SELECT g.G6_DETECTION_ID G6_DETECTION_ID, t.veh_clzzqymc sccmc, t.veh_clxh vehicle_model, g.VIN VIN, t.VIN TVIN, tt.VIN TTVIN, tt.CAR_TYPE CARTYPE, tt.XXGKBH XXGKBH, t.VEH_CLPP SB, t.VEH_CPSCDZ SCCDZ, to_char( t.VEH_CLSCRQ, 'yyyy"年"mm"月"dd"日"' ) SCDATE, t.VEH_FDJH FDJH, g.FDJSB FDJSB, g.FDJSCCDZ FDJSCCDZ, g.RH RH, g.ET ET, g.AP AP, g.TESTTYPE TEST_TYPE, g.TESTNO TESTNO, g.TESTDATE TEST_DATE, g.APASS APASS, g.OPASS OPASS, g.OTESTDATE OTESTDATE, g.EPASS EPASS, g.RESULT RESULT, g.YR YR, g.ANALYMANUF ANALYMANUF, g.ANALYNAME ANALYNAME, g.ANALYMODEL ANALYMODEL, g.ANALYDATE ANALYDATE, g.DYNOMODEL DYNOMODEL, g.DYNOMANUF DYNOMANUF, g.ISUPLOAD ISUPLOAD, g.UPLOADDATE UPLOADDATE, g.ISUPLOADSECCESS ISUPLOADSECCESS, g.UPLOADUSER UPLOADUSER, g.UPLOADPERSON UPLOADPERSON, g.UPLOADMES UPLOADMES, g.ISAUTOUPLOAD, t.is_sample ISSAMPLE, t.FRIST_PRINT_DATE BANCI, g.OBDINSPECTOR, SUBSTR( g.VIN, 8, 1 ) AS DLLX, T_DLLX.NAME AS DLLXNAME FROM G6_DETECTION_RESULT g LEFT JOIN T_VEHCERT_PRINT t ON g.VIN = t.VIN LEFT JOIN T_HBBQ_PRINT tt ON g.VIN = tt.VIN LEFT JOIN T_DLLX ON SUBSTR( g.VIN, 8, 1 ) = T_DLLX.CODE WHERE tt.car_type!='E' 通过car_type查询出来的数据应该有五条，但是自动去除car_type为空的四条数据，怎么做可以不去掉这四条数据

SELECT g.G6_DETECTION_ID G6_DETECTION_ID, t.veh_clzzqymc sccmc, t.veh_clxh vehicle_model, g.VIN VIN, t.VIN TVIN, tt.VIN TTVIN, tt.CAR_TYPE CARTYPE, tt.XXGKBH XXGKBH, t.VEH_CLPP SB, t.VEH_CPSCDZ SCCDZ, to_char( t.VEH_CLSCRQ, 'yyyy"年"mm"月"dd"日"' ) SCDATE, t.VEH_FDJH FDJH, g.FDJSB FDJSB, g.FDJSCCDZ FDJSCCDZ, g.RH RH, g.ET ET, g.AP AP, g.TESTTYPE TEST_TYPE, g.TESTNO TESTNO, g.TESTDATE TEST_DATE, g.APASS APASS, g.OPASS OPASS, g.OTESTDATE OTESTDATE, g.EPASS EPASS, g.RESULT RESULT, g.YR YR, g.ANALYMANUF ANALYMANUF, g.ANALYNAME ANALYNAME, g.ANALYMODEL ANALYMODEL, g.ANALYDATE ANALYDATE, g.DYNOMODEL DYNOMODEL, g.DYNOMANUF DYNOMANUF, g.ISUPLOAD ISUPLOAD, g.UPLOADDATE UPLOADDATE, g.ISUPLOADSECCESS ISUPLOADSECCESS, g.UPLOADUSER UPLOADUSER, g.UPLOADPERSON UPLOADPERSON, g.UPLOADMES UPLOADMES, g.ISAUTOUPLOAD, t.is_sample ISSAMPLE, t.FRIST_PRINT_DATE BANCI, g.OBDINSPECTOR, SUBSTR( g.VIN, 8, 1 ) AS DLLX, T_DLLX.NAME AS DLLXNAME FROM G6_DETECTION_RESULT g LEFT JOIN T_VEHCERT_PRINT t ON g.VIN = t.VIN LEFT JOIN T_HBBQ_PRINT tt ON g.VIN = tt.VIN LEFT JOIN T_DLLX ON SUBSTR( g.VIN, 8, 1 ) = T_DLLX.CODE WHERE tt.car_type!='E' 通过car_type预期查询出来应该有一条数据，但是结果因为这条数据car_type为空被自动去除了，如何让他不自动去除

可以改为使用 "IS NOT NULL" 条件来筛选非空值，例如将 WHERE 子句改为： WHERE tt.car_type IS NOT NULL AND tt.car_type != 'E' 这样可以保留 car_type 为空的数据，同时排除 car_type 为 'E' 的数据。

SELECT * FROM ( SELECT TMP_PAGE.*, ROWNUM ROW_ID FROM ( SELECT tt.ID, tt.VIN, tt.AON, tt.MSC, tt.VEH_CLSCRQ, tt.COLOR_CODE, tt.VEH_ZCHGZBH, tt.VEH_WZHGZBH, tt.VEH_FZRQ, tt.FRIST_PRINT_DATE, tt.VEH_CLZZQYMC, tt.VEH_CLPPMC, tt.IFLOCK, tt.UP_SYNCSTATE, tt.VEH_CLXH, tt.IS_SAMPLE, ta.XNYC AS XNYC, SUBSTR( VIN, 8, 1 ) AS DLLX, T_DLLX.NAME AS DLLXNAME FROM T_VEHCERT_PRINT tt LEFT JOIN T_ALL_PARAM ta ON tt.MSC = ta.MSC LEFT JOIN T_DLLX ON SUBSTR( VIN, 8, 1 ) = T_DLLX.CODE WHERE XNYC = '否' AND IFLOCK = '0' AND UP_SYNCSTATE ='1' ORDER BY frist_print_date DESC ) TMP_PAGE WHERE ROWNUM <= '10' ) WHERE ROW_ID > '0'这段sql查询半天都查不出结果

这段 SQL 查询语句的目的是从 T_VEHCERT_PRINT 表中选择一些列，并将它们与其他表连接，然后按照 frist_print_date 字段降序排序。然后，它将结果限制为前 10 行，并为每一行分配一个 ROW_ID。最后，它选择所有 ROW_...

std::string data; perception_obstacles_.SerializeToString(&data); VehData veh_data; veh_data.set_messagetype(5); veh_data.set_messagedes("PerceptionObstacles"); veh_data.set_contents(data); std::string serialized_data; veh_data.SerializeToString(&serialized_data); // webSocket.send("sv hello world"); webSocket.send(serialized_data); // webSocket.sendBinary("some serialized binary data"); 修改代码将proto结构的veh_data以二进制发送

您可以使用 sendBinary 函数将序列化后的 veh_data 数据以二进制形式发送。以下是修改后的代码示例： c++ std::string data; perception_obstacles_.SerializeToString(&data); VehData veh_data; veh_data...

优化这段代码 function [car, time_end] = Veh_following_IDM(car, time, time_step) time_end = 0; car.a_pre = car.a; car.d(:, :) = 0; %--------------更新速度和位置--------------% for car_n = length(car.v):-1:1 car.x(car_n) = car.v(car_n) * time_step + (car.a(car_n) * time_step^2) / 2 + car.x(car_n); car.v(car_n) = max(car.a(car_n) * time_step + car.v(car_n), 0); % 约束速度项大于等于0 end %--------------计算加速度--------------% sort_x = sort(car.x); car_n_last = length(sort_x); for car_id = length(sort_x):-1:1 car_n = car_id; if car_n ~= car_n_last car_n_front = car_id + 1; % 找出前车 [a_n] = acc_calculate(car, car_n, car_n_front); car.a(car_n) = a_n; if car.f(car_id) ~= 0 % 其他的操作 end else car.a(car_n) = 0; end end if sum(car.v(:,:)) <= 0.001 && time > 0.1 time_end = time; end end %% 车辆加速度计算函数，IDM模型 function [a_n] = acc_calculate(car, car_n, car_n_front) global road_length d_max h_safe car_length v_max a_max d_safe theta kappa_i road_width time_step =0.1; delta_x = car.x(car_n_front) - car.x(car_n) - car_length; delta_y = car.y(car_n_front)- car.y(car_n) ; theta = delta_y / delta_x; if delta_x < 0 delta_x = delta_x + road_length; end v_n_plus = car.v(car_n) * cos(theta); v_n_minus = car.v(car_n) * sin(theta); delta_v = v_n_plus - car.v(car_n_front)* cos(theta); term1 = 1 - (v_n_plus / v_max)^4; term2 = (((d_safe + v_n_plus * kappa_i * h_safe) + (v_n_plus * delta_v) / (2 * sqrt(a_maxd_max))) / (delta_x - car_length))^2; term3 =delta_y / road_width; term4 = (2 (v_n_minus time_step+ delta_y)) / (time_step^2); a_n = a_max (term1 - term2) + term3 *term4; end

function [car, time_end] = Veh_following_IDM(car, time, time_step) time_end = 0; car.a_pre = car.a; car.d(:, :) = 0; %--------------更新速度和位置--------------% car.x = car.v * time_step + ...

void UpdateDbMainInput(void) { #if (DB_TYPE != DB_IBC) /* read chassinf info 阅读追逐信息 / if (IsChassinfFunctionEnable(CHASSINF_RB) != FALSE) { DbIn.u.Flg.DbEnabled_b1 = TRUE; } else { DbIn.u.Flg.DbEnabled_b1 = FALSE; } / read roller bench mode status读取滚轮工作台模式状态 / DbIn.u.Flg.RollerBenchAct_b1 = IsRollerBenchModeActive(); / read main circuit pressure 读取主回路压力 / if (IsInputValid(INPUT_VALID_BRAKE_PRESSURE) == TRUE) { DbIn.u.Flg.McPressVal_b1 = TRUE; DbIn.McPress_s16 = GetMcpFiltered(); DbIn.McPressGrad_s16 = GetMcpDot(); } else { DbIn.u.Flg.McPressVal_b1 = FALSE; DbIn.McPress_s16 = 0; DbIn.McPressGrad_s16 = 0; } / get brake activity of any other performance subsystem 获取任何其他性能子系统的制动活动 / DbIn.u.Flg.OthPerfAct_b1 = IsBrakeControlArbitratorActive(); / read vehicle speed and acceleration 读取车速和加速度 / DbIn.VehSpd_s16 = GetZeroSlipVehicleVelocity(); DbIn.VehAcc_s16 = GetVehicleAccel(); / get actual inclination 获得实际倾斜度 / DbIn.HillAccAbs_s16 = ABS(GetHillAccelFilt()); / calculate inclination dependent hold pressure 计算倾斜相关的保持压力 / DbIn.HoldPress_s16 = (S16)((((((((((((S32)DbIn.HillAccAbs_s16 ((GetTireSize(FRONT_AXLE) + GetTireSize(REAR_AXLE) ) / AXLE_MAX ) ) / METER ) * TORQUE_PRESSURE_CONV_SF ) / VEH_ACCEL_RES ) * PI_SCALE_FACTOR ) / (S32)(2 * PI * PI_SCALE_FACTOR) ) * GetChassinfVehicleMass() ) / KILOGRAM ) * BAR ) / WHEEL_MAX ) / ((GetBrakeTorqueFactor(FRONT_AXLE) + GetBrakeTorqueFactor(REAR_AXLE) ) / AXLE_MAX ) );这段代码什么意思，有什么作用

6. 获取实际倾斜度的绝对值。 7. 根据倾斜度计算出倾斜相关的保持压力。具体而言，第7步是一个较为复杂的计算，其计算公式如下： (((((HillAccAbs_s16 * ((GetTireSize(FRONT_AXLE) + GetTireSize(REAR_AXLE)) / ...

Traceback (most recent call last): File "/home/yjy/Documents/code/MATP-with-HEAT/it_all_train.py", line 171, in <module> train_loss_ep = train_a_model(train_net, num_ep=ep) File "/home/yjy/Documents/code/MATP-with-HEAT/it_all_train.py", line 37, in train_a_model fut_pred = model_to_tr(data.to(args['device'])) File "/home/yjy/anaconda3/envs/MATP/lib/python3.7/site-packages/torch/nn/modules/module.py", line 889, in _call_impl result = self.forward(*input, **kwargs) File "/home/yjy/Documents/code/MATP-with-HEAT/it_heat_g_model.py", line 53, in forward fwd_Hist_Enc = self.RNN_Encoder(data_pyg.x, data_pyg.veh_mask, data_pyg.ped_mask) # Encode File "/home/yjy/Documents/code/MATP-with-HEAT/it_base_model.py", line 39, in RNN_Encoder _, veh_Hist_Enc = self.veh_enc_rnn(self.leaky_relu(self.ip_emb(Hist))) File "/home/yjy/anaconda3/envs/MATP/lib/python3.7/site-packages/torch/nn/modules/module.py", line 889, in _call_impl result = self.forward(*input, **kwargs) File "/home/yjy/anaconda3/envs/MATP/lib/python3.7/site-packages/torch/nn/modules/linear.py", line 94, in forward return F.linear(input, self.weight, self.bias) File "/home/yjy/anaconda3/envs/MATP/lib/python3.7/site-packages/torch/nn/functional.py", line 1753, in linear return torch._C._nn.linear(input, weight, bias) RuntimeError: mat1 and mat2 shapes cannot be multiplied (2320x4 and 128x32)

这个错误是由于你的代码中进行了矩阵乘法操作，但是两个矩阵的维度不匹配。根据错误信息，第一个矩阵的大小是2320x4，而第二个矩阵的大小是128x32。矩阵乘法要求第一个矩阵的列数与第二个矩阵的行数相等。...

if len(round_camera_obj_list) != 0: for i in range(len(round_camera_obj_list)): if -5 < round_camera_obj_list[i]['d_y'] < 15: if -4 < round_camera_obj_list[i]['d_x'] < 0: side_safe_flag_list[1] = 0 else: side_safe_flag_list[1] = 1 if 0 < round_camera_obj_list[i]['d_x'] < 4: side_safe_flag_list[0] = 0 else: side_safe_flag_list[0] = 1 else: side_safe_flag_list[0] = 1 side_safe_flag_list[1] = 1 color_print_lib.prRed('front_veh: ' + str(front_veh)) color_print_lib.prRed('left_side_front_veh: ' + str(left_side_front_veh)) color_print_lib.prRed('right_side_front_veh: ' + str(right_side_front_veh)) front_veh_v = front_veh left_side_front_veh_v = left_side_front_veh right_side_front_veh_v = right_side_front_veh side_safe_flag_list_v = side_safe_flag_list return front_veh, left_side_front_veh, right_side_front_veh, side_safe_flag_list解释一下

这段代码是一个函数，它接受一个参数 round_camera_obj_list，然后对其进行处理，并返回四个值：front_veh、left_side_front_veh、right_side_front_veh 和 side_safe_flag_list。在函数内部，首先判断 round_...

将veh_data以二进制通过ixwebsocket的sendBinary发送，std::string data; perception_obstacles_.SerializeToString(&data); VehData veh_data; veh_data.set_messagetype(5); veh_data.set_messagedes("PerceptionObstacles"); veh_data.set_contents(data);

要将 veh_data 以二进制形式通过 ixWebSocket 的 sendBinary 函数发送，您可以先将 veh_data 序列化为一个字符串，然后将该字符串转换为二进制数据并发送。以下是示例代码： c++ std::string data; ...

"xstream_workflow_file": "./video_box/configs/body_detection.json", "xstream_workflow_file_pic": "./video_box/configs/recog_flow.json", "xstream_workflow_file_feature": "./video_box/configs/feature_flow.json", "enable_recog": false, "enable_profile": 0, "profile_log_path": "", "enable_result_to_json": false, "box_face_thr": 0.95, "box_head_thr": 0.95, "box_body_thr": 0.95, "lmk_thr": 0.0, "kps_thr": 0.50, "box_veh_thr": 0.995, "plot_fps": false, "rtsp_config_file": "./video_box/configs/rtsp.json", "display_config_file": "./video_box/configs/display.json", "drop_frame_config_file": "./video_box/configs/drop_frames.json", "run_smart": true, "save_dec_video": false解释下

这段代码看起来像是一个配置文件，其中包含了一些参数的设置。这些参数的含义可能与视频分析相关，例如检测框的阈值（box_face_thr, box_head_thr, box_body_thr）和关键点的阈值（lmk_thr, kps_thr），还有视频流的...

import argparse import os from gen_kitti.label_coopcoord_to_cameracoord import gen_veh_lidar2veh_cam from gen_kitti.label_json2kitti import json2kitti, rewrite_label, label_filter from tools.data_converter.gen_kitti.gen_calib2kitti_coop import gen_calib2kitti_coop from gen_kitti.gen_ImageSets_from_split_data import gen_ImageSet_from_coop_split_data parser = argparse.ArgumentParser("Generate the Kitti Format Data") parser.add_argument("--source-root", type=str, default="D:\XINDE\EMIFF-main\data\DAIRV2X\cooperative-vehicle-infrastructure", help="Raw data root about DAIR-V2X.") parser.add_argument( "--target-root", type=str, default="./0623_vic_coop_v", help="The data root where the data with kitti format is generated", ) parser.add_argument( "--split-path", type=str, default="data/split_datas/cooperative-split-data.json", help="Json file to split the data into training/validation/testing.", ) parser.add_argument("--label-type", type=str, default="lidar", help="label type from ['lidar', 'camera']") parser.add_argument("--sensor-view", type=str, default="vehicle", help="Sensor view from ['infrastructure', 'vehicle']") parser.add_argument("--no-classmerge", action="store_true", help="Not to merge the four classes [Car, Truck, Van, Bus] into one class [Car]") parser.add_argument("--temp-root", type=str, default="./tmp_file", help="Temporary intermediate file root.") def mdkir_kitti(target_root): if not os.path.exists(target_root): os.makedirs(target_root) os.system("mkdir -p %s/training" % target_root) os.system("mkdir -p %s/training/calib" % target_root) os.system("mkdir -p %s/training/label_2" % target_root) os.system("mkdir -p %s/testing" % target_root) os.system("mkdir -p %s/ImageSets" % target_root) def rawdata_copy(source_root, target_root): os.system("cp -r %s/image %s/training/image_2" % (source_root, target_root)) # os.system("cp -r %s/velodyne %s/training" % (source_root, target_root))

f.write("P1: 7.070912e+02 0.000000e+00 6.018873e+02 0.000000e+00\n") if __name__ == "__main__": parser = argparse.ArgumentParser() parser.add_argument("--root", type=str, default="./kitti_data", ...

std::vector<VehiclePoint> ApaService::HandlePointMap(const ADCU_HmiServiceCommonTypes::VehiclePointMap &V_PointMap) { std::vector<VehiclePoint> V_VehiclePoint; if (V_PointMap.size() > 0) { V_VehiclePoint.reserve(V_PointMap.size()); uint16_t PointCnt = 0; for (auto &&veh_point : V_PointMap) { VehiclePoint point; point.set_x(veh_point.getPositionXSeN()); point.set_y(veh_point.getPositionYSeN()); V_VehiclePoint.emplace_back(point); LOGD("AdcuService %s PointCnt = %d, PositionXSeN = %d, PositionYSeN = %d", func, PointCnt, V_VehiclePoint[PointCnt].x(), V_VehiclePoint[PointCnt].y()); ++PointCnt; } } return V_VehiclePoint; }

在循环内部，代码创建了一个名为point的VehiclePoint对象，并使用veh_point.getPositionXSeN()和veh_point.getPositionYSeN()函数获取相应位置的数据，并将其设置到point对象中。接着，代码通过调用V_...

void ApaService::onSettingAPAInfoInfChanged(const ADCU_HmiServiceCommonTypes::SettingAPAInfo &_data) { SettingAPAInfo msg; m_APASwitch = static_cast<ADCU_HmiServiceCommonTypes::OnOff::Literal>(_data.getAPASwitchSeN().value_); msg.set_apaswitchsen(_data.getAPASwitchSeN().value_); LOGD("AdcuService %s, APASwitchSeN = %d, ", func, msg.apaswitchsen()); APA_MSG apa_msg; apa_msg.mutable_m_adcusettingapainfo()->CopyFrom(msg); Notify(ADCU_EVENT_ID::APA_E_SETTINGINFOINF, apa_msg); if(apa_callback_handle || !(LOGI("apa_callback_handle is NULL!!!!!"))) { apa_callback_handle(m_APASwitch); } } int VehicleBusProxy::Notify( uint32_t nId, CVehicleBusMsg& _bus_msg ) { int result = 0; uint32_t key = nId; if( 0 == m_veh_bus_notify_list.count( key ) ) { result = -1; } else { m_veh_bus_notify_list[key]->RecvBusValue( _bus_msg ); } return result; }

第二个函数是VehicleBusProxy::Notify，该函数接收一个类型为uint32_t的参数nId和一个类型为CVehicleBusMsg的引用参数_bus_msg。函数内部首先声明了一个名为result的整型变量，并初始化为0。然后，...

在ros项目中添加发送websocket wss消息的功能，修改如下代码并在CmakeLists.txt中添加依赖，实现将serialized_data发送到wss://autopilot-test.t3go.cn:443/api/v1/vehicle/push/message/LFB1FV696M2L43840。main.cpp：#include "ros/ros.h" #include "std_msgs/String.h" #include <boost/thread/locks.hpp> #include <boost/thread/shared_mutex.hpp> #include "third_party/apollo/proto/perception/perception_obstacle.pb.h" #include "t3_perception.pb.h" apollo::perception::PerceptionObstacles perception_obstacles_; void perceptionCallback(const std_msgs::String& msg) { ROS_WARN("t3 perceptionCallback parse"); if (perception_obstacles_.ParseFromString(msg.data)) { double timestamp = perception_obstacles_.header().timestamp_sec(); ROS_INFO("t3 perceptionCallback timestamp %f count:%d", timestamp, perception_obstacles_.perception_obstacle().size()); std::string data; perception_obstacles_.SerializeToString(&data); VehData veh_data; veh_data.set_messagetype(5); veh_data.set_messagedes("PerceptionObstacles"); veh_data.set_contents(data); std::string serialized_data; veh_data.SerializeToString(&serialized_data); } else { ROS_ERROR("t3 perceptionCallback parse fail!"); } } int main(int argc, char **argv) { ros::init(argc, argv, "listener"); ros::NodeHandle n; ros::Subscriber sub = n.subscribe("/perception_node/perception_objects", 1000, perceptionCallback); ros::spin(); return 0; }CMakeLists.txt：cmake_minimum_required(VERSION 3.0.2) project(t3) find_package(catkin REQUIRED COMPONENTS roscpp rospy pcl_ros std_msgs third_party ) find_package(Protobuf REQUIRED) include_directories(${Protobuf_INCLUDE_DIRS} ${CMAKE_CURRENT_BINARY_DIR}/..) find_package(Boost REQUIRED) include_directories(${Boost_INCLUDE_DIRS}) set(ixwebsocket_INCLUDE_DIR "/usr/local/include/ixwebsocket") set(ixwebsocket_LIBRARIES "/usr/local/lib/libixwebsocket.a") include_directories(${ixwebsocket_INCLUDE_DIR}) include_directories(${CATKIN_DEVEL_PREFIX}/${CATKIN_GLOBAL_INCLUDE_DESTINATION}/${PROJECT_NAME}) include_directories(${CATKIN_DEVEL_PREFIX}/${CATKIN_GLOBAL_INCLUDE_DESTINATION}/smartview) catkin_package(INCLUDE_DIRS ${PROJECT_INCLUDE_DIRS} DEPENDS ${GFLAGS_LIBRARIES} ) include_directories( ${catkin_INCLUDE_DIRS} ${PROTOBUF_INCLUDE_DIR} ${PROJECT_SOURCE_DIR}/.. ) add_executable(${PROJECT_NAME}_node src/main.cpp ) add_dependencies(${PROJECT_NAME}_node ${catkin_EXPORTED_TARGETS}) target_link_libraries(${PROJECT_NAME}_node ${catkin_LIBRARIES} ${PROTOBUF_LIBRARIES} smartview_proto ) install(TARGETS ${PROJECT_NAME}_node ARCHIVE DESTINATION ${CATKIN_PACKAGE_LIB_DESTINATION} LIBRARY DESTINATION ${CATKIN_PACKAGE_LIB_DESTINATION} RUNTIME DESTINATION ${CATKIN_GLOBAL_BIN_DESTINATION} )

veh_data.set_messagedes("PerceptionObstacles"); veh_data.set_contents(data); std::string serialized_data; veh_data.SerializeToString(&serialized_data); // create a WebSocket connection ...

if len(lidar_unknown_objs_final_left_front) > 0: for j in range(len(lidar_unknown_objs_final_left_front)): if lidar_unknown_objs_final_left_front[j]["s"] < lidar_unknown_objs_final_left_front_dis: lidar_unknown_objs_final_left_front_dis = lidar_unknown_objs_final_left_front[j]["s"] lidar_unknown_objs_final_left_front_veh = lidar_unknown_objs_final_left_front[j] if 45 > lidar_unknown_objs_final_left_front_veh["s"] >25: lat_lon_WIDC = [lidar_unknown_objs_final_left_front_veh['Lat'], lidar_unknown_objs_final_left_front_veh['Lon']] cone_pub_WIDC.publish(json.dumps(lat_lon_WIDC)) else: lidar_unknown_objs_final_left_front_veh = None解释一下

这段代码主要是用于检测左前方是否存在未知物体。首先判断从激光雷达获取的未知物体列表是否有物体存在。...如果不存在未知物体，则将变量 lidar_unknown_objs_final_left_front_veh 赋值为 None。

if (right_side_front_veh != None): if right_side_front_veh["count"] == 0 and rightobject_init == 1: try: rightobject_speed_s = (right_side_front_veh['s'] - dis_first_rightobject_s) / 0.53 rightobject_speed_l = (right_side_front_veh['l'] - dis_first_rightobject_l) / 0.53 right_side_front_veh['v_s'] = rightobject_speed_s right_side_front_veh['v_l'] = rightobject_speed_l dis_first_rightobject_s = right_side_front_veh['s'] dis_first_rightobject_l = right_side_front_veh['l'] except: color_print_lib.prRed("rightobject_speed初始化失败") if right_side_front_veh["count"] == 1 and rightobject_init == 1: try: rightobject_speed_s = (right_side_front_veh['s'] - dis_first_rightobject_s) / 0.53 rightobject_speed_l = (right_side_front_veh['l'] - dis_first_rightobject_l) / 0.53 right_side_front_veh['v_s'] = rightobject_speed_s right_side_front_veh['v_l'] = rightobject_speed_l except: color_print_lib.prRed("rightobject_speed初始化失败") if rightobject_init == 0: rightobject_init = 1 dis_first_rightobject_s = right_side_front_veh['s'] dis_first_rightobject_l = right_side_front_veh['l'] right_side_front_veh['v_s'] = None right_side_front_veh['v_l'] = None解释一下

这是一段Python代码，主要是针对一个名为right_side_front_veh的字典对象进行处理。这个字典对象包含了车辆的一些属性，如位置、速度等。下面是代码的解释： 1. 如果right_side_front_veh不为None，那么进行...

m_vehicle_proxy.RegNotify(AptivFdbMsg::FdbEcdAdapterTopicId::VALIDITY_ZEEKR_CAR_MODE_DATA, &m_Carmod_notify); int VehicleBusProxy::RegNotify( uint32_t nId, MsgNotify* pNotify ) { return MsgProxy::RegNotify( nId, pNotify ); } int VehicleBusProxy::RegNotify( uint32_t nId, VehicleBusMsgNotify* pNotify ) { uint32_t key = nId; int result = 0;; if( NULL == pNotify ) { result = -1; } else { if( 0 == m_veh_bus_notify_list.count( key ) ) { m_veh_bus_notify_list.insert( std::pair<uint32_t, VehicleBusMsgNotify*>(key, pNotify) ); } else { result = -2; } } if( 0 == result ) { MsgProxy::RegNotify( nId, &m_bus_msg, m_vehicle_value ); } return result; }

如果不存在，则将key和pNotify插入到m_veh_bus_notify_list中作为新的元素。如果存在，则将结果result设置为-2，表示注册失败。最后，如果注册成功（即result为0），则调用了基类的MsgProxy::RegNotify...

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