Vehicle state conversion method, device, electronic device, and storage medium

Abstract

The present application discloses a vehicle state conversion method, device, electronic device, and storage medium, including: obtaining a first reference index of a target vehicle; establishing a fuzzy state estimation model of the target vehicle based on the first reference index of the target vehicle and a preset fuzzy conversion matrix, and converting the fuzzy state estimation model to obtain a second reference index of the target vehicle, wherein the preset fuzzy conversion matrix includes a fuzzy evaluation vector; and using the corresponding index obtained according to the preset weight distribution in the second reference index of the target vehicle as the state conversion result of the target vehicle. By constructing a fuzzy state estimation model, the present application converts the operating state quantity of the target vehicle into a more intuitive safety index, comfort index, and efficiency index, thereby expanding the evaluation dimension of braking decisions and solving the problem of decision-making errors caused by a single evaluation index, so that the AEB system can execute emergency braking decisions more accurately, safely, and efficiently.

Description

Technical Field

[0001] The present application relates to the field of vehicle decision-making technology, and in particular to a vehicle state conversion method, device, electronic device, and storage medium. Background Art

[0002] As an active safety assistance system, the AEB system (Autonomous Emergency Braking) plays a vital role in reducing collisions and improving driving safety. In related technologies, the AEB system's TTC (time to collision) or predicted safe collision distance is often used as the sole indicator for early warning or emergency braking decisions.

[0003] However, the accuracy of TTC and safe collision distance calculations is heavily dependent on the target parameters of the sensory input. Due to the limited accuracy and stability of data acquired by the sensory module in existing technologies, inaccurate TTC and safe collision distance calculations are common. The single evaluation metric and its errors directly lead to deviations in the timing of AEB system warnings and braking, resulting in poor system safety and comfort. Summary of the Invention

[0004] The embodiments of the present application provide a vehicle state conversion method, device, electronic device, and storage medium to achieve the technical effects of effectively evaluating the operating state of the target vehicle, ensuring driving safety, and enabling the AEB system to make emergency braking decisions more accurately and efficiently.

[0005] The embodiments of this application adopt the following technical solutions:

[0006] According to one aspect of the present application, a vehicle state transition method is provided, the method comprising:

[0007] Obtaining a first reference index of a target vehicle;

[0008] Establishing a fuzzy state estimation model of the target vehicle according to a first reference index of the target vehicle and a preset fuzzy conversion matrix, and obtaining a second reference index of the target vehicle by calculating and converting the fuzzy state estimation model of the target vehicle, wherein the preset fuzzy conversion matrix includes a fuzzy evaluation vector;

[0009] The corresponding index obtained by distributing the preset weights in the second reference index of the target vehicle is used as the state transition result of the target vehicle.

[0010] Optionally, the state transition result of the target vehicle includes at least one of the following: a safety index of the target vehicle, a comfort index of the target vehicle, and an efficiency index of the target vehicle.

[0011] Among them, the safety index of the target vehicle is used to characterize the safe distance and the safe distance maintenance time, the comfort index of the target vehicle is used to characterize whether the acceleration is smooth, and the efficiency index of the target vehicle is used to characterize whether the speed is maintained and / or whether to change lanes.

[0012] Optionally, the fuzzy state estimation model of the target vehicle is established according to the first reference index of the target vehicle and a preset fuzzy conversion matrix, and the second reference index of the target vehicle is obtained by calculation and conversion using the fuzzy state estimation model of the target vehicle, wherein the preset fuzzy conversion matrix includes a fuzzy evaluation vector, including:

[0013] By constructing a conversion matrix performance optimization target, the preset fuzzy conversion matrix is optimized when establishing the fuzzy state estimation model of the target vehicle.

[0014] Optionally, the step of using the corresponding indicator obtained by distributing the second reference indicator of the target vehicle according to a preset weight as the state transition result of the target vehicle includes:

[0015] The state transition result output by the fuzzy state estimation model of the target vehicle is compensated by constructing a compensation fuzzy relationship model, wherein the compensation fuzzy relationship model is:

[0016] The second reference index at the next moment = the first weight * (the second reference index at the current moment - the second reference index at the previous moment) 2

[0017] + second weight (first reference index * preset fuzzy conversion matrix), wherein the first weight and the second weight are obtained by solving based on the experience set.

[0018] Optionally, obtaining a first reference index of the target vehicle includes:

[0019] The lateral relative position of the target vehicle, the longitudinal relative position of the target vehicle, the lateral relative speed of the target vehicle, the longitudinal relative speed of the target vehicle, and the offset angle of the target vehicle in the first reference index of the target vehicle are obtained, and a factor set is established.

[0020] Optionally, the fuzzy state estimation model of the target vehicle is established according to the first reference index of the target vehicle and a preset fuzzy conversion matrix, and the second reference index of the target vehicle is obtained by calculation and conversion using the fuzzy state estimation model of the target vehicle, wherein the preset fuzzy conversion matrix includes a fuzzy evaluation vector, including:

[0021] Establishing a preset fuzzy conversion matrix according to the fuzzy evaluation vectors of each factor, wherein the preset fuzzy conversion matrix is used to represent a fuzzy relationship from the factor set to the evaluation set, and the evaluation set includes the second reference indicator;

[0022] A fuzzy state estimation model of the target vehicle is established based on a ternary of {a first reference index, a second reference index, and a preset fuzzy conversion matrix};

[0023] A second reference index of the target vehicle is calculated based on the fuzzy state estimation model of the target vehicle.

[0024] Optionally, the method further includes:

[0025] When the speed of the target vehicle reaches a preset speed range, the evaluation set of the target vehicle starts to be converted and is used as a result of the second reference index of the target vehicle.

[0026] According to a second aspect of the present application, a vehicle state conversion device is provided, the device comprising:

[0027] An acquisition module, configured to acquire a first reference index of a target vehicle;

[0028] a conversion module, configured to establish a fuzzy state estimation model of the target vehicle according to a first reference index of the target vehicle and a preset fuzzy conversion matrix, and obtain a second reference index of the target vehicle by calculating and converting the fuzzy state estimation model of the target vehicle, wherein the preset fuzzy conversion matrix includes a fuzzy evaluation vector;

[0029] The allocation module is used to allocate the corresponding index obtained by allocating the second reference index of the target vehicle according to the preset weight as the state conversion result of the target vehicle.

[0030] According to a third aspect of the present application, an electronic device is provided, including:

[0031] processor; and

[0032] A memory arranged to store computer executable instructions, which when executed cause the processor to perform any of the methods described above.

[0033] According to the fourth aspect of the present application, a computer-readable storage medium is provided, which stores one or more programs. When the one or more programs are executed by an electronic device including multiple applications, the electronic device executes any of the methods described above.

[0034] At least one of the above technical solutions adopted in the embodiments of the present application can achieve the following beneficial effects:

[0035] Acquire a first reference index of the target vehicle; establish a fuzzy state estimation model of the target vehicle based on the first reference index of the target vehicle and a preset fuzzy conversion matrix; calculate and convert the second reference index of the target vehicle through the fuzzy state estimation model of the target vehicle, wherein the preset fuzzy conversion matrix includes a fuzzy evaluation vector; use the corresponding index obtained according to the preset weight distribution in the second reference index of the target vehicle as the state conversion result of the target vehicle. The present application converts the real-time state information of the target vehicle into safety index, comfort index and efficiency index through the fuzzy state estimation model, solving the problem of AEB system decision-making errors or deviations caused by a single evaluation index. It can not only effectively and accurately evaluate the operating status of the target vehicle, but also ensure driving safety, so that the AEB system can more accurately and efficiently execute emergency braking decisions, greatly improving driving comfort.

[0036] The above description is only an overview of the technical solution of the present application. In order to more clearly understand the technical means of the present application, it can be implemented in accordance with the contents of the specification. In order to make the above and other purposes, features and advantages of the present application more obvious and easy to understand, the specific implementation methods of the present application are listed below. BRIEF DESCRIPTION OF THE DRAWINGS

[0037] The drawings described herein are used to provide a further understanding of the present application and constitute a part of the present application. The illustrative embodiments of the present application and their descriptions are used to explain the present application and do not constitute an improper limitation on the present application. In the drawings:

[0038] Figure 1 A schematic flow chart of a vehicle state conversion method in one embodiment of the present application;

[0039] Figure 2 A conversion effect diagram of a preset fuzzy conversion matrix in a vehicle state conversion method in one embodiment of the present application;

[0040] Figure 3 This is a schematic structural diagram of a vehicle state conversion device in one embodiment of the present application;

[0041] Figure 4 This is a schematic structural diagram of an electronic device in one embodiment of the present application;

[0042] Figure 5 This is a schematic diagram of the structure of a computer-readable storage medium in one embodiment of the present application. DETAILED DESCRIPTION

[0043] To make the purpose, technical solutions, and advantages of this application more clear, the technical solutions of this application will be clearly and completely described below in conjunction with the specific embodiments of this application and the corresponding drawings. Obviously, the embodiments described are only part of the embodiments of this application, not all of them. Based on the embodiments in this application, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of this application.

[0044] The AEB system is an active automotive safety technology. Its operating principle is to use sensors or radars in the perception module to measure the distance between the vehicle and the vehicle in front, and use the vehicle information obtained from the perception to determine the collision time between the vehicle and the target vehicle, and then complete braking control through the vehicle's braking system. As a result, even in emergency situations where the driver does not have time to take braking measures, the AEB system can be relied upon to ensure safe travel. However, the safety models used in related technologies are based on collision time or collision distance, and their evaluation indicators for early warning or vehicle emergency braking decisions are relatively simple, which directly leads to poor safety, accuracy, and comfort of the vehicle system. Based on this, the embodiments of the present application propose a vehicle state conversion method to achieve the technical effect of effectively and intuitively evaluating the operating status of the target vehicle, expanding the dimensionality of the evaluation indicators, ensuring driving safety, and enabling the AEB system to make emergency braking decisions more accurately and efficiently.

[0045] The technical concept of this application is to establish a fuzzy state estimation model for the target vehicle, transform the acquired first reference index containing the target vehicle's state information through computation, solve the value of a preset fuzzy transformation matrix, and obtain the second reference index of the target vehicle, which is then distributed according to weights to obtain the state transformation result of the target vehicle. Simulation verification shows that this application can accurately and effectively obtain the evaluation index of the target vehicle, and apply three more intuitive safety indexes, comfort indexes, and efficiency indexes to the AEB system braking decision model. This not only expands the evaluation dimensions of vehicle emergency braking decisions and effectively solves the problem of AEB system decision errors or deviations caused by a single evaluation index, but also accurately evaluates the operating state of the target vehicle to ensure driving safety. At the same time, it enables the AEB system to more efficiently execute emergency braking decisions, greatly improving driving comfort.

[0046] The technical solutions provided by the embodiments of the present application are described in detail below with reference to the accompanying drawings.

[0047] like Figure 1 As shown, the method includes the following steps S110 to S130:

[0048] Step S110: obtaining a first reference index of the target vehicle.

[0049] In an embodiment of the present application, the perception module of the vehicle system can be used to obtain surrounding environmental information, including the real-time status and historical status of the vehicle and the target vehicle. After identifying, processing and analyzing the environmental information, the first reference index of the target vehicle is obtained.

[0050] Specifically, in one embodiment of the present application, obtaining the first reference index of the target vehicle includes: obtaining the lateral relative position of the target vehicle, the longitudinal relative position of the target vehicle, the lateral relative speed of the target vehicle, the longitudinal relative speed of the target vehicle, and the offset angle of the target vehicle in the first reference index of the target vehicle, and establishing a factor set as follows:

[0051]

[0052] In this embodiment, the factors of the first reference index U of the target vehicle include: the lateral relative position d of the target vehicle P , the longitudinal relative position d of the target vehicle H , the lateral relative velocity v of the target vehicle P , the longitudinal relative velocity v of the target vehicle H , the offset angle of the target vehicle

[0053] Step S120, establishing a fuzzy state estimation model of the target vehicle according to the first reference index of the target vehicle and a preset fuzzy conversion matrix, and obtaining a second reference index of the target vehicle by calculating and converting the fuzzy state estimation model of the target vehicle, wherein the preset fuzzy conversion matrix includes a fuzzy evaluation vector.

[0054] Specifically, by constructing a fuzzy state estimation model, the state quantity information such as real-time speed, historical speed, real-time position, historical position, real-time offset angle, historical offset angle, etc. contained in the first reference index U of the target vehicle is converted into a fuzzy state through a preset fuzzy transformation matrix R, and the second reference index V of the target vehicle is obtained.

[0055] In one embodiment of the present application, a fuzzy state estimation model of the target vehicle is established based on the first reference index of the target vehicle and a preset fuzzy conversion matrix, and the fuzzy state estimation model of the target vehicle is established. The second reference index of the target vehicle is obtained by calculation and conversion through the fuzzy state estimation model of the target vehicle, wherein the preset fuzzy conversion matrix includes a fuzzy evaluation vector, including: establishing a preset fuzzy conversion matrix based on the fuzzy evaluation vectors of each factor, wherein the fuzzy conversion matrix is used to characterize a fuzzy relationship from the factor set to the evaluation set, and the evaluation set includes the second reference index; establishing the fuzzy state estimation model of the target vehicle based on the ternary of {first reference index, second reference index, preset fuzzy conversion matrix}; and calculating the second reference index of the target vehicle based on the fuzzy state estimation model of the target vehicle.

[0056] The implementation is as follows:

[0057] Preset factor set composed of the first reference indicator The evaluation set composed of the second reference index V={δ s ,δ c ,δ e},

[0058] Among them, d P represents the lateral relative position of the target vehicle, d H Indicates the longitudinal relative position of the target vehicle, v P represents the lateral relative velocity of the target vehicle, v H represents the longitudinal relative velocity of the target vehicle, represents the offset angle of the target vehicle; δ s represents the safety index of the target vehicle, δ c represents the comfort index of the target vehicle, δ e Represents the efficiency index of the target vehicle.

[0059] A fuzzy transformation matrix R is preset. The fuzzy transformation matrix R is composed of the fuzzy evaluation vector R i =r i1 ,r i2 ,…,r im ), where r i1 -r im They represent the membership of each factor to the state in the evaluation set, reflecting a fuzzy relationship from the factor set to the evaluation set. The fuzzy evaluation vector R i The matrix relationship is specifically expressed as follows:

[0060]

[0061] It can be understood that the fuzzy state estimation model of the target vehicle is composed of the (U, V, R) triplet, and the conversion relationship in the fuzzy state estimation model of the target vehicle is: V = U × R,

[0062] Based on the above model and its conversion relationship, the fuzzy state conversion of the target vehicle can be realized. Substituting the various parameters in the above factor set and evaluation set, we get:

[0063]

[0064]

[0065] Step S130 , using the corresponding index obtained by distributing the second reference index of the target vehicle according to the preset weight as the state transition result of the target vehicle.

[0066] Because the state at the previous moment can affect the current state, a rolling optimization and feedback correction approach is necessary to obtain the true state indicator. In this embodiment, by constructing a compensatory fuzzy relationship model, the second reference indicator is assigned according to preset weights to obtain the corresponding indicator at the next moment. This corresponding indicator serves as the state transition result for the target vehicle. This further enables the evaluation of the target vehicle's braking risk factors and braking risk probability, providing an accurate and effective basis for the subsequent emergency braking decision-making of the AEB system.

[0067] In one embodiment of the present application, the state transition result of the target vehicle includes at least one of the following: the safety index of the target vehicle, the comfort index of the target vehicle, and the high efficiency index of the target vehicle. Specifically, the safety index of the target vehicle is used to characterize the safe distance between the target vehicle and the self-vehicle and the safe distance maintenance time, the comfort index of the target vehicle is used to characterize whether the acceleration of the target vehicle is smooth, and the high efficiency index of the target vehicle is used to characterize whether the speed of the target vehicle is maintained and / or whether the target vehicle changes lanes.

[0068] In one embodiment of the present application, a fuzzy state estimation model of the target vehicle is established based on the first reference index of the target vehicle and a preset fuzzy conversion matrix, and the second reference index of the target vehicle is obtained by calculating and converting the fuzzy state estimation model of the target vehicle, wherein the preset fuzzy conversion matrix includes a fuzzy evaluation vector, including: optimizing the preset fuzzy conversion matrix when establishing the fuzzy state estimation model of the target vehicle by constructing a conversion matrix performance optimization target.

[0069] Furthermore, in an embodiment of the present application, the corresponding indicator obtained by distributing the preset weights in the second reference indicator of the target vehicle as the state transition result of the target vehicle includes: compensating the state transition result output by the fuzzy state estimation model of the target vehicle by constructing a compensation fuzzy relationship model, wherein the compensation fuzzy relationship model is:

[0070] The second reference index at the next moment = the first weight * (the second reference index at the current moment - the second reference index at the previous moment) 2

[0071] + second weight (first reference index * preset fuzzy conversion matrix), wherein the first weight and the second weight are obtained by solving based on the experience set.

[0072] Specifically, in order to avoid the influence of the state at the previous moment on the state at the current moment, a more realistic state indicator can be obtained through rolling optimization. Therefore, the compensation fuzzy relationship model is constructed as follows:

[0073] V(t+1)=σ[V(t)-V(t-1)] 2 +τ(U×R)

[0074] Among them, σ represents the first weight, τ represents the second weight, V(t+1), V(t), and V(t-1) represent the second reference indicators of the target vehicle at time t+1, time t, and time t-1, respectively. It can be understood that time t in this application can be any time.

[0075] Since certain errors will occur in the calculation process of solving the second reference index by presetting the fuzzy transformation matrix, in order to further solve the limitations of the fuzzy state estimation model, a transformation matrix performance optimization target is constructed:

[0076]

[0077] The meaning of each parameter is as follows:

[0078] δ s Represents the safety index, used to characterize the safety distance and the safety distance maintenance time, Δδ s represents the difference between the safety index converted by the preset fuzzy conversion matrix and the real safety index, δ c Represents the comfort index, which is strongly correlated with acceleration smoothness, Δδ c represents the difference between the comfort index converted by the preset fuzzy conversion matrix and the real comfort index, δ e Represents the efficiency index, which is used to characterize whether the target vehicle maintains speed and / or changes lanes, Δδ erepresents the difference between the high efficiency index converted by the preset fuzzy conversion matrix and the real high efficiency index;

[0079] Among them, the above-mentioned real safety index, real comfort index and real efficiency index are all obtained through expert experience scoring; t0 represents the initial sampling time of the solution, t f represents the end sampling time of the solution; α, β, and γ represent different performance optimization parameters. In a preferred embodiment of the present application, the value of α is 0.85, the value of β is 0.41, and the value of γ is 0.37.

[0080] In this embodiment, the compensation fuzzy relationship model is solved in combination with the above-mentioned conversion matrix performance optimization goal to obtain the conversion matrix and the first and second weights as follows:

[0081]

[0082] Therefore, the corresponding index V(t+1) obtained according to the weight distribution can be used as the state transition result of the target vehicle, and combined with the result, a decision can be made as to whether the target vehicle needs to take emergency braking measures.

[0083] In some embodiments of the present application, the method further includes: when the speed of the target vehicle reaches a preset speed range, starting to convert the evaluation set of the target vehicle and using it as the result of the second reference indicator of the target vehicle.

[0084] like Figure 2 As shown, simulations conducted in the present application demonstrate that the compensation fuzzy relationship model constructed in the aforementioned embodiment can meet driving needs and usage requirements when the target vehicle speed is 38-82 kph, and can accurately predict the numerical results of the evaluation set after the target vehicle is converted and the evaluation set under real conditions, with an accuracy rate exceeding 50%. In particular, it can achieve an accuracy rate exceeding 90% when the vehicle speed is 60 kph.

[0085] The above simulation results show that the state quantity of the target vehicle can be converted into safety indicators, comfort indicators and efficiency indicators through the fuzzy state estimation model and the compensation fuzzy relationship model. The present application achieves the technical effect of effectively and accurately predicting and evaluating the operating state of the target vehicle; at the same time, the state conversion result of the target vehicle determined based on the first reference indicator and the second reference indicator in the present application can be used as an evaluation indicator for the emergency braking decision of the AEB system, and achieves the effect of enabling the system to make emergency braking decisions more accurately, safely and efficiently while ensuring safety.

[0086] In the embodiment of the present application, a vehicle state conversion device 300 is also provided. Figure 3 As shown, the device includes:

[0087] The acquisition module 310 is configured to acquire a first reference index of the target vehicle.

[0088] In an embodiment of the present application, the perception module of the vehicle system can be used to obtain surrounding environmental information, including the real-time status and historical status of the vehicle and the target vehicle. After identifying, processing and analyzing the environmental information, the first reference index of the target vehicle is obtained.

[0089] Specifically, in one embodiment of the present application, obtaining the first reference index of the target vehicle includes: obtaining the lateral relative position of the target vehicle, the longitudinal relative position of the target vehicle, the lateral relative speed of the target vehicle, the longitudinal relative speed of the target vehicle, and the offset angle of the target vehicle in the first reference index of the target vehicle, and establishing a factor set as follows:

[0090]

[0091] In this embodiment, the factors of the first reference index U of the target vehicle include: the lateral relative position d of the target vehicle P , the longitudinal relative position d of the target vehicle H , the lateral relative velocity v of the target vehicle P , the longitudinal relative velocity v of the target vehicle H , the offset angle of the target vehicle

[0092] a conversion module 320 for establishing a fuzzy state estimation model of the target vehicle based on the first reference index of the target vehicle and a preset fuzzy conversion matrix, and obtaining a second reference index of the target vehicle by calculating and converting the fuzzy state estimation model of the target vehicle, wherein the preset fuzzy conversion matrix includes a fuzzy evaluation vector;

[0093] Specifically, by constructing a fuzzy state estimation model, the state information such as real-time speed, historical speed, real-time position, historical position, real-time offset angle, historical offset angle, etc. contained in the first reference index U of the target vehicle is converted into a fuzzy state through a preset fuzzy transformation matrix R, and the second reference index V of the target vehicle is obtained.

[0094] In one embodiment of the present application, a fuzzy state estimation model of the target vehicle is established based on the first reference index of the target vehicle and a preset fuzzy conversion matrix, and the second reference index of the target vehicle is obtained by calculation and conversion through the fuzzy state estimation model of the target vehicle, wherein the preset fuzzy conversion matrix includes a fuzzy evaluation vector, including: establishing a preset fuzzy conversion matrix based on the fuzzy evaluation vectors of each factor, wherein the fuzzy conversion matrix is used to characterize a fuzzy relationship from the factor set to the evaluation set, and the evaluation set includes the second reference index; establishing the fuzzy state estimation model of the target vehicle based on the ternary of {first reference index, second reference index, preset fuzzy conversion matrix}; and calculating the second reference index of the target vehicle based on the fuzzy state estimation model of the target vehicle.

[0095] The implementation is as follows:

[0096] Preset factor set composed of the first reference indicator The evaluation set composed of the second reference index V={δ s ,δ c ,δ e},

[0097] Among them, d P represents the lateral relative position of the target vehicle, d H Indicates the longitudinal relative position of the target vehicle, v P represents the lateral relative velocity of the target vehicle, v H represents the longitudinal relative velocity of the target vehicle, represents the offset angle of the target vehicle; δ s represents the safety index of the target vehicle, δ c represents the comfort index of the target vehicle, δ e Represents the efficiency index of the target vehicle.

[0098] A fuzzy transformation matrix R is preset. The fuzzy transformation matrix R is composed of the fuzzy evaluation vector R i =r i1 ,r i2 ,…,r i m) composition, where r i1 -r i m represents the membership of each factor to the state in the evaluation set, reflecting a fuzzy relationship from the factor set to the evaluation set. The fuzzy evaluation vector R i The matrix relationship is specifically expressed as follows:

[0099]

[0100] It can be understood that the fuzzy state estimation model of the target vehicle is composed of the (U, V, R) triple. The conversion relationship in the fuzzy state estimation model of the target vehicle is: V = U × R. Based on the above model and its conversion relationship, fuzzy state conversion can be performed. Substituting the various parameters in the above factor set and evaluation set, we get:

[0101]

[0102]

[0103] The allocation module 330 is configured to allocate the corresponding index obtained by allocating the second reference index of the target vehicle according to the preset weight as the state transition result of the target vehicle.

[0104] Because the previous state affects the current state, rolling optimization and feedback correction are necessary to obtain the true state indicator. In this embodiment, by constructing a compensatory fuzzy relationship model, the second reference indicator is assigned a preset weight to obtain a corresponding indicator, which serves as the state transition result for the target vehicle. This further enables the evaluation of the target vehicle's braking risk factors and braking risk probability, providing an accurate and effective basis for the subsequent emergency braking decision-making of the AEB system.

[0105] In one embodiment of the present application, in the conversion module 320,

[0106] The state transition result of the target vehicle includes at least one of the following: a safety index of the target vehicle, a comfort index of the target vehicle, and an efficiency index of the target vehicle.

[0107] Among them, the safety index of the target vehicle is used to characterize the safe distance and the safe distance maintenance time, the comfort index of the target vehicle is used to characterize whether the acceleration is smooth, and the efficiency index of the target vehicle is used to characterize whether the speed is maintained and / or whether to change lanes.

[0108] In one embodiment of the present application, in the conversion module 320,

[0109] The fuzzy state estimation model of the target vehicle is established according to the first reference index of the target vehicle and a preset fuzzy conversion matrix, and the second reference index of the target vehicle is obtained by calculation and conversion through the fuzzy state estimation model of the target vehicle, wherein the preset fuzzy conversion matrix includes a fuzzy evaluation vector, including:

[0110] By constructing a conversion matrix performance optimization target, the preset fuzzy conversion matrix is optimized when establishing the fuzzy state estimation model of the target vehicle.

[0111] In one embodiment of the present application, in the allocation module 330,

[0112] The step of distributing the corresponding index obtained by allocating the preset weights in the second reference index of the target vehicle as the state transition result of the target vehicle includes:

[0113] The state transition result output by the fuzzy state estimation model of the target vehicle is compensated by constructing a compensation fuzzy relationship model, wherein the compensation fuzzy relationship model is:

[0114] The second reference index at the next moment = the first weight * (the second reference index at the current moment - the second reference index at the previous moment) 2

[0115] + second weight (first reference index * preset fuzzy conversion matrix), wherein the first weight and the second weight are obtained by solving based on the experience set.

[0116] In one embodiment of the present application, in the acquisition module 310,

[0117] The obtaining of a first reference index of the target vehicle includes:

[0118] The lateral relative position of the target vehicle, the longitudinal relative position of the target vehicle, the lateral relative speed of the target vehicle, the longitudinal relative speed of the target vehicle, and the offset angle of the target vehicle in the first reference index of the target vehicle are obtained, and a factor set is established.

[0119] In one embodiment of the present application, in the conversion module 320,

[0120] The fuzzy state estimation model of the target vehicle is established according to the first reference index of the target vehicle and a preset fuzzy conversion matrix, and the second reference index of the target vehicle is obtained by calculation and conversion through the fuzzy state estimation model of the target vehicle, wherein the preset fuzzy conversion matrix includes a fuzzy evaluation vector, including:

[0121] Establishing a preset fuzzy conversion matrix according to the fuzzy evaluation vectors of each factor, wherein the fuzzy conversion matrix is used to represent a fuzzy relationship from the factor set to the evaluation set, and the evaluation set includes the second reference indicator;

[0122] A fuzzy state estimation model of the target vehicle is established based on a ternary of {a first reference index, a second reference index, and a preset fuzzy conversion matrix};

[0123] A second reference index of the target vehicle is calculated based on the fuzzy state estimation model of the target vehicle.

[0124] In one embodiment of the present application, in the conversion module 320,

[0125] When the speed of the target vehicle reaches a preset speed range, the evaluation set of the target vehicle starts to be converted and is used as a result of the second reference index of the target vehicle.

[0126] It should be noted that the above-mentioned vehicle state conversion device can implement the various steps of the vehicle state conversion method provided in the aforementioned embodiment. The relevant explanations about the vehicle state conversion method are applicable to the vehicle state conversion device and will not be repeated here.

[0127] In summary, the technical solution of the present application achieves at least the following technical effects: obtaining a first reference index of the target vehicle; establishing a fuzzy state estimation model of the target vehicle based on the first reference index of the target vehicle and a preset fuzzy conversion matrix, and calculating and converting the second reference index of the target vehicle through the fuzzy state estimation model of the target vehicle, wherein the preset fuzzy conversion matrix includes a fuzzy evaluation vector; and using the corresponding index obtained according to the preset weight distribution in the second reference index of the target vehicle as the state conversion result of the target vehicle. The present application expands the evaluation dimension of vehicle emergency braking decisions. By constructing a fuzzy state estimation model, the operating state quantity of the target vehicle is converted into a safety index, a comfort index, and an efficiency index. This effectively solves the problem of AEB system decision-making errors or deviations caused by a single evaluation index in the prior art. It not only achieves the technical effect of accurately evaluating the operating state of the target vehicle to ensure driving safety, but also provides an effective judgment basis for the AEB system, enabling the system to execute emergency braking decisions more efficiently, greatly improving driving comfort.

[0128] It should be noted that:

[0129] The algorithms and displays provided herein are not inherently related to any particular computer, virtual device, or other device. Various general-purpose devices may also be used together with the teachings herein. Based on the above description, it is apparent that the structure required for constructing such devices is suitable. In addition, the present application is not directed to any specific programming language. It should be understood that various programming languages may be utilized to implement the present application described herein, and the description of the specific languages above is provided for the purpose of disclosing the preferred embodiment of the present application.

[0130] In the description provided herein, a large number of specific details are described. However, it is understood that the embodiments of the present application can be practiced without these specific details. In some instances, well-known methods, structures, and techniques are not shown in detail so as not to obscure the understanding of this description.

[0131] Similarly, it should be understood that in order to streamline the present application and aid in understanding one or more of the various inventive aspects, in the above description of the exemplary embodiments of the present application, various features of the present application are sometimes grouped together into a single embodiment, figure, or description thereof. However, this disclosed method should not be interpreted as reflecting an intention that the claimed application requires more features than are expressly recited in each claim. Rather, as reflected in the claims below, inventive aspects lie in fewer than all the features of the individual embodiments disclosed above. Accordingly, the claims following the detailed description are hereby expressly incorporated into this detailed description, with each claim itself serving as a separate embodiment of the present application.

[0132] Those skilled in the art will appreciate that the modules in the devices in the embodiments may be adaptively changed and arranged in one or more devices different from the embodiments. The modules or units or components in the embodiments may be combined into one module or unit or component, and in addition may be divided into multiple submodules or subunits or subcomponents. All features disclosed in this specification (including the accompanying claims, abstracts and drawings) and all processes or units of any method or device disclosed herein may be combined in any combination, except that at least some of such features and / or processes or units are mutually exclusive. Unless expressly stated otherwise, each feature disclosed in this specification (including the accompanying claims, abstracts and drawings) may be replaced by an alternative feature providing the same, equivalent or similar purpose.

[0133] Furthermore, those skilled in the art will appreciate that although some embodiments described herein include certain features included in other embodiments but not other features, combinations of features from different embodiments are intended to be within the scope of this application and to form different embodiments. For example, in the claims below, any of the claimed embodiments may be used in any combination.

[0134] The various component embodiments of the present application can be implemented in hardware, or in software modules running on one or more processors, or in a combination thereof. Those skilled in the art will appreciate that a microprocessor or digital signal processor (DSP) can be used in practice to implement some or all of the functions of some or all of the components in the vehicle state conversion device according to the embodiments of the present application. The present application can also be implemented as a device or apparatus program (e.g., a computer program and a computer program product) for executing part or all of the methods described herein. Such a program implementing the present application can be stored on a computer-readable medium, or can be in the form of one or more signals. Such a signal can be downloaded from an Internet website, or provided on a carrier signal, or provided in any other form.

[0135] For example, Figure 4 A schematic structural diagram of an electronic device according to an embodiment of the present application is shown. The electronic device 400 includes a processor 410 and a memory 420 arranged to store computer-executable instructions (computer-readable program code). The memory 420 can be an electronic memory such as a flash memory, an EEPROM (electrically erasable programmable read-only memory), an EPROM, a hard disk or a ROM. The memory 420 has a storage space 430 for storing a computer-readable program code 431 for executing any method step in the above method. For example, the storage space 430 for storing computer-readable program code may include various computer-readable program codes 431 respectively used to implement various steps in the above method. The computer-readable program code 431 can be read from or written to one or more computer program products. These computer program products include program code carriers such as a hard disk, a compact disk (CD), a memory card or a floppy disk. Such a computer program product is typically, for example Figure 5 The computer-readable storage medium shown.

[0136] Figure 5 A schematic diagram of the structure of a computer-readable storage medium according to one embodiment of the present application is shown. The computer-readable storage medium 500 stores computer-readable program code 431 for executing the method steps according to the present application and can be read by the processor 410 of the electronic device 400. When the computer-readable program code 431 is executed by the electronic device 400, the electronic device 400 executes each step of the method described above. Specifically, the computer-readable program code 431 stored in the computer-readable storage medium can execute the method described in any of the above embodiments. The computer-readable program code 431 can be compressed in an appropriate form.

[0137] It should be noted that the above embodiments illustrate rather than limit the present application, and that a person skilled in the art may devise alternative embodiments without departing from the scope of the appended claims. In the claims, any reference symbols placed between brackets should not be construed as limiting the claims. The word "comprising" does not exclude the presence of elements or steps not listed in the claims. The word "a" or "an" preceding an element does not exclude the presence of a plurality of such elements. The present application may be implemented by means of hardware comprising several different elements and by means of appropriately programmed computers. In a unit claim enumerating several means, several of these means may be embodied by the same item of hardware. The use of the words first, second, and third etc. does not indicate any order. These words may be interpreted as names.

Claims

1. A vehicle state conversion method, wherein: The method comprises: Obtaining a first reference index of a target vehicle; Establishing a fuzzy state estimation model of the target vehicle according to a first reference index of the target vehicle and a preset fuzzy conversion matrix, and obtaining a second reference index of the target vehicle by calculating and converting the fuzzy state estimation model of the target vehicle, wherein the preset fuzzy conversion matrix includes a fuzzy evaluation vector; Using the corresponding index obtained by distributing the preset weights in the second reference index of the target vehicle as the state transition result of the target vehicle; The step of distributing the corresponding index obtained by allocating the preset weights in the second reference index of the target vehicle as the state transition result of the target vehicle includes: The state transition result output by the fuzzy state estimation model of the target vehicle is compensated by constructing a compensation fuzzy relationship model, wherein the compensation fuzzy relationship model is: The second reference index at the next moment = the first weight * (the second reference index at the current moment - the second reference index at the previous moment) 2 +second weight*(first reference index*preset fuzzy conversion matrix), wherein the first weight and the second weight are obtained by solving based on the experience set.

2. The method according to claim 1, wherein: The state transition result of the target vehicle includes at least one of the following: a safety index of the target vehicle, a comfort index of the target vehicle, and an efficiency index of the target vehicle. Among them, the safety index of the target vehicle is used to characterize the safe distance and the safe distance maintenance time, the comfort index of the target vehicle is used to characterize whether the acceleration is smooth, and the efficiency index of the target vehicle is used to characterize whether the speed is maintained and / or whether to change lanes.

3. The method according to claim 2, wherein: The fuzzy state estimation model of the target vehicle is established according to the first reference index of the target vehicle and a preset fuzzy conversion matrix, and the second reference index of the target vehicle is obtained by calculation and conversion through the fuzzy state estimation model of the target vehicle, wherein the preset fuzzy conversion matrix includes a fuzzy evaluation vector, including: By constructing a conversion matrix performance optimization target, the preset fuzzy conversion matrix is optimized when establishing the fuzzy state estimation model of the target vehicle.

4. The method according to claim 1, wherein: The obtaining of a first reference index of the target vehicle includes: The lateral relative position of the target vehicle, the longitudinal relative position of the target vehicle, the lateral relative speed of the target vehicle, the longitudinal relative speed of the target vehicle, and the offset angle of the target vehicle in the first reference index of the target vehicle are obtained, and a factor set is established.

5. The method according to claim 4, wherein: The fuzzy state estimation model of the target vehicle is established according to the first reference index of the target vehicle and a preset fuzzy conversion matrix, and the second reference index of the target vehicle is obtained by calculation and conversion through the fuzzy state estimation model of the target vehicle, wherein the preset fuzzy conversion matrix includes a fuzzy evaluation vector, including: Establishing a preset fuzzy conversion matrix according to the fuzzy evaluation vectors of each factor, wherein the preset fuzzy conversion matrix is used to represent a fuzzy relationship from the factor set to the evaluation set, and the evaluation set includes the second reference indicator; A fuzzy state estimation model of the target vehicle is established based on a ternary of {a first reference index, a second reference index, and a preset fuzzy conversion matrix}; A second reference index of the target vehicle is calculated based on the fuzzy state estimation model of the target vehicle.

6. The method of claim 1, wherein: The method further comprises: When the speed of the target vehicle reaches a preset speed range, the evaluation set of the target vehicle starts to be converted and is used as a result of the second reference index of the target vehicle.

7. A vehicle state conversion device, wherein: The device comprises: An acquisition module, configured to acquire a first reference index of a target vehicle; a conversion module, configured to establish a fuzzy state estimation model of the target vehicle according to a first reference index of the target vehicle and a preset fuzzy conversion matrix, and obtain a second reference index of the target vehicle by calculating and converting the fuzzy state estimation model of the target vehicle, wherein the preset fuzzy conversion matrix includes a fuzzy evaluation vector; an allocating module, configured to allocate the corresponding index obtained by allocating the second reference index of the target vehicle according to a preset weight as a state transition result of the target vehicle; The step of distributing the corresponding index obtained by allocating the preset weights in the second reference index of the target vehicle as the state transition result of the target vehicle includes: The state transition result output by the fuzzy state estimation model of the target vehicle is compensated by constructing a compensation fuzzy relationship model, wherein the compensation fuzzy relationship model is: The second reference index at the next moment = the first weight * (the second reference index at the current moment - the second reference index at the previous moment) 2 +second weight*(first reference index*preset fuzzy conversion matrix), wherein the first weight and the second weight are obtained by solving based on the experience set.

8. An electronic device comprising: processor; as well as A memory arranged to store computer executable instructions, which when executed cause the processor to perform the method of any one of claims 1 to 6.

9. A computer-readable storage medium storing one or more programs, which, when executed by an electronic device including a plurality of application programs, causes the electronic device to execute the method according to any one of claims 1 to 6.

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