Automated Driving Control Method, Device and Scraper of Scraper

Abstract

The present invention discloses a control method, device and scraper for automatic driving of a scraper, belonging to the field of scraper control. The present invention obtains positioning information and environmental perception information through a preset sensing device, determines map behavior information and attitude positioning information in the positioning information; generates a planning navigation strategy in combination with the environmental perception information; uses the planning navigation strategy to predict route driving; obtains current driving scene information during driving; when the driving scene information meets the trigger conditions corresponding to the route driving strategy, generates corresponding control decisions according to the planning navigation strategy. The acquisition of the navigation planning strategy is realized by using the positioning information and environmental perception information obtained by the preset sensing device, and the corresponding control strategy is generated when the trigger conditions are met by predicting route driving, thereby realizing the improvement of the safety of the scraper during automatic driving and unmanned driving.

Description

Technical Field

[0001] The present invention relates to the field of scraper control, and particularly to an automatic driving control method, device and scraper for a scraper. Background Art

[0002] Trackless equipment in underground metal mines can be roughly divided into two categories according to working conditions. One category is equipment with the main working condition of driving function, such as scrapers and underground vehicles. The other category is equipment with the main working condition of interacting with unknown working objects at fixed positions, such as rock drilling jumbo, down-the-hole drill, tunneling jumbo, charging trolley, wet shotcreting trolley, bolting trolley, etc. Due to the differences in their working conditions and working objects, the directions of their intelligence are consistent, but there are also differences in research focuses.

[0003] At present, the unmanned driving technology is not mature, and effective control still cannot be carried out for emergencies and safety incidents.

[0004] Therefore, how to improve the safety of a scraper under unmanned driving during the mine production process has become an urgent technical problem to be solved. Summary of the Invention

[0005] The main purpose of the present invention is to solve the technical problem of how to improve the safety of a scraper under unmanned driving during the mine production process.

[0006] To achieve the above object, the present invention provides an automatic driving control method for a scraper, and the automatic driving control method for the scraper includes the following steps:

[0007] Obtain positioning information and environmental perception information through a preset sensing device, and determine map positioning information and attitude positioning information in the positioning information;

[0008] Generate a planning and navigation strategy according to the environmental perception information in combination with the map positioning information and the attitude positioning information;

[0009] Perform route driving prediction according to the planning and navigation strategy;

[0010] Drive according to the planning and navigation strategy and obtain current driving scene information;

[0011] When the current driving scene information meets the trigger condition corresponding to the route driving prediction, generate a corresponding control decision according to the planning and navigation strategy.

[0012] Optionally, the step of generating a planning and navigation strategy according to the environmental perception information in combination with the map positioning information and the attitude positioning information includes:

[0013] Determine obstacle information, driving road condition information and basic working condition information according to the environmental perception information;

[0014] Determine a strategy node based on the obstacle information, the driving road condition information, and the map positioning information;

[0015] Determine driving ability information based on the basic working condition information and the attitude positioning information;

[0016] Generate a planned navigation strategy based on the strategy node and the driving ability information.

[0017] Optionally, before the step of generating a planned navigation strategy based on the strategy node and the driving ability information, it further includes:

[0018] Determine the minimum driving ability condition based on the strategy node;

[0019] Judge whether the driving ability information meets the minimum ability condition;

[0020] If so, execute the step of generating a planned navigation strategy based on the strategy node and the driving ability information;

[0021] If not, generate a road condition exception report based on the strategy node and feedback the road condition exception report to a preset background port.

[0022] Optionally, the step of predicting route driving according to the planned navigation strategy includes:

[0023] Obtain driving route information in the navigation strategy;

[0024] Obtain all drivable route information in the driving route information;

[0025] Determine the driving weight information corresponding to each drivable route in all the drivable route information according to preset route weight conditions;

[0026] Perform route driving prediction according to the driving weight information.

[0027] Optionally, the step of determining the driving weight information corresponding to each drivable route in all the drivable route information according to preset route weight conditions includes:

[0028] Obtain the screening target rule in the preset route weight condition;

[0029] Determine the target object in each drivable route among all the drivable routes according to the screening target rule;

[0030] Determine the driving weight information corresponding to the target object according to the preset route weight condition.

[0031] Optionally, before the step of generating a corresponding control decision according to the planned navigation strategy when the current driving scenario information meets the trigger condition corresponding to the route driving prediction, the method further includes:

[0032] Obtaining the trigger condition corresponding to the route driving prediction;

[0033] Obtaining specific road condition trigger conditions and scenario trigger conditions from the trigger conditions;

[0034] Determining whether the current driving scenario information meets the road condition trigger condition and / or the scenario trigger condition.

[0035] Optionally, the step of generating a corresponding control decision according to the planned navigation strategy includes:

[0036] Determining the conflicting information in the current driving scenario information;

[0037] Matching a corresponding target strategy set in the navigation strategy according to the conflicting information;

[0038] Obtaining the current decision scenario and traversing the target strategy set according to the current decision scenario to generate a corresponding control decision.

[0039] In addition, to achieve the above object, the present invention further provides a control device for an automatic driving of a scraper, and the control device for an automatic driving of a scraper includes:

[0040] An information acquisition module, configured to acquire positioning information and environmental perception information through a preset sensing device, and determine map positioning information and attitude positioning information in the positioning information;

[0041] A navigation strategy generation module, configured to generate a planned navigation strategy according to the environmental perception information in combination with the map positioning information and the attitude positioning information;

[0042] A prediction module, configured to perform route driving prediction according to the planned navigation strategy;

[0043] A scenario acquisition module, configured to drive through the planned navigation strategy and acquire current driving scenario information;

[0044] A decision generation module, configured to generate a corresponding control decision according to the planned navigation strategy when the current driving scenario information meets the trigger condition corresponding to the route driving prediction.

[0045] In addition, to achieve the above object, the present invention further provides a scraper, and the scraper includes: a memory and a processor, and when the processor runs computer instructions stored in the memory, the method described above is executed.

[0046] In addition, to achieve the above object, the present invention further provides a medium including instructions that, when running on a scraper, cause the scraper to execute the method described above.

[0047] The present invention obtains positioning information and environmental perception information through a preset sensing device, determines map behavior information and attitude positioning information in the positioning information; generates a planning navigation strategy in combination with the environmental perception information; performs route driving prediction using the planning navigation strategy; obtains current driving scene information during driving; and generates a corresponding control decision according to the planning navigation strategy when the driving scene information meets the trigger condition corresponding to the route driving strategy. The acquisition of the navigation planning strategy is realized by using the positioning information and environmental perception information obtained by the preset sensing device, and the corresponding control strategy is generated when the trigger condition is met by performing route driving prediction, thereby improving the safety of the scraper during autonomous driving control and unmanned driving. BRIEF DESCRIPTION OF THE DRAWINGS

[0048] Figure 1 is a schematic structural diagram of a scraper in the hardware operating environment related to the embodiment of the present invention;

[0049] Figure 2 is a schematic flowchart of the first embodiment of the method for autonomous driving control of a scraper according to the present invention;

[0050] Figure 3 is a schematic overall architecture diagram of the first embodiment of the method for autonomous driving control of a scraper according to the present invention;

[0051] Figure 4 is a schematic overall network architecture diagram of the first embodiment of the method for autonomous driving control of a scraper according to the present invention;

[0052] Figure 5 is an architecture diagram of a UWB positioning system in the first embodiment of the method for autonomous driving control of a scraper according to the present invention;

[0053] Figure 6 is a schematic structural diagram of an intelligent driving control system in the first embodiment of the method for autonomous driving control of a scraper according to the present invention;

[0054] Figure 7 is a structural block diagram of the first embodiment of the device for autonomous driving control of a scraper according to the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0055] In order to make the objectives, technical solutions and advantages of the present application clearer, the present application will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present application and are not used to limit the present application.

[0056] Refer to Figure 1 , Figure 1Schematic diagram of the structure of a scraper for the hardware operating environment involved in the solution of the embodiment of the present invention.

[0057] As shown in Figure 1 , the scraper may include: a processor 1001, such as a central processing unit (CPU), a communication bus 1002, a user interface 1003, a network interface 1004, and a memory 1005. Among them, the communication bus 1002 is used to realize the connection and communication between these components. The user interface 1003 may include a display screen (Display) and an input unit such as a keyboard (Keyboard). Optionally, the user interface 1003 may further include a standard wired interface and a wireless interface. The network interface 1004 may optionally include a standard wired interface and a wireless interface (such as a wireless-fidelity (Wi-Fi) interface). The memory 1005 may be a high-speed random access memory (Random Access Memory, RAM), or a stable non-volatile memory (Non-Volatile Memory, NVM), such as a disk memory. Optionally, the memory 1005 may also be a storage device independent of the aforementioned processor 1001.

[0058] Those skilled in the art can understand that Figure 1 the structure shown in

[0059] does not constitute a limitation on the scraper, and may include more or fewer components than shown in the figure, or combine certain components, or have different component arrangements. Figure 1 As shown in

[0060] In Figure 1 the scraper shown, the network interface 1004 is mainly used for data communication with a network server; the user interface 1003 is mainly used for data interaction with a user; the processor 1001 and the memory 1005 in the scraper of the present invention may be provided in the scraper. The scraper calls the scraper automatic driving control program stored in the memory 1005 through the processor 1001 and executes the scraper automatic driving control method provided by the embodiment of the present invention.

[0061] The embodiment of the present invention provides a scraper automatic driving control method. Referring to Figure 2 , Figure 2 is a schematic flowchart of the first embodiment of the scraper automatic driving control method of the present invention.

[0062] In this embodiment, the scraper automatic driving control method includes the following steps:

[0063] Step S10: Obtain positioning information and environmental perception information through a preset sensing device, and determine map positioning information and attitude positioning information from the positioning information.

[0064] It should be noted that when retrofitting the scraper loader, it does not affect the driver's manual driving. The vehicle is retrofitted for wire control to meet the requirements of vehicle mechanical operation and command control. On the basis of the completion of the wire control retrofit, lidar, cameras, millimeter-wave radars, and various vehicle condition sensors are added to the scraper loader. The vehicle status data is collected in real time by deploying a signal acquisition system (vehicle networking system or others) at the vehicle end, and is linked with the remote cockpit of the operation center through the dedicated fiber optic network of the system. The cockpit is equipped with a professional remote driving system, which can monitor the vehicle transportation process, operation process, and surrounding environment information in real time. The vehicle condition sensors and the remote driving assistance subsystem ensure the safe operation of the vehicle under remote control. Thus, it is realized that by deploying a remote driving system in the dispatching center, the driving right of the vehicle can be taken over from a long distance to control the vehicle to drive. On the basis of completing the remote control driving, an intelligent control computer and environmental perception sensors are installed on the scraper loader, and software such as operation task scheduling is deployed on the server, and finally the underground scraper loader driverless system is completed. The overall architecture of the system is as Figure 3 shown.

[0065] In specific implementation, the system network architecture diagram is as Figure 4 shown.

[0066] To implement a safe and stable driverless scraper loader system, the communication network needs to meet the requirements: wired bandwidth rate ≥ 1Gbps, wireless bandwidth rate ≥ 500Mbps; delay ≤ 30ms; roaming time ≤ 50ms. Based on the research results of the remote control ore drawing technology of the scraper loader using the drift mining method in the early stage, the WIFI wireless communication scheme is adopted in this embodiment for the following reasons:

[0067] (1) Mature technology. After decades of development, the transmission bandwidth of the most common WiFi access points currently can meet the requirement of wireless bandwidth rate ≥ 500Mbps. The data delay in the local area network is much lower than 30ms. The pre-verified access technology is adopted in the WiFI-AP terminal devices planned to be invested in the project, so that the roaming time fully meets the performance requirements of the driverless wireless network.

[0068] (2)Economical and applicable. On the premise of meeting the requirements of unmanned driving wireless transmission performance, it is necessary to ensure "no duplication and no omission" to ensure the stable operation of the system. Different system configuration plans are formulated according to the on-site situation to reduce the system cost, improve the system function, and make the system have a high performance-price ratio. Considering the characteristics of many winding curves and connecting roads, the wireless access points to be deployed are relatively dense. Among the currently mature wireless communication technologies, WiFi wireless communication is the best choice. In addition, WiFi has irreplaceable advantages in later use and maintenance compared with other wireless communication technologies.

[0069] ① Technical advantages. Based on the communication platform architecture technology that combines a dedicated optical fiber backbone network with WiFi wireless access point coverage, its maturity is currently leading among other wireless communication technologies, and it can easily achieve bandwidth acceleration and functional expansion.

[0070] ② Operational advantages. After decades of development, WiFi technology has a wide range of applications and a low technical threshold for maintenance. General faults that occur during the system operation can be solved by itself without the need for professional communication technicians to provide on-site support. Using the WiFi wireless communication system as the communication system can better ensure the continuous and effective operation of unmanned driving, and will ultimately play a huge commercial value.

[0071] ③ System maintenance advantages. The mine information department has the ability to troubleshoot and maintain the communication platform of industrial Ethernet technology. The network construction topology of Ethernet is the clearest and most understandable network topology among current mainstream technologies, and the industrial chain is the most complete. The equipment of each manufacturer complies with a unified standard protocol, making the network communication platform built by the project have incomparable advantages in terms of emergency, temporary equipment replacement maintenance, and expansion.

[0072] ④ At present, WIFI coverage has been basically achieved in each panel of the second mining area. How to tap the potential of the existing WIFI system and quickly achieve the goal of "changing provincial roads to national roads and national roads to highways" for the underground network access by increasing the base station laying density and upgrading the base station level. The goal of reducing the cost of wireless communication construction and quickly meeting the conditions of unmanned driving communication technology can be achieved.

[0073] ⑤ Project requirements. Since the bandwidth of a single video and control data of the load-haul-dump vehicle for unmanned driving exceeds 15M at most, and there is a possibility that two pieces of equipment operate remotely at the same time. At the same time, reserve capacity for later system expansion and multi-system access to ensure that the communication load rate is in an excellent state. This implementation plan conducts WIFI 6 communication tests, which fully meet the communication requirements of this implementation plan.

[0074] (3)Selection of positioning technology

[0075] Compared with the outdoor open environment, the underground environment is relatively enclosed and complex. Since GPS technology cannot work in the underground environment. Therefore, there are various solutions for mine wireless positioning needs, and the main technical means adopted include the following:

[0076] ① WiFi technology. In WiFi positioning applications, wireless base stations are installed in the area. According to the signal characteristics of the WiFi device to be located and combined with the topological structure of the wireless base stations, the coordinates of the WiFi device to be located are comprehensively determined. WiFi positioning technology is convenient for using existing wireless devices to achieve the positioning function. However, WiFi positioning has natural defects such as poor security, high power consumption, low positioning accuracy, and the spectrum resources are approaching saturation.

[0077] ② Radio Frequency Identification (RFID) technology. It uses the principle of electromagnetic induction to wirelessly activate short-range wireless tags and realize the technology of information reading. The RFID distance ranges from a few centimeters to more than a dozen meters. The typical application of RFID for personnel positioning comes from the expansion of the personnel attendance system, mainly for identifying whether a person exists in a certain area, and it cannot achieve real-time tracking and precise positioning. Moreover, there is no standard network system for positioning applications. Therefore, if the continuous positioning requirements in the mine are to be met, the network establishment cost and difficulty are relatively large.

[0078] ③ UWB long-distance positioning technology. UWB (Ultra Wideband) is a carrierless communication technology that uses non-sinusoidal narrow pulses in the nanosecond to microsecond level to transmit data. UWB modulation uses fast-rising and falling pulses with a pulse width in the ns level. The spectrum covered by the pulses ranges from DC to GHz, and it does not require the RF frequency conversion required by conventional narrowband modulation. After pulse shaping, it can be directly sent to the antenna for transmission. The spectrum shape can be adjusted through the very narrow continuous single pulse shape and the antenna load characteristics. The radiation of UWB signals is very low, usually only one-thousandth of the radiation of mobile phones. Therefore, when applied in industry, there is no interference problem with other instruments and meters.

[0079] In specific implementation, for the increasingly tense frequency resources in the roadway, a new time-domain radio resource is studied and used to achieve the continuous positioning and wireless signal transmission of trackless equipment, ensuring that it can work simultaneously with other wireless communication devices without mutual interference. Traditional RFID and WiFi technologies can locate the target within a certain range, but they cannot obtain the precise position of the target in real time, and the construction volume is large and the debugging is complex, which cannot meet the actual needs of equipment positioning in the mine. UWB positioning technology is recognized as the positioning technology with the highest positioning accuracy in the limited space positioning industry, and it has characteristics such as high bandwidth, high-frequency carrierless, and strong anti-interference performance, which fully meet the requirements of loaders in the mine. The specific UWB positioning system architecture diagram is as Figure 5 shown.

[0080] It should be noted that for the intelligent driving control diagram of this embodiment, as Figure 6 shown, the main instruction compatibility work to be processed during the automatic operation of the underground loader is the compatibility of multiple priority control instructions. Under various operating conditions, different control instructions such as manual intervention, manual operation, line-of-sight remote control operation, remote control, autonomous operation, fault instructions, obstacle avoidance signals, etc., are related to the normal operation of the equipment and the safety and reliability of the equipment operation. Therefore, after different control signals reach the vehicle-mounted control terminal, it is first necessary to screen the control signals, distinguish the signal priorities, and execute them in order and with emphasis to avoid instruction conflicts, misoperations, and various dangerous situations.

[0081] Step S20: Generate a planning and navigation strategy based on the environmental perception information in combination with the map positioning information and the attitude positioning information.

[0082] Furthermore, in order to realize the generation of the planning and navigation strategy, the step of generating a planning and navigation strategy based on the environmental perception information in combination with the map positioning information and the attitude positioning information includes: determining obstacle information, driving road condition information, and basic working condition information according to the environmental perception information; determining strategy nodes according to the obstacle information and the driving road condition information in combination with the map positioning information; determining driving ability information according to the basic working condition information in combination with the attitude positioning information; and generating a planning and navigation strategy according to the strategy nodes and the driving ability information.

[0083] It should be noted that the environmental influence factors in the driving environment can be obtained according to the environmental perception information, such as air humidity or air temperature; the driving road condition information represents the road condition information, obstacle information, and relevant information of the driving surface during the driving process; the basic working condition information represents the basic situation of the loader itself, including the operating state of the equipment or the driving consumption situation.

[0084] It should be noted that before the step of generating a planning and navigation strategy according to the strategy nodes and the driving ability information, it also includes: determining the minimum driving ability condition according to the strategy nodes; judging whether the driving ability information meets the minimum ability condition; if so, executing the step of generating a planning and navigation strategy according to the strategy nodes and the driving ability information; if not, generating a road condition abnormality report according to the strategy nodes and feeding back the road condition abnormality report to the preset background port.

[0085] It should be noted that the minimum driving ability condition is the situation restricted by the driving ability of the loader itself. If the loader that does not meet the minimum driving ability condition will not be able to achieve passage. The condition that cannot meet the minimum driving ability is related to the basic attributes of the loader itself, such as the power torque information of the engine, the size information of the loader, or the vehicle weight information of the loader, etc.

[0086] It can be understood that the preset background port refers to the background administrator port set in advance. After the exception report is sent to the preset background port, it can be displayed on the corresponding display device or the mobile device of the background administrator.

[0087] Step S30: Perform route driving prediction according to the planned navigation strategy.

[0088] Furthermore, in order to achieve route driving prediction, the step of performing route driving prediction according to the planned navigation strategy includes: obtaining driving route information in the navigation strategy; obtaining all drivable route information in the driving route information; determining the driving weight information corresponding to each drivable route in all drivable route information according to the preset route weight conditions; and performing route driving prediction according to the driving weight information.

[0089] It should be noted that the step of determining the driving weight information corresponding to each drivable route in all drivable route information according to the preset route weight conditions includes: obtaining the screening target rules in the preset route weight conditions; determining the target object in each drivable route among all drivable routes according to the screening target rules; and determining the driving weight information corresponding to the target object in combination with the preset route weight conditions.

[0090] In specific implementation, the preset route weight conditions will consider the safety score of the route, the route path score, and the route recommendation score.

[0091] It should be noted that the determination of the route safety score is determined according to the safety score of the relevant environmental information during the route process, and the environmental information is obtained through a preset sensing device.

[0092] It can be understood that after obtaining the weight information of each drivable route, the route with the highest weight information will be used as the optimal route.

[0093] Step S40: Drive through the planned navigation strategy and obtain the current driving scene information.

[0094] It should be noted that the current driving scene information is the driving environment information during the driving process. By obtaining the current driving scene information, it can be judged whether decision generation is required.

[0095] Step S50: When the current driving scene information meets the trigger conditions corresponding to the route driving prediction, generate the corresponding control decision according to the planned navigation strategy.

[0096] Further, in order to reasonably generate control decisions, before the step of generating corresponding control decisions according to the planned navigation strategy when the current driving scenario information meets the trigger conditions corresponding to the route driving prediction, it further includes: obtaining the trigger conditions corresponding to the route driving prediction; obtaining specific road condition trigger conditions and scenario trigger conditions from the trigger conditions; and determining whether the current driving scenario information meets the road condition trigger conditions and / or the scenario trigger conditions.

[0097] In specific implementation, the situation where the road condition is triggered is that the route encounters a fork situation and there can be multiple choices; the scenario trigger condition refers to a situation where it is necessary to change the power or driving direction during driving, for example: a sudden obstacle blocking.

[0098] It should be noted that the step of generating corresponding control decisions according to the planned navigation strategy includes:

[0099] Determining the conflicting information in the current driving scenario information; matching the corresponding target strategy set in the navigation strategy according to the conflicting information; obtaining the current decision-making scenario and traversing the target strategy set according to the current decision-making scenario to generate the corresponding control decision.

[0100] Obtaining positioning information and environmental perception information through a preset sensing device, determining map behavior information and attitude positioning information in the positioning information; generating a planned navigation strategy by combining the environmental perception information; performing route driving prediction with the planned navigation strategy; obtaining the current driving scenario information during driving; and generating corresponding control decisions according to the planned navigation strategy when the driving scenario information meets the trigger conditions corresponding to the route driving strategy. The acquisition of the navigation planning strategy is realized by using the positioning information and environmental perception information obtained by the preset sensing device, and the corresponding control strategy is generated when the trigger conditions are met by performing route driving prediction, thereby realizing the improvement of the safety of the scraper during unmanned driving.

[0101] In addition, an embodiment of the present invention also proposes a medium, on which a program for scraper automatic driving control is stored, and when the program for scraper automatic driving control is executed by a processor, the steps of the method for scraper automatic driving control as described above are realized.

[0102] Refer to Figure 7 , Figure 7 which is the structural block diagram of the first embodiment of the scraper automatic driving control device of the present invention.

[0103] As Figure 7 shown, the scraper automatic driving control device proposed by the embodiment of the present invention includes:

[0104] An information acquisition module 10 is configured to acquire positioning information and environmental perception information through a preset sensing device, and determine map positioning information and attitude positioning information from the positioning information;

[0105] A navigation strategy generation module 20 is configured to generate a planned navigation strategy according to the environmental perception information in combination with the map positioning information and the attitude positioning information;

[0106] A prediction module 30 is configured to perform route driving prediction according to the planned navigation strategy;

[0107] A scene acquisition module 40 is configured to drive according to the planned navigation strategy and acquire current driving scene information;

[0108] A decision generation module 50 is configured to generate a corresponding control decision according to the planned navigation strategy when the current driving scene information meets the trigger condition corresponding to the route driving prediction.

[0109] In this embodiment, positioning information and environmental perception information are acquired through a preset sensing device, map behavior information and attitude positioning information are determined from the positioning information; a planned navigation strategy is generated in combination with the environmental perception information; route driving prediction is performed using the planned navigation strategy; current driving scene information is acquired during driving; when the driving scene information meets the trigger condition corresponding to the route driving strategy, a corresponding control decision is generated according to the planned navigation strategy. The acquisition of the navigation planning strategy is realized by using the positioning information and environmental perception information acquired by the preset sensing device, and a corresponding control strategy is generated when the trigger condition is met by performing route driving prediction, thereby improving the safety of the motor grader during unmanned driving.

[0110] In one embodiment, the information acquisition module is further configured to determine obstacle information, driving road condition information, and basic working condition information according to the environmental perception information; determine strategy nodes according to the obstacle information and driving road condition information in combination with the map positioning information; determine driving ability information according to the basic working condition information in combination with the attitude positioning information; and generate a planned navigation strategy according to the strategy nodes and the driving ability information.

[0111] In one embodiment, the information acquisition module is further configured to determine the minimum driving ability condition according to the strategy nodes; determine whether the driving ability information meets the minimum ability condition; if so, execute the step of generating a planned navigation strategy according to the strategy nodes and the driving ability information; if not, generate a road condition anomaly report according to the strategy nodes and feedback the road condition anomaly report to a preset background port.

[0112] In one embodiment, the prediction module is further configured to obtain driving route information from the navigation strategy; obtain all drivable route information from the driving route information; determine driving weight information corresponding to each drivable route from all the drivable route information according to a preset route weight condition; and perform route driving prediction according to the driving weight information.

[0113] In one embodiment, the prediction module is further configured to obtain a screening target rule from the preset route weight condition; determine a target object from each drivable route in all the drivable routes according to the screening target rule; and determine the driving weight information corresponding to the target object in combination with the preset route weight condition.

[0114] In one embodiment, the decision generation module is further configured to obtain a trigger condition corresponding to the route driving prediction; obtain specific road condition trigger conditions and scenario trigger conditions from the trigger condition; and determine whether the current driving scenario information meets the road condition trigger conditions and / or the scenario trigger conditions.

[0115] In one embodiment, the decision generation module is further configured to determine conflicting information in the current driving scenario information; match a corresponding target policy set in the navigation strategy according to the conflicting information; obtain the current decision scenario and traverse the target policy set according to the current decision scenario to generate a corresponding control decision.

[0116] It should be understood that the above is only an example for illustration and does not impose any limitation on the technical solution of the present invention. In specific applications, those skilled in the art can set according to needs, and the present invention does not limit this.

[0117] It should be noted that the above-described work process is only illustrative and does not limit the protection scope of the present invention. In practical applications, those skilled in the art can select some or all of them according to actual needs to achieve the purpose of the solution of this embodiment, and there is no limitation here.

[0118] In addition, for technical details not described in detail in this embodiment, reference can be made to the scraper automatic driving control method provided in any embodiment of the present invention, which will not be elaborated here.

[0119] In addition, it should be noted that in this text, the terms "include", "comprise" or any other variants thereof are intended to cover non-exclusive inclusion, such that a process, method, article or system comprising a series of elements not only includes those elements but also other elements not expressly listed, or elements inherent to such process, method, article or system. Without further limitation, an element defined by the statement "comprising an..." does not exclude the presence of additional identical elements in the process, method, article or system comprising that element.

[0120] The serial numbers of the above embodiments of the present invention are only for description and do not represent the superiority or inferiority of the embodiments.

[0121] Through the description of the above embodiments, those skilled in the art can clearly understand that the above embodiment methods can be implemented by means of software plus a necessary general hardware platform. Of course, they can also be implemented by hardware, but in many cases the former is a better implementation. Based on such an understanding, the technical solution of the present invention, in essence, or the part that contributes to the prior art can be embodied in the form of a software product. This computer software product is stored in a storage medium (such as a read-only memory (ROM) / RAM, magnetic disk, optical disk), and includes several instructions for causing a terminal device (which can be a mobile phone, computer, server, or network device, etc.) to execute the methods described in the various embodiments of the present invention.

[0122] The above are only the preferred embodiments of the present invention, and do not limit the patent scope of the present invention accordingly. Any equivalent structure or equivalent process transformation made by using the specification and drawings of the present invention, or directly or indirectly applied in other related technical fields, shall be equally included in the patent protection scope of the present invention.

Claims

1. An automatic driving control method for a scraper, characterized in that The self-driving control method of the scraper includes: obtaining positioning information and environmental perception information through a preset sensing device, and determining map positioning information and attitude positioning information in the positioning information; Generating a planning and navigation strategy according to the environmental perception information in combination with the map positioning information and the attitude positioning information; Performing route driving prediction according to the planning and navigation strategy; driving according to the planning and navigation strategy and obtaining current driving scene information; When the current driving scene information meets the trigger condition corresponding to the route driving prediction, generating a corresponding control decision according to the planning and navigation strategy; Among them, the step of generating a planning and navigation strategy according to the environmental perception information in combination with the map positioning information and the attitude positioning information includes: Determining obstacle information, driving road condition information, and basic working condition information according to the environmental perception information; Determining strategy nodes according to the obstacle information and driving road condition information in combination with the map positioning information; Determining driving ability information according to the basic working condition information in combination with the attitude positioning information; generating a planning and navigation strategy according to the strategy nodes and the driving ability information; Among them, before the step of generating a planning and navigation strategy according to the strategy nodes and the driving ability information, it further includes: Determining the minimum driving ability condition according to the strategy nodes; Judging whether the driving ability information meets the minimum ability condition; If so, execute the step of generating a planning and navigation strategy according to the strategy nodes and the driving ability information; If not, generating a road condition anomaly report according to the strategy nodes and feeding back the road condition anomaly report to a preset background port.

2. The control method for the automatic driving of a scraper according to claim 1, characterized in that, The step of performing route driving prediction according to the planning and navigation strategy includes: Obtaining driving route information in the navigation strategy; Obtaining all drivable route information in the driving route information; Determining driving weight information corresponding to each drivable route in the all drivable route information according to a preset route weight condition; Performing route driving prediction according to the driving weight information.

3. The control method for the automated driving of a scraper according to claim 2, wherein, The step of determining driving weight information corresponding to each drivable route in the all drivable route information according to a preset route weight condition includes: Obtaining a screening target rule in the preset route weight condition; Determining a target object in each drivable route in the all drivable routes according to the screening target rule; Determining the driving weight information corresponding to the target object according to the preset route weight condition in combination.

4. The control method for the automatic driving of a scraper according to claim 1, characterized in that Before the step of generating a corresponding control decision according to the planning and navigation strategy when the current driving scene information meets the trigger condition corresponding to the route driving prediction, it further includes: Obtaining the trigger condition corresponding to the route driving prediction; Obtaining specific road condition trigger conditions and scene trigger conditions in the trigger condition; Judging whether the current driving scene information meets the road condition trigger condition and / or the scene trigger condition.

5. The method for automatically driving and controlling a scraper according to claim 1, wherein The step of generating a corresponding control decision according to the planning and navigation strategy includes: Determining conflict information in the current driving scene information; Match the corresponding set of target strategies in the navigation strategy according to the contradictory information; Obtain the current decision-making scenario and traverse the set of target strategies according to the current decision-making scenario to generate a corresponding control decision.

6. An automatic driving control device for a scraper, characterized in that, Implement the method according to claim 1, the self-driving control device for a scraper includes: An information acquisition module, configured to acquire positioning information and environmental perception information through a preset sensing device, and determine map positioning information and attitude positioning information in the positioning information; A navigation strategy generation module, configured to generate a planned navigation strategy according to the environmental perception information in combination with the map positioning information and the attitude positioning information; A prediction module, configured to perform route driving prediction according to the planned navigation strategy; A scenario acquisition module, configured to drive through the planned navigation strategy and acquire current driving scenario information; A decision generation module, configured to generate a corresponding control decision according to the planned navigation strategy when the current driving scenario information meets the triggering condition corresponding to the route driving prediction.

7. A scraper, characterized in that, The scraper includes: a memory, a processor, and when the processor runs computer instructions stored in the memory, it executes the method according to any one of claims 1 to 5.

8. A medium, characterized in that, Includes instructions that, when running on a scraper, cause the scraper to execute the method according to any one of claims 1 to 5.

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