Vehicle control method and device, computer equipment and storage medium

Abstract

The invention relates to the technical field of vehicles, and discloses a vehicle control method and device, computer equipment and a storage medium, and the method comprises the steps: obtaining the road surface information of a road surface where a vehicle is located, and determining a target road surface type corresponding to the road surface information; determining a target friction coefficient of the vehicle on the road surface of the target road surface type; determining a basic safety distance of the vehicle according to the target friction coefficient; determining the confidence coefficient of the target friction coefficient, and adjusting the basic safety distance according to the confidence coefficient to obtain a target safety distance of the vehicle; and determining a control mode of the vehicle according to the target safety distance and the distance between the vehicle and the obstacle. The corresponding target friction coefficient is determined according to the road surface where the vehicle is located, the safety distance is adjusted according to the confidence coefficient of the target friction coefficient, the safety of the vehicle can be effectively improved, and driving safety can be guaranteed.

Description

Technical Field

[0001] The present invention relates to the field of vehicle technology, and in particular to a vehicle control method, device, computer equipment and storage medium. Background Art

[0002] Intelligent driving can guide and help drivers complete driving tasks, improve driving safety, and reduce the accident rate in dangerous driving scenarios. The intelligent braking system can assist in braking and ensure driving safety.

[0003] In existing intelligent braking systems, braking strategies are usually implemented based on the detected obstacle distance and the vehicle's own speed. However, in reality, the timing of braking is affected by many factors. Current braking strategies are difficult to achieve adaptive control and may even affect driving safety due to the failure to brake in time. Summary of the Invention

[0004] In view of this, the present invention provides a vehicle control method, apparatus, computer equipment and storage medium to solve the problem of poor adaptability of braking strategies.

[0005] In a first aspect, the present invention provides a vehicle control method, comprising:

[0006] Obtaining road surface information of the road on which the vehicle is located, and determining a target road surface type corresponding to the road surface information;

[0007] determining a target friction coefficient for the vehicle when traveling on a road of the target road surface type;

[0008] determining a basic safety distance of the vehicle according to the target friction coefficient; wherein the basic safety distance is not less than a minimum braking distance required for the vehicle to avoid collision with an obstacle;

[0009] Determining a confidence level of the target friction coefficient, and adjusting the basic safety distance according to the confidence level to obtain a target safety distance for the vehicle; the target safety distance is not less than the basic safety distance;

[0010] A control mode of the vehicle is determined according to the target safety distance and the distance between the vehicle and the obstacle.

[0011] In some optional embodiments, determining the target friction coefficient of the vehicle when driving on the target road surface type includes:

[0012] determining a slip ratio of the vehicle;

[0013] The target friction coefficient corresponding to the slip rate of the vehicle is determined according to a pre-calibrated correspondence between the slip rate and the friction coefficient under the target road surface type.

[0014] In some optional implementations, determining the target road surface type corresponding to the road surface information includes:

[0015] Performing road surface detection on the road surface information according to a preset road surface detection model, determining the probability values ​​of the road surface on which the vehicle is located belonging to various road surface types, and taking the road surface type with the highest probability value as the target road surface type to which the vehicle is located;

[0016] The determining, based on a pre-calibrated correspondence between the slip rate and the friction coefficient under the target road surface type, a target friction coefficient corresponding to the slip rate of the vehicle includes:

[0017] Determining the undetermined friction coefficients corresponding to the slip rates of the vehicle on various road types based on pre-calibrated correspondences between slip rates and friction coefficients on various road types;

[0018] Determining weights corresponding to the undetermined friction coefficients according to probability values ​​of the road surface on which the vehicle is located belonging to various road surface types;

[0019] The undetermined friction coefficients are weighted according to the weights corresponding to the respective undetermined friction coefficients to obtain a target friction coefficient of the road surface on which the vehicle is located.

[0020] In some optional implementations, determining the basic safety distance of the vehicle according to the target friction coefficient includes:

[0021] determining a target slope of the road on which the vehicle is located, and correcting the target friction coefficient according to the target slope;

[0022] A basic safety distance of the vehicle is determined according to the corrected target friction coefficient.

[0023] In some optional implementations, determining the target slope of the road on which the vehicle is located includes:

[0024] Determining the pitch angles of the vehicle collected at multiple sampling moments within a target time period, and determining the slopes at corresponding sampling moments based on the pitch angles;

[0025] Counting the variances corresponding to the slopes at multiple sampling moments within the target time period;

[0026] When the variance is less than a preset value, the slope at the last sampling moment is used as the target slope;

[0027] When the variance is greater than a preset value, the target slope is kept unchanged.

[0028] In some optional implementations, adjusting the basic safety distance according to the confidence level to obtain the target safety distance of the vehicle includes:

[0029] determining an adjustment coefficient of the basic safety distance according to the confidence level; wherein the adjustment coefficient and the confidence level are negatively correlated;

[0030] The basic safety distance is adjusted according to the adjustment coefficient to determine the target safety distance of the vehicle.

[0031] In some optional implementations, determining the control mode of the vehicle according to the target safety distance and the distance between the vehicle and the obstacle includes:

[0032] determining an obstacle distance between the vehicle and the obstacle;

[0033] When the obstacle distance is greater than a first distance threshold, performing early warning processing;

[0034] When the obstacle distance is between the first distance threshold and the second distance threshold, performing a braking operation on the vehicle at a first deceleration;

[0035] When the obstacle distance is less than the second distance threshold, performing a braking operation on the vehicle at a second deceleration;

[0036] The first distance threshold is greater than the second distance threshold, and the second distance threshold is greater than the target safety distance; and the first deceleration is less than the second deceleration.

[0037] In a second aspect, the present invention provides a vehicle control device, comprising:

[0038] A road surface recognition module is used to obtain road surface information of the road on which the vehicle is located and determine the target road surface type corresponding to the road surface information;

[0039] a friction coefficient determination module, configured to determine a target friction coefficient of the vehicle when the vehicle is on a road of the target road type;

[0040] a safety distance determination module, configured to determine a basic safety distance of the vehicle based on the target friction coefficient, wherein the basic safety distance is not less than a minimum braking distance required for the vehicle to avoid collision with an obstacle; determine a confidence level for the target friction coefficient, and adjust the basic safety distance based on the confidence level to obtain a target safety distance for the vehicle; wherein the target safety distance is not less than the basic safety distance;

[0041] A control module is used to determine a control mode of the vehicle according to the target safety distance and the distance between the vehicle and the obstacle.

[0042] In a third aspect, the present invention provides a computer device comprising: a memory and a processor, the memory and the processor being communicatively connected to each other, the memory storing computer instructions, and the processor executing the vehicle control method of the first aspect or any corresponding embodiment thereof by executing the computer instructions.

[0043] In a fourth aspect, the present invention provides a computer-readable storage medium having computer instructions stored thereon, the computer instructions being used to enable a computer to execute the vehicle control method of the first aspect or any corresponding embodiment thereof.

[0044] In a fifth aspect, the present invention provides a computer program product comprising computer instructions for causing a computer to execute the vehicle control method of the first aspect or any corresponding embodiment thereof.

[0045] The present invention determines a corresponding target friction coefficient according to the road surface on which the vehicle is located, preliminarily determines a basic safety distance based on this, and adjusts the basic safety distance according to the confidence level of the target friction coefficient. This can obtain a target safety distance with a safety margin. Based on this target safety distance, it is possible to more accurately characterize whether the vehicle is safe. Subsequently, controlling the vehicle based on the target safety distance can effectively improve the safety of the vehicle and help ensure driving safety. BRIEF DESCRIPTION OF THE DRAWINGS

[0046] In order to more clearly illustrate the specific embodiments of the present invention or the technical solutions in related technologies, the following briefly introduces the drawings required for use in the specific embodiments or related technical descriptions. Obviously, the drawings described below are some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0047] Figure 1 is a flow chart of a vehicle control method according to an embodiment of the present invention;

[0048] Figure 2 is a flow chart of another vehicle control method according to an embodiment of the present invention;

[0049] Figure 3 is a schematic diagram of force analysis when a vehicle is located on a slope according to an embodiment of the present invention;

[0050] Figure 4 is a structural block diagram of a vehicle control device according to an embodiment of the present invention;

[0051] Figure 5 Schematic diagram of the hardware structure of a computer device according to an embodiment of the present invention. DETAILED DESCRIPTION

[0052] To make the purpose, technical solutions, and advantages of the embodiments of the present invention more clear, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without making creative efforts shall fall within the scope of protection of the present invention.

[0053] Traditional automotive braking systems rely primarily on the driver's reactions and actions to achieve braking control. However, this reliance on human judgment and operation has numerous limitations. First, the driver's reaction speed and judgment accuracy are limited by physiological and psychological factors. In emergency situations, panic, fatigue, or inattention may prevent the driver from making timely and accurate braking decisions, leading to accidents. In existing intelligent braking systems, autonomous driving control methods rely heavily on pre-set vehicle dynamics models (such as tire and suspension models), typically executing braking strategies based on the detected obstacle distance and the vehicle's own speed.

[0054] However, road conditions, such as road friction, also play a key role in determining braking distance. For example, on dry roads with high friction, braking distance is shorter than on smooth roads. Traditional solutions struggle to adapt to complex and changing road conditions (such as sudden icing and dynamic changes in wetness), leading to large errors in braking distance calculations, which can compromise driving safety.

[0055] A vehicle control method provided by an embodiment of the present invention determines a target friction coefficient based on the road surface on which the vehicle is traveling, preliminarily determines a basic safety distance based on this target friction coefficient, and then adjusts the basic safety distance based on the target friction coefficient to obtain a target safety distance that more accurately represents the vehicle's safety. Controlling the vehicle based on this target safety distance can effectively improve vehicle safety.

[0056] According to an embodiment of the present invention, an embodiment of a vehicle control method is provided. It should be noted that the steps shown in the flowchart of the accompanying drawings can be executed in a computer system such as a set of computer-executable instructions, and although a logical order is shown in the flowchart, in some cases, the steps shown or described can be executed in an order different from that shown here.

[0057] In this embodiment, a vehicle control method is provided, which can be applied to a device capable of controlling a vehicle, such as an onboard controller of a vehicle, or a remote server for controlling a vehicle. Figure 1 is a flow chart of a vehicle control method according to an embodiment of the present invention. Figure 1 As shown, the process includes the following steps.

[0058] Step S101: obtaining road surface information of the road on which the vehicle is located, and determining a target road surface type corresponding to the road surface information.

[0059] In this embodiment, the vehicle has an automatic control function. For example, the vehicle may be provided with an intelligent braking system, and the method provided in this embodiment is executed based on the intelligent braking system to achieve control of the vehicle.

[0060] During driving, a vehicle may locate on a road surface. At this point, road surface information can be obtained for the road surface on which the vehicle is located. This road surface information can represent the relevant conditions of the road surface on which the vehicle is located. For example, a vehicle may be equipped with sensors such as cameras and lidars, which can collect road surface information representing road surface conditions. For example, a road surface image can be captured by an onboard camera and used as the road surface information for the road surface on which the vehicle is located.

[0061] In this embodiment, the road surface is pre-classified into multiple types, such as dry road surface type, wet road surface type, icy road surface type, etc. This embodiment does not limit the method of classifying the road surface types. After obtaining the road surface information, road surface detection can be performed based on the road surface information to determine the road surface type to which the vehicle is located, i.e., the target road surface type.

[0062] For example, if it is recognized based on the road surface image that the vehicle is located on an icy or snowy road surface, then the target road surface type is an icy or snowy road surface type.

[0063] Step S102 , determining a target friction coefficient when the vehicle is on a road of a target road type.

[0064] In this embodiment, when the vehicle is on different types of road surfaces, the friction coefficient between the vehicle and the road surface is different; after determining the type of road surface the vehicle is currently on (i.e., the target road surface type), the friction coefficient of the vehicle on the target road surface type can be obtained, i.e., the target friction coefficient.

[0065] For example, the target friction coefficient may be determined based on a table lookup, or may be calculated based on a formula for calculating the friction coefficient.

[0066] Step S103: determining a basic safety distance of the vehicle based on the target friction coefficient. The basic safety distance is not less than the minimum braking distance required for the vehicle to avoid collision with an obstacle.

[0067] In this embodiment, the safety distance of a vehicle is different when it is on a road surface with different friction coefficients. In addition, when determining the safety distance of the vehicle, a reference distance, i.e., a basic safety distance, is preliminarily determined, and then the final safety distance used is determined based on the basic safety distance, i.e., the subsequent target safety distance.

[0068] Specifically, after determining the target friction coefficient, the vehicle's basic safety distance can be preliminarily calculated. It can be understood that the larger the target friction coefficient, the shorter the vehicle's braking distance, and the correspondingly smaller the basic safety distance can be set. In other words, there is a negative correlation between the target friction coefficient and the basic safety distance.

[0069] The minimum braking distance required for the vehicle to avoid a collision with a forward obstacle can be calculated, and the basic safety distance can be determined based on the minimum braking distance. In this embodiment, the basic safety distance is not less than the minimum braking distance; in general, the minimum braking distance can be directly used as the basic safety distance, that is, the basic safety distance is equal to the minimum braking distance.

[0070] For example, the basic safety distance may be the minimum braking distance of the vehicle. Specifically, the basic safety distance d1 may be calculated based on the following formula (1).

[0071]

[0072] Where v represents the vehicle's current speed, μ is the target friction coefficient, and g is the acceleration due to gravity. The vehicle speed v can be obtained from the vehicle's wheel speed sensors or positioning system.

[0073] Step S104: Determine the confidence level of the target friction coefficient and adjust the basic safety distance according to the confidence level to obtain the target safety distance of the vehicle. The target safety distance is not less than the basic safety distance.

[0074] In this embodiment, due to errors in vehicle sensors and other reasons, the target friction coefficient determined above is not absolutely accurate. In this embodiment, the degree of credibility of the target friction coefficient is represented by the confidence level, and the value range of the confidence level is [0, 1]. The higher the confidence level, the more reliable the calculated target friction coefficient is, and the more accurate it is.

[0075] The confidence level of the target friction coefficient is used to represent the confidence level of the basic safety distance d1. The higher the confidence level of the basic safety distance d1, the more reliable the calculated basic safety distance d1 is, and the more likely it is that the basic safety distance d1 can be directly used as the final required safety distance, that is, the target safety distance. The target safety distance is subsequently represented by d2.

[0076] Therefore, based on the confidence level, the basic safety distance d1 can be adjusted to obtain the vehicle's target safety distance d2, ensuring that even when the confidence level is low, the target safety distance d2 used ultimately has sufficient safety margin.

[0077] The target safety distance d2 is no less than the basic safety distance d1, for example, d2 > d1. Furthermore, given a constant basic safety distance d1, the lower the confidence level of the target friction coefficient, the larger the target safety distance d2 needs to be to ensure sufficient safety margin. This means there is a negative correlation between the confidence level and the target safety distance d2.

[0078] Step S105 : determining a vehicle control method based on the target safety distance and the distance between the vehicle and the obstacle.

[0079] In this embodiment, after determining the target safety distance d2, the control method of the vehicle-to-vehicle linkage can be adaptively determined based on the target safety distance d2 to achieve vehicle control. Specifically, the risk identification result between the vehicle and the obstacle can be determined based on the target safety distance; and the vehicle's braking control method can be determined based on the risk identification result. The risk identification result is used to represent the comparison between the target safety distance and the obstacle distance (i.e., the distance between the vehicle and the obstacle), such as the magnitude relationship between the two.

[0080] By comparing the target safety distance with the distance between the vehicle and the obstacle, a corresponding risk identification result can be determined, and then a specific control method can be determined. This control method may include maintaining the vehicle speed (for example, maintaining the current speed) or braking. For example, if the distance between the vehicle and the obstacle ahead is about to fall below the target safety distance d2, the vehicle may need to be braked to prevent collision with the obstacle to ensure driving safety.

[0081] The vehicle control method provided in this embodiment determines a corresponding target friction coefficient according to the road surface on which the vehicle is located, preliminarily determines a basic safety distance based on this, and adjusts the basic safety distance according to the confidence level of the target friction coefficient. This can obtain a target safety distance with a safety margin. Based on this target safety distance, it is possible to more accurately characterize whether the vehicle is safe. Subsequently, controlling the vehicle based on this target safety distance can effectively improve the safety of the vehicle and help ensure driving safety.

[0082] In this embodiment, a vehicle control method is provided, which can be applied to a device capable of controlling a vehicle, such as an onboard controller of a vehicle, or a remote server for controlling a vehicle. Figure 2 is a flow chart of a vehicle control method according to an embodiment of the present invention. Figure 2 As shown, the process includes the following steps.

[0083] Step S201: Obtain road surface information of the road on which the vehicle is located, and determine a target road surface type corresponding to the road surface information.

[0084] For details, please see Figure 1 Step S101 of the illustrated embodiment will not be described in detail here.

[0085] Step S202 : determining a target friction coefficient of the vehicle when it is on a road of a target road type.

[0086] Specifically, the above step S202 “determining the target friction coefficient of the vehicle when it is on a road of a target road type” includes steps S2021 to S2022 .

[0087] Step S2021, determining the slip ratio of the vehicle.

[0088] Step S2022: Determine a target friction coefficient corresponding to the slip rate of the vehicle based on a pre-calibrated correspondence between the slip rate and the friction coefficient under the target road surface type.

[0089] The slip ratio is the ratio of the slip component in the wheel motion. Specifically, the slip ratio of a vehicle is the difference between the actual vehicle speed v and the ground and the vehicle's wheel speed v. w The speed difference between them is the ratio of the actual vehicle speed v. For example, the slip ratio is: (vv w ) / v.

[0090] In this embodiment, since there is a certain correspondence between the slip rate of the vehicle and the corresponding friction coefficient, the corresponding friction coefficient, ie, the target friction coefficient, is determined based on the slip rate of the vehicle.

[0091] Among them, there are certain differences in the corresponding relationship between the slip rate and the friction coefficient under different road surfaces. In order to ensure the accuracy of the friction coefficient, the corresponding relationship between the slip rate and the friction coefficient under each road surface type is calibrated in advance, that is, the corresponding relationship between the slip rate and the friction coefficient under each road surface type is determined in advance, that is, each road surface type has a corresponding corresponding relationship.

[0092] The Burckhardt formula can be used to express the corresponding relationship between the slip rate and the friction coefficient. The corresponding relationship can be specifically shown in the following formula (2):

[0093]

[0094] Where s represents the slip rate, and μ(s) represents the friction coefficient when the slip rate is s. c1, c2, and c3 are coefficients that need to be calibrated. For example, they can be calibrated based on the least squares method. This embodiment does not limit the process for determining the coefficients c1, c2, and c3.

[0095] The values ​​of coefficients c1, c2, and c3 vary for different road surface types, indicating a different relationship between slip rate and friction coefficient. By determining the values ​​of coefficients c1, c2, and c3 for each road surface type, we can determine the relationship between slip rate and friction coefficient for each road surface type. For example, for dry roads, coefficients c1, c2, and c3 are 1.0, 15, and 0.5, respectively. For wet roads, coefficients c1, c2, and c3 are 0.7, 12, and 0.3, respectively.

[0096] Specifically, during vehicle travel, the vehicle's slip ratio can be determined. This slip ratio is the relative difference between the actual wheel speed and the vehicle's forward speed. Once the target road surface type is determined, the friction coefficient corresponding to the vehicle's slip ratio, i.e., the target friction coefficient, can be calculated based on the corresponding relationship between the target road surface type (the relationship between slip ratio and friction coefficient).

[0097] This embodiment utilizes a method of determining the road surface friction coefficient based on slip rate, combined with parameter settings for different road surface types, to accurately reflect the friction characteristics of the actual road surface. Whether the road surface is dry, wet, or icy and snowy, the corresponding friction coefficient can be estimated quickly and accurately.

[0098] In some optional implementations, the above step S201 "determining the target road surface type corresponding to the road surface information" specifically includes the following steps A1.

[0099] In step A1, the road surface information is detected according to a preset road surface detection model to determine the probability values ​​of the road surface where the vehicle is located belonging to various road surface types, and the road surface type with the highest probability value is used as the target road surface type to which the vehicle is located.

[0100] Furthermore, the above-mentioned step S2022 of “determining the target friction coefficient corresponding to the slip rate of the vehicle according to the pre-calibrated correspondence between the slip rate and the friction coefficient under the target road surface type” specifically includes the following steps B1 to B3.

[0101] Step B1, determining the undetermined friction coefficient corresponding to the slip rate of the vehicle on various road types based on the pre-calibrated correspondence between the slip rate and the friction coefficient on various road types.

[0102] Step B2: Determine the weights corresponding to the various undetermined friction coefficients based on the probability values ​​of the road surface on which the vehicle is located belonging to various road surface types.

[0103] Step B3: performing weighted processing on the to-be-determined friction coefficients according to the weights corresponding to the to-be-determined friction coefficients to obtain the target friction coefficient of the road surface on which the vehicle is located.

[0104] In this embodiment, a model for detecting road surface types, namely a road surface detection model, is pre-established. For example, this road surface detection model can be implemented based on a convolutional neural network (CNN). After collecting road surface information, this information is input into the road surface detection model. Based on the output of the road surface detection model, the probability of the vehicle's road surface belonging to various road surface types is determined. The road surface type with the highest probability is the most likely road surface type and is therefore used as the target road surface type to which the vehicle belongs.

[0105] For example, if there are three types of road surface: dry road surface type, wet road surface type, and icy road surface type, the road surface detection model determines that the probability values ​​of the road surface where the vehicle is located belonging to the three road surface types are 0.1, 0.2, and 0.7 respectively. That is, the probability value of the icy road surface type is the highest, so it can be determined that the target road surface type to which the vehicle is located belongs is the icy road surface type.

[0106] When determining the target friction coefficient, due to the large number of actual road surface types, calibrating the correspondence between slip rate and friction coefficient for all road surface types would not only be labor-intensive but also susceptible to inaccurate road surface type recognition. In this embodiment, only the correspondence between slip rate and friction coefficient for a small number of road surface types needs to be calibrated, and the target friction coefficient is determined using a weighted approach.

[0107] Specifically, after determining the vehicle's slip rate s, the corresponding friction coefficients for each road surface type can be calculated based on the corresponding relationship between the slip rate and the friction coefficient. For ease of description, the friction coefficient determined at this time is referred to as the undetermined friction coefficient. Still using the above three road surface types as an example, three undetermined friction coefficients can be calculated, and they are: μ 干燥 (s), μWet (s), μIce and Snow (s).

[0108] Furthermore, the road surface detection model can output probabilities for each road type the vehicle is on. Based on these probabilities, weights for each undetermined friction coefficient are determined. The greater the probability, the greater the corresponding weight. For example, each probability can be directly used as the weight for the corresponding undetermined friction coefficient. Since the target road surface type is the most important reference value, the weight corresponding to the target road surface type can be adaptively increased.

[0109] After determining the weights corresponding to the various undetermined friction coefficients, the undetermined friction coefficients can be weighted based on these weights to ultimately obtain the target friction coefficient of the road surface on which the vehicle is located.

[0110] For example, if the weights corresponding to the three undetermined friction coefficients are w1, w2, and w3, respectively, then the target friction coefficient μ is: μ = w1 × μ 干燥 (s)+w2×μ湿滑 (s)+w3×μ 冰雪 (s).

[0111] In this embodiment, only the correspondence between the slip rate and the friction coefficient in a small number of road types needs to be calibrated. The target friction coefficient can be determined simply and quickly by weighting, which reduces the amount of calculation and ensures the accuracy of the calculation results.

[0112] Step S203: determining a basic safety distance of the vehicle based on the target friction coefficient. The basic safety distance is not less than the minimum braking distance required for the vehicle to avoid collision with an obstacle.

[0113] For details, please see Figure 1 Step S103 of the illustrated embodiment will not be described in detail here.

[0114] In some optional implementations, step S203 “determine a basic safety distance of the vehicle according to a target friction coefficient” includes steps C1 to C2.

[0115] Step C1, determining a target slope of the road on which the vehicle is located, and correcting a target friction coefficient according to the target slope.

[0116] In this embodiment, the terrain on which the vehicle is located also affects the friction coefficient, particularly when the road surface has a certain slope. Specifically, the slope of the road surface on which the vehicle is located, i.e., the target slope, can be determined. For example, the pitch angle of the vehicle can be determined based on the vehicle's inertial measurement unit (IMU), and the target slope of the road surface can be determined based on the pitch angle.

[0117] After determining the target slope, the target friction coefficient can be corrected. It can be understood that when the vehicle is going uphill, the greater the target slope, the greater the corrected target friction coefficient; while when the vehicle is going downhill, the greater the target slope, the smaller the corrected target friction coefficient.

[0118] Step C2: determining the basic safety distance of the vehicle according to the corrected target friction coefficient.

[0119] After determining the corrected target friction coefficient, the basic safety distance of the vehicle can be calculated. For example, the basic safety distance d1 can be calculated based on the above formula (1), where the friction coefficient μ in the above formula (1) is the corrected target friction coefficient.

[0120] In this embodiment, the friction coefficient can be corrected using a simple method. For example, if the target friction coefficient is μ, a linear correction can be performed based on the target slope θ. For example, when the vehicle is traveling uphill, the corrected target friction coefficient is μ(1+kθ); when the vehicle is traveling downhill, the corrected target friction coefficient is μ(1-kθ). Here, k is a preset correction factor. This method simplifies calculations but sacrifices accuracy.

[0121] Optionally, the corrected target friction coefficient is μcosθ±sinθ, where it takes a positive value when the vehicle is going uphill, i.e., μcosθ+sinθ, and takes a negative value when the vehicle is going downhill, i.e., μcosθ-sinθ.

[0122] Figure 3 Figure 2 shows a schematic diagram of the force analysis when the vehicle is on a slope. Figure 3 As shown, the target slope of the road where the vehicle is located is θ, and it will receive gravity G and support force F N And the influence of friction f; if the mass of the vehicle is m, the acceleration of gravity is g, and the target friction coefficient is μ, then G = mg, and f = μ × F N .

[0123] When a vehicle brakes on a slope, there is also an inertia force F along the slope road surface. g , the inertial force F g The direction of is specifically related to the direction of travel of the vehicle. Figure 3 As shown, when the vehicle goes downhill, the inertia force F g The downward direction along the slope is the direction of travel, and the following explanation will be given using the downhill slope as an example.

[0124] Performing force analysis in the direction perpendicular to the road surface, we can obtain formula (3): mg×cosθ=F N .

[0125] By analyzing the force along the road surface, we can obtain formula (4): mg×sinθ+ma=f.

[0126] Where a is the absolute value of the vehicle's acceleration when braking.

[0127] Since, f = μ × F N Therefore, f = μ × mg × cosθ. Substituting the sliding friction force f into the above formula (4), we can obtain: mg × sinθ + ma = mg × μcosθ, from which we can obtain: a = g(μcosθ - sinθ).

[0128] Based on kinematics, we know that when a vehicle starts braking from a speed v and stops, then v 2 =2a×d, d is the braking distance, and the braking distance d is used as the basic safety distance d1, then:

[0129] Similarly, when the vehicle is going uphill, a=g(μcosθ+sinθ), then the basic safety distance d1 satisfies:

[0130] Comparing this with equation (1), we can see that the target friction coefficient can be corrected based on the target slope. The corrected target friction coefficient is μcosθ±sinθ, where the value is positive when the vehicle is going uphill (μcosθ+sinθ) and negative when the vehicle is going downhill (μcosθ-sinθ). This correction method accurately represents the effect of slope on the friction coefficient and ensures the accuracy of the subsequent basic safety distance d1.

[0131] In this embodiment, a slope correction mechanism is introduced when calculating the safety distance. The road slope is detected by the inertial measurement unit to correct the friction coefficient, making the calculation of the safety distance on uphill and downhill roads more accurate, and effectively reducing the safety distance error caused by the slope factor.

[0132] Optionally, the above step C1 “determining the target slope of the road on which the vehicle is located” may include steps C11 to C14.

[0133] Step C11 : determining the pitch angles of the vehicle collected at multiple sampling moments within the target time period, and determining the slopes at corresponding sampling moments according to the pitch angles.

[0134] Step C12: Calculate the variance corresponding to the slope at multiple sampling moments within the target time period.

[0135] Step C13: When the variance is less than the preset value, the slope at the last sampling moment is used as the target slope.

[0136] Step C14: When the variance is greater than the preset value, the target slope is kept unchanged.

[0137] In this embodiment, the vehicle's inertial measurement unit has a sampling cycle that periodically collects the vehicle's pitch angle. This means that the corresponding pitch angle can be determined at each sampling moment. If the pitch angle is directly used as the target slope, the pitch angle may change suddenly when the vehicle is jolted by an uneven road surface. To ensure slope accuracy, this embodiment uses multiple pitch angles over a period of time to determine the target slope.

[0138] Specifically, the latest time period can be used as the target time period, that is, the last sampling moment of the target time period corresponds to the latest sampling moment. Within the target time period, there are multiple sampling moments, each of which can collect a corresponding pitch angle, and then the slope corresponding to the pitch angle can be determined.

[0139] The variance of these slopes is calculated. If the variance is less than a preset value, it indicates that the pitch angle (or slope) collected during the target time period is relatively stable. Therefore, the slope corresponding to the last sampling moment (i.e., the most recent sampling moment) can be used as the target slope. Conversely, if the variance is greater than the preset value, it indicates that there is a sudden change in the pitch angle (or slope) during the target time period. In this case, the slope collected at the last sampling moment may be inaccurate. Therefore, the target slope remains unchanged, that is, the target slope determined last time is continued to be used. In other words, the target slope determined last time is used as the current target slope.

[0140] In this embodiment, the variance of the slope over a period of time can be used to simply and conveniently determine whether the slope determined at the last sampling moment is accurate, thereby ensuring the accuracy of the target slope.

[0141] Step S204: Determine the confidence level of the target friction coefficient and adjust the basic safety distance based on the confidence level to obtain the target safety distance of the vehicle. The target safety distance is not less than the basic safety distance.

[0142] For details, please see Figure 1 Step S104 of the illustrated embodiment will not be described in detail here.

[0143] In some optional implementations, when setting a safe distance based on the braking distance, a sufficient fixed multiple can be set, such as twice the braking distance. While this can ensure safety, this strategy is overly conservative and can sacrifice traffic efficiency. Setting a smaller fixed multiple may pose a safety hazard. In this embodiment, adaptively adjusting the basic safe distance based on the confidence level of the target friction coefficient can balance safety and vehicle efficiency, avoiding multiple, meaningless braking operations.

[0144] Specifically, the above-mentioned step S204 "adjusting the basic safety distance according to the confidence level to obtain the target safety distance of the vehicle" may include: determining an adjustment coefficient of the basic safety distance according to the confidence level; the adjustment coefficient and the confidence level are negatively correlated; and adjusting the basic safety distance according to the adjustment coefficient to determine the target safety distance of the vehicle.

[0145] In this embodiment, an adjustment coefficient K is set for the basic safety distance d1 based on the confidence level of the target friction coefficient. The confidence level and the adjustment coefficient K are negatively correlated; that is, the higher the confidence level, the smaller the adjustment coefficient K can be. Furthermore, the basic safety distance d1 is adjusted based on the adjustment coefficient K, and the adjusted basic safety distance serves as the target safety distance d2. Specifically, the adjustment coefficient K can be a multiple of the basic safety distance d1. The target safety distance d2 is the product of the adjustment coefficient K and the basic safety distance d1, i.e., d2 = K × d1.

[0146] Only two adjustment coefficients can be divided. If the confidence level is greater than a certain threshold (for example, 90%), the adjustment coefficient K is set to a smaller value, for example, K=1.2; if the confidence level is greater than the threshold, the adjustment coefficient K is set to a larger value, for example, K=1.5.

[0147] Alternatively, when the confidence level is greater than a first threshold, the adjustment coefficient is set to a minimum value; when the confidence level is less than a second threshold, the adjustment coefficient is set to a maximum value; when the confidence level is between the first threshold and the second threshold, the adjustment coefficient is set to a value between the minimum value and the maximum value, and there is a negative correlation between the adjustment coefficient and the confidence level.

[0148] The first threshold is greater than the second threshold. For example, the first threshold is 90% and the second threshold is 70%. Specifically, if the confidence level is greater than 90%, the adjustment coefficient is set to the minimum value, such as K = 1.2. When the confidence level is less than 70%, the adjustment coefficient is set to the maximum value, such as K = 1.5. When the confidence level is between 70% and 90%, the adjustment coefficient K is determined to be between a minimum value of 1.2 and a maximum value of 1.5, depending on the confidence level.

[0149] In this embodiment, the basic safety distance is adaptively adjusted based on the confidence level of the target friction coefficient, which can balance safety and vehicle driving efficiency and avoid multiple meaningless braking.

[0150] Step S205 : determining a vehicle control method based on the target safety distance and the distance between the vehicle and the obstacle.

[0151] For details, please see Figure 1 Step S105 of the illustrated embodiment will not be described in detail here.

[0152] In some optional implementations, when controlling the vehicle based on a safe distance, binary braking is generally used, i.e., determining whether to brake. This simple and crude braking strategy can easily cause occupant discomfort (such as motion sickness from sudden braking) or miss opportunities for steering to avoid the vehicle. In this embodiment, a multi-level control strategy is adopted.

[0153] Specifically, according to the size relationship between the obstacle distance and the target safety distance, the vehicle is controlled to perform corresponding braking operations. The above step S205 "determining the vehicle control method according to the target safety distance and the distance between the vehicle and the obstacle" includes steps D1 to D4.

[0154] Step D1, determining the obstacle distance between the vehicle and the obstacle.

[0155] Step D2: When the obstacle distance is greater than the first distance threshold, perform early warning processing.

[0156] In step D3 , when the obstacle distance is between the first distance threshold and the second distance threshold, a braking operation is performed on the vehicle at a first deceleration.

[0157] Step D4: When the obstacle distance is less than the second distance threshold, braking the vehicle at a second deceleration.

[0158] In this embodiment, two distance thresholds are pre-determined based on the target safety distance d2: a first distance threshold Th1 and a second distance threshold Th2. Both are greater than the target safety distance d2, and the first distance threshold Th1 is greater than the second distance threshold Th2. For example, Th1 = 2 × d2, and Th2 = 1.5 × d2. The corresponding risk identification result is determined by comparing the distance between the vehicle and the obstacle with the above distance thresholds.

[0159] While the vehicle is in motion, it can use cameras or lidar to identify surrounding obstacles and determine the distance between the vehicle and the obstacle, i.e., the obstacle distance. When identifying obstacles, the obstacle can be classified to determine whether it requires avoidance or poses a safety hazard. For example, if a low obstacle in the lane is less than the height of the chassis, it does not need to be avoided, and the corresponding obstacle distance does not need to be calculated.

[0160] If the obstacle distance is greater than the larger first distance threshold Th1, it indicates that the obstacle is still far away, so only early warning measures are required, such as audio and visual reminders, or maintaining the current vehicle speed. If the obstacle distance is between the first distance threshold Th1 and the second distance threshold Th2, it indicates that there is a certain risk, and pre-braking can be performed, that is, braking the vehicle at a smaller first deceleration, that is, braking with a smaller braking torque.

[0161] If the obstacle is less than the second distance threshold, there's a significant risk of collision. Therefore, the vehicle needs to be braked at a higher second deceleration rate, applying a larger braking torque for emergency braking. For example, full braking can be used. Furthermore, if the vehicle's speed falls below a certain threshold, the vehicle can be steered to avoid the obstacle.

[0162] In this embodiment, through the progressive response of three risk levels (warning / pre-braking / emergency braking), while ensuring safety, the warning-level sound and light reminders can reserve takeover time for the driver, and pre-braking with a smaller first deceleration can avoid the discomfort caused by excessive braking; emergency braking with a larger second deceleration can achieve maximum risk avoidance and ensure driving safety.

[0163] The vehicle control method provided in this embodiment can automatically sense road conditions and detect obstacles based on data collected by vehicle sensors; and, by calculating a safe distance based on road conditions and slip rate, can safely avoid obstacles ahead and adaptively control the vehicle based on the corresponding risk level.

[0164] This embodiment also provides a vehicle control device for implementing the above-described embodiments and preferred embodiments. Details already described will not be repeated. As used below, the term "module" may refer to a combination of software and / or hardware that implements a predetermined function. Although the devices described in the following embodiments are preferably implemented in software, implementation using hardware, or a combination of software and hardware, is also possible and contemplated.

[0165] This embodiment provides a vehicle control device, such as Figure 4 Shown, including:

[0166] The road surface recognition module 401 is used to obtain road surface information of the road on which the vehicle is located and determine the target road surface type corresponding to the road surface information;

[0167] A friction coefficient determination module 402 is configured to determine a target friction coefficient of the vehicle when the vehicle is on a road of the target road type;

[0168] The safety distance determination module 403 is configured to determine a basic safety distance for the vehicle based on the target friction coefficient, where the basic safety distance is not less than a minimum braking distance required for the vehicle to avoid collision with an obstacle; determine a confidence level for the target friction coefficient, and adjust the basic safety distance based on the confidence level to obtain a target safety distance for the vehicle; where the target safety distance is not less than the basic safety distance;

[0169] The control module 404 is configured to determine a control method for the vehicle according to the target safety distance and the distance between the vehicle and the obstacle.

[0170] In some optional implementations, the friction coefficient determination module 402 determines the target friction coefficient of the vehicle when driving on the target road surface type, including:

[0171] determining a slip ratio of the vehicle;

[0172] The target friction coefficient corresponding to the slip rate of the vehicle is determined according to a pre-calibrated correspondence between the slip rate and the friction coefficient under the target road surface type.

[0173] In some optional implementations, the road surface recognition module 401 determines the target road surface type corresponding to the road surface information, including:

[0174] Performing road surface detection on the road surface information according to a preset road surface detection model, determining the probability values ​​of the road surface on which the vehicle is located belonging to various road surface types, and taking the road surface type with the highest probability value as the target road surface type to which the vehicle is located;

[0175] The friction coefficient determination module 402 determines a target friction coefficient corresponding to the slip rate of the vehicle according to a pre-calibrated correspondence between the slip rate and the friction coefficient under the target road surface type, including:

[0176] Determining the undetermined friction coefficients corresponding to the slip rates of the vehicle on various road types based on pre-calibrated correspondences between slip rates and friction coefficients on various road types;

[0177] Determining weights corresponding to the undetermined friction coefficients according to probability values ​​of the road surface on which the vehicle is located belonging to various road surface types;

[0178] The undetermined friction coefficients are weighted according to the weights corresponding to the respective undetermined friction coefficients to obtain a target friction coefficient of the road surface on which the vehicle is located.

[0179] In some optional implementations, the safety distance determination module 403 determines the basic safety distance of the vehicle according to the target friction coefficient, including:

[0180] determining a target slope of the road on which the vehicle is located, and correcting the target friction coefficient according to the target slope;

[0181] A basic safety distance of the vehicle is determined according to the corrected target friction coefficient.

[0182] In some optional implementations, the safety distance determination module 403 determines the target slope of the road on which the vehicle is located, including:

[0183] Determining the pitch angles of the vehicle collected at multiple sampling moments within a target time period, and determining the slopes at corresponding sampling moments based on the pitch angles;

[0184] Counting the variances corresponding to the slopes at multiple sampling moments within the target time period;

[0185] When the variance is less than a preset value, the slope at the last sampling moment is used as the target slope;

[0186] When the variance is greater than a preset value, the target slope is kept unchanged.

[0187] In some optional implementations, the safety distance determination module 403 adjusts the basic safety distance according to the confidence level to obtain the target safety distance of the vehicle, including:

[0188] determining an adjustment coefficient of the basic safety distance according to the confidence level; wherein the adjustment coefficient and the confidence level are negatively correlated;

[0189] The basic safety distance is adjusted according to the adjustment coefficient to determine the target safety distance of the vehicle.

[0190] In some optional implementations, the control module 404 determines the vehicle control mode according to the target safety distance and the distance between the vehicle and the obstacle, including:

[0191] determining an obstacle distance between the vehicle and the obstacle;

[0192] When the obstacle distance is greater than a first distance threshold, performing early warning processing;

[0193] When the obstacle distance is between the first distance threshold and the second distance threshold, performing a braking operation on the vehicle at a first deceleration;

[0194] When the obstacle distance is less than the second distance threshold, performing a braking operation on the vehicle at a second deceleration;

[0195] The first distance threshold is greater than the second distance threshold, and the second distance threshold is greater than the target safety distance; and the first deceleration is less than the second deceleration.

[0196] The further functional description of each of the above modules and units is the same as that of the above corresponding embodiments and will not be repeated here.

[0197] The vehicle control device in this embodiment is presented in the form of a functional unit, where the unit refers to an ASIC (Application Specific Integrated Circuit) circuit, including a processor and memory that executes one or more software or fixed programs, and / or other devices that can provide the above functions.

[0198] The embodiment of the present invention also provides a computer device having the above Figure 4 Vehicle controls shown.

[0199] See also Figure 5 , Figure 5 is a structural diagram of a computer device provided by an optional embodiment of the present invention, such as Figure 5As shown, the computer device includes: one or more processors 10, a memory 20, and interfaces for connecting various components, including high-speed interfaces and low-speed interfaces. Various components utilize different buses to communicate with each other and can be installed on a common mainboard or installed in other ways as needed. The processor can process the instructions executed in the computer device, including instructions stored in or on the memory to display the graphical information of a GUI on an external input / output device (such as, a display device coupled to the interface). In some optional embodiments, if necessary, multiple processors and / or multiple buses can be used together with multiple memories. Equally, multiple computer devices can be connected, and each device provides part of the necessary operations (for example, as a server array, a group of blade servers, or a multi-processor system). Figure 5 A processor 10 is taken as an example.

[0200] The processor 10 may be a central processing unit, a network processor, or a combination thereof. The processor 10 may further include a hardware chip. The hardware chip may be an application-specific integrated circuit, a programmable logic device, or a combination thereof. The programmable logic device may be a complex programmable logic device, a field programmable gate array, a general purpose array logic, or any combination thereof.

[0201] The memory 20 stores instructions that can be executed by at least one processor 10, so that the at least one processor 10 executes the method shown in the above embodiment.

[0202] The memory 20 may include a program storage area and a data storage area, wherein the program storage area may store an operating system and application programs required for at least one function; the data storage area may store data created based on the use of the computer device, etc. In addition, the memory 20 may include a high-speed random access memory, and may also include a non-transient memory, such as at least one disk storage device, a flash memory device, or other non-transient solid-state storage device. In some optional embodiments, the memory 20 may optionally include a memory remotely located relative to the processor 10, and these remote memories may be connected to the computer device via a network. Examples of the above-mentioned network include, but are not limited to, the Internet, an intranet, a local area network, a mobile communication network, and combinations thereof.

[0203] The memory 20 may include a volatile memory, such as a random access memory; the memory may also include a non-volatile memory, such as a flash memory, a hard disk or a solid-state drive; the memory 20 may also include a combination of the above types of memory.

[0204] The computer device further includes a communication interface 30 for the computer device to communicate with other devices or a communication network.

[0205] The embodiment of the present invention also provides a computer-readable storage medium. The above-mentioned method according to the embodiment of the present invention can be implemented in hardware, firmware, or implemented as a computer code that can be recorded in a storage medium, or implemented as a computer code that is originally stored in a remote storage medium or a non-temporary machine-readable storage medium and downloaded through a network and will be stored in a local storage medium, so that the method described herein can be stored in such software processing on a storage medium using a general-purpose computer, a dedicated processor, or programmable or dedicated hardware. Among them, the storage medium can be a magnetic disk, an optical disk, a read-only storage memory, a random access memory, a flash memory, a hard disk or a solid-state drive, etc.; further, the storage medium can also include a combination of the above-mentioned types of memory. It can be understood that a computer, a processor, a microprocessor controller or programmable hardware includes a storage component that can store or receive software or computer code. When the software or computer code is accessed and executed by a computer, a processor or hardware, the method shown in the above embodiment is implemented.

[0206] A portion of the present invention may be applied as a computer program product, such as a computer program instruction, which, when executed by a computer, can call or provide the method and / or technical solution according to the present invention through the operation of the computer. Those skilled in the art should understand that the form in which the computer program instruction exists in a computer-readable medium includes, but is not limited to, a source file, an executable file, an installation package file, etc. Accordingly, the way in which the computer program instruction is executed by the computer includes, but is not limited to: the computer directly executes the instruction, or the computer compiles the instruction and then executes the corresponding compiled program, or the computer reads and executes the instruction, or the computer reads and installs the instruction and then executes the corresponding installed program. Here, the computer-readable medium may be any available computer-readable storage medium or communication medium that can be accessed by the computer.

[0207] Although the embodiments of the present invention have been described with reference to the accompanying drawings, those skilled in the art may make various modifications and variations without departing from the spirit and scope of the present invention, and such modifications and variations should all be included in the scope of protection of the present invention.

Claims

1. A vehicle control method, characterized in that: The method comprises: Obtaining road surface information of the road on which the vehicle is located, and determining a target road surface type corresponding to the road surface information; determining a target friction coefficient for the vehicle when traveling on a road of the target road surface type; determining a basic safety distance of the vehicle according to the target friction coefficient; wherein the basic safety distance is not less than a minimum braking distance required for the vehicle to avoid collision with an obstacle; Determining a confidence level of the target friction coefficient, and adjusting the basic safety distance according to the confidence level to obtain a target safety distance for the vehicle; the target safety distance is not less than the basic safety distance; A control mode of the vehicle is determined according to the target safety distance and the distance between the vehicle and the obstacle.

2. The method according to claim 1, characterized in that Determining a target friction coefficient of the vehicle on the road of the target road type includes: determining a slip ratio of the vehicle; The target friction coefficient corresponding to the slip rate of the vehicle is determined according to a pre-calibrated correspondence between the slip rate and the friction coefficient under the target road surface type.

3. The method according to claim 2, characterized in that Determining the target road surface type corresponding to the road surface information includes: Performing road surface detection on the road surface information according to a preset road surface detection model, determining the probability values ​​of the road surface on which the vehicle is located belonging to various road surface types, and taking the road surface type with the highest probability value as the target road surface type to which the vehicle is located; The determining, based on a pre-calibrated correspondence between the slip rate and the friction coefficient under the target road surface type, a target friction coefficient corresponding to the slip rate of the vehicle includes: Determining the undetermined friction coefficients corresponding to the slip rates of the vehicle on various road types based on pre-calibrated correspondences between slip rates and friction coefficients on various road types; Determining weights corresponding to the undetermined friction coefficients according to probability values ​​of the road surface on which the vehicle is located belonging to various road surface types; The undetermined friction coefficients are weighted according to the weights corresponding to the respective undetermined friction coefficients to obtain a target friction coefficient of the road surface on which the vehicle is located.

4. The method according to claim 1, wherein Determining the basic safety distance of the vehicle according to the target friction coefficient includes: determining a target slope of the road on which the vehicle is located, and correcting the target friction coefficient according to the target slope; A basic safety distance of the vehicle is determined according to the corrected target friction coefficient.

5. The method according to claim 4, characterized in that Determining the target slope of the road on which the vehicle is located includes: Determining the pitch angles of the vehicle collected at multiple sampling moments within a target time period, and determining the slopes at corresponding sampling moments based on the pitch angles; Counting the variances corresponding to the slopes at multiple sampling moments within the target time period; When the variance is less than a preset value, the slope at the last sampling moment is used as the target slope; When the variance is greater than a preset value, the target slope is kept unchanged.

6. The method according to claim 1, characterized in that The adjusting the basic safety distance according to the confidence level to obtain the target safety distance of the vehicle includes: determining an adjustment coefficient of the basic safety distance according to the confidence level; wherein the adjustment coefficient and the confidence level are negatively correlated; The basic safety distance is adjusted according to the adjustment coefficient to determine the target safety distance of the vehicle.

7. The method according to claim 1, characterized in that The determining of the vehicle control mode according to the target safety distance and the distance between the vehicle and the obstacle includes: determining an obstacle distance between the vehicle and the obstacle; When the obstacle distance is greater than a first distance threshold, performing early warning processing; When the obstacle distance is between the first distance threshold and the second distance threshold, performing a braking operation on the vehicle at a first deceleration; When the obstacle distance is less than the second distance threshold, performing a braking operation on the vehicle at a second deceleration; The first distance threshold is greater than the second distance threshold, and the second distance threshold is greater than the target safety distance; and the first deceleration is less than the second deceleration.

8. A vehicle control device, characterized in that: The device comprises: A road surface recognition module is used to obtain road surface information of the road on which the vehicle is located and determine the target road surface type corresponding to the road surface information; a friction coefficient determination module, configured to determine a target friction coefficient of the vehicle when the vehicle is on a road of the target road type; a safety distance determination module, configured to determine a basic safety distance of the vehicle based on the target friction coefficient, wherein the basic safety distance is not less than a minimum braking distance required for the vehicle to avoid collision with an obstacle; determine a confidence level for the target friction coefficient, and adjust the basic safety distance based on the confidence level to obtain a target safety distance for the vehicle; wherein the target safety distance is not less than the basic safety distance; A control module is used to determine a control mode of the vehicle according to the target safety distance and the distance between the vehicle and the obstacle.

9. A computer device, characterized in that: include: A memory and a processor, wherein the memory and the processor are communicatively connected to each other, the memory stores computer instructions, and the processor executes the vehicle control method according to any one of claims 1 to 7 by executing the computer instructions.

10. A computer-readable storage medium, characterized in that The computer-readable storage medium stores computer instructions, and the computer instructions are used to enable a computer to execute the vehicle control method according to any one of claims 1 to 7.

Images