A vehicle safety control method and related products based on historical driving data

Abstract

The present invention provides a vehicle safety control method and related products based on historical driving data. The above method includes: obtaining the historical driving data of the current vehicle in a set time period, and clustering the historical driving data to obtain a first safe driving model; obtaining the new driving data of the current vehicle after the set time period, and when the data volume of the new driving data is greater than the first set data volume threshold, clustering each new driving data to obtain a second safe driving model; detecting the current driving environment, and when the driving risk index is greater than the set risk threshold, controlling the current vehicle to drive safely according to the first safe driving model and the second safe driving model. The technical solution of the present invention can improve the safety of vehicle driving and achieve the purpose of improving user experience.

Description

Technical Field

[0001] The present invention relates to the technical field of vehicle safety control, and in particular to a vehicle safety control method based on historical driving data 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 roads is also increasing, and the road driving safety problem is becoming more and more serious.

[0003] Intelligent assisted driving technology can develop an optimal driving strategy based on the vehicle's current driving environment, helping the driver cope with the situation and improving driving safety. Existing intelligent assisted driving technology uses historical vehicle driving data to capture the driver's responses to various driving conditions, constructing a data model and then generating a driving strategy for the current driving environment based on this data model. Because the driver's response to different driving conditions changes with driving skills, seasons, and driving environments, the data model is regularly updated based on the vehicle's current driving data.

[0004] However, updating the data model takes a long time. If the update is performed while driving, the assisted driving function cannot be provided during the update process, which affects the driving safety of the vehicle. Summary of the Invention

[0005] One purpose of the present invention is to provide a vehicle safety control method and related products based on historical driving data, so as to improve the safety of vehicle driving and achieve the purpose of improving user experience.

[0006] Specifically, in a first aspect, the present invention provides a vehicle safety control method based on historical driving data, comprising:

[0007] The steps of constructing a safe driving model for the current vehicle, and controlling the safe driving of the current vehicle according to the safe driving model;

[0008] The step of constructing a safe driving model for the current vehicle includes:

[0009] Acquiring historical driving data of the current vehicle in a set time period, and obtaining optimal driving modes for multiple historical driving environments from the historical driving data;

[0010] Obtaining adjacent driving environments of each of the historical driving environments according to the similarities between the historical driving environments, and selecting a plurality of reference driving environments from each of the historical driving environments according to the number of the adjacent driving environments;

[0011] Clustering the similarities between the reference driving environments to obtain a plurality of historical environment clusters, and calculating the cluster center of each of the historical environment clusters to obtain a first safe driving model;

[0012] Acquire new driving data of the current vehicle after the set time period, and if the amount of the new driving data is greater than a first set data amount threshold, obtain optimal driving modes for multiple new driving environments from the new driving data;

[0013] Clustering each of the newly added driving environments to obtain a plurality of newly added environment clusters, and calculating a cluster center of each of the newly added environment clusters to obtain a second safe driving model;

[0014] The step of controlling the current vehicle to drive safely according to the safe driving model includes:

[0015] detecting a current driving environment, and if the driving risk index is greater than a set risk threshold, selecting a target historical environment cluster from each of the historical environment clusters based on similarity between the cluster center and the current driving environment, and using the reference driving environment in the target historical environment cluster that has the greatest similarity to the current driving environment as the target historical driving environment;

[0016] selecting a new target cluster from each of the new environment clusters according to the similarity between the cluster center and the current driving environment, and taking multiple new driving environments in the new target clusters that have the greatest similarity with the current driving environment as new target driving environments;

[0017] The neighboring driving environments of the target historical driving environment are integrated with the optimal driving mode of the newly added target driving environment to generate a safe driving strategy, and the current vehicle is controlled to drive safely according to the safe driving strategy.

[0018] Furthermore, after the step of obtaining the newly added driving data of the current vehicle after the set time period, the method further includes:

[0019] Obtaining an update condition for the safe driving model based on the historical driving data;

[0020] Determining whether the amount of the newly added driving data is greater than a second set data amount threshold;

[0021] If so, checking whether the current vehicle meets the update conditions;

[0022] If it is less, return to the step of obtaining the historical driving data of the set time period.

[0023] Furthermore, the step of calculating the cluster center of each historical environment cluster includes:

[0024] The fitness value of each reference driving environment as the cluster center of the corresponding historical environment cluster is calculated respectively, and the reference driving environment with the largest fitness value in each historical environment cluster is used as the cluster center of the corresponding historical environment cluster.

[0025] Furthermore, the step of clustering each of the newly added driving environments to obtain a plurality of newly added environment clusters includes:

[0026] A first preset number of the newly added environment clusters is obtained, and a k-means clustering algorithm is used to cluster each of the newly added driving environments according to the first preset number to obtain a plurality of the newly added environment clusters.

[0027] Furthermore, the step of selecting a new target cluster from each of the new environment clusters according to the similarity between the cluster center and the current driving environment includes:

[0028] A second preset number is obtained, and the second preset number of newly added environment clusters whose cluster centers have the greatest similarity with the current driving environment are all used as the newly added target clusters.

[0029] 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 any one of the above vehicle safety control methods are implemented.

[0030] In a third aspect, the present invention further provides a computer program product, comprising a computer program, which, when executed by a processor, implements the steps of any of the above vehicle safety control methods.

[0031] In the technical solution of the present invention, a first safe driving model can be constructed based on the historical driving data of the current vehicle during a set time period. When the amount of newly added driving data for the current vehicle exceeds a first set data volume threshold, a second safe driving model can be constructed based on the newly added driving data. During the driving process of the current vehicle, a target historical driving environment most similar to the current driving environment is obtained based on the first safe driving model, and a newly added target driving environment most similar to the current driving environment is obtained based on the second safe driving model. The optimal driving mode for the newly added target driving environment is then integrated with the neighboring driving environments of the target historical driving environment to generate a safe driving strategy, and the current vehicle is controlled to drive safely according to the safe driving strategy. In the technical solution of the present invention, after obtaining the newly added driving data for the current vehicle, the newly added driving data is not directly added to the first safe driving model. Instead, a second safe driving model is constructed based on the newly added driving data. Constructing the second safe driving model only requires clustering the newly added driving data, which requires less time. This can avoid the inability to provide safe driving services due to updates to the safe driving model, and can also utilize the newly added driving data to improve the reliability of safe driving.

[0032] 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

[0033] 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:

[0034] Figure 1 is a schematic flow chart of a vehicle safety control method based on historical driving data according to an embodiment of the present invention;

[0035] Figure 2 is a schematic flowchart of constructing a safe driving model for a current vehicle according to one embodiment of the present invention;

[0036] 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 one embodiment of the present invention;

[0037] Figure 4 is a schematic flowchart of building a safe driving model for a current vehicle according to another embodiment of the present invention;

[0038] Figure 5is a schematic flow chart of calculating the cluster center of each historical environment cluster according to one embodiment of the present invention;

[0039] Figure 6 is a schematic flow chart of clustering based on similarities between newly added driving environments according to one embodiment of the present invention;

[0040] Figure 7 is a schematic flow chart of updating each initial cluster according to an embodiment of the present invention;

[0041] Figure 8 is a schematic diagram of a computer program product according to one embodiment of the present invention; and

[0042] Figure 9 is a schematic diagram of a computer-readable storage medium according to one embodiment of the present invention. DETAILED DESCRIPTION

[0043] Refer to the following Figures 1 to 9 To describe a vehicle safety control method and related products based on historical driving data in 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.

[0044] See also Figure 1 , Figure 1 This is a schematic flow chart of a vehicle safety control method based on historical driving data according to an embodiment of the present invention. This method can not only improve the safety of current vehicle driving, but also improve the timeliness and reliability of the current vehicle safe driving response.

[0045] like Figure 1 As shown, the vehicle safety control method based on historical driving data of the present invention can generally include:

[0046] Step S101: constructing a safe driving model for the current vehicle;

[0047] Step S102: Control the current vehicle to drive safely according to the constructed safe driving model.

[0048] In this embodiment, the safe driving model constructed in step S101 includes a first safe driving model and a second safe driving model, and the method for constructing the safe driving model is as follows: Figure 2 As shown, the following steps are included:

[0049] Step S111: obtaining historical driving data of the current vehicle in a set time period, and obtaining multiple historical driving environments from the historical driving data;

[0050] Step S112: constructing data vectors of each historical driving data, and calculating similarities between each historical driving environment based on the data vectors of each historical driving data;

[0051] Step S113: obtaining neighboring driving environments of each historical driving data according to the similarity of each historical driving data, selecting reference driving environments from each historical driving data according to the number of neighboring driving environments, and obtaining the optimal driving mode for each reference driving environment;

[0052] Step S114: performing clustering based on the similarity between the reference driving environments to obtain a plurality of historical environment clusters, and calculating the cluster center of each historical environment cluster to obtain a first safe driving model;

[0053] Step S115: Acquire newly added driving data, and determine whether the amount of the newly added driving data is greater than a first set data amount threshold;

[0054] If yes, proceed to step S116;

[0055] Step S116: Acquire multiple newly added driving environments and the optimal driving mode for each newly added driving environment from the newly added driving data;

[0056] Step S117: constructing data vectors for each newly added driving environment, and calculating similarities between each newly added driving environment based on the data vectors for each newly added driving environment;

[0057] Step S118: Clustering is performed based on the similarities between the newly added driving environments to obtain a plurality of newly added driving environment clusters, and the cluster center of each newly added driving environment cluster is calculated to obtain a second safe driving model.

[0058] In the above step S111, the historical driving environment obtained based on the historical driving data of the current vehicle includes a traffic jam environment, a road maintenance environment, a vehicle accident environment, a road fault environment, a waiting streetlight environment, and a road turning environment.

[0059] In the above step S112, taking one of the historical driving environments as an example, the method for constructing the data vector of the historical driving environment includes: obtaining data of the historical driving environment in multiple preset dimensions, and constructing the data vector of the historical driving environment based on the data of each preset dimension.

[0060] In this embodiment, the preset dimensions may include the distance between the current vehicle and the preceding vehicle or obstacle, the speed of the preceding vehicle or obstacle, the distance between the current vehicle and the adjacent vehicle or obstacle, the speed of the adjacent vehicle or obstacle, ambient light intensity, weather severity index, and road smoothness. In constructing a data vector for one of the historical driving environments, the data values ​​for each preset dimension of the historical driving environment may be normalized to obtain the data vector for the historical driving environment.

[0061] Taking one of the preset dimensions of the above-mentioned driving history environment as an example, the method for normalizing the data value of the preset dimension includes:

[0062] Get the data value of the preset dimension as T, the maximum value of the preset dimension is T max , then the data value of the preset dimension after the normalization process is T'=T / T max .

[0063] Taking two driving environments as an example, the method for calculating the similarity between the two driving environments includes:

[0064] 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:

[0065]

[0066] In the above step S113, taking one of the historical driving environments as an example, the method for obtaining the neighboring driving environment of the historical driving environment includes: taking the historical driving environment whose similarity with the historical driving environment is greater than a preset similarity threshold as the neighboring driving environment of the historical driving environment.

[0067] In this embodiment, the method of selecting a reference driving environment from each historical driving data according to the number of neighboring driving environments includes:

[0068] The historical driving environments are sorted in descending order of the number of neighboring driving environments, and the historical driving environments that represent the first set proportion in the sorting are used as reference driving environments. For example, if there are N historical driving environments and the first set proportion is 80%, then the N×80% historical driving environments with the most neighboring driving environments are used as reference driving environments.

[0069] Taking one of the reference driving environments as an example, the method for obtaining the optimal driving mode for the reference driving environment includes:

[0070] First, multiple driving modes corresponding to the reference driving environment are obtained from various historical driving data, then the risk index of each driving mode is calculated, and finally the optimal driving mode of the reference driving environment is obtained according to each risk index.

[0071] Taking one driving mode in a reference driving environment as an example, the method for obtaining the risk index of the driving mode includes:

[0072] First, determine whether a collision with surrounding vehicles or obstacles occurs during the driving mode. If a collision occurs, the risk index of the driving mode is set to 0.

[0073] If no collision occurs, the number of turns and the angle of each turn in the driving mode are obtained, and a first risk value of the driving mode is calculated based on the number of turns and the steering angles, including:

[0074] First, obtain the preset correspondence between the number of turns and the risk value, then obtain the number of turns in the driving mode in the number of turns interval, and according to the above correspondence, obtain the preset risk value k1 corresponding to the number interval; let the turning angle of the i-th turn in the driving mode be θ i , then the first risk value d1 of this driving mode is:

[0075]

[0076] Where α1 is the first steering matching coefficient, α2 is the second steering matching coefficient, and a is the steering angle correction coefficient.

[0077] Periodically obtaining the driving speed, the driving speed of the preceding vehicle or obstacle, and the distance to the preceding vehicle or obstacle in the driving mode to obtain a second risk value for the driving mode, including:

[0078] Assume that the obtained i-th driving speed 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:

[0079]

[0080] Where β1 is the motion correction coefficient.

[0081] The number of times the distance between the vehicle and the side vehicle or obstacle is less than a set vehicle distance threshold in the driving mode is periodically obtained as sid, and a third risk value d3 of the driving mode is obtained based on the number of times, including:

[0082]

[0083] Among them, β2 is the side vehicle distance correction coefficient, and β3 is the correction constant.

[0084] Finally, the risk index p of the driving mode is calculated based on the first risk value, the second risk value, and the third risk value, including:

[0085] p=ω1d1+ω2d2+ω3d3

[0086] Wherein, ω1, ω2, and ω3 are the first safety factor, the second safety factor, and the third safety factor, respectively, and ω1+ω2+ω3=1.

[0087] After obtaining the risk index of each driving mode in one of the driving environments, the driving mode with the smallest risk index is used as the optimal driving mode for the reference driving environment.

[0088] In step S114, a preset clustering algorithm may be used to cluster the reference driving environments based on their similarities, thereby obtaining a plurality of historical environment clusters. Then, based on the similarities between the reference driving environments within each historical environment cluster, the cluster center of each historical environment cluster may be calculated.

[0089] In this embodiment, after obtaining the cluster centers of each historical environment cluster, the obtained first safe driving model includes multiple historical environment clusters, the cluster centers of each historical environment cluster, the optimal driving mode of each reference driving environment, and the neighboring driving environment.

[0090] In the above step S115, if the amount of newly added driving data of the current vehicle is less than the first set data amount threshold, it can be considered that the newly added driving data is too small to provide a reference for the safe driving of the current vehicle. Therefore, when the amount of newly added driving data of the current vehicle is greater than the first set data amount threshold, a second safe driving model is constructed based on the newly added driving data.

[0091] In step S116, the newly added driving environments obtained from the newly added driving data include traffic jams, road maintenance, vehicle accidents, road breakdowns, waiting for streetlights, and road turns. In this embodiment, the method for obtaining the optimal driving mode for each newly added driving environment is the same as the method for obtaining the optimal driving mode for each reference driving environment described above. To avoid redundancy, a detailed description is omitted here.

[0092] In the above step S117, the method of constructing the data vectors of each newly added driving environment and the method of calculating the similarity between each newly added driving environment based on the data vectors of each newly added driving environment are respectively the same as the method of constructing the data vectors of each historical driving environment and the method of calculating the similarity between each historical driving environment in the above text. In order to avoid redundancy, they are not described here.

[0093] In step S118, a preset clustering algorithm may be used to cluster the newly added driving environments based on their similarities, thereby obtaining a plurality of newly added environment clusters. Then, based on the similarities between the newly added driving environments in each newly added environment cluster, a cluster center of each newly added environment cluster may be calculated.

[0094] In this embodiment, the obtained second safe driving model includes multiple newly added environmental clusters and the cluster center of each newly added environmental cluster.

[0095] In some embodiments of the present invention, the method of controlling the safe driving of the current vehicle according to the constructed safe driving model in step S102 is as follows: Figure 3 As shown, the following steps are included:

[0096] 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;

[0097] If yes, proceed to step S122;

[0098] Step S122: Calculating the similarity between the cluster center of each historical environment cluster and the current driving environment, and taking the historical environment cluster whose cluster center has the greatest similarity with the current driving environment as the target historical environment cluster;

[0099] Step S123: Calculating the similarity between each reference driving environment in the target historical environment cluster and the current driving environment, and taking the reference historical driving environment with the greatest similarity as the target historical driving environment;

[0100] Step S124: calculating the similarity between the cluster center of each newly added environmental cluster and the current driving environment, and selecting the newly added environmental cluster whose cluster center has the greatest similarity with the current driving environment as the newly added target cluster;

[0101] Step S125: calculating the similarity between each newly added driving environment in the newly added target cluster and the current driving environment, and selecting a first set number of newly added driving environments with the greatest similarity as newly added target driving environments;

[0102] Step S126: The optimal driving modes of the neighboring driving environments of the target historical driving environment and the optimal driving modes of each newly added target driving environment are integrated to obtain a safe driving method, and the current vehicle driving is controlled according to the safe driving method.

[0103] In step S121, the current driving environment can be determined using driving environment detection equipment installed on the vehicle and traffic flow information on the road at the vehicle's location. A driving risk assessment model is then used to determine a driving risk index for the current driving environment based on the current traffic flow, surrounding vehicle information, and obstacle information.

[0104] For example, assuming 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 volumes in the lanes on both sides are h1 and h2 respectively, then if the lane width L is greater than the set width threshold, the driving risk index R of the current driving environment is:

[0105]

[0106] When the lane width L is greater than the set width threshold, the driving risk index R of the current driving environment is

[0107]

[0108] Among them, ε1, ε2, and ε3 are the first risk matching coefficient, the second risk matching coefficient, and the third risk matching coefficient, respectively.

[0109] 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 that matches the current driving environment needs to be obtained to improve the safety of the current vehicle.

[0110] 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 historical driving environment in the above text; after constructing the data vector of the current driving environment, the similarity between the cluster center of each historical environment cluster and the current driving environment can be calculated respectively based on the data vector and the data vector of the cluster center of each historical environment cluster. The method of calculating the similarity is the same as the method of calculating the similarity between the historical driving environments in the above text, and is not introduced here to avoid redundancy.

[0111] The method for calculating the similarity between each reference driving environment in the target historical environment cluster and the current driving environment in the above step S123, the method for calculating the similarity between the cluster center of each newly added environment cluster and the current driving environment in the above step S124, and the method for calculating the similarity between each newly added driving environment in the newly added target cluster and the current driving environment in the above step S125 are all the same as the calculation method for calculating the similarity between each historical driving environment in the above text, and are not introduced here to avoid repetition.

[0112] In the above step S126, each neighboring driving environment of the target historical driving environment and each newly added target driving environment can be used as a standby driving environment, and then the optimal driving mode of each standby driving environment is integrated to obtain a safe driving method for the current driving environment.

[0113] In this embodiment, the method for fusing the optimal driving modes for each ready-to-use driving environment includes:

[0114] Assume that there are M waiting driving environments. Take the ith waiting driving environment as an example. Let the ambient light intensity of the ith waiting driving environment be light i , the ambient light intensity of the current driving environment is light0, then the similarity of the ambient light intensity between the i-th waiting driving environment and the current driving environment is sli i for:

[0115]

[0116] The weather severity index of the i-th standby driving environment is weather i , the weather severity index of the current driving environment is weather0, then the weather severity index similarity swea between the i-th waiting driving environment and the current driving environment i for:

[0117]

[0118] The road smoothness of the i-th driving environment is smooth i, if the road smoothness of the current driving environment is smooth0, then the road smooth similarity sm between the i-th待用 driving environment and the current driving environment i is

[0119]

[0120] The environment similarity senv between the i-th待用 driving environment and the current driving environment i is

[0121] senv i = sli i × swea i × sm i

[0122] Assume that in the current driving environment, the distance between the current vehicle and the vehicle or obstacle in front is l0, then the similarity sfrobs of the front obstacle between the i-th待用 driving environment and the current driving environment i is[[ID=2...]]

[0123]

[0124] Assume that in the current driving environment, the moving speed of the vehicle or obstacle in front is v, then the speed similarity sv between the i-th待用 driving environment and the current driving environment i is

[0125] ...

[0126] Assume that in the current driving environment, the distance between the current vehicle and the vehicle or obstacle on the left is lef0, and the distance between the current vehicle and the vehicle or obstacle on the right is rig0. In the i-th待用 driving environment, the distance between the current vehicle and the vehicle or obstacle on the left is lef i , and the distance between the current vehicle and the vehicle or obstacle on the right is rig i , then the side obstacle similarity sidobs between the i-th待用 driving environment and the current driving environment [[ID=4...]] i [[ID=4...]]is [[ID=4...]]

[0127] ...

[0128] Among the optimal driving methods of each待用 driving environment obtained, the number of optimal driving methods with steering at the t-th moment and the same steering direction is m. If m < M × μ, then in the safe driving method of the previous driving environment, at the t-th moment, control the current vehicle not to turn, and the driving speed V t is

[0129]

[0130] Among them, μ is the second setting ratio, v i,t is the driving speed of the current vehicle at the tth moment in the optimal driving mode of the i-th standby driving environment.

[0131] If m≥M×μ, then the optimal driving mode of the m waiting driving environments is obtained. In the safe driving mode of the previous driving environment, the current vehicle is controlled to turn at time t, and the direction of the turning is the turning direction of the waiting driving mode. The driving speed V t and steering angle They are:

[0132]

[0133] Among them, senv′ i 、sv′ i 、sfrobsi′ i and sidobs′ i The environment similarity, speed similarity, front obstacle similarity and side obstacle similarity between the i-th standby driving environment and the current driving environment are obtained in the m standby driving environments, respectively, v′ i,t and θ′ i,t are respectively the moving speed and turning angle of the current vehicle at the tth moment in the optimal driving modes of the m standby driving environments obtained.

[0134] After obtaining a safe driving mode for the front driving environment, the driving speed and turning angle of the current vehicle at each moment are controlled according to the safe driving mode to improve the driving safety of the current vehicle.

[0135] As can be seen from the above, the technical solution of this embodiment can construct a first safe driving model based on the historical driving data of the current vehicle during a set time period, and construct a second safe driving model based on the newly added driving data when the amount of newly added driving data of the current vehicle exceeds a first set data amount threshold. During the driving process of the current vehicle, the first safe driving model is used to obtain the target historical driving environment that is most similar to the current driving environment, and the second safe driving model is used to obtain the newly added target driving environment that is most similar to the current driving environment. The neighboring driving environments of the target historical driving environment are then integrated with the optimal driving mode of the newly added target driving environment to generate a safe driving strategy, and the current vehicle is controlled to drive safely according to the safe driving strategy. In the technical solution of this embodiment, after obtaining the newly added driving data of the current vehicle, the newly added driving data is not directly added to the first safe driving model. Instead, the second safe driving model is constructed based on the newly added driving data. Since the construction of the second safe driving model only requires clustering the newly added driving data, the time required is shorter. Therefore, it can avoid the failure to provide safe driving services due to the update of the safe driving model, and can also utilize the newly added driving data to improve the reliability of safe driving.

[0136] In some embodiments of the present invention, Figure 4 As shown, after the above step S115 acquires the newly added driving data, the following steps are also included:

[0137] Step S131: obtaining an update condition for a safe driving model of the current vehicle based on historical driving data of the current vehicle;

[0138] Step S132: determining whether the amount of newly added driving data of the current vehicle is greater than a second set data amount threshold;

[0139] If yes, execute step S133;

[0140] Step S133: Detect whether the current vehicle meets the above-mentioned update conditions;

[0141] If satisfied, return to step S111.

[0142] In the above step S131, the usage pattern of the current vehicle can be obtained based on the historical driving data of the current vehicle to obtain the time period with the least data processing of the current vehicle, and this time period is used as the time period for model update. The time period when the safety driving model is updated and the interval between the time period and the last time the safety driving model was generated is greater than the set interval time, which is used as the update condition of the safety driving model.

[0143] For example, assuming that the current vehicle's usage time period is obtained based on the current vehicle's usage record data, it 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 vehicle is used in other time periods accounts for a relatively small proportion. Therefore, a specific time period can be selected outside the time periods of 7:00 to 9:00 in the morning and 6:00 to 10:00 in the afternoon on weekdays, and reaching the specific time period can be used as the update condition for the current vehicle's safe driving model.

[0144] Therefore, after determining in the above step S132 that the amount of newly added driving data of the current vehicle is greater than the second set data amount threshold, if the selected specific time period is entered, the update conditions of the safety driving model of the current vehicle are implemented.

[0145] Through the technical solution of this embodiment, the update conditions of the safe driving model can be determined based on the historical driving data of the current vehicle, so as 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.

[0146] In some embodiments of the present invention, the method for calculating the cluster center of each historical environment cluster in step S114 is as follows: Figure 5 As shown, the following steps are included:

[0147] Step S321: Calculate the fitness of each reference driving environment as the cluster center of the corresponding historical environment cluster;

[0148] Step S322: taking the reference driving environment with the greatest fitness in each historical environment cluster as the cluster center of the corresponding historical environment cluster.

[0149] Taking one of the historical environment clusters as an example, the method for obtaining the cluster center of the historical environment cluster includes:

[0150] A reference driving environment in the historical environment cluster is randomly obtained, and then the similarity between the reference driving environment and other reference driving environments in the historical environment cluster is calculated, and the average value of each similarity is used as the fitness of the historical reference driving environment.

[0151] Through the technical solution of this embodiment, the reference driving environment with the greatest fitness in each historical environment cluster can be used as the corresponding cluster center, so that each cluster center is an element in the corresponding historical environment cluster, so that in the process of controlling the safe driving of the current vehicle according to the safe driving model, the target historical cluster can be accurately and quickly obtained, further improving the response speed of the current vehicle's safe driving.

[0152] In some embodiments of the present invention, the clustering algorithm used in the above step S114 for clustering according to the similarity between the reference driving environments, and the clustering algorithm used in the above step S118 for clustering according to the similarity between the newly added driving environments, are both k-means clustering algorithms.

[0153] For example, the clustering is performed based on the similarity between the newly added driving environments in step S118, specifically as follows: Figure 6 As shown, the following steps are included:

[0154] Step S301: obtaining a first preset number of newly added environment clusters, then randomly selecting a newly added driving environment from each newly added driving environment as a first initial cluster center, and moving the first initial cluster center to the cluster center set;

[0155] Step S302: determining whether the number of initial cluster centers in the cluster center set is a first preset number;

[0156] If not, execute step S303; if yes, execute step S304;

[0157] Step S303: Calculate the similarity between each of the remaining newly added driving environments and each of the initial cluster centers in the cluster center set, obtain a new initial cluster center based on the similarity, move the new initial cluster center to the cluster center set, and then return to step S302;

[0158] Step S304: clustering the remaining newly added driving environments according to the similarities between the initial cluster centers to obtain a first preset number of initial clusters;

[0159] Step S305: updating each initial cluster to obtain a first preset number of newly added environment clusters.

[0160] In step S303, taking one of the newly added driving environments as an example, after calculating the similarity between the newly added driving environment and each initial cluster center in the cluster center set, the average of the similarities is used as the average similarity of the newly added driving environment. After obtaining the average similarity of each newly added driving environment, the newly added driving environment with the highest average phase velocity is selected as the new initial cluster center.

[0161] In the above step S305, the method for updating each initial cluster is as follows: Figure 7 As shown, the following steps are included:

[0162] Step S311: calculating new cluster centers of each initial cluster, and re-clustering each newly added driving environment according to each new cluster center to obtain a new cluster;

[0163] Step S312: determining whether the update of the initial clustering satisfies a preset termination condition;

[0164] If not, return to step S311; if satisfied, execute step S313;

[0165] Step S313: The cluster obtained by the last update is used as a new environment cluster.

[0166] In the above step S312, the preset termination conditions include 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.

[0167] In this embodiment, the k-means clustering algorithm can be used to cluster each reference driving environment into multiple historical environment clusters, and each new driving environment into multiple new environment clusters, so as to improve the controllability and work efficiency of constructing a safe driving model.

[0168] In some embodiments of the present invention, the method of selecting the newly added environment cluster whose cluster center has the greatest similarity with the current driving environment as the newly added target cluster in step S124 includes the following steps:

[0169] A second set number is obtained, and the second set number of newly added environmental clusters whose cluster centers have the greatest similarity with the current driving environment are all used as newly added target clusters.

[0170] In this embodiment, the value of the second preset number can be 2 or other integers greater than 1. If there is only one new target cluster, there may be a problem of inaccurate target historical driving environment due to edge effect. The edge effect refers to the edge part of the first new environment cluster that exists in the reference driving environment with the greatest similarity to the current driving environment, but the similarity between the cluster center of the second new environment cluster and the current driving environment is greater than the similarity between the cluster center of the first new environment cluster and the current driving environment. Therefore, the target new driving environment will be searched in the second new environment cluster, but not in the first new environment cluster, resulting in inaccurate target historical driving environment and affecting the reliability of the obtained safe driving method.

[0171] Through the technical solution of this embodiment, the second preset number of new environmental clusters whose cluster centers have the greatest similarity with the current driving environment can be used as target new clusters to avoid the problem of affecting the safe driving mode of the current driving environment due to edge effects.

[0172] The flowcharts provided in this embodiment are not intended to indicate that the operations of the method will be performed in any particular order, or that all operations of the method are included in all cases. In addition, each of the above methods may include additional operations. Additional changes may be made to the above method within the scope of the technical ideas provided by the method of this embodiment.

[0173] 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.

[0174] This embodiment also provides a computer program product 10 and a computer-readable storage medium 20 . Figure 8 is a schematic diagram of a computer program product 10 according to one embodiment of the present invention, Figure 9 is a schematic diagram of a computer-readable storage medium 20 according to one embodiment of the present invention. The computer program product 10 includes a computer program 11. When executed by a processor 32, the computer program 11 implements the steps of any of the aforementioned vehicle safety control methods based on historical driving data. The computer-readable storage medium 20 stores the computer program 11. When executed by the processor 32, the computer program 11 implements the steps of any of the aforementioned vehicle safety control methods based on historical driving data. The computer device 30 may include a memory 31, a processor 32, and the computer program 11 stored in the memory 31 and executed by the processor 32.

[0175] The computer program 11 for performing the operations of the present invention may be assembly instructions, instruction set architecture (ISA) instructions, machine instructions, machine-dependent instructions, microcode, firmware instructions, state setting data, configuration data for an integrated circuit, or source code or object code written in any combination of one or more programming languages ​​and procedural programming languages. The computer program 11 may be executed entirely on the user's computer, partially on the user's computer, as a standalone 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., via the Internet using an Internet service provider). In some embodiments, to perform various aspects of the present invention, an electronic circuit, such as a programmable logic circuit, a field programmable gate array (FPGA), or a programmable logic array (PLA), may execute computer-readable program instructions by utilizing state information of the computer-readable program instructions to personalize the electronic circuit.

[0176] In the description of this embodiment, the computer program product 10 is a related product including the computer program 11 .

[0177] For the purposes of the description of this embodiment, the computer-readable storage medium 20 is a tangible device capable of retaining and storing the computer program 11, and can be any device that can contain, store, communicate, propagate, or use the computer program 11 for an instruction execution system, apparatus, or device, or in conjunction with such an instruction execution system, apparatus, or device. 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 disc 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 foregoing.

[0178] At this point, those skilled in the art will recognize that, although a number of exemplary embodiments of the present invention have been shown and described in detail herein, many other variations or modifications consistent with the principles of the present invention may be directly determined or derived from the disclosure of 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 deemed to cover all such other variations or modifications.

Claims

1. A vehicle safety control method based on historical driving data, characterized in that: include: The steps of constructing a safe driving model for the current vehicle, and controlling the safe driving of the current vehicle according to the safe driving model; The step of constructing a safe driving model for the current vehicle includes: Acquiring historical driving data of the current vehicle in a set time period, and obtaining optimal driving modes for multiple historical driving environments from the historical driving data; Obtaining adjacent driving environments of each of the historical driving environments according to the similarities between the historical driving environments, and selecting a plurality of reference driving environments from each of the historical driving environments according to the number of the adjacent driving environments; Clustering the similarities between the reference driving environments to obtain a plurality of historical environment clusters, and calculating the cluster center of each of the historical environment clusters to obtain a first safe driving model; Acquire new driving data of the current vehicle after the set time period, and if the amount of the new driving data is greater than a first set data amount threshold, obtain optimal driving modes for multiple new driving environments from the new driving data; Clustering each of the newly added driving environments to obtain a plurality of newly added environment clusters, and calculating a cluster center of each of the newly added environment clusters to obtain a second safe driving model; The step of controlling the current vehicle to drive safely according to the safe driving model includes: detecting a current driving environment, and if a driving risk index is greater than a set risk threshold, selecting a target historical environment cluster from each of the historical environment clusters based on similarity between the cluster center and the current driving environment, and using the reference driving environment in the target historical environment cluster that has the greatest similarity to the current driving environment as the target historical driving environment; selecting a new target cluster from each of the new environment clusters according to the similarity between the cluster center and the current driving environment, and taking multiple new driving environments in the new target clusters that have the greatest similarity with the current driving environment as new target driving environments; The neighboring driving environments of the target historical driving environment are integrated with the optimal driving mode of the newly added target driving environment to generate a safe driving strategy, and the current vehicle is controlled to drive safely according to the safe driving strategy.

2. The vehicle safety control method according to claim 1, characterized in that: After the step of obtaining the newly added driving data of the current vehicle after the set time period, the method further includes: Obtaining an update condition for the safe driving model based on the historical driving data; Determining whether the amount of the newly added driving data is greater than a second set data amount threshold; If so, checking whether the current vehicle meets the update conditions; If it is less, return to the step of obtaining the historical driving data of the set time period.

3. The vehicle safety control method according to claim 1, wherein: The step of calculating the cluster center of each historical environment cluster includes: The fitness value of each reference driving environment as the cluster center of the corresponding historical environment cluster is calculated respectively, and the reference driving environment with the largest fitness value in each historical environment cluster is used as the cluster center of the corresponding historical environment cluster.

4. The vehicle safety control method according to claim 1, characterized in that: The step of clustering each of the newly added driving environments to obtain a plurality of newly added environment clusters includes: A first preset number of the newly added environment clusters is obtained, and a k-means clustering algorithm is used to cluster each of the newly added driving environments according to the first preset number to obtain a plurality of the newly added environment clusters.

5. The vehicle safety control method according to claim 1, characterized in that: The step of selecting a new target cluster from each of the new environment clusters according to the similarity between the cluster center and the current driving environment includes: A second preset number is obtained, and the second preset number of newly added environment clusters whose cluster centers have the greatest similarity with the current driving environment are all used as the newly added 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 vehicle safety control method according to 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 vehicle safety control method according to any one of claims 1 to 5 are implemented.

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