A vehicle control system for an agricultural vehicle including a processing circuit including a processor and memory, the memory having instructions stored thereon that, when executed by the processor, cause the processing circuit to receive (i) image data depicting at least a portion of a hazard area associated with the agricultural vehicle and (ii) ultrasonic sensor data from an ultrasonic sensor monitoring the hazard area, determine, based on a combination of the image data and the ultrasonic sensor data, whether an obstacle is positioned at least partially within the hazard area, and perform an action with respect to the agricultural vehicle in response to a determination that the obstacle is positioned at least partially within the hazard area.

In various examples, surface profile estimation and bump detection may be performed based on a three-dimensional (3D) point cloud. The 3D point cloud may be filtered in view of a portion of an environment including drivable free-space, and within a threshold height to factor out other objects or obstacles other than a driving surface and protuberances thereon. The 3D point cloud may be analyzed—e.g., using a sliding window of bounding shapes along a longitudinal or other heading direction—to determine one-dimensional (1D) signal profiles corresponding to heights along the driving surface. The profile itself may be used by a vehicle—e.g., an autonomous or semi-autonomous vehicle—to help in navigating the environment, and/or the profile may be used to detect bumps, humps, and/or other protuberances along the driving surface, in addition to a location, orientation, and geometry thereof.

A stereo camera apparatus includes a first imaging unit including a first imaging optical system provided with a plurality of lens groups, and a first actuator configured to change a focal length by driving at least one of the plurality of lens groups of the first imaging optical system; a second imaging unit including a second imaging optical system provided with a plurality of lens groups, and a second actuator configured to change a focal length by driving at least one of the plurality of lens groups of the second imaging optical system; a focal length controller configured to output synchronized driving signals to the first and second actuators; and an image processing unit configured to calculate a distance to a subject by using images captured by the first imaging unit and the second imaging unit.

Provided are methods for prediction error scenario mining for machine learning methods, which can include determining a prediction error indicative of a difference between a planned decision of an autonomous vehicle and an ideal decision of the autonomous vehicle. The prediction error is associated with an error-prone scenario for which a machine learning model of an autonomous vehicle is to make planned movements. The method includes searching a scenario database for the error-prone scenario based on the prediction error. The scenario database includes a plurality of datasets representative of data received from an autonomous vehicle sensor system in which the plurality of datasets is marked with at least one attribute of the set of attributes. The method further includes obtaining the error-prone scenario from the scenario database for inputting into the machine learning model for training the machine learning model. Systems and computer program products are also provided.

In various examples, a deep neural network (DNN) is trained—using image data alone—to accurately predict distances to objects, obstacles, and/or a detected free-space boundary. The DNN may be trained with ground truth data that is generated using sensor data representative of motion of an ego-vehicle and/or sensor data from any number of depth predicting sensors—such as, without limitation, RADAR sensors, LIDAR sensors, and/or SONAR sensors. The DNN may be trained using two or more loss functions each corresponding to a particular portion of the environment that depth is predicted for, such that—in deployment—more accurate depth estimates for objects, obstacles, and/or the detected free-space boundary are computed by the DNN. In some embodiments, a sampling algorithm may be used to sample depth values corresponding to an input resolution of the DNN from a predicted depth map of the DNN at an output resolution of the DNN.

An electronic apparatus and method for assisting with driving of a vehicle are provided. The electronic apparatus includes: a processor configured to execute one or more instructions stored in a memory, to: obtain a surrounding image of the vehicle via at least one sensor, recognize an object from the obtained surrounding image, obtain three-dimensional (3D) coordinate information for the object by using the at least one sensor, determine a number of planar regions constituting the object, based on the 3D coordinate information corresponding to the object, determine whether the object is a real object, based on the number of planar regions constituting the object, and control a driving operation of the vehicle based on a result of the determining whether the object is the real object.

The road obstacle detection device includes a semantic label estimation unit that estimates a semantic label for each pixel of an image using a classifier learned in advance and generates a semantic label image, an original image estimation unit for reconstruction of the original image from the semantic label image, a difference calculating unit for calculating a difference between the original image and the reconstructed image from the original image estimation unit as a calculation result, and a road obstacle detection unit for detecting a road obstacle based on the calculation result.

This application relates to a planar contour recognition method and apparatus, a computer device, and a storage medium. The method includes obtaining a target frame image collected from a target environment; fitting edge points of an object plane in the target frame image and edge points of a corresponding object plane in a previous frame image to obtain a fitting graph, the previous frame image being collected from the target environment before the target frame image; deleting edge points that do not appear on the object plane of the previous frame image, in the fitting graph; and recognizing a contour constructed by remaining edge points in the fitting graph as a planar contour.

An obstacle detection method can improve the accuracy of determining a relative positional relationship between two or more obstacles that are obstructed or obscured during automated driving. A road scene image of a road where a target vehicle is located is acquired. Obstacle recognition is performed to obtain region information and depth-of-field information corresponding to each obstacle in the road scene image. Target obstacles in an occlusion relationship and a relative depth-of-field relationship between the target obstacles are determined. A ranging result of each obstacle is acquired using a ranging apparatus corresponding to the target vehicle. An obstacle detection result of the road is determined based on the relative depth of field relationship between the target obstacles and the ranging result of each obstacle, thereby improving the accuracy of determining a positional relationship of obstructed or obscured obstacles during automated driving.

The present disclosure relates to a method of selecting an accident image using the results of the recognition of an obstacle on a road, which can distinguish between an actual accident image and a fake accident image by previously recognizing an obstacle on a road while a vehicle travels and determining an impact event to have a low accident possibility, the impact event occurring in a section in which the vehicle goes over the obstacle, or suppressing the impact event, and can secure a space of a storage medium by deleting the fake accident image or prevent a data usage fee and unnecessary management expenses by blocking the transmission of the fake accident image to a remote cloud server.