Disclosed herein are a driver assistance system and a vehicle including the same. The driver assistance system of the present disclosure includes a camera, an obstacle detector configured to detect an obstacle and output obstacle information about the detected obstacle, and a processor configured to recognize an image of the obstacle based on image information acquired by the camera, obtain a rate of change in size of the recognized image of the obstacle, obtain relative bearing information of the obstacle based on the obstacle information detected by the obstacle detector, determine whether the obstacle and a vehicle are present on a collision path based on the obtained rate of change in size of the image of the obstacle and the relative bearing information of the obstacle, and upon determining that the vehicle and the obstacle are present on the collision path, output a collision risk warning.

Systems and methods are provided for generating a rear view image display for a motor vehicle. A rear view system includes an optical sensor disposed at the motor vehicle and configured to capture image data, a computational unit coupled to the optical sensor by a cable connection and configured to execute program instructions stored on a computer-readable medium to modify the image data for presentation, and a display device coupled to the computational unit and configured to receive the modified image data from the computational unit and display the modified image data to a driver of the motor vehicle. The computational unit is further configured to receive software calibration to optimize modification of the image data.

An obstacle detection device includes a stereo camera that captures IR images on the right side and left side; a point cloud data creation unit that creates point cloud data having three-dimensional position information from the IR images captured by the stereo camera; an obstacle detection unit that detects an obstacle according to point cloud data; an invalid area identification unit that identifies whether an invalid area with no point cloud is present in point cloud data; and a target recognition unit that recognizes a particular target in the invalid area with no point cloud according to an IR image. When a particular target is recognized by the target recognition unit, the obstacle detection unit does not determine the invalid area with no point cloud to be an obstacle.

Systems and methods for detecting objects and predicting their motion are provided. In particular, a computing system can obtain a plurality of sensor sweeps. The computing system can determine movement data associated with movement of the autonomous vehicle. For each sensor sweep, the computing system can generate an image associated with the sensor sweep. The computing system can extract, using the respective image as input to one or more machine-learned models, feature data from the respective image. The computing system can transform the feature data into a coordinate frame associated with a next time step. The computing system can generate a fused image. The computing system can generate a final fused image. The computing system can predict, based, at least in part, on the final fused representation of the plurality of sensors sweeps from the plurality of sensor sweeps, movement associated with the feature data at one or more time steps in the future.

A method for machine-based training of a computer-implemented network for common detecting, tracking, and classifying of at least one object in a video image sequence having a plurality of successive individual images. A combined error may be determined during the training, which error results from the errors of the determining of the class identification vector, determining of the at least one identification vector, the determining of the specific bounding box regression, and the determining of the inter-frame regression.

An object detection apparatus 100 is an apparatus for detecting an object in a fish-eye image. The object detection apparatus 100 includes a normalized image acquisition unit 10 configured to acquire a normalized image obtained by normalizing a fish-eye image in which an object appears; a position detecting unit 20 configured to detect position coordinates of the object in the normalized image; and a determination unit 30 configured to determine a positional relationship between the object and the object detection apparatus 100 using the position coordinates of the object in the normalized image.

The present disclosure discloses an engineering machinery equipment, and a method, system, and storage medium for safety control thereof, and relates to the field of artificial intelligence, automatic control, and engineering machinery technologies. A method can include: acquiring spatial sensing data of a work area; performing obstacle detection based on the spatial sensing data to determine a position of an obstacle within the work area; determining a safe working range of a mechanical structural component of the engineering machinery equipment based on the position of the obstacle within the work area; and controlling a working range of the mechanical structural component of the engineering machinery equipment based on the safe working range.

A vehicular trailer assist system includes a camera disposed at a rear portion of a vehicle and having an exterior field of view that includes at least a portion of a trailer hitched to the vehicle. A control, responsive to processing of frames of image data captured by the camera and during a calibration maneuver by the vehicle, determines a trailer template of trailer hitched to the vehicle. The control, during a turning portion of the calibration maneuver, determines a trailer collision angle based on the determined trailer template. The control, after completion of the calibration maneuver, determines a current trailer angle of the trailer relative to the vehicle as the vehicle is driven along a road. The control alerts an operator of the vehicle if the current trailer angle is within a threshold amount of the determined trailer collision angle.

A vehicle detection information sharing system includes a detection information acquiring unit, a vehicle information acquiring unit, a risk degree calculator, and an information transmitter. The detection information acquiring unit is configured to acquire detection information on an obstacle detected by a first vehicle traveling on a road. The vehicle information acquiring unit is configured to acquire vehicle information on a second vehicle traveling on the road. The risk degree calculator is configured to calculate a risk degree on the basis of the detection information and the vehicle information. The risk degree is a degree of risk that arises between the second vehicle and the obstacle. The information transmitter is configured to transmit, to the second vehicle, risk avoidance information corresponding to the risk degree.

An unmanned aerial vehicle (UAV) includes one or more propulsion units that effect flight of the UAV, an application processing circuit configured to verify a validity of a system image of the UAV in a secure environment, and a flight control circuit operably coupled to the application processing circuit. Generation and/or transmission of control signals from the flight control circuit to one or more electronic speed controllers (ESC controllers) is prevented prior to verification of the validity of the system image.