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Vision Detection Generator

Detect objects and lanes from visual measurements

  • Library:
  • Automated Driving Toolbox / Driving Scenario and Sensor Modeling

  • Vision Detection Generator block

Description

The Vision Detection Generator block generates detections from camera measurements taken by a vision sensor mounted on an ego vehicle.

The block derives detections from simulated actor poses and generates these detections at intervals equal to the sensor update interval. By default, detections are referenced to the coordinate system of the ego vehicle. The block can simulate real detections with added random noise and also generate false positive detections. A statistical model generates the measurement noise, true detections, and false positives. To control the random numbers that the statistical model generates, use the random number generator settings on the Measurements tab of the block.

You can use the Vision Detection Generator to create input to a Multi-Object Tracker block. When building scenarios and sensor models using the Driving Scenario Designer app, the camera sensors exported to Simulink® are output as Vision Detection Generator blocks.

Ports

Input

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Scenario actor poses in ego vehicle coordinates, specified as a Simulink bus containing a MATLAB structure.

The structure must contain these fields.

FieldDescriptionType
NumActorsNumber of actorsNonnegative integer
TimeCurrent simulation timeReal-valued scalar
ActorsActor posesNumActors-length array of actor pose structures

Each actor pose structure in Actors must have these fields.

FieldDescription
ActorID

Scenario-defined actor identifier, specified as a positive integer.

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-direction, 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-direction, specified as a real-valued vector of the form [ωx, ωy, ωz]. Units are in degrees per second.

Dependencies

To enable this input port, set the Types of detections generated by sensor parameter to Objects only, Lanes with occlusion, or Lanes and objects.

Lane boundaries in ego vehicle coordinates, specified as a Simulink bus containing a MATLAB structure.

The structure must contain these fields.

FieldDescriptionType
NumLaneBoundariesNumber of lane boundariesNonnegative integer
TimeCurrent simulation timeReal scalar
LaneBoundariesLane boundariesNumLaneBoundaries-length array of lane boundary structures

Each lane boundary structure in LaneBoundaries must have these fields.

FieldDescription

Coordinates

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.

  • In MATLAB, specify these distances by using the 'XDistance' name-value pair argument of the laneBoundaries function.

  • In Simulink, specify these distances by using the Distances from ego vehicle for computing boundaries (m) parameter of the Scenario Reader block or the Distance from parent for computing lane boundaries parameter of the Simulation 3D Vision Detection Generator block.

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.

Curvature

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.

CurvatureDerivative

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.

HeadingAngle

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.

LateralOffset

Distance of the lane boundary from the ego vehicle position, 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.

BoundaryType

Type of lane boundary marking, specified as one of these values:

  • 'Unmarked' — No physical lane marker exists

  • 'Solid' — Single unbroken line

  • 'Dashed' — Single line of dashed lane markers

  • 'DoubleSolid' — Two unbroken lines

  • 'DoubleDashed' — Two dashed lines

  • 'SolidDashed' — Solid line on the left and a dashed line on the right

  • 'DashedSolid' — Dashed line on the left and a solid line on the right

Strength

Saturation strength of the lane boundary marking, specified as a real scalar from 0 to 1. A value of 0 corresponds to a marking whose color is fully unsaturated. The marking is gray. A value of 1 corresponds to a marking whose color is fully saturated.

Width

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

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.

Space

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 input port, set the Types of detections generated by sensor parameter to Lanes only, Lanes only, Lanes with occlusion, or Lanes and objects.

Output

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Object detections, returned as a Simulink bus containing a MATLAB structure. For more details about buses, see Create Nonvirtual Buses (Simulink).

You can pass object detections from these sensors and other sensors to a tracker, such as a Multi-Object Tracker block, and generate tracks.

The detections structure has this form:

FieldDescriptionType
NumDetectionsNumber of detectionsInteger
IsValidTimeFalse when updates are requested at times that are between block invocation intervalsBoolean
DetectionsObject detectionsArray of object detection structures of length set by the Maximum number of reported detections parameter. Only NumDetections of these detections are actual detections.

The object detection structure contains these properties.

PropertyDefinition
TimeMeasurement time
MeasurementObject measurements
MeasurementNoiseMeasurement noise covariance matrix
SensorIndexUnique ID of the sensor
ObjectClassIDObject classification
ObjectAttributesAdditional information passed to tracker
MeasurementParametersParameters used by initialization functions of nonlinear Kalman tracking filters

The Measurement field reports the position and velocity of a measurement in the coordinate system specified by Coordinate system used to report detections. This field is a real-valued column vector of the form [x; y; z; vx; vy; vz]. Units are in meters per second.

The MeasurementNoise field is a 6-by-6 matrix that reports the measurement noise covariance for each coordinate in the Measurement field.

The MeasurementParameters field is a structure with these fields.

ParameterDefinition
Frame Enumerated type indicating the frame used to report measurements. The Vision Detection Generator block reports detections in either ego and sensor Cartesian coordinates, which are both rectangular coordinate frames. Therefore, for this block, Frame is always set to 'rectangular'.
OriginPosition3-D vector offset of the sensor origin from the ego vehicle origin. The vector is derived from the Sensor's (x,y) position (m) and Sensor's height (m) parameters of the block.
OrientationOrientation of the vision sensor coordinate system with respect to the ego vehicle coordinate system. The orientation is derived from the Yaw angle of sensor mounted on ego vehicle (deg), Pitch angle of sensor mounted on ego vehicle (deg), and Roll angle of sensor mounted on ego vehicle (deg) parameters of the block.
HasVelocityIndicates whether measurements contain velocity.

The ObjectAttributes property of each detection is a structure with these fields.

FieldDefinition
TargetIndexIdentifier of the actor, ActorID, that generated the detection. For false alarms, this value is negative.

Dependencies

To enable this output port, set the Types of detections generated by sensor parameter to Objects only, Lanes with occlusion, or Lanes and objects.

Lane boundary detections, returned as a Simulink bus containing a MATLAB structure. The structure had these fields:

FieldDescriptionType
TimeLane detection timeReal scalar
IsValidTimeFalse when updates are requested at times that are between block invocation intervalsBoolean
SensorIndexUnique identifier of sensorPositive integer
NumLaneBoundariesNumber of lane boundary detectionsNonnegative integer
LaneBoundariesLane boundary detectionsArray of clothoidLaneBoundary objects

Dependencies

To enable this output port, set the Types of detections generated by sensor parameter to Lanes only, Lanes with occlusion, or Lanes and objects.

Parameters

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Parameters

Sensor Identification

Unique sensor identifier, specified as a positive integer. The sensor identifier distinguishes detections that come from different sensors in a multisensor system. If a model contains multiple sensor blocks with the same sensor identifier, the Bird's-Eye Scope displays an error.

Example: 5

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, no road information is used to occlude actors.

  • When set to Lanes only, no actor information is used to detect lanes.

  • When set to Lanes with occlusion, actors in the camera field of view can impair the sensor ability to detect lanes.

  • When set to Lanes and objects, the sensor generates object both object detections and occluded lane detections.

Required time interval between sensor updates, specified as a positive real scalar. The value of this parameter must be an integer multiple of the Actors input port data interval. Updates requested from the sensor between update intervals contain no detections. Units are in seconds.

Required time interval between lane detection updates, specified as a positive real scalar. The vision detection generator is called at regular time intervals. The vision detector generates new lane detections at intervals defined by this parameter 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.

Sensor Extrinsics

Location of the vision sensor center, specified as a real-valued 1-by-2 vector. The Sensor's (x,y) position (m) and Sensor's height (m) parameters define the coordinates of the vision sensor with respect to the ego vehicle coordinate system. The default value corresponds to a forward-facing vision sensor mounted to a sedan dashboard. Units are in meters.

Vision sensor height above the ground plane, specified as a positive real scalar. The height is defined with respect to the vehicle ground plane. The Sensor's (x,y) position (m) and Sensor's height (m) parameters define the coordinates of the vision sensor with respect to the ego vehicle coordinate system. The default value corresponds to a forward-facing vision sensor mounted a sedan dashboard. Units are in meters.

Example: 0.25

Yaw angle of vision sensor, specified as a real scalar. Yaw angle is the angle between the center line of the ego vehicle and the optical axis of the camera. 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: -4.0

Pitch angle of sensor, specified as a real scalar. The pitch angle is the angle between the optical axis of the camera 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: 3.0

Roll angle of the vision sensor, specified as a real scalar. The roll angle is the angle of rotation of the optical axis of the camera 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.

Output Port Settings

Source of object bus name, specified as Auto or Property. If you select Auto, the block automatically creates a bus name. If you select Property, specify the bus name using the Specify an object bus name parameter.

Example: Property

Source of output lane bus name, specified as Auto or Property. If you choose Auto, the block will automatically create a bus name. If you choose Property, specify the bus name using the Specify an object bus name parameter.

Example: Property

Name of object bus, specified as a valid bus name.

Example: objectbus

Dependencies

To enable this parameter, set the Source of object bus name parameter to Property.

Namer of output lane bus, specified as a valid bus name.

Example: lanebus

Dependencies

To enable this parameter, set the Source of output lane bus name parameter to Property.

Detection Reporting

Maximum number of detections reported by the sensor, specified as a positive integer. Detections are reported in order of increasing distance from the sensor until the maximum number is reached.

Example: 100

Dependencies

To enable this parameter, set the Types of detections generated by sensor parameter to Objects only or Lanes and objects.

Maximum number of reported lanes, specified as a positive integer.

Example: 100

Dependencies

To enable this parameter, set the Types of detections generated by sensor parameter to Lanes only, Lanes with occlusion, or Lanes and objects.

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.

Simulation
  • Interpreted execution — Simulate the model using the MATLAB interpreter. This option shortens startup time. In Interpreted execution mode, you can debug the source code of the block.

  • Code generation — Simulate the model using generated C/C++ code. The first time you run a simulation, Simulink generates C/C++ code for the block. The C code is reused for subsequent simulations as long as the model does not change. This option requires additional startup time.

Measurements

Settings

Maximum detection range, specified as a positive real scalar. The vision sensor cannot detect objects beyond this range. Units are in meters.

Example: 250

Object Detector Settings

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: 9

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 meters per second squared.

Example: 2

Maximum detectable object speed, specified as a nonnegative real scalar. Units are in meters per second.

Example: 20

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 that is not visible to the sensor. A value of 1 indicates that the object is fully occluded. Units are dimensionless.

Example: 0.2

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: [25 20]

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

Number of false detections generated by the vision sensor per image, specified as a nonnegative real scalar.

Example: 1.0

Lane Detector Settings

Minimum size of a projected lane marking in the camera image 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. Units are in pixels.

Dependencies

To enable this parameter, set the Types of detections generated by sensor parameter to Lanes only, Lanes only, or Lanes and objects.

Accuracy of lane boundaries, specified as a positive real scalar. This parameter defines the accuracy with which the lane sensor can place a lane boundary. Units are in pixels.

Example: 2.5

Dependencies

To enable this parameter, set the Types of detections generated by sensor parameter to Lanes only, Lanes only, or Lanes and objects.

Random Number Generator Settings

Select this parameter to add noise to vision sensor measurements. Otherwise, the measurements are noise-free. The MeasurementNoise property of each detection is always computed and is not affected by the value you specify for the Add noise to measurements parameter.

Method to set the random number generator seed, specified as one of the options in the table.

OptionDescription
Repeatable

The block generates a random initial seed for the first simulation and reuses this seed for all subsequent simulations. Select this parameter to generate repeatable results from the statistical sensor model. To change this initial seed, at the MATLAB command prompt, enter: clear all.

Specify seedSpecify your own random initial seed for reproducible results by using the Specify seed parameter.
Not repeatableThe block generates a new random initial seed after each simulation run. Select this parameter to generate nonrepeatable results from the statistical sensor model.

Random number generator seed, specified as a nonnegative integer less than 232.

Example: 2001

Dependencies

To enable this parameter, set the Random Number Generator Settings parameter to Specify seed.

Actor Profiles

Method to specify actor profiles, specified as Parameters or MATLAB expression. When you select Parameters, set the actor profiles using the parameters in the Actor Profiles tab. When you select MATLAB expression, set the actor profiles using the MATLAB expression for actor profiles parameter.

MATLAB expression for actor profiles, specified as a MATLAB structure, a MATLAB structure array, or a valid MATLAB expression that produces such a structure or structure array.

If your Scenario Reader block reads data from a drivingScenario object, to obtain the actor profiles directly from this object, set this expression to call the actorProfiles function on the object. For example: actorProfiles(scenario).

Example: struct('ClassID',5,'Length',5.0,'Width',2,'Height',2,'OriginOffset',[-1.55,0,0])

Dependencies

To enable this parameter, set the Select method to specify actor profiles parameter to MATLAB expression.

Scenario-defined actor identifier, specified as a positive integer or length-L vector of unique positive integers. L must equal the number of actors input into the Actors input port. The vector elements must match ActorID values of the actors. You can specify Unique identifier for actors as []. In this case, the same actor profile parameters apply to all actors.

Example: [1,2]

Dependencies

To enable this parameter, set the Select method to specify actor profiles parameter to Parameters.

User-defined classification identifier, specified as an integer or length-L vector of integers. When Unique identifier for actors is a vector, this parameter is a vector of the same length with elements in one-to-one correspondence to the actors in Unique identifier for actors. When Unique identifier for actors is empty, [], you must specify this parameter as a single integer whose value applies to all actors.

Example: 2

Dependencies

To enable this parameter, set the Select method to specify actor profiles parameter to Parameters.

Length of cuboid, specified as a positive real scalar or length-L vector of positive values. When Unique identifier for actors is a vector, this parameter is a vector of the same length with elements in one-to-one correspondence to the actors in Unique identifier for actors. When Unique identifier for actors is empty, [], you must specify this parameter as a positive real scalar whose value applies to all actors. Units are in meters.

Example: 6.3

Dependencies

To enable this parameter, set the Select method to specify actor profiles parameter to Parameters.

Width of cuboid, specified as a positive real scalar or length-L vector of positive values. When Unique identifier for actors is a vector, this parameter is a vector of the same length with elements in one-to-one correspondence to the actors in Unique identifier for actors. When Unique identifier for actors is empty, [], you must specify this parameter as a positive real scalar whose value applies to all actors. Units are in meters.

Example: 4.7

Dependencies

To enable this parameter, set the Select method to specify actor profiles parameter to Parameters.

Height of cuboid, specified as a positive real scalar or length-L vector of positive values. When Unique identifier for actors is a vector, this parameter is a vector of the same length with elements in one-to-one correspondence to the actors in Unique identifier for actors. When Unique identifier for actors is empty, [], you must specify this parameter as a positive real scalar whose value applies to all actors. Units are in meters.

Example: 2.0

Dependencies

To enable this parameter, set the Select method to specify actor profiles parameter to Parameters.

Rotational center of the actor, specified as a length-L cell array of real-valued 1-by-3 vectors. Each vector represents the offset of the rotational center of the actor from the bottom-center of the actor. For vehicles, the offset corresponds to the point on the ground beneath the center of the rear axle. When Unique identifier for actors is a vector, this parameter is a cell array of vectors with cells in one-to-one correspondence to the actors in Unique identifier for actors. When Unique identifier for actors is empty, [], you must specify this parameter as a cell array of one element containing the offset vector whose values apply to all actors. Units are in meters.

Example: [ -1.35, .2, .3 ]

Dependencies

To enable this parameter, set the Select method to specify actor profiles parameter to Parameters.

Camera Intrinsics

Camera focal length, in pixels, specified as a two-element real-valued vector. See also the FocalLength (Computer Vision Toolbox) property of cameraIntrinsics.

Example: [480,320]

Optical center of the camera, in pixels, specified as a two-element real-valued vector. See also the PrincipalPoint (Computer Vision Toolbox) property of cameraIntrinsics.

Example: [480,320]

Image size produced by the camera, in pixels, specified as a two-element vector of positive integers. See also the ImageSize (Computer Vision Toolbox) property of cameraIntrinsics.

Example: [240,320]

Radial distortion coefficients, specified as a two-element or three-element real-valued vector. For details on setting these coefficients, see the RadialDistortion (Computer Vision Toolbox) property of cameraIntrinsics.

Example: [1,1]

Tangential distortion coefficients, specified as a two-element real-valued vector. For details on setting these coefficients, see the TangentialDistortion (Computer Vision Toolbox) property of cameraIntrinsics.

Example: [1,1]

Skew angle of the camera axes, specified as a real scalar. See also the Skew (Computer Vision Toolbox) property of cameraIntrinsics.

Example: 0.1

Extended Capabilities

C/C++ Code Generation
Generate C and C++ code using Simulink® Coder™.

Introduced in R2017b