visionDetectionGenerator
Generate vision detections for driving scenario or RoadRunner Scenario
Description
The visionDetectionGenerator
System object™ generates detections from a monocular camera sensor mounted on an ego
vehicle. All detections are referenced to the coordinate system of the ego vehicle or
the vehicle-mounted sensor. You can use the visionDetectionGenerator
object in a scenario containing actors and
trajectories, which you can create by using a drivingScenario
object. Using a statistical mode, the generator can
simulate real detections with added random noise and also generate false alarm
detections. In addition, you can use the visionDetectionGenerator
object to create input to a multiObjectTracker
. When building scenarios using the Driving Scenario
Designer app, the camera sensors mounted on the ego vehicle are output as
visionDetectionGenerator
objects.
You can also use the visionDetectionGenerator
object with vehicle
actors in RoadRunner Scenario simulation. First you must create a SensorSimulation
object to interface sensors with RoadRunner Scenario and then register the lidar as a sensor model using the addSensors
object function before simulation.
To generate visual detections:
Create the
visionDetectionGenerator
object and set its properties.Call the object with arguments, as if it were a function.
To learn more about how System objects work, see What Are System Objects?
Note
The sensor model created using visionDetectionGenerator is designed for applications with ground vehicles traveling on approximately flat surfaces.
Creation
Syntax
Description
creates a vision detection generator object with default property values.sensor
= visionDetectionGenerator
creates a vision detection generator object using the sensor
= visionDetectionGenerator(cameraConfig
)monoCamera
configuration object,
cameraConfig
.
sets properties using one or more
name-value pairs. For example,
sensor
= visionDetectionGenerator(Name,Value
)visionDetectionGenerator('DetectionCoordinates','Sensor
Cartesian','MaxRange',200)
creates a vision detection generator
that reports detections in the sensor Cartesian coordinate system and has a
maximum detection range of 200 meters. Enclose each property name in
quotes.
Properties
Unless otherwise indicated, properties are nontunable, which means you cannot change their
values after calling the object. Objects lock when you call them, and the
release
function unlocks them.
If a property is tunable, you can change its value at any time.
For more information on changing property values, see System Design in MATLAB Using System Objects.
DetectorOutput
— Types of detections generated by sensor
'Objects only'
(default) | 'Lanes only'
| 'Lanes with occlusion'
| 'Lanes and objects'
Types of detections generated by the sensor, specified as
'Objects only'
, 'Lanes only'
,
'Lanes with occlusion'
, or 'Lanes and
objects'
.
When set to
'Objects only'
, only actors are detected.When set to
'Lanes only'
, only lanes are detected.When set to
'Lanes with occlusion'
, only lanes are detected but actors in the camera field of view can impair the sensor ability to detect lanes.When set to
'Lanes and objects'
, the sensor generates both object detections and occluded lane detections.
Example: 'Lanes with occlusion'
Data Types: char
| string
SensorIndex
— Unique sensor identifier
positive integer
Unique sensor identifier, specified as a positive integer. This property distinguishes detections that come from different sensors in a multi-sensor system.
Example: 5
Data Types: double
UpdateInterval
— Required time interval between sensor updates
0.1
| positive real scalar
Required time interval between sensor updates, specified as a positive
real scalar. The drivingScenario
object calls
the vision detection generator at regular time intervals. The vision
detector generates new detections at intervals defined by the
UpdateInterval
property. The value of the
UpdateInterval
property must be an integer multiple
of the simulation time interval. Updates requested from the sensor between
update intervals contain no detections. Units are in seconds.
Example: 5
Data Types: double
SensorLocation
— Sensor location
[3.4 0]
| [x y]
vector
Location of the vision sensor center, specified as an [x
y]
. The SensorLocation
and
Height
properties define the coordinates of the
vision sensor with respect to the ego vehicle coordinate system. The default
value corresponds to a forward-facing sensor mounted on a vehicle dashboard.
Units are in meters.
Example: [4 0.1]
Data Types: double
Height
— Sensor height above ground plane
1.1
| positive real scalar
Sensor height above the vehicle ground plane, specified as a positive real scalar. The default value corresponds to a forward-facing vision sensor mounted on the dashboard of a sedan. Units are in meters.
Example: 1.5
Data Types: double
Yaw
— Yaw angle of vision sensor
0
| real scalar
Yaw angle of vision sensor, specified as a real scalar. The yaw angle is the angle between the center line of the ego vehicle and the down-range axis of the vision sensor. A positive yaw angle corresponds to a clockwise rotation when looking in the positive direction of the z-axis of the ego vehicle coordinate system. Units are in degrees.
Example: -4
Data Types: double
Pitch
— Pitch angle of vision sensor
0
| real scalar
Pitch angle of vision sensor, specified as a real scalar. The pitch angle is the angle between the down-range axis of the vision sensor and the x-y plane of the ego vehicle coordinate system. A positive pitch angle corresponds to a clockwise rotation when looking in the positive direction of the y-axis of the ego vehicle coordinate system. Units are in degrees.
Example: 3
Data Types: double
Roll
— Roll angle of vision sensor
0
| real scalar
Roll angle of the vision sensor, specified as a real scalar. The roll angle is the angle of rotation of the down-range axis of the vision sensor around the x-axis of the ego vehicle coordinate system. A positive roll angle corresponds to a clockwise rotation when looking in the positive direction of the x-axis of the coordinate system. Units are in degrees.
Example: -4
Data Types: double
Intrinsics
— Intrinsic calibration parameters of vision sensor
cameraIntrinsics([800 800],[320 240],[480
640])
(default) | cameraIntrinsics
object
Intrinsic calibration parameters of vision sensor, specified as a cameraIntrinsics
object.
FieldOfView
— Angular field of view of vision sensor
real-valued 1-by-2 vector of positive values
This property is read-only.
Angular field of view of vision sensor, specified as a real-valued 1-by-2
vector of positive values, [azfov,elfov]
. The field of
view defines the azimuth and elevation extents of the sensor image. Each
component must lie in the interval from 0 degrees to 180 degrees. The field
of view is derived from the intrinsic parameters of the vision sensor.
Targets outside of the angular field of view of the sensor are not detected.
Units are in degrees.
Data Types: double
MaxRange
— Maximum detection range
150
| positive real scalar
Maximum detection range, specified as a positive real scalar. The sensor cannot detect a target beyond this range. Units are in meters.
Example: 200
Data Types: double
MaxSpeed
— Maximum detectable object speed
100
(default) | nonnegative real scalar
Maximum detectable object speed, specified as a nonnegative real scalar. Units are in meters per second.
Example: 10.0
Data Types: double
MaxAllowedOcclusion
— Maximum allowed occlusion of an object
0.5
(default) | real scalar in the range (0 1]
Maximum allowed occlusion of an object, specified as a real scalar in the range [0 1]. Occlusion is the fraction of the total surface area of an object not visible to the sensor. A value of one indicates that the object is fully occluded. Units are dimensionless.
Example: 0.2
Data Types: double
DetectionProbability
— Probability of detection
0.9
(default) | positive real scalar less than or equal to 1
Probability of detecting a target, specified as a positive real scalar less than or equal to 1. This quantity defines the probability that the sensor detects a detectable object. A detectable object is an object that satisfies the minimum detectable size, maximum range, maximum speed, and maximum allowed occlusion constraints.
Example: 0.95
Data Types: double
FalsePositivesPerImage
— Number of false detections per image
0.1
(default) | nonnegative real scalar
Number of false detections that the vision sensor generates for each image, specified as a nonnegative real scalar.
Example: 2
Data Types: double
MinObjectImageSize
— Minimum image size of detectable object
[15 15]
(default) | 1-by-2 vector of positive values
Minimum height and width of an object that the vision sensor detects
within an image, specified as a [minHeight,minWidth]
vector of positive values. The 2-D projected height of an object must be
greater than or equal to minHeight
. The projected width
of an object must be greater than or equal to minWidth
.
Units are in pixels.
Example: [30 20]
Data Types: double
BoundingBoxAccuracy
— Bounding box accuracy
5
(default) | positive real scalar
Bounding box accuracy, specified as a positive real scalar. This quantity defines the accuracy with which the detector can match a bounding box to a target. Units are in pixels.
Example: 4
Data Types: double
ProcessNoiseIntensity
— Noise intensity used for filtering position and velocity measurements
5
(default) | positive real scalar
Noise intensity used for filtering position and velocity measurements, specified as a positive real scalar. Noise intensity defines the standard deviation of the process noise of the internal constant-velocity Kalman filter used in a vision sensor. The filter models the process noise using a piecewise-constant white noise acceleration model. Noise intensity is typically of the order of the maximum acceleration magnitude expected for a target. Units are in m/s2.
Example: 2.5
Data Types: double
HasNoise
— Enable adding noise to vision sensor measurements
true
(default) | false
Enable adding noise to vision sensor measurements, specified as
true
or false
. Set this property
to true
to add noise to the sensor measurements.
Otherwise, the measurements have no noise. Even if you set
HasNoise
to false
, the object
still computes the MeasurementNoise
property of each
detection.
Data Types: logical
MaxNumDetectionsSource
— Source of maximum number of detections reported
'Auto'
(default) | 'Property'
Source of maximum number of detections reported by the sensor, specified
as 'Auto'
or 'Property'
. When this
property is set to 'Auto'
, the sensor reports all
detections. When this property is set to 'Property'
, the
sensor reports no more than the number of detections specified by the
MaxNumDetections
property.
Data Types: char
| string
MaxNumDetections
— Maximum number of reported detections
50
(default) | positive integer
Maximum number of detections reported by the sensor, specified as a positive integer. The detections closest to the sensor are reported.
Dependencies
To enable this property, set the
MaxNumDetectionsSource
property to
'Property'
.
Data Types: double
DetectionCoordinates
— Coordinate system of reported detections
'Ego Cartesian'
(default) | 'Sensor Cartesian'
Coordinate system of reported detections, specified as one of these values:
'Ego Cartesian'
— Detections are reported in the ego vehicle Cartesian coordinate system.'Sensor Cartesian'
— Detections are reported in the sensor Cartesian coordinate system.
Data Types: char
| string
LaneUpdateInterval
— Required time interval between lane detection updates
0.1
(default) | positive real scalar
Required time interval between lane detection updates, specified as a
positive real scalar. The drivingScenario
object calls
the vision detection generator at regular time intervals. The vision
detector generates new lane detections at intervals defined by this property
which must be an integer multiple of the simulation time interval. Updates
requested from the sensor between update intervals contain no lane
detections. Units are in seconds.
Example: 0.4
Data Types: double
MinLaneImageSize
— Minimum lane size in image
[20 5]
(default) | 1-by-2 real-valued vector
Minimum size of a projected lane marking that can be detected by the
sensor after accounting for curvature, specified as a 1-by-2 real-valued
vector, [minHeight minWidth]
. Lane markings must exceed
both of these values to be detected. This property is used only when
detecting lanes. Units are in pixels.
Example: [5,7]
Data Types: double
LaneBoundaryAccuracy
— Accuracy of lane boundaries
3
| positive real scalar
Accuracy of lane boundaries, specified as a positive real scalar. This property defines the accuracy with which the lane sensor can place a lane boundary. Units are in pixels. This property is used only when detecting lanes.
MaxNumLanesSource
— Source of maximum number of reported lanes
'Property'
(default) | 'Auto'
Source of maximum number of reported lanes, specified as
'Auto'
or 'Property'
. When
specified as 'Auto'
, the maximum number of lanes is
computed automatically. When specified as 'Property'
, use
the MaxNumLanes
property to set the maximum number or
lanes.
Data Types: char
| string
MaxNumLanes
— Maximum number of reported lanes
30
(default) | positive integer
Maximum number of reported lanes, specified as a positive integer.
Dependencies
To enable this property, set the
MaxNumLanesSource
property to
'Property'
.
Data Types: char
| string
ActorProfiles
— Actor profiles
structure | array of structures
Actor profiles, specified as a structure or as an array of structures. Each structure contains the physical and radar characteristics of an actor.
If
ActorProfiles
is a single structure, all actors passed into thevisionDetectionGenerator
object use this profile.If
ActorProfiles
is an array, each actor passed into the object must have a unique actor profile.
To generate an array of structures for your driving scenario, use the actorProfiles
function. The table shows the valid structure fields. If you do
not specify a field, that field is set to its default value. If no actors are passed into the
object, then the ActorID
field is not included.
Field | Description |
---|---|
ActorID | Scenario-defined actor identifier, specified as a positive integer. |
ClassID | Classification identifier, specified as a
nonnegative integer. 0 is
reserved for an object of an unknown or unassigned
class. |
Length | Length of actor, specified as a positive
real scalar. The default is
4.7 . Units are in
meters. |
Width | Width of actor, specified as a positive
real scalar. The default is
1.8 . Units are in
meters. |
Height | Height of actor, specified as a positive
real scalar. The default is
1.4 . Units are in
meters. |
OriginOffset | Offset of the rotational center of
the actor from its geometric center, specified as
an [x
y
z] real-valued vector. The
rotational center, or origin, is located at the
bottom center of the actor. For vehicles, the
rotational center is the point on the ground
beneath the center of the rear axle. The default
is |
RCSPattern | Radar cross-section pattern of actor,
specified as a
numel(RCSElevationAngles) -by-numel(RCSAzimuthAngles)
real-valued matrix. The default is [10
10; 10 10] . Units are in decibels per
square meter. |
RCSAzimuthAngles | Azimuth angles corresponding to rows of
RCSPattern , specified as a
vector of real values in the range [–180, 180].
The default is [-180 180] .
Units are in degrees. |
RCSElevationAngles | Elevation angles corresponding to rows of
RCSPattern , specified as a
vector of real values in the range [–90, 90]. The
default is [-90 90] . Units are
in degrees. |
For full definitions of the structure fields, see the actor
and vehicle
functions.
Usage
Syntax
Description
creates visual detections, dets
= sensor(actors
,time
)dets
, from sensor measurements
taken of actors
at the current simulation
time
. The object can generate sensor detections for
multiple actors simultaneously. Do not include the ego vehicle as one of the
actors.
To enable this syntax, set DetectionOutput
to
'Objects only'
.
generates lane detections, lanedets
= sensor(laneboundaries
,time
)lanedets
, from lane boundary
structures, laneboundaries
.
To enable this syntax set DetectionOutput
to
'Lanes only'
. The lane detector generates lane boundaries
at intervals specified by the LaneUpdateInterval
property.
generates lane detections, lanedets
= sensor(actors
,laneboundaries
,time
)lanedets
, from lane boundary
structures, laneboundaries
.
To enable this syntax, set DetectionOutput
to
'Lanes with occlusion'
. The lane detector generates lane
boundaries at intervals specified by the LaneUpdateInterval
property.
[___,
also returns the number of valid detections reported,
numValidDets
]
= sensor(___)numValidDets
.
[___,
also returns a logical value,
numValidDets
,isValidTime
]
= sensor(___)isValidTime
, indicating that the
UpdateInterval
time to generate detections has
elapsed.
[
returns both object detections, dets
,numValidDets
,isValidTime
,lanedets
,numValidLaneDets
,isValidLaneTime
] = sensor(actors
,laneboundaries
,time
)dets
, and lane detections
lanedets
. This syntax also returns the number of valid
lane detections reported, numValidLaneDets
, and a flag,
isValidLaneTime
, indicating whether the required
simulation time to generate lane detections has elapsed.
To enable this syntax, set DetectionOutput
to
'Lanes and objects'
.
Input Arguments
actors
— Scenario actor poses
structure | structure array
Scenario actor poses, specified as a structure or structure array.
Each structure corresponds to an actor. You can generate this structure
using the actorPoses
function.
You can also create these structures manually. The table shows the
fields that the object uses to generate detections. All other fields are
ignored.
Field | Description |
---|---|
ActorID | Scenario-defined actor identifier, specified as a positive integer. |
In R2024b:
| Front-axle position of the vehicle, specified as a three-element row vector in the form [x y z]. Units are in meters. Note If the driving scenario does not contain a
front-axle trajectory for at least one vehicle,
then the
|
Position | Position of actor, specified as a real-valued vector of the form [x y z]. Units are in meters. |
Velocity | Velocity (v) of actor in the x- y-, and z-directions, specified as a real-valued vector of the form [vx vy vz]. Units are in meters per second. |
Roll | Roll angle of actor, specified as a real-valued scalar. Units are in degrees. |
Pitch | Pitch angle of actor, specified as a real-valued scalar. Units are in degrees. |
Yaw | Yaw angle of actor, specified as a real-valued scalar. Units are in degrees. |
AngularVelocity | Angular velocity (ω) of actor in the x-, y-, and z-directions, specified as a real-valued vector of the form [ωx ωy ωz]. Units are in degrees per second. |
For full definitions of the structure fields, see the actor
and vehicle
functions.
Dependencies
To enable this argument, set the
DetectorOutput
property to
'Objects only'
, 'Lanes with
occlusion'
, or 'Lanes and
objects'
.
laneboundaries
— Lane boundaries
array of lane boundary structures
Lane boundaries, specified as an array of lane boundary structures. The table shows the fields for each structure.
Field | Description |
| Lane boundary coordinates, specified as a real-valued N-by-3 matrix, where N is the number of lane boundary coordinates. Lane boundary coordinates define the position of points on the boundary at specified longitudinal distances away from the ego vehicle, along the center of the road.
This matrix also includes the boundary coordinates at zero distance from the ego vehicle. These coordinates are to the left and right of the ego-vehicle origin, which is located under the center of the rear axle. Units are in meters. |
| Lane boundary curvature at each row of the Coordinates matrix, specified
as a real-valued N-by-1 vector. N is the
number of lane boundary coordinates. Units are in radians per meter. |
| Derivative of lane boundary curvature at each row of the Coordinates
matrix, specified as a real-valued N-by-1 vector.
N is the number of lane boundary coordinates. Units are
in radians per square meter. |
| Initial lane boundary heading angle, specified as a real scalar. The heading angle of the lane boundary is relative to the ego vehicle heading. Units are in degrees. |
| Lateral offset of the ego vehicle position from the lane boundary, specified as a real scalar. An offset to a lane boundary to the left of the ego vehicle is positive. An offset to the right of the ego vehicle is negative. Units are in meters. In this image, the ego vehicle is offset 1.5 meters from the left lane and 2.1 meters from the right lane. |
| Type of lane boundary marking, specified as one of these values:
|
| Saturation strength of the lane boundary marking, specified as a real scalar from 0 to
1. A value of |
| Lane boundary width, specified as a positive real scalar. In a double-line lane marker, the same width is used for both lines and for the space between lines. Units are in meters. |
| Length of dash in dashed lines, specified as a positive real scalar. In a double-line lane marker, the same length is used for both lines. |
| Length of space between dashes in dashed lines, specified as a positive real scalar. In a dashed double-line lane marker, the same space is used for both lines. |
Dependencies
To enable this argument, set the
DetectorOutput
property to 'Lanes
only'
, 'Lanes with occlusion'
, or
'Lanes and objects'
.
Data Types: struct
time
— Current simulation time
positive real scalar
Current simulation time, specified as a positive real scalar. The
drivingScenario
object
calls the vision detection generator at regular time intervals. The
vision detector generates new detections at intervals defined by the
UpdateInterval
property. The values of the
UpdateInterval
and
LanesUpdateInterval
properties must be an
integer multiple of the simulation time interval. Updates requested from
the sensor between update intervals contain no detections. Units are in
seconds.
Example: 10.5
Data Types: double
Output Arguments
dets
— Object detections
cell array of objectDetection
objects
Object detections, returned as a cell array of objectDetection
objects.
Each object contains these fields:
Property | Definition |
---|---|
Time | Measurement time |
Measurement | Object measurements |
MeasurementNoise | Measurement noise covariance matrix |
SensorIndex | Unique ID of the sensor |
ObjectClassID | Object classification |
MeasurementParameters | Parameters used by initialization functions of nonlinear Kalman tracking filters |
ObjectAttributes | Additional information passed to tracker |
Measurement
,
MeasurementNoise
, and
MeasurementParameters
are reported in the
coordinate system specified by the
DetectionCoordinates
property of the visionDetectionGenerator
.
Measurement
DetectionCoordinates
Property | Measurement and Measurement Noise Coordinates |
---|---|
'Ego Cartesian' | [x;y;z;vx;vy;vz] |
'Sensor Cartesian' |
MeasurementParameters
Parameter | Definition |
---|---|
Frame | Enumerated type indicating the frame used to
report measurements. When Frame
is set to 'rectangular' ,
detections are reported in Cartesian coordinates.
When Frame is set
'spherical' , detections are
reported in spherical coordinates. |
OriginPosition | 3-D vector offset of the sensor origin from the
ego vehicle origin. The vector is derived from the
SensorLocation and
Height properties specified
in the visionDetectionGenerator . |
Orientation | Orientation of the vision sensor coordinate
system with respect to the ego vehicle coordinate
system. The orientation is derived from the
Yaw ,
Pitch , and
Roll properties of the
visionDetectionGenerator . |
HasVelocity | Indicates whether measurements contain velocity or range rate components. |
ObjectAttributes
Attribute | Definition |
---|---|
TargetIndex | Identifier of the actor,
ActorID , that generated the
detection. For false alarms, this value is
negative. |
numValidDets
— Number of detections
nonnegative integer
Number of detections returned, defined as a nonnegative integer.
When the
MaxNumDetectionsSource
property is set to'Auto'
,numValidDets
is set to the length ofdets
.When the
MaxNumDetectionsSource
is set to'Property'
,dets
is a cell array with length determined by theMaxNumDetections
property. No more thanMaxNumDetections
number of detections are returned. If the number of detections is fewer thanMaxNumDetections
, the firstnumValidDets
elements ofdets
hold valid detections. The remaining elements ofdets
are set to the default value.
Data Types: double
isValidTime
— Valid detection time
0
| 1
Valid detection time, returned as 0
or
1
. isValidTime
is
0
when detection updates are requested at times
that are between update intervals specified by
UpdateInterval
.
Data Types: logical
lanedets
— Lane boundary detections
lane boundary detection structure
Lane boundary detections, returned as an array structures. The fields of the structure are:
Lane Boundary Detection Structure
Field | Description |
Time | Lane detection time |
SensorIndex | Unique identifier of sensor |
LaneBoundaries | Array of clothoidLaneBoundary objects. |
numValidLaneDets
— Number of detections
nonnegative integer
Number of lane detections returned, defined as a nonnegative integer.
When the
MaxNumLanesSource
property is set to'Auto'
,numValidLaneDets
is set to the length oflanedets
.When the
MaxNumLanesSource
is set to'Property'
,lanedets
is a cell array with length determined by theMaxNumLanes
property. No more thanMaxNumLanes
number of lane detections are returned. If the number of detections is fewer thanMaxNumLanes
, the firstnumValidLaneDetections
elements oflanedets
hold valid lane detections. The remaining elements oflanedets
are set to the default value.
Data Types: double
isValidLaneTime
— Valid lane detection time
0
| 1
Valid lane detection time, returned as 0
or
1
. isValidLaneTime
is
0
when lane detection updates are requested at
times that are between update intervals specified by
LaneUpdateInterval
.
Data Types: logical
Object Functions
To use an object function, specify the
System object as the first input argument. For
example, to release system resources of a System object named obj
, use
this syntax:
release(obj)
Examples
Generate Visual Detections of Multiple Vehicles
Generate detections using a forward-facing automotive vision sensor mounted on an ego vehicle. Assume that there are two target vehicles:
Vehicle 1 is directly in front of the ego vehicle and moving at the same speed.
Vehicle 2 vehicle is driving faster than the ego vehicle by 12 kph in the left lane.
All positions, velocities, and measurements are relative to the ego vehicle. Run the simulation for ten steps.
dt = 0.1; car1 = struct('ActorID',1,'Position',[100 0 0],'Velocity', [5*1000/3600 0 0]); car2 = struct('ActorID',2,'Position',[150 10 0],'Velocity',[12*1000/3600 0 0]);
Create an automotive vision sensor having a location offset from the ego vehicle. By default, the sensor location is at (3.4,0) meters from the vehicle center and 1.1 meters above the ground plane.
sensor = visionDetectionGenerator('DetectionProbability',1, ... 'MinObjectImageSize',[5 5],'MaxRange',200,'DetectionCoordinates','Sensor Cartesian'); tracker = multiObjectTracker('FilterInitializationFcn',@initcvkf, ... 'ConfirmationParameters',[3 4],'NumCoastingUpdates',6);
Generate visual detections for the non-ego actors as they move. The output detections form a cell array. Extract only position information from the detections to pass to the multiObjectTracker
, which expects only position information. Then update the tracker for each new set of detections.
simTime = 0; nsteps = 10; for k = 1:nsteps dets = sensor([car1 car2],simTime); n = size(dets,1); for k = 1:n meas = dets{k}.Measurement(1:3); dets{k}.Measurement = meas; measmtx = dets{k}.MeasurementNoise(1:3,1:3); dets{k}.MeasurementNoise = measmtx; end [confirmedTracks,tentativeTracks,allTracks] = updateTracks(tracker,dets,simTime); simTime = simTime + dt; car1.Position = car1.Position + dt*car1.Velocity; car2.Position = car2.Position + dt*car2.Velocity; end
Use birdsEyePlot
to create an overhead view of the detections. Plot the sensor coverage area. Extract the x and y positions of the targets by converting the Measurement
fields of the cell into a MATLAB® array. Then, plot the detections using birdsEyePlot
functions.
BEplot = birdsEyePlot('XLim',[0 220],'YLim',[-75 75]); caPlotter = coverageAreaPlotter(BEplot,'DisplayName','Vision Coverage Area'); plotCoverageArea(caPlotter,sensor.SensorLocation,sensor.MaxRange, ... sensor.Yaw,sensor.FieldOfView(1)) detPlotter = detectionPlotter(BEplot,'DisplayName','Vision Detections'); detPos = cellfun(@(d)d.Measurement(1:2),dets,'UniformOutput',false); detPos = cell2mat(detPos')'; if ~isempty(detPos) plotDetection(detPlotter,detPos) end
Generate Visual Detections from Monocular Camera
Create a vision sensor by using a monocular camera configuration, and generate detections from that sensor.
Specify the intrinsic parameters of the camera and create a monoCamera
object from these parameters. The camera is mounted on top of an ego vehicle at a height of 1.5 meters above the ground and a pitch of 1 degree toward the ground.
focalLength = [800 800];
principalPoint = [320 240];
imageSize = [480 640];
intrinsics = cameraIntrinsics(focalLength,principalPoint,imageSize);
height = 1.5;
pitch = 1;
monoCamConfig = monoCamera(intrinsics,height,'Pitch',pitch);
Create a vision detection generator using the monocular camera configuration.
visionSensor = visionDetectionGenerator(monoCamConfig);
Generate a driving scenario with an ego vehicle and two target cars. Position the first target car 30 meters directly in front of the ego vehicle. Position the second target car 20 meters in front of the ego vehicle but offset to the left by 3 meters.
scenario = drivingScenario; egoVehicle = vehicle(scenario,'ClassID',1); targetCar1 = vehicle(scenario,'ClassID',1,'Position',[30 0 0]); targetCar2 = vehicle(scenario,'ClassID',1,'Position',[20 3 0]);
Use a bird's-eye plot to display the vehicle outlines and sensor coverage area.
figure bep = birdsEyePlot('XLim',[0 50],'YLim',[-20 20]); olPlotter = outlinePlotter(bep); [position,yaw,length,width,originOffset,color] = targetOutlines(egoVehicle); plotOutline(olPlotter,position,yaw,length,width); caPlotter = coverageAreaPlotter(bep,'DisplayName','Coverage area','FaceColor','blue'); plotCoverageArea(caPlotter,visionSensor.SensorLocation,visionSensor.MaxRange, ... visionSensor.Yaw,visionSensor.FieldOfView(1))
Obtain the poses of the target cars from the perspective of the ego vehicle. Use these poses to generate detections from the sensor.
poses = targetPoses(egoVehicle); [dets,numValidDets] = visionSensor(poses,scenario.SimulationTime);
Display the (X,Y) positions of the valid detections. For each detection, the (X,Y) positions are the first two values of the Measurement
field.
for i = 1:numValidDets XY = dets{i}.Measurement(1:2); detXY = sprintf('Detection %d: X = %.2f meters, Y = %.2f meters',i,XY); disp(detXY) end
Detection 1: X = 19.09 meters, Y = 2.79 meters Detection 2: X = 27.81 meters, Y = 0.08 meters
Generate Object and Lane Boundary Detections
Create a driving scenario containing an ego vehicle and a target vehicle traveling along a three-lane road. Detect the lane boundaries by using a vision detection generator.
scenario = drivingScenario;
Create a three-lane road by using lane specifications.
roadCenters = [0 0 0; 60 0 0; 120 30 0];
lspc = lanespec(3);
road(scenario,roadCenters,'Lanes',lspc);
Specify that the ego vehicle follows the center lane at 30 m/s.
egovehicle = vehicle(scenario,'ClassID',1);
egopath = [1.5 0 0; 60 0 0; 111 25 0];
egospeed = 30;
smoothTrajectory(egovehicle,egopath,egospeed);
Specify that the target vehicle travels ahead of the ego vehicle at 40 m/s and changes lanes close to the ego vehicle.
targetcar = vehicle(scenario,'ClassID',1);
targetpath = [8 2; 60 -3.2; 120 33];
targetspeed = 40;
smoothTrajectory(targetcar,targetpath,targetspeed);
Display a chase plot for a 3-D view of the scenario from behind the ego vehicle.
chasePlot(egovehicle)
Create a vision detection generator that detects lanes and objects. The pitch of the sensor points one degree downward.
visionSensor = visionDetectionGenerator('Pitch',1.0); visionSensor.DetectorOutput = 'Lanes and objects'; visionSensor.ActorProfiles = actorProfiles(scenario);
Run the simulation.
Create a bird's-eye plot and the associated plotters.
Display the sensor coverage area.
Display the lane markings.
Obtain ground truth poses of targets on the road.
Obtain ideal lane boundary points up to 60 m ahead.
Generate detections from the ideal target poses and lane boundaries.
Display the outline of the target.
Display object detections when the object detection is valid.
Display the lane boundary when the lane detection is valid.
bep = birdsEyePlot('XLim',[0 60],'YLim',[-35 35]); caPlotter = coverageAreaPlotter(bep,'DisplayName','Coverage area', ... 'FaceColor','blue'); detPlotter = detectionPlotter(bep,'DisplayName','Object detections'); lmPlotter = laneMarkingPlotter(bep,'DisplayName','Lane markings'); lbPlotter = laneBoundaryPlotter(bep,'DisplayName', ... 'Lane boundary detections','Color','red'); olPlotter = outlinePlotter(bep); plotCoverageArea(caPlotter,visionSensor.SensorLocation,... visionSensor.MaxRange,visionSensor.Yaw, ... visionSensor.FieldOfView(1)); while advance(scenario) [lmv,lmf] = laneMarkingVertices(egovehicle); plotLaneMarking(lmPlotter,lmv,lmf) tgtpose = targetPoses(egovehicle); lookaheadDistance = 0:0.5:60; lb = laneBoundaries(egovehicle,'XDistance',lookaheadDistance,'LocationType','inner'); [obdets,nobdets,obValid,lb_dets,nlb_dets,lbValid] = ... visionSensor(tgtpose,lb,scenario.SimulationTime); [objposition,objyaw,objlength,objwidth,objoriginOffset,color] = targetOutlines(egovehicle); plotOutline(olPlotter,objposition,objyaw,objlength,objwidth, ... 'OriginOffset',objoriginOffset,'Color',color) if obValid detPos = cellfun(@(d)d.Measurement(1:2),obdets,'UniformOutput',false); detPos = vertcat(zeros(0,2),cell2mat(detPos')'); plotDetection(detPlotter,detPos) end if lbValid plotLaneBoundary(lbPlotter,vertcat(lb_dets.LaneBoundaries)) end end
Configure Ideal Vision Sensor
Generate detections from an ideal vision sensor and compare these detections to ones from a noisy sensor. An ideal sensor is one that always generates detections, with no false positives and no added random noise.
Create a Driving Scenario
Create a driving scenario in which the ego vehicle is positioned in front of a diagonal array of target cars. With this configuration, you can later plot the measurement noise covariances of the detected targets without having the target cars occlude one another.
scenario = drivingScenario; egoVehicle = vehicle(scenario,'ClassID',1); numTgts = 6; x = linspace(20,50,numTgts)'; y = linspace(-20,0,numTgts)'; x = [x;x(1:end-1)]; y = [y;-y(1:end-1)]; numTgts = numel(x); for m = 1:numTgts vehicle(scenario,'ClassID',1,'Position',[x(m) y(m) 0]); end
Plot the driving scenario in a bird's-eye plot.
bep = birdsEyePlot('XLim',[0 60]); legend('hide')
olPlotter = outlinePlotter(bep); [position,yaw,length,width,originOffset,color] = targetOutlines(egoVehicle); plotOutline(olPlotter,position,yaw,length,width, ... 'OriginOffset',originOffset,'Color',color)
Create an Ideal Vision Sensor
Create a vision sensor by using the visionDetectionGenerator
System object™. To generate ideal detections, set DetectionProbability
to 1
, FalsePositivesPerImage
to 0
, and HasNoise
to false
.
DetectionProbability = 1
— The sensor always generates detections for a target, as long as the target is not occluded and meets the range, speed, and image size constraints.FalsePositivesPerImage = 0
— The sensor generates detections from only real targets in the driving scenario.HasNoise = false
— The sensor does not add random noise to the reported position and velocity of the target. However, theobjectDetection
objects returned by the sensor have measurement noise values set to the noise variance that would have been added ifHasNoise
weretrue
. With these noise values, you can process ideal detections using themultiObjectTracker
. This technique is useful for analyzing maneuver lag without needing to run time-consuming Monte Carlo simulations.
idealSensor = visionDetectionGenerator( ... 'SensorIndex',1, ... 'UpdateInterval',scenario.SampleTime, ... 'SensorLocation',[0.75*egoVehicle.Wheelbase 0], ... 'Height',1.1, ... 'Pitch',0, ... 'Intrinsics',cameraIntrinsics(800,[320 240],[480 640]), ... 'BoundingBoxAccuracy',50, ... % Make the noise large for illustrative purposes 'ProcessNoiseIntensity',5, ... 'MaxRange',60, ... 'DetectionProbability',1, ... 'FalsePositivesPerImage',0, ... 'HasNoise',false, ... 'ActorProfiles',actorProfiles(scenario))
idealSensor = visionDetectionGenerator with properties: SensorIndex: 1 UpdateInterval: 0.0100 SensorLocation: [2.1000 0] Height: 1.1000 Yaw: 0 Pitch: 0 Roll: 0 Intrinsics: [1x1 cameraIntrinsics] DetectorOutput: 'Objects only' FieldOfView: [43.6028 33.3985] MaxRange: 60 MaxSpeed: 100 MaxAllowedOcclusion: 0.5000 MinObjectImageSize: [15 15] DetectionProbability: 1 FalsePositivesPerImage: 0 Use get to show all properties
Plot the coverage area of the ideal vision sensor.
legend('show')
caPlotter = coverageAreaPlotter(bep,'DisplayName','Coverage area','FaceColor','blue'); mountPosition = idealSensor.SensorLocation; range = idealSensor.MaxRange; orientation = idealSensor.Yaw; fieldOfView = idealSensor.FieldOfView(1); plotCoverageArea(caPlotter,mountPosition,range,orientation,fieldOfView);
Simulate Ideal Vision Detections
Obtain the positions of the targets. The positions are in ego vehicle coordinates.
gTruth = targetPoses(egoVehicle);
Generate timestamped vision detections. These detections are returned as a cell array of objectDetection
objects.
time = scenario.SimulationTime; dets = idealSensor(gTruth,time);
Inspect the measurement and measurement noise variance of the first (leftmost) detection. Even though the detection is ideal and therefore has no added random noise, the MeasurementNoise
property shows the values as if the detection did have noise.
dets{1}.Measurement
ans = 6×1
31.0000
-11.2237
0
0
0
0
dets{1}.MeasurementNoise
ans = 6×6
1.5427 -0.5958 0 0 0 0
-0.5958 0.2422 0 0 0 0
0 0 100.0000 0 0 0
0 0 0 0.5398 -0.1675 0
0 0 0 -0.1675 0.1741 0
0 0 0 0 0 100.0000
Plot the ideal detections and ellipses for the 2-sigma contour of the measurement noise covariance.
pos = cell2mat(cellfun(@(d)d.Measurement(1:2)',dets, ... 'UniformOutput',false)); cov = reshape(cell2mat(cellfun(@(d)d.MeasurementNoise(1:2,1:2),dets, ... 'UniformOutput',false))',2,2,[]); plotter = trackPlotter(bep,'DisplayName','Ideal detections', ... 'MarkerEdgeColor','blue','MarkerFaceColor','blue'); sigma = 2; plotTrack(plotter,pos,sigma^2*cov)
Simulate Noisy Detections for Comparison
Create a noisy sensor based on the properties of the ideal sensor.
noisySensor = clone(idealSensor); release(noisySensor) noisySensor.HasNoise = true;
Reset the driving scenario back to its original state.
restart(scenario)
Collect statistics from the noisy detections.
numMonte = 1e3; pos = []; for itr = 1:numMonte time = scenario.SimulationTime; dets = noisySensor(gTruth,time); % Save noisy measurements pos = [pos;cell2mat(cellfun(@(d)d.Measurement(1:2)',dets,'UniformOutput',false))]; advance(scenario); end
Plot the noisy detections.
plotter = detectionPlotter(bep,'DisplayName','Noisy detections', ... 'Marker','.','MarkerEdgeColor','red','MarkerFaceColor','red'); plotDetection(plotter,pos)
Algorithms
The vision sensor models a monocular camera that produces 2-D camera images. To project the coordinates of these 2-D images into the 3-D world coordinates used in driving scenarios, the sensor algorithm assumes that the z-position (height) of all image points of the bottom edge of the target’s image bounding box lie on the ground. The plane defining the ground is defined by the height property of the vision detection generator, which defines the offset of the monocular camera above the ground plane. With this projection, the vertical locations of objects in the produced images are strongly correlated to their heights above the road. However, if the road is not flat and the heights of objects differ from the height of the sensor, then the sensor reports inaccurate detections. For an example that shows this behavior, see Model Vision Sensor Detections.
Extended Capabilities
C/C++ Code Generation
Generate C and C++ code using MATLAB® Coder™.
Usage notes and limitations:
For driving scenario workflows, the
visionDetectionGenerator
System object supports standalone deployment using MATLAB Coder™, and also for Simulink Real-Time™ targets.See System Objects in MATLAB Code Generation (MATLAB Coder).
Version History
Introduced in R2017a
See Also
Objects
lidarPointCloudGenerator
|objectDetection
|drivingScenario
|laneMarking
|lanespec
|monoCamera
|multiObjectTracker
|drivingRadarDataGenerator
|insSensor
Functions
Apps
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