Automatic driving control method and device and computer equipment

Abstract

The invention relates to an automatic driving control method and device and computer equipment. Obtaining a target dust concentration grade corresponding to the current dust concentration in the target vehicle driving environment; determining a target driving control strategy matched with the target dust concentration grade according to a preset corresponding relation; the preset corresponding relationship comprises at least one group of corresponding relationship between the dust concentration grade and the driving control strategy; the target vehicle is controlled to execute the target action corresponding to the target driving control strategy, it is ensured that the target vehicle can effectively meet the driving requirements in different dust concentration environments, and potential risks are avoided; by introducing a dust concentration sensing and response mechanism, the operation capability and safety of the unmanned vehicle in a complex environment are effectively improved.

Description

Technical Field

[0001] The present application relates to the field of autonomous driving technology, and particularly to an autonomous driving control method, apparatus, and computer device. Background Art

[0002] With the continuous deepening of the application of driverless technology in mining areas, unmanned mining trucks have achieved remarkable results in improving production efficiency and operation safety. Existing unmanned mining trucks are equipped with advanced perception systems, including various sensors such as millimeter-wave radars, lidar, and high-definition cameras, which can acquire and process environmental information in real time, thereby optimizing driving parameters and ensuring efficient and stable operation. Summary of the Invention

[0003] Although existing driverless solutions perform well in conventional environments, they still have limitations when dealing with extreme environmental conditions. Especially in environments with high dust concentrations, such as mines, construction sites, or areas prone to sandstorms, the high-density particulate matter in the air poses a severe challenge to the vehicle's perception system; these particulate matters will reduce the effective detection distance and accuracy of the sensors, seriously affecting the safety and reliability of the vehicle. However, the existing technologies have not fully considered these complex environmental factors, resulting in potential safety hazards and reduced production capacity in actual applications. Based on this, it is necessary to provide an autonomous driving control method, apparatus, computer device, and readable storage medium for the above technical problems.

[0004] In a first aspect, the present application provides an autonomous driving control method, and the method includes:

[0005] Obtain a target dust concentration level corresponding to the current dust concentration in the driving environment of the target vehicle;

[0006] Determine a target driving control strategy that matches the target dust concentration level according to a preset corresponding relationship; the preset corresponding relationship includes at least one set of corresponding relationships between dust concentration levels and driving control strategies;

[0007] Control the target vehicle to execute a target action corresponding to the target driving control strategy.

[0008] In one embodiment, the obtaining of the target dust concentration level corresponding to the current dust concentration in the driving environment of the target vehicle includes:

[0009] Obtain a first concentration threshold and a second concentration threshold; wherein, the second concentration threshold is less than the first concentration threshold; the first concentration threshold refers to the lowest dust concentration value at which the target vehicle cannot drive safely due to excessive dust concentration; the second concentration threshold refers to the highest dust concentration value at which the target vehicle can maintain normal driving;

[0010] Determine the target dust concentration level corresponding to the current dust concentration in the driving environment of the target vehicle according to the current dust concentration, the first concentration threshold, and the second concentration threshold.

[0011] In one embodiment, the determining the target dust concentration level corresponding to the current dust concentration in the driving environment of the target vehicle according to the current dust concentration, the first concentration threshold, and the second concentration threshold includes:

[0012] If the current dust concentration is less than or equal to the second concentration threshold, determine that the target dust concentration level is the first level;

[0013] If the current dust concentration is greater than the second concentration threshold and less than the first concentration threshold, determine that the target dust concentration level is the second level;

[0014] If the current dust concentration is greater than or equal to the first concentration threshold, determine that the target dust concentration level is the third level.

[0015] In one embodiment, the controlling the target vehicle to execute the target action corresponding to the target driving control strategy includes:

[0016] When the target dust concentration level is the first level, control the target vehicle to travel at a first driving speed;

[0017] When the target dust concentration level is the second level, control the target vehicle to travel at a second driving speed; the second driving speed is less than the first driving speed;

[0018] When the target dust concentration level is the third level, control the target vehicle to stop driving.

[0019] In one embodiment, the controlling the target vehicle to travel at the second driving speed includes:

[0020] Obtain the current driving speed of the target vehicle;

[0021] Determine a second driving speed calculation formula according to a second preset proportional parameter, the current driving speed, the first concentration threshold, and the current dust concentration;

[0022] Adjust the current driving speed to the second driving speed according to the second driving speed calculation formula;

[0023] Control the target vehicle to travel at the second driving speed;

[0024] Wherein, the second driving speed calculation formula is:

[0025]

[0026] v t is the second driving speed, k1 is the second preset proportional parameter, t v is the first concentration threshold, t is the current dust concentration, and v is the current driving speed.

[0027] In one embodiment, the obtaining of the first concentration threshold and the second concentration threshold includes:

[0028] Determine the first detection area detected by the lidar along a preset direction in the first environment; the dust concentration in the first environment is lower than the preset threshold; the first detection area refers to the conical area detected by the lidar along the preset direction in the first environment; the preset threshold refers to the preset initial dust concentration value;

[0029] Gradually adjust the dust concentration in the first environment to obtain a plurality of second environments with adjusted dust concentrations; the dust concentration in the second environment is higher than that in the first environment;

[0030] For the plurality of second environments with adjusted dust concentrations, determine the second detection area detected by the lidar along the preset direction in the second environment; the second detection area refers to the conical area detected by the lidar along the preset direction in the second environment;

[0031] If the spatial proportional relationship between the second detection area and the first detection area satisfies the preset proportional relationship, then determine the dust concentration of the current second environment as the first concentration threshold; the preset proportional relationship refers to the standard proportional value;

[0032] Determine the second concentration threshold according to the first preset proportional parameter and the first concentration threshold; the first preset proportional parameter is used to adjust the first concentration threshold to obtain the second concentration threshold.

[0033] In one embodiment, the controlling the target vehicle to execute the target action corresponding to the target driving control strategy further includes:

[0034] When the target dust concentration level is the second level, obtain the first radar data through the lidar provided on the target vehicle;

[0035] Obtain the second radar data through the millimeter-wave radar provided on the target vehicle;

[0036] Perform filtering processing on the first radar data to obtain the third radar data;

[0037] Perform data fusion processing on the second radar data and the third radar data to obtain target radar data;

[0038] Control the target vehicle to make corresponding decisions according to the target radar data.

[0039] In one embodiment, the performing data fusion processing on the second radar data and the third radar data to obtain target radar data includes:

[0040] Determine a first weight coefficient corresponding to the second radar data and a second weight coefficient corresponding to the third radar data according to a third preset proportional parameter, the current dust concentration, and the first concentration threshold;

[0041] Based on the first weight coefficient and the second weight coefficient, perform data fusion processing on the second radar data and the third radar data to obtain target radar data; wherein, the calculation formula for the first weight coefficient is:

[0042]

[0043] The calculation formula for the second weight coefficient is:

[0044] W l = 1 - W m ;

[0045] The calculation formula for the target radar data is:

[0046] D = W m × d m + W l × d l ;

[0047] wherein, W m is the first weight coefficient, k2 is the third preset proportional parameter, t v is the first concentration threshold, t is the current dust concentration, W l is the second weight coefficient, D is the position information in the target radar data, d m is the position information in the second radar data, d l is the position information in the third radar data.

[0048] In one embodiment, the controlling the target vehicle to make corresponding decisions according to the target radar data includes:

[0049] Determine the position information of the target obstacle according to the target radar data;

[0050] If the target obstacle is outside the driving route of the target vehicle, control the target vehicle to drive along the driving route;

[0051] If the target obstacle is located in the driving route of the target vehicle, determine the height of the target obstacle;

[0052] If the height of the target obstacle is higher than the chassis height corresponding to the target vehicle, control the target vehicle to bypass;

[0053] If the height of the target obstacle is lower than the chassis height corresponding to the target vehicle, control the target vehicle to travel along the driving route.

[0054] In a second aspect, the present application further provides an automatic driving control device, and the device includes:

[0055] An acquisition module, configured to acquire a target dust concentration level corresponding to the current dust concentration in the driving environment of the target vehicle;

[0056] A policy matching module, configured to determine a target driving control policy matching the target dust concentration level according to a preset corresponding relationship; the preset corresponding relationship includes at least one set of corresponding relationships between dust concentration levels and driving control policies;

[0057] A control module, configured to control the target vehicle to execute a target action corresponding to the target driving control policy.

[0058] In a third aspect, the present application further provides a computer device, including a memory and a processor, where the memory stores a computer program, and when the processor executes the computer program, the steps of the method described in any one of the embodiments of the first aspect above are implemented.

[0059] In a fourth aspect, the present application further provides a computer-readable storage medium, on which a computer program is stored, and when the computer program is executed by a processor, the steps of the method described in any one of the embodiments of the first aspect above are implemented.

[0060] In a fifth aspect, the present application further provides a computer program product, including a computer program, and when the computer program is executed by a processor, the steps of the method described in any one of the embodiments of the first aspect above are implemented.

[0061] The above-mentioned automatic driving control method, device, and computer equipment first obtain the target dust concentration level corresponding to the current dust concentration in the driving environment of the target vehicle; subsequently, according to the preset corresponding relationship, the target driving control strategy matching the target dust concentration level can be quickly and accurately determined; among them, the preset corresponding relationship includes at least one set of corresponding relationships between the dust concentration level and the driving control strategy; further, by controlling the target vehicle to execute the target actions corresponding to the target driving control strategy, it is ensured that the target vehicle can effectively meet the driving requirements in different dust concentration environments and avoid potential risks; based on this, by introducing the dust concentration perception and response mechanism, the operation ability and safety of the driverless vehicle in complex environments are effectively improved. BRIEF DESCRIPTION OF THE DRAWINGS

[0062] Figure 1 It is an application environment diagram of the automatic driving control method in an embodiment;

[0063] Figure 2 It is a schematic flowchart of the automatic driving control method in an embodiment;

[0064] Figure 3 It is a schematic flowchart of the step of obtaining the first concentration threshold and the second concentration threshold in an embodiment;

[0065] Figure 4a It is a side view of the first detection area in an embodiment;

[0066] Figure 4b It is a side view of the second detection area in an embodiment;

[0067] Figure 5 It is a schematic flowchart of the step of controlling the target vehicle to execute the target actions corresponding to the target driving control strategy in an embodiment;

[0068] Figure 6 It is a schematic flowchart of the step of controlling the target vehicle to execute the target actions corresponding to the target driving control strategy in another embodiment;

[0069] Figure 7 It is a schematic flowchart of the step of controlling the target vehicle to make corresponding decisions according to the target radar data in an embodiment;

[0070] Figure 8 It is a schematic flowchart of the automatic driving control method in a specific embodiment;

[0071] Figure 9 It is a structural block diagram of the automatic driving control device in an embodiment;

[0072] Figure 10 It is an internal structure diagram of the computer equipment in an embodiment. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0073] In order to make the objectives, technical solutions, and advantages of the present application clearer and more understandable, 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.

[0074] The automatic driving control method provided by the embodiment of the present application can be applied to an application environment as Figure 1 shown. Among them, the terminal 102 communicates with the server 104 through a network. The data storage system can store the data that the server 104 needs to process. The data storage system can be integrated on the server 104, or can be placed in the cloud or on other network servers. Among them, the terminal 102 can be, but is not limited to, an Internet of Things device, and the Internet of Things device can be an intelligent vehicle-mounted device, etc. The server 104 can be an independent physical server, or a server cluster or distributed system composed of multiple physical servers, or a cloud server providing cloud computing services.

[0075] In one embodiment, as Figure 2 shown, Figure 2 is a schematic flowchart of the automatic driving control method in one embodiment; in this embodiment, the method is exemplified by being applied to the terminal. It can be understood that the method can also be applied to the server, and can also be applied to a system including the terminal and the server, and is implemented through the interaction between the terminal and the server. In this embodiment, the method includes the following steps:

[0076] Step S201, obtain the target dust concentration level corresponding to the current dust concentration in the driving environment of the target vehicle.

[0077] Among them, the target vehicle can be, but is not limited to, an unmanned vehicle, such as an unmanned mining truck; it can be understood that the target vehicle is equipped with a variety of vehicle-mounted sensors; among them, the vehicle-mounted sensors can be, but are not limited to, dust sensors, lidar, millimeter-wave radar, cameras, etc.

[0078] Among them, the driving environment of the target vehicle refers to the road and the surrounding environment where the target vehicle is located. The current dust concentration refers to the real-time concentration of dust in the air in the driving environment of the target vehicle; the current dust concentration can be measured and determined by a dust sensor; in an exemplary embodiment, the dust sensor can be, but is not limited to, installed at the front of the target vehicle.

[0079] Among them, the target dust concentration level is used to quantify the high or low level of the current dust concentration in the driving environment of the target vehicle. In an exemplary embodiment, the target dust concentration level depends on the current dust concentration; the target dust concentration level can be any one of multiple dust concentration levels; among them, the dust concentration levels are successively divided into a first level, a second level, and a third level according to the dust concentration from low to high; that is, the dust concentration corresponding to the first level is lower than the dust concentration corresponding to the second level, and the dust concentration corresponding to the second level is lower than the dust concentration corresponding to the third level.

[0080] Step S202: Determine a target driving control strategy that matches the target dust concentration level according to a preset correspondence.

[0081] Among them, the preset correspondence includes at least one set of correspondences between dust concentration levels and driving control strategies.

[0082] In an exemplary embodiment, the dust concentration levels include a first level, a second level, and a third level, and the driving control strategies include a normal driving mode, a speed-limited driving mode, and a safety driving mode; the preset correspondence includes: the correspondence between the first level and the normal driving mode, the correspondence between the second level and the speed-limited driving mode, and the correspondence between the third level and the safety driving mode.

[0083] It should be noted that in the normal driving mode, the target vehicle travels at a normal driving speed and there is no need to turn on the high beam and fog lights; in the speed-limited driving mode, the target vehicle reduces its speed and needs to turn on the high beam and fog lights; in the safety driving mode, the target vehicle stops and needs to turn on the hazard lights and taillights. It can be understood that this embodiment is only taken as one example. In actual applications, the preset correspondence needs to be adaptively set according to the performance of the target vehicle and the actual driving environment, and no specific limitation is made here.

[0084] Step S203: Control the target vehicle to execute the target actions corresponding to the target driving control strategy.

[0085] Among them, the target actions refer to the specific execution actions in the target driving control strategy, such as adjusting the vehicle speed, turning on / off the lights, etc.

[0086] Exemplarily, taking the target vehicle as an autonomous mining truck as an example for illustration, the autonomous mining truck is equipped with a variety of on-vehicle sensors (such as dust sensors, lidar, millimeter-wave radars, etc.) and an on-vehicle control terminal; the on-vehicle control terminal is used to implement the autonomous driving control method; specifically, when the autonomous mining truck is driving and operating in an open-pit mine, the dust sensor at the front of the vehicle head detects the current dust concentration in the driving environment in real time, and transmits the current dust concentration to the on-vehicle control terminal. The on-vehicle control terminal determines the target dust concentration level corresponding to the current dust concentration, and according to the preset corresponding relationship, determines the target driving control strategy matching the target dust concentration level. Further, it controls the target vehicle to execute the target action corresponding to the target driving control strategy to ensure the safety of the autonomous mining truck in the mining area environment and avoid potential risks caused by low visibility.

[0087] In this embodiment, the target dust concentration level corresponding to the current dust concentration in the driving environment of the target vehicle is obtained; subsequently, according to the preset corresponding relationship, the target driving control strategy matching the target dust concentration level can be quickly and accurately determined; further, by controlling the target vehicle to execute the target action corresponding to the target driving control strategy, it is ensured that the target vehicle can effectively meet the driving requirements in different dust concentration environments and avoid potential risks; based on this, by introducing the dust concentration perception and response mechanism, the operation ability and safety of the autonomous vehicle in a complex environment are effectively improved.

[0088] In one embodiment, obtaining the target dust concentration level corresponding to the current dust concentration in the driving environment of the target vehicle includes the following steps:

[0089] Step 1, obtain the first concentration threshold and the second concentration threshold.

[0090] Among them, the second concentration threshold is less than the first concentration threshold; the first concentration threshold refers to the lowest dust concentration value at which the target vehicle cannot drive safely due to too high dust concentration; the second concentration threshold refers to the highest dust concentration value at which the target vehicle maintains normal driving.

[0091] It can be understood that the target vehicle cannot drive safely means that the visibility of the driving environment is not sufficient to support the target vehicle to continue to maintain the driving state; the target vehicle driving normally means that the visibility of the driving environment can support the target vehicle to drive at a normal speed; among them, the normal speed corresponding to the target vehicle is related to factors such as the type of the target vehicle and the type of the driving environment corresponding to the target vehicle, and no specific limitation is made here.

[0092] In an exemplary embodiment, taking an autonomous mining truck as the target vehicle as an example, considering the scenarios of stone mines and coal mines, as well as various road sections such as straight roads, curves, uphill and downhill, the normal speed range of the autonomous mining truck is 15 km / h to 40 km / h; if the current dust concentration in the driving environment of the autonomous mining truck reaches 80 mg / m 3 when it is not sufficient to support the autonomous mining truck to continue maintaining the driving state, then 80 mg / m 3 is set as the first concentration threshold; if the current dust concentration in the driving environment of the autonomous mining truck is at 30 mg / m 3 when it can support the target vehicle to drive at the normal speed (15 km / h to 40 km / h), then 30 mg / m 3 is set as the second concentration threshold.

[0093] Step 2, according to the current dust concentration, the first concentration threshold and the second concentration threshold, determine the target dust concentration level corresponding to the current dust concentration in the driving environment of the target vehicle.

[0094] Exemplarily, compare the current dust concentration with the first concentration threshold and the second concentration threshold to determine the magnitude relationship between the current dust concentration and the first concentration threshold and the second concentration threshold, and then according to the magnitude relationship between the current dust concentration and the first concentration threshold and the second concentration threshold, determine the target dust concentration level corresponding to the current dust concentration in the driving environment of the target vehicle.

[0095] In this embodiment, by obtaining the first concentration threshold and the second concentration threshold, and determining the target dust concentration level according to the comparison of the current dust concentration with these two thresholds, it is possible to achieve precise driving control of the autonomous mining truck in different dust concentration environments.

[0096] In one embodiment, as Figure 3 shown, Figure 3 is a schematic flow chart of the steps for obtaining the first concentration threshold and the second concentration threshold in an embodiment; obtaining the first concentration threshold and the second concentration threshold includes the following steps:

[0097] Step S301, determine the first detection area detected by the lidar along a preset direction in the first environment.

[0098] Among them, the dust concentration in the first environment is lower than the preset threshold; the preset threshold refers to the initially set dust concentration value; it should be noted that the setting of the preset threshold needs to ensure that the detection performance of the lidar is optimal in the first environment. For example, the best detection performance can be that the lidar can detect the farthest distance it can support, usually 100 m - 200 m; in an exemplary embodiment, the preset threshold can be 10 mg / m 3, indicating that in the first environment, i.e., when the dust concentration is lower than 10 mg / m 3 , it can ensure that the detection distance of the lidar is 100 m - 200 m. The maximum distance is related to the performance of the lidar and is not specifically limited herein.

[0099] Among them, the first detection area refers to the conical area detected by the lidar along a preset direction in the first environment; among them, the preset direction can be but is not limited to the vehicle driving direction, a preset reference direction, etc., and is not specifically limited herein.

[0100] In an exemplary embodiment, the method for determining the first detection area may be: determining the farthest straight-line distance that the lidar can detect along the preset direction in the first environment, as well as the horizontal angle range and vertical angle range that can be covered along the preset direction; determining the first detection area according to the farthest straight-line distance, the horizontal angle range, and the vertical angle range. The side view of the first detection area is as Figure 4a shown.

[0101] Step S302, gradually adjust the dust concentration of the first environment to obtain multiple second environments with adjusted dust concentrations.

[0102] Among them, the dust concentration of the second environment is higher than that of the first environment.

[0103] It should be noted that the dust concentration of the first environment can be gradually adjusted according to a preset dust concentration step size to obtain corresponding multiple second environments; among them, the preset dust concentration step size needs to be set according to actual adjustment requirements and is not specifically limited herein.

[0104] Step S303, for multiple second environments with adjusted dust concentrations, determine the second detection area detected by the lidar along the preset direction in the second environment.

[0105] Among them, the second detection area refers to the conical area detected by the lidar along the preset direction in the second environment. It can be understood that the method for determining the second detection area is the same as the principle of the method for determining the above-mentioned first detection area and will not be elaborated herein. The side view of the second detection area is as Figure 4b shown.

[0106] Step S304, if the spatial proportional relationship between the second detection area and the first detection area satisfies a preset proportional relationship, then determine the dust concentration of the current second environment as the first concentration threshold.

[0107] It should be noted that since both the first detection area and the second detection area are conical areas, the spatial proportional relationship between the second detection area and the first detection area refers to the volume ratio of the second detection area to the first detection area; through the spatial proportional relationship between the second detection area and the first detection area, the influence of dust concentration on the detection ability of lidar can be accurately evaluated.

[0108] Among them, the preset proportional relationship refers to the standard proportional value; the preset proportional relationship needs to be set according to the performance of the lidar, the driving requirements of the target vehicle, etc., and will not be specifically limited here. For example, the value range of the preset proportional relationship can be 85% - 95%, and the preset proportional relationship can be 90%.

[0109] In an exemplary embodiment, taking the preset proportional relationship as 90% as an example, for each second detection area, calculate the spatial proportional relationship between the second detection area and the first detection area; determine whether the spatial proportional relationship between the second detection area and the first detection area meets the preset proportional relationship, that is, 90%. If the spatial proportional relationship between the second detection area and the first detection area meets the preset proportional relationship of 90%, then determine the dust concentration of the current second environment as the first concentration threshold.

[0110] Step S305, determine the second concentration threshold according to the first preset proportional parameter and the first concentration threshold.

[0111] Among them, the first preset proportional parameter is used to adjust the first concentration threshold to obtain the second concentration threshold; the first preset proportional parameter needs to be set according to the environmental type where the target vehicle is located, and will not be specifically limited here; exemplarily, the value range of the first preset proportional parameter can be 20% - 50%, for example, the first preset proportional parameter can be 30%, and the first preset proportional parameter can also be 40%.

[0112] Exemplarily, taking the target vehicle as an unmanned mining truck, the preset proportional relationship as 90%, and the first preset proportional parameter as 30% as an example for illustration, when the unmanned mining truck is in the initial state, determine the first detection area detected by the lidar in the first environment along the preset direction; among them, the initial state refers to the state where the unmanned mining truck works normally after all equipment is installed; the conditions of the initial state can include, but are not limited to: high visibility (that is, the visual visibility range of people is 20km - 30km, and the maximum detection distance range of the lidar is 100m - 200m), the road in front of the unmanned mining truck is flat, spacious and unobstructed, the power supply is started normally, all hardware components of the unmanned mining truck can communicate and work normally, the sensor calibration is correct, the communication link is smooth, and the safety system works normally, etc.

[0113] Further, gradually adjust the dust concentration in the first environment according to a preset dust concentration step size to obtain the second environment after each dust concentration adjustment; for the second environment after each dust concentration adjustment, determine the second detection area detected by the lidar in the second environment along a preset direction, and calculate the spatial ratio relationship, i.e., the volume ratio, between the second detection area and the first detection area; determine whether the spatial ratio relationship between the second detection area and the first detection area meets a preset ratio relationship, i.e., 90%. If the spatial ratio relationship between the second detection area and the first detection area meets 90%, then determine the dust concentration of the current second environment as the first concentration threshold; further, adjust the first concentration threshold according to a first preset ratio parameter, i.e., 30%, to determine the second concentration threshold.

[0114] In this embodiment, by determining the detection areas of the lidar in the first environment and the second environment, and based on the spatial ratio relationship between the second detection area and the first detection area, the preset ratio relationship, and the first preset ratio parameter, the first concentration threshold and the second concentration threshold are determined, ensuring the scientificity and reliability of the threshold setting, and providing a reliable basis for the driving control of the target vehicle in different dust concentration environments.

[0115] In one embodiment, according to the current dust concentration, the first concentration threshold, and the second concentration threshold, determining the target dust concentration level corresponding to the current dust concentration in the driving environment of the target vehicle includes the following steps:

[0116] Step 1, if the current dust concentration is less than or equal to the second concentration threshold, then determine that the target dust concentration level is the first level.

[0117] Step 2, if the current dust concentration is greater than the second concentration threshold and the current dust concentration is less than the first concentration threshold, then determine that the target dust concentration level is the second level.

[0118] Step 3, if the current dust concentration is greater than or equal to the first concentration threshold, then determine that the target dust concentration level is the third level.

[0119] Exemplarily, taking the first concentration threshold as 80mg / m 3 and the second concentration threshold as 30mg / m 3 as an example for illustration, according to the current dust concentration level, determine whether the current dust concentration level is less than or equal to the second concentration threshold of 30mg / m 3 ; if the current dust concentration is less than or equal to 30mg / m 3 , then determine that the target dust concentration level is the first level; if the current dust concentration is greater than 30mg / m 3 , then determine whether the current dust concentration is less than the first concentration threshold of 80mg / m 3 ; if the current dust concentration is greater than 30mg / m 3and the current dust concentration is less than 80 mg / m 3 , then determine that the target dust concentration level is the second level; if the current dust concentration is greater than or equal to 80 mg / m 3 , then determine that the target dust concentration level is the third level.

[0120] In this embodiment, by comparing the current dust concentration with the first concentration threshold and the second concentration threshold, the target dust concentration level is divided into the first level, the second level, and the third level, laying a foundation for realizing the accurate assessment of the driving environment of the target vehicle.

[0121] In one embodiment, as Figure 5 shown, Figure 5 is a schematic flowchart of the target action steps for controlling the target vehicle to execute the target driving control strategy in one embodiment; controlling the target vehicle to execute the target action corresponding to the target driving control strategy includes the following steps:

[0122] Step S501, when the target dust concentration level is the first level, control the target vehicle to travel at the first driving speed.

[0123] Among them, the first driving speed refers to the normal driving speed corresponding to the target vehicle; it should be noted that the first driving speed is related to the type of the target vehicle, the type of the driving environment, etc., and needs to be set according to the actual driving requirements, and no specific limitation is made here. For example, the range of the first driving speed corresponding to the driverless mining truck in the mining area environment is 15 km / h to 40 km / h.

[0124] Step S502, when the target dust concentration level is the second level, control the target vehicle to travel at the second driving speed.

[0125] Among them, the second driving speed is less than the first driving speed. It can be understood that the dust concentration corresponding to the second level is higher than the dust concentration corresponding to the first level. Therefore, it is necessary to control the target vehicle to reduce the speed to reduce the possibility of accidents.

[0126] Step S503, when the target dust concentration level is the third level, control the target vehicle to stop.

[0127] It can be understood that the dust concentration corresponding to the third level is higher than the dust concentration corresponding to the second level. There are relatively high potential safety hazards for the target vehicle to continue to maintain the driving state. Therefore, it is necessary to control the target vehicle to stop to further avoid potential risks.

[0128] Optionally, after the target dust concentration level reaches the third level and the target vehicle is controlled to stop, a request can be sent to the background to dispatch a sprinkler truck to the location of the target vehicle for dust suppression work. At the same time, since dust will adhere to the lidar, it is likely to affect the normal operation and judgment of the lidar, and relevant personnel need to be informed to promptly remove the pollutants attached to the surface of the lidar.

[0129] It should be noted that in this embodiment, the dust concentration levels include the first level, the second level, and the third level, and the driving control strategies include the normal driving mode, the speed-limited driving mode, and the safe driving mode; there is a corresponding relationship between the first level and the normal driving mode, between the second level and the speed-limited driving mode, and between the third level and the safe driving mode. Among them, in the normal driving mode, the target vehicle travels at the normal driving speed (i.e., the first driving speed) and does not need to turn on the high beam and fog lights; in the speed-limited driving mode, the target vehicle reduces its speed and travels at the second driving speed, and needs to turn on the high beam and fog lights; in the safe driving mode, the target vehicle stops and needs to turn on the hazard lights and taillights.

[0130] In this embodiment, in an environment with a dust concentration level of the first level, the vehicle maintains the normal driving speed to maximize the transportation efficiency while avoiding unnecessary speed reduction or stopping; in an environment with a dust concentration level of the second level, the vehicle reduces its speed to reduce the possibility of accidents; in an environment with a dust concentration level of the third level, the vehicle is controlled to stop to effectively avoid potential risks caused by low visibility; based on this, by dynamically adjusting the driving speed or stopping of the target vehicle according to the target dust concentration level, precise control of the vehicle in a complex dust environment is achieved, effectively improving the stability and safety of the vehicle in different environments.

[0131] In one embodiment, controlling the target vehicle to travel at the second driving speed includes the following steps:

[0132] Step 1, obtain the current driving speed of the target vehicle.

[0133] Among them, the current driving speed refers to the actual driving speed of the target vehicle at the current moment.

[0134] Step 2, determine the second driving speed calculation formula according to the second preset proportional parameter, the current driving speed, the first concentration threshold, and the current dust concentration.

[0135] Among them, the second driving speed calculation formula is:

[0136]

[0137] v t is the second driving speed, k1 is the second preset proportional parameter, tv where \(t_1\) is the first concentration threshold, \(t\) is the current dust concentration, and \(v\) is the current driving speed.

[0138] Among them, the second preset proportional parameter is used to ensure the rationality of the driving speed of the target vehicle in the second-level dust concentration environment.

[0139] Exemplarily, taking an unmanned mining truck as an example, it is necessary to determine the second driving speed calculation formula based on the second preset proportional parameter, the current driving speed, the first concentration threshold, and the current dust concentration, so as to ensure that the range of the second driving speed is between 10 km / h and 15 km / h, avoiding potential risks.

[0140] It can be understood that the second preset proportional parameter needs to be set according to the actual driving speed adjustment requirements, which are not specifically limited here. For example, the value range of the second preset proportional parameter can be 0.3 - 0.5, or it can also be 0.3 - 0.4.

[0141] Step 3: Adjust the current driving speed to the second driving speed according to the second driving speed calculation formula.

[0142] Step 4: Control the target vehicle to drive at the second driving speed.

[0143] It can be understood that when the current dust concentration is between the second concentration threshold and the first concentration threshold, according to formula (1), the ratio of the current dust concentration \(t\) to the first concentration threshold \(t_1\) is less than 1, and then multiplied by the second preset proportional parameter \(k_1\) less than 1, making the whole between 0 and 1, which can ensure that the second driving speed \(v_2\) v is less than the current driving speed \(v\); at the same time, according to formula (1), it can be seen that there is an inverse proportional relationship between the second driving speed \(v_2\) and the current dust concentration \(t\), that is, the second driving speed \(v_2\) t will decrease as the current dust concentration \(t\) increases. Based on this, it can effectively avoid potential safety hazards caused by high-speed driving in an environment with too high dust concentration. t t t will decrease as the current dust concentration \(t\) increases. Based on this, it can effectively avoid potential safety hazards caused by high-speed driving in an environment with too high dust concentration.

[0144] In this embodiment, by obtaining the current driving speed of the target vehicle and dynamically adjusting the second driving speed according to the relationship between the second preset proportional parameter, the current dust concentration and the first concentration threshold, the precise speed control of the vehicle in the second-level dust concentration environment is realized, laying a foundation for improving the safety and adaptability of the target vehicle in the complex dust environment.

[0145] In one embodiment, as Figure 6 shown, Figure 6It is a schematic flowchart for controlling a target vehicle to execute target action steps corresponding to a target driving control strategy; controlling the target vehicle to execute the target action corresponding to the target driving control strategy further includes the following steps:

[0146] Step S601, when the target dust concentration level is the second level, obtain first radar data through a lidar installed on the target vehicle.

[0147] Among them, the first radar data may, but is not limited to, include information such as the position, speed, and direction of objects around the vehicle. It can be understood that the lidar determines the corresponding position, speed, direction, etc. of objects by emitting laser beams and analyzing the returned signals.

[0148] Step S602, obtain second radar data through a millimeter-wave radar installed on the target vehicle.

[0149] Among them, the second radar data may, but is not limited to, include information such as the position, speed, and direction of objects around the vehicle. It can be understood that the millimeter-wave radar measures distance, speed, direction, etc. by emitting electromagnetic waves in the millimeter-wave frequency band and receiving the signals reflected from the target obstacles.

[0150] Step S603, perform filtering processing on the first radar data to obtain third radar data.

[0151] It should be noted that since the lidar is vulnerable to extreme weather and harsh environments, when the target dust concentration level is the second level, it is necessary to perform filtering processing on the first radar data to ensure the accuracy and reliability of the data.

[0152] In an exemplary embodiment, dust can be regarded as noise in the environment, which will affect the recognition of obstacles in the environment. In order to effectively remove this noise of dust, a bilateral filtering method is used to perform filtering processing on the first radar data; among them, bilateral filtering is a non-linear filtering method, which is essentially based on Gaussian filtering. The purpose is to solve the edge blurring caused by Gaussian filtering. It is a compromise processing that combines the spatial proximity and pixel value similarity of the image, taking into account both spatial domain information and gray-scale similarity to achieve the purpose of edge-preserving denoising, and has the characteristics of simplicity, non-iterative, and local. The formula and principle of the bilateral filtering method are shown in formula (2):

[0153]

[0154] Among them, the output pixel of the bilateral filtering depends on the neighborhood of the currently convolved pixel. i and j are the coordinate points of the currently convolved pixel, k and l are the coordinate points of the neighborhood pixels, g(i,j) is the output point, f(k,l) is the input point, and the weighting coefficient w is determined by the domain kernel and the range kernel.

[0155] Among them, the domain kernel d(i, j, k, l) is a Gaussian kernel, and σ d is the smoothing parameter. The domain kernel d(i, j, k, l) is shown in Formula (3):

[0156]

[0157] The range kernel r(i, j, k, l) is used to "infer" whether it is an edge point, and σ r is the smoothing parameter. The range kernel r(i, j, k, l) is shown in Formula (4):

[0158]

[0159] It should be noted that the size of the range kernel depends on the difference between the gray value of the pixel to be convolved and the gray values of the neighboring pixels. When there is a large gray change at the edge, a smaller weight value will be generated, and a larger weight value will be generated in the area similar to the gray value of the pixel to be convolved.

[0160] The weighting coefficient w is equal to the product of the domain kernel d(i, j, k, l) and the range kernel r(i, j, k, l), as shown in Formula (5):

[0161]

[0162] Step S604: Perform data fusion processing on the second radar data and the third radar data to obtain the target radar data.

[0163] It should be noted that lidar has accurate distance perception and is suitable for distance measurement. With the development of multi-line radar, its resolution is relatively high, but its volume and installation position are relatively limited, and it is greatly affected by extreme weather and harsh environments; millimeter-wave radar has accurate speed and distance perception and is suitable for moving target detection. It is not easily affected by harsh weather (such as rain, snow, fog, dust, etc.), but its resolution is relatively low and its perception ability for stationary objects is weak. Therefore, combining lidar and millimeter-wave radar can ensure that the obtained data is more accurate and is beneficial to improving the positioning accuracy of driverless vehicles.

[0164] Although filtering the first radar data through the bilateral filtering method can improve and enhance the perception ability of lidar in a second-level dust environment, there will inevitably be errors and the perception effect is limited. Therefore, increase the weight of the millimeter-wave radar that is not easily affected by dust during data fusion, that is, increase its credibility, and at the same time reduce the credibility of lidar, so as to combine the advantages of multiple sensors and achieve the optimal effect of multi-sensor fusion.

[0165] Step S605: Control the target vehicle to make corresponding decisions according to the target radar data.

[0166] It should be noted that the target radar data combines the advantages of lidar and millimeter-wave radar, providing more comprehensive and accurate environmental perception information. Based on the target radar data, the target vehicle can be accurately controlled to make corresponding decisions.

[0167] Among them, the decision can, but is not limited to, include path planning and behavior decision-making, and specific limitations are not made here; for example, path planning can, but is not limited to, include determining the best path to bypass an obstacle when an obstacle is detected ahead; behavior decision-making can, but is not limited to, include acceleration, deceleration, steering, etc.

[0168] In this embodiment, by filtering the lidar data, noise can be effectively removed and the data can be smoothed, improving the quality and reliability of the data; by combining the high resolution of lidar and the reliability of millimeter-wave radar, through data fusion technology, a more comprehensive and accurate target radar data set can be generated, thus significantly improving the positioning accuracy and environmental perception ability of the driverless vehicle, ensuring safe driving in various complex environments and adverse weather conditions.

[0169] In one embodiment, data fusion processing is performed on the second radar data and the third radar data to obtain the target radar data, including the following steps:

[0170] Step 1, determine the first weight coefficient corresponding to the second radar data and the second weight coefficient corresponding to the third radar data according to the third preset ratio parameter, the current dust concentration, and the first concentration threshold.

[0171] Among them, the third preset ratio parameter needs to be set according to the actual data fusion requirements, and specific limitations are not made here. For example, the value range of the third preset ratio parameter can be 0.5 to 0.8, and the value range of the third preset ratio parameter can also be 0.6 to 0.7.

[0172] Among them, the calculation formula for the first weight coefficient is:

[0173]

[0174] According to formula (6), it can be seen that the first weight coefficient W m is positively correlated with the current dust concentration t, that is, as the current dust concentration t increases, the first weight coefficient of the second radar data increases.

[0175] Among them, the calculation formula for the second weight coefficient is:

[0176] W l = 1 - W m (7)

[0177] Among them, W m is the first weight coefficient, k2 is the third preset ratio parameter, tv is the first concentration threshold, t is the current dust concentration, and W l is the second weight coefficient.

[0178] It should be noted that the first weight coefficient corresponding to the second radar data and the second weight coefficient corresponding to the third radar data can be adaptively adjusted according to the current dust concentration. It can be understood that in extreme weather or harsh environments, millimeter-wave radars perform more stably, while lidars may be interfered. By adaptively adjusting the first weight coefficient and the second weight coefficient, it can be ensured that the system can still provide reliable perception data in such situations.

[0179] Step 2: Based on the first weight coefficient and the second weight coefficient, perform data fusion processing on the second radar data and the third radar data to obtain target radar data.

[0180] Among them, the calculation formula for the target radar data is:

[0181] D = W m ×d m +W l ×d l (8)

[0182] Among them, D is the position information in the target radar data, d m is the position information in the second radar data, and d l is the position information in the third radar data. It can be understood that the position information includes distance information, and the distance information is used to determine the distance between the obstacle and the target vehicle.

[0183] In this embodiment, the first weight coefficient and the second weight coefficient are dynamically adjusted according to the current dust concentration to generate more accurate target radar data, effectively improving the safety and reliability of the driverless vehicle.

[0184] In one embodiment, as Figure 7 shown, Figure 7 is a schematic flowchart of the steps for controlling the target vehicle to make corresponding decisions according to the target radar data in one embodiment; controlling the target vehicle to make corresponding decisions according to the target radar data includes the following steps:

[0185] Step S701: According to the target radar data, determine the position information of the target obstacle.

[0186] Among them, the position information of the target obstacle includes coordinate information.

[0187] It should be noted that the target radar data is the comprehensive environmental perception information obtained by fusing millimeter-wave radar data and lidar data; the target radar data can, but is not limited to, include information such as the position, direction, and speed of objects around the vehicle. Therefore, by analyzing the target radar data, the target obstacle can be accurately identified. Further, based on the target radar data and using geometric calculation methods, the position information of the target obstacle in a two-dimensional or three-dimensional coordinate system can be accurately determined.

[0188] Step S702, determine whether the target obstacle is outside the driving route of the target vehicle.

[0189] If so, execute step S703; if not, execute step S704.

[0190] In an exemplary embodiment, the method for determining whether the target obstacle is outside the driving route of the target vehicle can be: determine the driving route corresponding to the target vehicle according to the vehicle width of the target vehicle, the target path, and the preset swing distance; determine whether the target obstacle is outside the driving route of the target vehicle according to the position information corresponding to the target obstacle; wherein, the target path refers to the path determined according to the actual driving requirements; the preset swing distance refers to the deviation range of the swing during the driving of the target vehicle; it should be noted that the preset swing distance needs to be set according to the performance of the target vehicle and is not specifically limited here. For example, the preset swing distance can be 0.5m.

[0191] Step S703, if the target obstacle is outside the driving route of the target vehicle, control the target vehicle to drive along the driving route.

[0192] Exemplarily, if the target obstacle is outside the driving route of the target vehicle, it indicates that the target obstacle can be ignored. At this time, control the target vehicle to continue driving along the driving route.

[0193] Step S704, if the target obstacle is in the driving route of the target vehicle, determine the height of the target obstacle.

[0194] It can be understood that based on the target radar data, the size information of the target obstacle can be accurately determined, where the size information includes the height, width, and length of the target obstacle.

[0195] Step S705, determine whether the height of the target obstacle is higher than the chassis height corresponding to the target vehicle.

[0196] If so, execute step S706; if not, execute step S707.

[0197] Step S706, if the height of the target obstacle is higher than the chassis height corresponding to the target vehicle, control the target vehicle to detour.

[0198] Exemplarily, if the target obstacle is in the driving route of the target vehicle and the height of the target obstacle is higher than the chassis height corresponding to the target vehicle, a path planning algorithm is used to re-plan a safe route around the target obstacle to control the target vehicle to bypass and avoid potential risks.

[0199] Step S707, if the height of the target obstacle is lower than the chassis height corresponding to the target vehicle, control the target vehicle to drive along the driving route.

[0200] In this embodiment, the driving decision can be dynamically adjusted according to the target radar data to ensure that the vehicle can drive safely and efficiently in various complex environments, further improving the safety of the driverless vehicle.

[0201] In a specific embodiment, the first-level dust concentration environment corresponds to the normal driving mode, the second-level dust concentration environment corresponds to the speed-limited driving mode, and the third-level dust concentration environment corresponds to the safe driving mode; wherein, in the normal driving mode, the target vehicle drives at a normal driving speed, does not need to turn on the high beam and fog lights, the braking distance is controlled within the range of 15m - 20m, and the vehicle distance is controlled within the range of 20m - 40m; in the speed-limited driving mode, the target vehicle drives at a reduced speed, needs to turn on the high beam and fog lights, the braking distance is controlled within the range of 22.5m - 30m, and the vehicle distance is controlled within the range of 30m - 60m; in the safe driving mode, the target vehicle stops driving and needs to turn on the hazard lights and tail lights.

[0202] In a specific embodiment, refer to Figure 8 , taking the target vehicle as an autonomous mining truck as an example for illustration, the autonomous driving control method includes the following steps:

[0203] Step S801, obtain the current dust concentration in the driving environment of the target vehicle.

[0204] Step S802, obtain the first concentration threshold and the second concentration threshold.

[0205] Wherein, the second concentration threshold is less than the first concentration threshold. Optionally, the first concentration threshold is 80mg / m 3 and the second concentration threshold is 30mg / m 3 .

[0206] Step S803, determine the target dust concentration level corresponding to the current dust concentration in the driving environment of the target vehicle according to the current dust concentration, the first concentration threshold, and the second concentration threshold.

[0207] Step S804, when the target dust concentration level is the first level, control the target vehicle to drive at the first driving speed.

[0208] Step S805, when the target dust concentration level is the second level, control the target vehicle to travel at the second driving speed, and obtain the first radar data through the lidar installed on the target vehicle; obtain the second radar data through the millimeter-wave radar installed on the target vehicle; perform filtering processing on the first radar data to obtain the third radar data; perform data fusion processing on the second radar data and the third radar data to obtain the target radar data; control the target vehicle to make corresponding decisions according to the target radar data.

[0209] Among them, the second driving speed is less than the first driving speed.

[0210] Step S806, when the target dust concentration level is the third level, control the target vehicle to stop driving, and send a request to the background to notify the sprinkler to go to the location where the target vehicle is to perform dust removal work.

[0211] The above-mentioned autonomous driving control method effectively improves the operation ability and safety of driverless vehicles in complex environments by introducing a dust concentration perception and response mechanism; in an environment with a dust concentration level of the first level, the vehicle maintains a normal driving speed to maximize the transportation efficiency while avoiding unnecessary speed reduction or parking; in an environment with a dust concentration level of the second level, by reducing the vehicle speed, the possibility of accidents is reduced. At the same time, by combining the data of the lidar and the millimeter-wave radar, more comprehensive and accurate environmental information can be provided, effectively making up for the deficiencies of a single sensor and improving the accuracy of obstacle detection and path planning; in an environment with a dust concentration level of the third level, by controlling the vehicle to stop driving, potential risks caused by low visibility are effectively avoided; based on this, by dynamically adjusting the driving speed or stopping the target vehicle according to the target dust concentration level, precise control of the vehicle in a complex dust environment is achieved, effectively improving the stability and safety of the vehicle in different environments.

[0212] It should be understood that although the steps in the flowcharts involved in the above-mentioned embodiments are shown in sequence according to the arrows, these steps are not necessarily executed in the order indicated by the arrows. Unless there is a clear indication in this article, the execution of these steps has no strict order restriction, and these steps can be executed in other orders. Moreover, at least a part of the steps in the flowcharts involved in the above-mentioned embodiments may include multiple steps or multiple stages. These steps or stages are not necessarily executed at the same time, but can be executed at different times. The execution order of these steps or stages is not necessarily sequential, but can be executed alternately or in turn with at least a part of other steps or steps or stages in other steps.

[0213] Based on the same inventive concept, an embodiment of the present application further provides an automatic driving control device for implementing the above-mentioned automatic driving control method. The solution provided by this device to solve the problem is similar to the solution described in the above method. Therefore, the specific limitations in one or more embodiments of the automatic driving control device provided below can refer to the limitations on the automatic driving control method in the above text, and will not be elaborated here.

[0214] In an exemplary embodiment, as Figure 9 shown, an automatic driving control device is provided, including: an acquisition module 901, a policy matching module 902, and a control module 903, where:

[0215] The acquisition module 901 is configured to acquire a target dust concentration level corresponding to the current dust concentration in the driving environment of the target vehicle;

[0216] The policy matching module 902 is configured to determine a target driving control policy matching the target dust concentration level according to a preset corresponding relationship; the preset corresponding relationship includes at least one set of corresponding relationships between dust concentration levels and driving control policies;

[0217] The control module 903 is configured to control the target vehicle to execute a target action corresponding to the target driving control policy.

[0218] The above automatic driving control device acquires a target dust concentration level corresponding to the current dust concentration in the driving environment of the target vehicle; subsequently, according to the preset corresponding relationship, it can quickly and accurately determine a target driving control policy matching the target dust concentration level; where the preset corresponding relationship includes at least one set of corresponding relationships between dust concentration levels and driving control policies; further, by controlling the target vehicle to execute a target action corresponding to the target driving control policy, it ensures that the target vehicle can effectively meet the driving requirements in different dust concentration environments and avoid potential risks; based on this, by introducing a dust concentration perception and response mechanism, the operation ability and safety of the driverless vehicle in a complex environment are effectively improved.

[0219] In one embodiment, the acquisition module 901 is further configured to

[0220] acquire a first concentration threshold and a second concentration threshold; where the second concentration threshold is less than the first concentration threshold; the first concentration threshold refers to the lowest dust concentration value at which the target vehicle cannot drive safely due to excessive dust concentration; the second concentration threshold refers to the highest dust concentration value at which the target vehicle can maintain normal driving;

[0221] Determine the target dust concentration level corresponding to the current dust concentration in the driving environment of the target vehicle according to the current dust concentration, the first concentration threshold, and the second concentration threshold.

[0222] In one embodiment, the obtaining module 901 is further configured to

[0223] If the current dust concentration is less than or equal to the second concentration threshold, determine that the target dust concentration level is the first level;

[0224] If the current dust concentration is greater than the second concentration threshold and less than the first concentration threshold, determine that the target dust concentration level is the second level;

[0225] If the current dust concentration is greater than or equal to the first concentration threshold, determine that the target dust concentration level is the third level.

[0226] In one embodiment, the control module 903 is further configured to

[0227] When the target dust concentration level is the first level, control the target vehicle to travel at the first traveling speed;

[0228] When the target dust concentration level is the second level, control the target vehicle to travel at the second traveling speed; the second traveling speed is less than the first traveling speed;

[0229] When the target dust concentration level is the third level, control the target vehicle to stop traveling.

[0230] In one embodiment, the control module 903 is further configured to

[0231] Obtain the current traveling speed of the target vehicle;

[0232] Determine the second traveling speed calculation formula according to the second preset ratio parameter, the current traveling speed, the first concentration threshold, and the current dust concentration;

[0233] Adjust the current traveling speed to the second traveling speed according to the second traveling speed calculation formula;

[0234] Control the target vehicle to travel at the second traveling speed;

[0235] Wherein, the second traveling speed calculation formula is:

[0236]

[0237] v t is the second traveling speed, k1 is the second preset ratio parameter, t v is the first concentration threshold, t is the current dust concentration, and v is the current traveling speed.

[0238] In one embodiment, the obtaining module 901 is further configured to

[0239] Determine the first detection area detected by the lidar in the first environment along a preset direction; the dust concentration in the first environment is lower than a preset threshold; the first detection area refers to the conical area detected by the lidar along the preset direction in the first environment; the preset threshold refers to the preset initial dust concentration value;

[0240] Gradually adjust the dust concentration in the first environment to obtain multiple second environments with adjusted dust concentrations; the dust concentration in the second environment is higher than that in the first environment;

[0241] For multiple second environments with adjusted dust concentrations, determine the second detection area detected by the lidar in the second environment along the preset direction; the second detection area refers to the conical area detected by the lidar along the preset direction in the second environment;

[0242] If the spatial proportional relationship between the second detection area and the first detection area satisfies a preset proportional relationship, then determine the dust concentration of the current second environment as the first concentration threshold; the preset proportional relationship refers to the standard proportional value;

[0243] Determine the second concentration threshold according to the first preset proportional parameter and the first concentration threshold; the first preset proportional parameter is used to adjust the first concentration threshold to obtain the second concentration threshold.

[0244] In one embodiment, the control module 903 is further configured to

[0245] In the case where the target dust concentration level is the second level, obtain the first radar data through the lidar set on the target vehicle;

[0246] Obtain the second radar data through the millimeter-wave radar set on the target vehicle;

[0247] Perform filtering processing on the first radar data to obtain the third radar data;

[0248] Perform data fusion processing on the second radar data and the third radar data to obtain the target radar data;

[0249] Control the target vehicle to make corresponding decisions according to the target radar data.

[0250] In one embodiment, the control module 903 is further configured to

[0251] Determine the first weight coefficient corresponding to the second radar data and the second weight coefficient corresponding to the third radar data according to the third preset proportional parameter, the current dust concentration, and the first concentration threshold;

[0252] Based on the first weight coefficient and the second weight coefficient, perform data fusion processing on the second radar data and the third radar data to obtain target radar data; among them, the calculation formula for the first weight coefficient is:

[0253]

[0254] The calculation formula for the second weight coefficient is:

[0255] W l = 1 - W m ;

[0256] The calculation formula for the target radar data is:

[0257] D = W m ×d m + W l ×d l ;

[0258] Among them, W m is the first weight coefficient, k2 is the third preset ratio parameter, t v is the first concentration threshold, t is the current dust concentration, W l is the second weight coefficient, D is the target radar data, d m is the second radar data, d l is the third radar data.

[0259] In one embodiment, the control module 903 is further configured to

[0260] Determine the position information of the target obstacle according to the target radar data;

[0261] If the target obstacle is outside the driving route of the target vehicle, control the target vehicle to drive along the driving route;

[0262] If the target obstacle is in the driving route of the target vehicle, determine the height of the target obstacle;

[0263] If the height of the target obstacle is higher than the chassis height corresponding to the target vehicle, control the target vehicle to bypass;

[0264] If the height of the target obstacle is lower than the chassis height corresponding to the target vehicle, control the target vehicle to drive along the driving route.

[0265] Each module in the above autonomous driving control device can be implemented in whole or in part by software, hardware, and their combination. The above modules can be embedded in the processor of the computer device in hardware form or independent of it, or stored in the memory of the computer device in software form, so that the processor can call and execute the operations corresponding to the above modules.

[0266] In an exemplary embodiment, a computer device is provided. The computer device may be a server, and its internal structural diagram may be as shown in Figure 10 . The computer device includes a processor, a memory, an input / output interface (Input / Output, abbreviated as I / O), and a communication interface. Among them, the processor, the memory, and the input / output interface are connected through a system bus, and the communication interface is connected to the system bus through the input / output interface. Among them, the processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system, a computer program, and a database. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The database of the computer device is used to store data related to autonomous driving control. The input / output interface of the computer device is used to exchange information between the processor and external devices. The communication interface of the computer device is used to communicate with external terminals through a network connection. When the computer program is executed by the processor, it implements an autonomous driving control method.

[0267] Those skilled in the art can understand that Figure 10 the structure shown in is only a block diagram of some structures related to the solution of the present application, and does not constitute a limitation on the computer device to which the solution of the present application is applied. The specific computer device may include more or fewer components than those shown in the figure, or combine some components, or have different component arrangements.

[0268] In one embodiment, a computer device is further provided, including a memory and a processor. A computer program is stored in the memory, and when the processor executes the computer program, it implements the steps in the above method embodiments.

[0269] In one embodiment, a computer-readable storage medium is provided, on which a computer program is stored. When the computer program is executed by the processor, it implements the steps in the above method embodiments.

[0270] In one embodiment, a computer program product is provided, including a computer program. When the computer program is executed by the processor, it implements the steps in the above method embodiments.

[0271] Those of ordinary skill in the art can understand that all or part of the processes in the methods of the above embodiments can be completed by instructing relevant hardware through a computer program. The computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiments of the above methods. Among them, any reference to a memory, database, or other medium used in the embodiments provided in the present application can include at least one of non-volatile memory and volatile memory. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical memory, high-density embedded non-volatile memory, resistive random access memory (ReRAM), magnetoresistive random access memory (MRAM), ferroelectric random access memory (FRAM), phase change memory (PCM), graphene memory, etc. Volatile memory can include random access memory (RAM) or external cache memory, etc. By way of illustration and not limitation, RAM can be in various forms, such as static random access memory (SRAM) or dynamic random access memory (DRAM), etc. The databases involved in the embodiments provided in the present application can include at least one of relational databases and non-relational databases. Non-relational databases can include distributed databases based on blockchain, etc., without limitation. The processors involved in the embodiments provided in the present application can be general-purpose processors, central processors, graphics processors, digital signal processors, programmable logic devices, data processing logics based on quantum computing, artificial intelligence (AI) processors, etc., without limitation.

[0272] The technical features of the above embodiments can be combined arbitrarily. For the sake of brevity of description, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, it should be considered to be within the scope recorded in the present application.

[0273] The above-described embodiments merely represent several implementation manners of the present application. The description is relatively specific and detailed, but it should not be construed as a limitation on the patent scope of the present application. It should be noted that for those of ordinary skill in the art, without departing from the concept of the present application, several modifications and improvements can still be made, and these all fall within the protection scope of the present application. Therefore, the protection scope of the present application shall be subject to the appended claims.

Claims

1. An automatic driving control method, characterized in that, The method includes: Obtaining a target dust concentration level corresponding to the current dust concentration in the driving environment of the target vehicle; Determining a target driving control strategy matching the target dust concentration level according to a preset corresponding relationship; the preset corresponding relationship includes at least one set of corresponding relationships between dust concentration levels and driving control strategies; Controlling the target vehicle to execute a target action corresponding to the target driving control strategy.

2. The method according to claim 1, characterized in that, The obtaining of the target dust concentration level corresponding to the current dust concentration in the driving environment of the target vehicle includes: Obtaining a first concentration threshold and a second concentration threshold; wherein, the second concentration threshold is less than the first concentration threshold; the first concentration threshold refers to the lowest dust concentration value at which the target vehicle cannot drive safely due to excessive dust concentration; the second concentration threshold refers to the highest dust concentration value at which the target vehicle can maintain normal driving; Determining a target dust concentration level corresponding to the current dust concentration in the driving environment of the target vehicle according to the current dust concentration, the first concentration threshold, and the second concentration threshold.

3. The method according to claim 2, characterized in that, The determining of the target dust concentration level corresponding to the current dust concentration in the driving environment of the target vehicle according to the current dust concentration, the first concentration threshold, and the second concentration threshold includes: If the current dust concentration is less than or equal to the second concentration threshold, determining that the target dust concentration level is the first level; If the current dust concentration is greater than the second concentration threshold and less than the first concentration threshold, determining that the target dust concentration level is the second level; If the current dust concentration is greater than or equal to the first concentration threshold, determining that the target dust concentration level is the third level.

4. The method according to claim 3, characterized in that, The controlling of the target vehicle to execute a target action corresponding to the target driving control strategy includes: When the target dust concentration level is the first level, controlling the target vehicle to drive at a first driving speed; When the target dust concentration level is the second level, controlling the target vehicle to drive at a second driving speed; the second driving speed is less than the first driving speed; When the target dust concentration level is the third level, controlling the target vehicle to stop driving.

5. The method according to claim 4, wherein The controlling of the target vehicle to drive at the second driving speed includes: Obtaining the current driving speed of the target vehicle; Determining a second driving speed calculation formula according to a second preset proportional parameter, the current driving speed, the first concentration threshold, and the current dust concentration; Adjusting the current driving speed to the second driving speed according to the second driving speed calculation formula; Controlling the target vehicle to drive at the second driving speed; Wherein, the second driving speed calculation formula is: v t is the second driving speed, k1 is the second preset proportional parameter, t v is the first concentration threshold, t is the current dust concentration, and v is the current driving speed.

6. The method according to claim 2, wherein The obtaining of the first concentration threshold and the second concentration threshold includes: Determining a first detection area detected by a lidar along a preset direction in a first environment; the dust concentration in the first environment is lower than a preset threshold; the first detection area refers to a conical area detected by the lidar along the preset direction in the first environment; the preset threshold refers to a preset initial dust concentration value; Gradually adjust the dust concentration of the first environment to obtain multiple second environments with adjusted dust concentrations; the dust concentration of the second environment is higher than that of the first environment; For multiple second environments with adjusted dust concentrations, determine the second detection area obtained by the lidar detecting along the preset direction in the second environment; the second detection area refers to the conical area obtained by the lidar detecting along the preset direction in the second environment; If the spatial proportional relationship between the second detection area and the first detection area satisfies the preset proportional relationship, determine the dust concentration of the current second environment as the first concentration threshold; the preset proportional relationship refers to the standard proportional value; Determine the second concentration threshold according to the first preset proportional parameter and the first concentration threshold; the first preset proportional parameter is used to adjust the first concentration threshold to obtain the second concentration threshold.

7. The method according to claim 3, wherein The controlling the target vehicle to execute the target action corresponding to the target driving control strategy further includes: In the case where the target dust concentration level is the second level, obtain the first radar data through the lidar provided on the target vehicle; Obtain the second radar data through the millimeter wave radar provided on the target vehicle; Perform filtering processing on the first radar data to obtain the third radar data; Perform data fusion processing on the second radar data and the third radar data to obtain the target radar data; Control the target vehicle to make corresponding decisions according to the target radar data.

8. The method according to claim 7, wherein The performing data fusion processing on the second radar data and the third radar data to obtain the target radar data includes: Determine the first weight coefficient corresponding to the second radar data and the second weight coefficient corresponding to the third radar data according to the third preset proportional parameter, the current dust concentration, and the first concentration threshold; Based on the first weight coefficient and the second weight coefficient, perform data fusion processing on the second radar data and the third radar data to obtain the target radar data; wherein, the calculation formula of the first weight coefficient is: The calculation formula of the second weight coefficient is: W l = 1 - W m ; The calculation formula of the target radar data is: D = W m × d m + W l × d l ; Among them, W m is the first weight coefficient, k2 is the third preset ratio parameter, t v is the first concentration threshold, t is the current dust concentration, W l is the second weight coefficient, D is the position information in the target radar data, d m is the position information in the second radar data, d l is the position information in the third radar data.

9. The method according to claim 7, wherein The controlling the target vehicle to make corresponding decisions according to the target radar data includes: Determine the position information of the target obstacle according to the target radar data; If the target obstacle is outside the driving route of the target vehicle, control the target vehicle to drive along the driving route; If the target obstacle is in the driving route of the target vehicle, determine the height of the target obstacle; If the height of the target obstacle is higher than the chassis height corresponding to the target vehicle, control the target vehicle to bypass; If the height of the target obstacle is lower than the chassis height corresponding to the target vehicle, control the target vehicle to drive along the driving route.

10. An automatic driving control device, characterized in that, The device includes: An acquisition module, configured to acquire the target dust concentration level corresponding to the current dust concentration in the driving environment of the target vehicle; A policy matching module, configured to determine a target driving control policy that matches the target dust concentration level according to a preset corresponding relationship; the preset corresponding relationship includes at least one set of corresponding relationships between dust concentration levels and driving control policies; A control module, configured to control the target vehicle to execute a target action corresponding to the target driving control policy.

11. A computer device, comprising a memory and a processor, the memory storing a computer program, characterized in that, When the processor executes the computer program, the steps of the method according to any one of claims 1 to 9 are implemented.

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