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.

The present invention discloses an intelligent sweeping robot which is used for detecting whether a foreground object is an obstacle or not according to the extracted foreground object features; marking an area, located by the foreground object, as an obstacle point if a detection result is that the foreground object is the obstacle, and resetting a second sweeping path for avoiding the obstacle point; and further determining a first conditional probability of the foreground object being the obstacle according to the extracted scene features and foreground object features if the detection result is that whether the foreground object is the obstacle or not cannot be determined, determining the foreground object to be the obstacle if the first conditional probability is larger than a preset threshold value, marking the area, located by the foreground object, as the obstacle point, and resetting the second sweeping path for avoiding the obstacle point.

Disclosed embodiments pertain to a method for determining position information of a target vehicle relative to an ego vehicle. The method may comprise: obtaining, by at least one image sensor, first images of one or more target vehicles and classifying at least one target vehicle from the one or more target vehicles based on the one or more first images. Further, vehicle characteristics corresponding to the least one target vehicle may be obtained based on the classification of the least one target vehicle. Position information of the at least one target vehicle relative to the ego vehicle may be determined based on the vehicle characteristics.

A sensing device senses an object present in a blind spot in the surrounding environment of a moving body. The sensing device is provided with a detection unit, a distance measurement unit, and a control unit. The detection unit radiates a physical signal from the moving body to the surrounding environment, and detects a reflection signal which is a reflection of the radiated physical signal. The distance measurement unit detects distance information indicating the distance from the moving body to the surrounding environment. The control unit analyzes the result of the detection by the detection unit. The control unit: senses, on the basis of the distance information, a blind spot region indicating a blind spot in the surrounding environment, and another moving body travelling in front of the moving body towards the blind spot region; and senses an object in the blind spot region on the basis of a reflected signal reflected by the other moving body, in the result of the detection by the detection unit.

In one embodiment, a method includes receiving sensor data corresponding to an environment external of a vehicle. The sensor data include data points. The method includes determining one or more subsets of the data points. The method includes comparing the one or more subsets of the data points to one or more predetermined data patterns. Each of the one or more predetermined data patterns corresponds to an object classification. The method includes computing a confidence score for each subset of data points of the one or more subsets of the data points as corresponding to each of the one or more predetermined data patterns based on the comparison. The method includes generating a classification for an object in the environment external of the vehicle based on the confidence score.

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. During a parking event, and responsive to processing of captured image data, the system detects objects, gathers information pertaining to detected objects exterior the vehicle and generates an environmental model based at least in part on the gathered information. With the vehicle parked, the system determines location of the vehicle relative to at least one detected object of the environmental model and determines if the model includes an object in a region that would be swept by a hatch of the parked vehicle when the hatch is being opened or closed. Responsive to determination that the object is in the region that would be swept by the hatch, the system stops movement of the hatch to avoid impact with the object.

A vehicular safety feature control system includes a camera disposed at a vehicle that views exterior of the equipped vehicle. The vehicular safety feature control system includes an electronic control unit (ECU) for processing image data captured by the camera to detect presence of objects. The ECU, responsive to processing of image data captured by the camera, detects an object on a collision course with the equipped vehicle. The ECU, responsive to detecting the object on the collision course with the equipped vehicle, classifies the detected object. The ECU estimates a mass of the detected object based on the classification. The ECU estimates a kinetic energy of a potential collision and, based on the estimated kinetic energy of the potential collision, estimates a severity of the potential collision. The ECU controls a safety feature of the equipped vehicle based on the estimated severity of the potential collision.

A system and a method for detecting a pile of material by an autonomous machine. A 3D point cloud method includes obtaining a 3D point cloud indicative of an environment having a material pile, performing a ground surface estimation on the point cloud to identify non-ground points, grouping the non-ground points into clusters based on a proximity metric, creating a normalized height histogram for each of the clusters, comparing the normalized height histogram of each cluster to a generalized pile histogram, and identifying a cluster as a pile based on the similarity between the normalized height histogram and the generalized pile histogram. A 2D image method includes obtaining a 2D image from an imaging device, calibrating the imaging device with respect to a coordinate frame of the machine, and autonomously detecting an image of a material pile in the two-dimensional image using a trained deep-learning neural network.

A display image to be displayed on a display unit is obtained in accordance with motion of a viewpoint of a driver, on the basis of a captured image obtained by capturing an image on a rear side from a vehicle, when a line-of-sight of the driver is in a certain region including the display unit. For example, the display image is obtained in accordance with a deviation of a viewpoint position of the driver from a reference viewpoint position. For example, the reference viewpoint position is updated on the basis of a long-term fluctuation of the viewpoint position. For example, the reference viewpoint position is not updated when a line-of-sight of the driver is in a certain region including a display unit that displays a display image.

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.