Abstract: Systems and methods for generating a map. The methods comprise: performing, by a computing device, ray-casting operations to generate a 3D point cloud with a reduced number of data points associated with moving objects; generating, by the computing device, a 2D binary mask for at least one semantic label class of the 3D point cloud; determining, by the computing device, x-coordinates and y-coordinates for a 2D volume defining an object of the at least one semantic label class; identifying, by the computing device, data points in the 3D point cloud based on the 2D volume; comparing, by the computing device, z-coordinates of the identified data points to at least one threshold value selected for the at least one semantic label class; and generating, by the computing device, the map by removing data points from the 3D point cloud based on results of the comparing.

Abstract: Disclosed herein are system, method, and computer program product embodiments for localizing three-dimensional objects relative to a vehicle. The system includes: at least one sensor for generating two-dimensional (2D) data and a three-dimensional (3D) point cloud of an environment external to a vehicle. The 3D point cloud includes object points associated with a stationary traffic control object. The localization system also includes a memory and at least one processor coupled to the memory. The processor is programmed to: select a bounding box associated with the object from the memory based on the 2D data; arrange the bounding box proximate to the object points in the 3D point cloud; assign a weight to each point of the 3D point cloud based on a position of the point relative to the bounding box; filter the weighted points; and generate a 3D location of the object based on the filtered points.

Abstract: Systems and methods for processing and using sensor data. The methods comprise: obtaining semantic labels assigned to data points; performing a supervised machine learning algorithm and an unsupervised machine learning algorithm to respectively generate a first confidence score and a second confidence score for each semantic label of said semantic labels, the first and second confidence scores each representing a degree of confidence that the semantic label is correctly assigned to a respective one of the data points; generating a final confidence score for each said semantic label based on the first and second confidence scores; selecting subsets of the data points based on the final confidence scores; and aggregating the data points of the subsets to produce an aggregate set of data points.

Abstract: Systems and methods for generating a map. The methods comprise: performing, by a computing device, ray-casting operations to generate a 3D point cloud with a reduced number of data points associated with moving objects; generating, by the computing device, a 2D binary mask for at least one semantic label class of the 3D point cloud; determining, by the computing device, x-coordinates and y-coordinates for a 2D volume defining an object of the at least one semantic label class; identifying, by the computing device, data points in the 3D point cloud based on the 2D volume; comparing, by the computing device, z-coordinates of the identified data points to at least one threshold value selected for the at least one semantic label class; and generating, by the computing device, the map by removing data points from the 3D point cloud based on results of the comparing.

Abstract: A system and method for identifying navigational hazards is disclosed. In some examples, the system can be included in, or the method can be performed by, a vehicle. One or more sensors included in the vehicle can gather perception data related to potentially dangerous driving conditions, such as blind corners, reckless drivers, accidents, and other hazards. The data can be associated with the location at which it was collected to predict future hazardous conditions at the same location. In some examples, a navigational system can use the data to determine the relative safety of one or more routes for driving to a destination. In some examples, a fully or partially autonomous vehicle can modify its driving behavior based on the relative safety of its location. Modifying driving behavior can include, for example, changing a top speed, a following distance, and conditions for changing lanes.

Abstract: Systems and methods relating to determining a position or orientation of a vehicle are disclosed. A landmark is identified using sensor data presented by one or more sensors included with the vehicle. The landmark is identified using map data relating to an approximate location of the vehicle in a world coordinate system. A position or orientation of the vehicle relative to the landmark is determined. A position or orientation of the landmark relative to the world coordinate system is determined. Using the determined position or orientation of the vehicle relative to the landmark and the determined position or orientation of the landmark relative to the world coordinate system, a position or orientation of the vehicle relative to the world coordinate system is determined.

Abstract: A system that performs a method is disclosed. Whether a charging station is available to charge the vehicle is determined. In accordance with a determination that a charging station is available, one or more position parameters for a charging arm of the charging station are obtained using the one or more scanners. Based on the obtained one or more position parameters, one or more commands are generated to control the motion of the charging arm to couple the charging arm to the vehicle to charge the vehicle.

Abstract: Three-dimensional data are registered by selecting a first set of primitives from the data in a first coordinate system, wherein the first set of primitives includes at least one plane, at least one point, and a third primitive that is either a point or a plane, and selecting a second set of primitives from the data in a second coordinate system, wherein the second set of primitives includes at least one plane, at least one point, and a third primitive corresponding to the third primitive in the first set of primitives. Then, the planes are registered with each other, as are the points, to obtain registered primitives.

Abstract: Three-dimensional data are registered by selecting a first set of primitives from the data in a first coordinate system, wherein the first set of primitives includes at least one plane, at least one point, and a third primitive that is either a point or a plane, and selecting a second set of primitives from the data in a second coordinate system, wherein the second set of primitives includes at least one plane, at least one point, and a third primitive corresponding to the third primitive in the first set of primitives. Then, the planes are registered with each other, as are the points, to obtain registered primitives.