A positioning method and device for automatic driving equipment

Abstract

This specification discloses a positioning method and device for an autonomous driving device, and specifically discloses determining the current positioning data of the autonomous driving device and the historical motion state data of the autonomous driving device, simulating the driving process of the autonomous driving device through a preset simulation test system, so as to simulate the positioning data corresponding to the current moment after the autonomous driving device drives according to the historical motion state data, and use it as reference positioning data, determine the positioning deviation between the current positioning data and the reference positioning data, adjust the current positioning data according to the positioning deviation, obtain the adjusted positioning data, and locate the autonomous driving device based on the adjusted positioning data. In this way, the current positioning data is calibrated with the simulated positioning data, thereby ensuring the accuracy of the positioning data of the obtained autonomous driving device.

Description

Technical Field

[0001] The present invention relates to the field of computer technology, and more particularly to a positioning method and apparatus for an autonomous driving device. Background Art

[0002] During the driving process of unmanned equipment, the positioning data of the unmanned equipment is usually obtained and saved through the Global Navigation Satellite System (GNSS) to facilitate subsequent data processing such as fault analysis.

[0003] In actual scenarios, when the signal quality of unmanned equipment is poor, there will be problems with accurate positioning data. At this time, the positioning data of the unmanned equipment in each measurement cycle will be inferred based on the attitude data of the unmanned equipment monitored by the Inertial Measurement Unit (IMU) and the positioning data determined when the signal quality is good. The positioning data obtained by GNSS measurement will be corrected based on the inferred positioning data. However, the positioning data calculated based on the attitude data monitored by the IMU has certain errors. As the signal quality is poor for a longer period of time, the errors in the predicted positioning data will gradually accumulate, resulting in reduced accuracy of the positioning data.

[0004] In summary, when the GNSS set on the unmanned device fails or the communication signal is poor for a long time, and / or the IMU set on the unmanned device fails, there will be a problem of inaccurate positioning data. Summary of the invention

[0005] This specification provides a positioning method and device for an autonomous driving device to partially solve the above-mentioned problems existing in the prior art.

[0006] This manual adopts the following technical solutions:

[0007] This specification provides a positioning method for an autonomous driving device, including:

[0008] Determining current positioning data of the autonomous driving device and historical motion state data of the autonomous driving device;

[0009] The driving process of the autonomous driving device is simulated by a preset simulation test system to simulate the positioning data corresponding to the current moment after the autonomous driving device drives according to the historical motion state data, as reference positioning data;

[0010] Determining a positioning deviation between the current positioning data and the reference positioning data;

[0011] According to the positioning deviation, the current positioning data is adjusted to obtain adjusted positioning data, so as to position the automatic driving device according to the adjusted positioning data.

[0012] Optionally, determining the current positioning data of the autonomous driving device includes:

[0013] Acquire initial positioning data measured by a positioning module provided on the autonomous driving device at a current moment;

[0014] The current positioning data is determined according to the initial positioning data and the historical posture data of the automatic driving device.

[0015] Optionally, the driving process of the autonomous driving device is simulated by a preset simulation test system to simulate the positioning data corresponding to the current moment after the autonomous driving device drives according to the historical motion state data as reference positioning data, specifically including:

[0016] The current positioning data and the historical motion state data are input into the simulation test system, so that the simulation test system can retrieve a high-precision map that matches the geographical location corresponding to the current positioning data according to the current positioning data, and simulate the driving process of the autonomous driving device on the high-precision map according to the historical motion state data to obtain the reference positioning data.

[0017] Optionally, the current positioning data and the historical motion state data are input into the simulation test system, so that the simulation test system retrieves a high-precision map that matches the geographical location corresponding to the current positioning data according to the current positioning data, and simulates the driving process of the autonomous driving device on the high-precision map according to the historical motion state data to obtain the reference positioning data, specifically including:

[0018] The current positioning data and the historical motion state data are input into the simulation test system, so that the simulation test system can simulate, on the retrieved high-precision map, the driving trajectory formed by the automatic driving device after driving according to the historical motion state data, and determine the reference positioning data from the driving trajectory.

[0019] Optionally, simulating the driving process of the autonomous driving device through a preset simulation test system specifically includes:

[0020] The current positioning data and the historical motion state data are input into the simulation test system, so that the simulation test system can simulate the driving trajectory formed by the automatic driving device after driving according to the historical motion state data through a predetermined power simulation model of the automatic driving device, and determine the reference positioning data from the driving trajectory.

[0021] Optionally, determining a power simulation model of the autonomous driving device specifically includes:

[0022] In response to the automatic driving device being started, the model of the automatic driving device is obtained and transmitted to the simulation test system, so that the simulation test system determines, based on the model, a power simulation model that matches the model from a pre-built power simulation model database as the power simulation model of the automatic driving device.

[0023] Optionally, adjusting the current positioning data according to the positioning deviation to obtain adjusted positioning data specifically includes:

[0024] Determine whether the positioning deviation is greater than a set deviation threshold;

[0025] If yes, the reference positioning data is used as the adjusted positioning data.

[0026] Optionally, determining current positioning data of the autonomous driving device and historical motion state data of the autonomous driving device specifically includes:

[0027] The arrival of a set measurement period is monitored to determine the current positioning data of the autonomous driving device and the historical motion state data of the autonomous driving device, wherein the measurement period includes 100 milliseconds.

[0028] This specification provides a positioning device for an autonomous driving device, including:

[0029] A determination module, used to determine current positioning data of the autonomous driving device and historical motion state data of the autonomous driving device;

[0030] A simulation module, used to simulate the driving process of the automatic driving device through a preset simulation test system, so as to simulate the positioning data corresponding to the current moment after the automatic driving device drives according to the historical motion state data, as reference positioning data;

[0031] A deviation determination module, used to determine the positioning deviation between the current positioning data and the reference positioning data;

[0032] An adjustment module is used to adjust the current positioning data according to the positioning deviation to obtain adjusted positioning data, so as to position the automatic driving device according to the adjusted positioning data.

[0033] This specification provides a computer-readable storage medium, which stores a computer program. When the computer program is executed by a processor, the positioning method of the above-mentioned autonomous driving device is implemented.

[0034] This specification provides an electronic device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements the positioning method of the above-mentioned autonomous driving device when executing the program.

[0035] At least one of the above technical solutions adopted in this specification can achieve the following beneficial effects:

[0036] In the positioning method of the autonomous driving device provided in this specification, the current positioning data of the autonomous driving device and the historical motion state data of the autonomous driving device are determined, and then the driving process of the autonomous driving device is simulated through a preset simulation test system to simulate the positioning data corresponding to the current moment after the autonomous driving device drives according to the historical motion state data, which is used as reference positioning data, and then the positioning deviation between the current positioning data and the reference positioning data is determined. Finally, according to the positioning deviation, the current positioning data is adjusted to obtain the adjusted positioning data, so as to position the autonomous driving device according to the adjusted positioning data.

[0037] It can be seen from the above method that during the driving process of the automatic driving device, the method uses the simulation test system to simulate the driving process of the automatic driving device in real time, and adjusts the current positioning data of the automatic driving device according to the positioning deviation between the current positioning data actually measured and the simulated reference positioning data. In this way, the current positioning data is calibrated with the simulated positioning data, thereby ensuring the accuracy of the positioning data of the automatic driving device. BRIEF DESCRIPTION OF THE DRAWINGS

[0038] The drawings described herein are used to provide a further understanding of this specification and constitute a part of this specification. The illustrative embodiments and descriptions of this specification are used to explain this specification and do not constitute an improper limitation on this specification. In the drawings:

[0039] Figure 1 A flowchart of a positioning method for an autonomous driving device in this specification;

[0040] Figure 2 This is a detailed flowchart of the positioning method of the autonomous driving device in this manual;

[0041] Figure 3 A schematic diagram of a positioning device for an autonomous driving device provided in this specification;

[0042] Figure 4 The corresponding Figure 1 Schematic diagram of autonomous driving equipment. DETAILED DESCRIPTION

[0043] In order to make the purpose, technical solutions and advantages of this specification more clear, the technical solutions of this specification will be clearly and completely described below in combination with the specific embodiments of this specification and the corresponding drawings. Obviously, the described embodiments are only part of the embodiments of this specification, not all of them. Based on the embodiments in this specification, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of this specification.

[0044] The positioning solution of the autonomous driving equipment provided in this specification will be described in detail below in conjunction with embodiments.

[0045] Figure 1 The following is a flowchart of a positioning method for an autonomous driving device in this specification, which specifically includes the following steps:

[0046] Step S100, determining the current positioning data of the autonomous driving device and the historical motion state data of the autonomous driving device.

[0047] At present, in the field of autonomous driving technology, during the driving process of the autonomous driving equipment, the autonomous driving system carried by the autonomous driving equipment will periodically collect the positioning data of the autonomous driving equipment, and perform fault analysis on the driving process of the autonomous driving equipment in real time or after completing the driving task based on the collected positioning data, so as to optimize the algorithm for realizing autonomous driving based on the obtained fault analysis results.

[0048] At present, when positioning an autonomous driving device, it is generally based on the positioning module set on the autonomous driving device, through the Global Navigation Satellite System (GNSS) or the Global Positioning System (GPS). However, when the autonomous driving device passes through geographical areas such as tunnels, high-rise dense areas, mountainous areas, etc., the communication quality of the positioning module will be poor. At this time, there will be a large deviation between the positioning data determined by the positioning module and the actual position of the autonomous driving device, and the accuracy of the positioning data is low.

[0049] To address this issue, quality inspection is usually performed on the determined positioning data. When the quality of the positioning data does not meet the preset quality requirements (that is, the accuracy of the determined positioning data is low), the historical posture data monitored by the inertial measurement unit (IMU) installed on the autonomous driving device is obtained, and the most recent positioning data that meets the preset quality requirements is found. Then, based on the found positioning data and the historical posture data, the theoretical positioning data of the autonomous driving device at the current moment is inferred. Finally, the inferred positioning data is used to replace the positioning data determined by the positioning module as the positioning data of the autonomous driving device at that moment.

[0050] In this way, the positioning data determined in the case of poor communication quality can be adjusted based on the continuity of the movement process of the autonomous driving device to improve the accuracy of the positioning data. However, once the poor communication quality of the positioning module lasts for a long time, the error between the calculated positioning data and the actual location of the autonomous driving device will gradually accumulate over time, causing the accuracy of the positioning data to decrease again.

[0051] In order to solve this problem, this specification provides a positioning method for an autonomous driving device. In this method, a preset simulation test system is used to simulate the driving process of the autonomous driving device in real time. In this way, when determining the positioning data of the autonomous driving device, the determined positioning data is compared with the data at the same time predicted by the simulation test system. When it is determined that the position deviation between the two is greater than the set deviation threshold, the positioning data predicted by the simulation test system is used to replace the determined positioning data as the positioning data of the autonomous driving device at that moment.

[0052] The implementation of the positioning method for the autonomous driving device provided in this specification will be explained in detail below.

[0053] In a specific implementation, the autonomous driving device first determines the current positioning data of the autonomous driving device and the historical motion state data of the autonomous driving device. When determining the current positioning data, the autonomous driving device may first obtain the initial positioning data measured by the positioning module at the current moment, and then determine the current positioning data based on the initial positioning data and the historical posture data of the autonomous driving device.

[0054] Specifically, after the autonomous driving device obtains the initial positioning data measured by the positioning module at the current moment, it determines whether the initial positioning data meets the preset quality requirements. If so, the initial positioning data is used as the current positioning data of the autonomous driving device. If not, the positioning data closest to the initial positioning data is determined from the positioning data before the initial positioning data, and the historical posture data collected by the IMU in the time period between the moment corresponding to the positioning data and the moment corresponding to the initial positioning data is obtained. Then, based on the determined positioning data closest to the initial positioning data and the historical posture data, the theoretical positioning data of the autonomous driving device at the current moment is inferred as the current positioning data of the autonomous driving device.

[0055] In addition, the above-mentioned historical motion state data includes the motion speed and motion direction of the autonomous driving device, wherein the motion direction can be determined based on the state data of the autonomous driving device collected by the IMU.

[0056] It should be noted that the autonomous driving equipment mentioned in this specification may refer to unmanned driving equipment that can realize autonomous driving, including unmanned vehicles, robots, automatic delivery equipment, etc. Based on this, the unmanned driving equipment in this specification can be used to perform delivery tasks in the delivery field, such as using unmanned driving equipment for express delivery, logistics, take-out and other delivery business scenarios.

[0057] In this specification, when the autonomous driving device executes the positioning method of the autonomous driving device, it will be completed by the autonomous driving system carried by the autonomous driving device, or an independent processing unit. For the sake of ease of description in this specification, the operations performed by the autonomous driving system or the independent processing unit will be abbreviated as being completed by the autonomous driving device.

[0058] Step S102, simulating the driving process of the autonomous driving device through a preset simulation test system to simulate the positioning data corresponding to the current moment after the autonomous driving device drives according to the historical motion state data as reference positioning data.

[0059] Among them, the above-mentioned simulation test system can be directly set up on the autonomous driving equipment. Of course, the simulation test system can also be set up on a server that provides business support for autonomous driving.

[0060] In a specific implementation, the unmanned driving device inputs the determined current positioning data and the historical motion state data into a preset simulation test system, and then the simulation test system retrieves a high-precision map that matches the geographical location corresponding to the current positioning data based on the current positioning data. Next, the simulation test system renders the power simulation model of the automatic driving device on the retrieved high-precision map through the power simulation model of the automatic driving device determined in advance, and simulates the driving process of the automatic driving device based on the historical motion state data to simulate the driving trajectory formed by the automatic driving device after driving according to the historical motion state data, and then determines from the driving trajectory the positioning data corresponding to the automatic driving device at the current moment after driving according to the historical motion state data as the reference positioning data.

[0061] Specifically, when the autonomous driving device determines the power simulation model, in response to the startup of the autonomous driving device, the model of the autonomous driving device is obtained and transmitted to the simulation test system. Then, after receiving the model, the simulation test system determines the power simulation model that matches the model from the pre-built power simulation model database as the power simulation model of the autonomous driving device. The power simulation model of the autonomous driving device is constructed based on the simulation models of each module component in the autonomous driving device. Among them, if the autonomous driving device is an autonomous driving car, the module is each car component of each autonomous driving car, including an engine, a transmission, a drive shaft, a tire, a car profile, etc. The power simulation model image of the autonomous driving device is rendered based on the profile information bound to the model of the autonomous driving device.

[0062] In actual business, the autonomous driving device periodically obtains its current positioning data and historical posture data and inputs them into the simulation test system, and then determines the reference positioning data of the autonomous driving device from the simulated driving trajectory, so as to locate the autonomous driving device according to the periodically determined current positioning data and the corresponding reference positioning data. The autonomous driving device can determine the current positioning data and the corresponding reference positioning data every 100 milliseconds.

[0063] Step S104: determining a positioning deviation between the current positioning data and the reference positioning data.

[0064] Step S106: Adjust the current positioning data according to the positioning deviation to obtain adjusted positioning data, so as to position the automatic driving device according to the adjusted positioning data.

[0065] In a specific implementation, the autonomous driving device determines the distance between the geographical location corresponding to the current positioning data and the geographical location corresponding to the reference positioning data, and uses the distance as the positioning deviation between the current positioning data and the reference positioning data. Then, the autonomous driving device determines whether the positioning deviation is greater than the set deviation threshold. If so, the reference positioning data is used as the adjusted positioning data, and the adjusted positioning data is used as the positioning data of the autonomous driving device at the current moment. If not, it is determined that the current positioning data is the positioning data of the autonomous driving device at the current moment.

[0066] In actual business, the simulation test system can also directly determine the positioning deviation between the current positioning data and the reference positioning data, and determine whether the positioning deviation is greater than the set deviation threshold. If so, the reference positioning data is sent to the autonomous driving device, and the autonomous driving device adjusts the current positioning data according to the reference positioning data to obtain the adjusted positioning data, so as to locate the autonomous driving device according to the adjusted positioning data. If not, the simulation test system returns feedback information to the autonomous driving device indicating that the positioning deviation is not greater than the set deviation threshold. After receiving the feedback information, the autonomous driving device will locate the autonomous driving device according to the current positioning data.

[0067] Through the above steps, during the driving process of the automatic driving device, the driving process of the automatic driving device is simulated in real time through the simulation test system, and the current positioning data of the automatic driving device is adjusted according to the positioning deviation between the current positioning data actually measured and the simulated reference positioning data. In this way, the current positioning data is calibrated with the simulated positioning data, thereby ensuring the accuracy of the positioning data of the automatic driving device.

[0068] It should be noted that the simulation test system involved in the positioning method of the autonomous driving device provided in this specification can also be set on the server side that provides business support for the autonomous driving device. During the specific execution, the autonomous driving device determines the current positioning data and the historical motion state data, and sends them to the server. Then, the server inputs the received current positioning data and historical motion state data into the preset simulation test system, and the simulation test system retrieves the high-precision map that matches the geographical location corresponding to the current positioning data based on the current positioning data, and then simulates the driving trajectory formed by the autonomous driving device after driving according to the historical motion state data on the retrieved high-precision map through the power simulation model of the autonomous driving device, and determines the reference positioning data from the driving trajectory.

[0069] In addition, after determining the reference positioning data, the server can continue to determine the positioning deviation between the reference positioning data and the above-mentioned current positioning data, and then determine whether the positioning deviation is greater than the set deviation threshold. If so, the reference positioning data is returned to the automatic driving device. Then, the automatic driving device uses the reference positioning data as the adjusted positioning data and performs positioning according to the adjusted positioning data.

[0070] Of course, the server can also directly send the determined reference positioning data to the autonomous driving device, and then the autonomous driving device determines the positioning deviation between the reference positioning data and the above-mentioned current positioning data, and then determines whether the positioning deviation is greater than the set deviation threshold. If so, the reference positioning data is used as the adjusted positioning data, and positioning is performed according to the adjusted positioning data.

[0071] The following will combine the diagrams and take the simulation test system set on the autonomous driving device as an example to provide a detailed flow chart of the execution of the positioning method of the autonomous driving device provided in this specification. Figure 2 .

[0072] Step S200, after the autonomous driving device is started, the model of the autonomous driving device, the current positioning data and the current motion state data are determined, and timing is started.

[0073] In step S202, the autonomous driving device transmits the model, current positioning data and motion status data to the simulation test system.

[0074] In step S204, the simulation test system determines a power simulation model that matches the model from a pre-built power simulation model database according to the model as the power simulation model of the automatic driving device.

[0075] Step S206: The simulation test system determines, based on the model, from a pre-built power simulation model database, a power simulation model that matches the model as the power simulation model of the autonomous driving device.

[0076] Step S208: The simulation test system renders a power simulation model of the autonomous driving device on the retrieved high-precision map.

[0077] In step S210, the automatic driving device determines whether the current timing duration reaches the set duration. If so, execute step S212; otherwise, execute step S210.

[0078] Step S212: The autonomous driving device determines the current positioning data and current motion state data of the autonomous driving device and restarts the timing.

[0079] In step S214, the autonomous driving device transmits the current positioning data and the current motion state data to the simulation test system.

[0080] Step S216: The simulation test system uses a dynamic simulation model on the retrieved high-precision map to simulate the driving trajectory formed by the automatic driving device after driving according to the obtained motion state data based on the current positioning data and motion state data determined at the previous moment and the current positioning data and motion state data determined at the current moment.

[0081] In step S218, the simulation test system determines, from the obtained driving trajectory, the positioning data corresponding to the current moment after the automatic driving device drives according to the obtained motion state data, as reference positioning data.

[0082] Step S220: the simulation test system determines a positioning deviation between the current positioning data and the reference positioning data at the current moment.

[0083] In step S222, the simulation test system determines whether the positioning deviation is greater than a set deviation threshold, and if so, executes step S224, otherwise, executes step S228.

[0084] Step S224: The simulation test system sends the reference positioning data to the automatic driving device.

[0085] In step S226, the autonomous driving device adjusts the current positioning data according to the reference positioning data to obtain adjusted positioning data, and saves the adjusted positioning data as the positioning data of the autonomous driving device at the current moment, and returns to execute step S210.

[0086] In step S228, the simulation test system returns feedback information to the autonomous driving device indicating that the positioning deviation between the current positioning data and the reference positioning data is less than a set deviation threshold.

[0087] In step S230, the autonomous driving device saves the current positioning data as the positioning data of the autonomous driving device at the current moment based on the feedback information, and returns to execute step S210.

[0088] The above is a positioning method for an autonomous driving device provided in one or more embodiments of this specification. Based on the same idea, this specification also provides a corresponding positioning device for an autonomous driving device, such as Figure 3 shown.

[0089] Figure 3 A schematic diagram of a positioning device for an autonomous driving device provided in this specification specifically includes:

[0090] A determination module 300, configured to determine current positioning data of the autonomous driving device and historical motion state data of the autonomous driving device;

[0091] The simulation module 301 is used to simulate the driving process of the automatic driving device through a preset simulation test system, so as to simulate the positioning data corresponding to the current moment after the automatic driving device drives according to the historical motion state data, as reference positioning data;

[0092] The deviation determination module 302 is used to determine the positioning deviation between the current positioning data and the reference positioning data;

[0093] The adjustment module 303 is used to adjust the current positioning data according to the positioning deviation to obtain adjusted positioning data, so as to position the automatic driving device according to the adjusted positioning data.

[0094] Optionally, the determination module 300 is specifically used to obtain initial positioning data measured by a positioning module set on the autonomous driving device at the current moment; and determine the current positioning data based on the initial positioning data and the historical posture data of the autonomous driving device.

[0095] Optionally, the simulation module 301 is specifically used to input the current positioning data and the historical motion status data into the simulation test system, so that the simulation test system can retrieve a high-precision map that matches the geographical location corresponding to the current positioning data based on the current positioning data, and simulate the driving process of the autonomous driving device on the high-precision map based on the historical motion status data to obtain the reference positioning data.

[0096] Optionally, the simulation module 301 is specifically used to input the current positioning data and the historical motion state data into the simulation test system, so that the simulation test system can simulate, on the retrieved high-precision map, the driving trajectory formed by the autonomous driving device after driving according to the historical motion state data, and determine the reference positioning data from the driving trajectory.

[0097] Optionally, the simulation module 301 is specifically used to input the current positioning data and the historical motion state data into the simulation test system, so that the simulation test system can simulate the driving trajectory formed by the autonomous driving device after driving according to the historical motion state data through a predetermined power simulation model of the autonomous driving device, and determine the reference positioning data from the driving trajectory.

[0098] Optionally, the simulation module 301 is specifically used to obtain the model of the autonomous driving device and transmit it to the simulation test system in response to the startup of the autonomous driving device, so that the simulation test system can determine, based on the model, a power simulation model that matches the model from a pre-built power simulation model database as the power simulation model of the autonomous driving device.

[0099] Optionally, the adjustment module 303 is specifically used to determine whether the positioning deviation is greater than a set deviation threshold; if so, use the reference positioning data as the adjusted positioning data.

[0100] Optionally, the determination module 300 is specifically used to monitor the arrival of a set measurement cycle, determine the current positioning data of the autonomous driving device, and the historical motion state data of the autonomous driving device, and the measurement cycle includes 100 milliseconds.

[0101] This specification also provides a computer-readable storage medium, which stores a computer program, which can be used to execute the above Figure 1 A positioning method for an autonomous driving device is provided.

[0102] This manual also provides Figure 4 The schematic structure diagram of the automatic driving equipment is shown in FIG. Figure 4 As mentioned above, at the hardware level, the autonomous driving device includes a processor, an internal bus, a network interface, a memory, and a non-volatile memory, and may also include hardware required for other services. The processor reads the corresponding computer program from the non-volatile memory into the memory and then runs it to achieve the above Figure 1 The positioning method of the autonomous driving device. Of course, in addition to the software implementation, this specification does not exclude other implementation methods, such as logic devices or a combination of software and hardware, etc., that is, the execution subject of the following processing flow is not limited to each logic unit, but can also be hardware or logic devices.

[0103] In the 1990s, improvements to a technology could be clearly distinguished as hardware improvements (for example, improvements to the circuit structure of diodes, transistors, switches, etc.) or software improvements (improvements to the method flow). However, with the development of technology, many improvements to the method flow today can be regarded as direct improvements to the hardware circuit structure. Designers almost always obtain the corresponding hardware circuit structure by programming the improved method flow into the hardware circuit. Therefore, it cannot be said that an improvement in a method flow cannot be implemented using a hardware entity module. For example, a programmable logic device (PLD) (such as a field programmable gate array (FPGA)) is such an integrated circuit whose logical function is determined by the user's programming of the device. Designers can "integrate" a digital system on a PLD by programming it themselves, without having to ask a chip manufacturer to design and produce a dedicated integrated circuit chip. Moreover, nowadays, instead of manually making integrated circuit chips, this kind of programming is mostly implemented by "logic compiler" software, which is similar to the software compiler used when developing and writing programs, and the original code before compilation must also be written in a specific programming language, which is called hardware description language (HDL). There is not only one HDL, but many kinds, such as ABEL (Advanced Boolean Expression Language), AHDL (Altera Hardware Description Language), Confluence, CUPL (Cornell University Programming Language), HDCal, JHDL (Java Hardware Description Language), Lava, Lola, MyHDL, PALASM, RHDL (Ruby Hardware Description Language), etc. The most commonly used ones are VHDL (Very-High-Speed ​​Integrated Circuit Hardware Description Language) and Verilog. Those skilled in the art should also know that it is only necessary to program the method flow slightly in the above-mentioned hardware description languages ​​and program it into the integrated circuit, and then it is easy to obtain the hardware circuit that implements the logic method flow.

[0104] The controller can be implemented in any appropriate manner, for example, the controller can take the form of a microprocessor or processor and a computer-readable medium storing a computer-readable program code (such as software or firmware) that can be executed by the (micro)processor, a logic gate, a switch, an application-specific integrated circuit (ASIC), a programmable logic controller, and an embedded microcontroller. Examples of controllers include, but are not limited to, the following microcontrollers: ARC 625D, Atmel AT91SAM, Microchip PIC18F26K20, and Silicone Labs C8051F320. The memory controller can also be implemented as part of the control logic of the memory. Those skilled in the art also know that in addition to implementing the controller in a purely computer-readable program code manner, the controller can be implemented in the form of a logic gate, a switch, an application-specific integrated circuit, a programmable logic controller, and an embedded microcontroller by logically programming the method steps. Therefore, this controller can be considered as a hardware component, and the devices included therein for implementing various functions can also be regarded as structures within the hardware component. Or even, the devices for implementing various functions can be regarded as both software modules for implementing the method and structures within the hardware component.

[0105] The systems, devices, modules or units described in the above embodiments may be implemented by computer chips or entities, or by products with certain functions. A typical implementation device is a computer. Specifically, the computer may be, for example, a personal computer, a laptop computer, a cellular phone, a camera phone, a smart phone, a personal digital assistant, a media player, a navigation device, an email device, a game console, a tablet computer, a wearable device, or a combination of any of these devices.

[0106] For the convenience of description, the above device is described in various units according to their functions. Of course, when implementing this specification, the functions of each unit can be implemented in the same or multiple software and / or hardware.

[0107] Those skilled in the art will appreciate that embodiments of the present invention may be provided as methods, systems, or computer program products. Therefore, the present invention may take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware. Moreover, the present invention may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0108] The present invention is described with reference to flowcharts and / or block diagrams of methods, devices (systems), and computer program products according to embodiments of the present invention. It should be understood that each process and / or block in the flowchart and / or block diagram, as well as the combination of processes and / or blocks in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0109] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0110] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.

[0111] In a typical configuration, a computing device includes one or more processors (CPU), input / output interfaces, network interfaces, and memory.

[0112] The memory may include non-permanent storage in a computer-readable medium, random access memory (RAM) and / or non-volatile memory in the form of read-only memory (ROM) or flash RAM. The memory is an example of a computer-readable medium.

[0113] Computer readable media include permanent and non-permanent, removable and non-removable media that can be implemented by any method or technology to store information. Information can be computer readable instructions, data structures, program modules or other data. Examples of computer storage media include, but are not limited to, phase change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other memory technology, compact disk read-only memory (CD-ROM), digital versatile disk (DVD) or other optical storage, magnetic cassettes, magnetic tape magnetic disk storage or other magnetic storage devices or any other non-transmission media that can be used to store information that can be accessed by a computing device. As defined herein, computer readable media does not include temporary computer readable media (transitory media), such as modulated data signals and carrier waves.

[0114] It should also be noted that the terms "include", "comprises" or any other variations thereof are intended to cover non-exclusive inclusion, so that a process, method, commodity or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, commodity or device. In the absence of more restrictions, the elements defined by the sentence "comprises a ..." do not exclude the existence of other identical elements in the process, method, commodity or device including the elements.

[0115] Those skilled in the art will appreciate that the embodiments of this specification may be provided as methods, systems or computer program products. Therefore, this specification may take the form of a complete hardware embodiment, a complete software embodiment or an embodiment combining software and hardware. Moreover, this specification may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0116] This specification may be described in the general context of computer-executable instructions executed by a computer, such as program modules. Generally, program modules include routines, programs, objects, components, data structures, etc. that perform specific tasks or implement specific abstract data types. This specification may also be practiced in distributed computing environments where tasks are performed by remote processing devices connected through a communication network. In a distributed computing environment, program modules may be located in local and remote computer storage media, including storage devices.

[0117] Each embodiment in this specification is described in a progressive manner, and the same or similar parts between the embodiments can be referred to each other, and each embodiment focuses on the differences from other embodiments. In particular, for the system embodiment, since it is basically similar to the method embodiment, the description is relatively simple, and the relevant parts can be referred to the partial description of the method embodiment.

[0118] The above description is only an embodiment of the present specification and is not intended to limit the present specification. For those skilled in the art, the present specification may have various changes and variations. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present specification shall be included in the scope of the claims of the present specification.

Claims

1. A positioning method for an autonomous driving device, characterized in that: include: Determining current positioning data of the autonomous driving device and historical motion state data of the autonomous driving device; The driving process of the autonomous driving device is simulated by a preset simulation test system to simulate the positioning data corresponding to the current moment after the autonomous driving device drives according to the historical motion state data, as reference positioning data; Determining a positioning deviation between the current positioning data and the reference positioning data; According to the positioning deviation, the current positioning data is adjusted to obtain adjusted positioning data, so as to position the automatic driving device according to the adjusted positioning data; The simulation of the driving process of the automatic driving device by a preset simulation test system specifically includes: The current positioning data and the historical motion state data are input into the simulation test system, so that the simulation test system can simulate the driving trajectory formed by the automatic driving device after driving according to the historical motion state data through the predetermined power simulation model of the automatic driving device, and determine the reference positioning data from the driving trajectory; wherein, when the automatic driving device determines the power simulation model, in response to the automatic driving device being started, the model of the automatic driving device is obtained and transmitted to the simulation test system.

2. The method according to claim 1, characterized in that Determine the current positioning data of the autonomous driving device, including: Acquire initial positioning data measured by a positioning module provided on the autonomous driving device at a current moment; The current positioning data is determined according to the initial positioning data and the historical posture data of the automatic driving device.

3. The method according to claim 1, characterized in that The driving process of the autonomous driving device is simulated by a preset simulation test system to simulate the positioning data corresponding to the current moment after the autonomous driving device drives according to the historical motion state data as reference positioning data, specifically including: The current positioning data and the historical motion state data are input into the simulation test system, so that the simulation test system can retrieve a high-precision map that matches the geographical location corresponding to the current positioning data according to the current positioning data, and simulate the driving process of the autonomous driving device on the high-precision map according to the historical motion state data to obtain the reference positioning data.

4. The method according to claim 3, characterized in that Inputting the current positioning data and the historical motion state data into the simulation test system so that the simulation test system retrieves a high-precision map matching the geographical location corresponding to the current positioning data according to the current positioning data, and simulates the driving process of the autonomous driving device on the high-precision map according to the historical motion state data to obtain the reference positioning data, specifically including: The current positioning data and the historical motion state data are input into the simulation test system, so that the simulation test system can simulate, on the retrieved high-precision map, the driving trajectory formed by the automatic driving device after driving according to the historical motion state data, and determine the reference positioning data from the driving trajectory.

5. The method according to claim 1, characterized in that Determining a power simulation model of the autonomous driving device specifically includes: In response to the automatic driving device being started, the model of the automatic driving device is obtained and transmitted to the simulation test system, so that the simulation test system determines, based on the model, a power simulation model that matches the model from a pre-built power simulation model database as the power simulation model of the automatic driving device.

6. The method according to claim 1, characterized in that According to the positioning deviation, the current positioning data is adjusted to obtain adjusted positioning data, specifically including: Determine whether the positioning deviation is greater than a set deviation threshold; If yes, the reference positioning data is used as the adjusted positioning data.

7. The method according to claim 1, characterized in that Determine the current positioning data of the autonomous driving device and the historical motion status data of the autonomous driving device, monitor the arrival of a set measurement period, determine the current positioning data of the autonomous driving device and the historical motion status data of the autonomous driving device, and the measurement period includes 100 milliseconds.

8. A positioning device for an automatic driving device, characterized in that: include: A determination module, used to determine current positioning data of the autonomous driving device and historical motion state data of the autonomous driving device; A simulation module, used to simulate the driving process of the automatic driving device through a preset simulation test system, so as to simulate the positioning data corresponding to the current moment after the automatic driving device drives according to the historical motion state data, as reference positioning data; The simulation of the driving process of the automatic driving device by a preset simulation test system specifically includes: The current positioning data and the historical motion state data are input into the simulation test system, so that the simulation test system can simulate the driving trajectory formed by the automatic driving device after driving according to the historical motion state data through the power simulation model of the automatic driving device determined in advance, and determine the reference positioning data from the driving trajectory; wherein, when the automatic driving device determines the power simulation model, in response to the automatic driving device being started, the model of the automatic driving device is obtained and transmitted to the simulation test system; A deviation determination module, used to determine the positioning deviation between the current positioning data and the reference positioning data; An adjustment module is used to adjust the current positioning data according to the positioning deviation to obtain adjusted positioning data, so as to position the automatic driving device according to the adjusted positioning data.

9. A computer-readable storage medium, characterized in that: The storage medium stores a computer program, and when the computer program is executed by a processor, the method according to any one of claims 1 to 7 is implemented.

10. An automatic driving device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that: When the processor executes the program, the method according to any one of claims 1 to 7 is implemented.

Images