Abstract: A method and a device for self-supervised learning, a storage medium, and an electronic device are provided. The method includes: organizing real points in one column along a vertical direction into a pillar; determining a predicted point in a next frame; determining a first loss term based on a minimum distance among distances between predicted points in the next frame and real points in the next frame, and generating a loss function including the first loss term; and performing self-supervised learning processing based on the loss function. A pillar motion parameter representing motion of a real point is determined with the pillar as a unit, so as to enhance correlation between point clouds. Self-supervised learning can be realized in a case of no precise correspondence between the predicted point and the real point, and training is performed based on a large number of unlabeled point clouds.

Abstract: A method, an apparatus and an electronic device for real-time object detection are provided according to the present disclosure. In the method, target point cloud data in a range of a preset angle is acquired, where the target point cloud data includes one or more data points, and the preset angle is less than the round angle. A one-dimensional LiDAR point feature of each of the data points in the target point cloud data is determined. A previous frame of LiDAR feature vector is updated based on the one-dimensional LiDAR point feature of each of the data points in the target point cloud data to generate a current frame of LiDAR feature vector. Object information is determined in a real-time manner based on the current frame of LiDAR feature vector.

Abstract: A method and an apparatus for asynchronous data fusion, a storage medium and an electronic device are provided. The method includes: obtaining current frame LiDAR data, and determining current frame LiDAR three-dimensional embeddings; determining a previous frame fused hidden state, and performing a temporal fusion process based on the previous frame fused hidden state and the current frame LiDAR three-dimensional embeddings to generate a current frame temporary hidden state and a current frame output result; and obtaining current frame camera data, determining current frame camera three-dimensional embeddings, and generating a current frame fused hidden state based on the current frame camera three-dimensional embeddings and the current frame temporary hidden state. Asynchronous fusion is performed on the current frame LiDAR data and previous frame camera data, which leads to a low processing latency.

Abstract: A method and an apparatus for generating an interactive scenario, and an electronic device are provided. The method includes performing encoding processing on a first basic coordinate sequence of a target object and a second basic coordinate sequence of an interactive object to generate an encoded implicit state; determining an implicit state probability distribution corresponding to the encoded implicit state based on the encoded implicit state, and determining an initial implicit state by sampling; and performing decoding processing on the initial implicit state to determine a first coordinate sequence probability distribution of the target object and a second coordinate sequence probability distribution of the interactive object, determining a new coordinate sequence of the target object and a new coordinate sequence of the interactive object by sampling.

Abstract: A method and an apparatus for asynchronous data fusion, a storage medium and an electronic device are provided. The method includes: obtaining current frame LiDAR data, and determining current frame LiDAR three-dimensional embeddings; determining a previous frame fused hidden state, and performing a temporal fusion process based on the previous frame fused hidden state and the current frame LiDAR three-dimensional embeddings to generate a current frame temporary hidden state and a current frame output result; and obtaining current frame camera data, determining current frame camera three-dimensional embeddings, and generating a current frame fused hidden state based on the current frame camera three-dimensional embeddings and the current frame temporary hidden state. Asynchronous fusion is performed on the current frame LiDAR data and previous frame camera data, which leads to a low processing latency.