An apparatus for performing driving aid control to cause a travel trajectory of a mobile object to follow a setpoint trajectory (La) by transmitting a control command value (δ) to a yaw moment controller capable of controlling a yaw moment of the mobile object. In the apparatus, a setpoint trajectory setter is configured to set the setpoint trajectory (La) of the mobile object. A control command value calculator is configured to calculate the control command value (δ) based on an integrated value (δI) of a lateral error that is an error between a position of the mobile object and the setpoint trajectory. The control command value calculator is further configured to decrease the integrated value (δI) with decreasing a curvature (ρ) of a road on which the mobile object is traveling.

The technology relates to controlling a vehicle in an autonomous driving mode. For instance, sensor data identifying an object in an environment of the vehicle may be received. A grid including a plurality of cells may be projected around the object. For each given one of the plurality of cells, a likelihood that the object will enter the given one within a period of time into the future is predicted. A contour is generated based on the predicted likelihoods. The vehicle is then controlled in the autonomous driving mode in order to avoid an area within the contour.

An ECU determines that detected first and second objects are identical as an identical object based on a first position of the first object and a second position of the second object, and calculates the position of a target object that is determined as the identical object. The ECU determines whether a current situation is such that a reliability of the identicalness determination is at a predetermined level or higher. In a case of determining the current situation to be such that the reliability is at the predetermined level or higher, the ECU calculates the position of the target object based on a first distance and a second orientation. In a case of determining the current situation not to be such that the reliability is at the predetermined level or higher, the ECU calculates the position of the target object based on a second distance and the second orientation.

Disclosed systems and methods prevent collisions in a carwash property. Disclosed systems and methods include software that brings together computer vision and machine learning algorithms to track the interaction of vehicles and equipment within the environment of a carwash to improve safety and optimize production.

A method and apparatus for identifying laser point cloud data of an autonomous vehicle provided with a lidar. A specific implementation of the method includes: acquiring current pose information of the autonomous vehicle in a world coordinate system in response to receiving a latest frame of laser point cloud data collected by the lidar; acquiring from the cache, based on the current pose information, N×N map blocks centered on map blocks corresponding to the current pose information in a preset three-dimensional grid map and pre-loaded into a cache and are; and executing, for each laser point data in the received laser point cloud data, the laser point data identification operations. The implementation realizes identifying whether each laser point data is a static laser point, and can improve the accuracy rate of identifying a laser point data obstacle.

A vehicle recognition device includes: an acquisition section acquiring image information based on an image captured by a vehicle-mounted camera; a boundary line detection section detecting, based on the image information, boundary lines on both left and right sides demarcating vehicle traveling lanes; a vehicle detection section detecting position information on an object vehicle; and a recognition section recognizing lane-to-lane movement of the object vehicle between an own lane in which the subject vehicle is traveling and an adjacent lane. The recognition section recognizes the lane-to-lane movement based on a second boundary line of the boundary lines, a lane width between a first boundary line of the boundary lines and the second boundary line, and the position information on the object vehicle, the first boundary line dividing the own lane and the adjacent lane, the second boundary line being different from the first boundary line.

A vehicle hatch clearance determining system includes a plurality of cameras disposed at the vehicle and a control having a processor for processing image data captured by the cameras to determine structure and objects present in the fields of view of the cameras while the vehicle is being driven during a parking event. The control, responsive to processing of captured image data, determines and stores information pertaining to structure and objects present in the fields of view of the cameras during the parking event. When the vehicle is parked, the control determines if stored information includes structure or an object at or near the parking location and in a region that may be swept by the vehicle hatch when it is opened or closed. Responsive to determination of a potential collision of the hatch with structure or an object, the clearance determining system controls the hatch and/or generates an alert.

The present disclosure provides a ranging method, a ranging device, and a ranging system. The method includes: acquiring a video image and a radar signal set that are collected respectively by a camera and radar at the same time; determining position information corresponding to each target object in the video image; determining one or more target object that have matched a radar signal in the video image; determining a ranging algorithm based on the position information corresponding to the target objects that have matched radar signals and the radar signals matching the target objects; and calculating a distance between a target object that does not match a radar signal in the video image and a designated vehicle according to position information corresponding to the target object that does not match a radar signal in the video image and through the ranging algorithm.

An information processing device (10) includes: an acquisition unit (181) that acquires an image captured by an imaging unit (13); a recognition unit (182) that recognizes attributes of an object shown in the image captured by the imaging unit (13); and a generation unit (183) that generates a bird's-eye view map showing the attributes of the object on the basis of the image captured by the imaging unit (13) and information on the attributes of the object recognized by the recognition unit.

A display control device includes a video data acquiring unit that acquires video data from a plurality of cameras photographing a periphery of a vehicle, a bird's-eye view video generator that applies view point conversion processing and synthesis processing to the video data to generate a bird's-eye view video, an adjacent information acquiring unit that acquires first obstacle information serving as information about obstacles around the vehicle when the vehicle has moved backward and entered the parked state, and second obstacle information serving as the information about the obstacles around the vehicle when the vehicle moves forward to exit the parked state, a comparing unit that compares the first obstacle information with the second obstacle information to determine whether the number of obstacles around the vehicle has increased when the vehicle exits the parked state, and a display controller.