Abstract: A support stand for photography includes a support main body having supporting ends and a mounting end and an adapting device provided at the mounting end. The supporting ends are formed on supporting legs of the support main body. The supporting legs can be unfolded to allow two adjacent supporting ends to move away from each other. The supporting legs can be folded to allow the two adjacent supporting ends to move close to each other and form a gap between two adjacent supporting legs. The adapting device includes a fixed seat provided with an adapting structure for mounting a first photographic equipment and a clamping seat configured to clamp a second photographic equipment. The clamping seat is rotatably connected to the fixed seat. The clamping seat can rotate toward a side where the supporting ends are located to be accommodated in the gap between the two adjacent supporting legs.

Abstract: A method for transmitting high bandwidth camera data through a SerDes links is provided. The method includes steps of: calculating transmission bandwidth required for transmitting image data, and the image data is obtained by a high bandwidth camera; determining a maximum bandwidth capacity of each SerDes link of a plurality of SerDes links; cutting the image data into a plurality of sub images according to the transmission bandwidth and the maximum bandwidth capacity of each SerDes link; assigning each sub image to a sub image transmission area in a corresponding SerDes link, and each SerDes link containing the sub image transmission area and the sub image reception area; acquiring a plurality of sub images transmitted in the plurality of the SerDes links from the corresponding sub image reception area; and splicing the plurality of sub images into the image data.

Abstract: A generation method of high-precision map for recognizing traffic lights is provided. The generation method comprised steps of: obtaining road test data comprising video data of traffic lights; marking the video data in order to obtain marked data of the traffic lights, the marked data comprising states of the traffic lights and traffic lights information; using the video data and the marked data to generate a recognition model of the traffic lights; and storing the recognition model and the traffic lights information in a high-precision map to generate a high-precision map for recognizing the traffic lights. Furthermore, a method and system for recognizing traffic lights using high-precision map are also provided. The recognition model is stored in the high-precision map, and cooperating with the high-precision map to effectively recognize the traffic lights.

Abstract: A method for selecting parking location for an autonomous driving vehicle is provided. The method comprises steps of: obtaining a request of a user; constructing a first map according to a current location of the user, a first parking location, and a high-precision map; obtaining a second parking location selected by the user on the first map; controlling the autonomous driving vehicle to drive to the second parking location, and calculating distance between a current location of the autonomous driving vehicle and the second parking location; when the distance is less than a preset distance, constructing a second map according to the current location of the autonomous driving vehicle, the second parking location, and the high-precision map; controlling the autonomous driving vehicle to drive to the second parking location selected by the user on the second map. Furthermore, an intelligent control device and an autonomous driving vehicle are also provided.

Abstract: A training method for a multi-task recognition network based on end-to-end provided includes: obtaining a plurality of data and location information by a plurality of different sensors located at different positions of an autonomous driving vehicle; inputting the plurality of data into a corresponding data processing network to obtain a plurality of first samples; inputting the first samples into a feature extraction network to obtain a plurality of first-sample features; inputting the plurality of the first-sample features and the plurality of location information into a feature recognition network to obtain a plurality of second samples; training an initial multi-task recognition network based on the second samples to obtain a target multi-task recognition network with recognition and prediction functions. A prediction method for road targets and target behaviors, a computer-readable storage media, and a computer device are also provided.

Abstract: A method of digging valuable data and a server using the same are provided. The method comprises steps of: grabbing source data of one or more regions within a period of time, the source data of one or more autonomous driving vehicles within a period of time, and the source data of one or more sensors within a period of time from the storage area in parallel to form several initial data packets; analyzing corresponding data in each initial data packet to obtain abnormal data; adding labels to the abnormal data to form first version data packets; adding labels to data associated with label requests in some of the initial data packets to form second version data packets; analyzing the data analysis requests in parallel to obtain corresponding labels; obtaining data from the first version data packets and/or the second version data packets in parallel to be valuable data.

Abstract: An intelligent parking method of an autonomous driving vehicle is provided. The intelligent parking method includes steps of: recommending a first parking location according to an acquired target location; constructing an observation area according to the first parking location; obtaining environmental data of the observation area when the autonomous driving vehicle reaches the observation area; determining whether the first parking location is available or not according to the environmental data of the observation area; driving to the first parking location to park when the first parking location is available; or acquiring a second parking location according to a predetermined evaluation rule when the first parking location is unavailable, and recommending the one or more second parking locations to the user for selection; and driving to the second parking location selected by the user to park.

Abstract: A hybrid lidar is provided, the hybrid lidar includes a laser transceiver. The laser transceiver includes a first laser transceiver and a second laser transceiver, the first laser transceiver transmits first transmitted signals and receives first echo signals reflected back by a target object of the first transmitted signals, the first transmitted signals being frequency modulated continuous waves, the second laser transceiver being configured to transmit second transmitted signals and receive second echo signals reflected back by the target object of the second transmitted signals, the second transmitted signals are pulse waves. Furthermore, a vehicle using the hybrid lidar is also provided.

Abstract: A panoramic FMCW lidar is provided. The panoramic FMCW lidarincludes a rotating member and a laser sensor. The rotating member is capable of being operatively rotated. The laser sensor, is arranged on the rotating member and rotated with the rotating member, the laser sensor includes one or more pairs of laser emitters and laser receivers, all laser emitters being arranged on the same side of the rotating member; each pair of the laser transmitters and the laser receivers are arranged adjacently, each laser transmitter is configured to transmit a frequency-modulated continuous wave optical signals, and each laser receiver is configured to receive reflected signals the reflected signals is configured to generate a panoramic point cloud related to an panoramic image of surrounding environment of the panoramic FMCW lidar, and the panoramic point cloud containing speed information.

Abstract: A hybrid dispatch method for autonomous vehicles and manually-driven vehicles is provided and includes steps of: receiving travel information from a terminal, the travel information including a start position and an end position; matching manually-driven vehicle adapted to the travel information according to manually-driven vehicle matching rules; determining whether the travel information meets compliance rules for autonomous vehicles; matching autonomous vehicles adapted to the travel information according to autonomous vehicle matching rules when the travel information meets the compliance rules for autonomous vehicles; providing the matched manually-driven vehicles and the matched autonomous vehicles to the terminal for the user to select. Furthermore, a server and a storage medium are also provided.

Abstract: A method for transmitting high bandwidth camera data through a SerDes links is provided. The method includes steps of: calculating transmission bandwidth required for transmitting image data, and the image data is obtained by a high bandwidth camera; determining a maximum bandwidth capacity of each SerDes link of a plurality of SerDes links; cutting the image data into a plurality of sub images according to the transmission bandwidth and the maximum bandwidth capacity of each SerDes link; assigning each sub image to a sub image transmission area in a corresponding SerDes link, and each SerDes link containing the sub image transmission area and the sub image reception area; acquiring a plurality of sub images transmitted in the plurality of the SerDes links from the corresponding sub image reception area; and splicing the plurality of sub images into the image data.

Abstract: A simulation method based on three-dimensional contour for an autonomous driving vehicle is provided. The simulation method includes steps of: obtaining a path ID and obtaining image data, and point cloud data according to the path ID, the path ID containing an image data path ID, and a point cloud data path ID; rendering a two-dimensional contour of each of a plurality of objects in the image data according to the image data to obtain a plurality of two-dimensional contours; rendering a plurality of three-dimensional contours according to the plurality of two-dimensional contours and the point cloud data; and performing simulation according to the plurality of the three-dimensional contours. A computer equipment and a storage medium are also provided.

Abstract: The disclosure provides an simulation method based on events including steps of: loading a virtual autonomous vehicle into a simulation scene; acquiring a first operation data of the virtual autonomous vehicle in the simulation scene, the first operation data is current operation data of autonomous vehicle; determining whether a first trigger event exists in the first operation data and the environment data or not; changing the first part of the obstacles from current movement trajectories to first movement trajectories according to a first predetermined movement rule when the first trigger event exists; acquiring second operation data of the virtual autonomous vehicle in the simulation scene; and obtaining simulation results of the autonomous driving system on the virtual autonomous vehicle in the simulation scene according to the second operation data. A computer equipment is also provided.

Abstract: A multi-sensor synchronization method is provided. The method comprises steps of: obtaining a first field of viewing direction of a first sensor, and a second field of viewing direction of a second sensor, the second sensor being rotatable; calculating a synchronization time when the second sensor is rotated to change the second field of viewing direction to be consistent with the first field of viewing direction; when the time of the current moment is earlier than the synchronization time by a preset time, triggering the first sensor to output a first image; adjusting first sensing parameters of the first sensor to obtain second sensing parameters according to the first image; and when the second sensor is rotated to change the second field of viewing direction to be consistent with the first field of viewing direction, triggering the first sensor to output a second image based on the second sensing parameters.

Abstract: A control method for an autonomous driving vehicle is provided. The control method comprises steps of: determining whether trigger information is received; when the trigger information is received and the autonomous driving vehicle has been located at a predetermined location at current moment, obtaining an arrival time required for a predetermined target to reach the predetermined location, the predetermined location is a preset connection location; determining whether the arrival time is greater than a preset value; when the arrival time is greater than the preset value, controlling the autonomous driving vehicle to drive to a parking area; and when the arrival time is not greater than the preset value, controlling the autonomous driving vehicle to drive according to preset rules. Furthermore, a computer device is also provided.

Abstract: A method for selecting parking location for an autonomous driving vehicle is provided. The method comprises steps of: obtaining a request of a user; constructing a first map according to a current location of the user, a first parking location, and a high-precision map; obtaining a second parking location selected by the user on the first map; controlling the autonomous driving vehicle to drive to the second parking location, and calculating distance between a current location of the autonomous driving vehicle and the second parking location; when the distance is less than a preset distance, constructing a second map according to the current location of the autonomous driving vehicle, the second parking location, and the high-precision map; controlling the autonomous driving vehicle to drive to the second parking location selected by the user on the second map. Furthermore, an intelligent control device and an autonomous driving vehicle are also provided.

Abstract: An autonomous driving prediction method based on big data, wherein the autonomous driving prediction method based on big data includes steps of: providing a plurality of prediction algorithm models associated with a target road; obtaining sensing data of sensors, the sensing data including a current position of the autonomous driving vehicle, surrounding environment data of the autonomous driving vehicle, and, driving data of the autonomous driving vehicle; obtaining current scene data of the autonomous driving vehicle; loading the optimal prediction algorithm model; calculating current scene data of the autonomous driving vehicle by the optimal prediction algorithm model to obtain prediction data; generating a control command based on the prediction data; and controlling the autonomous driving vehicle to drive according to the control command.

Abstract: A generation method of high-precision map for recognizing traffic lights is provided. The generation method comprised steps of: obtaining road test data comprising video data of traffic lights; marking the video data in order to obtain marked data of the traffic lights, the marked data comprising states of the traffic lights and traffic lights information; using the video data and the marked data to generate a recognition model of the traffic lights; and storing the recognition model and the traffic lights information in a high-precision map to generate a high-precision map for recognizing the traffic lights. Furthermore, a method and system for recognizing traffic lights using high-precision map are also provided. The recognition model is stored in the high-precision map, and cooperating with the high-precision map to effectively recognize the traffic lights.

Abstract: The invention provides a man-machine hybrid decision method based on cloud, the man-machine hybrid decision method based on cloud comprising: the autonomous driving vehicle sending a request to a cloud server, and the request includes real-time data about the autonomous driving vehicle, an abnormal event that triggering the request, and vehicle information; the cloud server obtaining the request; the cloud server distributing the request to corresponding terminals; the terminal obtaining the request; the terminal generating a solution according to the real-time data and the abnormal event, and sending the solution to the cloud server; the cloud server transmitting the solution to the autonomous driving vehicle.

Abstract: A method for preventing fogging is provided.