A vehicle navigation system includes an electronic control unit. The electronic control unit receives image data regarding a source of a traffic jam on a roadway from a plurality of sensors of a plurality of vehicles in a mesh network. Moreover, the electronic control unit generates a bird's eye view of the traffic jam based on the image data, wherein the bird's eye view includes a graphical representation of the source of the traffic jam and a graphical representation of vehicles on the roadway within the traffic jam. A display device displays the bird's eye view.

An image processing device includes a processor; and a memory to store the program which performs processing including: measuring a first texture amount indicating luminance variation in image regions, based on the image regions obtained by dividing the captured image and previously determining an image processing target region based on the first texture amount; judging whether the image processing on a current captured image is necessary based on luminance of the image processing target region in the current captured image; calculating a second texture amount indicating luminance variation in the image processing target region, and judging whether the image processing should be performed on the image processing target region or not based on the second texture amount; and performing the image processing on the image processing target region on which the judging based on the second texture amount is that the image processing should be performed.

A system and method for providing an interpretable and unified representation for trajectory prediction that includes receiving birds-eye image data associated with travel of at least one agent within a roadway environment. The system and method also include analyzing the birds-eye image data to determine a potential field associated with the roadway environment and analyzing the birds-eye image data to determine a potential field associated with a past trajectory of the at least one agent. The system and method further include predicting a future trajectory of the at least one agent based on analysis of the potential fields.

An image processing apparatus including a determination unit configured to determine, for each of a plurality of partial areas set in an input image, a priority for executing a first image analysis process, based on an image of the partial area and a corresponding image corresponding to the partial area, the corresponding image being in a past image analyzed before the input image, a selection unit configured to select at least one of partial areas from the plurality of partial areas, based on the determined priority, and a first analysis unit configured to execute the first image analysis process on each of the at least one of partial areas selected.

A computer, including a processor and a memory, the memory including instructions to be executed by the processor to detect a moving object in video stream data based on determining an eccentricity map. The instructions can further include instructions to determine a magnitude and direction of motion of the moving object, transform the magnitude and direction to global coordinates and operate a vehicle based on the transformed magnitude and direction.

Techniques are disclosed for determining a location of an object based at least in part on a motion of the object. The techniques include generating a motion profile based at least in part on motion data received from a mobile device that is associated with the object. The techniques further include receiving, from a camera at a location, a plurality of images that identifies a candidate motion of a candidate object through at least a portion of the location. The techniques further include generating a candidate motion profile corresponding to the candidate motion of the candidate object based at least in part on the plurality of images. Based at least in part on a score generated by comparing the motion profile with the candidate motion profile, the techniques may determine that the candidate object is the object.

A system and method for determining a dimension of a target. The method includes: determining a camera parameter, the camera parameter including at least one of a focal length, a yaw angle, a roll angle, a pitch angle, or a height of one or more cameras; acquiring a first image and a second image of an target captured by the one or more cameras; generating a first corrected image and a second corrected image by correcting the first image and the second image; determining a parallax between a pixel in the first corrected image and a corresponding pixel in the second corrected image; determining an outline of the target; and determining a dimension of the target based at least in part on the camera parameter, the parallax, and the outline of the target.

A vehicle includes a controller that may be configured to, responsive to receiving a delivery request associated with a drone, periodically transmit a current location, trip route information, and acceleration data of the vehicle to guide the drone to a rendezvous location, and responsive to receiving a proximity notification associated with the drone, open a delivery opening of the vehicle.

The present disclosure relates to a method of recovering an image from which a motion blur is removed and an image recovering apparatus and the image recovering apparatus according to an exemplary embodiment of the present disclosure includes a signal generator which receives at least one of illuminance information and speed information from a sensor to determine a random flickering pattern and a triggering signal based on the received signal, output the determined random flickering pattern to a lighting unit, and output the triggering signal to a camera; and an image processor which receives an image including a motion blur from the camera to recover the received image based on the random flickering pattern determined by the signal generator.

A peccancy monitoring system and a peccancy monitoring method are provided in the present application. The peccancy monitoring system includes: a first camera, configured to monitor whether a peccancy vehicle is present in a designated area and generate alarm message when the peccancy vehicle is present; a control unit, connected to the first camera and configured to trigger the second camera to shoot according to the alarm message; a second camera, connected to the control unit and configured to shoot the peccancy vehicle to acquire a video of the vehicle and an image of the vehicle, here the video of the vehicle is a video containing a license plate of the peccancy vehicle and the image of the vehicle is an image containing the license plate of the peccancy vehicle. By using the technical solutions in the present application, the road can be monitored after the school bus is docked.