Abstract: Provided are methods, systems, and computer program products for generating an output map indicating a likelihood of individual elements of an image as corresponding to particular road elements, such as lane dividers, road dividers, and road boundaries. An example method may include applying a machine learning architecture to the image, which architecture includes a convolutional neural network and a sub-network capturing global context from feature maps generated by the convolutional neural network.

Abstract: Provided are methods for enhanced semantic labeling in mapping with a semantic labeling system, which can include receiving, from a LiDAR sensor of a vehicle, LiDAR point cloud information including at least one raw point feature for a point, receiving, from a camera of the vehicle, image data associated with an image captured using the camera, generating at least one rich point feature for the point based on the image data, predicting, using a LiDAR segmentation neural network and based on the at least one raw point feature and the at least one rich point feature, a point-level semantic label for the point, and providing the point-level semantic label to a mapping engine to generate a map based on the point-level semantic label Systems and computer program products are also provided.

Abstract: Provided are methods for automatically detecting traffic signals using sensor data. The methods can include obtaining first sensor data and second sensor data regarding an environment of an autonomous vehicle, where the first sensor data represents at least one image of the environment generated by at least one image sensor of the autonomous vehicle, where the second sensor data represents a three-dimensional point cloud associated with the environment, and where the second sensor data is generated by at least one range sensor of the autonomous vehicle. The method can also include determining a portion of the at least one image corresponding to a traffic signal in the environment, determining a point cluster of the point cloud corresponding to the portion of the at least one image, and determining a location of the traffic signal based on the point cluster. Systems and computer program products are also provided.

Abstract: Provided are methods for enhanced semantic labeling in mapping with a semantic labeling system, which can include receiving, from a LiDAR sensor of a vehicle, LiDAR point cloud information including at least one raw point feature for a point, receiving, from a camera of the vehicle, image data associated with an image captured using the camera, generating at least one rich point feature for the point based on the image data, predicting, using a LiDAR segmentation neural network and based on the at least one raw point feature and the at least one rich point feature, a point-level semantic label for the point, and providing the point-level semantic label to a mapping engine to generate a map based on the point-level semantic label Systems and computer program products are also provided.