Control method and device of vehicle, vehicle and storage medium

Abstract

The application discloses a vehicle control method and device, a vehicle and a storage medium, and relates to the technical field of vehicle control. The disclosed vehicle control method comprises the following steps: acquiring a position error of an obstacle perceived during vehicle driving; performing chi-square test based on the position error of the obstacle to determine whether the obstacle is located in a safety area; if the obstacle is located in the safety area, acquiring the width of a lane where the obstacle is currently located; and if it is determined that the vehicle meets overtaking conditions according to the safety area and the width of the lane, controlling the vehicle to perform an overtaking action, so that the risk of vehicle collision can be reduced.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of vehicle control, in particular to a vehicle control method and device, a vehicle and a storage medium. BACKGROUND

[0002] Currently, in a complex road environment, a vehicle needs to face various obstacles such as vehicles, pedestrians, road facilities, etc., which pose a serious challenge to the safe driving of the vehicle. In order to ensure the safety of driving, the vehicle needs to actively avoid multiple obstacles in the road, which usually depends on the accurate identification of the obstacle position by the vehicle perception system.

[0003] Most of the existing driving obstacle avoidance techniques are based on an idealized assumption that the perception system is perfect and can accurately identify the obstacle position. Under this assumption, the vehicle can establish a safe driving corridor according to the obstacle position and make a passing decision accordingly.

[0004] However, in actual situations, due to the limitations of environmental perception sensor hardware, factors such as sensor accuracy, resolution, coverage range and environmental interference will cause perception errors, so that the vehicle perception system often cannot accurately identify the precise obstacle position. When the vehicle makes a passing decision based on the wrong obstacle position, there is a risk of vehicle collision. SUMMARY

[0005] The main purpose of the present application is to provide a vehicle control method, device, vehicle and storage medium, aiming to solve the problem of low obstacle position error identification accuracy during vehicle driving, which leads to the risk of vehicle collision.

[0006] To achieve the above purpose, the present application provides a vehicle control method, which comprises:

[0007] obtaining an obstacle position error perceived during vehicle driving;

[0008] performing a chi-square test based on the obstacle position error to determine whether the obstacle is located in a safe area;

[0009] if the obstacle is located in the safe area, obtaining the width of the lane where the obstacle is currently located;

[0010] if it is determined that the vehicle meets the passing condition according to the safe area and the width of the lane, controlling the vehicle to perform a passing action.

[0011] In an embodiment, the obstacle position error includes a lateral position error and a longitudinal position error between the vehicle and the obstacle, and the step of performing a chi-square test based on the obstacle position error to determine whether the obstacle is located in a safe area comprises:

[0012] determine a chi-square statistic based on the lateral position error and the longitudinal position error of the historical period;

[0013] determine a degree of freedom and a risk parameter;

[0014] determine a chi-square quantile based on the degree of freedom and the risk parameter, wherein the risk parameter represents a probability that the obstacle is located in a confidence region within the safety region;

[0015] determine whether the obstacle is located in the safety region according to the chi-square statistic and the chi-square quantile.

[0016] In an embodiment, the step of determining the chi-square statistic based on the lateral position error and the longitudinal position error of the historical period comprises:

[0017] determine a lateral position mean error and a lateral position error variance based on each of the lateral position errors in the historical period, and determine a longitudinal position mean error and a longitudinal position error variance based on each of the longitudinal position errors in the historical period;

[0018] determine a lateral position error standard deviation according to the lateral position error variance, and determine a longitudinal position error standard deviation according to the longitudinal position error variance;

[0019] perform standardization processing based on the lateral position error, the lateral position mean error and the lateral position error standard deviation to obtain a first standard random variable, and perform standardization processing based on the longitudinal position error, the longitudinal position mean error and the longitudinal position error standard deviation to obtain a second standard random variable;

[0020] obtain the chi-square statistic based on a sum of a square of the first standard random variable and a square of the second standard random variable.

[0021] In an embodiment, the step of determining whether the obstacle is located in the safety region according to the chi-square statistic and the chi-square quantile comprises:

[0022] if the chi-square statistic is less than or equal to the chi-square quantile, determine that the obstacle is located in the safety region;

[0023] if the chi-square statistic is greater than the chi-square quantile, determine that the obstacle is located outside the safety region.

[0024] In an embodiment, the step of controlling the vehicle to perform the overtaking action if it is determined that the vehicle satisfies the overtaking condition according to the safety region and the width of the lane comprises:

[0025] determining a virtual position of the vehicle according to the obstacle position, if it is determined that the vehicle satisfies the overtaking condition according to the safety zone and the width of the lane;

[0026] constructing a safety driving corridor of the vehicle according to the virtual position of the vehicle and the current position of the vehicle;

[0027] controlling the vehicle to drive along the safety driving corridor.

[0028] In an embodiment, the step of constructing the safety driving corridor of the vehicle according to the virtual position of the vehicle and the current position of the vehicle comprises:

[0029] path planning using a quintic polynomial curve according to the virtual position of the vehicle and the current position of the vehicle to obtain the safety driving corridor of the vehicle.

[0030] In an embodiment, the step of constructing the safety driving corridor of the vehicle according to the virtual position of the vehicle and the current position of the vehicle is followed by:

[0031] performing feasibility verification on the safety driving corridor to obtain a feasibility verification result and performing safety verification on the safety driving corridor to obtain a safety verification result;

[0032] if the feasibility verification result is that the safety driving corridor is feasible and the safety verification result is that the safety driving corridor is safe, then performing the step of controlling the vehicle to drive along the safety driving corridor.

[0033] In an embodiment, when the obstacle is a social vehicle, the step of, if the obstacle is located in the safety zone, obtaining the width of the lane in which the obstacle is currently located is followed by:

[0034] if it is determined that the vehicle does not satisfy the overtaking condition according to the safety zone and the width of the lane, then controlling the vehicle to follow the social vehicle to drive.

[0035] In an embodiment, the step of, if the obstacle is located in the safety zone, obtaining the width of the lane in which the obstacle is currently located is followed by:

[0036] determining a passable width of the vehicle in the lane according to the safety zone and the width of the lane;

[0037] obtaining a lateral safety driving threshold of the vehicle;

[0038] if a difference between the passable width and the lateral safety driving threshold is greater than a vehicle width, then determining that the vehicle satisfies the overtaking condition;

[0039] If the difference between the passable width and the lateral safe driving threshold is less than or equal to the vehicle width, it is determined that the vehicle does not meet the overtaking condition.

[0040] In addition, to achieve the above object, the present application also provides a control device of a vehicle, which comprises:

[0041] The first obtaining module is configured to obtain a position error of an obstacle perceived during driving of the vehicle.

[0042] The chi-square test module is configured to perform a chi-square test based on the position error of the obstacle to determine whether the obstacle is located in a safe area.

[0043] The second obtaining module is configured to obtain a width of a lane in which the obstacle is currently located if the obstacle is located in the safe area.

[0044] The overtaking module is configured to control the vehicle to perform an overtaking action if it is determined that the vehicle meets the overtaking condition based on the safe area and the width of the lane.

[0045] In addition, to achieve the above object, the present application also provides a vehicle, which comprises a memory, a processor, and a computer program stored in the memory and executable on the processor, and the computer program is configured to implement the steps of the control method of the vehicle as described above.

[0046] In addition, to achieve the above object, the present application also provides a storage medium, which is a computer-readable storage medium, and the storage medium stores a computer program, and the computer program is executed by a processor to implement the steps of the control method of the vehicle as described above.

[0047] The present application obtains the position error of the obstacle perceived during driving of the vehicle, performs a chi-square test based on the position error of the obstacle to determine whether the obstacle is located in a safe area. Since the chi-square test is mainly used to determine whether there is a significant difference between the perceived position error of the obstacle and the theoretical position error of the obstacle, when the obstacle is located in the safe area, it indicates that there is no significant difference between the position error of the obstacle and the theoretical position error of the obstacle, i.e., the current perceived position of the obstacle is accurate. At this time, when it is determined that the vehicle meets the overtaking condition based on the safe area corresponding to the accurate position of the obstacle, the width of the lane, and the vehicle width, the vehicle is controlled to perform an overtaking action, which can reduce the risk of vehicle collision. BRIEF DESCRIPTION OF DRAWINGS

[0048] The accompanying drawings, which are incorporated herein and form part of the specification, illustrate embodiments consistent with the present application and, together with the description, further serve to explain the principles of the application.

[0049] In order to more clearly illustrate the technical solutions of the embodiments of the present application or the prior art, the accompanying drawings needed to be used in the embodiments or prior art description will be briefly introduced as follows. Obviously, those skilled in the art can obtain other drawings according to these drawings without any creative effort.

[0050] Figure 1 A flowchart provided for a first embodiment of the vehicle control method of the present application;

[0051] Figure 2 A flowchart provided for a second embodiment of the vehicle control method of the present application;

[0052] Figure 3 A schematic diagram of the safety area of the present application;

[0053] Figure 4 A flowchart provided for a third embodiment of the vehicle control method of the present application;

[0054] Figure 5 A schematic diagram for the present application to perform path planning for a social vehicle to realize overtaking;

[0055] Figure 6 Another schematic diagram for the present application to perform path planning for multiple social vehicles to realize overtaking;

[0056] Figure 7 A schematic diagram of the module structure of the vehicle control device of the present application;

[0057] Figure 8 A schematic diagram of the vehicle structure of the present application.

[0058] The purpose of the present application, functional features and advantages will be further described with reference to the accompanying drawings. DETAILED DESCRIPTION

[0059] It should be understood that the specific embodiments described herein are merely intended to explain the technical solutions of the present application, and not to limit the present application.

[0060] In order to better understand the technical solutions of the present application, the following will be described in detail in conjunction with the drawings and specific embodiments of the specification.

[0061] Currently, most of the existing driving obstacle avoidance techniques are based on an idealized assumption that the perception system is perfect and can accurately identify the obstacle position. Under this assumption, the vehicle can establish a safe driving corridor according to the obstacle position and make a passing decision accordingly. However, in actual situations, due to the limitations of environmental perception sensor hardware, factors such as sensor accuracy, resolution, coverage range, and environmental interference will cause perception errors, making the vehicle perception system often unable to accurately identify the precise obstacle position. When the vehicle makes a passing decision based on the wrong obstacle position, there is a risk of vehicle collision.

[0062] To solve the above problems, the present application proposes a control method for a vehicle, the main technical solution includes: obtaining the obstacle position error perceived during the driving of the vehicle; performing chi-square test based on the obstacle position error to determine whether the obstacle is located in a safe area; if the obstacle is located in the safe area, obtaining the width of the lane where the obstacle is currently located; if it is determined that the vehicle meets the passing condition according to the safe area and the width of the lane, controlling the vehicle to perform a passing action.

[0063] By obtaining the obstacle position error perceived during the driving of the vehicle, and performing chi-square test based on the obstacle position error to determine whether the obstacle is located in a safe area, since chi-square test is mainly used to judge whether there is a significant difference between the perceived obstacle position error and the theoretical obstacle position error, when the obstacle is located in the safe area, it indicates that there is no significant difference between the obstacle position error and the theoretical obstacle position error, i.e. the current perceived obstacle position is accurate. At this time, based on the safe area corresponding to the accurate obstacle position, the width of the lane and the width of the vehicle, when the vehicle meets the passing condition, the vehicle is controlled to perform a passing action, which can reduce the risk of vehicle collision.

[0064] It should be noted that the execution subject of the embodiments of the present application can be a vehicle, for example, a vehicle-mounted terminal of the vehicle, and the control method of the vehicle is executed on the vehicle-mounted terminal. The execution subject of the embodiments of the present application can also be a computing service device with data processing, network communication and program running functions, such as a tablet computer, a personal computer, a mobile phone and the like. These computing service devices are connected with the vehicle-mounted terminal of the present application. The vehicle-mounted terminal can receive the obstacle position error perceived by the environment perception sensor and send the perceived obstacle position error to the computing service device. The control method of the vehicle is executed on the computing service device. After the computing service device processes the obstacle position error, the computing service device sends the overtaking action instruction to the vehicle-mounted terminal, and the vehicle-mounted terminal executes the corresponding overtaking action. Alternatively, the computing service device sends the processing result of whether the vehicle overtaking condition is met to the vehicle-mounted terminal, and the vehicle-mounted terminal determines whether to execute the overtaking action based on the processing result. In this way, through the cooperation of the vehicle-mounted terminal and the computing service device, the data processing pressure of the vehicle-mounted terminal can be reduced, and the vehicle-mounted terminal can have more computing power to perform other operations. The following describes the embodiments of the present application and the following embodiments by taking the vehicle as an execution subject and executing the control method of the vehicle on the vehicle.

[0065] It should be noted that the safe driving corridor mentioned in the embodiments of the present application is a virtual space range, which ensures that the vehicle maintains a sufficient safety distance from the obstacle during driving, thereby avoiding collision and other safety problems. It can be regarded as a drivable path for the vehicle to implement overtaking. The safe driving corridor is composed of multiple track points.

[0066] It should be noted that the chi-square test technology used in the embodiments of the present application is a hypothesis testing method in statistics. The chi-square statistic is used to measure the difference between the actual perceived obstacle position error and the theoretical obstacle position error, so as to determine whether there is a significant relationship between the two variables. If there is a significant difference, the original hypothesis is rejected, and it is considered that the actual perceived obstacle position error does not match the theoretical obstacle position error. Otherwise, the original hypothesis is accepted, and it is considered that the actual perceived obstacle position error matches the theoretical obstacle position error.

[0067] The original hypothesis here can be that the obstacle is located in the safe area. That is, when the actual perceived obstacle position error does not match the theoretical obstacle position error, it means that the perceived obstacle position is inaccurate. At this time, the original hypothesis is rejected, and it is determined that the obstacle position is located outside the safe area, and there is a risk of collision if overtaking is performed. When the actual perceived obstacle position error matches the theoretical obstacle position error, it means that the perceived obstacle position is accurate. At this time, the original hypothesis is accepted, and it is determined that the obstacle is located in the safe area, and overtaking can be performed.

[0068] Based on this, the embodiment of the present application provides a vehicle control method, referring to Figure 1 , Figure 1 The flowchart of the first embodiment of the vehicle control method of the present application is shown.

[0069] In this embodiment, the vehicle control method comprises:

[0070] Step S10, obtaining the obstacle position error perceived in the vehicle driving process;

[0071] It should be noted that the obstacle can be a dynamic obstacle such as a pedestrian or a social vehicle, or a static obstacle such as a road facility. The present application takes the social vehicle as an example, and finally realizes the purpose of passing or bypassing the front social vehicle or following the front social vehicle.

[0072] The number of the above-mentioned obstacles can be one or more.

[0073] The above-mentioned obstacles and the vehicle can be located in the same lane or different lanes. The obstacle can be located in front of the vehicle, such as the obstacle located in front of the current driving lane of the vehicle, or can be located behind or on the side of the vehicle, etc.

[0074] It should be noted that the obstacle position error refers to the difference between the actual perceived obstacle position and the theoretical obstacle position in the vehicle driving process. Due to the limitations of the hardware of the environment perception sensor, factors such as the accuracy, resolution, coverage range and environmental interference of the sensor will cause the perceived obstacle position in the vehicle driving process to have errors, and the obstacle position error will increase with the increase of the longitudinal position distance between the vehicle and the obstacle, so that the obstacle position cannot be accurately identified.

[0075] Obtaining the obstacle position error perceived in the vehicle driving process includes any of the following ways:

[0076] In a feasible implementation, the measurement of the obstacle position error is realized based on the ranging principle of the monocular camera. Specifically, the road image is captured by the monocular camera, and the image matching algorithm is used to identify the obstacle such as a car, a pedestrian, etc. According to the size, shape and other characteristics of the obstacle in the image, the distance of the obstacle is estimated by using a pre-established ranging model. The real distance of the obstacle is measured in the actual environment by using high-precision ranging devices such as laser range finder, radar, etc. The estimated distance is compared with the actually measured distance, and the obstacle position error perceived in the vehicle driving process is calculated.

[0077] In another possible implementation, the measurement of the obstacle position error during the vehicle driving process can be realized based on the ranging principle of the binocular camera. Specifically, the binocular camera is used to capture the road image simultaneously. The images of the left and right cameras are corrected to ensure that they are on the same plane, and the corresponding obstacle feature points in the left and right images are found. According to the coordinate difference of the obstacle feature points in the left and right images, the distance of the obstacle is calculated by using a three-dimensional reconstruction algorithm. The real distance of the obstacle is measured by using a high-precision ranging device, and the calculated distance is compared with the real distance to obtain the obstacle position error perceived during the vehicle driving process.

[0078] In another possible implementation, the obstacle position error perceived during the vehicle driving process can also be obtained based on the perception model training. Specifically, high-precision sensors such as lidar, millimeter wave radar, camera, etc. are used to collect obstacle position data perceived during the vehicle driving process in various driving environments. At the same time, the actual obstacle position data is recorded, which can be obtained by high-precision map, GPS positioning or other reliable means. The collected data is cleaned to remove noise and outliers. The data is labeled to clearly indicate the obstacle type, position, etc. information corresponding to each data point. According to the task requirements, the data is preprocessed by scaling, cropping, color space conversion, etc. to facilitate subsequent model training. According to the task requirements, a suitable perception model architecture is selected, such as convolutional neural network, recurrent neural network or their combination, etc. For the obstacle position perception task, a target detection model such as YOLO, Faster R-CNN, etc. or a semantic segmentation model such as U-Net, DeepLab, etc. can be considered. The perception model is trained using the preprocessed data set. During the training process, the hyperparameters of the model such as learning rate, batch size, optimizer, etc. are constantly adjusted to optimize the model performance. Regularization techniques such as Dropout, weight decay, etc. and early stopping strategy are used to avoid model overfitting. After the model training is completed, the performance of the model is evaluated using an independent validation data set. The position error of the obstacle is calculated by comparing the difference between the predicted obstacle position of the model and the actual obstacle position. The error can be measured by various indicators, such as mean square error, mean absolute error, accuracy, precision, recall and F1 score, etc. Finally, the trained perception model is deployed in the autonomous driving system for real-time perception of the obstacle position error.

[0079] In another possible implementation, high-precision positioning technology can also be used to obtain the position of the obstacle perceived by the vehicle during driving. For example, an autonomous fusion positioning method based on feature point calibration is used to determine the position of the obstacle, or a vehicle infrastructure cooperative positioning method is used to achieve more accurate positioning and perception through communication and data exchange between the vehicle and the road infrastructure. For example, a camera and radar at the edge of the lane are used to assist in perceiving and positioning the obstacle, and the position of the obstacle perceived by these devices is obtained. The position of the obstacle perceived by these devices is analyzed to obtain the position error of the obstacle perceived by the vehicle during driving.

[0080] In summary, obtaining the position error of the obstacle perceived by the vehicle during driving requires the use of various sensor technologies, multi-sensor fusion technologies, and high-precision positioning technologies. The comprehensive use of these technologies can achieve accurate perception and identification of the position error of the obstacle, and provide strong protection for the safe driving of the vehicle.

[0081] Step S20: performing chi-square test based on the position error of the obstacle to determine whether the obstacle is located in a safety area;

[0082] A safety area can be defined according to the position and driving direction of the vehicle, which is usually an area with a certain radius or boundary centered on the vehicle. It should be noted that the safety area is a spatial position area where the obstacle is expected to appear in the future, and the obstacle needs to be in the safety area. If the obstacle exceeds the safety area, there is a risk of collision with the vehicle.

[0083] In an embodiment, determining whether the obstacle is located in the safety zone based on the obstacle position error comprises: dividing the obstacle position error into several discrete regions, such as a safety zone, a dangerous zone, etc., which should be defined based on the actual application scenario and safety standards. A classification variable is defined to represent whether the obstacle is located in the safety zone, for example, 1 represents in the safety zone, and 0 represents not in the safety zone. According to the defined classification variable, the obstacle position error perceived during the vehicle driving is marked as in the safety zone or not in the safety zone. A contingency table is created to show the relationship between the obstacle position error and the safety zone. The contingency table should contain two dimensions: one dimension is the position of the obstacle divided by region, and the other dimension is whether the obstacle is located in the safety zone. The statistical method of chi-square test is used to analyze the data in the contingency table to determine whether there is a significant correlation between the obstacle position error and the safety zone. According to the result of chi-square test, it is determined whether there is a significant correlation between the obstacle position error and the safety zone. If the result is significant, it can be considered that there is some correlation between the obstacle position error and the safety zone, and if the result is not significant, it can be considered that there is no correlation between the obstacle position error and the safety zone, thereby achieving the purpose of determining whether the obstacle is located in the safety zone based on the obstacle position error by chi-square test.

[0084] In step S30, if the obstacle is located in the safety zone, the width of the lane where the obstacle is currently located is obtained.

[0085] In an embodiment, obtaining the width of the lane where the obstacle is currently located comprises: using image processing algorithms such as edge detection, Hough transform, etc. to recognize lane lines from camera images, and calculating the width of the lane according to the detected lane line position.

[0086] In an embodiment, obtaining the width of the vehicle comprises: establishing a database containing various vehicle models and their sizes. Using a camera and machine learning algorithms to identify the vehicle model of the current vehicle. According to the identified vehicle model, the corresponding vehicle width is queried from the database. Alternatively, if the vehicle itself is an autonomous vehicle and is equipped with corresponding sensors, the width of the vehicle can be directly measured using sensors such as ultrasonic sensors, cameras, etc. on the vehicle.

[0087] In step S40, if it is determined that the vehicle meets the overtaking condition according to the safety zone and the width of the lane, the vehicle is controlled to perform an overtaking action.

[0088] When overtaking, it is necessary to ensure that there is enough safety area for obstacles, so that the vehicle can deal with possible emergencies. This includes ensuring that there is enough space in front for acceleration after overtaking and returning to the original lane. Check the rearview mirror to ensure that the vehicle behind is far enough away to avoid being rear-ended during overtaking. When overtaking, especially on a two-way single lane, ensure that there is enough lateral safety distance from oncoming vehicles to prevent collisions with oncoming vehicles during overtaking.

[0089] In this embodiment, by acquiring the obstacle position error perceived during vehicle driving, a chi-square test is performed based on the obstacle position error to determine whether the obstacle is located in the safety area. Since the chi-square test is mainly used to determine whether there is a significant difference between the perceived obstacle position error and the theoretical obstacle position error, when the obstacle is located in the safety area, it indicates that there is no significant difference between the obstacle position error and the theoretical obstacle position error, i.e. the current perceived obstacle position is accurate. At this time, based on the safety area corresponding to the accurate obstacle position, the width of the lane and the width of the vehicle, it is determined that the vehicle meets the overtaking condition, and the vehicle is controlled to perform the overtaking action, which can reduce the risk of vehicle collision.

[0090] Based on the first embodiment of the present application, in the second embodiment of the present application, the same or similar contents as the above first embodiment can be referred to the above introduction, and will not be repeated hereinafter. On this basis, the obstacle position error includes the lateral position error and the longitudinal position error between the vehicle and the obstacle, and when performing the chi-square test, the lateral position error and the longitudinal position error need to be considered comprehensively to improve the accuracy of the obstacle position error. Specifically, referring to Figure 2 , step S20 comprises:

[0091] Step S21, based on the lateral position error and the longitudinal position error of the historical period, determine the chi-square statistic;

[0092] It should be noted that the historical period can be set according to actual conditions, and the lateral position error and the longitudinal position error can be obtained every preset time interval within the historical period. Assuming that the historical period is 5 minutes and the preset time interval is 10 seconds, the lateral position error and the longitudinal position error obtained can be the data corresponding to every 10 seconds in the past 5 minutes. At this time, there are multiple lateral position errors and longitudinal position errors obtained.

[0093] It should be noted that the lateral position error refers to the difference between the actual position of the social vehicle and the position of the social vehicle detected by the vehicle perception system in the lateral direction, i.e., the vertical direction to the driving direction of the vehicle. The lateral position error is usually manifested as that the vehicle perception system considers that the social vehicle is located at a certain lateral position of the lane line, while in fact the social vehicle is located on the other side of the lane line or deviates from the lane line. The longitudinal position error refers to the difference between the actual position of the social vehicle and the position of the social vehicle detected by the vehicle perception system in the longitudinal direction, i.e., the driving direction of the vehicle. The longitudinal position error is usually manifested as that the vehicle perception system considers that the social vehicle is located at a certain longitudinal position, while in fact the social vehicle is located at another longitudinal position.

[0094] It should be noted that the chi-square statistic is mainly used to measure the difference between the actual perceived obstacle position error and the theoretical obstacle position error. The calculation formula of the chi-square statistic is:

[0095] χ² = ∑((Oi - Ei)² / Ei).

[0096] wherein, Oi represents the actual perceived obstacle position error, Ei represents the theoretical obstacle position error, and ∑ represents the summation of all categories. The theoretical obstacle position error here can be the obstacle position average error. Since the lateral position error and the longitudinal position error are independent, the obstacle position error includes the lateral position average error and the longitudinal position average error.

[0097] In a feasible implementation, determining the chi-square statistic based on the lateral position error and the longitudinal position error obtained in the historical period includes: determining the lateral position average error and the lateral position error variance based on each lateral position error in the historical period, and determining the longitudinal position average error and the longitudinal position error variance based on each longitudinal position error in the historical period; determining the lateral position error standard deviation according to the lateral position error variance, and determining the longitudinal position error standard deviation according to the longitudinal position error variance; performing standardization processing based on the lateral position error, the lateral position average error and the lateral position error standard deviation to obtain a first standard random variable, and performing standardization processing based on the longitudinal position error, the longitudinal position average error and the longitudinal position error standard deviation to obtain a second standard random variable; obtaining the chi-square statistic based on the sum of the square of the first standard random variable and the square of the second standard random variable.

[0098] For example, assuming that the lateral position error is represented as , the longitudinal position error is represented as , the lateral position average error is represented as , the lateral position error variance is represented as , the longitudinal position average error is represented as , and the longitudinal position error variance is represented as The standard deviation of the lateral position error can be obtained by taking the square root of the variance of the lateral position error, denoted as The standard deviation of the longitudinal position error can be obtained by taking the square root of the variance of the longitudinal position error, denoted as . .

[0099] It is assumed that the lateral position error and the longitudinal position error form a bivariate normal distribution, and the lateral position error and the longitudinal position error are independent of each other. Therefore, two independent standard random variables conforming to a standard normal distribution can be obtained respectively:

[0100] .

[0101] wherein the first standard random variable is and the second standard random variable is The average error of the lateral position and the average error of the longitudinal position determine the center position of the corresponding standard normal distribution, and the standard deviation of the lateral position error and the standard deviation of the longitudinal position error determine the width or dispersion of the corresponding standard normal distribution. The average error of the lateral position and the average error of the longitudinal position can be set to 0, so that the center position of the corresponding standard normal distribution is at the 0 point, and the lateral position error and the longitudinal position error are symmetrically distributed around the corresponding 0 point.

[0102] It can be understood that by using the above method, even if the average value and the standard deviation of the lateral position error and the longitudinal position error are different, the corresponding first standard random variable and the second standard random variable can be calculated to compare the differences between the corresponding data points, so that the comparison and explanation between different data become easier.

[0103] Step S22, determine the degrees of freedom and the risk parameter;

[0104] It should be noted that considering that there are two independent standard random variables, the degrees of freedom here can be set to 2.

[0105] It should be noted that the risk parameter represents the probability of the obstacle appearing in the confidence region, and the confidence region is located in the safety region, and the confidence region becomes larger as the risk parameter increases. The risk parameter here can be pre-set according to the actual situation.

[0106] ​Step S23: Determine the chi-square quantile based on the degrees of freedom and the risk parameter, wherein the risk parameter represents the probability that the obstacle is located in the confidence region, and the confidence region is located in the safe region;

[0107] It should be noted that the chi-square quantiles mentioned above are used to describe the confidence range around the obstacle. The chi-square quantile refers to the critical value of the chi-square distribution at a given degree of freedom and significance level. The chi-square critical value is commonly used in chi-square tests to determine whether there is a significant difference between the perceived obstacle position error and the theoretical obstacle position error. This critical value is also used in statistical hypothesis testing to determine whether to reject the null hypothesis. Chi-square quantiles can be obtained by looking up a chi-square distribution table or by theoretical calculation using specific mathematical formulas, such as the probability density function and cumulative distribution function of the chi-square distribution.

[0108] It should be noted that the confidence level region mentioned above is derived based on the properties of the chi-square distribution. This confidence level region indicates that the population parameter has a certain probability of falling within this region, that is, the lateral position error and the longitudinal position error have a certain probability of falling within this region. Typically, a confidence level needs to be set, which is the aforementioned risk parameter. Then, the chi-square quantiles are determined based on the confidence level and degrees of freedom, and subsequently, a confidence level region is determined using the chi-square quantiles.

[0109] Reference Figure 3 As shown, the confidence region can be enclosed by an elliptical contour line. The variance of the lateral position error can be determined based on the lateral position error, and the variance of the longitudinal position error can be determined based on the longitudinal position error; the safe region can be determined based on the chi-square quantile, the variance of the lateral position error, and the variance of the longitudinal position error. The safe region is... Figure 3 The ellipse shown.

[0110] For example, the semi-major axis and semi-minor axis lengths of the ellipse boundary corresponding to the confidence region can be obtained according to the following formulas:

[0111] ;

[0112] ;

[0113] Where a represents Figure 3 The semi-major axis of the ellipse, b represents Figure 3 The semi-minor axis of the medium ellipse, Here are the chi-square quantiles.

[0114] Step S24: Determine whether the obstacle is located in a safe area based on the chi-square statistic and the chi-square quantile.

[0115] In the chi-square test, if the calculated chi-square statistic is greater than the chi-square quantile, the null hypothesis is rejected, considering that there is a significant difference between the perceived obstacle position error and the theoretical obstacle position error, and it is determined that the obstacle is located outside the safety area. Conversely, if the chi-square statistic is less than or equal to the chi-square quantile, the null hypothesis is not rejected, considering that there is no significant difference between the perceived obstacle position error and the theoretical obstacle position error, and it is determined that the obstacle is located in the safety area.

[0116] In this embodiment, the chi-square test is performed by comprehensively considering the lateral position error and the longitudinal position error, so as to improve the accuracy of identification of the obstacle position error.

[0117] Based on the above embodiments of the present application, in the third embodiment of the present application, the same or similar contents as the above embodiments can be referred to the above description, and will not be described hereinafter. On this basis, please refer to Figure 4 , step S30 comprises:

[0118] Step S31, if it is determined that the vehicle satisfies the overtaking condition according to the safety area and the width of the lane, then the virtual position of the vehicle is determined according to the obstacle position;

[0119] It should be noted that the virtual position of the vehicle refers to the position that the vehicle is expected to reach at a future time point, which is usually predicted based on the current speed, acceleration, driving direction and road conditions of the vehicle and other factors. The virtual position of the vehicle can also be the virtual center point position of the vehicle, which is the position of the vehicle predicted at the current position when the vehicle overtakes the obstacle. Referring to Figure 5 , the virtual position of the vehicle can be the position when the vehicle is parallel to the obstacle, for example, the center point position of the obstacle can be obtained, the virtual center point position of the vehicle is located on the same horizontal line as the center point position of the obstacle, and a certain safety distance is maintained between the virtual center point position of the vehicle and the obstacle.

[0120] In this embodiment, if the lane width is sufficient and the safety area is sufficient, the overtaking condition is satisfied, and the vehicle can smoothly perform the overtaking operation. At this time, the turn signal should be turned on in advance, the speed should be reduced, the safety distance with the overtaken vehicle should be maintained, and the situation of the opposite lane should be observed. If the lane width is insufficient or the safety area is insufficient, the overtaking condition is not satisfied, and it is not recommended to perform the overtaking operation.

[0121] Step S32, constructing a safety driving corridor of the vehicle according to the virtual position of the vehicle and the current position of the vehicle;

[0122] It should be noted that the current position of the vehicle refers to the actual position of the vehicle on the road at present.

[0123] It should be noted that the safe driving corridor is a collision-free driving path planned by the vehicle within a certain time and space range, which ensures that the vehicle maintains a sufficient safety distance from obstacles during driving, thereby avoiding collisions and other safety problems.

[0124] In a feasible implementation, a road model can be constructed using high-precision map data, including lane lines, road width, curvature, etc. Based on the current position, virtual position and road model of the vehicle, a path planning algorithm is used to determine the safe driving corridor of the vehicle. Specifically, a path planning algorithm such as A* algorithm, Dijkstra algorithm, etc. is used to generate a preliminary driving path. The preliminary path should avoid known obstacles and consider the driving constraints of the vehicle such as maximum steering angle, maximum driving speed, etc. On the basis of the preliminary path, according to the position of the obstacle and the obstacle avoidance safety distance after virtualization, the safe driving corridor is generated. The safe driving corridor should have a certain width to accommodate the driving error and possible lateral movement of the vehicle. The safe driving corridor can be represented by a polygon such as a rectangle or a more complex shape, and the specific shape should be determined according to the actual situation and algorithm performance. In addition, the generated safe driving corridor can be optimized to reduce unnecessary curvature and width changes, improve driving efficiency and safety. Alternatively, the safe driving corridor is adjusted and re-planned online according to sensor data and vehicle state to adapt to dynamically changing environment.

[0125] In another feasible implementation, with reference to Figure 5 The virtual position of the vehicle and the current position of the vehicle can also be used to plan the path using a quintic polynomial curve to obtain the safe driving corridor of the vehicle. Specifically, the following steps are included:

[0126] (1) The current position of the vehicle is taken as the starting point of the path, and the virtual position of the vehicle is taken as the end point of the path. The quintic polynomial curve is defined as: y(x) = ax 5 +bx 4 +cx 3 +dx 2 +ex+f, where x and y represent the horizontal coordinate and vertical coordinate on the path respectively.

[0127] (2) At the starting point and end point of the path, the continuity conditions of position, velocity and acceleration need to be met, which can be converted into boundary conditions and derivative conditions of the quintic polynomial curve.

[0128] (3) According to the constraint conditions, a system of equations is established, and numerical methods such as Gaussian elimination, matrix inversion, etc. are used to solve the system of equations to obtain the coefficients a, b, c, d, e, f of the quintic polynomial.

[0129] (4) Based on the quintic polynomial curve, the offset of the path is calculated considering the driving error and possible lateral movement of the vehicle. The offset can be determined according to the width of the vehicle, driving speed, road conditions, and obstacle avoidance safety distance, etc.

[0130] (5) Taking the quintic polynomial curve as the center line, the left and right safety boundary lines are generated according to the offset. The safety boundary line should have a certain width to accommodate the driving error and possible lateral movement of the vehicle. A polygon such as a rectangle or a more complex shape can be used to represent the safe driving corridor.

[0131] (6) During the generation of the safe driving corridor, real-time detection and avoidance of surrounding obstacles are required. If the obstacle is located within the safe driving corridor, the path needs to be re-planned to ensure the safe driving of the vehicle.

[0132] By using the quintic polynomial curve for path planning, the safe driving corridor of the vehicle is obtained, which can realize accurate planning and real-time adjustment of the vehicle's driving path, thereby improving the safety and reliability of vehicle driving.

[0133] In another possible implementation, referring to Figure 6 , if there are multiple social vehicles, it is necessary to check whether there is a conflict in obstacle avoidance, and the following conditions need to be met: respectively for each obstacle to establish a safe driving corridor, check whether the passing width of the virtual position of the vehicle meets the passable width. Respectively plan the quintic polynomial curve from point A to point B, and from point B to point C. If the obstacle avoidance from B to C does not meet the requirements, it indicates that there is a conflict, and the obstacle Obs2 needs to be followed, and the obstacle 1 needs to be avoided.

[0134] Step S33, controlling the vehicle to drive along the safe driving corridor.

[0135] The generated safe driving corridor can be converted into actual control instructions of the vehicle, such as steering angle, acceleration / braking instructions, etc. The vehicle control system is used to execute these instructions to realize overtaking. During overtaking, the deviation of the actual driving trajectory of the vehicle from the safe driving corridor can be monitored in real time, and the control instructions of the vehicle can be adjusted in time according to the deviation, to ensure that the vehicle always drives within the safe driving corridor, so as to avoid collision with obstacles.

[0136] In this embodiment, when the vehicle meets the overtaking conditions according to the safe area and the width of the lane, the safe driving corridor of the vehicle is constructed, which can realize accurate planning of the vehicle's driving path, thereby improving the safety and reliability of vehicle driving.

[0137] Further, in order to ensure that the vehicle can safely drive, after the safe driving corridor is constructed, the safe driving corridor can be subjected to feasibility verification and safety verification. Specifically, after step S32, the following steps are included:

[0138] Step S34, the feasibility verification result of the safe driving corridor is obtained by performing feasibility verification on the safe driving corridor, and the safety verification result of the safe driving corridor is obtained by performing safety verification on the safe driving corridor;

[0139] It should be noted that the feasibility verification result includes that the safe driving corridor is feasible or the safe driving corridor is not feasible, and the feasibility is verified by checking whether the maximum curvature of the path meets the non-holonomic constraint of the vehicle. The safety verification result includes that the safe driving corridor is safe or the safe driving corridor is not safe, and the safety is verified by checking whether the confidence region of each point of the path and the current obstacle collides.

[0140] If the feasibility verification result is that the safe driving corridor is feasible and the safety verification result is that the safe driving corridor is safe, step S33 is performed to control the vehicle to drive along the safe driving corridor.

[0141] In an embodiment, obtaining the feasibility verification result of the safe driving corridor by performing feasibility verification on the safe driving corridor includes: obtaining the curvature of each trajectory point of the safe driving corridor, wherein the safe driving corridor is composed of a plurality of trajectory points; if the inverse of the curvature of each trajectory point meets the minimum turning radius of the vehicle, it is determined that the feasibility verification result is that the safe driving corridor is feasible; if the inverse of the curvature of the trajectory point does not meet the minimum turning radius of the vehicle, it is determined that the feasibility verification result is that the safe driving corridor is not feasible.

[0142] In an embodiment, obtaining the safety verification result of the safe driving corridor by performing safety verification on the safe driving corridor includes: if each trajectory point region of the safe driving corridor and the obstacle region do not overlap, it is determined that the safety verification result is that the safe driving corridor is safe; according to the rear vehicle predicted trajectory, the self-vehicle obstacle avoidance time is deduced, and the position error of the rear vehicle at this time is deduced. If the transverse distance between the deduced self-vehicle and the rear vehicle is within a certain threshold, and the longitudinal distance is less than a certain safety distance, it is considered that there is a safety risk, and it is determined that the safe driving corridor is not safe, and the vehicle continues to follow without avoiding obstacles.

[0143] In this embodiment, by performing feasibility verification and safety verification on the safe driving corridor, it is ensured that the vehicle can safely drive.

[0144] Based on the above embodiments of the present application, in the fourth embodiment of the present application, the same or similar contents as the above embodiments can be referred to the above introduction, and will not be described hereinafter. On this basis, when the obstacle is a social vehicle, after step S30, the following steps are included:

[0145] Step S50, if it is determined that the vehicle does not meet the overtaking condition according to the safety area and the width of the lane, the vehicle is controlled to follow the social vehicle.

[0146] In this embodiment, when it is determined that the vehicle does not meet the overtaking condition according to the safety area and the width of the lane, the vehicle is controlled to follow the social vehicle to avoid collision between the vehicle and the social vehicle, and improve the driving safety of the vehicle.

[0147] Based on the above embodiments, in the fifth embodiment of the present application, the same or similar contents as the above embodiments can be referred to the above description, and will not be described hereinafter. On this basis, after step S30, it includes: determining whether the vehicle meets the overtaking condition, including: determining the passable width of the vehicle in the lane according to the safety area and the width of the lane; obtaining the lateral safety driving threshold of the vehicle; if the difference between the passable width and the lateral safety driving threshold is greater than the width of the vehicle, it is determined that the vehicle meets the overtaking condition; if the difference between the passable width and the lateral safety driving threshold is less than or equal to the width of the vehicle, it is determined that the vehicle does not meet the overtaking condition. Wherein, the passable width of the vehicle in the lane can be obtained according to the difference between the width of the lane and the width of the lane occupied by the safety area, for example, pass_width in the formula (1) represents. Figure 5 The lateral safety driving threshold can be a fixed value, or can be adjusted in real time according to different vehicle types and / or the width of the lane, for example, safe_buff in the formula (2) represents. Figure 5

[0148] In other embodiments, determining whether the vehicle meets the overtaking condition can also be: determining the passable width of the vehicle in the lane according to the safety area and the width of the lane, and determining that the vehicle meets the overtaking condition when the passable width is greater than the width of the vehicle, and determining that the vehicle does not meet the overtaking condition when the passable width is less than or equal to the width of the vehicle.

[0149] In this embodiment, if the difference between the passable width and the lateral safety driving threshold is greater than the width of the vehicle, it is determined that the vehicle meets the overtaking condition; if the difference between the passable width and the lateral safety driving threshold is less than or equal to the width of the vehicle, it is determined that the vehicle does not meet the overtaking condition, and the accurate judgment of overtaking is realized by setting the corresponding overtaking condition.

[0150] It should be noted that the above examples are only used for understanding the present application, and do not constitute a limitation on the control method of the vehicle of the present application. More forms of simple transformation based on this technical concept are within the protection scope of the present application.

[0151] ​Based on the same inventive purpose, the application further provides a control device of a vehicle, which comprises Figure 7 , and the control device of the vehicle comprises:

[0152] a first obtaining module 10, configured to obtain an obstacle position error perceived during vehicle driving;

[0153] a chi-square test module 20, configured to perform chi-square test based on the obstacle position error to determine whether the obstacle is located in a safety area;

[0154] a second obtaining module 30, configured to obtain a width of a lane where the obstacle is currently located if the obstacle is located in the safety area;

[0155] a passing module 40, configured to control the vehicle to perform a passing action if it is determined that the vehicle meets a passing condition based on the safety area and the width of the lane.

[0156] Optionally, the chi-square test module 20 is further configured to: determine a chi-square statistic based on lateral position errors and longitudinal position errors in a historical period; determine a degree of freedom and a risk parameter; determine a chi-square quantile based on the degree of freedom and the risk parameter, wherein the risk parameter represents a probability that the obstacle is located in a confidence area, and the confidence area is located in the safety area; and determine whether the obstacle is located in the safety area based on the chi-square statistic and the chi-square quantile.

[0157] Optionally, the chi-square test module 20 is further configured to: determine a lateral position mean error and a lateral position error variance based on each of the lateral position errors in the historical period, and determine a longitudinal position mean error and a longitudinal position error variance based on each of the longitudinal position errors in the historical period; determine a lateral position error standard deviation based on the lateral position error variance, and determine a longitudinal position error standard deviation based on the longitudinal position error variance; perform standardization processing based on the lateral position error, the lateral position mean error and the lateral position error standard deviation to obtain a first standard random variable, and perform standardization processing based on the longitudinal position error, the longitudinal position mean error and the longitudinal position error standard deviation to obtain a second standard random variable; and obtain the chi-square statistic based on a sum of squares of the first standard random variable and squares of the second standard random variable.

[0158] Optionally, the chi-square test module 20 is further configured to: determine that the obstacle is located in the safety area if the chi-square statistic is less than or equal to the chi-square quantile; and determine that the obstacle is located outside the safety area if the chi-square statistic is greater than the chi-square quantile.

[0159] Optionally, the overtaking module 40 is further configured to: determine a virtual position of the vehicle according to the obstacle position, if it is determined that the vehicle meets the overtaking condition according to the safety area and the width of the lane; construct a safety driving corridor of the vehicle according to the virtual position of the vehicle and the current position of the vehicle; and control the vehicle to drive along the safety driving corridor.

[0160] Optionally, the overtaking module 40 is further configured to: plan a path of the vehicle using a quintic polynomial curve according to the virtual position of the vehicle and the current position of the vehicle, to obtain the safety driving corridor of the vehicle.

[0161] Optionally, the overtaking module 40 is further configured to: perform feasibility verification on the safety driving corridor to obtain a feasibility verification result, and perform safety verification on the safety driving corridor to obtain a safety verification result; and perform the step of controlling the vehicle to drive along the safety driving corridor, if the feasibility verification result indicates that the safety driving corridor is feasible and the safety verification result indicates that the safety driving corridor is safe.

[0162] Optionally, the control device of the vehicle further comprises a following module, which is configured to control the vehicle to follow the social vehicle, if it is determined that the vehicle does not meet the overtaking condition according to the safety area and the width of the lane.

[0163] Optionally, the control device of the vehicle further comprises an overtaking judgment module, which is configured to: determine a passable width of the vehicle in the lane according to the safety area and the width of the lane; obtain a lateral safety driving threshold of the vehicle; determine that the vehicle meets the overtaking condition, if a difference between the passable width and the lateral safety driving threshold is greater than a vehicle width; and determine that the vehicle does not meet the overtaking condition, if the difference between the passable width and the lateral safety driving threshold is less than or equal to the vehicle width.

[0164] The control device of the vehicle provided in the present application adopts the control method of the vehicle in the above embodiments, and can solve the technical problem of low obstacle position error recognition accuracy during vehicle driving, which leads to vehicle collision risk. Compared with the prior art, the control device of the vehicle provided in the present application has the same beneficial effects as the control method of the vehicle provided in the above embodiments, and other technical features in the control device of the vehicle are the same as the features disclosed in the above embodiments, which will not be repeated here.

[0165] Based on the same inventive purpose, the application further provides a vehicle, comprising: at least one processor; and a memory connected with the at least one processor in communication; wherein the memory stores instructions executable by the at least one processor, and the instructions are executed by the at least one processor to enable the at least one processor to perform the control method of the vehicle in Embodiment I.

[0166] Reference will now be made to the following description Figure 8 , which shows a structural schematic diagram of a vehicle suitable for implementing embodiments of the application. The vehicle in embodiments of the application can include, but is not limited to, mobile terminals such as mobile phones, notebook computers, digital broadcast receivers, PDAs (Personal Digital Assistants), PADs (Portable Application Descriptions), PMPs (Portable Media Players), vehicle-mounted terminals (for example, vehicle-mounted navigation terminals), and the like, and fixed terminals such as digital TVs, desktop computers, and the like. Figure 8 The vehicle shown is merely an example and should not impose any limitation on the functions and use range of embodiments of the application.

[0167] As shown in Figure 8 , the vehicle can include a processing device 1001 (for example, a central processor, a graphics processor, or the like) that can perform various appropriate actions and processes according to programs stored in a read-only memory (ROM) 1002 or loaded from a storage device 1003 into a random access memory (RAM) 1004. Various programs and data required for operation of the vehicle are also stored in the RAM 1004. The processing device 1001, the ROM 1002, and the RAM 1004 are connected to each other through a bus 1005. An input / output (I / O) interface 1006 is also connected to the bus. In general, the following systems can be connected to the I / O interface 1006: input devices 1007 including, for example, a touch screen, a touch pad, a keyboard, a mouse, an image sensor, a microphone, an accelerometer, a gyroscope, and the like; output devices 1008 including, for example, a liquid crystal display (LCD), a speaker, a vibrator, and the like; the storage device 1003 including, for example, a magnetic tape, a hard disk, and the like; and a communication device 1009. The communication device 1009 can allow the vehicle to communicate wirelessly or by wire with other devices to exchange data. Although a vehicle with various systems is shown in the figure, it should be understood that it is not required to implement or have all the systems shown. More or fewer systems can be alternatively implemented or provided.

[0168] In particular, according to the embodiments disclosed in the present application, the process described above with reference to the flowchart can be implemented as a computer software program. For example, the embodiments disclosed in the present application include a computer program product comprising a computer program carried on a computer readable medium, the computer program containing program codes for executing the method shown in the flowchart. In such embodiments, the computer program can be downloaded and installed from a network through a communication device, or installed from the storage device 1003, or installed from the ROM 1002. When the computer program is executed by the processing device 1001, the above-mentioned functions defined in the method of the embodiments disclosed in the present application are executed.

[0169] The vehicle provided by the present application adopts the control method of the vehicle in the above-mentioned embodiments, and can solve the technical problem of low obstacle position error recognition accuracy during vehicle driving, which leads to the risk of vehicle collision. Compared with the prior art, the vehicle provided by the present application has the same beneficial effects as the control method of the vehicle provided by the above-mentioned embodiments, and other technical features in the vehicle are the same as those disclosed in the previous embodiment method, which will not be repeated here.

[0170] It should be understood that various parts of the present application can be realized by hardware, software, firmware or a combination thereof. In the description of the above-mentioned embodiments, specific features, structures, materials or characteristics can be combined in any one or more embodiments or examples in a suitable manner.

[0171] The above is only a specific implementation of the present application, but the protection scope of the present application is not limited thereto, and any person skilled in the art can easily think of changes or replacements within the technical scope disclosed in the present application, which should be covered within the protection scope of the present application. Therefore, the protection scope of the present application should be subject to the protection scope of the claims.

[0172] Based on the same invention purpose, the present application provides a computer readable storage medium having computer readable program instructions (i.e. computer program) stored thereon, the computer readable program instructions being used to execute the control method of the vehicle in the above-mentioned embodiments.

[0173] The computer readable storage medium provided in the present application may, for example, be a U disk, but is not limited to an electric, magnetic, optical, electromagnetic, infrared, or semiconductor system, system, or device, or any combination of the above. More specific examples of the computer readable storage medium can include, but are not limited to, an electric connection with one or more conductive wires, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the above. In the present embodiment, the computer readable storage medium can be any tangible medium containing or storing a program that can be used by or in conjunction with an instruction execution system, system, or device. The program code contained on the computer readable storage medium can be transmitted by any suitable medium, including but not limited to an electric wire, an optical cable, an RF (Radio Frequency), etc., or any suitable combination of the above.

[0174] The above computer readable storage medium can be contained in a vehicle, or can exist separately without being assembled into a vehicle.

[0175] The above computer readable storage medium carries one or more programs, which, when executed by the vehicle, cause the vehicle to obtain an obstacle position error perceived during driving of the vehicle; perform a chi-square test based on the obstacle position error to determine whether the obstacle is located in a safety area; if the obstacle is located in the safety area, obtain a width of a lane in which the obstacle is currently located; and if it is determined that the vehicle satisfies a passing condition according to the safety area and the width of the lane, control the vehicle to perform a passing action.

[0176] Computer program code for carrying out operations of the present application can be written in any combination of one or more programming languages, including an object oriented programming language such as Java, Smalltalk, C++ or the like and conventional procedural programming languages, such as the "C" programming language or similar programming languages. The program code can execute entirely on the user's computer, partly on the user's computer, as a stand-alone software package, partly on the user's computer and partly on a remote computer or entirely on the remote computer or server. In the latter scenario, the remote computer can be connected to the user's computer through any type of network, including a local area network (LAN) or a wide area network (WAN), or the connection can be made to an external computer (for example, through the Internet using an Internet Service Provider).

[0177] The flow diagrams and the block diagrams in the drawings are illustrations of architectures, functionalities, and operations of possible implementations of systems, methods, and computer program products according to various embodiments of the present application. In this regard, each block in the flow diagrams or block diagrams can represent a module, a segment, or a portion of code, which comprises one or more executable instructions for implementing the specified logical function(s). It should also be noted that in some alternative implementations, the functions noted in the blocks can occur out of the order noted in the figures. For example, two blocks shown in succession may, in fact, be executed substantially concurrently or the blocks may

[0178] The modules involved in the embodiments of the present application can be implemented in the form of software or in the form of hardware. In some cases, the name of the module does not constitute a limitation on the module itself.

[0179] The readable storage medium provided by the present application is a computer readable storage medium, which stores computer readable program instructions (i.e., a computer program) for executing the control method of the vehicle, and can solve the technical problem of low obstacle position error identification accuracy during vehicle driving, which leads to the risk of vehicle collision. Compared with the prior art, the computer readable storage medium provided by the present application has the same beneficial effects as the vehicle control method provided by the above-mentioned embodiments, and will not be described here.

[0180] The above merely describes some embodiments of the present application, and does not limit the patent scope of the present application. Any equivalent structural transformation, direct / indirect application in other related technical fields, or the like, which is made based on the technical concept of the present application and the content of the specification and drawings, is included in the patent protection scope of the present application.

Claims

1. A method for controlling a vehicle, characterized in that, The method includes: Obtain the positional error of obstacles perceived during vehicle operation; The chi-square statistic is determined based on the horizontal and vertical positional errors of historical time periods. Determine the degrees of freedom and risk parameters; The chi-square quantile is determined based on the degrees of freedom and the risk parameter, wherein the risk parameter represents the probability that the obstacle is located in the confidence region, and the confidence region is located in the safe region; Based on the chi-square statistic and the chi-square quantile, determine whether the obstacle is located in a safe area; If the obstacle is located within the safe zone, obtain the width of the lane where the obstacle is currently located; The permissible width of the vehicle in the lane is determined based on the width of the safety zone and the width of the lane; Obtain the lateral safe driving threshold of the vehicle; If, based on the difference between the passable width and the lateral safe driving threshold, and a comparison with the vehicle width, it is determined that the vehicle meets the overtaking conditions, the vehicle is controlled to perform an overtaking maneuver.

2. The method as described in claim 1, characterized in that, The steps for determining the chi-square statistic based on the horizontal and vertical positional errors of historical time periods include: The average lateral position error and the variance of the lateral position error are determined based on the lateral position errors within the historical period, and the average longitudinal position error and the variance of the longitudinal position error are determined based on the longitudinal position errors within the historical period. The standard deviation of the lateral position error is determined based on the variance of the lateral position error, and the standard deviation of the longitudinal position error is determined based on the variance of the longitudinal position error. The first standard random variable is obtained by standardizing the lateral position error, the average lateral position error, and the standard deviation of the lateral position error; and the second standard random variable is obtained by standardizing the longitudinal position error, the average longitudinal position error, and the standard deviation of the longitudinal position error. The chi-square statistic is obtained by summing the squares of the first and second standard random variables.

3. The method as described in claim 1, characterized in that, The step of determining whether the obstacle is located in a safe zone based on the chi-square statistic and the chi-square quantile includes: If the chi-square statistic is less than or equal to the chi-square quantile, the obstacle is determined to be located within the safe area; If the chi-square statistic is greater than the chi-square quantile, the obstacle is determined to be outside the safe zone.

4. The method according to any one of claims 1 to 3, characterized in that, The step of controlling the vehicle to perform an overtaking maneuver when it is determined that the vehicle meets the overtaking conditions based on the width of the safe area and the lane includes: If the vehicle meets the overtaking conditions based on the width of the safe area and the lane, then the virtual position of the vehicle is determined based on the position of the obstacle. Construct a safe driving corridor for the vehicle based on its virtual location and current location. Control the vehicle to travel along the safe driving corridor.

5. The method as described in claim 4, characterized in that, The step of constructing a safe driving corridor for the vehicle based on its virtual location and current location includes: Based on the vehicle's virtual location and current location, a path is planned using a fifth-order polynomial curve to obtain the vehicle's safe driving corridor.

6. The method as described in claim 5, characterized in that, After the step of constructing a safe driving corridor for the vehicle based on the vehicle's virtual location and current location, the method further includes: The feasibility of the safe driving corridor was verified to obtain the feasibility verification result, and the safety of the safe driving corridor was verified to obtain the safety verification result. If the feasibility verification result indicates that the safe driving corridor is feasible and the safety verification result indicates that the safe driving corridor is safe, then the step of controlling the vehicle to drive along the safe driving corridor is executed.

7. The method as described in claim 1, characterized in that, When the obstacle is a public vehicle, after the step of obtaining the width of the lane where the obstacle is currently located if the obstacle is located in the safe area, the method further includes: If, based on the width of the safe zone and the lane, it is determined that the vehicle does not meet the overtaking conditions, the vehicle is controlled to follow the other vehicles.

8. The method as described in claim 1 or 7, characterized in that, The method further includes: If the difference between the passable width and the lateral safe driving threshold is greater than the vehicle width, then the vehicle is determined to meet the overtaking conditions. If the difference between the passable width and the lateral safe driving threshold is less than or equal to the vehicle width, then the vehicle is determined not to meet the overtaking conditions.

9. A vehicle control device, characterized in that, The vehicle control device includes: The first acquisition module is used to acquire the position error of obstacles perceived during vehicle movement; The chi-square test module is used to determine the chi-square statistic based on the lateral and longitudinal positional errors over a historical period; determine the degrees of freedom and risk parameters; determine the chi-square quantiles based on the degrees of freedom and the risk parameters, wherein the risk parameters represent the probability that the obstacle is located in the confidence region, and the confidence region is located in the safe region; and determine whether the obstacle is located in the safe region based on the chi-square statistic and the chi-square quantiles. The second acquisition module is used to acquire the width of the lane where the obstacle is currently located if the obstacle is located in the safe area; The overtaking module is used to determine the passable width of the vehicle in the lane based on the width of the safe area and the lane; obtain the lateral safe driving threshold of the vehicle; and if the vehicle meets the overtaking conditions based on the comparison result between the difference between the passable width and the lateral safe driving threshold and the vehicle width, control the vehicle to perform an overtaking action.

10. A vehicle, characterized in that, The vehicle includes: a memory, a processor, and a computer program stored in the memory and executable on the processor, the computer program being configured to implement the steps of the vehicle control method as claimed in any one of claims 1 to 8.

11. A storage medium, characterized in that, The storage medium is a computer-readable storage medium, and a computer program is stored on the storage medium. When the computer program is executed by a processor, it implements the steps of the vehicle control method as described in any one of claims 1 to 8.

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