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.
| `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](python_api.md#carla.Transform) | Location and rotation in world coordinates of the sensor at the time of the measurement. |
| `actor` | [carla.Actor](python_api.md#carla.Actor) | Actor that measured the collision (sensor's parent). |
| `other_actor` | [carla.Actor](python_api.md#carla.Actor) | Actor against whom the parent collided. |
| `normal_impulse` | [carla.Vector3D](python_api.md#carla.Vector3D) | Normal impulse result of the collision. |
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
The output [carla.Image](python_api.md#carla.Image) should then be saved to disk using a [carla.colorConverter](python_api.md#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](python_api.md#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.
| `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](python_api.md#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. |
Reports current [gnss position](https://www.gsa.europa.eu/european-gnss/what-gnss) of its parent object. This is calculated by adding the metric position to an initial geo reference location defined within the OpenDRIVE map definition.
| `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](python_api.md#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. |
Provides measures that accelerometer, gyroscope and compass would retrieve for the parent object. The data is collected from the object's current state.
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.
| `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](python_api.md#carla.Transform) | Location and rotation in world coordinates of the sensor at the time of the measurement. |
| `actor` | [carla.Actor](python_api.md#carla.Actor) | Vehicle that invaded another lane (parent actor). |
| `crossed_lane_markings` | list([carla.LaneMarking](python_api.md#carla.LaneMarking)) | List of lane markings that have been crossed. |
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:
A Lidar measurement contains a packet 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, can be iterated to retrieve a list of their [`carla.Location`](python_api.md#carla.Location):
Running the simulator at [fixed time-step](adv_synchrony_timestep.md) it is possible to tune the rotation for each measurement. Adjust the step and the rotation frequency to get, for instance, a 360 view each measurement.
| `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](python_api.md#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 this 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 (XYZ of each point). |
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.
| `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](python_api.md#carla.Transform) | Location and rotation in world coordinates of the sensor at the time of the measurement. |
| `actor` | [carla.Actor](python_api.md#carla.Actor) | Actor that detected the obstacle (parent actor). |
| `other_actor` | [carla.Actor](python_api.md#carla.Actor) | Actor detected as an obstacle. |
| `distance` | float | Distance from `actor` to `other_actor`. |
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](python_api.md#carla.RadarMeasurement) as an array of [carla.RadarDetection](python_api.md#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__:
```py
# To get a numpy [[vel, altitude, azimuth, depth],...[,,,]]:
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:
| `fov` | float | 90.0 | Horizontal field of view in degrees. |
| `fstop` | float | 1.4 | 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 | 1200.0 | The camera sensor sensitivity. |
| `gamma` | float | 2.2 | Target gamma value of the camera. |
| `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][AutomaticExposure.gamesetting]. |
| `exposure_compensation` | float | 3.0 | 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. |
| `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. |
| `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](python_api.md#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. |
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](python_api.md#carla.Image) can be stored and converted it with the help of __CityScapesPalette__ in [carla.ColorConverter](python_api.md#carla.ColorConverter) to apply the tags information and show picture with the semantic segmentation.
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".
| `timestamp` | double | Simulation time of the measurement in seconds since the beginning of the episode. |
| `transform` | [carla.Transform](python_api.md#carla.Transform) | Location and rotation in world coordinates of the sensor at the time of the measurement. |
