import proto.cameraobjectarray_pb2 as cameraobjectarray_pb2 import proto.decitionspeedlimit_pb2 as decitionspeedlimit_pb2 import proto.lightarray_pb2 as lightarray_pb2 import proto.objectarray_pb2 as objectarray_pb2 import proto.chassis_pb2 as chassis_pb2 import math from typing import List from datetime import datetime, timedelta import time from xdg.Config import setIconSize class Point2D: def __init__(self, x, y,hdg): self.mx = x self.my = y self.mhdg = hdg while self.mhdg < 0: self.mhdg = self.mhdg + 2.0* math.pi while self.mhdg >= 2.0*math.pi: self.mhdg = self.mhdg - 2.0* math.pi def __str__(self): return f"Point2D({self.mx}, {self.my})" class CameraDecision: def __init__(self): self.mendacc = -0.7 # 抵达终点时的减速度为 -0.7 m/s² self.mmaxwheel = 430 # 最大方向盘角度 self.mdefaultacc = 1.0 # 加速时的默认加速度 self.mspeed = 10.0 # 目标速度 10 km/h self.cspeed = self.mspeed self.mstopdistoobs = 6.0 # 距离障碍物6米时停车 self.mstopdisacc = -1.0 # 障碍物检测时的减速度 self.mvehwidth = 2.3 # 车辆宽度 self.speed5_nodetect = 0 self.is_stop = False pass def CalcDecision(self, x_chassis, xobjarray_sign, xobjarray_light, xobjarray_lidar): acc = 0.0 # 加速度，m/s² (>0 加速, <0 制动) wheel = 0.0 # 方向盘转角 (>0 左转, <0 右转, 范围: -430 ~ 430) speed = 0.0 # 车速 leftLamp = False # 左转向灯 rightLamp = False # 右转向灯 # 获取当前车辆速度 veh_speed = x_chassis.vel print("veh speed: ",veh_speed) # 控制加速度，使车速维持在目标速度 mspeed 附近 if not self.is_stop: if veh_speed < self.cspeed: acc = min(0.5, self.cspeed - veh_speed) # 根据差值调整加速度，最大为0.5 m/s² elif veh_speed > self.cspeed: acc = max(-0.5, self.cspeed - veh_speed) # 根据差值调整减速度，最小为-0.5 m/s² ######################决策逻辑代码######################## if self.speed5_nodetect < 20: self.speed5_nodetect += 1 # 基于标志物的决策 for pobj in xobjarray_sign.obj: print("sign type: ",pobj.type) if pobj.type == "Left": # 左转标志 wheel = min(self.mmaxwheel, 30) # 控制转角 leftLamp = True # 打开右转向灯 if pobj.type == "Right": # 右转标志 wheel = max(-self.mmaxwheel, -30) # 控制转角 rightLamp = True # 打开左转向灯 if pobj.type == "Speed5": # 限速5 km/h标志 self.cspeed = 5 # 将目标速度调整为 5 km/h self.speed5_nodetect = 0 if self.speed5_nodetect > 10: self.cspeed = self.mspeed # 基于交通信号灯的决策 for light in xobjarray_light.light: if light.type == 1: # 绿灯，加速通过 self.is_stop = False acc = 0.5 # 加速 elif light.type == 2: # 红灯，停止 self.is_stop = True acc = -1.0 # 大幅减速 ############################################################ # 创建并返回决策对象 xdecisiion = decitionspeedlimit_pb2.decitionspeedlimit() xdecisiion.wheelAngle = wheel xdecisiion.accelerator = acc xdecisiion.brake = 0 xdecisiion.speed = speed xdecisiion.leftLamp = leftLamp xdecisiion.rightLamp = rightLamp print("acc: ",acc) # 制动决策 if acc < 0: xdecisiion.brake = abs(acc) # 将负加速度转换为制动值 xdecisiion.torque = 0 # 加速度为负时，无需扭矩输出 else: xdecisiion.brake = 0 # 没有制动 fVehWeight = 1800 # 车辆重量，单位：kg fRollForce = 50 # 滚动阻力 fRatio = 2.5 # 扭矩比例因子 fNeed = fRollForce + fVehWeight * acc # 计算所需的动力 xdecisiion.torque = 100 # 计算输出扭矩 if acc == 0: xdecisiion.torque = 0 return xdecisiion def is_point_in_rotated_rectangle(self,x, y, x1, y1, yaw, l, w): # 将长方形的左下角坐标转换到原点 x_rel = x - x1 y_rel = y - y1 # 计算旋转矩阵（逆时针旋转） # | cos(yaw) -sin(yaw) | # | sin(yaw) cos(yaw) | cos_yaw = math.cos(yaw) sin_yaw = math.sin(yaw) # 应用旋转矩阵到相对坐标 x_rotated = x_rel * cos_yaw + y_rel * sin_yaw y_rotated = -x_rel * sin_yaw + y_rel * cos_yaw # 判断点是否在旋转后的长方形内 # 长方形的边界在旋转后的坐标系中是 [-l/2, l/2] x [-w/2, w/2] if -l/2 <= x_rotated <= l/2 and -w/2 <= y_rotated <= w/2: return True else: return False def GaussProj(self,lon,lat): iPI = 0.0174532925199433 ZoneWide = 6 a = 6378245.0 f = 1.0 / 298.3 ProjNo = int(lon / ZoneWide) longitude0 = ProjNo * ZoneWide + ZoneWide / 2 longitude0 = longitude0 * iPI latitude0 = 0 longitude1 = lon * iPI #经度转换为弧度 latitude1 = lat * iPI #//纬度转换为弧度 e2 = 2 * f - f * f ee = e2 * (1.0 - e2) NN = a / math.sqrt(1.0 - e2 * math.sin(latitude1)*math.sin(latitude1)) T = math.tan(latitude1)*math.tan(latitude1) C = ee * math.cos(latitude1)*math.cos(latitude1) A = (longitude1 - longitude0)*math.cos(latitude1) M = a * ((1 - e2 / 4 - 3 * e2*e2 / 64 - 5 * e2*e2*e2 / 256)*latitude1 - (3 * e2 / 8 + 3 * e2*e2 / 32 + 45 * e2*e2*e2 / 1024)*math.sin(2 * latitude1)+ (15 * e2*e2 / 256 + 45 * e2*e2*e2 / 1024)*math.sin(4 * latitude1) - (35 * e2*e2*e2 / 3072)*math.sin(6 * latitude1)) xval = NN * (A + (1 - T + C)*A*A*A / 6 + (5 - 18 * T + T * T + 72 * C - 58 * ee)*A*A*A*A*A / 120) yval = M + NN * math.tan(latitude1)*(A*A / 2 + (5 - T + 9 * C + 4 * C*C)*A*A*A*A / 24 + (61 - 58 * T + T * T + 600 * C - 330 * ee)*A*A*A*A*A*A / 720) X0 = 1000000 * (ProjNo + 1) + 500000 Y0 = 0 xval = xval + X0; yval = yval + Y0; X = xval Y = yval return X,Y def GaussProjInvCal(self,X,Y): iPI = 0.0174532925199433 #3.1415926535898/180.0; a = 6378245.0 f = 1.0 / 298.3 # //54年北京坐标系参数 #////a=6378140.0; f=1/298.257; //80年西安坐标系参数 ZoneWide = 6 # ////6度带宽 ProjNo = int(X / 1000000) # //查找带号 longitude0 = (ProjNo - 1) * ZoneWide + ZoneWide / 2 longitude0 = longitude0 * iPI # //中央经线 X0 = ProjNo * 1000000 + 500000 Y0 = 0 xval = X - X0; yval = Y - Y0 #//带内大地坐标 e2 = 2 * f - f * f e1 = (1.0 - math.sqrt(1 - e2)) / (1.0 + math.sqrt(1 - e2)) ee = e2 / (1 - e2) M = yval u = M / (a*(1 - e2 / 4 - 3 * e2*e2 / 64 - 5 * e2*e2*e2 / 256)) fai = u + (3 * e1 / 2 - 27 * e1*e1*e1 / 32)*math.sin(2 * u) + (21 * e1*e1 / 16 - 55 * e1*e1*e1*e1 / 32)*math.sin(4 * u)+ (151 * e1*e1*e1 / 96)*math.sin(6 * u) + (1097 * e1*e1*e1*e1 / 512)*math.sin(8 * u) C = ee * math.cos(fai)*math.cos(fai) T = math.tan(fai)*math.tan(fai) NN = a / math.sqrt(1.0 - e2 * math.sin(fai)*math.sin(fai)) R = a * (1 - e2) / math.sqrt((1 - e2 * math.sin(fai)*math.sin(fai))*(1 - e2 * math.sin(fai)*math.sin(fai))*(1 - e2 * math.sin(fai)*math.sin(fai))) D = xval / NN #//计算经度(Longitude) 纬度(Latitude) longitude1 = longitude0 + (D - (1 + 2 * T + C)*D*D*D / 6 + (5 - 2 * C + 28 * T - 3 * C*C + 8 * ee + 24 * T*T)*D*D*D*D*D / 120) / math.cos(fai) latitude1 = fai - (NN*math.tan(fai) / R)*(D*D / 2 - (5 + 3 * T + 10 * C - 4 * C*C - 9 * ee)*D*D*D*D / 24 + (61 + 90 * T + 298 * C + 45 * T*T - 256 * ee - 3 * C*C)*D*D*D*D*D*D / 720) #//转换为度 DD longitude = longitude1 / iPI latitude = latitude1 / iPI return longitude,latitude