Abstract: Embodiments of the present disclosure provide a control method of an unmanned vehicle and an unmanned vehicle, which have excellent safety. The control method of the unmanned vehicle includes: detecting vibration information and running attitude information of the unmanned vehicle; according to the vibration information, the running attitude information and a running status of the unmanned vehicle, determining a condition of the unmanned vehicle, wherein the running status of the unmanned vehicle includes a stop status and a driving status; and when the condition of the unmanned vehicle is abnormal, controlling the unmanned vehicle according to an abnormal condition coping strategy.

Abstract: This specification discloses an obstacle trajectory prediction method and apparatus. In embodiments of the present disclosure, a global interaction feature under joint action of a vehicle and obstacles is determined according to historical status information and current status information of the vehicle, historical status information and current status information of the obstacles, and a future motion trajectory planned by the vehicle; an individual interaction feature of a to-be-predicted obstacle is determined according to the global interaction feature and current status information of the to-be-predicted obstacle; and a future motion trajectory of the to-be-predicted obstacle is predicted through the individual interaction feature and information about an environment around the vehicle.

Abstract: Disclosed herein is a visual positioning method and apparatus, the method including: acquiring a video captured by an image sensor; determining visual positioning information respectively corresponding to a plurality of key image frames in the video; determining a capture pose transformation relationship between each of the plurality of key image frames according to inertial navigation information of the image sensor recorded when taking the video; performing, according to the visual positioning information corresponding to each of the plurality of key image frames, graph optimization processing on the visual positioning information corresponding to each of the plurality of key image frames by using the capture pose transformation relationship between each of the plurality of key image frames as an edge constraint; and determining, according to a result of the graph optimization processing, a visual positioning result of the image sensor when taking the video.

Abstract: An unmanned device acquires sensing data of surrounding obstacles; determines, for each obstacle, at least one predicted track of the obstacle in a future period of time based on the sensing data; determines, for each moment in the future period of time and according to the predicted track corresponding to the obstacle, a collision probability that a collision with the obstacle occurs at each position in a target region at the moment; and determines a global collision probability that the collision with the obstacle occurs in the entire target region at the moment. According to the global collision probability corresponding to each obstacle at each moment, the unmanned device controls the unmanned device in the future period of time.

Abstract: An image acquisition method and apparatus are provided. By controlling a motion device, at least one of an image acquisition device or a photographed target object moves under the driving of the motion device, so that a sample image including the target object can be acquired in a preset designated acquisition pose after movement, thereby improving the accuracy of a relative pose between the image acquisition device and the target object during acquisition, reducing human intervention during acquisition, improving the automation degree, and providing the possibility for subsequent services that need to be performed according to sample images captured with relatively high shooting pose accuracy.

Abstract: A method for trajectory planning, an apparatus, a storage medium, and an electronic device are provided. A constraint set of a space including a target device is determined according to a velocity of an unmanned device and velocities of designated obstacles, so that during optimization of a preliminary reference trajectory, a solution can be obtained with the space in the constraint set as a solution space under the constraint of the constraint set, so as to ensure that the solution space is a convex space, and relatively satisfactory reference trajectory points can be solved.

Abstract: A method and apparatus for controlling an unmanned driving device are provided. An unmanned driving device obtains a travel trajectory of the unmanned driving device as a first trajectory, obtains a travel trajectory of a target object around as a second trajectory, and determines a spatial relation between the unmanned driving device and the target object according to the first trajectory and the second trajectory. Subsequently, the unmanned driving device can code the spatial relation according to a preset coding manner to obtain spatial coding information, where each code in the spatial coding information is configured to represent a spatial relation between the unmanned driving device and the target object at a moment corresponding to the code.

Abstract: Embodiments provide extracting information respectively corresponding to predetermined dimensions from environment information corresponding to an unmanned driving environment. In some embodiments, the information respectively corresponding to the dimensions is input into an identification model to obtain a driving feature. Then a risk value representing a driving risk degree of an unmanned device is determined, and a maximum variation of the information corresponding to at least one dimension is determined when a variation of the driving feature is less than a predetermined threshold. A maximum variation of the information corresponding to each dimension is used as a risk contribution feature. A variation representative value of the information corresponding to each dimension is determined from the risk contribution feature. According to the variation representative values of the dimensions, a driving risk factor corresponding to the risk value is determined based on the driving feature.

Abstract: A method and apparatus for controlling an unmanned device, a storage medium, and an electronic device are provided. In some embodiments, not only a control policy is determined according to a single frame of a current image, but a safety representation value of an unmanned device at a current moment and safety representation values of the unmanned device at respective historical moments are respectively determined according to a current image and environment images of several historical moments. In those embodiments, a control policy of the unmanned device at a next moment is determined according to a safety representation value of the unmanned device at current moment and the safety representation values of the unmanned device at respective historical moments.

Abstract: A trajectory prediction method and apparatus, a storage medium, and an electronic device are provided. In embodiments of this disclosure, according to a historical trajectory of a designated target and a historical trajectory of each obstacle, a historical interaction feature between the designated target and each obstacle is determined, and a motion trajectory of the designated target is predicted to obtain an initial predicted trajectory. A future interaction feature between each obstacle and the designated target is then determined according to the initial predicted trajectory and a planned trajectory of each obstacle. According to the future interaction feature, a final predicted trajectory of the designated target is obtained.

Abstract: An obstacle tracking method and apparatus, a storage medium and an unmanned driving device are provided. An obstacle aggregation region in a first point cloud may be determined according to position information of each obstacle in the first point cloud acquired by the unmanned driving device. Then, an aggregated obstacle and a non-aggregated obstacle in the second point cloud acquired by the unmanned driving device are determined according to the obstacle aggregation region. In addition, a matching result of each aggregated obstacle is respectively determined based on a group matching rule, and a matching result of each non-aggregated obstacle is respectively determined based on a non-group matching rule. Finally, an obstacle tracking result is determined according to the matching result of each obstacle in the second point cloud.

Abstract: A data noise reduction method and apparatus are provided. In some embodiments, the method includes: obtaining to-be-processed point cloud data; for each of points in the to-be-processed point cloud data, determining a point feature corresponding to the point, the point feature comprising at least one of a spatial distribution difference feature between the point and other points adjacent to the point or a point group distribution feature corresponding to a point group comprising all points in a local space in which the point is located; recognizing a noise point from the to-be-processed point cloud data according to point features corresponding to the points in the to-be-processed point cloud data; and performing noise reduction on the to-be-processed point cloud data according to the recognized noise point.

Abstract: An obstacle tracking method, a storage medium, and an electronic device are provided. In various embodiments for obstacles in every two frames of laser point clouds, first, the obstacles in the two frames of laser point clouds are matched according to types of the obstacles in the two frames of laser point clouds. Next, unmatched obstacles in the two frames of laser point clouds are matched according to point cloud data of the unmatched obstacles in the two frames of laser point clouds. After two times of matching, motion states of the obstacles in the two frames of laser point clouds are updated.

Abstract: First, environment image data acquired by an unmanned driving device is obtained, and for each piece of reference image data matching the environment image data, a predicted pose of the unmanned driving device when acquiring the environment image data is determined according to an actual pose corresponding to the reference image data; and then pose deviation representation information of the reference image data is determined according to the predicted pose and actual poses corresponding to other pieces of reference image data. Finally, target image data is selected from the reference image data, and a pose of the unmanned driving device when acquiring the environment image data is determined.