*当前定位个数)
forestcastArray.append(distribution)
# 遍历概率矩阵选出最合理的定位
maxK = []
maxV = []
# [概率， car索引]
yArray = []
print('预测行驶概率矩阵 = ', forestcastArray)
for m in range(len(v)):
# 找出每辆车最大概率的索引
maxIndex = np.argmax(forestcastArray[m])
# 保存这个概率值
maxK.append(forestcastArray[m][maxIndex])
# 保存这个概率对应的car索引
maxV.append(maxIndex)
# [概率，car索引]
for i in range(len(maxK)):
yArray.append([maxK[i], maxV[i]])
print('操作矩阵 = ', yArray)
# 去除定位点一样的概率更低的点
cleanData = getClean(yArray)
gaiLv = cleanData[1]
carIndex = cleanData[0]
# 存放筛选出的最有可能的车
vIndex = []
if len(gaiLv) == 0 or (len(gaiLv) == 0 and gaiLv[0] == 0):
return
for g in gaiLv:
vIndex.append(np.where(np.array(maxK) == g)[0])
for i in range(len(gaiLv)):
if len(vIndex[i]) > 1:
vNo = int(vIndex[i][0])
else:
vNo = int(vIndex[i])
carNo = carIndex[i]
lastSpeed = v[vNo][vSpeed]
if len(car2[carNo]) == 0:
continue
realSpeed = ceLiangSpeed(v[vNo][vPosition], car2[carNo][cPotition], v[vNo][vTime], car2[carNo][cTime])
# newSpeed = (realSpeed + lastSpeed) / 2
print('第' + str(vNo) + '个车以概率' + str(gaiLv[i]) + '到达' + str(car2[carNo]) + "他的速度为：", v[vNo][vSpeed])
if gaiLv[i] > zhiXing and realSpeed * lastSpeed >= 0 and v[vNo][vCartype] == car2[carNo][cCartype] and abs(
v[vNo][vTongDao] - car2[carNo][cTongDao]) <= 1 and car2[carNo][cTime] - v[vNo][vTime] > 0:
# if v[vNo][_ch] != car2[carNo][_ch]:
# count = 0
# for car_no in v:
# if car2[carNo][_ch] == car_no[_ch] and (car2[carNo][_p] - car_no[_p]) * (v[vNo][_p] - car_no[_p]) < 0:
# count += 1
# if count > 2:
# continue
print('预测匹配:', v[vNo], '匹配到', car2[carNo], '概率为:', gaiLv[i], ' , 它的速度为：', v[vNo][vSpeed])
print('lastSpeed = ', lastSpeed)
print('reakSpeed = ', realSpeed)
v[vNo][vPosition] = car2[carNo][cPotition]
v[vNo][vScope] = car2[carNo][cScope]
v[vNo][vCartype] = car2[carNo][cCartype]
v[vNo][vTime] = car2[carNo][cTime]
v[vNo][vTongDao] = car2[carNo][cTongDao]
vDeleteTime[vNo] = time.time() * 1000
foreCastAndBianDaoStack.remove(foreCastAndBianDaoStack[foreList[carNo]])
def getSpeed(carSta):
# print('carsta = ', carSta)
p1 = carSta[0][cPotition]
direction = []
# for j in range(len(carSta)):
# if j != 0:
# direction.append((carSta[j][cPotition] - p1) / abs((carSta[j][cPotition] - p1)))
direction = np.array(direction)
num1 = np.where(direction == 1)[0]
num2 = np.where(direction == -1)[0]
print(carSta[0][cTime])
speed = abs(carSta[-1][cPotition] - p1) / ((carSta[-1][cTime] - carSta[0][cTime] + 1) / 1000)
# print('时间差:', ((carSta[-1][cTime] - carSta[0][cTime])) if num1.sh)
# print('速度是：', speed)
# #ape[0] > num2.shape[0]:
# return speed
# else:
# return -1 * speed
if abs(speed) == 0:
return 10
else:
return abs(speed)
# 攒点
def zanDian():
print('hehe1')
# 在最开始获取数组长度
length = len(foreCastAndBianDaoStack)
for c in car:
pushFlag = False
if len(c) > 0:
if c[cPotition] != 0 and c[cPotition] not in entrance and c[cPotition] > entrance[-1]:
for i in range(length):
# print('dd, ', foreCastAndBianDaoStack[i][-1][cPotition])
if 0 < c[cPotition] - foreCastAndBianDaoStack[i][-1][cPotition] < zanDianThreshold and c[
cTongDao] == foreCastAndBianDaoStack[i][-1][cTongDao] and len(
foreCastAndBianDaoStack[i]) <= zanDianNeedNum:
foreCastAndBianDaoStack[i].append(c)
pushFlag = True
if not pushFlag:
foreCastAndBianDaoStack.append([c])
# print('foreCastAndBianDaoStack = ', foreCastAndBianDaoStack)
if __name__ == '__main__':
encoder = RoadEncoder(lanes=4)
msg = MessageServer()
msg_s = MessagePublisher()
msg_s.mile_range = encoder.mil_range
t_r = threading.Thread(target=msg.ReceiveThread, args=())
t = threading.Thread(target=msg_s.SendThread, args=())
t.setDaemon(True)
t_r.setDaemon(True)
t_r.start()
t.start()
map_data = []
# 预备初始化的记录
carStack = []
# 已使用过的record
usedRecord = []
# 车的个数
car_num = 0
# 初始化最小距离
threshold = 20
# 初始化攒点个数
initialNum = 1
# 攒点删除时间
deleteZanDian = 1200
# 删除车辆时间
timeThreshold = 5000
# 栈时间
stackTime = 1000
k = []
v = []
kType = []
_ch = 0 # 通道号
_p = 1 # 位置
_max = 2
_scope = 3 # 范围
_t = 5 # 时间
_car_type = 4
_v = 6
# _max_time_limited = 0.2 # 0.5滑窗设为0.5 0.1设为0.1
_min_time_limited = 0.01 # 0.5滑窗设为0.3 0.1设为0.05, 0.2设为0.01
# v
vTime = 5 # v中存放时间的索引
vSpeed = 6 # v中存放速度的索引
vPosition = 1 # v中存放定位的索引
vScope = 3 # v中存放范围的索引
vCartype = 4 # v中存放车型的索引
vTongDao = 0 # v中存放车型的索引
# car
cCartype = 4 # car中存放车型的索引
cTime = 5 # car中存放时间的索引
cPotition = 1 # car中存放定位的索引
cScope = 3 # car中存放范围的索引
cTongDao = 0 # car中存放通道的索引
pushT = 0
car = []
vDeleteTime = []
cStackDeleteTime = []
t = 0
# 车辆入口
start = 10048
end = 9998
entrance = [i for i in range(end, start)]
car_his_pos = []
car_his_pos_time = []
line_change = []
road_change = []
push_v_predict = []
zhiXingDu = 0.85
chuKou = 10708
foreCastAndBianDaoStack = []
push_tmp_vaild = []
zanDianNeedNum = 1
zanDianThreshold = 35
carState = [] # 存放车辆行驶状态（0表示停车态、1表示行车态）
flag = True
is_single_cross = []
while True:
print()
print('start:*****************************')
time.sleep(_min_time_limited)
data = msg.Dequeue()
# if data is None:
# pass
# else:
# map_data = encoder.MappingData(data)
# if map_data is None:
# pass
# # print('数据不全')
# else:
# print(map_data)
if data is None:
pass
else:
try:
map_data = encoder.MappingData(data)
if map_data is None:
pass
# print('数据不全')
else:
print()
print("@@@@@@@@@@")
print('map_data:', map_data)
# file = open("E:\\推送信息6.txt", 'a+', encoding='UTF-8')
# file.writelines("第" + str(t) + "次：" + str(map_data) + "\n")
# file.close()
# for da in map_data:
# channnelNo = da[0]
# file = open("E:\\"+str(channnelNo)+"定位信息.txt", 'a+', encoding='utf-8')
# file.writelines(str(da)+"\n")
# file.close()
car = copy.deepcopy(map_data)
first_car = car[0]
if len(car) > 0:
pushT = car[0][-1]
# 矩形判断筛选
# getMaxtrixLocation()
tmp = []
for c in car:
if len(c) > 0:
if c[cTongDao] == 3 or c[cTongDao] == 2 or c[cTongDao] == 1:
tmp.append(c)
car = tmp
print('car = ', car)
for i in range(len(is_single_cross)):
is_single_cross[i] = True
# 影响范围判断
former_v = copy.deepcopy(v)
is_cross_match(k, v, car, kType)
is_path_judge(former_v, v)
# 匹配不上的点攒下来
zanDian()
# 预测匹配
forecastDistance(zhiXingDu)
# 推送时间
print('k = ', k)
print('v = ', v)
print('carStack = ', carStack)
print('stackLen = ', len(carStack))
print('car_num = ', car_num)
print('kType = ', kType)
# if len(v) == 4:
# v.append([1, v[-1][vPosition], 0.3121134638786316, v[-1][vScope], 0, v[-1][vTime], v[-1][vSpeed]])
# k.append(car_num + 1)
# vDeleteTime.append(time.time() * 1000)
# kType.append(0)
# car_num += 1
# car_his_pos.append([v[-1][_p]])
# car_his_pos_time.append([v[-1][_t]])
# seven
# for i in range(len(v)):