Abstract: Systems and methods for navigating a host vehicle are disclosed. In one implementation, a system includes a processor configured to receive a first image acquired by a first camera and a second image acquired by a second camera onboard the host vehicle; identify a first representation of an object in the first image and a second representation of the object in the second image; input to a first trained model at least a portion of the first image; input to a second trained model at least a portion of the second image; receive the first signature encoding determined by the first trained model and the second signature encoding determined by the second trained model; input to a third trained model the first signature encoding and the second signature encoding; and receive an indicator of a location of the object determined by the third trained model.

Abstract: Systems and methods are provided for controlling vehicle operation. A processor may access route information for navigation of a route by the vehicle including data relating to speed along the route and calculate a speed of the vehicle along the route based on the route information. The processor may cause the vehicle to be operated at the calculated speed along the route; obtain dynamic information for the route based on data collected from one or more other vehicles on the route and indicating current conditions on the route which affect the speed of the vehicle along the route; and cause the vehicle to be operated at an updated speed along the route, based on the dynamic information.

Abstract: A navigation system for a host vehicle may include a processor programmed to: receive from a center camera onboard the host vehicle a captured center image including a representation of at least a portion of an environment of the host vehicle, receive from a left surround camera onboard the host vehicle a captured left surround image including a representation of at least a portion of the environment of the host vehicle, and receive from a right surround camera onboard the host vehicle a captured right surround image including a representation of at least a portion of the environment of the host vehicle; provide the center image, the left surround image, and the right surround image to an analysis module configured to generate an output relative to the at least one captured center image; and cause a navigational action by the host vehicle based on the generated output.

Abstract: Systems and methods are provided for training and using a model to predict image blockages. In one implementation, a system may comprise at least one processor. The at least one processor may be programmed to obtain a plurality of training images, each of the plurality of training images being associated with a blockage indicator representing a presence of a blockage or an absence of a blockage; analyze intensities of pixels located at corresponding pixel coordinates of the plurality of training images; and cause the model to undergo at least one training process based on the plurality of training images, the blockage indicator associated with each of the plurality of training images, and the analysis of the intensities of the pixels.

Abstract: Systems and methods navigate a vehicle by determining a free space region in which the vehicle can travel. In one implementation, a system may include at least one processor programmed to receive from an image capture device, a plurality of images associated with the environment of a vehicle, analyze at least one of the plurality of images to identify a first free space boundary on a driver side of the vehicle and extending forward of the vehicle, a second free space boundary on a passenger side of the vehicle and extending forward of the vehicle, and a forward free space boundary forward of the vehicle and extending between the first free space boundary and the second free space boundary. The first free space boundary, the second free space boundary, and the forward free space boundary may define a free space region forward of the vehicle.

Abstract: A system and method estimate a future path ahead of a current location of a vehicle. The system includes at least one processor programmed to: obtain an image of an environment ahead of a current arbitrary location of a vehicle navigating a road; obtain a trained system that was trained to estimate a future path on a first plurality of images of environments ahead of vehicles navigating roads; apply the trained system to the image of the environment ahead of the current arbitrary location of the vehicle; and provide, based on the application of the trained system to the image, an estimated future path of the vehicle ahead of the current arbitrary location.

Abstract: Systems and methods for navigating a host vehicle are disclosed. In one implementation, a system includes a processor configured to receive a first image acquired by a first camera and a second image acquired by a second camera onboard the host vehicle; identify a first representation of an object in the first image and a second representation of the object in the second image; input to a first trained model at least a portion of the first image; input to a second trained model at least a portion of the second image; receive the first signature encoding determined by the first trained model and the second signature encoding determined by the second trained model; input to a third trained model the first signature encoding and the second signature encoding; and receive an indicator of a location of the object determined by the third trained model.

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: Systems and methods are provided for vehicle navigation.

Abstract: A navigation system for a host vehicle may include a processor programmed to: receive from a center camera onboard the host vehicle a captured center image including a representation of at least a portion of an environment of the host vehicle, receive from a left surround camera onboard the host vehicle a captured left surround image including a representation of at least a portion of the environment of the host vehicle, and receive from a right surround camera onboard the host vehicle a captured right surround image including a representation of at least a portion of the environment of the host vehicle; provide the center image, the left surround image, and the right surround image to an analysis module configured to generate an output relative to the at least one captured center image; and cause a navigational action by the host vehicle based on the generated output.

Abstract: A navigation system for a host vehicle may include a processor programmed to: receive from a camera onboard the host vehicle at least one captured image representative of an environment of the host vehicle, wherein the camera is positioned at a first location relative to the host vehicle; receive point cloud information from a LIDAR system onboard the host vehicle, wherein the LIDAR system is positioned at a second location relative to the host vehicle; analyze the at least one captured image and the received point cloud information to detect one or more objects in the shared field of view region; determine whether a vantage point difference between the first location of the camera and the second location of the LIDAR system accounts for the one or more detected objects being represented in only one of the at least one captured image or the received point cloud information.

Abstract: Systems and methods for creating maps used in navigating autonomous vehicles are disclosed. In one implementation at least one processor is programmed to receive drive information from each of a plurality of vehicles that traverse different entrance-exit combinations of a road junction; for each of the entrance-exit combinations, align three-dimensional feature points in the drive information to generate a plurality of aligned three-dimensional feature point groups, one for each entrance-exit combination of the road junction; correlate one or more three-dimensional feature points in each of the plurality of aligned three-dimensional feature point groups with one or more three-dimensional feature points included in every other aligned three-dimensional feature point group from among the plurality of aligned three-dimensional feature point groups; and generate a sparse map based on the correlation, the sparse map including a target trajectory associated with each of the entrance-exit combinations.

Abstract: 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.

Abstract: 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.

Abstract: Systems and methods navigate a vehicle on a road at least partially covered with snow. In one implementation, a system may include at least one processor programmed to receive from an image capture device, a plurality of images captured of an environment forward of the vehicle, including an area where snow covers a road on which the vehicle travels, analyze at least one of the plurality of images to identify a first free space boundary on a first side of the vehicle and extending forward of the vehicle, a second free space boundary on a passenger side of the vehicle and extending forward of the vehicle, and a forward free space boundary forward of the vehicle and extending between the first free space boundary and the second free space boundary.

Abstract: A system and method estimate a future path ahead of a current location of a vehicle. The system includes at least one processor programmed to: obtain an image of an environment ahead of a current arbitrary location of a vehicle navigating a road; obtain a trained system that was trained to estimate a future path on a first plurality of images of environments ahead of vehicles navigating roads; apply the trained system to the image of the environment ahead of the current arbitrary location of the vehicle; and provide, based on the application of the trained system to the image, an estimated future path of the vehicle ahead of the current arbitrary location.

Abstract: A system and method estimate a future path ahead of a current location of a vehicle. The system includes at least one processor programmed to obtain a trained system that was trained to estimate a future path on a first plurality of images of environments ahead of vehicles navigating roads. The at least one processor is also programmed to obtain an image of an environment ahead of a current arbitrary location of a vehicle navigating a road, and provide, based on an application of the trained system to the obtained image, an estimated future path of the vehicle ahead of the current arbitrary location.

Abstract: A detection system for a vehicle is provided. The detection system may include at least one image capture device configured to acquire a plurality of images of an area forward of the vehicle, the area including a curb separating a road surface from an off-road surface and a data interface. The detection system may also include at least one processing device programmed to receive the plurality of images via the data interface, and determine a plurality of curb edge line candidates in the plurality of images. The at least one processing device may be further programmed to identify at least one edge line candidate as an edge line of the curb.

Abstract: Systems and methods are provided for vehicle navigation. In one implementation, a navigation system for a vehicle may comprise at least one processor. The at least one processor may be programmed to receive, from at least one sensor of the vehicle, information captured from an environment of the vehicle and determine, based on the information, a first position of the vehicle relative to a road navigation model. The at least one processor may further determine, based on at least one signal received from a satellite, a second position of the vehicle and determine, based on a comparison of the first position and the second position, error information associated with the second position. The at least one processor may cause a transmission of the error information to a server.

Abstract: A system for autonomously navigating a vehicle along a road segment may include at least one processor. The at least one processor may be programmed to receive from at least one sensor information relating to one or more aspects of the road segment. The processor may also be programmed to determine a local feature of the road segment based on the received information. Further the processor may be programmed to compare the local feature to a predetermined signature feature for the road segment. The processor may be programmed to determine a current location of the vehicle along a predetermined road model trajectory associated with the road segment based on the comparison of the received information and the predetermined signature feature. The processor may also be programmed to determine an autonomous steering action for the vehicle based on a direction of the predetermined road model trajectory at the determined location.