Techniques for generating more accurate determinations of object proximity by using vectors in data structures based on vehicle sensor data are disclosed. Vectors reflecting a distance and direction to a nearest object edge from a reference point in a data structure are used to determine a distance and direction from a point of interest in an environment to a nearest surface. In some examples, a weighted average query point response vector is determined using the determined distance vectors of cells neighboring the cell in which the point of interest is located and nearest to the same object as the query point, providing a more accurate estimate of the distance to the nearest object from the point of interest.

Provided is a moving object detection device including a storage medium storing computer-readable commands and a processor connected to the storage medium, the processor executing the computer-readable commands to: acquire image data including a plurality of frames representing a surrounding condition of a mobile object, which are photographed by a camera mounted in the mobile object in time series; calculate a difference image between the plurality of frames by calculating differences between the plurality of frames and binarizing the differences using a first value and a second value; extract a grid for which the density of pixels with the first value is equal to or larger than a first threshold value from among a plurality of grids set in the difference image; and detect the extracted grid as a moving object.

A monitoring system for an aircraft has sensors configured to sense objects around the aircraft and provide data indicative of the sensed objects. The system contains a first type of computing module that processes data obtained from all the sensors and a second type of computing module dedicated to processing data from a particular sensor. The second module may characterize and locate a detected object within the processed image data. Both the first and second modules generate a likelihood of detection of an object within their processed image data. A scheduler module calculates a percentage of computing resources that should be assigned to processing data from a respective image sensor in view of this likelihood and assigns a dedicated compute module to an image sensor requiring a higher percentage of attention. Processing resources may therefore be focused on geospatial areas with a high likelihood of object detection.

A target object detection method, including: obtaining images collected by more than one camera installed on a target vehicle; determining a high-dimensional parameter feature in a high-dimensional space corresponding to parameter information of each camera; and fusing features of the images via a target object detection model according to the high-dimensional parameter features, and determining position information of a target object based on the fused features, an order of the cameras corresponding to the images being the same as an order of the cameras corresponding to the high-dimensional parameter features.

A method for drivable area detection and autonomous obstacle avoidance of unmanned haulage equipment in deep confined spaces is disclosed, which includes the following steps: acquiring 3D point cloud data of a roadway; computing a 2D image drivable area of the coal mine roadway; acquiring a 3D point cloud drivable area of the coal mine roadway; establishing a 2D grid map and a risk map, and performing autonomous obstacle avoidance path planning by using an improved particle swarm path planning method designed for deep confined roadways; and acquiring an optimal end point to be selected of a driving path by using a greedy strategy, and enabling an unmanned auxiliary haulage vehicle to drive according to the optimal end point and an optimal path. According to the present disclosure, images of a coal mine roadway are acquired actively by use of a single-camera sensor device, a 3D spatial drivable area of an auxiliary haulage vehicle in a deep underground space can be computed stably, accurately and rapidly, and the autonomous obstacle avoidance driving of the unmanned auxiliary haulage vehicle in a deep confined roadway is completed according to the drivable area detection and safety assessment information, and therefore, the method of the present disclosure is of great significance to the implementation of an automatic driving technology for an auxiliary haulage vehicle for coal mines.

An information processing apparatus includes: a risk area identification unit configured to identify a risk area outside a moving object; and a transmission control unit configured to perform control for transmitting risk area information representing the risk area identified by the risk area identification unit to a server configured to retain information related to a risk area, in which the risk area identification unit is configured to identify an area defined by a plurality of points as the risk area, and the transmission control unit is configured to perform control for transmitting coordinate information of some of the plurality of points to the server as the risk area information.

An object distance measurement apparatus may include: a camera to capture an image of an area around a vehicle; a distance sensor to detect a distance from an object by scanning around the vehicle; and a distance measurement unit that detects a vehicle moving distance using vehicle information generated by operation of the vehicle, and measures the distance from the object in response to each of frames between scan periods of the distance sensor, among frames of the image, based on the vehicle moving distance and the location pixel coordinates of the object within the images before and after the vehicle moves.

Generally, the disclosed systems and methods implement improved detection of objects in three-dimensional (3D) space. More particularly, an improved 3D object detection system can exploit continuous fusion of multiple sensors and/or integrated geographic prior map data to enhance effectiveness and robustness of object detection in applications such as autonomous driving. In some implementations, geographic prior data (e.g., geometric ground and/or semantic road features) can be exploited to enhance three-dimensional object detection for autonomous vehicle applications. In some implementations, object detection systems and methods can be improved based on dynamic utilization of multiple sensor modalities. More particularly, an improved 3D object detection system can exploit both LIDAR systems and cameras to perform very accurate localization of objects within three-dimensional space relative to an autonomous vehicle. For example, multi-sensor fusion can be implemented via continuous convolutions to fuse image data samples and LIDAR feature maps at different levels of resolution.

A vehicle body transport system includes an unmanned carrier carrying and transporting a vehicle body between work stations; and an imaging device including an imaging part imaging a traveling route of the unmanned carrier and the surroundings of the traveling route from above, an analysis part analyzing an image captured by the imaging part, and a transmission part transmitting a signal to the unmanned carrier. When a moving object other than the unmanned carrier carrying the vehicle body is present in the image, the analysis part predicts whether a movement trajectory that the vehicle body passes after a predetermined time intersects a movement position where the moving object is located after the predetermined time. When predicting that the movement trajectory and the movement position intersect after the predetermined time, the transmission part transmits an emergency operation signal to the unmanned carrier before the predetermined time elapses.

Approaches for environment reconstruction and path planning for autonomous machine systems and applications are described. An iterative volumetric mapping function for an ego-machine may compute a distance field, and from the distance field derive a cost map representing a volumetric reconstruction of the physical environment around the ego-machine. The cost map may be used for collision avoidance and path planning. The iterative volumetric mapping function may also optionally compute a color integration map and visualization mesh from the distance field that can be used for visualization of the physical environment around the ego-machine. The cost map may be computed as a Euclidean Signed Distance Field (ESDF) and the distance field from which the cost map is computed may include a Truncated Signed Distance Field (TSDF). The distance field, cost map, color integration map and visualization mesh may each be stored in memory as maps of a plurality of map layers.