A road rage monitoring and early warning method and device, electronic equipment and storage medium

Abstract

The application provides a road rage monitoring and early warning method and device, electronic equipment and a storage medium. The method comprises the following steps: obtaining driver emotion information and vehicle driving records of a plurality of target vehicles around; obtaining the number of illegal behaviors and the number of courtesy behaviors of the target vehicles based on the vehicle driving records; obtaining an illegal behavior index of the target vehicles based on the number of illegal behaviors; obtaining a courtesy behavior index of the target vehicles based on the number of courtesy behaviors; establishing a target behavior matrix according to the illegal behavior index and the courtesy behavior index of each target vehicle; obtaining a behavior parameter of each target vehicle based on the target behavior matrix; obtaining a road rage score based on the behavior parameter, a scoring weight coefficient and a preset influence parameter; performing early warning classification on the target vehicles according to the road rage score; and executing a corresponding preset early warning scheme based on the early warning classification. The method combines the driver driving records and emotion monitoring to solve the technical problems of untimely or inaccurate monitoring and early warning of road rage behaviors.

Description

Technical Field

[0001] The present application relates to the field of intelligent vehicle technology, and specifically to a road rage monitoring and early warning method, device, electronic device and storage medium. Background Art

[0002] Road rage is aggressive or angry behavior displayed by drivers of cars or other motor vehicles while driving. These behaviors may include intentionally driving in an unsafe or safety-threatening manner, such as suddenly and intentionally blocking others from entering one's lane, suddenly braking or accelerating, and following vehicles too closely. Road rage driving behavior can easily lead to traffic accidents, resulting in damage to the driver's own property and personal safety, and may also pose safety hazards to other vehicles.

[0003] With the rapid development of intelligent transportation system technology, the target of driver emotion monitoring can now be achieved through the driver's physiological signals or facial expression recognition. However, such monitoring methods are affected by many factors, such as individual differences, environmental factors and the driver's self-regulation ability. These influencing factors may lead to inaccurate monitoring results, thereby affecting the untimely or inaccurate road rage warning. Summary of the Invention

[0004] In view of the shortcomings of the related technologies mentioned above, the present application provides a road rage monitoring and early warning method, device, electronic device and storage medium to solve the technical problem of untimely or inaccurate monitoring and early warning of road rage behavior.

[0005] The present application provides a road rage monitoring and early warning method, which includes: obtaining driver emotional information and vehicle driving records of multiple surrounding target vehicles; obtaining the number of violations and the number of courteous behaviors of the target vehicles based on the vehicle driving records, obtaining the violation behavior index of the target vehicle based on the number of violations, and obtaining the courteous behavior index of the target vehicle based on the number of courteous behaviors; establishing a target behavior matrix according to the violation behavior index and the courteous behavior index of each target vehicle, and calculating the behavior parameters of each target vehicle based on the target behavior matrix; scoring the behavior parameters to obtain a road rage score, and the scoring calculation includes calculation based on a scoring weight coefficient, a preset influencing parameter and the behavior parameter, and the scoring weight coefficient is obtained based on the driver emotional information; performing early warning classification on the target vehicles according to the road rage score, and executing a corresponding preset early warning plan based on the warning classification.

[0006] In an embodiment of the present application, the number of rule-breaking behaviors and the number of courtesy behaviors of the target vehicle are obtained based on the vehicle driving records, the rule-breaking behavior index of the target vehicle is obtained based on the number of rule-breaking behaviors, and the courtesy behavior index of the target vehicle is obtained based on the number of courtesy behaviors, including: taking the value of the number of courtesy behaviors as the courtesy behavior index of the target vehicle; and taking the value of the number of rule-breaking behaviors after inverse processing, and taking the processed value of the number of rule-breaking behaviors as the rule-breaking behavior index of the target vehicle.

[0007] In an embodiment of the present application, the target behavior matrix is established according to the rule-breaking behavior index and the courtesy behavior index of each target vehicle, and the behavior parameter of each target vehicle is calculated based on the target behavior matrix, including: obtaining an initial behavior matrix based on the rule-breaking behavior index and the courtesy behavior index, wherein one row of the initial behavior matrix represents the rule-breaking behavior index and the courtesy behavior index of the same target vehicle, and one column of the initial behavior matrix represents the rule-breaking behavior index or the courtesy behavior index of each target vehicle; performing positive normalization on all values in the initial behavior matrix to obtain a normalized target behavior matrix, wherein one row of the normalized target behavior matrix represents the normalized rule-breaking behavior index and the normalized courtesy behavior index of the same target vehicle, and one column of the normalized target behavior matrix represents the normalized rule-breaking behavior index or the normalized courtesy behavior index of each target vehicle; taking the maximum value in the first column and the maximum value in the second column in the target behavior matrix as the matrix maximum value, and taking the minimum value in the first column and the minimum value in the second column in the target behavior matrix as the matrix minimum value; obtaining the initial parameter of each target vehicle based on the matrix maximum value, the matrix minimum value, and a preset behavior formula; and obtaining the behavior parameter of each target vehicle after normalizing the initial parameter.

[0008] In an embodiment of the present application, the score weight coefficient is obtained based on the driver emotional information, including: performing facial emotion recognition on the facial information to obtain an emotion recognition result of the facial information, wherein the emotion recognition result includes an angry state and a calm state, and the driver emotional information includes facial information and heartbeat information; if the heart rate value of the heartbeat information is less than a preset heart rate threshold value and the emotion recognition result is the calm state, the score weight coefficient is less than a preset weight standard value; if the heart rate value of the heartbeat information is greater than or equal to the preset heart rate threshold value and the emotion recognition result is the angry state, the score weight coefficient is greater than the preset weight standard value; if the heart rate value of the heartbeat information is less than a preset heart rate threshold value and the emotion recognition result is the angry state, the score weight coefficient is equal to the preset weight standard value; and if the heart rate value of the heartbeat information is greater than or equal to the preset heart rate threshold value and the emotion recognition result is the calm state, the score weight coefficient is equal to the preset weight standard value.

[0009] In an embodiment of the present application, before the calculation based on the score weight coefficient, the preset influence parameter and the behavior parameter, further comprising: constructing a judgment matrix of multiple dimension indicators, the judgment matrix comprising influence quantization values between each two of the dimension indicators; obtaining an influence matrix by normalizing the judgment matrix by column, and obtaining the influence weight values corresponding to the dimension indicators by the arithmetic mean of the influence matrix by row; obtaining the preset influence parameter based on the influence weight values.

[0010] In an embodiment of the present application, after obtaining the influence weight values corresponding to the multiple dimension indicators by the arithmetic mean of the influence matrix by row, further comprising: obtaining a check parameter based on the influence matrix and the influence weight values corresponding to each dimension indicator; obtaining a first check value and a second check value according to the check parameter and the number of the dimension indicators; taking the ratio of the first check value and the second check value as a target check value; if the target check value is less than a preset check threshold, the check is passed, and the influence weight values are valid.

[0011] In an embodiment of the present application, the target vehicle is pre-alert graded according to the road rage score, and a corresponding preset pre-alert scheme is executed based on the pre-alert grading, comprising: if the road rage score of the target vehicle is less than a preset first pre-alert score threshold, the target vehicle is green pre-alert graded, and the target vehicle is marked as green; if the road rage score of the target vehicle is greater than or equal to the preset first pre-alert score threshold and less than a preset second pre-alert score threshold, the target vehicle is yellow pre-alert graded, the target vehicle is marked as yellow, and a preset yellow pre-alert scheme is executed on the target vehicle; if the road rage score of the target vehicle is greater than or equal to the second pre-alert score threshold, the target vehicle is red pre-alert graded, the target vehicle is marked as red, and a red pre-alert scheme is executed on the target vehicle.

[0012] The embodiment of the present application further provides a road rage monitoring and early warning device, which comprises: an information input module, which is used to acquire driver emotion information and vehicle driving records of a plurality of target vehicles; an index calculation module, which is used to obtain a number of rule violation behaviors and a number of courtesy behaviors of the target vehicles based on the vehicle driving records, obtain a rule violation behavior index of the target vehicles based on the number of rule violation behaviors, and obtain a courtesy behavior index of the target vehicles based on the number of courtesy behaviors; a parameter calculation module, which is used to establish a target behavior matrix according to the rule violation behavior index and the courtesy behavior index of each target vehicle, and obtain a behavior parameter of each target vehicle based on the target behavior matrix; a score calculation module, which is used to perform score calculation on the behavior parameter to obtain a road rage score, wherein the score calculation comprises calculation based on a score weight coefficient, a preset influence parameter and the behavior parameter, and the score weight coefficient is obtained based on the driver emotion information; and a hierarchical early warning module, which is used to perform early warning classification on the target vehicles according to the road rage score, and perform a corresponding preset early warning scheme based on the early warning classification.

[0013] The embodiment of the present application further provides an electronic device, one or more processors; a storage device, used to store one or more programs, when the one or more programs are executed by the one or more processors, the electronic device implements the road rage monitoring and early warning method as any one of the above embodiments.

[0014] Beneficial effects of the present application: the embodiment of the present application provides a road rage monitoring and early warning method, device, electronic equipment and storage medium, the method comprises: obtaining the driver emotion information and vehicle driving record of a plurality of target vehicles around, the driver emotion information and driving record are combined, the road rage can be monitored more accurately, the number of illegal behaviors and the number of courtesy behaviors of the target vehicle are obtained based on the vehicle driving record, the illegal behavior index of the target vehicle is obtained based on the number of illegal behaviors, the courtesy behavior index of the target vehicle is obtained based on the number of courtesy behaviors, the target behavior matrix is established according to the illegal behavior index and the courtesy behavior index of each target vehicle, the behavior parameter of each target vehicle is obtained based on the target behavior matrix, the vehicle behavior is scored based on the illegal behavior and courtesy behavior of the vehicle, the road rage behavior of the vehicle owner is warned based on the historical behavior of the vehicle, the road rage score is obtained based on the behavior parameter, the scoring weight coefficient and the preset influence parameter, the road rage score is evaluated according to the historical behavior of the vehicle, the current emotional state of the driver and the influence parameter, the accuracy of the score is improved, the target vehicle is warned and classified according to the road rage score, the corresponding preset warning scheme is executed based on the warning classification, the corresponding warning measures can be taken in time through the warning and classification of the target vehicle, the timeliness of road rage monitoring and early warning is improved, the adverse consequences caused by road rage are avoided, and the technical problems that the road rage behavior monitoring and early warning are not timely or not accurate are solved by combining the driver behavior parameter and emotion monitoring.

[0015] It should be understood that the above general description and the following detailed description are only exemplary and explanatory, and cannot limit the present application. BRIEF DESCRIPTION OF DRAWINGS

[0016] Figure 1 is a road rage monitoring and early warning method environment diagram shown by an exemplary embodiment of the present application;

[0017] Figure 2 is a flowchart of a road rage monitoring and early warning method shown by an exemplary embodiment of the present application;

[0018] Figure 3 is an analytic hierarchy process diagram shown by an exemplary embodiment of the present application;

[0019] Figure 4 is a block diagram of a road rage monitoring and early warning device shown by an exemplary embodiment of the present application;

[0020] Figure 5 is a structure diagram of an electronic equipment shown by an exemplary embodiment of the present application. DETAILED DESCRIPTION

[0021] The following detailed description is presented in order to describe the embodiments of the present application and it is not intended to limit the present application. The present application can be implemented in various manners, and the details of the present application can be modified based on different views and applications without departing from the spirit of the present application. It should be noted that the following embodiments and features in the embodiments can be combined with each other without conflict.

[0022] It should be noted that the diagrams provided in the following embodiments only schematically illustrate the basic concepts of the present application, and only the components related to the present application are shown in the diagrams, rather than the number, shape and size of the components when actually implemented. The shapes, numbers and proportions of the components when actually implemented can be arbitrarily changed, and the layout of the components can be more complex.

[0023] It should be noted that in the present application, "first", "second", and the like are only for distinguishing similar objects, and are not limited in order or sequence. The described "include", "have" and the like mean that the subject covered by the word also covers the examples shown by the word, and is not exclusive.

[0024] It can be understood that the various numbers, step numbers and the like in the present application are distinguished for convenience of description, and are not used to limit the scope of the present application. The size of the numbers in the present application does not mean the order of execution, and the execution order of the processes should be determined by its function and inherent logic.

[0025] In the following description, a large number of details are discussed to provide a more thorough explanation of the embodiments of the present application, however, it is obvious to those skilled in the art that the embodiments of the present application can be implemented without these specific details, and in other embodiments, well-known structures and devices are shown in the form of block diagrams rather than in detail, to avoid making the embodiments of the present application difficult to understand.

[0026] It should be noted that the V2X communication technology is a vehicle-to-everything communication, which covers various communication forms such as vehicle-to-vehicle (V2V), vehicle-to-infrastructure (V2I), vehicle-to-network (V2N) and vehicle-to-pedestrian (V2P). Through these communication modes, vehicles can obtain real-time traffic information, road information, pedestrian information and a series of traffic information, thereby improving driving safety, reducing congestion and improving traffic efficiency.

[0027] The embodiments of the present application respectively propose a road rage monitoring and warning method, a road rage monitoring and warning device, an electronic device, a computer readable storage medium and a computer program product, which will be described in detail below.

[0028] Please refer to Figure 1 , Figure 1 is a schematic diagram of an implementation environment of a road rage monitoring and early warning method according to an example embodiment of the present application.

[0029] As Figure 1 shown, the implementation environment can include target vehicle 101, target vehicle 102 and target vehicle 103, and current vehicle 104. It should be understood that the three target vehicles in the present embodiment are only exemplary, and in actual road conditions, vehicles around that can perform V2X communication can all be target vehicles, and the number of target vehicles can be one or more. First, driver emotion information and vehicle driving records are acquired from target vehicle 102, target vehicle 102 and target vehicle 103, and the vehicle computer of current vehicle 104 performs road rage scoring and early warning classification on the target vehicles based on the driver emotion information and vehicle driving records. The early warning classification includes green early warning classification, yellow early warning classification and red early warning classification. If there are vehicles around with yellow early warning classification or red early warning classification, the current vehicle 104 performs corresponding early warning measures based on the preset early warning scheme of each level, including avoiding the target vehicles marked with red and yellow, etc.

[0030] In an embodiment of the present application, target vehicle 101, target vehicle 102 and target vehicle 103 and current vehicle 104 can also perform road rage monitoring and early warning in their own vehicle computers. Based on the vehicle's own vehicle computer, the road rage score and early warning classification are obtained through the driver's emotion information and vehicle driving records of the vehicle itself. When the early warning classification of the vehicle itself is yellow early warning or red early warning, a voice of "please keep calm" or soothing music can be played automatically to help the driver relax.

[0031] In an embodiment of the present application, not only can the current vehicle 104 monitor and early warn the target vehicles by acquiring the driver emotion information and vehicle driving records of the target vehicles, but each target vehicle can also acquire the driver emotion information and vehicle driving records of the current vehicle 104 to monitor and early warn the current vehicle 104.

[0032] Please refer to Figure 2 , Figure 2 is a flowchart of a road rage monitoring and early warning method according to an example embodiment of the present application. The method can be applied to Figure 1 the implementation environment as shown, and the method can also be applicable to other example implementation environments and be specifically executed by devices in other implementation environments. The present embodiment does not limit the implementation environment to which the method is applicable.

[0033] As Figure 2As shown, in an exemplary embodiment, the road rage monitoring and early warning method includes at least steps S210 to S250, which are described in detail as follows:

[0034] In step S210, driver emotion information and vehicle driving records of a plurality of target vehicles around the vehicle are obtained.

[0035] In an embodiment of the present application, a high-resolution camera is installed in the target vehicle, and the resolution of the camera should be at least 1080P or above, so as to clearly capture the facial image of the driver of the target vehicle. Meanwhile, the target vehicle is provided with a sensor capable of detecting heart rate, such as a smart bracelet worn by the driver to monitor heart rate. The smart bracelet has a high-precision heart rate sensor and can monitor the heart rate of the driver in real time. The camera and the smart bracelet transmit the facial image collected as facial information and the heart rate data as heartbeat information to the vehicle computer through a wireless manner. The vehicle computer is provided with a biological recognition software, which can analyze the current emotion information of the driver through facial recognition. For example, if the facial expression shows obvious anger, the software will determine that the driver is in an angry state. If the facial expression shows a relatively relaxed state, the software will determine that the driver is in a calm state. The target vehicle sends the emotion recognition result of the facial information and the heartbeat information as the driver emotion information to other vehicles around the vehicle, so that other vehicles can monitor and early warn the target vehicle based on the driver emotion information.

[0036] In an embodiment of the present application, during the driving of the vehicle, the illegal driving behaviors of the vehicle are recorded, including sudden acceleration, sudden braking, sudden lane changing or following too close, and the driving environment (such as traffic congestion, driving behaviors of other vehicles) is combined to determine whether the vehicle is in illegal behavior. If so, the number of illegal behaviors of the target vehicle is increased once. The courtesy driving behaviors of the vehicle are recorded, including monitoring the courtesy behavior of the vehicle to pedestrians at traffic intersections through a vehicle event data recorder, or judging whether the vehicle is in courtesy behavior when giving priority to other vehicles in traffic congestion. If so, the number of courtesy behaviors of the target vehicle is increased once. The number of illegal behaviors and the number of courtesy behaviors of the vehicle are sent to other vehicles around the vehicle as vehicle driving records, so that other vehicles can monitor and early warn the target vehicle based on the vehicle driving records.

[0037] In an embodiment of the present application, the target vehicle itself also monitors and early warns its own road rage. For example, when the heart rate of the driver of the vehicle exceeds 90 times per minute, and the facial expression of the driver shows obvious anger, it is determined that the driver of the vehicle is in a road rage state at this time. The vehicle can issue a prompt such as “Your current emotion may affect your driving safety, please keep calm”, and the vehicle can also automatically play some soothing music to help the driver relax.

[0038] In an embodiment of the present application, the on-board computer of the target vehicle also transmits the driver's emotional information and vehicle driving record of the target vehicle to other vehicles on the road in a safe manner through V2X communication technology, so that other vehicles can monitor and warn the target vehicle of road rage based on the driver's emotional information and vehicle driving record. The on-board computer packages the driver's emotional state, the vehicle's driving state, and the environmental factors of the road into a data packet, and then sends the data packet to other vehicles nearby through V2X communication technology. After receiving the data packet, other vehicles analyze the driver's emotional state through the built-in warning software, and if potential safety risks are found, they will also issue a warning prompt to the driver through voice or visual means. In this way, the driver's road rage can be effectively monitored and warned, and driving safety can be improved.

[0039] In an embodiment of the present application, if the target vehicle itself monitors the driver's road rage driving behavior, the on-board computer will issue a warning prompt to the driver through voice. This method can improve the timeliness and accuracy of the warning, effectively reduce traffic accidents caused by the driver's emotional excitement, and at the same time, the target vehicle will transmit its driver's emotional information and vehicle driving record to other vehicles on the road in a safe manner through V2X communication technology, achieving information sharing. At the same time, through encryption and anonymous processing and other technical means, the privacy of the driver is protected. This method can fully utilize the advantages of V2X communication technology while protecting the privacy of the driver, and achieve safe transmission and sharing of the driver's emotional information.

[0040] In step S220, the number of illegal behaviors and the number of courtesy behaviors of the target vehicle are obtained based on the vehicle driving record, the illegal behavior index of the target vehicle is obtained based on the number of illegal behaviors, and the courtesy behavior index of the target vehicle is obtained based on the number of courtesy behaviors.

[0041] In an embodiment of the present application, the number of illegal behaviors and the number of courtesy behaviors of the target vehicle are obtained based on the vehicle driving record, the illegal behavior index of the target vehicle is obtained based on the number of illegal behaviors, and the courtesy behavior index of the target vehicle is obtained based on the number of courtesy behaviors. The value of the number of courtesy behaviors is taken as the courtesy behavior index of the target vehicle, and the value of the number of illegal behaviors is processed by taking the reciprocal, and the processed value of the number of illegal behaviors is taken as the illegal behavior index of the target vehicle.

[0042] In an embodiment of the present application, the target vehicle is evaluated by the distance between the best solution and the worst solution, the minimum (benefit) type index is the number of illegal behaviors, and the maximum (cost) type index is the number of courtesy behaviors. The method for converting the minimum type index to the maximum type index is 1 / x (x is the minimum type index). After conversion, it is shown in Table 1:

[0043] Table 1

[0044] Target vehicle Misconduct indicator Courtesy indicator Vehicle 1 1 / n1 n1’ Vehicle 2 1 / n2 n2’ Indicator type Minimally sized Maximally sized

[0045] In Table 1, n1 is the number of violations of vehicle 1, and 1 / n1 is taken as the violation index of vehicle 1, n1' is the number of courtesy behaviors of vehicle 1, and n1' is taken as the courtesy index of vehicle 1; n2 is the number of violations of vehicle 2, and 1 / n2 is taken as the violation index of vehicle 2, n2' is the number of courtesy behaviors of vehicle 2, and n2' is taken as the courtesy index of vehicle 2.

[0046] In step S230, a target behavior matrix is established according to the violation index and the courtesy index of each target vehicle, and a behavior parameter of each target vehicle is calculated based on the target behavior matrix.

[0047] In an embodiment of the present application, the target behavior matrix is established according to the violation index and the courtesy index of each target vehicle, and the behavior parameter of each target vehicle is obtained based on the target behavior matrix, including: obtaining an initial behavior matrix based on the violation index and the courtesy index, one row of the initial behavior matrix representing the violation index and the courtesy index of the same target vehicle, and one column of the initial behavior matrix representing the violation index or the courtesy index of each target vehicle; performing positive normalization on all values in the initial behavior matrix to obtain a normalized target behavior matrix, one row of the target behavior matrix representing the normalized violation index and the normalized courtesy index of the same target vehicle, and the same column of the target behavior matrix representing the normalized violation index or the normalized courtesy index of each target vehicle; taking the maximum value in the first column and the maximum value in the second column in the target behavior matrix as a matrix maximum value, and taking the minimum value in the first column and the minimum value in the second column in the target behavior matrix as a matrix minimum value; obtaining an initial parameter of each target vehicle based on the matrix maximum value, the matrix minimum value, and a preset behavior formula; and obtaining the behavior parameter of each target vehicle after normalizing the initial parameter.

[0048] In an embodiment of the present application, the initial behavior matrix is normalized to eliminate the influence of different index dimensions.

[0049]

[0050] In formula (1), X is the initial behavior matrix, x11 is the violation index of the first target vehicle, x12 is the courtesy index of the first target vehicle, x21 is the violation index of the second target vehicle, x22 is the courtesy index of the second target vehicle, …, xn1 is the violation index of the nth target vehicle, and xn2 is the courtesy index of the nth target vehicle.

[0051] In one embodiment of the present application, the target behavior matrix obtained by normalizing the initial behavior matrix is denoted as Z, each element of Z being:

[0052]

[0053] In formula (2), z ij is the element in the i-th row and the j-th column of the target behavior matrix, x ij is the element in the i-th row and the j-th column of the initial behavior matrix, and n is the number of target vehicles.

[0054] In one embodiment of the present application, the maximum value in the first column and the maximum value in the second column of the target behavior matrix are taken as the matrix maximum value, and the minimum value in the first column and the minimum value in the second column of the target behavior matrix are taken as the matrix minimum value; the initial parameters of each target vehicle are obtained based on the matrix maximum value, the matrix minimum value and a preset behavior formula, including: defining the matrix maximum value as Z + =(Z1 + , Z + 2) = (max{z 11 , z 21 , …, z n1}, max{z 12 , z 22 , …, z n2}) and defining the matrix minimum value as Z - =(Z1 - , Z - 2) = (min{z 11 , z 21 , …, z n1}, min{z 12 , z 22 , …, z n2}

[0055] In one embodiment of the present application, the distance between the i-th (i = 1, 2, …, n) target vehicle and the matrix maximum value is defined as: wherein is the distance between the i-th target vehicle and the matrix maximum value, is the matrix maximum value, and z ij is the element in the i-th row and the j-th column of the target behavior matrix.

[0056] In one embodiment of the present application, the distance between the i-th (i = 1, 2, …, n) evaluation object and the matrix minimum value is defined as: wherein is the distance between the i-th target vehicle and the matrix minimum value, is the matrix minimum value, and z ij is the element in the i-th row and the j-th column of the target behavior matrix.

[0057] In an embodiment of the present application, the initial parameter of each target vehicle is obtained based on the maximum value of the matrix and the minimum value of the matrix and a preset behavior formula, and the initial parameter of the i-th evaluation object is calculated, and the preset behavior formula is: wherein S i is the initial parameter of the i-th target vehicle, is the distance between the i-th target vehicle and the minimum value of the matrix, is the distance between the i-th target vehicle and the maximum value of the matrix.

[0058] In an embodiment of the present application, the normalized behavior parameter of the i-th evaluation object is calculated: wherein S i ′ is the normalized behavior parameter of the i-th target vehicle, S i is the initial parameter of the i-th target vehicle, is the distance between the i-th target vehicle and the minimum value of the matrix, is the distance between the i-th target vehicle and the maximum value of the matrix.

[0059] In an embodiment of the present application, the behavior parameters of the plurality of target vehicles are shown in Table 2:

[0060] Table 2

[0061]

[0062] As shown in Table 2, the distance between vehicle 1 and the maximum value of the matrix is D1 + , the distance between vehicle 1 and the minimum value of the matrix is D1 - , the initial behavior parameter is S1, and the normalized behavior parameter is S1'; the distance between vehicle 2 and the maximum value of the matrix is D2 + , the distance between vehicle 2 and the minimum value of the matrix is D2 - , the initial behavior parameter is S2, and the normalized behavior parameter is S2'.

[0063] In step S240, the road rage score is calculated by scoring the behavior parameter, and the scoring calculation includes calculating based on a scoring weight coefficient, a preset influence parameter and a behavior parameter, and the scoring weight coefficient is obtained based on the driver emotional information.

[0064] In an embodiment of the present application, the behavior parameter is multiplied by the scoring weight coefficient and the preset influence parameter to obtain the road rage score.

[0065] In an embodiment of the present application, the score weight coefficient is obtained based on the driver emotion information, and the score weight coefficient comprises: performing facial emotion recognition on the facial information to obtain an emotion recognition result of the facial information, the emotion recognition result comprising an angry state and a calm state, and the driver emotion information comprising the facial information and heartbeat information; if the heart rate value of the heartbeat information is less than a preset heart rate threshold and the emotion recognition result is the calm state, the score weight coefficient is less than a preset weight standard value; if the heart rate value of the heartbeat information is greater than or equal to the preset heart rate threshold and the emotion recognition result is the angry state, the score weight coefficient is greater than the preset weight standard value; if the heart rate value of the heartbeat information is less than a preset heart rate threshold and the emotion recognition result is the angry state, the score weight coefficient is equal to the preset weight standard value; and if the heart rate value of the heartbeat information is greater than or equal to the preset heart rate threshold and the emotion recognition result is the calm state, the score weight coefficient is equal to the preset weight standard value.

[0066] In an embodiment of the present application, the preset heart rate threshold is set to 90 beats per minute, if the heartbeat threshold is greater than or equal to 90 beats per minute, it is considered that the heartbeat is too fast at this time, and there is a risk of road rage, and in this embodiment, the preset weight standard value can be 1, if the emotion information of the driver is high road rage risk, the score weight coefficient is greater than 1, and if the emotion information of the driver is low road rage risk, the score weight coefficient is less than 1.

[0067] In an embodiment of the present application, before the behavior parameter is multiplied by the score weight coefficient and the preset influence parameter to obtain the road rage score, further comprising: constructing a judgment matrix of multiple dimension indicators, the judgment matrix comprising influence quantization values between each two dimension indicators; obtaining an influence matrix by normalizing the judgment matrix by column, and obtaining influence weight values corresponding to each dimension indicator by arithmetic mean of the influence matrix by row; and obtaining the preset influence parameter based on the influence weight values.

[0068] In an embodiment of the present application, the preset influence parameter is calculated by an analytic hierarchy process method, and the multiple dimension indicators comprise a pathological dimension, a personal dimension and an objective dimension. The influence quantization values between each two dimensions can be preset in advance, and the higher the influence quantization value between dimension A and dimension B, the more important dimension A is compared with dimension B.

[0069] In an embodiment of the present application, after the influence weight values corresponding to the multiple dimension indicators are obtained by calculating the arithmetic mean of the influence matrix by row, further comprising: obtaining a verification parameter based on the influence matrix and the influence weight values corresponding to each dimension indicator; obtaining a first verification value and a second verification value according to the verification parameter and the number of the dimension indicators; taking the ratio of the first verification value and the second verification value as a target verification value; if the target verification value is less than a preset verification threshold, the verification is passed, and the influence weight values are valid.

[0070] In an embodiment of the present application, after the influence weight values are calculated, consistency of each influence weight value is judged to avoid contradiction.

[0071] In an embodiment of the present application, the check parameter is: λ is the check parameter, Aω is the influence matrix multiplied by the vector ω of the arithmetic mean of each row of the influence matrix, i represents the i-th row of the influence matrix, and n represents the number of dimension indicators (i.e. n-order matrix). Meanwhile, the first check value (CI), the second check value (RI), and the third check value (CR) of the consistency indicator are calculated: CI = (λ-n) / (n-1), where n is the order of the matrix, i.e. the number of dimension indicators, RI can be obtained directly based on the number of dimension indicators from a preset relationship corresponding table, and CR = CI / RI. The obtained CR and 0.1 are compared in size, and if CR < 0.1, the one-time test passes.

[0072] In an embodiment of the present application, the road rage condition of the plurality of target vehicles is scored, including multiplying the behavior parameter by a scoring weight coefficient and a preset influence parameter to obtain a road rage score, and the scoring weight coefficient is obtained based on the driver emotional information. Therefore, the road rage score formula is:

[0073] P = k * ω * S n Formula (3)

[0074] In formula (3), k is the scoring weight coefficient, ω is the preset influence parameter, Sn is the behavior parameter of the n-th target vehicle, and P is the road rage score. The larger the P value is, the more serious the road rage condition of the vehicle is.

[0075] Step S250, according to the road rage score, the target vehicle is classified and warned, and the corresponding warning scheme is executed based on the warning classification.

[0076] In an embodiment of the present application, the target vehicle is classified and warned according to the road rage score, and the corresponding preset warning scheme is executed based on the warning classification, including: if the road rage score of the target vehicle is less than a preset first warning score threshold, the target vehicle is classified as a green warning level, and the target vehicle is marked as green; if the road rage score of the target vehicle is greater than or equal to the preset first warning score threshold and less than a preset second warning score threshold, the target vehicle is classified as a yellow warning level, the target vehicle is marked as yellow, and a preset yellow warning scheme is executed on the target vehicle; and if the road rage score of the target vehicle is greater than or equal to the second warning score threshold, the target vehicle is classified as a red warning level, the target vehicle is marked as red, and a red warning scheme is executed on the target vehicle.

[0077] In an embodiment of the present application, if the target vehicle is marked green, it means that the vehicle is currently in a relatively safe and stable driving state, and no further action or warning is needed; if the target vehicle is marked yellow, it means that the vehicle has a certain degree of road rage risk, and a warning message can be sent to the driver of the vehicle to remind the driver to pay attention to the driving behavior of the target vehicle marked yellow; if the target vehicle is marked red, a warning message can be sent to the driver of the vehicle to remind the driver to avoid or evade the driving behavior of the target vehicle marked red.

[0078] In an embodiment of the present application, the road rage monitoring and early warning method can be embedded in the traffic management system, navigation map system, ADAS high-precision city map, etc. The vehicles in red warning classification are marked red, the vehicles in yellow warning classification are marked yellow, and the vehicles in green warning classification are marked green. The traffic management system supervises the yellow vehicles and monitors the red vehicles. Other vehicles pay attention to the yellow vehicles and take measures such as avoiding the red vehicles in advance.

[0079] Please refer to Figure 3 , Figure 3 is a schematic diagram of an analytic hierarchy process according to an exemplary embodiment of the present application. In this embodiment, the dimensional indicators of the influencing factors causing the emotional changes of the driver include pathological dimension, personal dimension, and objective dimension. There is a hierarchical structure between the multiple dimensional indicators as shown in Figure 3 The personal subjectivity of the pathological dimension is small or relatively small, the personal subjectivity of the personal dimension is relatively small or the personal subjectivity is relatively large, and the personal subjectivity of the objective dimension is small or relatively small. Based on the comparison quantization values between the preset dimensional indicators, the judgment matrix of the target layer and the indicator judgment layer is constructed, as shown in Table 3:

[0080] Table 3

[0081] Z A1 Pathology A2 Personal A3 Objective A1 Pathology 1 1 / 6 4 A2 Personal 6 1 3 A3 Objective 1 / 4 1 / 3 1

[0082] The comparison quantization values between the preset dimensional indicators include: if dimension 1 is as important as dimension 2, the quantization value is 1; if dimension 1 is slightly more important than dimension 2, the quantization value is 3; if dimension 1 is relatively more important than dimension 2, the quantization value is 5; if dimension 1 is strongly more important than dimension 2, the quantization value is 7; if dimension 1 is extremely more important than dimension 2, the quantization value is 9; if the importance of dimension 1 is between the above-mentioned importance levels, the intermediate value of the two adjacent judgments is taken, i.e. 2, 4, 6, 8; for example, if dimension 1 is between slightly more important and relatively more important, the quantization value is 4; if dimension 1 is between relatively more important and strongly more important, the quantization value is 6; otherwise, the reciprocal is taken, for example, if dimension 1 is slightly less important than dimension 2, the reciprocal of 3 is taken, which is 1 / 3. In summary, the larger the quantization value is, the higher the importance of dimension 1 compared to dimension 2 is.

[0083] In Table 3, the vertical table represents dimension 1, the horizontal table represents dimension 2, and the middle number represents the quantization value of both, according to the above-mentioned comparison quantization value specification, the greater the quantization value, the more important the dimension 1 is compared with the dimension 2.

[0084] The judgment matrix of Table 3 is normalized to obtain the influence matrix as shown in Table 4:

[0085] Table 4

[0086] Z A1 Pathology A2 Personal A3 Objective ω (arithmetic mean) A1 Pathology 0.138 0.111 .0.5 0.2497 A2 Personal 0.828 0.667 0.375 0.623 A3 Objective 0.034 0.222 0.125 0.381

[0087] In Table 4, ω is the arithmetic mean calculated for each row of the influence matrix, and the influence weight value corresponding to the pathology dimension is 0.2497, the influence weight value corresponding to the personal dimension is 0.623, and the influence weight value corresponding to the objective dimension is 0.381.

[0088] In an embodiment of the present application, the preset influence parameter is obtained based on the influence weight values of the three dimensions, which can be the average value of the influence weight values, or one of the influence weight values.

[0089] Please refer to Figure 4 , Figure 4 is a block diagram of a road rage monitoring and early warning device according to an exemplary embodiment of the present application. The device can be applied to Figure 1 the implementation environment as shown, and the device can also be applied to other exemplary implementation environments and be specifically configured in other devices, and the present embodiment does not limit the implementation environment to which the device is applied.

[0090] As shown in Figure 4 , the exemplary road rage monitoring and early warning device includes an information input module 401, an index calculation module 402, a parameter calculation module 403, a score calculation module 404, and a grading early warning module 405.

[0091] The information input module 401 is configured to obtain driver emotional information and vehicle driving records of a plurality of target vehicles around;

[0092] The index calculation module 402 is configured to obtain the number of violation behaviors and the number of courtesy behaviors of the target vehicle based on the vehicle driving records, obtain the violation behavior index of the target vehicle based on the number of violation behaviors, and obtain the courtesy behavior index of the target vehicle based on the number of courtesy behaviors;

[0093] The parameter calculation module 403 is configured to establish a target behavior matrix according to the violation behavior index and the courtesy behavior index of each target vehicle, and calculate the behavior parameter of each target vehicle based on the target behavior matrix;

[0094] The score calculation module 404 is configured to perform score calculation on the behavior parameters to obtain the road rage score, and the score calculation comprises calculation based on a score weight coefficient, a preset influence parameter and the behavior parameters, and the score weight coefficient is obtained based on the driver emotion information;

[0095] The grading warning module 405 is configured to perform warning grading on the target vehicle according to the road rage score, and perform a corresponding preset warning scheme based on the warning grading.

[0096] Figure 5 A structural schematic diagram of a computer system of an electronic device suitable for implementing the embodiments of the present application is shown. It should be noted that, Figure 5 The computer system 500 of the electronic device shown is only an example, and should not impose any limitation on the functions and use range of the embodiments of the present application.

[0097] As Figure 5 shown, the computer system 500 includes a central processing unit (CPU) 501, which can perform various appropriate actions and processes according to programs stored in a read-only memory (ROM) 502 or loaded from a storage portion 508 into a random access memory (RAM) 503, such as performing the methods described in the above embodiments. In the RAM 503, various programs and data required for system operation are also stored. The CPU 501, the ROM 502 and the RAM 503 are connected to each other through a bus 504. An input / output (I / O) interface 505 is also connected to the bus 504.

[0098] The following components are connected to the I / O interface 505: an input portion 506 including a keyboard, a mouse, and the like; an output portion 507 including a cathode ray tube (CRT), a liquid crystal display (LCD), and the like, and a speaker, and the like; a storage portion 508 including a hard disk, and the like; and a communication portion 509 including a network interface card such as a LAN (Local Area Network) card, a modem, and the like. The communication portion 509 performs communication processing via a network such as the Internet. A drive 510 is also connected to the I / O interface 505 as needed. A removable medium 511 such as a magnetic disk, an optical disk, a magneto-optical disk, a semiconductor memory, and the like is mounted on the drive 510 as needed, so that a computer program read therefrom is installed in the storage portion 508 as needed.

[0099] In particular, according to embodiments of the present application, the processes described above with reference to the flowcharts can be implemented as a computer software program. For example, embodiments of the present application include a computer program product comprising a computer program carried on a computer readable medium, the computer program comprising computer programs for executing the methods illustrated by the flowcharts. In such embodiments, the computer program can be downloaded and installed from a network by the communication section 509, and / or installed from the removable medium 511. When the computer program is executed by the central processing unit (CPU) 501, various functions defined in the system of the present application are executed.

[0100] It should be noted that the computer readable medium shown in the embodiments of the present application can be a computer readable signal medium or a computer readable storage medium or any combination of the two. The computer readable storage medium may, for example, be an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, device or apparatus, or any combination of the above. More specific examples of the computer readable storage medium can include, but are not limited to, an electrical connection having one or more wires, a portable computer diskette, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM), a flash memory, an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the above. In the present application, the computer readable signal medium can include a data signal propagated in a baseband or as a carrier wave in a propagated data signal, in which the computer readable computer program is carried. Such a propagated data signal can take on many forms, including but not limited to an electromagnetic signal, an optical signal, or any suitable combination of the above. The computer readable signal medium can also be any computer readable medium other than the computer readable storage medium that can send, propagate or transfer the program for use by or in connection with the instruction execution system, apparatus or device. The computer program contained on the computer readable medium can be transmitted in any suitable medium, including but not limited to wireless, wired, or the like, or any suitable combination of the above.

[0101] The flow and block diagrams in the drawings illustrate the architecture, functionality, and operation of possible implementations of systems, methods and computer program products according to various embodiments of the present application. In this regard, each block in the flow and block diagrams can represent a module, segment, or portion of code, which comprises one or more executable instructions for implementing the specified logical functions. It should also be noted that in some alternative implementations, the functions noted in the blocks can occur out of the order noted in the figures. For example, two blocks shown in succession may, in fact, be executed substantially concurrently or the blocks may be executed in the reverse order, depending on the functionality involved. It will also be noted that each block of the block diagrams and / or flowchart illustrations, and combinations thereof, can be implemented by special purpose hardware-based systems that perform the specified functions or operations, or combinations of special purpose hardware and computer instructions.

[0102] The units described in the embodiments of the present application can be implemented by software, or by hardware, or by a combination of software and hardware. The units described may

[0103] Another aspect of the present application provides a computer readable storage medium, having stored thereon a computer program, which, when executed by a processor of a computer, causes the computer to perform the road rage monitoring and early warning method as described above. The computer readable storage medium can be included in the electronic device described in the above embodiments, or can exist separately and not be assembled into the electronic device.

[0104] Another aspect of the present application provides a computer program product or computer program, which comprises computer instructions stored in a computer readable storage medium. A processor of a computer device reads the computer instructions from the computer readable storage medium, and the processor executes the computer instructions, so that the computer device performs the road rage monitoring and early warning method provided in the above embodiments.

[0105] The above embodiments only illustrate the principles of the present application and its effects, and are not used to limit the present application. Any person skilled in the art can make modifications or changes to the above embodiments without departing from the spirit and scope of the present application. Therefore, all equivalent modifications or changes made by those skilled in the art without departing from the spirit and technical ideas of the present application shall be covered by the claims of the present application.

Claims

1. A road rage monitoring and early warning method, characterized in that: The road rage monitoring and early warning method includes: Obtain driver emotion information and vehicle driving records of multiple surrounding target vehicles; Obtaining the number of violations and the number of courteous behaviors of the target vehicle based on the vehicle driving record, obtaining a violation index of the target vehicle based on the number of violations, and obtaining a courteous behavior index of the target vehicle based on the number of courteous behaviors; Establishing a target behavior matrix according to the violation behavior index and the courtesy behavior index of each target vehicle, and calculating the behavior parameters of each target vehicle based on the target behavior matrix; Scoring the behavior parameters to obtain a road rage score, wherein the scoring calculation includes calculation based on a scoring weight coefficient, a preset influencing parameter, and the behavior parameter, wherein the scoring weight coefficient is obtained based on the driver's emotional information, and the preset influencing parameter is calculated by analyzing multiple dimensional indicators that cause changes in the driver's emotions through a hierarchical analysis method; The target vehicle is classified into warning levels according to the road rage score, and a corresponding preset warning plan is executed based on the warning level.

2. The road rage monitoring and warning method according to claim 1, characterized in that: Obtaining the number of violations and the number of courteous behaviors of the target vehicle based on the vehicle driving record, obtaining the violation index of the target vehicle based on the number of violations, and obtaining the courteous behavior index of the target vehicle based on the number of courteous behaviors includes: The value of the number of courteous behaviors is used as the courteous behavior index of the target vehicle; After performing a countdown process on the value of the number of violation behaviors, the processed value of the number of violation behaviors is used as the violation behavior index of the target vehicle.

3. The road rage monitoring and warning method according to claim 1, characterized in that: A target behavior matrix is ​​established based on the violation behavior index and the courtesy behavior index of each target vehicle, and the behavior parameters of each target vehicle are calculated based on the target behavior matrix, including: An initial behavior matrix is ​​obtained based on the violation behavior index and the courtesy behavior index, wherein a row of the initial behavior matrix represents the violation behavior index and the courtesy behavior index of the same target vehicle, and a column of the initial behavior matrix represents the violation behavior index or the courtesy behavior index of each target vehicle; Forward normalizing all values ​​in the initial behavior matrix to obtain a standardized target behavior matrix, wherein a row of the target behavior matrix represents a standardized violation behavior index and a standardized courtesy behavior index of the same target vehicle, and a column of the target behavior matrix represents a standardized violation behavior index or a standardized courtesy behavior index of each target vehicle; The maximum value in the first column and the maximum value in the second column of the target behavior matrix are used as the matrix maximum value, and the minimum value in the first column and the minimum value in the second column of the target behavior matrix are used as the matrix minimum value; Obtaining initial parameters of each target vehicle based on the matrix maximum value, the matrix minimum value, and a preset behavior formula; The behavior parameters of each target vehicle are obtained after normalizing the initial parameters.

4. The road rage monitoring and warning method according to claim 1, characterized in that: The scoring weight coefficient is obtained based on the driver's emotional information and includes: Performing facial emotion recognition on the facial information to obtain an emotion recognition result of the facial information, wherein the emotion recognition result includes an angry state and a calm state, and the driver's emotion information includes the facial information and heartbeat information; If the heart rate value of the heartbeat information is less than the preset heart rate threshold and the emotion recognition result is a calm state, the scoring weight coefficient is less than the preset weight standard value; If the heart rate value of the heartbeat information is greater than or equal to the preset heart rate threshold and the emotion recognition result is an angry state, the scoring weight coefficient is greater than the preset weight standard value; If the heart rate value of the heartbeat information is less than the preset heart rate value threshold and the emotion recognition result is an angry state, the scoring weight coefficient is equal to the preset weight standard value; If the heart rate value of the heartbeat information is greater than or equal to a preset heart rate threshold and the emotion recognition result is a calm state, the scoring weight coefficient is equal to the preset weight standard value.

5. The road rage monitoring and early warning method according to any one of claims 1 to 4, characterized in that: Before the calculation based on the scoring weight coefficient, the preset influence parameter and the behavior parameter, the following is also included: Constructing a judgment matrix of multiple dimensional indicators, wherein the judgment matrix includes quantitative influence values ​​between each dimensional indicator; Normalizing the judgment matrix by column to obtain an influence matrix, and taking the arithmetic mean of the influence matrix by row to obtain the influence weight value corresponding to each dimensional indicator; The preset influence parameter is obtained based on the influence weight value.

6. The road rage monitoring and warning method according to claim 5, characterized in that: After obtaining the influence weight value corresponding to each dimensional indicator by calculating the arithmetic mean of the influence matrix row by row, the method further includes: Obtain verification parameters based on the impact matrix and the impact weight values ​​corresponding to the various dimensional indicators; Obtaining a first check value and a second check value according to the check parameter and the number of dimension indicators; using the ratio of the first check value to the second check value as a target check value; If the target verification value is less than the preset verification threshold, the verification passes and the influence weight value is valid.

7. The road rage monitoring and early warning method according to any one of claims 1 to 4, characterized in that: The target vehicle is classified into a warning level according to the road rage score, and a corresponding preset warning plan is executed based on the warning level, including: If the road rage score of the target vehicle is less than a preset first warning score threshold, the target vehicle is classified as a green warning level and is marked green; If the road rage score of the target vehicle is greater than or equal to the preset first warning score threshold and less than the preset second warning score threshold, the target vehicle is classified as a yellow warning, the target vehicle is marked as yellow, and a preset yellow warning plan is executed for the target vehicle; If the road rage score of the target vehicle is greater than or equal to the second warning score threshold, the target vehicle is classified as a red warning, the target vehicle is marked as red, and a red warning plan is executed for the target vehicle.

8. A road rage monitoring and warning device, characterized in that: The road rage monitoring and warning device includes: An information input module is used to obtain driver emotion information and vehicle driving records of multiple surrounding target vehicles; an index calculation module, configured to obtain the number of violations and the number of courteous behaviors of the target vehicle based on the vehicle driving record, obtain the violation index of the target vehicle based on the number of violations, and obtain the courteous behavior index of the target vehicle based on the number of courteous behaviors; a parameter calculation module, configured to establish a target behavior matrix according to the violation behavior index and the courtesy behavior index of each target vehicle, and calculate the behavior parameters of each target vehicle based on the target behavior matrix; a scoring calculation module, configured to score and calculate the behavior parameters to obtain a road rage score, wherein the scoring calculation includes calculation based on a scoring weight coefficient, a preset influencing parameter, and the behavior parameter, wherein the scoring weight coefficient is obtained based on the driver's emotional information, and the preset influencing parameter is calculated by analyzing multiple dimensional indicators that cause changes in the driver's emotions using a hierarchical analysis method; A graded warning module is used to classify the target vehicle according to the road rage score and execute a corresponding preset warning plan based on the warning classification.

9. An electronic device, characterized in that: The electronic device comprises: one or more processors; A storage device for storing one or more programs, which, when executed by the one or more processors, enables the electronic device to implement the road rage monitoring and warning method as described in any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that A computer program is stored thereon, and when the computer program is executed by a processor of a computer, the computer is caused to execute the road rage monitoring and warning method according to any one of claims 1 to 7.

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