A safe driving control method for intelligent networked vehicles and related products

Abstract

The present invention provides a method for safe driving control of an intelligent networked car and related products. The above method includes obtaining the driving modes under multiple driving environments from a server, obtaining the optimal driving mode under each driving environment, and constructing a safe driving model according to the similarity between each driving environment; when the current car detects that the driving risk index of the current driving environment is greater than the set risk threshold, the target driving environment with the greatest similarity to the current driving environment is obtained according to the constructed safe driving model, and the optimal driving modes of each target driving environment are merged to generate a safe driving mode for the current driving environment. Due to the technical solution of the present invention, the safe driving mode of the current car in the current driving environment can be quickly obtained according to the safe driving model, so compared with the prior art, the response speed to the safety control of the current car can be improved to improve the reliability of the safe driving control of the current car.

Description

Technical Field

[0001] The present invention relates to the technical field of vehicle safety control, and in particular to a method for controlling safe driving of an intelligent networked vehicle and related products. Background Art

[0002] With the continuous improvement of people's living standards and the rapid development of science and technology, cars have become a common industrial product in many families. At the same time, the traffic volume on the road is also increasing, and the problem of road driving safety is becoming more and more serious.

[0003] Intelligent assisted driving technology can formulate the best driving strategy according to the current driving environment of the car to help the driver cope with the current driving environment and improve the safety of the car. However, the existing intelligent assisted driving technology requires a long time to formulate a driving plan, and the driving environment of the car will change at any time. Therefore, the obtained driving strategy may have a certain lag relative to the driving environment and cannot reliably guarantee the safety of the car. Summary of the invention

[0004] An object of the present invention is to provide a method for controlling safe driving of an intelligent networked vehicle and related products, so as to solve the problem that the existing intelligent assisted driving technology cannot reliably guarantee the safety of vehicle driving due to the generated driving strategy that may have a certain lag relative to the driving environment, so as to achieve the purpose of improving user experience.

[0005] Specifically, in a first aspect, the present invention provides a method for controlling safe driving of an intelligent networked vehicle, comprising:

[0006] The steps of building a safe driving model based on Internet data, and controlling the safe driving of the current vehicle based on the safe driving model;

[0007] The step of constructing a safe driving model includes:

[0008] Sending a data request instruction to the Internet to obtain driving modes under multiple driving environments from the Internet, and calculating the risk index of each driving mode, and obtaining the optimal driving mode for each preset driving environment according to each risk index;

[0009] respectively constructing data vectors of the current driving environment and each of the driving environments, calculating similarities between the driving environments according to the data vectors, and obtaining adjacent driving environments of each of the driving environments according to the similarities;

[0010] Selecting a plurality of reference driving environments according to the number of the neighboring driving environments, clustering the reference driving environments according to the similarity to obtain a plurality of environment clusters, and calculating a cluster center of each of the environment clusters;

[0011] The step of controlling the current vehicle to drive safely according to the safe driving model comprises:

[0012] Detecting the current driving environment of the current vehicle, and when the current driving environment is a preset driving environment, calculating the similarity between each cluster center and the current driving environment, and taking the environment cluster whose cluster center has the greatest similarity with the current driving environment as the target cluster;

[0013] Calculating the similarity between each of the reference driving environments in the target cluster and the current driving environment, and taking the reference driving environment with the greatest similarity as the target driving environment;

[0014] The optimal driving modes of the neighboring driving environments of the target driving environment are integrated to generate a safe driving mode for the current driving environment, and the current vehicle is controlled according to the safe driving mode.

[0015] Furthermore, before the step of sending the data request instruction to the Internet, it also includes:

[0016] Acquiring vehicle usage record data of the current vehicle, and determining an update condition of the safe driving model according to the vehicle usage record data;

[0017] When it is detected that the current car meets the update condition, the step of sending a data request instruction to the Internet is executed.

[0018] Furthermore, the step of clustering the reference driving environments according to the similarity to obtain a plurality of environment clusters includes:

[0019] The number of types of the preset driving environment is obtained, and a k-means clustering algorithm is used to cluster the reference driving environments according to the number of types and the similarities to obtain a plurality of environment clusters.

[0020] Furthermore, the step of calculating the cluster center of each of the environment clusters includes:

[0021] The fitness of each of the reference driving environments as the cluster center of the corresponding cluster is calculated respectively, and the reference driving environment with the smallest fitness in each of the clusters is used as the cluster center of the corresponding cluster.

[0022] Furthermore, the step of taking the environment cluster whose cluster center has the greatest similarity with the current driving environment as the target cluster includes:

[0023] A set number of environment clusters whose cluster centers have the greatest similarity to the current driving environment are all used as the target clusters.

[0024] In a second aspect, the present invention further provides a computer-readable storage medium having a computer program stored thereon, and when the computer program is executed by a processor, the steps of the smart networked vehicle safe driving control method described in any one of the above are implemented.

[0025] In a third aspect, the present invention further provides a computer program product, including a computer program, which, when executed by a processor, implements the steps of any of the above-mentioned methods for controlling safe driving of an intelligent networked vehicle.

[0026] The technical solution of the present invention can obtain the optimal driving mode in each driving environment after obtaining the driving modes in multiple driving environments from the server, and construct a safe driving model based on the similarity between the driving environments; when the current car detects that the driving risk index of the current driving environment is greater than the set risk threshold, the target driving environment with the greatest similarity to the current driving environment is obtained according to the constructed safe driving model, and the optimal driving modes of each target driving environment are merged to generate a safe driving mode for the current driving environment. Due to the technical solution of the present invention, the safe driving mode of the current car in the current driving environment can be quickly obtained according to the safe driving model, so compared with the prior art, the response speed of the current car safety control can be improved to improve the reliability of the current car safety driving control.

[0027] Based on the following detailed description of specific embodiments of the present invention in conjunction with the accompanying drawings, those skilled in the art will become more aware of the above and other objects, advantages and features of the present invention. BRIEF DESCRIPTION OF THE DRAWINGS

[0028] Hereinafter, some specific embodiments of the present invention will be described in detail in an exemplary and non-limiting manner with reference to the accompanying drawings. The same reference numerals in the accompanying drawings indicate the same or similar components or parts. It should be understood by those skilled in the art that these drawings are not necessarily drawn to scale. In the accompanying drawings:

[0029] Figure 1 is a schematic flow chart of a safe driving control method for an intelligent networked vehicle according to an embodiment of the present invention;

[0030] Figure 2 is a schematic flow chart of building a safe driving model based on Internet data according to an embodiment of the present invention;

[0031] Figure 3 is a schematic flow chart of controlling the safe driving of a current vehicle according to a constructed safe driving model according to an embodiment of the present invention;

[0032] Figure 4 is a schematic flow chart of building a safe driving model based on Internet data according to another embodiment of the present invention;

[0033] Figure 5 is a schematic flow chart of clustering reference driving environments using a k-means clustering algorithm according to an embodiment of the present invention;

[0034] Figure 6 is a schematic flow chart of updating each initial cluster according to an embodiment of the present invention;

[0035] Figure 7 is a schematic flow chart of calculating the cluster center of each environment cluster according to an embodiment of the present invention;

[0036] Figure 8 is a schematic diagram of a computer program product according to an embodiment of the present invention; and

[0037] Fig. 9 is a schematic diagram of a computer-readable storage medium according to an embodiment of the present invention. DETAILED DESCRIPTION

[0038] Refer to the following Figures 1 to 9 To describe a method for safe driving control of an intelligent networked car and related products according to an embodiment of the present invention. In the description of this embodiment, it should be understood that the terms "first" and "second" are used for descriptive purposes only, and cannot be understood as indicating or implying relative importance or implicitly indicating the number of technical features indicated. Therefore, the features defined as "first" and "second" may explicitly or implicitly include at least one of the features, that is, include one or more of the features. In the description of the present invention, the meaning of "multiple" is at least two, such as two, three, etc., unless otherwise clearly and specifically defined. When a feature "includes or contains" one or some of the features it covers, unless otherwise specifically described, this indicates that other features are not excluded and may further include other features.

[0039] See also Figure 1 , Figure 1 It is a schematic flow chart of a safe driving control method for an intelligent networked vehicle according to an embodiment of the present invention. The method can not only improve the current vehicle driving safety, but also improve the timeliness and reliability of the current vehicle safe driving response.

[0040] like Figure 1As shown, the safe driving control method of the intelligent networked vehicle of the present invention may generally include:

[0041] Step S101: constructing a safe driving model based on Internet data;

[0042] Step S102: Control the current vehicle to drive safely according to the constructed safe driving model.

[0043] In this embodiment, the method of constructing a safe driving model based on Internet data in step S101 is as follows: Figure 2 As shown, the following steps are included:

[0044] Step S111: sending a data request instruction to a server via the Internet to obtain driving modes under various driving environments from the server;

[0045] Step S112: respectively calculating the risk index of each driving mode obtained, and obtaining the optimal driving mode for each driving environment according to the risk index of each driving mode;

[0046] Step S113: constructing data vectors of each driving environment respectively, calculating similarities between the driving environments according to the data vectors, and obtaining adjacent driving environments of each driving environment according to the similarities;

[0047] Step S114: selecting a plurality of reference driving environments from each driving environment according to the number of adjacent driving environments, clustering the reference driving environments according to the similarity between the reference driving environments to obtain a plurality of environment clusters, and calculating the cluster center of each environment cluster.

[0048] In the above step S111, the intelligent networked car can monitor the current driving environment and the driving operation performed in the current driving environment in real time, and send the current driving environment and driving operation to the server through the Internet for storage. After receiving the data request instruction sent by the current car through the Internet, the server sends the driving modes of multiple reference vehicles in multiple driving environments stored within a set time period to the current car, where the reference vehicle refers to a vehicle whose vehicle length, vehicle height and vehicle width differ from the vehicle length, vehicle height and vehicle width of the current car by less than a preset body threshold.

[0049] In this embodiment, the acquired driving environment includes a traffic jam environment, a road maintenance environment, a vehicle accident environment, a road failure environment, a waiting street light environment, and a road turning environment, and the driving mode includes the driving speed, the distance to the vehicle or obstacle in front, the distance to the side vehicle or obstacle, the number of turns, and the angle of each turn.

[0050] In the above step S112, a preset safety evaluation model may be used to evaluate each driving mode under each driving environment to obtain a risk index of each driving mode.

[0051] Taking one of the driving environments as an example, the driving environment corresponds to multiple driving modes, and the method for obtaining the risk index of one of the driving modes includes:

[0052] First, determine whether the reference vehicle has collided with surrounding vehicles or obstacles in the driving mode. If a collision occurs, the risk index of the driving mode is set to 0;

[0053] If no collision occurs, the number of turns and the turning angle of each turn of the reference vehicle in the driving mode are obtained, and a first risk value of the driving mode is calculated according to the number of turns and each turning angle, which specifically includes:

[0054] First, obtain the preset correspondence between the turning number interval and the risk value, then obtain the turning number interval where the reference vehicle turns in the driving mode, and obtain the preset risk value k1 corresponding to the number interval according to the above correspondence; let the turning angle of the reference vehicle in the driving mode be θ i , then the first risk value d1 of this driving mode is:

[0055]

[0056] Wherein α1 is the first steering matching coefficient, α2 is the second steering matching coefficient, and a is the steering angle correction coefficient.

[0057] Periodically obtaining the driving speed of the reference vehicle, the driving speed of the front vehicle or obstacle, and the distance between the reference vehicle and the front vehicle or obstacle in the driving mode to obtain a second risk value of the driving mode, including:

[0058] Assume that the speed of the reference vehicle obtained in the i-th driving mode is v i , the distance between the vehicle or obstacle in front is l i , the driving speed is v i , the speed of the vehicle or obstacle in front is v' i , then the second risk value of this driving mode is:

[0059]

[0060] Among them, β1 is the motion correction coefficient.

[0061] The number of times that the distance between the vehicle and the side vehicle or obstacle is less than the set vehicle distance threshold in the driving mode is periodically obtained as sid, and the third risk value d3 of the driving mode is obtained according to the number, including:

[0062]

[0063] Among them, β2 is the side vehicle distance correction coefficient, and β3 is the correction constant.

[0064] Finally, according to the first risk value, the second risk value and the third risk value, the risk index p of the driving mode is calculated, including:

[0065] p=ω1d1+ω2d2+ω3d3

[0066] Among them, ω1, ω2, ω3 are the first safety factor, the second safety factor and the third safety factor respectively, and ω1+ω2+ω3=1.

[0067] After the risk index of each driving mode in one of the driving environments is obtained, the driving mode with the smallest risk index is used as the optimal driving mode for the driving environment.

[0068] In the above step S113, taking one of the driving environments as an example, the method for constructing the data vector of the driving environment includes: acquiring data of the driving environment in multiple preset dimensions, and constructing the data vector of the driving environment according to the data of each preset dimension.

[0069] In this embodiment, the preset dimensions may include the distance to the front vehicle or obstacle, the moving speed of the front vehicle or obstacle, the distance to the side vehicle or obstacle, the moving speed of the side vehicle or obstacle, the ambient light intensity, the weather severity index, and the road smoothness. In the process of constructing the data vector of one of the driving environments, the data values ​​of each preset dimension of the driving environment may be normalized to obtain the data vector of the driving environment.

[0070] Taking one of the preset dimensions of the above driving environment as an example, the method for normalizing the data value of the preset dimension includes:

[0071] Get the data value of the preset dimension as T, the maximum value of the preset dimension as T max , then the data value of the preset dimension after the normalization process is T'=T / T max .

[0072] Taking two of the driving environments as an example, the method for calculating the similarity of the two driving environments includes:

[0073] Assume that both driving environments have n preset dimensions, and in the data vector of one driving environment, the index of the i-th preset dimension is P 1,i , in another driving environment data vector, the index of the i-th preset dimension is P 2,i, then the similarity between the two driving environments for:

[0074]

[0075] Taking one of the driving environments as an example, the method for obtaining the neighboring driving environment of the driving environment includes: taking a driving environment having a similarity with the driving environment greater than a preset similarity threshold as the neighboring driving environment of the driving environment.

[0076] In the above step S114, the method of selecting multiple reference driving environments according to the number of neighboring driving environments of each driving environment includes: arranging each driving environment in order from the largest to the smallest number of neighboring driving environments, and taking the driving environments with the first set proportion in the arrangement as the reference driving environments. For example, assuming that there are N types of driving environments, and the first set proportion is 80%, then N×80% of the driving environments with the most neighboring driving environments are taken as the reference driving environments.

[0077] After obtaining the reference driving environment, a preset clustering algorithm is used to cluster the reference driving environments according to their similarities, thereby obtaining multiple environmental clusters. The cluster centers of each environmental cluster can be calculated based on the similarities between the reference driving environments in each environmental cluster.

[0078] In this embodiment, in the above step S102, the method for controlling the current vehicle to drive safely according to the constructed safe driving model is as follows: Figure 3 As shown, the following steps are included:

[0079] Step S121: detecting the current driving environment, calculating the driving risk index of the current driving environment, and determining whether the driving risk index is greater than a set risk threshold;

[0080] If yes, execute step S122;

[0081] Step S122: calculating the similarity between the cluster center of each environmental cluster and the current driving environment, and taking the environmental cluster with the greatest similarity between the cluster center and the current driving environment as the target cluster;

[0082] Step S123: calculating the similarity between each reference driving environment in the target cluster and the current driving environment, and taking the reference driving environment with the greatest similarity to the current driving environment as the target driving environment;

[0083] Step S124: The optimal driving modes of the neighboring driving environments of the target driving environment are integrated to generate a safe driving mode for the current driving environment, and the current vehicle is controlled according to the safe driving mode.

[0084] In the above step S121, the current driving environment can be obtained by using the driving environment detection device installed on the current car and the traffic flow information of the road at the location. Then, the driving risk evaluation model is used to obtain the driving risk index of the current driving environment according to the traffic flow of the current driving environment, the surrounding vehicle information and the obstacle information.

[0085] For example, assuming that the current lane width is L, the speed of the vehicle ahead is V, the distance to the vehicle ahead is D, and the traffic volume of the lanes on both sides is h1 and h2 respectively, then when the lane width L is greater than the set width threshold, the driving risk index R of the current driving environment is:

[0086]

[0087] When the lane width L is greater than the set width threshold, the driving risk index R of the current driving environment is

[0088]

[0089] Among them, ε1, ε2, and ε3 are the first risk matching coefficient, the second risk matching coefficient, and the third risk matching coefficient, respectively.

[0090] If the driving risk index of the current driving environment is greater than the set risk threshold, it can be considered that the current driving environment has a high risk of causing a traffic accident, and a safe driving method matching the current driving environment needs to be obtained to improve the safety of the current car driving.

[0091] In the above step S122, the method of constructing the data vector of the current driving environment is the same as the method of constructing the data vectors of each driving environment mentioned above, and after constructing the data vector of the current driving environment, the similarity between the cluster center of each environment cluster cluster and the current driving environment is calculated respectively based on the data vector and the data vector of each cluster center.

[0092] In the above step S123, the similarity between each reference driving environment in the target cluster and the current driving environment can be calculated based on the data vectors of each reference driving environment in the target cluster and the data vector of the current driving environment. The calculation method of the similarity is the same as the calculation method of the similarity between the driving environments mentioned above, and will not be introduced here to avoid redundancy.

[0093] In the above step S124, it is assumed that the target driving environment has M neighboring driving environments, and the i-th neighboring driving environment is taken as an example, and the ambient light intensity of the i-th neighboring driving environment is light i , the ambient light intensity of the current driving environment is light0, then the similarity between the ambient light intensity of the ith neighbor driving environment and the current driving environment is slii for:

[0094]

[0095] The weather severity index of the i-th neighbor driving environment is weather i , the weather severity index of the current driving environment is weather0, then the similarity between the weather severity index of the ith neighbor driving environment and the current driving environment is swea i for:

[0096]

[0097] The road smoothness of the i-th neighbor driving environment is smooth i , the road smoothness of the current driving environment is smooth0, then the road smoothness similarity sm between the i-th neighbor driving environment and the current driving environment i for

[0098]

[0099] The environmental similarity senv between the i-th neighbor driving environment and the current driving environment i for

[0100] senv i =1-sli i ×swea i ×sm i

[0101] Assume that in the current driving environment, the distance between the current car and the vehicle or obstacle in front is l0, then the similarity between the obstacle in front of the i-th neighbor driving environment and the current driving environment is sfrobs i for:

[0102]

[0103] Assume that the moving speed of the vehicle or obstacle in the current driving environment is v0, then the speed similarity sv between the ith neighbor driving environment and the current driving environment is i for:

[0104]

[0105] Assume that in the current driving environment, the distance between the current car and the vehicle or obstacle on the left is lef0, and the distance between the current car and the vehicle or obstacle on the right is rig0. In the i-th neighbor driving environment, the distance between the reference vehicle and the vehicle or obstacle on the left is lef i , the distance to the right vehicle or obstacle is rig iThen, the similarity sidobs between the i-th neighboring driving environment and the current driving environment with respect to the lateral obstacles i is as follows:

[0106]

[0107] In obtaining the optimal driving mode of the neighboring driving environments of the target driving environment, the number of optimal driving modes with steering occurring at the t-th moment and the same steering direction is m. If m < M×μ, where μ is the second set ratio, then in the safe driving mode of the previous driving environment, at the t-th moment, control the current vehicle not to steer, and the driving speed V t is

[0108]

[0109] where, v i,t is the driving speed of the reference vehicle at the t-th moment in the optimal driving mode of the i-th neighboring driving environment.

[0110] If m ≥ M×μ, then obtain the optimal driving modes of these m neighboring driving environments. In the safe driving mode of the previous driving environment, at the t-th moment, control the current vehicle to steer, and the steering direction is the steering direction of these m optimal driving modes, and the driving speed V t and the steering angle are respectively as follows:

[0111]

[0112] where, senv' i 、sv' i 、sfrobs' i and sidobs' i are respectively the environmental similarity, speed similarity, front obstacle similarity and lateral obstacle similarity between the i-th neighboring driving environment and the current driving environment among the m neighboring driving environments obtained, and v' i,t and θ' i,t are respectively the moving speed and turning angle of the reference vehicle at the t-th moment in the optimal driving modes of the m neighboring driving environments obtained.

[0113] After obtaining the safe driving mode of the previous driving environment, control the driving speed and turning angle of the current vehicle at each moment according to this safe driving mode to improve the driving safety of the current vehicle.

[0114] According to the above content, the technical solution of this embodiment can obtain the optimal driving mode in each driving environment after the driving modes in multiple driving environments are obtained from the server, and a safe driving model is constructed according to the similarity between the driving environments; when the current car detects that the driving risk index of the current driving environment is greater than the set risk threshold, the target driving environment with the greatest similarity to the current driving environment is obtained according to the constructed safe driving model, and the optimal driving modes of each target driving environment are merged to generate a safe driving mode for the current driving environment. Due to the technical solution of this embodiment, the safe driving mode of the current car in the current driving environment can be quickly obtained according to the safe driving model, so compared with the prior art, the response speed of the current car safety control can be improved to improve the reliability of the current car safety driving control.

[0115] In some embodiments of the present invention, the method of constructing a safe driving model based on Internet data in step S101 is as follows: Figure 4 As shown, before sending the data request instruction to the Internet in step S111, the following steps are also included:

[0116] Step S201: obtaining vehicle usage record data of the current vehicle, and determining an update condition of a safe driving model according to the vehicle usage record data;

[0117] Step S202: Detect whether the current car meets the above update conditions;

[0118] If satisfied, execute the above step S111.

[0119] In the above step S201, the usage pattern of the current car can be obtained based on the usage record data of the current car to obtain the time period with the least data processing of the current car, and this time period is used as the time period for model update. The interval time between reaching this time period and the last time a safe driving model was generated is greater than the set interval time, which is used as the update condition of the safe driving model.

[0120] For example, assuming that the usage time period of the current car is obtained based on the usage record data of the current car, which is concentrated between 7:00 and 9:00 in the morning and between 6:00 and 10:00 in the afternoon on weekdays, and the number of times the car is used at night accounts for a relatively small proportion, it can be determined that the current car is used for commuting to and from work, and the time period between 10:00 p.m. and 7:00 a.m. the next day has the least data processing volume for the current car. Therefore, a time point can be selected in the time period between 10:00 p.m. and 7:00 a.m. the next day as the model update time period, and when the model update time period is reached, it is determined to be a condition for triggering the update of the safe driving model, so as to update the safe driving model of the current vehicle.

[0121] Through the technical solution of this embodiment, the update conditions of the safe driving model can be determined according to the current vehicle usage record data to avoid the problem of being unable to perform safety control due to the update of the safe driving model during the driving of the current vehicle.

[0122] In some embodiments of the present invention, the method of clustering according to the similarity between the reference driving environments in step S114 includes:

[0123] A first preset number of environmental clusters is obtained, and a k-means clustering algorithm is used to cluster the reference driving environments according to the first preset number and the similarity between the reference driving environments to obtain a plurality of environmental clusters.

[0124] In this embodiment, the method of clustering each reference driving environment using the k-means clustering algorithm is as follows: Figure 5 As shown, the following steps are included:

[0125] Step S301: randomly selecting a reference driving environment from each reference driving environment as a first initial cluster center, and moving the first initial cluster center to a cluster center set;

[0126] Step S302: determining whether the number of initial cluster centers in the cluster center set is a first preset number;

[0127] If not, execute step S303; if yes, execute step S304;

[0128] Step S303: calculating the similarity between each remaining reference driving environment and each initial cluster center in the cluster center set, and obtaining a new initial cluster center according to the similarity, and moving the new initial cluster center to the cluster center set, and then returning to step S302;

[0129] Step S304: clustering according to the similarities between the remaining reference driving environments and the initial cluster centers to obtain a first preset number of initial cluster clusters;

[0130] Step S305: updating each initial cluster to obtain a first preset number of environment clusters.

[0131] In the above step S303, taking one of the reference driving environments as an example, after calculating the similarity between the reference driving environment and each initial cluster center in the cluster center set, the average value of each similarity is used as the average similarity of the reference driving environment. After obtaining the average similarity of each reference driving environment, the reference driving environment with the largest average phase velocity is used as a new initial cluster center.

[0132] In the above step S305, the method for updating each initial cluster is as follows: Figure 6 As shown, the following steps are included:

[0133] Step S311: calculating new cluster centers of each initial cluster, and re-clustering each reference driving environment according to each new cluster center to obtain a new cluster;

[0134] Step S312: determining whether the update of the initial clustering cluster satisfies a preset termination condition;

[0135] If not, return to step S311; if satisfied, execute step S313;

[0136] Step S313: The cluster obtained by the last update is used as the environment cluster.

[0137] In the above step S312, the preset termination condition includes that the number of updates to the initial clusters reaches a preset update number threshold, or the cluster centers of the clusters obtained twice in succession are the same.

[0138] In this embodiment, a k-means clustering algorithm may be used to cluster each reference driving environment into a first preset number of environment clusters to improve the controllability and work efficiency of building a safe driving model.

[0139] In some embodiments of the present invention, the method for calculating the cluster center of each environment cluster in the above step S114 is as follows: Figure 7 As shown, the following steps are included:

[0140] Step S321: Calculate the fitness of each reference driving environment as the cluster center of the corresponding environment cluster respectively;

[0141] Step S322: taking the reference driving environment with the greatest fitness in each environment cluster as the cluster center of the corresponding environment cluster.

[0142] Taking one of the environment clusters as an example, the method for obtaining the cluster center of the environment cluster includes:

[0143] A reference driving environment in the environment cluster is randomly obtained, and then the similarity between the reference driving environment and other reference driving environments in the environment cluster is calculated, and the average value of each similarity is used as the fitness of the reference driving environment.

[0144] Through the technical solution of this embodiment, the reference driving environment with the highest fitness in each environmental clustering cluster can be used as the corresponding clustering center, so that each clustering center is an element in the corresponding environmental clustering cluster, so that in the process of controlling the safe driving of the current car according to the safe driving model, the target clustering cluster can be accurately and quickly obtained, thereby further improving the response speed of the current car's safe driving.

[0145] In some embodiments of the present invention, in the above step S122, the method of taking the environment cluster with the greatest similarity between the cluster center and the current driving environment as the target cluster includes:

[0146] A second preset number of environmental clusters is obtained, and the second preset number of environmental clusters whose cluster centers have the greatest similarity with the current driving environment are all taken as target clusters.

[0147] In this embodiment, the value of the second preset number can be 2 or other integers greater than 1. If there is only one target clustering cluster, there may be a problem of inaccurate target driving environment obtained due to edge effect. The edge effect refers to the edge part of the first environment clustering cluster where the reference driving environment with the greatest similarity to the current driving environment exists, but the similarity between the cluster center of the second environment clustering cluster and the current driving environment is greater than the similarity between the cluster center of the first environment clustering cluster and the current driving environment. Therefore, the target driving environment will be searched in the second environment clustering cluster, but not in the first environment clustering cluster, resulting in inaccurate target driving environment queried, affecting the reliability of the obtained safe driving method.

[0148] Through the technical solution of this embodiment, the second preset number of environmental clusters whose cluster centers have the greatest similarity with the current driving environment can be used as target clusters to avoid the problem of affecting the safe driving mode of the current driving environment due to edge effects.

[0149] The flow chart provided by the present embodiment is not intended to indicate that the operation of the method will be performed in any particular order, or that all operations of the method are included in all every case. In addition, the method may include additional operations. Within the scope of the technical thinking provided by the present embodiment method, additional changes may be made to the above method.

[0150] It should be understood that in some embodiments, each part can be implemented by hardware, software, firmware or a combination thereof. In the above embodiments, multiple steps or methods can be implemented by software or firmware stored in a memory and executed by a suitable instruction execution system.

[0151] This embodiment also provides a computer program product 10 and a computer readable storage medium 20 . Figure 8is a schematic diagram of a computer program product 10 according to an embodiment of the present invention, Fig. 9 is a schematic diagram of a computer-readable storage medium 20 according to an embodiment of the present invention. The computer program product 10 includes a computer program 11, which implements the steps of any of the above-mentioned intelligent networked vehicle safe driving control methods when executed by a processor 32. The computer-readable storage medium 20 stores the above-mentioned computer program 11, which implements the steps of any of the above-mentioned intelligent networked vehicle safe driving control methods when executed by a processor 32. The computer device 30 may include a memory 31, a processor 32, and a computer program 11 stored in the memory 31 and running on the processor 32.

[0152] The computer program 11 for performing the operation of the present invention may be an assembly instruction, an instruction set architecture (ISA) instruction, a machine instruction, a machine-related instruction, a microcode, a firmware instruction, a state setting data, a configuration data of an integrated circuit, or a source code or an object code written in any combination of one or more programming languages ​​and process programming languages. The computer program 11 may be executed entirely on the user's computer, partially on the user's computer, as an independent software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In the latter case, the remote computer may be connected to the user's computer via any type of network (including a local area network (LAN) or a wide area network (WAN), or may be connected to an external computer (e.g., using an Internet service provider via the Internet). In some embodiments, in order to perform various aspects of the present invention, an electronic circuit including, for example, a programmable logic circuit, a field programmable gate array (FPGA) or a programmable logic array (PLA) may execute computer-readable program instructions by utilizing the state information of the computer-readable program instructions to personalize the electronic circuit.

[0153] In the description of this embodiment, the computer program product 10 is a related product including the computer program 11 .

[0154] For the purpose of the description of the present embodiment, the computer readable storage medium 20 is a tangible device capable of retaining and storing the computer program 11, which can be any device that can contain, store, communicate, propagate or use the computer program 11 for an instruction execution system, device or apparatus or in conjunction with these instruction execution systems, devices or apparatuses. More specific examples (a non-exhaustive list) of the computer readable storage medium 20 include the following: 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), a static random access memory (SRAM), a portable compact disk read-only memory (CD-ROM), a digital versatile disk (DVD), a memory stick, a floppy disk, a mechanical encoding device, and any suitable combination of the above.

[0155] At this point, those skilled in the art should recognize that, although multiple exemplary embodiments of the present invention have been shown and described in detail herein, many other variations or modifications that conform to the principles of the present invention can still be directly determined or derived based on the content disclosed in the present invention without departing from the spirit and scope of the present invention. Therefore, the scope of the present invention should be understood and recognized as covering all these other variations or modifications.

Claims

1. A method for controlling safe driving of an intelligent networked vehicle, characterized in that: include: The steps of building a safe driving model based on Internet data, and controlling the safe driving of the current vehicle based on the safe driving model; The step of constructing a safe driving model includes: Sending a data request instruction to a server via the Internet to obtain driving modes under multiple driving environments, and calculating a risk index of each driving mode, and obtaining an optimal driving mode for each driving environment according to each risk index; constructing data vectors of the driving environments respectively, and calculating similarities between the driving environments according to the data vectors, and obtaining adjacent driving environments of the driving environments according to the similarities; Selecting a plurality of reference driving environments according to the number of the neighboring driving environments, clustering the reference driving environments according to the similarity between the reference driving environments to obtain a plurality of environment clusters, and calculating the cluster center of each of the environment clusters to obtain the safe driving model; The step of controlling the current vehicle to drive safely according to the safe driving model comprises: Inspecting the current driving environment to obtain a driving risk index of the current driving environment; When the driving risk index is greater than a set risk threshold, calculating the similarity between each cluster center and the current driving environment, and taking the environment cluster with the greatest similarity between the cluster center and the current driving environment as the target cluster; Calculating the similarity between each reference driving environment in the target cluster and the current driving environment respectively, and taking the reference driving environment with the greatest similarity to the current driving environment as the target driving environment; The optimal driving modes of the neighboring driving environments of the target driving environment are integrated to generate a safe driving mode for the current driving environment, and the current vehicle is controlled according to the safe driving mode.

2. The method for controlling safe driving of an intelligent networked vehicle according to claim 1, characterized in that: Before the step of sending the data request instruction to the server via the Internet, the method further includes: Acquiring vehicle usage record data of the current vehicle, and determining an update condition of the safe driving model according to the vehicle usage record data; When it is detected that the current car meets the update condition, the step of sending a data request instruction to the server via the Internet is performed.

3. The method for controlling safe driving of an intelligent networked vehicle according to claim 1, characterized in that: The step of clustering the reference driving environments according to their similarities to obtain a plurality of environment clusters comprises: A first preset number of the environmental clusters is obtained, and a k-means clustering algorithm is used to cluster the reference driving environments according to the first preset number and the similarities to obtain a plurality of the environmental clusters.

4. The method for controlling safe driving of an intelligent networked vehicle according to claim 1, characterized in that: The step of calculating the cluster center of each of the environment clusters comprises: The fitness of each of the reference driving environments as the cluster center of the corresponding environment cluster is calculated respectively, and the reference driving environment with the largest fitness in each of the environment clusters is used as the cluster center of the corresponding environment cluster.

5. The method for controlling safe driving of an intelligent networked vehicle according to claim 1, characterized in that: The step of taking the environment cluster whose cluster center has the greatest similarity with the current driving environment as the target cluster includes: A second preset number of environmental clusters is obtained, and the second preset number of environmental clusters whose cluster centers have the greatest similarity with the current driving environment are all used as the target clusters.

6. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the method for safe driving control of an intelligent networked vehicle as described in any one of claims 1 to 5 are implemented.

7. A computer program product, comprising a computer program, characterized in that When the computer program is executed by a processor, the steps of the safe driving control method for an intelligent networked vehicle described in any one of claims 1 to 5 are implemented.

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