Abstract: An obstacle recognition method and apparatus, a computer device, and a storage medium are provided. The method comprises: acquiring point cloud data scanned by LiDAR and time-sequence pose information of a vehicle (S101); determining a spliced image of a bird's eye view according to the point cloud data, the time-sequence pose information, and a historical frame embedded image (S102); inputting the spliced image into a preset first CNN model to obtain a current frame embedded image and pixel-level information of the bird's eye view (S103); determining recognition information of at least one obstacle according to the current frame embedded image and pixel-level information (S104).

Abstract: An aluminum alloy metro vehicle body section bar capable of matching and combining includes a roof section bar, an upper edge beam section bar, a side wall section bar, a lower edge beam section bar and a floor section bar, wherein the roof section bar comprises roof edge section bars of two widths; the upper edge beam section bar comprises an upper edge beam of a drum-shaped vehicle and an upper edge beam 4b of the trapezoidal vehicle; the side wall section bar comprises a side wall middle section bar of the drum-shaped vehicle and a side wall middle section bar of the trapezoidal vehicle; the lower edge beam section bar comprises a lower edge beam of the drum-shaped vehicle and a lower edge beam of the trapezoidal vehicle; and the floor section bar comprises underframe edge section bars of two widths.

Abstract: An optical computing apparatus and system and a computing method are provided. The optical computing apparatus includes a linear operation module, a first delay module, and a coupler. The linear operation module can modulate, based on received electrical signals, optical signals input to the linear operation module; the first delay module may adjust a delay of optical signals output by the linear operation module; and after the first delay module adjusts the delay of the optical signals output by the linear operation module, the coupler may combine a plurality of groups of optical signals successively output by the linear operation module, to output one group of optical signals used to indicate a computing result that is obtained after a multiply-add operation is performed on one group of data and weights.

Abstract: An obstacle recognition method and apparatus, a computer device, and a storage medium are provided. The method comprises: acquiring point cloud data scanned by LiDAR and time-sequence pose information of a vehicle; determining a spliced image of an eye bird view according to the point cloud data, the time-sequence pose information, and a historical frame embedded image; inputting the spliced image into a preset first CNN model to obtain a current frame embedded image and pixel-level information of the eye bird view; determining recognition information of at least one obstacle according to the current frame embedded image and pixel-level information.

Abstract: The present disclosure provides an obstacle detecting method and apparatus, a device and a storage medium, wherein the method comprises: obtaining a 3D point cloud collected by an unmanned vehicle during travel; projecting the 3D point cloud on a 2-dimensional grid to respectively obtain feature information of each grid; inputting the feature information of each grid into a pre-trained prediction model to respectively obtain obstacle prediction parameters of each grid; performing grid clustering according to the obstacle prediction parameters of each grid to obtain obstacle detection results. The solutions of the present disclosure can be applied to improve the accuracy of detection results.

Abstract: The present disclosure provides an obstacle detecting method and apparatus, a device and a storage medium, wherein the method comprises: obtaining a 3D point cloud collected by an unmanned vehicle during travel; projecting the 3D point cloud on a 2-dimensional grid to respectively obtain feature information of each grid; inputting the feature information of each grid into a pre-trained prediction model to respectively obtain obstacle prediction parameters of each grid; performing grid clustering according to the obstacle prediction parameters of each grid to obtain obstacle detection results. The solutions of the present disclosure can be applied to improve the accuracy of detection results.