Abstract:
Systems and methods of processing crowdsourced navigation information for use in autonomous vehicle navigation are disclosed. A method may include processing, by a mapping server, crowdsourced 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.
Abstract:
A non-transitory computer-readable medium is provided. The computer-readable medium includes a sparse map for autonomous vehicle navigation along a road segment. The sparse map includes a polynomial representation of a target trajectory for the autonomous vehicle along the road segment, and a plurality of predetermined landmarks associated with the road segment. The plurality of predetermined landmarks are spaced apart by at least 50 meters, and the sparse map has a data density of no more than 1 megabyte per kilometer.
Abstract:
A method of processing vehicle navigation information for use in autonomous vehicle navigation is provided. The method includes receiving, by a server, navigation information from a plurality of vehicles. The navigation information from the plurality of vehicles is associated with a common road segment. The method also includes storing, by the server, the navigation information associated with the common road segment. The method also includes generating, by the server, at least a portion of an autonomous vehicle road navigation model for the common road segment based on the navigation information from the plurality of vehicles. The method further includes distributing, by the server, the autonomous vehicle road navigation model to one or more autonomous vehicles for use in autonomously navigating the one or more autonomous vehicles along the common road segment.
Abstract:
A computerized system mountable on a vehicle operable to detect an object by processing first image frames from a first camera and second image frames from a second camera. A first range is determined to said detected object using the first image frames. An image location is projected of the detected object in the first image frames onto an image location in the second image frames. A second range is determined to the detected object based on both the first and second image frames. The detected object is tracked in both the first and second image frames When the detected object leaves a field of view of the first camera, a third range is determined responsive to the second range and the second image frames.
Abstract:
Systems and methods are disclosed for mapping lanes for use in vehicle navigation. In one implementation, at least one processing device may be programmed to receive navigational information from a first vehicle and a second vehicle that have navigated along a road segment including a lane split feature; receive at least one image associated with the road segment; determine, from the first navigational information, a first actual trajectory of the first vehicle and a second actual trajectory of the second vehicle; determine a divergence between the first actual trajectory and the second actual trajectory; determine, based on analysis of the at least one image, that the divergence between the first actual trajectory and the second actual trajectory is indicative of the lane split feature; and update a vehicle road navigation model to include a first target trajectory and a second target trajectory that branches from the first target trajectory after the lane split feature.
Abstract:
Systems and methods may selectively collect information from a host vehicle. In one example, a method may include causing collection of navigational information associated with an environment traversed by the host vehicle; storing the collected navigational information; determining, based on an output of at least one navigational sensor, a location of the host vehicle; transmitting the determined location of the host vehicle to a server; receiving, from the server and in response to the transmitted determined location, a request for transmission of a selected subset of the navigational information collected by the host vehicle; and transmitting the selected subset of the navigational information to the server.
Abstract:
Systems and methods are provided for self-aware adaptive navigation. In one implementation, a navigation system for a vehicle may include at least one processor. The at least one processor may be programmed to determine a navigational maneuver for the vehicle based, at least in part, on a comparison of a motion of the vehicle with respect to a predetermined model representative of a road segment. The at least one processor may be further programmed to receive, from a camera, at least one image representative of an environment of the vehicle. The at least one processor may be further programmed to determine, based on analysis of the at least one image, an existence in the environment of the vehicle of an navigational adjustment condition, cause the vehicle to adjust the navigational maneuver based on the existence of the navigational adjustment condition, and store information relating to the navigational adjustment condition.
Abstract:
Systems and methods are disclosed for providing maps to an autonomous vehicle. Methods include maintaining a road model that includes trajectories associated with a road segment, the trajectories used to assist the autonomous vehicle to navigate on a target trajectory consistent with the road model; determining, based on analysis of image data, an existence of a non-transient condition that is inconsistent with the road model, the image data from a camera integrated with the autonomous vehicle, wherein the autonomous vehicle is configured to deviate from the target trajectory based on the existence of the non-transient condition; and storing information about the non-transient condition for updating the road model.
Abstract:
Systems and methods are provided for interacting with a plurality of autonomous vehicles. In one implementation, a system may include at least one processor. The at least one processor may be programmed to receive from each of the plurality of autonomous vehicles navigational situation information associated with an occurrence of an adjustment to a determined navigational maneuver, analyze the navigational situation information, determine, based on the analysis of the navigational situation information, whether the adjustment to the determined navigational maneuver was due to a transient condition, and update the predetermined model representative of the at least one road segment if the adjustment to the determined navigational maneuver was not due to a transient condition.
Abstract:
A system is provided for determining a location of a landmark for use in navigation of an autonomous vehicle. The system includes a processor programmed to receive a measured position of the landmark. The processor is also programmed to determine a refined position of the landmark based on the measured position of the landmark and at least one previously acquired position for the landmark. The measured position and the previously acquired position are determined based on acquisition of an environmental image associated with the host vehicle, analysis of the environmental image to identify the landmark, reception of global positioning system (GPS) data representing a location of the host vehicle, analysis of the environmental image to determine a relative position of the identified landmark with respect to the host vehicle, and determination of a globally localized position of the landmark based on the GPS data and the relative position.