A system for automatically mapping a road segment may include: at least one processor programmed to: receive, from at least one camera mounted on a vehicle, a plurality of images acquired as the vehicle traversed the road segment; convert each of the plurality of images to a corresponding top view image to provide a plurality of top view images; aggregate the plurality of top view images to provide an aggregated top view image of the road segment; analyze the aggregated top view image to identify at least one road feature associated with the road segment; automatically annotate the at least one road feature relative to the aggregated top view image; and output to at least one memory the aggregated top view image including the annotated at least one road feature.

Systems and methods are provided for constructing, using, and updating the sparse map for autonomous vehicle navigation. A system may comprise a processor and a memory. The memory may include instructions, which when executed on the processor, cause the processor to maintain a map; determine, based on analysis of image data, an existence of a non-transient condition that is inconsistent with the map, the image data from a camera integrated with the autonomous vehicle; and update the map.

A method of annotating road images, the method comprising implementing, at an image processing system, the following steps: receiving a time sequence of two dimensional images as captured by an image capture device of travelling vehicle; processing the images to reconstruct, in three-dimensional space, a path travelled by the vehicle; using the reconstructed vehicle path to determine expected road structure extending along the reconstructed vehicle path; and generating road annotation data for marking at least one of the images with an expected road structure location, by performing a geometric projection of the expected road structure in three-dimensional space onto a two-dimensional plane of that image.

Among other things, we describe techniques for automatic annotation of environmental features in a map during navigation of a vehicle. The techniques include receiving, by the vehicle located within an environment, a map of the environment. Sensors of the vehicle receive sensor data and semantic data. The sensor data includes a plurality of features of the environment. A geometric model of a feature of the plurality of features is generated. The feature is associated with a drivable area within the environment. A drivable segment is extracted from the drivable area. The drivable segment is segregated into a plurality of geometric blocks, wherein each geometric block corresponds to a characteristic of the drivable area and the geometric model of the feature includes the plurality of geometric blocks. The geometric model is annotated using the semantic data. The annotated geometric model is embedded within the map.

A georeferenced trajectory system for vehicles receives trajectory data generated by a plurality of vehicle sensors and scaffolds of previously generated maps and aligns geometry data for a geographic region and trajectory data from the received data from different map builds. A scaffold of a geographic region to be mapped during an initial map build is generated, and the trajectory data from respective map builds is aligned with the scaffold of previously generated maps to generate a map of the geographic region. The resulting map expands the coverage of the existing map such that old and new map data is in a common consistent reference frame whereby the map may be built incrementally by merging or expanding local scaffolds and filling in the merged or expanded scaffold while ensuring global consistency.

A method and apparatus for annotating a virtual lane at a crossing is provided, which relates to the field of intelligent transportation, and specifically includes: calculating a virtual connecting probability of various lanes that do not connected to each other at the crossing based on driving trajectory data of a vehicles that passing through the crossing within a detection time, and generating the virtual lane at the crossing for two disconnected lanes whose virtual connecting probability is greater than a threshold, and annotating the virtual lane on a map. In this process, the virtual lane at the crossing can be automatically generated according to the driving trajectory data of the vehicles passing through the crossing, and because the driving trajectory data of the vehicles passing through the crossing is real trajectory data, which is more in conformity with actual driving rules of the vehicles, and has a higher annotation accuracy.

A plurality of instances of probe data are received and used to generate a plurality of probe trajectories. A set of selected probe trajectories is defined using spatial familiarity sampling wherein a familiarity score is determined for each instance of probe data of a probe trajectory based on a familiarity model corresponding to already selected probe trajectories. A least familiar familiarity score for a probe trajectory is identified from the familiarity scores determined for the probe trajectory and a determination of whether the probe trajectory is included in the set of selected probe trajectories is performed based on the least familiar familiarity score for the probe trajectory. Only the probe trajectories of the set of selected probe trajectories are map-matched. The map-matched probe trajectories are then used to generate traffic and/or map information/data which is provided to consumer apparatuses for use in performing navigation functions, for example.

A hybrid approach for using reference frames is presented in which a series of anchor frames is used, effectively resetting a global frame upon a trigger event. With each new anchor frame, parameter values for lane boundary estimates (known as lane boundary states) can be recalculated with respect to the new anchor frame. Triggering events may a based on a length of time, distance traveled, and/or an uncertainty value.

Systems and methods are provided for autonomous navigation based on user intervention. In one implementation, a navigation system for a vehicle may include least one processor. The at least one processor may be programmed to receive from a camera, at least one environmental image associated with the vehicle, determine a navigational maneuver for the vehicle based on analysis of the at least one environmental image, cause the vehicle to initiate the navigational maneuver, receive a user input associated with a user's navigational response different from the initiated navigational maneuver, determine navigational situation information relating to the vehicle based on the received user input, and store the navigational situation information in association with information relating to the user input.

Systems and methods are provided for constructing, using, and updating the sparse map for autonomous vehicle navigation. A method may comprise processing, by a mapping server, collected navigation information from a plurality of vehicles obtained by sensors coupled to the plurality of vehicles, wherein the navigation information describes road lanes of a road segment; collecting data about landmarks identified proximate to the road segment, the landmarking including a traffic sign; generating, by the mapping server, an autonomous vehicle map for the road segment, wherein the autonomous vehicle map includes a spline corresponding to a lane in the road segment and the landmarks identified proximate to the road segment; and distributing, by the mapping server, the autonomous vehicle map to an autonomous vehicle for use in autonomous navigation over the road segment.