However there are some things to be taken into consideration: 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. Registers an event each time its parent crosses a lane marking. Output: carla.LaneInvasionEvent per crossing.Standard deviation parameter in the noise model for the gyroscope (Z axis). Standard deviation parameter in the noise model for the gyroscope (Y axis). Standard deviation parameter in the noise model for the gyroscope (X axis). Mean parameter in the noise model for the gyroscope (Z axis). Mean parameter in the noise model for the gyroscope (Y axis). Mean parameter in the noise model for the gyroscope (X axis). Standard deviation parameter in the noise model for acceleration (Z axis). Standard deviation parameter in the noise model for acceleration (Y axis).
Standard deviation parameter in the noise model for acceleration (X axis). The data is collected from the object's current state. Provides measures that accelerometer, gyroscope and compass would retrieve for the parent object. Output: carla.IMUMeasurement per step (unless sensor_tick says otherwise).Initializer for a pseudorandom number generator. Standard deviation parameter in the noise model for longitude. Mean parameter in the noise model for longitude. Standard deviation parameter in the noise model for latitude. Mean parameter in the noise model for latitude. Standard deviation parameter in the noise model for altitude. Mean parameter in the noise model for altitude. This is calculated by adding the metric position to an initial geo reference location defined within the OpenDRIVE map definition. Reports current gnss position of its parent object. Output: carla.GNSSMeasurement per step (unless sensor_tick says otherwise).Simulation seconds between sensor captures (ticks).Ĭamera lens distortion attributes Blueprint attribute Raw_image.save_to_disk("path/to/save/converted/image",carla.Depth)īasic camera attributes Blueprint attribute The precision is milimetric in both, but the logarithmic approach provides better results for closer objects. There are two options in lorConverter to get a depth view: Depth and Logaritmic depth. The output carla.Image should then be saved to disk using a lorConverter that will turn the distance stored in RGB channels into a float containing the distance and then translate this to grayscale. The actual distance in meters can beĭecoded with: normalized = (R + G * 256 + B * 256 * 256) / (256 * 256 * 256 - 1) 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 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. Output: carla.Image per step (unless sensor_tick says otherwise).Location and rotation in world coordinates of the sensor at the time of the measurement.Īctor that measured the collision (sensor's parent). Simulation time of the measurement in seconds since the beginning of the episode. Output attributes Sensor data attributeįrame number when the measurement took place. 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.Ĭollision detectors do not have any configurable attribute. Several collisions may be detected during a single simulation step. This sensor registers an event each time its parent actor collisions against something in the world. Output: carla.CollisionEvent per collision.Many invert the Y-axis, so visualizing the sensor data directly may result in mirrored outputs. When using any visualization software, pay attention to its coordinate system. All the sensors use the UE coordinate system ( x- forward, y- right, z- up), and return coordinates in local space.
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