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 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.

An active sensor for performing active measurements of a scene is presented. The active sensor includes at least one transmitter configured to emit light pulses toward at least one target object in the scene, wherein the at least one target object is recognized in an image acquired by a passive sensor; at least one receiver configured to detect light pulses reflected from the at least one target object; a controller configured to control an energy level, a direction, and a timing of each light pulse emitted by the transmitter, wherein the controller is further configured to control at least the direction for detecting each of the reflected light pulses; and a distance measurement circuit configured to measure a distance to each of the at least one target object based on the emitted light pulses and the detected light pulses.

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.

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 images acquired by a camera from an environment of a vehicle; determine a navigational maneuver for the vehicle based on analysis of one or more of the plurality of images; cause the vehicle to initiate the navigational maneuver; receive a user input causing an override to alter the initiated navigational maneuver; determine navigational situation information relating to the vehicle via analysis of the images; determine, based on the navigational situation information, whether the user input is associated with a transient condition; and when the user input is not associated with a transient condition, store the navigational situation information in association with information relating to the user input.

A ray is cast into a volume described by a volumetric data structure, which describes the volume at a plurality of levels of detail. A first entry in the volumetric data structure includes a first set of bits representing voxels at a lowest one of the plurality of levels of detail, and values of the first set of bits indicate whether a corresponding one of the voxels is at least partially occupied by respective geometry. A set of second entries in the volumetric data structure describe voxels at a second level of detail, which represent subvolumes of the voxels at the first lowest level of detail. The ray is determined to pass through a particular subset of the voxels at the first level of detail and at least a particular one of the particular subset of voxels is determined to be occupied by geometry.

A driving support system includes a first monitoring device on a first object, the first monitoring device having a first controller, a first camera, and a first display, a second monitoring device on a second object, the second monitoring device having a second controller and a second camera, and a server in communication with the first and second monitoring devices. The first and second controllers are each detect a target in images acquired from the respective first or second camera, calculate target information for the target, and transmit the target information to the server. The server generates list information including the target information from the first and second monitoring devices, and transmits the list information to the first and second monitoring devices. The first controller further generates a map according to the list information received from the server, and displays the map on the first display.

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.

A method for simultaneous localization of a movable robot and mapping by the robot of an object in a zone. The method comprises providing the robot with at least a distance measurement sensor, whereby the robot is enabled to detect the object by means of the at least one distance measurement sensor; execute a wall following algorithm enabling to lead the robot around the object based on a plurality of measurements made with the at least one distance measurement sensor, along a first circumnavigated path obtained by the wall following algorithm, hence causing the robot to travel between a plurality of successive positions around the object; collect the plurality of measurements from the at least one distance measurement sensor while the robot is at the respective successive positions on the first circumnavigated path; aggregate the plurality of measurements taken respectively at the plurality of successive positions into an initial local snapshot of the zone, thereby obtaining a scanned shape of the object after each first circumnavigation; constructing a determined path from the first circumnavigated path, whereby the determined path is intended to lead to robot around the object on subsequent circumnavigations; lead the robot on the determined path on subsequent circumnavigations; position the robot at further determined positions on the determined path during the subsequent circumnavigations; collect further measurement from the at least one distance measurement sensor while the robot is at the further determined positions: aggregate the further measurements into further local snapshots of the zone for each of the subsequent circumnavigations; and perform a scanmatch algorithm for each of the further local snapshots with the initial local snapshot to determine what is the real position of the robot with respect to the object.

A system and method for online real-time multi-object tracking is disclosed. A particular embodiment can be configured to: receive image frame data from at least one camera associated with an autonomous vehicle; generate similarity data corresponding to a similarity between object data in a previous image frame compared with object detection results from a current image frame; use the similarity data to generate data association results corresponding to a best matching between the object data in the previous image frame and the object detection results from the current image frame; cause state transitions in finite state machines for each object according to the data association results; and provide as an output object tracking output data corresponding to the states of the finite state machines for each object.