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The traffic_light_occlusion_predictor Package#

Overview#

traffic_light_occlusion_predictor receives the detected traffic lights rois and calculates the occlusion ratios of each roi with point cloud.

For each traffic light roi, hundreds of pixels would be selected and projected into the 3D space. Then from the camera point of view, the number of projected pixels that are occluded by the point cloud is counted and used for calculating the occlusion ratio for the roi. As shown in follow image, the red pixels are occluded and the occlusion ratio is the number of red pixels divided by the total pixel numbers.

image

If no point cloud is received or all point clouds have very large stamp difference with the camera image, the occlusion ratio of each roi would be set as 0.

Input topics#

Name Type Description
~input/vector_map autoware_auto_mapping_msgs::HADMapBin vector map
~/input/rois autoware_auto_perception_msgs::TrafficLightRoiArray traffic light detections
~input/camera_info sensor_msgs::CameraInfo target camera parameter
~/input/cloud sensor_msgs::PointCloud2 LiDAR point cloud

Output topics#

Name Type Description
~/output/occlusion autoware_auto_perception_msgs::TrafficLightOcclusionArray occlusion ratios of each roi

Node parameters#

Parameter Type Description
azimuth_occlusion_resolution_deg double azimuth resolution of LiDAR point cloud (degree)
elevation_occlusion_resolution_deg double elevation resolution of LiDAR point cloud (degree)
max_valid_pt_dist double The points within this distance would be used for calculation
max_image_cloud_delay double The maximum delay between LiDAR point cloud and camera image
max_wait_t double The maximum time waiting for the LiDAR point cloud