An information display device includes an attention object recognition unit that recognizes a plurality of attention objects, a traveling state recognition unit that recognizes a vehicle speed of a vehicle, a HUD that displays attention display, an attention depth calculation unit that calculates TTC and calculates attention depths for the attention objects on the basis of the TTC, a display information generation unit that determines a first attention object having the largest attention depth among the plurality of attention objects and generates first display information regarding first attention display for highlighting the first attention object and second display information regarding second attention display which is a line surrounding the first attention object and second attention objects other than the first attention object among the plurality of attention objects, and a display control unit that displays the first attention display and the second attention display on the HUD.

An approaching-object detection system detects an approaching object based on images captured by an imaging device. The approaching-object detection system comprises an extraction unit, a distortion correction unit, an object detection unit, and an approaching-object classification unit. The extraction unit extracts a partial image of one far side and a partial image of the other far side from each of the images. The distortion correction unit corrects distortion of the partial images. The object detection unit detects an object from the corrected partial images through pattern matching with reference to preinstalled image information on the object. The approaching-object classification unit classifies the detection result of the object detection unit as an approaching object or not.

Provided is a vehicle surroundings recognition apparatus capable of enhancing a calculation accuracy of a relative position and a relative speed with respect to an object such as a pedestrian even in a case where the object cannot be detected accurately in some of the images obtained by performing image-capturing around a vehicle, for example. In a case where a first state in which a first detection unit and a second detection unit detect first position information and second position information about an object and a second state in which only a second detection unit detects second position information about the object occur in a time sequence, first position information interpolation information for interpolating the first position information about the object in the second state is calculated on the basis of the first position information and the second position information in the first state and the second position information in the second state, and a relative distance and a relative speed to the object are measured on the basis of the first position information and the first position information interpolation information.

System, methods, and other embodiments described herein relate to tracking dynamic objects in a surrounding environment of a vehicle. In one embodiment, a method includes, in response to acquiring sensor data from at least one sensor, generating a current occupancy map that indicates locations of occupied grid cells as identified by the sensor data. The method includes updating a difference map according to the current occupancy map. The difference map encodes temporal changes in relation to prior states of occupancy of the grid cells to track dynamic objects in the surrounding environment over a defined temporal horizon. The method includes computing dynamics of the dynamic objects according to the difference map. The method includes providing the dynamics to at least one vehicle system within the vehicle.

The invention relates to a method for generating a surroundings map (14) of a surrounding area (7) of a motor vehicle (1) in which an object in the surrounding area (7) is detected by means of a sensor device (9) of the motor vehicle (1), a position value (P) that describes a position of the object is determined on the basis of sensor data of the sensor device (9) by means of a control device (3) of the motor vehicle (1) and the determined position value (P) is transferred into the surroundings map (14), wherein a vector (v′) between the object and a predetermined reference point (11) of the motor vehicle (1) that forms an origin (0′) of a vehicle coordinate system (12) is determined, the determined vector (v′) is transformed from the vehicle coordinate system (12) into a global coordinate system (13) of the surroundings map (14) and the position value (P) in the surroundings map (14) is determined on the basis of the transformed vector (v).

An object recognition apparatus learns an axis displacement amount of a reference axis of first object detecting means, combines and integrates, as information belonging to a same object, a plurality of pieces of information present within a first combining area and a second combining area, when a positional relationship between the first combining area and the second combining area meets a predetermined combinable condition. The first combining area is set as an area in which pieces of information related to the object acquired by the first object detecting means are combined. The second combining area is set as an area in which pieces of information related to the object acquired by second object detecting means are combined. The object recognition apparatus variably sets sizes of the first combining area and the second combining area based on a learning state of the axis displacement amount of the reference axis.

An imaging device for tracking moving objects includes an image output device outputting first and second images that include a moving object to be tracked. A memory stores image data from the image output device. An image processing device includes local storage and performs a block matching processing comprising evaluating a first block in the first image, dividing the second image into multiple regions, storing image data for a divided region in the local storage unit, determining similarity of the first block to blocks in each of the divided regions in turn, and then determining the similarity of the first and second images according to results obtained for each of the divided regions.

An apparatus and a method capable of accurately recognizing that a person will appear on a road and certainly notifying the person that an autonomous moving body has recognized a place where the person will appear are disclosed. An apparatus according to an embodiment of the present disclosure detects, as a recognition target, a person appearance area, in which a person will appear, formed by a recognition target presentation apparatus on a road and presents, to the person, a result of recognition indicating that the autonomous moving body has recognized the person appearance area as a result of the detection of the recognition target.

An image processing apparatus includes a processor configured to generate vertical direction distribution data which is a distribution of distance values to a vertical direction of a distance image from the distance image having distance values according to distance of a road surface in captured images. There is an extraction of a pixel having the highest frequency value for each of the distance values from pixels in a search area. There is a detection of the road surface based on each pixel extracted.

Systems, methods, tangible non-transitory computer-readable media, and devices for associating objects are provided. For example, the disclosed technology can receive sensor data associated with the detection of objects over time. An association dataset can be generated and can include information associated with object detections of the objects at a most recent time interval and object tracks of the objects at time intervals in the past. A subset of the association dataset including the object detections that satisfy some association subset criteria can be determined. Association scores for the object detections in the subset of the association dataset can be determined. Further, the object detections can be associated with the object tracks based on the association scores for each of the object detections in the subset of the association dataset that satisfy some association criteria.