Systems and methods use cameras to provide autonomous navigation features. In one implementation, a driver-assist object detection system is provided for a vehicle. One or more processing devices associated with the system receive at least two images from a plurality of captured images via a data interface. The device(s) analyze the first image and at least a second image to determine a reference plane corresponding to the roadway the vehicle is traveling on. The processing device(s) locate a target object in the first two images, and determine a difference in a size of at least one dimension of the target object between the two images. The system may use the difference in size to determine a height of the object. Further, the system may cause a change in at least a directional course of the vehicle if the determined height exceeds a predetermined threshold.

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.

A system and a method for processing a plurality of images, each image of the plurality of images being acquired by a respective image acquisition module of a vehicle and each image acquisition module being oriented outwardly with respect to the vehicle, the method comprising: elaborating a bird's eye view image of surroundings of the vehicle using pixel values of pixels of at least one portion of each image of the plurality of images as pixel values of the bird's eye view image, and performing, on the bird's eye view image, a detection of at least one lane marked on a surface on which the vehicle is and visible on the bird's eye view image.

Provided is an Artificial Intelligence (AI) system for simulating a human brain's functions, such as recognition, decision, etc., by using a machine learning algorithm such as deep learning, etc. and applications of the AI system. Provided is an electronic device including: a camera configured to capture an outside image of a vehicle, and a processor configured to execute one or more instructions stored in a memory, wherein the processor executes the one or more instructions to: determine, from the captured image, at least one object for estimating lane information; estimate, from the image, lane information of a road on which the vehicle is traveling, based on a distance between the determined at least one object and the vehicle and a vanishing point of the image; and output guide information for guiding driving of the vehicle based on the estimated lane information.

Provided herein are platforms for determining a real-time human behavior analysis of an unmanned vehicle by a plurality of autonomous or semi-autonomous land vehicles through infrastructure recognition and assessment. The platforms determine a real-time parking status for a plurality of parking locations, a platform for detecting a traffic violation by a manned vehicle at a roadway location, and a platform for monitoring security of a physical location.

An approach is provided for a redundant feature detection engine. The approach, for instance, involves segmenting an input image into a plurality of grid cells for processing by the redundant feature detection engine. The redundant feature detection engine includes a neural network. The approach also involves, for each of the plurality of grid cells, initiating a prediction of an object code by the redundant feature detection engine. The object code is a predicted feature that uniquely identifies an object depicted in the input image. The approach further involves aggregating the plurality of grid cells into one or more clusters based on the object code predicted for said each grid cell. The approach further involves predicting one or more features of the object corresponding to a respective cluster of the one or more clusters by merging one or more feature prediction outputs of said each grid cell in the respective cluster.

A display control device includes a front image acquiring portion configured to acquire front images by a front camera that images a front of a vehicle traveling on a traveling lane, a lane line position calculation portion configured to calculate a position of a lane line separating the traveling lane and an adjacent lane adjacent to the traveling lane based on movement of the vehicle in a period from a point at which the front image is acquired by the front camera, a movement track calculation portion configured to calculate a movement track of the vehicle, a map creating portion configured to create a map, and a display control portion configured to display, a synthesized image in which a pseudo lane line image corresponding to the lane line is superimposed onto a rear side image acquired after the movement of the vehicle by a rear side camera based on the map.

A system for navigating a host vehicle includes at least one electronic horizon processor to determine an electronic horizon for the host vehicle based on localization of the host vehicle relative to a map, generate a navigation information packet including information associated with the determined electronic horizon, and output the generated navigation information packet to one or more navigation system processors configured to cause the host vehicle to execute at least one navigational maneuver based on the information included in the navigation information packet.

This application discloses a road constraint determining method and apparatus, applied to the intelligent driving field, and in particular, to a sensor in an advanced driver assistance system ADAS or an autonomous driving system, for example, radar and/or a photographing apparatus. In this method, a moving state of a target is determined based on detection information of the target; at least one road geometry of a road on which the target is located is determined based on the detection information of the target; and a road constraint of the target is determined based on the at least one road geometry and the moving state of the target. The road constraint includes at least one of a road direction constraint and a road width constraint. According to the solutions in this application, road constraint determining accuracy can be improved, and target tracking accuracy can be further improved.