该笔记参考链接：

• https://blog.csdn.net/zhanshen112/article/details/88565400

Prescan中目前提供的传感器一共有三种类型：

• Idealized Sensor 理想传感器：包括了理论研究需要的传感器，适用于自动驾驶算法开发前期逻辑的验证。
• Detailed Sensor详细传感器：真实存在的传感器，对应的是有相应的传感器模型，考虑了在实际使用过程中的传感器信息的损失等（例如：雷达就考虑了路径发散衰减、大气衰减和目标反射衰减，相机则考虑相机畸变等），适用于自动驾驶算法的鲁棒性验证
• Ground-Truth Sensor真值传感器：提供的是真值（主要是视觉传感器），适用于算法的早期开发阶段

1. Idealized Sensor

理想传感器主要包括：

1. SELF snesor/GPS receiver 自车/GPS接收器
2. AIR sensor( radar/lidar/ultrasonic ) 执行器信息接收器 （挖坑）
3. Antenna&DSRC transmitter/receiver 天线和DSRC的发送器/接收器（挖坑）
4. Beacon/OBU 信标（信号塔）/车载单元

1.1 GPS接收器

作用：输出本车精确的GPS位置信息

PreScan中是否可以模拟GPS信号不好的这种状态？

1.2 AIR Sensor 执行器信息传感器

作用：输出目标物的检测信息
对于每个检测到的物体，都有如下参数：
Range[m] 从传感器坐标系到检测到目标物体的距离
Azimuth[deg] 方位角
Elevation[deg] 高程角
ID[n] 检测到目标的ID
Velocity[m/s] 目标物纵向速度
Heading[deg] 目标物的航向角（N-0 deg, E – 90 deg）

3种不同的可能检测方法：

Bounding Box；检测边界
Center of Bounding Box； 检测中心
Cebter of Gravity；检测质心

注意：AIR传感的局限：该传感不考虑重叠、遮挡和目标的实际形状，检测点可能落在检测范围之外。具体应用时应该根据具体情况判断。

1.3 Beacon/OBU

作用：当车载装置在beacon检测范围之内，可实现V2I通信；双向通信（Beacon依附于infrastructure上，OBU依附于车辆上）
根据传感原理，分为以下两种：

• RF射频：OBU应位于Beacon的光束中
• IR红外：OBU应位于Beacon的光束中，需要考虑遮挡

RF	IR
fast	slower
no obstructed view (no occluded objects)	obstructed view (occluded objects)

RF OBU体现为一个点，IR OBU体现为一个矩形box（尺寸可以编辑，默认0.1m）
输出参数：

• Position & orientation 位置&方位角
• Field of view 视野角度
• Range 距离（Only Beacon，默认RF 50m IR 20m）
• Cone angle （Only Beacon，默认45°）
• Maximum number of detectable OBUs （Only Beacon，默认 5）
• Maximum number of detectable beacons （Only OBU，默认 2）

2. Detailed Sensor

Detailed Sensor主要包括：

1. Camera Sensor
2. Fisheye Camera Sensor
3. TIS：Technology Independent Sensor
4. Lidar Sensor
5. Radar Sensor
6. Ultrasonic Sensor

2.1 Camera Sensor

作用：发送图像信息（pixels）到Simulink，便于用户自定义相应的算法（例如：车道线识别、目标分类检测、融合算法等）

可配置项：

• Position & Orientation 位置和方位
• Mono Vision /Stereo Vision 单目/双目
• Field of View 视野范围
• Resolution 分辨率
• Frame-rate 帧频率
• Color/monochrome 彩色/黑白
• Misalignment （position / orientation）
• Drift （偏移）
  Simulink中摄像头输出：
  

2.2 Fish eye Camera

作用：发送图像信息（pixels）到Simulink，便于用户自定义相应的算法（例如：泊车辅助时障碍物识别等）

可配置项：

2.3 Lidar

激光雷达可用于多种目标，包括非金属物体、岩石、雨水等。
PreScan基于两种工作原理给出了对应的Lidar模型：

• pulse time-of-flight ranging
• beam modulation telemetry
  这两种模型中，laser scanner都包含了发射器和接收器。
  
  为了计算距离，被测信号的功率必须足够大。
  可配置项：
• 波长
• 发散角（对激光雷达而言，一般是0.01-0.08度）
• 最大目标输出数量（最大为5），针对最大目标输出数量，下面的图给出了更为具体的解释（下图max object to detect设置为3）
  

输出到Simulink中的数据

Signal name	Description
ActiveBeamID[-]	ID of the beam in the current simulation time step. Value is 0 when there’s no detection.
Range[m]	Range at which the target object has been detected.
DopplerVelocity [m/s]	Velocity of target point, relative to the sensor, along the beam.
DopplerVelocityX/Y/Z [ms-1]	Velocity of target point, relative to the sensor, along the beam, decomposed into X,Y,Z of the sensor’s coordinate system.
Theta[deg]	Azimuth angle in the sensor coordinate system at which the target is detected.
Phi[deg]	Elevation angle in the sensor coordinate system at which the target is detected.
TargetID[-]	Numerical ID of the detected target.
TargetTypeID[-]	The Type ID of the detected object.
EnergyLoss[dB]	Ratio received power / transmitted power.
Alpha[deg]	Azimuthal incidence angle of Lidar on the target object.
Beta[deg]	Elevation incidence angle of Lidar on the target object.

2.3 附 - Lidar Equation

<这部分先挖坑>
对于采用脉冲time of flight这种测量方法，假设$t_r$为发射端发生信号到接收端收到信号的时间，$c$为光速，在PreScan中为常数，则Range $R$与$t_r$之间的关系可以表达如下：
$2R=t_{r}c$

对于采用光束调制测量方法，激光由相对低频正弦波调制，因此采用的是相位进行间接测量。假设$f_{mod}$是调制频率，${\varphi }_r$为发射波和接收波之间的相位差，则$t_r$可以表示为：

2.4 Radar Sensor

Radar Sensor是一个更加详细的TIS传感器版本，与TIS传感器有所区别的是：

• 增加项：
  1. 支持对Antenna Gain Maps的使用
  2. 将大气衰减建模为频率和降雨的函数
  3. 可由外部提供扫描模式（即Simulink提供）
  4. 改进的Radar模型
• 删减项：
  1. 运用在Lidar上的pencil beam功能被移除了
  2. 分层阵列扫描功能被移除了（可以用多个Radar Sensor实现）

输出到Simulink中的数据

Signal name	Description
ActiveBeamID[-]	ID of the beam in the current simulation time step. Value is 0 when there’s no detection.
Range[m]	Range at which the target object has been detected.
DopplerVelocity [ms-1]	Velocity of target point, relative to the sensor, along the line-of-sight between sensor and target point.
DopplerVelocityX/Y/Z[ms-1]	Velocity of target point, relative to the sensor, along the line-of-sight between sensor and target point, decomposed into X,Y,Z of the sensor’s coordinate system.
Theta[deg]	Azimuth angle in the sensor coordinate system at which the target is detected.
Phi[deg]	Elevation angle in the sensor coordinate system at which the target is detected.
TargetID[-]	Numerical ID of the detected target.
TargetTypeID[-]	The Type ID of the detected object.
EnergyLoss[dB]	Ratio received power / transmitted power.
Alpha[deg]	Azimuthal incidence angle of the Radar beam on the target object.
Beta[deg]	Elevation incidence angle of the Radar beam on the target object.

车载雷达的分类及基本属性

类型	工作形式	频率	覆盖距离	水平视角	应用场景
SRR短距雷达	脉冲	24GHz	30m	±65°~±80°	BSD、PA、LCA、FCW、RCW
MRR中距离雷达	连续波/脉冲	24GHz / 76-77GHz	70m	±40°~±50°	LCA
LRR长距离雷达	连续波/脉冲	76-77GHz	200m	±4°~±8°	ACC

2.4 Ultrasonic Sensor

反射强度取决于透射波的强度、辐射模式、到物体的距离、介质的透射率和目标物体的特性。

输出到Simulink中的数据

signal name	description
ObjectDetection[-]	Indicates if an object is detected. (1 if an object is detected, 0 otherwise)
Range[m]	Range at which the target object has been detected.
DopplerVelocity [ms-1]	Velocity of target point, relative to the sensor, along the line-of-sight between sensor and target point.
DopplerVelocityX/Y/Z[ms-1]	Velocity of target point, relative to the sensor, along the line-of-sight between sensor and target point, decomposed into X,Y,Z of the sensor’s coordinate system.
Theta[deg]	Azimuth angle in the sensor coordinate system at which the target is detected.
Phi[deg]	Elevation angle in the sensor coordinate system at which the target is detected.
TargetID[-]	Numerical ID of the detected target.
TargetTypeID[-]	The Type ID of the detected object.
EnergyLoss[dB]	Ratio received power / transmitted power, same as ΔSPL.
Alpha[deg]	Azimuthal incidence angle of sound wave on the target object.
Beta[deg]	Elevation incidence angle of sound wave on the target object.

3. Ground Truth Sensor

3.1 Lane Marker Sensor

lane marker传感器提供道路上存在的车道线信息。
输出到Simulink的数据

输出结果以Bus形式给出，主要包括了5个信号：

• sliceCout [int] 前视距离数量
• ScanAtSensor [sub-bus:LaneMarkerSliceData]
• ScanAtDistance1(,2,3)

3.2 Analytical Lane Marker Sensor

分析车道标志传感器是车道传感器的一种新的实现方法，提供道路上车道线的信息。
车道线信息以多项式的形式给出，需要注意一下，因为是用多项式进行车道线的拟合，所以这个传感器输出的在一定程度上不算是真值，是对真值的逼近。

该传感器仅考虑传感器视野范围内的车道线，同一条车道线在视野范围内穿过了，会视为不同的车道线。



在Simulink中可视化车道线，可以借助Prescan提供的’ALMS XY Polynomial Plot’实现，该模块利用open('PreScanUsefulBlocks')查找。

3.3 Depth Camera

Depth摄像机包含的是深度值，用来校准和验证双目相机的深度计算。
输出到Simulink的数据

深度的分辨率是非线性的，如下：
$d=\frac{z^2}{z_{near}{2}^{24}-z}$

3.4 Bounding Rectangle Sensor

bounding rectangle传感器提供传感器可检测目标的包围矩形信息，并为相机的bounding rectangle算法做参考。例如行人识别算法，实现照明条件不良情况下的行人检测，输出按距离排序。

输出到Simulink的数据


在Simulink中可视化bounding box，可以借助Prescan提供的’BRS data on Camera image’实现，该模块利用open('PreScanUsefulBlocks')查找。

3.5 Object Camera Sensor

该传感器对包含摄像机单元和图像处理单元的系统进行建模。此外，它还提供了有关目标的距离和多普勒速度信息。
OCS传感器检测所有标记为sensor detectable的目标物。

输出到Simulink的数据

Signal	Description
Object ID [-]	Numerical ID of the detected object.
ObjectTypeID [-] The Type ID of the detected object.
Left [-]	Horizontal screen coordinate of the left side of the bounding box
Right [-]	Horizontal screen of the right side of the bounding box
Bottom [-]	Vertical screen coordinate of the bottom side of the bounding box
Top [-]	Vertical screen coordinate of the top side of the bounding box
Range [m]	Range at which the target object has been detected. The distance to the nearest point is returned.
RangeX [m]	X component of the Range, in sensor coordinates.
RangeY [m]	Y component of the Range, in sensor coordinates.
RangeZ [m]	Z component of the Range, in sensor coordinates.
DopplerVelocity [m/s]	Velocity of target point, relative to the sensor, along the line-of-sight between sensor and target point.
DopplerVelocityX/Y/Z[m/s]	Velocity of target point, relative to the sensor, along the line-of-sight between sensor and target point, decomposed into X,Y,Z of the sensor’s coordinate system.
Theta [deg]	Azimuth angle in the sensor’s coordinate system at which the target is detected.
Phi [deg]	Elevation angle in the sensor’s coordinate system at which the target is detected.

3.6 Image Segmentation Sensor

ISS传感器输出到Simulink的数据

3.7 Point Cloud Sensor

点云传感器用于构建周边环境的点云，用于算法的开发、激光雷达的尺寸验证以及HIL测试。
基本参数：

Parameter	Description	Defaut	Min.	Max.
FoV in Azimuth[deg]	The horizontal field of view of the sensor in degrees.	60	0.1	120
FoV in Elevation[deg]	For each azimuth direction, the same vertical field of view in degrees.	30	0.1	60
#horizontal samples	The number of equi-angular-distant samples in the azimuth direction.	320	1	3840
#vertical samples	The number of equi-angular-distant samples in the elevation direction.	160	1	2160

传感器输出到Simulink的数据

PCS mux for World Position

PCS mux for World Position and Intensity

PCS mux for Range.

PCS mux for Range and Intensity.

Data Model API通过matlab脚本提供了对传感器参数的便捷式访问。

%% Sample code for configuring the PCS via the Data Model API
%% Part 1
% Get the model
models = prescan.experiment.readDataModels();
% Find camera sensor CameraSensor_1
sensorData = prescan.sensors.findByName(models, 'PointCloudSensor_1');% Exit the script when the sensor is not found
if isempty(sensorData)
display('Sensor with the specified name is not found.');
return;
end%% Part 2
% Create copies of point cloud sensor structures
sensor = models.(sensorData{1}.modelName).sensor{sensorData{1}.indices(1)};
pointCloudSensor = sensor.pointCloudSensor;
sensorBase = sensor.sensorBase;
% Update settings
pointCloudSensor.sensorOutputMode = 'worldPosition'; % Can also be 'range'.
pointCloudSensor.outputIntensity = false;
pointCloudSensor.nearClippingDistance = 0.1; % [m]
pointCloudSensor.farClippingDistance = 150; % [m]
pointCloudSensor.extrapolateRange = true;
pointCloudSensor.sampleAccuracy.x = 0.05; % [deg]
pointCloudSensor.sampleAccuracy.y = 0.05; % [deg]
pointCloudSensor.integerOutput = false; % Do not use sensorOutputMode =
%'worldPosition' with integerOutput = true.
% Doing so will result in undefined
behaviour.
%sensorBase.name = 'PointCloudSensor_1';
sensorBase.fovAzimuth = 60 * pi/180; % [rad]
sensorBase.fovElevation = 30 * pi/180; % [rad]
sensor.resolution.x = 320; % [#samples]
sensor.resolution.y = 160; % [#samples]
sensor.frameRate = int32(20); % [Hz]% Configure sensor's pose, defaults depend on the actor it is placed on.
% sensorBase.relativePose.position.x = 1.56; % [m]
% sensorBase.relativePose.position.y = 0; % [m]
% sensorBase.relativePose.position.z = 1.22; % [m]
% sensorBase.relativePose.orientation.roll = 0; % [rad]
% sensorBase.relativePose.orientation.pitch = 0; % [rad]
% sensorBase.relativePose.orientation.yaw = 0; % [rad]
% Copy updated structures back into the model.
sensor.pointCloudSensor = pointCloudSensor;
sensor.sensorBase = sensorBase;
models.cameramodel.sensor{1} = sensor;%% Part 3
% Run the experiment for 10 seconds
simOut = prescan.experiment.runWithDataModels(models, 'StopTime', '10.0');

4 Tripod

目的：用来进行传感器标定，Tripod对传感器是不可见的。

5 Physics Based

5.1 Physics Based Camera Sensor

5.2 V2X Transceiver

以上两个目前没有用到，先挖坑吧