Sensors reference


Collision detector

This sensor registers an event each time its parent actor collisions against something in the world. Several collisions may be detected during a single simulation step. To ensure that collisions with any kind of object are detected, the server creates "fake" actors for elements such as buildings or bushes so the semantic tag can be retrieved to identify it.

Collision detectors do not have any configurable attribute.

Output attributes

Sensor data attribute Type Description
frame int Frame number when the measurement took place.
timestamp double Simulation time of the measurement in seconds since the beginning of the episode.
transform carla.Transform Location and rotation in world coordinates of the sensor at the time of the measurement.
actor carla.Actor Actor that measured the collision (sensor's parent).
other_actor carla.Actor Actor against whom the parent collided.
normal_impulse carla.Vector3D Normal impulse result of the collision.

Depth camera

  • Blueprint: sensor.camera.depth
  • Output: carla.Image per step (unless sensor_tick says otherwise).

The camera provides a raw data of the scene codifying the distance of each pixel to the camera (also known as depth buffer or z-buffer) to create a depth map of the elements.

The image codifies depth value per pixel using 3 channels of the RGB color space, from less to more significant bytes: R -> G -> B. The actual distance in meters can be decoded with:

normalized = (R + G * 256 + B * 256 * 256) / (256 * 256 * 256 - 1)
in_meters = 1000 * normalized

The output carla.Image should then be saved to disk using a carla.colorConverter that will turn the distance stored in RGB channels into a [0,1] float containing the distance and then translate this to grayscale. There are two options in carla.colorConverter to get a depth view: Depth and Logaritmic depth. The precision is milimetric in both, but the logarithmic approach provides better results for closer objects.

ImageDepth

Basic camera attributes

Blueprint attribute Type Default Description
image_size_x int 800 Image width in pixels.
image_size_y int 600 Image height in pixels.
fov float 90.0 Horizontal field of view in degrees.
sensor_tick float 0.0 Simulation seconds between sensor captures (ticks).


Camera lens distortion attributes

Blueprint attribute Type Default Description
lens_circle_falloff float 5.0 Range: [0.0, 10.0]
lens_circle_multiplier float 0.0 Range: [0.0, 10.0]
lens_k float -1.0 Range: [-inf, inf]
lens_kcube float 0.0 Range: [-inf, inf]
lens_x_size float 0.08 Range: [0.0, 1.0]
lens_y_size float 0.08 Range: [0.0, 1.0]


Output attributes

Sensor data attribute Type Description
frame int Frame number when the measurement took place.
timestamp double Simulation time of the measurement in seconds since the beginning of the episode.
transform carla.Transform Location and rotation in world coordinates of the sensor at the time of the measurement.
width int Image width in pixels.
height int Image height in pixels.
fov float Horizontal field of view in degrees.
raw_data bytes Array of BGRA 32-bit pixels.

GNSS sensor

  • Blueprint: sensor.other.gnss
  • Output: carla.GNSSMeasurement per step (unless sensor_tick says otherwise).

Reports current gnss position of its parent object. This is calculated by adding the metric position to an initial geo reference location defined within the OpenDRIVE map definition.

GNSS attributes

Blueprint attribute Type Default Description
noise_alt_bias float 0.0 Mean parameter in the noise model for altitude.
noise_alt_stddev float 0.0 Standard deviation parameter in the noise model for altitude.
noise_lat_bias float 0.0 Mean parameter in the noise model for latitude.
noise_lat_stddev float 0.0 Standard deviation parameter in the noise model for latitude.
noise_lon_bias float 0.0 Mean parameter in the noise model for longitude.
noise_lon_stddev float 0.0 Standard deviation parameter in the noise model for longitude.
noise_seed int 0 Initializer for a pseudorandom number generator.
sensor_tick float 0.0 Simulation seconds between sensor captures (ticks).


Output attributes

Sensor data attribute Type Description
frame int Frame number when the measurement took place.
timestamp double Simulation time of the measurement in seconds since the beginning of the episode.
transform carla.Transform Location and rotation in world coordinates of the sensor at the time of the measurement.
latitude double Latitude of the actor.
longitude double Longitude of the actor.
altitude double Altitude of the actor.

IMU sensor

  • Blueprint: sensor.other.imu
  • Output: carla.IMUMeasurement per step (unless sensor_tick says otherwise).

Provides measures that accelerometer, gyroscope and compass would retrieve for the parent object. The data is collected from the object's current state.

IMU attributes

Blueprint attribute Type Default Description
noise_accel_stddev_x float 0.0 Standard deviation parameter in the noise model for acceleration (X axis).
noise_accel_stddev_y float 0.0 Standard deviation parameter in the noise model for acceleration (Y axis).
noise_accel_stddev_z float 0.0 Standard deviation parameter in the noise model for acceleration (Z axis).
noise_gyro_bias_x float 0.0 Mean parameter in the noise model for the gyroscope (X axis).
noise_gyro_bias_y float 0.0 Mean parameter in the noise model for the gyroscope (Y axis).
noise_gyro_bias_z float 0.0 Mean parameter in the noise model for the gyroscope (Z axis).
noise_gyro_stddev_x float 0.0 Standard deviation parameter in the noise model for the gyroscope (X axis).
noise_gyro_stddev_y float 0.0 Standard deviation parameter in the noise model for the gyroscope (Y axis).
noise_gyro_stddev_z float 0.0 Standard deviation parameter in the noise model for the gyroscope (Z axis).
noise_seed int 0 Initializer for a pseudorandom number generator.
sensor_tick float 0.0 Simulation seconds between sensor captures (ticks).


Output attributes

Sensor data attribute Type Description
frame int Frame number when the measurement took place.
timestamp double Simulation time of the measurement in seconds since the beginning of the episode.
transform carla.Transform Location and rotation in world coordinates of the sensor at the time of the measurement.
accelerometer carla.Vector3D Measures linear acceleration in m/s^2.
gyroscope carla.Vector3D Measures angular velocity in rad/sec.
compass float Orientation in radians. North is (0.0, -1.0, 0.0) in UE.

Lane invasion detector

Registers an event each time its parent crosses a lane marking. The sensor uses road data provided by the OpenDRIVE description of the map to determine whether the parent vehicle is invading another lane by considering the space between wheels. However there are some things to be taken into consideration:

  • Discrepancies between the OpenDRIVE file and the map will create irregularities such as crossing lanes that are not visible in the map.
  • The output retrieves a list of crossed lane markings: the computation is done in OpenDRIVE and considering the whole space between the four wheels as a whole. Thus, there may be more than one lane being crossed at the same time.

This sensor does not have any configurable attribute.

Important

This sensor works fully on the client-side.

Output attributes

Sensor data attribute Type Description
frame int Frame number when the measurement took place.
timestamp double Simulation time of the measurement in seconds since the beginning of the episode.
transform carla.Transform Location and rotation in world coordinates of the sensor at the time of the measurement.
actor carla.Actor Vehicle that invaded another lane (parent actor).
crossed_lane_markings list(carla.LaneMarking) List of lane markings that have been crossed.

LIDAR sensor

  • Blueprint: sensor.lidar.ray_cast
  • Output: carla.LidarMeasurement per step (unless sensor_tick says otherwise).

This sensor simulates a rotating LIDAR implemented using ray-casting. The points are computed by adding a laser for each channel distributed in the vertical FOV. The rotation is simulated computing the horizontal angle that the Lidar rotated in a frame. The point cloud is calculated by doing a ray-cast for each laser in every step.
points_per_channel_each_step = points_per_second / (FPS * channels)

A LIDAR measurement contains a package with all the points generated during a 1/FPS interval. During this interval the physics are not updated so all the points in a measurement reflect the same "static picture" of the scene.

This output contains a cloud of simulation points and thus, it can be iterated to retrieve a list of their carla.Location:

for location in lidar_measurement:
    print(location)

The information of the LIDAR measurement is enconded 4D points. Being the first three, the space points in xyz coordinates and the last one intensity loss during the travel. This intensity is computed by the following formula.

LidarIntensityComputation

a — Attenuation coefficient. This may depend on the sensor's wavelenght, and the conditions of the atmosphere. It can be modified with the LIDAR attribute atmosphere_attenuation_rate.
d — Distance from the hit point to the sensor.

For a better realism, points in the cloud can be dropped off. This is an easy way to simulate loss due to external perturbations. This can done combining two different.

  • General drop-off — Proportion of points that are dropped off randomly. This is done before the tracing, meaning the points being dropped are not calculated, and therefore improves the performance. If dropoff_general_rate = 0.5, half of the points will be dropped.
  • Instensity-based drop-off — For each point detected, and extra drop-off is performed with a probability based in the computed intensity. This probability is determined by two parameters. dropoff_zero_intensity is the probability of points with zero intensity to be dropped. dropoff_intensity_limit is a threshold intensity above which no points will be dropped. The probability of a point within the range to be dropped is a linear proportion based on these two parameters.

Additionally, the noise_stddev attribute makes for a noise model to simulate unexpected deviations that appear in real-life sensors. For positive values, each point is randomly perturbed along the vector of the laser ray. The result is a LIDAR sensor with perfect angular positioning, but noisy distance measurement.

The rotation of the LIDAR can be tuned to cover a specific angle on every simulation step (using a fixed time-step). For example, to rotate once per step (full circle output, as in the picture below), the rotation frequency and the simulated FPS should be equal.
1. Set the sensor's frequency sensors_bp['lidar'][0].set_attribute('rotation_frequency','10').
2. Run the simulation using python config.py --fps=10.

LidarPointCloud

Lidar attributes

Blueprint attribute Type Default Description
channels int 32 Number of lasers.
range float 10.0 Maximum distance to measure/raycast in meters (centimeters for CARLA 0.9.6 or previous).
points_per_second int 56000 Points generated by all lasers per second.
rotation_frequency float 10.0 LIDAR rotation frequency.
upper_fov float 10.0 Angle in degrees of the highest laser.
lower_fov float -30.0 Angle in degrees of the lowest laser.
atmosphere_attenuation_rate float 0.004 Coefficient that measures the LIDAR instensity loss per meter. Check the intensity computation above.
dropoff_general_rate float 0.45 General proportion of points that are randomy dropped.
dropoff_intensity_limit float 0.8 For the intensity based drop-off, the threshold intensity value above which no points are dropped.
dropoff_zero_intensity float 0.4 For the intensity based drop-off, the probability of each point with zero intensity being dropped.
sensor_tick float 0.0 Simulation seconds between sensor captures (ticks).
noise_stddev float 0.0 Standard deviation of the noise model to disturb each point along the vector of its raycast.


Output attributes

Sensor data attribute Type Description
frame int Frame number when the measurement took place.
timestamp double Simulation time of the measurement in seconds since the beginning of the episode.
transform carla.Transform Location and rotation in world coordinates of the sensor at the time of the measurement.
horizontal_angle float Angle (radians) in the XY plane of the LIDAR in the current frame.
channels int Number of channels (lasers) of the LIDAR.
get_point_count(channel) int Number of points per channel captured this frame.
raw_data bytes Array of 32-bits floats (XYZI of each point).

Obstacle detector

Registers an event every time the parent actor has an obstacle ahead. In order to anticipate obstacles, the sensor creates a capsular shape ahead of the parent vehicle and uses it to check for collisions. To ensure that collisions with any kind of object are detected, the server creates "fake" actors for elements such as buildings or bushes so the semantic tag can be retrieved to identify it.

Blueprint attribute Type Default Description
distance float 5 Distance to trace.
hit_radius float 0.5 Radius of the trace.
only_dynamics bool False If true, the trace will only consider dynamic objects.
debug_linetrace bool False If true, the trace will be visible.
sensor_tick float 0.0 Simulation seconds between sensor captures (ticks).


Output attributes

Sensor data attribute Type Description
frame int Frame number when the measurement took place.
timestamp double Simulation time of the measurement in seconds since the beginning of the episode.
transform carla.Transform Location and rotation in world coordinates of the sensor at the time of the measurement.
actor carla.Actor Actor that detected the obstacle (parent actor).
other_actor carla.Actor Actor detected as an obstacle.
distance float Distance from actor to other_actor.

Radar sensor

The sensor creates a conic view that is translated to a 2D point map of the elements in sight and their speed regarding the sensor. This can be used to shape elements and evaluate their movement and direction. Due to the use of polar coordinates, the points will concentrate around the center of the view.

Points measured are contained in carla.RadarMeasurement as an array of carla.RadarDetection, which specifies their polar coordinates, distance and velocity. This raw data provided by the radar sensor can be easily converted to a format manageable by numpy:

# To get a numpy [[vel, altitude, azimuth, depth],...[,,,]]:
points = np.frombuffer(radar_data.raw_data, dtype=np.dtype('f4'))
points = np.reshape(points, (len(radar_data), 4))

The provided script manual_control.py uses this sensor to show the points being detected and paint them white when static, red when moving towards the object and blue when moving away:

ImageRadar

Blueprint attribute Type Default Description
horizontal_fov float 30.0 Horizontal field of view in degrees.
points_per_second int 1500 Points generated by all lasers per second.
range float 100 Maximum distance to measure/raycast in meters.
sensor_tick float 0.0 Simulation seconds between sensor captures (ticks).
vertical_fov float 30.0 Vertical field of view in degrees.


Output attributes

Sensor data attribute Type Description
raw_data carla.RadarDetection The list of points detected.


RadarDetection attributes Type Description
altitude float Altitude angle in radians.
azimuth float Azimuth angle in radians.
depth float Distance in meters.
velocity float Velocity towards the sensor.

RGB camera

  • Blueprint: sensor.camera.rgb
  • Output: carla.Image per step (unless sensor_tick says otherwise)..

The "RGB" camera acts as a regular camera capturing images from the scene. carla.colorConverter

If enable_postprocess_effects is enabled, a set of post-process effects is applied to the image for the sake of realism:

  • Vignette: darkens the border of the screen.
  • Grain jitter: adds some noise to the render.
  • Bloom: intense lights burn the area around them.
  • Auto exposure: modifies the image gamma to simulate the eye adaptation to darker or brighter areas.
  • Lens flares: simulates the reflection of bright objects on the lens.
  • Depth of field: blurs objects near or very far away of the camera.

The sensor_tick tells how fast we want the sensor to capture the data. A value of 1.5 means that we want the sensor to capture data each second and a half. By default a value of 0.0 means as fast as possible.

ImageRGB

Basic camera attributes

Blueprint attribute Type Default Description
bloom_intensity float 0.675 Intensity for the bloom post-process effect, 0.0 for disabling it.
fov float 90.0 Horizontal field of view in degrees.
fstop float 8.0 Opening of the camera lens. Aperture is 1/fstop with typical lens going down to f/1.2 (larger opening). Larger numbers will reduce the Depth of Field effect.
image_size_x int 800 Image width in pixels.
image_size_y int 600 Image height in pixels.
iso float 200.0 The camera sensor sensitivity.
gamma float 2.2 Target gamma value of the camera.
lens_flare_intensity float 0.1 Intensity for the lens flare post-process effect, 0.0 for disabling it.
sensor_tick float 0.0 Simulation seconds between sensor captures (ticks).
shutter_speed float 200.0 The camera shutter speed in seconds (1.0/s).


Camera lens distortion attributes

Blueprint attribute Type Default Description
lens_circle_falloff float 5.0 Range: [0.0, 10.0]
lens_circle_multiplier float 0.0 Range: [0.0, 10.0]
lens_k float -1.0 Range: [-inf, inf]
lens_kcube float 0.0 Range: [-inf, inf]
lens_x_size float 0.08 Range: [0.0, 1.0]
lens_y_size float 0.08 Range: [0.0, 1.0]

Advanced camera attributes

Since these effects are provided by UE, please make sure to check their documentation:

Blueprint attribute Type Default Description
min_fstop float 1.2 Maximum aperture.
blade_count int 5 Number of blades that make up the diaphragm mechanism.
exposure_mode str manual Can be manual or histogram. More in UE4 docs.
exposure_compensation float -2.2 Logarithmic adjustment for the exposure. 0: no adjustment, -1:2x darker, -2:4 darker, 1:2x brighter, 2:4x brighter.
exposure_min_bright float 0.1 In exposure_mode: "histogram". Minimum brightness for auto exposure. The lowest the eye can adapt within. Must be greater than 0 and less than or equal to exposure_max_bright.
exposure_max_bright float 2.0 In `exposure_mode: "histogram"`. Maximum brightness for auto exposure. The highestthe eye can adapt within. Must be greater than 0 and greater than or equal to `exposure_min_bright`.
exposure_speed_up float 3.0 In exposure_mode: "histogram". Speed at which the adaptation occurs from dark to bright environment.
exposure_speed_down float 1.0 In exposure_mode: "histogram". Speed at which the adaptation occurs from bright to dark environment.
calibration_constant float 16.0 Calibration constant for 18% albedo.
focal_distance float 1000.0 Distance at which the depth of field effect should be sharp. Measured in cm (UE units).
blur_amount float 1.0 Strength/intensity of motion blur.
blur_radius float 0.0 Radius in pixels at 1080p resolution to emulate atmospheric scattering according to distance from camera.
motion_blur_intensity float 0.45 Strength of motion blur [0,1].
motion_blur_max_distortion float 0.35 Max distortion caused by motion blur. Percentage of screen width.
motion_blur_min_object_screen_size float 0.1 Percentage of screen width objects must have for motion blur, lower value means less draw calls.
slope float 0.88 Steepness of the S-curve for the tonemapper. Larger values make the slope steeper (darker) [0.0, 1.0].
toe float 0.55 Adjusts dark color in the tonemapper [0.0, 1.0].
shoulder float 0.26 Adjusts bright color in the tonemapper [0.0, 1.0].
black_clip float 0.0 This should NOT be adjusted. Sets where the crossover happens and black tones start to cut off their value [0.0, 1.0].
white_clip float 0.04 Set where the crossover happens and white tones start to cut off their value. Subtle change in most cases [0.0, 1.0].
temp float 6500.0 White balance in relation to the temperature of the light in the scene. White light: when this matches light temperature. Warm light: When higher than the light in the scene, it is a yellowish color. Cool light: When lower than the light. Blueish color.
tint float 0.0 White balance temperature tint. Adjusts cyan and magenta color ranges. This should be used along with the white balance Temp property to get accurate colors. Under some light temperatures, the colors may appear to be more yellow or blue. This can be used to balance the resulting color to look more natural.
chromatic_aberration_intensity float 0.0 Scaling factor to control color shifting, more noticeable on the screen borders.
chromatic_aberration_offset float 0.0 Normalized distance to the center of the image where the effect takes place.
enable_postprocess_effects bool True Post-process effects activation.


Output attributes

Sensor data attribute Type Description
frame int Frame number when the measurement took place.
timestamp double Simulation time of the measurement in seconds since the beginning of the episode.
transform carla.Transform Location and rotation in world coordinates of the sensor at the time of the measurement.
width int Image width in pixels.
height int Image height in pixels.
fov float Horizontal field of view in degrees.
raw_data bytes Array of BGRA 32-bit pixels.



RSS sensor

  • Blueprint: sensor.other.rss
  • Output: carla.RssResponse per step (unless sensor_tick says otherwise).

Important

It is highly recommended to read the specific rss documentation before reading this.

This sensor integrates the C++ Library for Responsibility Sensitive Safety in CARLA. It is disabled by default in CARLA, and it has to be explicitly built in order to be used.

The RSS sensor calculates the RSS state of a vehicle and retrieves the current RSS Response as sensor data. The carla.RssRestrictor will use this data to adapt a carla.VehicleControl before applying it to a vehicle.

These controllers can be generated by an Automated Driving stack or user input. For instance, hereunder there is a fragment of code from PythonAPI/examples/manual_control_rss.py, where the user input is modified using RSS when necessary.

1. Checks if the RssSensor generates a valid response containing restrictions.
2. Gathers the current dynamics of the vehicle and the vehicle physics.
3. Applies restrictions to the vehicle control using the response from the RssSensor, and the current dynamics and physicis of the vehicle.

rss_restriction = self._world.rss_sensor.acceleration_restriction if self._world.rss_sensor and self._world.rss_sensor.response_valid else None
if rss_restriction:
    rss_ego_dynamics_on_route = self._world.rss_sensor.ego_dynamics_on_route
    vehicle_physics = world.player.get_physics_control()
...
        vehicle_control = self._restrictor.restrict_vehicle_control(
            vehicle_control, rss_restriction, rss_ego_dynamics_on_route, vehicle_physics)

The carla.RssSensor class

The blueprint for this sensor has no modifiable attributes. However, the carla.RssSensor object that it instantiates has attributes and methods that are detailed in the Python API reference. Here is a summary of them.

carla.RssSensor variables Type Description
ego_vehicle_dynamics libad_rss_python.RssDynamics RSS parameters to be applied for the ego vehicle
other_vehicle_dynamics libad_rss_python.RssDynamics RSS parameters to be applied for the other vehicles
road_boundaries_mode carla.RssRoadBoundariesMode Enables/Disables the stay on road feature. Default is Off.
visualization_mode carla.RssVisualizationMode States the visualization of the RSS calculations. Default is All.


# Fragment of manual_control_rss.py
# The carla.RssSensor is updated when listening for a new carla.RssResponse
def _on_rss_response(weak_self, response):
...
        self.timestamp = response.timestamp
        self.response_valid = response.response_valid
        self.proper_response = response.proper_response
        self.acceleration_restriction = response.acceleration_restriction
        self.ego_dynamics_on_route = response.ego_dynamics_on_route

Warning

This sensor works fully on the client side. There is no blueprint in the server. Changes on the attributes will have effect after the listen() has been called.

The methods available in this class are related to the routing of the vehicle. RSS calculations are always based on a route of the ego vehicle through the road network.

The sensor allows to control the considered route by providing some key points, which could be the carla.Transform in a carla.Waypoint. These points are best selected after the intersections to force the route to take the desired turn.

carla.RssSensor methods Description
routing_targets Get the current list of routing targets used for route.
append_routing_target Append an additional position to the current routing targets.
reset_routing_targets Deletes the appended routing targets.
drop_route Discards the current route and creates a new one.
register_actor_constellation_callback Register a callback to customize the calculations.
set_log_level Sets the log level.


# Update the current route
self.sensor.reset_routing_targets()
if routing_targets:
    for target in routing_targets:
        self.sensor.append_routing_target(target)

Note

If no routing targets are defined, a random route is created.

Output attributes

carla.RssResponse attributes Type Description
response_valid bool Validity of the response data.
proper_response libad_rss_python.ProperResponse Proper response that the RSS calculated for the vehicle.
acceleration_restriction libad_rss_python.AccelerationRestriction Acceleration restrictions of the RSS calculation.
rss_state_snapshot libad_rss_python.RssStateSnapshot RSS states at the current point in time.
ego_dynamics_on_route carla.RssEgoDynamicsOnRoute Current ego vehicle dynamics regarding the route.
situation_snapshot carla.RssEgoDynamicsOnRoute Current situation snapshot extracted from the world model.

In case a actor_constellation_callback is registered, a call is triggered for:

  1. default calculation (actor_constellation_data.other_actor=None)
  2. per-actor calculation
# Fragment of manual_control_rss.py
# The function is registered as actor_constellation_callback
def _on_actor_constellation_request(self, actor_constellation_data):
    actor_constellation_result = carla.RssActorConstellationResult()
    actor_constellation_result.rss_calculation_mode = rssmap.RssMode.NotRelevant
    actor_constellation_result.restrict_speed_limit_mode = rssmap.RssSceneCreation.RestrictSpeedLimitMode.IncreasedSpeedLimit10
    actor_constellation_result.ego_vehicle_dynamics = self.current_vehicle_parameters
    actor_constellation_result.actor_object_type = rss.ObjectType.Invalid
    actor_constellation_result.actor_dynamics = self.current_vehicle_parameters

    actor_id = -1
    actor_type_id = "none"
    if actor_constellation_data.other_actor != None:
        # customize actor_constellation_result for specific actor
        ...
    else:
        # default
        ...
    return actor_constellation_result

Semantic LIDAR sensor

This sensor simulates a rotating LIDAR implemented using ray-casting that exposes all the information about the raycast hit. Its behaviour is quite similar to the LIDAR sensor, but there are two main differences between them.

  • The raw data retrieved by the semantic LIDAR includes more data per point.
    • Coordinates of the point (as the normal LIDAR does).
    • The cosine between the angle of incidence and the normal of the surface hit.
    • Instance and semantic ground-truth. Basically the index of the CARLA object hit, and its semantic tag.
  • The semantic LIDAR does not include neither intensity, drop-off nor noise model attributes.

The points are computed by adding a laser for each channel distributed in the vertical FOV. The rotation is simulated computing the horizontal angle that the LIDAR rotated in a frame. The point cloud is calculated by doing a ray-cast for each laser in every step.

points_per_channel_each_step = points_per_second / (FPS * channels)

A LIDAR measurement contains a package with all the points generated during a 1/FPS interval. During this interval the physics are not updated so all the points in a measurement reflect the same "static picture" of the scene.

This output contains a cloud of lidar semantic detections and therefore, it can be iterated to retrieve a list of their carla.SemanticLidarDetection:

for detection in semantic_lidar_measurement:
    print(detection)

The rotation of the LIDAR can be tuned to cover a specific angle on every simulation step (using a fixed time-step). For example, to rotate once per step (full circle output, as in the picture below), the rotation frequency and the simulated FPS should be equal.
1. Set the sensor's frequency sensors_bp['lidar'][0].set_attribute('rotation_frequency','10').
2. Run the simulation using python config.py --fps=10.

LidarPointCloud

SemanticLidar attributes

Blueprint attribute Type Default Description
channels int 32 Number of lasers.
range float 10.0 Maximum distance to measure/raycast in meters (centimeters for CARLA 0.9.6 or previous).
points_per_second int 56000 Points generated by all lasers per second.
rotation_frequency float 10.0 LIDAR rotation frequency.
upper_fov float 10.0 Angle in degrees of the highest laser.
lower_fov float -30.0 Angle in degrees of the lowest laser.
sensor_tick float 0.0 Simulation seconds between sensor captures (ticks).


Output attributes

Sensor data attribute Type Description
frame int Frame number when the measurement took place.
timestamp double Simulation time of the measurement in seconds since the beginning of the episode.
transform carla.Transform Location and rotation in world coordinates of the sensor at the time of the measurement.
horizontal_angle float Angle (radians) in the XY plane of the LIDAR in the current frame.
channels int Number of channels (lasers) of the LIDAR.
get_point_count(channel) int Number of points per channel captured in the current frame.
raw_data bytes Array containing the point cloud with instance and semantic information. For each point, four 32-bits floats are stored.
- XYZ coordinates.
- cosine of the incident angle.
- Unsigned int containing the index of the object hit.
- Unsigned int containing the semantic tag of the object it.

Semantic segmentation camera

  • Blueprint: sensor.camera.semantic_segmentation
  • Output: carla.Image per step (unless sensor_tick says otherwise).

This camera classifies every object in sight by displaying it in a different color according to its tags (e.g., pedestrians in a different color than vehicles). When the simulation starts, every element in scene is created with a tag. So it happens when an actor is spawned. The objects are classified by their relative file path in the project. For example, meshes stored in Unreal/CarlaUE4/Content/Static/Pedestrians are tagged as Pedestrian.

The server provides an image with the tag information encoded in the red channel: A pixel with a red value of x belongs to an object with tag x. This raw carla.Image can be stored and converted it with the help of CityScapesPalette in carla.ColorConverter to apply the tags information and show picture with the semantic segmentation. The following tags are currently available:

Value Tag Converted color
0 Unlabeled ( 0, 0, 0)
1 Building ( 70, 70, 70)
2 Fence (190, 153, 153)
3 Other (250, 170, 160)
4 Pedestrian (220, 20, 60)
5 Pole (153, 153, 153)
6 Road line (157, 234, 50)
7 Road (128, 64, 128)
8 Sidewalk (244, 35, 232)
9 Vegetation (107, 142, 35)
10 Car ( 0, 0, 142)
11 Wall (102, 102, 156)
12 Traffic sign (220, 220, 0)


Note

Adding new tags: It requires some C++ coding. Add a new label to the ECityObjectLabel enum in "Tagger.h", and its corresponding filepath check inside GetLabelByFolderName() function in "Tagger.cpp".

ImageSemanticSegmentation

Basic camera attributes

Blueprint attribute Type Default Description
fov float 90.0 Horizontal field of view in degrees.
image_size_x int 800 Image width in pixels.
image_size_y int 600 Image height in pixels.
sensor_tick float 0.0 Simulation seconds between sensor captures (ticks).


Camera lens distortion attributes

Blueprint attribute Type Default Description
lens_circle_falloff float 5.0 Range: [0.0, 10.0]
lens_circle_multiplier float 0.0 Range: [0.0, 10.0]
lens_k float -1.0 Range: [-inf, inf]
lens_kcube float 0.0 Range: [-inf, inf]
lens_x_size float 0.08 Range: [0.0, 1.0]
lens_y_size float 0.08 Range: [0.0, 1.0]


Output attributes

Sensor data attribute Type Description
fov float Horizontal field of view in degrees.
frame int Frame number when the measurement took place.
height int Image height in pixels.
raw_data bytes Array of BGRA 32-bit pixels.
timestamp double Simulation time of the measurement in seconds since the beginning of the episode.
transform carla.Transform Location and rotation in world coordinates of the sensor at the time of the measurement.
width int Image width in pixels.



DVS camera

  • Blueprint: sensor.camera.dvs
  • Output: carla.DVSEventArray per step (unless sensor_tick says otherwise).

A Dynamic Vision Sensor (DVS) or Event camera is a sensor that works radically differently from a conventional camera. Instead of capturing intensity images at a fixed rate, event cameras measure changes of intensity asynchronously, in the form of a stream of events, which encode per-pixel brightness changes. Event cameras possess outstanding properties when compared to standard cameras. They have a very high dynamic range (140 dB versus 60 dB), no motion blur, and high temporal resolution (in the order of microseconds). Event cameras are thus sensors that can provide high-quality visual information even in challenging high-speed scenarios and high dynamic range environments, enabling new application domains for vision-based algorithms.

The DVS camera outputs a stream of events. An event e=(x,y,t,pol) is triggered at a pixel x, y at a timestamp t when the change in logarithmic intensity L reaches a predefined constant threshold C (typically between 15% and 30%).

L(x,y,t) - L(x,y,t-\delta t) = pol C

t-\delta t is the time when the last event at that pixel was triggered and pol is the polarity of the event according to the sign of the brightness change. The polarity is positive +1 when there is increment in brightness and negative -1 when a decrement in brightness occurs. The working principles depicted in the following figure. The standard camera outputs frames at a fixed rate, thus sending redundant information when no motion is present in the scene. In contrast, event cameras are data-driven sensors that respond to brightness changes with microsecond latency. At the plot, a positive (resp. negative) event (blue dot, resp. red dot) is generated whenever the (signed) brightness change exceeds the contrast threshold C for one dimension x over time t. Observe how the event rate grows when the signal changes rapidly.

DVSCameraWorkingPrinciple

The current implementation of the DVS camera works in a uniform sampling manner between two consecutive synchronous frames. Therefore, in order to emulate the high temporal resolution (order of microseconds) of a real event camera, the sensor requires to execute at a high frequency (much higher frequency than a conventional camera). Effectively, the number of events increases as faster a CARLA car drives. Therefore, the sensor frequency should increase accordingly with the dynamic of the scene. The user should find their balance between time accuracy and computational cost.

The provided script manual_control.py uses the DVS camera in order to show how to configure the sensor, how to get the stream of events and how to depict such events in an image format, usually called event frame.

DVSCameraWorkingPrinciple

DVS is a camera and therefore has all the attributes available in the RGB camera. Nevertheless, there are few attributes exclusive to the working principle of an Event camera.

DVS camera attributes

Blueprint attribute Type Default Description
positive_threshold float 0.3 Positive threshold C associated to a increment in brightness change (0-1).
negative_threshold float 0.3 Negative threshold C associated to a decrement in brightness change (0-1).
sigma_positive_threshold float 0 White noise standard deviation for positive events (0-1).
sigma_negative_threshold float 0 White noise standard deviation for negative events (0-1).
refractory_period_ns int 0.0 Refractory period (time during which a pixel cannot fire events just after it fired one), in nanoseconds. It limits the highest frequency of triggering events.
use_log bool true Whether to work in the logarithmic intensity scale.
log_eps float 0.001 Epsilon value used to convert images to log: L = log(eps + I / 255.0).
Where I is the grayscale value of the RGB image:
I = 0.2989*R + 0.5870*G + 0.1140*B.