Safety evaluation method and device for automobile NOA system and storage medium

Abstract

The invention provides a safety evaluation method and device for an automobile NOA system and a storage medium, and relates to the technical field of navigation aided driving safety, and the method comprises the steps: constructing an initial weight through the direct influence and fluctuation influence of a scene factor on each evaluation index; and then the initial weight is corrected by adopting the secondary index, so that the accuracy of the index weight is improved, and the scientific rationality of the safety evaluation scheme is improved.

Description

Technical Field

[0001] The present invention relates to the technical field of navigation-assisted driving safety, and in particular to a safety evaluation method, device and storage medium for a vehicle NOA system. Background Art

[0002] NOA, or navigation automatic driving or navigation automatic assistance function, is essentially a deep integration of navigation technology and assisted driving function. In the current safety evaluation of NOA system, the evaluation results may be affected by factors such as the evaluator's personal experience, cognition and preference, and lack objectivity and consistency. This subjectivity may limit the accuracy and reliability of NOA system safety evaluation, thereby affecting the quality and effectiveness of decision-making.

[0003] In order to solve this problem, researchers have been seeking more objective and accurate methods to determine the weights of NOA system safety evaluation indicators. Some researchers have tried to use machine learning algorithms, data mining techniques, etc. to distribute weights to reduce the influence of subjective factors. However, these methods still have some problems, such as difficulty in interpretation and lack of generalization ability, which need further improvement and perfection.

[0004] Therefore, it is of great practical significance and theoretical value to conduct in-depth research and explore more objective and accurate methods to address the subjective problem of judging the weight of indicators in the safety evaluation of NOA systems. This can help us better understand the safety status of NOA systems, improve the accuracy and reliability of evaluation, and provide a more reliable basis for relevant decisions. At present, the commonly used methods for determining indicator weights in the evaluation field are hierarchical analysis method and entropy weight method, and both methods have certain limitations in terms of objectivity of evaluation and simplicity of calculation.

[0005] Based on the above problems, the present invention is proposed. Summary of the invention

[0006] In view of the above problems, the present invention proposes a safety evaluation method, device and storage medium for an automobile NOA system, which constructs initial weights through the direct impact and fluctuation impact of scenario factors on each evaluation indicator; then, secondary indicators are used to correct the initial weights, thereby improving the accuracy of the indicator weights and thus improving the scientific rationality of the safety evaluation scheme.

[0007] In a first aspect, the present invention provides a safety evaluation method for an automobile NOA system, comprising: Obtain multiple safety evaluation indicators of the car navigation and autonomous driving system, as well as multiple scenario factors for each evaluation indicator; Using statistical methods, determine the static influence degree of each scenario factor on each evaluation index, and construct a first influence vector of multiple scenario factors on each evaluation index according to the static influence degree; Using an experimental method, determine the dynamic impact degree of each scenario factor on each evaluation index when it fluctuates, and construct a second impact vector of multiple scenario factors on each evaluation index according to the dynamic impact degree; Determine an influence value of each evaluation indicator according to the first influence vector and the second influence vector; The impact value of each evaluation indicator is combined to obtain the initial weight of each evaluation indicator; Obtaining the target evaluation index with inaccurate initial weight, and splitting the target evaluation index into multiple secondary indicators, wherein the multiple secondary indicators include a severity indicator, an exposure probability indicator, and a controllability indicator; Determine the initial weight of each secondary indicator separately, and combine the initial weights of multiple secondary indicators to obtain the latest weight of the target evaluation indicator; The weighted sum of each evaluation index is performed according to the weight to obtain the safety evaluation result of the vehicle navigation automatic driving system.

[0008] In a second aspect, the present invention provides an electronic device, comprising: at least one processor, and a memory communicatively coupled to at least one of the processors; The memory stores instructions that can be executed by at least one of the processors, and the instructions are executed by at least one of the processors so that at least one of the processors can execute the safety evaluation method of the automobile NOA system described in any embodiment.

[0009] In a third aspect, the present invention provides a computer-readable storage medium having computer instructions stored thereon, wherein the computer instructions can be executed by one or more processors to implement the safety evaluation method of the automobile NOA system described in any embodiment.

[0010] Compared with the prior art, the present invention has the following technical effects: 1. The present invention uses statistical methods to obtain the static influence of the existence of scene factors on the evaluation indicators, and uses experimental methods to obtain the dynamic influence of the fluctuation of scene factors on the evaluation indicators. The influence value of each evaluation indicator is determined from both static and dynamic aspects, and the influence on the evaluation indicators is comprehensively evaluated. By combining the influence value of each evaluation indicator, the initial weight of each evaluation indicator is obtained, and different evaluation indicators are associated so that the initial weight can reflect the importance of each evaluation indicator among all evaluation indicators. The present invention also takes into account that when the initial weight is inaccurate, the initial weight is split into different types of secondary indicators, and the initial weights of the secondary indicators are combined to update the inaccurate initial weight.

[0011] 2. The present invention adopts a single-scenario analysis method to independently analyze the corresponding indicators in each scenario, so as to determine the weight of the corresponding evaluation indicators according to the characteristics of each scenario.

[0012] 3. The present invention introduces a judgment mechanism for whether the initial weight is accurate, so as to further improve the objectivity of determining the weight of the vehicle NOA system safety evaluation index by splitting the index and setting different secondary index weights. In this way, more accurate and objective vehicle NOA system safety evaluation index weights can be obtained, thereby better evaluating the safety performance of the vehicle.

[0013] The above description is only an overview of the technical solution of the present invention. In order to more clearly understand the technical means of the present invention, it can be implemented in accordance with the contents of the specification. In order to make the above description and other purposes, features and advantages of the present invention more obvious and easy to understand, the preferred embodiments are specifically cited and described in detail as follows. BRIEF DESCRIPTION OF THE DRAWINGS

[0014] Various other advantages and benefits will become apparent to those of ordinary skill in the art by reading the detailed description of the preferred embodiments below. The accompanying drawings are only for the purpose of illustrating the preferred embodiments and are not to be considered as limiting the present invention. Moreover, the same reference symbols are used throughout the accompanying drawings to represent the same components. In the accompanying drawings: Figure 1 A flowchart of a safety evaluation method for an automobile NOA system provided by an embodiment of the present invention; Figure 2 A structural diagram of an electronic device provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0015] The exemplary embodiments of the present disclosure will be described in more detail below with reference to the accompanying drawings. Although the exemplary embodiments of the present disclosure are shown in the accompanying drawings, it should be understood that the present disclosure can be implemented in various forms and should not be limited by the embodiments described herein. On the contrary, these embodiments are provided in order to enable a more thorough understanding of the present disclosure and to fully convey the scope of the present disclosure to those skilled in the art.

[0016] In the description of the present invention, unless otherwise clearly specified and limited, the terms "installed", "connected", "connected", "fixed" and the like should be understood in a broad sense, for example, it can be connected, detachably connected, or integrated; it can be mechanically connected or electrically connected; it can be directly connected or indirectly connected through an intermediate medium, it can be the internal connection of two elements or the interaction relationship between two elements. For ordinary technicians in this field, the specific meanings of the above terms in the present invention can be understood according to specific circumstances.

[0017] Example 1 The present invention proposes a safety evaluation method for a vehicle NOA system, which is suitable for evaluating the safety of a vehicle NOA system in a single scenario. Different from the method of evaluating various scenarios in the prior art, the present invention uses a single scene as the basic evaluation unit, sets corresponding evaluation indicators for each single scene, and thus uses personalized evaluation indicators to achieve safety evaluation of a single scene. The single scene is a scene of a certain driving condition, a certain weather scene, or a certain test scene. For example, a lane keeping scene, a rainy day obstacle avoidance scene.

[0018] See also Figure 1 , the method specifically comprises the following steps: S110, obtaining multiple safety evaluation indicators of the vehicle navigation automatic driving system and multiple scenario factors for each evaluation indicator.

[0019] For example, in the lane keeping scenario, multiple evaluation indicators include average speed, distance between the vehicle and the center line of the lane, distance between the vehicle and the yellow line, number of occurrences of acceleration and deceleration, and average steering wheel angle.

[0020] Each evaluation index is affected by multiple scene factors in a single scene, and the scene factors corresponding to different evaluation indicators can be the same or different. For example, the scene factors of average speed include: road adhesion coefficient, traffic density, etc. The scene factors of average steering wheel angle include: weather, road curvature, etc.

[0021] S120: Determine the static influence degree of each scenario factor on each evaluation index by using a statistical method, and construct a first influence vector of multiple scenario factors on each evaluation index according to the static influence degree.

[0022] Statistics is a comprehensive science that uses search, collation, analysis, and description of data to infer the nature of the measured object and even predict the future of the object. The present invention can collect the static influence of the existence of each scenario factor on each evaluation indicator from industry experts, and average the same static influence degree to obtain the static influence degree of each scenario factor on each evaluation indicator. Its essence is a value, which can be set in the range of -100~100. 0 means no influence, and negative values ​​indicate that the scenario factor has a negative influence on the evaluation indicator, which will reduce the safety performance of the NOA system. Positive values ​​indicate that the scenario factor has a positive influence on the evaluation indicator, which will improve the safety performance of the NOA system. The higher the absolute value of the static influence degree, the greater the influence degree of the scenario factor. Assume that there are m scenario factors and n evaluation indicators. Both m and n are integers greater than or equal to 1.

[0023] Preferably, the present invention abandons the specific value of the influence degree, no longer considers the change range of the influence degree, and sets a first threshold (eg, 10) and a second threshold (eg, -10). The first threshold is greater than the second threshold.

[0024] If the static influence degree exceeds the first threshold, the influence value of the evaluation index is set to the first value, for example, 1. If the static influence degree exceeds the second threshold and does not exceed the first threshold, the influence value of the evaluation index is set to the second value, for example, 0. If the static influence degree does not exceed the second threshold, the influence value of the evaluation index is set to a third value, for example, -1. Construct a first influence vector of multiple scenario factors on each evaluation index based on the first value, the second value, and the third value. Exemplarily, assuming that an evaluation index has 5 scenario factors, the first scenario factor of the evaluation index is K=[-1,0,-1,1,1], and each vector element in K is an influence value.

[0025] S130. Determine the dynamic influence degree of each scenario factor on each evaluation index when it fluctuates by using an experimental method, and construct a second influence vector of multiple scenario factors on each evaluation index according to the dynamic influence degree.

[0026] In some examples, each scene factor is fluctuated within a threshold range to construct a first data sequence for each scene factor; the first data sequence includes the fluctuation value of the scene factor; the threshold range is determined according to the type of the scene factor, for example, the threshold range of the road adhesion coefficient is, 0.1~1. According to the set step size 0.1, the first data sequence is determined in order from small to large: 0.1, 0.2, ..., 0.9, 1. Each value in each data sequence is used as a test condition to test the car navigation automatic driving system to obtain a second data sequence for each evaluation index; the second data sequence includes the value of the evaluation index. For example, a lane keeping test is performed under various adhesion coefficients to measure the values ​​of evaluation indicators such as average speed and distance from the center line of the lane. Then, the variance of the second data sequence is calculated, and the dynamic influence of each scene factor on each evaluation index when it fluctuates is determined according to the relationship between the variance and the set threshold. For example, multiple average speeds are obtained under different adhesion coefficients, and the variance of the average speed is calculated. If the variance is greater than the set threshold, it means that the average speed has obvious fluctuations and is greatly affected by the dynamic influence of the adhesion coefficient. If the evaluation index tends to be negative as the scenario factor gradually increases, that is, it is not conducive to the safety of the NOA system, the negative value of the variance of the evaluation index is used as the dynamic impact degree; if the evaluation index tends to be positive as the scenario factor gradually increases, that is, it is conducive to the safety of the NOA system, the positive value of the variance of the evaluation index is used as the dynamic impact degree. In the dynamic impact degree, 0 means no impact, a negative value means that the fluctuation of the scenario factor has a negative impact on the evaluation index, which will reduce the safety performance of the NOA system, and a positive value means that the scenario factor has a positive impact on the evaluation index, which will improve the safety performance of the NOA system. The higher the absolute value of the dynamic impact degree, the greater the dynamic impact degree of the scenario factor.

[0027] Preferably, the present invention abandons the specific value of the influence degree, no longer considers the change range of the influence degree, and sets a third threshold (eg, 5) and a fourth threshold (eg, -5). The third threshold is greater than the fourth threshold.

[0028] If the dynamic impact degree exceeds the third threshold, the impact value of the evaluation index is set to a fourth value, for example, 1. If the dynamic impact degree exceeds the fourth threshold and does not exceed the third threshold, the impact value of the evaluation index is set to a fifth value, for example, 0. If the dynamic impact degree does not exceed the fourth threshold, the impact value of the evaluation index is set to a sixth value, for example, -1. Construct a second impact vector of multiple scenario factors on each evaluation index based on the fourth value, the fifth value, and the sixth value. Exemplarily, assuming that an evaluation index has 5 scenario factors, the second scenario factor of the evaluation index is T=[1,0,-1,-1,-1], and each vector element in T is an impact value.

[0029] S140: Determine an influence value of each evaluation indicator according to the first influence vector and the second influence vector.

[0030] The influence values ​​of the same evaluation index in the first influence vector and the second influence vector are averaged to obtain the influence value of each evaluation index. For example, K=[-1,0,-1,1,1], T=[1,0,-1,-1,-1], the influence values ​​of the same scene factor are averaged to obtain the intermediate vector w=[0,0,-2,0,0]; then all the influence values ​​in w are added and divided by the number of scene factors to obtain the influence value weight=-0.4 of the evaluation index.

[0031] In some cases, a scenario factor may have a static positive impact and a dynamic negative impact on an evaluation indicator, which may be caused by experimental errors or unknown reasons. After averaging, the impact value is neutralized to 0, thus avoiding the impact of such situations on the final evaluation results.

[0032] S150, comprehensively analyzing the influence value of each evaluation indicator to obtain the initial weight of each evaluation indicator.

[0033] The influence value of each evaluation indicator is used as the numerator, and the absolute value of the influence value of each evaluation indicator is weighted and summed to obtain the denominator; the numerator is divided by the denominator to obtain the initial weight of each evaluation indicator. Optionally, refer to the following formula: ; in, is the i-th initial weight, is the influence value of the i-th evaluation index, d can be determined according to the importance of the evaluation index and can be set in advance. n is the number of evaluation indicators.

[0034] The initial weights are obtained by combining statistical knowledge and experiments, and have a certain degree of accuracy, but there are still inaccuracies, which may be caused by experimental errors. Therefore, the present invention selects the initial weights of those inaccurate evaluation indicators and continues to optimize.

[0035] S160: Acquire the target evaluation index with inaccurate initial weight, and split the target evaluation index into multiple secondary indexes.

[0036] A reasonable range of weights is set for each evaluation indicator based on expert experience. If the range is exceeded, it means that the initial weight is wrong. For example, in the scenario of vehicle obstacle avoidance, the reasonable range of the weight of the evaluation indicator of the distance between the vehicle and the obstacle is [0.8,1]. If the initial weight is not within the reasonable range, the distance between the vehicle and the obstacle is considered to be an inaccurate target evaluation indicator.

[0037] The various secondary indicators include severity indicators, exposure probability indicators and controllability indicators. For example, the target evaluation indicator is the lane keeping function, which is divided into severity indicator S: disengagement rate, object detection ability, and ability to avoid sudden intrusion of objects; controllability indicator C: vehicle speed control, vehicle direction control; exposure probability indicator E: traffic signal visibility, road construction area density, pedestrian and bicycle flow.

[0038] S170, respectively determine the initial weight of each secondary indicator, and combine the initial weights of multiple secondary indicators to obtain the latest weight of the target evaluation indicator.

[0039] Similar to the method of determining the initial weights of the evaluation indicators, the same method is used to determine the initial weights for each secondary indicator (such as detachment rate and object detection ability).

[0040] Specifically, a statistical method is used to determine the static influence of each scenario factor on each secondary indicator, and a third influence vector of multiple scenario factors on each secondary indicator is constructed based on the static influence. An experimental method is used to determine the dynamic influence of each scenario factor on each secondary indicator when it fluctuates, and a fourth influence vector of multiple scenario factors on each secondary indicator is constructed based on the dynamic influence. The influence value of each secondary indicator is determined based on the third influence vector and the fourth influence vector. The initial weight of each evaluation indicator is obtained by combining the influence value of each evaluation indicator under the comprehensive target evaluation indicator. Please refer to the process of determining the initial weight of the evaluation indicator for details, which will not be repeated here.

[0041] Then, the impact values ​​of the same secondary indicators are averaged to obtain the impact value of each secondary indicator. For example, the initial weight of the detachment rate is 0.3, the initial weight of the object detection ability is 0.2, and the initial weight of the ability to avoid sudden intrusion of objects is 0.1. Then the impact value of the severity indicator S is (0.3+0.2+0.1) / 3=0.2 Then, according to the first weight of the severity index S, the second weight of the exposure probability index E and the third weight of the controllability index C, the influence value of each secondary index is weighted and summed to obtain the latest weight of the target evaluation index. See the following formula, is the latest weight of the target evaluation index, is the first weight, is the second weight, is the third weight. S is the impact value of the severity index, E is the impact value of the exposure probability index, and C is the impact value of the controllability index.

[0042] ; The security and controllability of the system are the aspects that need to be focused on. Therefore, in view of the NOA security issue, the weights of S and C are increased, that is, the third weight> the first weight> the second weight, and the specific values ​​can be set by yourself.

[0043] S180. Perform weighted summation on each evaluation index according to the weight to obtain a safety evaluation result of the vehicle navigation automatic driving system.

[0044] Combined with the above description, for each evaluation indicator, if its initial weight is within a reasonable range, the initial weight is taken as the final weight; if its initial weight is not within a reasonable range, the latest weight is calculated by splitting the indicator, and the latest weight is taken as the final weight. The final weight of each evaluation indicator is used to perform weighted summation on each evaluation indicator to obtain the safety evaluation result of the car navigation automatic driving system in a single scenario, that is, a safety evaluation value.

[0045] Compared with the prior art, the present invention has the following technical effects: 1. The present invention uses statistical methods to obtain the static influence of the existence of scene factors on the evaluation indicators, and uses experimental methods to obtain the dynamic influence of the fluctuation of scene factors on the evaluation indicators. The influence value of each evaluation indicator is determined from both static and dynamic aspects, and the influence on the evaluation indicators is comprehensively evaluated. By combining the influence value of each evaluation indicator, the initial weight of each evaluation indicator is obtained, and different evaluation indicators are associated so that the initial weight can reflect the importance of each evaluation indicator among all evaluation indicators. The present invention also takes into account that when the initial weight is inaccurate, the initial weight is split into different types of secondary indicators, and the initial weights of the secondary indicators are combined to update the inaccurate initial weight.

[0046] 2. The present invention adopts a single-scenario analysis method to independently analyze the corresponding indicators in each scenario, so as to determine the weight of the corresponding evaluation indicators according to the characteristics of each scenario.

[0047] 3. The present invention introduces a judgment mechanism for whether the initial weight is accurate, so as to further improve the objectivity of determining the weight of the vehicle NOA system safety evaluation index by splitting the index and setting different secondary index weights. In this way, more accurate and objective vehicle NOA system safety evaluation index weights can be obtained, thereby better evaluating the safety performance of the vehicle.

[0048] Example 2 like Figure 2 As shown, an embodiment of the present application provides an electronic device, including: at least one processor; and a memory communicatively connected to at least one of the processors; wherein, The memory stores instructions executable by at least one of the processors, and the instructions are executed by at least one of the processors to enable at least one of the processors to perform the above method. At least one processor in the electronic device can perform the above method, and thus has at least the same advantages as the above method.

[0049] Optionally, the electronic device also includes interfaces for connecting various components, including high-speed interfaces and low-speed interfaces. The various components are connected to each other using different buses and can be installed on a common mainboard or installed in other ways as needed. The processor can process instructions executed in the electronic device, including instructions stored in or on a memory to display graphical information of a GUI (Graphical User Interface) on an external input / output device (such as a display device coupled to an interface). In other embodiments, if necessary, multiple processors can be used together with multiple memories, and / or multiple buses can be used together with multiple memories. Similarly, multiple electronic devices can be connected (for example, as a server array, a group of blade servers, or a multi-processor system), and each device provides some necessary operations. Figure 2 A processor 301 is taken as an example.

[0050] The memory 302 is a computer-readable storage medium that can be used to store software programs, computer executable programs and modules, such as program instructions / modules corresponding to the safety evaluation method of the automobile NOA system in the embodiment of the present application. The processor 301 executes various functional applications and data processing of the electronic device by running the software programs, instructions and modules stored in the memory 302, that is, realizes the above-mentioned safety evaluation method of the automobile NOA system.

[0051] The memory 302 may mainly include a program storage area and a data storage area, wherein the program storage area may store an operating system and at least one application required for a function; the data storage area may store data created according to the use of the electronic device, etc. In addition, the memory 302 may include a high-speed random access memory, and may also include a non-volatile memory, such as at least one disk storage device, a flash memory device, or other non-volatile solid-state storage device. In some examples, the memory 302 may further include a memory remotely arranged relative to the processor 301, and these remote memories may be connected to the electronic 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.

[0052] The electronic device may further include: an input device 303 and an output device 304. The processor 301, the memory 302, the input device 303 and the output device 304 may be connected via a bus or other means, and the figure takes the connection via a bus as an example.

[0053] The input device 303 can receive input digital or character information, and the output device 304 can include a display device, an auxiliary lighting device (e.g., LED), and a tactile feedback device (e.g., a vibration motor), etc. The display device can include, but is not limited to, a liquid crystal display (LCD), a light emitting diode (LED) display, and a plasma display. In some embodiments, the display device can be a touch screen.

[0054] Example 3 This embodiment provides a computer-readable storage medium, on which computer instructions are stored, and the computer instructions are used to enable the computer to execute the above method. The computer instructions on the computer-readable storage medium are used to enable the computer to execute the above method, and thus have at least the same advantages as the above method.

[0055] The computer-readable storage medium in this application may adopt any combination of one or more computer-readable media. The medium may be a computer-readable signal medium or a computer-readable storage medium. The medium may be, for example, but not limited to, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, device or device, or any combination thereof. More specific examples of the medium (a non-exhaustive list) include: an electrical connection with one or more 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 thereof. In this document, the medium may be any tangible medium containing or storing a program, which may be used by or in combination with an instruction execution system, device, or device.

[0056] Computer-readable signal media may include data signals propagated in baseband or as part of a carrier wave, which carry computer-readable program code. Such propagated data signals may take a variety of forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination of the above. Computer-readable signal media may also be any computer-readable medium other than a computer-readable storage medium, which may send, propagate, or transmit a program for use by or in conjunction with an instruction execution system, apparatus, or device.

[0057] The program code contained on the computer-readable medium can be transmitted using any appropriate medium, including but not limited to wireless, wire, optical cable, RF (Radio Frequency), etc., or any suitable combination of the above.

[0058] Computer program code for performing the operation of the present application can be written in one or more programming languages ​​or a combination thereof, including object-oriented programming languages, such as Java, Smalltalk, C++, and conventional procedural programming languages, such as "C" language or similar programming languages. The program code can be executed entirely on the user's computer, partially on the user's computer, as a separate software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In the case of a remote computer, 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 can be connected to an external computer (e.g., using an Internet service provider to connect through the Internet).

[0059] The above is only a preferred specific embodiment of the present invention, but the protection scope of the present invention is not limited thereto. Any changes or substitutions that can be easily thought of by a person skilled in the art within the technical scope disclosed by the present invention should be included in the protection scope of the present invention. Therefore, the protection scope of the present invention shall be based on the protection scope of the claims.

Claims

1. A safety evaluation method for an automobile NOA system, characterized in that: include: Obtain multiple safety evaluation indicators of the car navigation and autonomous driving system, as well as multiple scenario factors for each evaluation indicator; Using statistical methods, determine the static influence degree of each scenario factor on each evaluation index, and construct a first influence vector of multiple scenario factors on each evaluation index according to the static influence degree; Using an experimental method, determine the dynamic impact degree of each scenario factor on each evaluation index when it fluctuates, and construct a second impact vector of multiple scenario factors on each evaluation index according to the dynamic impact degree; Determine an influence value of each evaluation indicator according to the first influence vector and the second influence vector; The impact value of each evaluation indicator is combined to obtain the initial weight of each evaluation indicator; Obtaining the target evaluation index with inaccurate initial weight, and splitting the target evaluation index into multiple secondary indicators, wherein the multiple secondary indicators include a severity index, an exposure probability index, and a controllability index; Determine the initial weight of each secondary indicator separately, and combine the initial weights of multiple secondary indicators to obtain the latest weight of the target evaluation indicator; The weighted sum of each evaluation index is performed according to the weight to obtain the safety evaluation result of the vehicle navigation automatic driving system.

2. The method according to claim 1, characterized in that Using experimental methods, determine the dynamic impact of each scenario factor on each evaluation indicator when it fluctuates, including: Fluctuate each scenario factor within a threshold range to construct a first data sequence for each scenario factor; the first data sequence includes a fluctuation value of the scenario factor; Using each value in each data sequence as a test condition, testing the vehicle navigation automatic driving system, and obtaining a second data sequence for each evaluation index; the second data sequence includes the value of the evaluation index; The variance of the second data sequence is calculated, and the dynamic influence of each scenario factor on each evaluation index when it fluctuates is determined according to the relationship between the variance and the set threshold.

3. The method according to claim 1, characterized in that Constructing a first influence vector of multiple scenario factors on each evaluation index according to the static influence degree, including: If the static influence degree exceeds a first threshold, setting the influence value of the evaluation index to a first value; If the static influence degree exceeds the second threshold value and does not exceed the first threshold value, setting the influence value of the evaluation index to a second value; If the static influence degree does not exceed the second threshold, setting the influence value of the evaluation index to a third value; Constructing a first influence vector of multiple scene factors on each evaluation index according to the first value, the second value and the third value; the first threshold value is greater than the second threshold value; Constructing a second impact vector of multiple scenario factors on each evaluation index according to the dynamic impact degree, including: If the dynamic impact degree exceeds a third threshold, setting the impact value of the evaluation index to a fourth value; If the dynamic impact degree exceeds the fourth threshold value and does not exceed the third threshold value, setting the impact value of the evaluation index to a fifth value; If the dynamic impact degree does not exceed the fourth threshold, setting the impact value of the evaluation index to a sixth value; A second influence vector of multiple scenario factors on each evaluation index is constructed according to the fourth value, the fifth value and the sixth value; and the third threshold is greater than the fourth threshold.

4. The method according to claim 1, characterized in that: Determining the influence value of each evaluation indicator according to the first influence vector and the second influence vector includes: The influence values ​​of the same evaluation indicator in the first influence vector and the second influence vector are averaged to obtain the influence value of each evaluation indicator.

5. The method according to claim 1, characterized in that The impact value of each evaluation indicator is combined to obtain the initial weight of each evaluation indicator, including: The influence value of each evaluation index is taken as the numerator, and the absolute value of the influence value of each evaluation index is weighted and summed to obtain the denominator; The numerator is divided by the denominator to obtain the initial weight of each evaluation indicator.

6. The method according to claim 1, characterized in that Obtaining the target evaluation index for the inaccurate initial weight includes: Set a reasonable range of weights for each evaluation indicator; If the initial weight of an evaluation indicator exceeds the reasonable range, the evaluation indicator is used as the target evaluation indicator.

7. The method according to claim 1, characterized in that Determine the initial weight of each secondary indicator separately, including: Using statistical methods, determine the static influence degree of each scenario factor on each secondary indicator, and construct a third influence vector of multiple scenario factors on each secondary indicator according to the static influence degree; Using an experimental method, determine the dynamic impact degree of each scenario factor on each secondary indicator when it fluctuates, and construct a fourth impact vector of multiple scenario factors on each secondary indicator based on the dynamic impact degree; Determining an impact value of each secondary indicator according to the third impact vector and the fourth impact vector; The impact value of each evaluation indicator under the comprehensive target evaluation index is calculated to obtain the initial weight of each evaluation indicator.

8. The method according to claim 1, characterized in that The initial weights of various secondary indicators are combined to obtain the latest weights of the target evaluation indicators, including: The impact values ​​of the same secondary indicators are averaged to obtain the impact value of each secondary indicator; According to the first weight of the severity index, the second weight of the exposure probability index and the third weight of the controllability index, the impact value of each secondary index is weighted and summed to obtain the latest weight of the target evaluation index; Among them, the third weight>first weight>second weight.

9. An electronic device, characterized in that: include: at least one processor, and a memory communicatively coupled to at least one of the processors; The memory stores instructions that can be executed by at least one of the processors, and the instructions are executed by at least one of the processors to enable at least one of the processors to execute the safety evaluation method for the automobile NOA system according to any one of claims 1 to 8.

10. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores computer instructions, which can be executed by one or more processors to implement the safety evaluation method of the automobile NOA system according to any one of claims 1 to 8.

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