Vehicle-to-vehicle communication data transmission control method and apparatus for taxis

Abstract

The application relates to the field of traffic communication transmission control, in particular to a car-to-car communication data transmission control method and equipment for taxis. The method obtains the congestion degree of each taxi at the current moment by analyzing the distance distribution between each vehicle in the local area of each taxi and the speed difference, obtains the trajectory disorder index of each vehicle in the local area at the current moment according to the position point change of each vehicle in the local area at each moment in a preset time period before the current moment of each taxi, and obtains the communication transmission weight of each taxi at the current moment in combination with the congestion degree of each taxi at the current moment. The communication transmission of each taxi is carried out with different priorities based on the communication transmission weight. The application can make the taxis with greater congestion or collision risk transmit communication data in time, and ensure the timeliness of taxi communication.

Description

TECHNICAL FIELD

[0001] The present application relates to the field of traffic communication transmission control, in particular to a car-to-car communication data transmission control method and device for taxis. BACKGROUND

[0002] The data transmission of car-to-car communication is one of the core technologies of intelligent transportation system. Through the information exchange between vehicles, the single vehicle is transformed from an "information island" to an "intelligent node" in the traffic network, forming a dynamic data interaction network covering the entire road. Through the real-time sharing of dynamic information between vehicles, the traffic efficiency, safety and intelligent level are comprehensively improved. The car-to-car communication data transmission of taxis has important significance in improving traffic safety, operation efficiency and service quality.

[0003] With the increasing number of taxis, especially during peak hours, a large number of taxis simultaneously transmit data, causing communication channel congestion. When the data volume exceeds the channel carrying capacity, data transmission will be delayed and packets will be lost, seriously affecting the accuracy and speed of data transmission, so that taxis with higher congestion or collision risk cannot transmit communication data in time, thereby failing to ensure the timeliness of taxi communication. SUMMARY

[0004] In order to solve the technical problem that the presence of a large number of taxis will cause taxis with higher congestion or collision risk to be unable to transmit communication data in time, thereby failing to ensure the timeliness of taxi communication, the purpose of the present application is to provide a car-to-car communication data transmission control method and device for taxis, and the technical solution adopted is as follows:

[0005] The present application provides a car-to-car communication data transmission control method for taxis, which comprises:

[0006] Real-time acquisition of vehicle speed data and position points of each vehicle in different lanes;

[0007] Any taxi in the lane is taken as a target taxi, and the first congestion performance of the target taxi at the current time is obtained according to the distribution of the distance between adjacent vehicles in the same lane in the preset area of the target taxi at the current time. The second congestion performance of the target taxi at the current time is obtained according to the distance between each vehicle and the adjacent previous vehicle in the same lane in the preset area of the target taxi at the current time, the vehicle speed data of each vehicle at the current time, and the difference between the vehicle speed data of each vehicle and the adjacent previous vehicle at the current time. The congestion degree of the target taxi at the current time is obtained based on the first congestion performance and the second congestion performance.

[0008] obtaining a trajectory disorder index of each vehicle in the preset area of the target taxi at the current time according to the change of the position point of each vehicle in the preset area of the target taxi at each time within a preset time period before the current time, and the distance between each vehicle at the current time, and the congestion degree of the target taxi at the current time;

[0009] performing communication transmission of different priorities for each taxi based on the communication transmission weight of each taxi at the current time.

[0010] Further, the obtaining of the first congestion performance degree of the target taxi at the current time comprises:

[0011] taking any one lane in the preset area of the target taxi as a target lane, and taking the distance between each vehicle at the current time and the adjacent previous vehicle in the target lane as a distance parameter of each vehicle at the current time in the target lane;

[0012] analyzing the dispersion degree of the distance parameter of all vehicles at the current time in the target lane to obtain a distance distribution disorder degree of the target lane at the current time;

[0013] performing negative correlation mapping on the average value of the distance parameter of all vehicles at the current time in the target lane to obtain a distance proximity degree of the target lane at the current time;

[0014] comprehensively obtaining a congestion risk coefficient of the target lane at the current time by comprehensively combining the distance distribution disorder degree and the distance proximity degree;

[0015] taking the average value of the congestion risk coefficient of all lanes in the preset area of the target taxi at the current time as the first congestion performance degree of the target taxi at the current time.

[0016] Further, the obtaining of the second congestion performance degree of the target taxi at the current time comprises:

[0017] in the target lane, taking the distance parameter of each vehicle at the current time as the numerator, taking the speed data of each vehicle at the current time as the denominator, taking the ratio as the following time length of each vehicle at the current time, and taking the vehicle with the following time length less than a preset safety time threshold as the first risk vehicle of the target lane at the current time;

[0018] obtaining a following time length difference degree of the preset area of the target taxi at the current time according to the distribution of the following time length of each vehicle at the current time in all lanes in the preset area of the target taxi;

[0019] In the target lane, the difference value of the vehicle speed data between each vehicle and the previous vehicle at the current time is the numerator, the vehicle distance parameter of each vehicle at the current time is the denominator, and the ratio is the vehicle speed gradient value of each vehicle in the target lane at the current time. The vehicle with a vehicle speed gradient value greater than a preset gradient value threshold in the target lane is the second risk vehicle of the target lane at the current time;

[0020] According to the number of the first risk vehicle and the number of the second risk vehicle of each lane in the preset area of the target taxi at the current time, the congestion risk assessment value of the preset area of the target taxi at the current time is obtained.

[0021] The following is a summary of the following:

[0022] Further, the obtaining of the following is a summary of the following:

[0023] The dispersion degree of the following time of all vehicles in all lanes in the preset area of the target taxi at the current time is analyzed, and the following time confusion degree of the preset area of the target taxi at the current time is obtained.

[0024] Any two vehicles in all lanes in the preset area of the target taxi are taken as a vehicle control group, the absolute value of the difference value of the following time between the two vehicles in each vehicle control group at the current time is taken as the following time difference value of each vehicle control group at the current time, and the average value of the following time difference value of all vehicle control groups at the current time is taken as the overall difference value of the preset area of the target taxi at the current time.

[0025] The following time confusion degree and the overall difference value are integrated to obtain the following time difference degree of the preset area of the target taxi at the current time.

[0026] Further, the obtaining of the following is a summary of the following:

[0027] The number of the first risk vehicle of the target lane at the current time is the numerator, the number of all vehicles of the target lane at the current time is the denominator, and the ratio is the first risk vehicle proportion of the target lane at the current time.

[0028] The number of the second risk vehicle of the target lane at the current time is the numerator, the number of all vehicles of the target lane at the current time is the denominator, and the ratio is the second risk vehicle proportion of the target lane at the current time.

[0029] comprehensive risk vehicle proportion of the target lane at the current time;

[0030] an average value of the comprehensive risk vehicle proportions of all lanes in the preset area of the target taxi at the current time is taken as a congestion risk evaluation value of the preset area of the target taxi at the current time.

[0031] Further, the obtaining of the congestion degree of the target taxi at the current time comprises:

[0032] comprehensive risk vehicle proportion of the target lane at the current time;

[0033] Further, the obtaining of the trajectory disorder index of each vehicle in the preset area at the current time comprises:

[0034] any one vehicle in the preset area of the target taxi is taken as a target vehicle, a line between a position point of each time and a position point of a next adjacent time within a preset time period before the current time of the target vehicle is taken as a driving direction line of the target vehicle at each time, a dispersion degree of an included angle between the driving direction lines of all adjacent two times within the preset time period is analyzed and normalized to obtain the trajectory disorder index of the target vehicle at the current time.

[0035] Further, the obtaining of the communication transmission weight of the target taxi at the current time comprises:

[0036] the vehicle with the trajectory disorder index greater than a preset trajectory disorder threshold value in the preset area of the target taxi is taken as a collision risk vehicle in the preset area;

[0037] an average value of the trajectory disorder indexes of all collision risk vehicles in the preset area of the target taxi at the current time is taken as an overall trajectory disorder value of the preset area of the target taxi at the current time.

[0038] a reference collision risk vehicle of each collision risk vehicle is selected from other collision risk vehicles in the preset area of the target taxi, wherein a distance between each collision risk vehicle and the corresponding reference collision risk vehicle is the closest, and an average value of distances between all collision risk vehicles and the corresponding reference collision risk vehicles is negatively correlated to obtain a collision vehicle aggregation degree of the preset area of the target taxi at the current time.

[0039] The congestion degree, the overall trajectory disorder value, the collision vehicle aggregation degree and the number of the collision risk vehicles of the target taxi in the preset area at the current time are comprehensively obtained and normalized to obtain the communication transmission weight of the target taxi at the current time.

[0040] Further, the different priority communication transmission of each taxi comprises:

[0041] According to the order of the communication transmission weight from large to small, the communication data of each taxi is transmitted.

[0042] The application further provides a computer device, which comprises a memory and a processor, the memory stores a computer program, and the processor realizes the steps of any one of the vehicle-to-vehicle communication data transmission control methods for taxis when executing the computer program.

[0043] The application has the following beneficial effects:

[0044] The application considers that a large number of taxis will make the taxis with a larger congestion or collision risk unable to transmit communication data in time, thereby failing to guarantee the timeliness of taxi communication. Therefore, the application first collects the speed data and position points of each vehicle in different lanes in real time, analyzes the distance distribution between adjacent vehicles in the same lane at the current time in the preset area of the target taxi, and the speed difference of each vehicle in the preset area, reflects the congestion of the vehicles in the preset area of the target taxi through the obtained congestion degree, analyzes the change of the position points of each vehicle in the preset area of the target taxi at each time in a preset time period before the current time, reflects the possibility of collision risk of each vehicle due to the chaotic driving trajectory through the obtained trajectory disorder index, and reflects the degree of information transmission priority in the local area of the target taxi due to the existence of vehicle congestion or collision risk through the obtained communication transmission weight, thereby performing different priority communication transmission of each taxi to guarantee that the taxis with a larger congestion or collision risk can transmit communication data in time and improve the timeliness of taxi communication. BRIEF DESCRIPTION OF DRAWINGS

[0045] In order to more clearly illustrate the technical solutions in the embodiments of the application or the prior art, and the advantages thereof, the following will briefly introduce the drawings needed in the embodiments or the prior art description. Obviously, the drawings in the following description are only some embodiments of the application, and for those skilled in the art, other drawings can be obtained from these drawings without creative labor.

[0046] Figure 1A flow chart of a vehicle-to-vehicle communication data transmission control method for taxis is provided in an embodiment of the present application. DETAILED DESCRIPTION

[0047] In order to further clarify the technical means and effects taken by the present application to achieve the predetermined inventive objectives, the specific embodiments, structures, features and effects of a vehicle-to-vehicle communication data transmission control method and device for taxis according to the present application are described in detail below in combination with the accompanying drawings and preferred embodiments. In the following description, different "one embodiment" or "another embodiment" do not necessarily refer to the same embodiment. In addition, the specific features, structures or characteristics in one or more embodiments can be combined in any suitable form.

[0048] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which the present application belongs.

[0049] The specific scheme of a vehicle-to-vehicle communication data transmission control method and device for taxis provided by the present application is described in detail below in combination with the accompanying drawings.

[0050] Please refer to Figure 1 which shows a flow chart of a vehicle-to-vehicle communication data transmission control method for taxis provided in an embodiment of the present application. The method comprises:

[0051] Step S1: Real-time acquisition of vehicle speed data and position points of each vehicle in different lanes.

[0052] Since the embodiment of the present application needs to judge the risk of congestion or collision around each taxi according to the driving conditions of vehicles around the taxi during driving, and then perform information transmission with different priority levels for different taxis based on the degree of congestion or collision risk of different taxis, the embodiment of the present application first uses a vehicle speed recorder or a vehicle speed sensor in each vehicle to collect vehicle speed data of each vehicle on different lanes. It should be noted that the lanes include not only taxis but also other types of vehicles, such as family cars or trucks, etc.

[0053] At the same time, the embodiment of the present application also needs to use the GPS system in the vehicle to collect the position information of each vehicle on different roads, i.e. the position points, which can reflect the position information of the vehicles on the road in real time.

[0054] Step S2: taking any one taxi in the lane as a target taxi, obtaining a first congestion performance degree of the target taxi at the current time according to a distance distribution between adjacent vehicles in the same lane in a preset area of the target taxi at the current time, obtaining a second congestion performance degree of the target taxi at the current time according to a distance between each vehicle and an adjacent previous vehicle in the same lane in the preset area of the target taxi at the current time, speed data of each vehicle at the current time, and a difference between speed data between each vehicle and the adjacent previous vehicle at the current time, and obtaining a congestion degree of the target taxi at the current time based on the first congestion performance degree and the second congestion performance degree.

[0055] The car-to-car communication of the taxi is performed through radio communication, so that the vehicles share data such as positions, speeds and driving intentions in real time, traffic information is shared, the traffic state in the region is transparentized, the traffic efficiency of the road is improved, and traffic congestion is reduced. When a large number of taxis simultaneously perform data transmission through radio, the limited communication channel may be congested, and the regions where different taxis are located are different, and the importance of the information to be transmitted by the taxis is also different. The congestion of the transmission channel may cause delay in transmission of important information. Therefore, in order to perform fast and effective communication data transmission, it is necessary to determine the importance of the data to be transmitted by different taxis, and the information of the taxis with high importance is preferentially transmitted. The importance of taxi information transmission can be reflected by the driving conditions of the vehicles around the taxi. The greater the possibility of congestion or collision in the area around a certain taxi, the more likely the taxi is to appear congestion or collision phenomenon, and the greater the importance of information transmission of the taxi.

[0056] Therefore, the embodiment of the present application first takes any one taxi in the lane as a target taxi, and sets a preset area with the target taxi as the center. In an embodiment of the present application, the preset area can be a circular area with a radius of 200 meters. The specific value of the radius of the preset area can also be set by the implementer according to the specific real-time scene, which is not limited herein. The smaller the distance between the two vehicles in the same lane in the preset area of the target taxi and the more inconsistent the distance distribution, the greater the possibility of congestion risk in the local area of the target taxi. Therefore, the distance distribution between adjacent vehicles in the same lane in the preset area of the target taxi at the current time can be analyzed, and the possibility of congestion risk around the target taxi at the current time is reflected through the obtained first congestion performance degree. It should be noted that the vehicles analyzed in the embodiment of the present application are all in the preset area.

[0057] Preferably, in an embodiment of the present application, the method for obtaining the first congestion performance degree of the target taxi at the current time specifically comprises:

[0058] Any one lane in the preset area of the target taxi is taken as a target lane, and a distance between each vehicle in the target lane at the current time and an adjacent previous vehicle is taken as a distance parameter of each vehicle in the target lane at the current time, wherein the distance between each vehicle and the adjacent previous vehicle can be represented by a distance between position points of the two vehicles.

[0059] The dispersion degree of the distance parameters of all vehicles in the target lane at the current time is analyzed to obtain a distance distribution disorder degree of the target lane at the current time, and the greater the distance distribution disorder degree, the more inconsistent the distance distribution of adjacent vehicles on the target lane, and the more likely the target lane to have a congestion risk at the current time.

[0060] In an embodiment of the present application, a standard deviation, a variance, or a range of the distance parameters of all vehicles in the target lane at the current time can be taken as the distance distribution disorder degree of the target lane at the current time, thereby realizing the analysis of the dispersion degree of the distance parameters of all vehicles in the target lane at the current time, and the analysis of the dispersion degree of data in subsequent steps can also be processed using the same method, which is not described and limited here.

[0061] The average value of the distance parameters of all vehicles in the target lane at the current time is negatively correlated to obtain a distance proximity degree of the target lane at the current time, and the greater the distance proximity degree, the closer the distance between adjacent vehicles on the target lane, and the more likely the target lane to have a congestion risk at the current time.

[0062] Then, the distance distribution disorder degree and the distance proximity degree are integrated to obtain a congestion risk coefficient of the target lane at the current time, and the congestion risk coefficient of each lane in the preset area of the target taxi at the current time can be obtained by the same method, and the average value of the congestion risk coefficients of all lanes in the preset area of the target taxi at the current time can be taken as a first congestion performance degree of the target taxi at the current time.

[0063] In an embodiment of the present application, the sum or product of the distance distribution disorder degree and the distance proximity degree can be taken as the congestion risk coefficient of the target lane at the current time, thereby realizing the comprehensive analysis of the two, and the comprehensive processing of two or more data in subsequent steps can also be realized using the same method.

[0064] As an example, in an embodiment of the present application, the expression of the first congestion performance degree of the target taxi at the current time can be specifically, for example:

[0065]

[0066] wherein S1 represents the first congestion performance degree of the target taxi at the current time; σn This represents the standard deviation of the distance parameters of all vehicles in the nth lane within the preset area of ​​the target taxi at the current time, which is the disorder of the distance distribution in the nth lane at the current time. This represents the average distance parameter between all vehicles in the nth lane at the current moment. In real-world scenarios, the distance between two adjacent vehicles in the same lane is not zero, therefore... This indicates the degree of proximity of vehicles in the nth lane at the current moment; This represents the congestion risk coefficient of the nth lane at the current moment; N represents the number of lanes within the preset area of ​​the target taxi.

[0067] It should be noted that negative correlation mapping can also be achieved through other basic mathematical operations in other embodiments of the present invention, which will not be elaborated here.

[0068] The above process analyzes the risk of congestion around the target taxi from the perspective of vehicle distance distribution. In addition, the speed distribution in the same lane and the speed differences between vehicles can also reflect the risk of congestion around the target taxi. For example, in the same lane, when a vehicle is close to the vehicle in front and its speed is high, the following time is short, and a rear-end collision is likely to occur during emergency braking, thus posing a greater risk of congestion. At the same time, the greater the speed of the vehicle relative to the vehicle in front and the smaller the distance between them, the greater the risk of a rear-end collision and congestion. Therefore, the distance between each vehicle and its adjacent vehicle in the same lane within the preset area of ​​the target taxi at the current moment, the speed data of each vehicle at the current moment, and the difference in speed data between each vehicle and its adjacent vehicle at the current moment can be analyzed. The obtained second congestion performance index reflects the probability of congestion around the target taxi at the current moment from the perspective of vehicle speed distribution.

[0069] Preferably, in one embodiment of the present invention, the method for obtaining the second congestion performance of the target taxi at the current moment specifically includes:

[0070] Firstly, in the target lane, the distance parameter of each vehicle at the current time is taken as the numerator, the speed data of each vehicle at the current time is taken as the denominator, and the ratio is taken as the following time of each vehicle in the target lane at the current time. The shorter the following time, the more likely the rear-end phenomenon occurs when the vehicle is in emergency braking, indicating that the vehicle has a greater safety risk, so the vehicle with a following time less than a preset safety time threshold in the target lane at the current time can be taken as the first risk vehicle of the target lane at the current time. The following time is usually greater than 2 seconds to ensure enough time for braking, so the preset safety time threshold has a value range of [1.5, 1.8] seconds. In an embodiment of the present application, the preset safety time threshold is set to 1.8 seconds. The specific value of the preset safety time threshold can also be set by the implementer according to the specific implementation scene, which is not limited here.

[0071] Then, the more inconsistent the following time of each vehicle in the preset area of the target taxi at the current time, the more likely the congestion risk around the target taxi, so the following time difference degree of the preset area of the target taxi at the current time can be obtained according to the distribution of the following time of each vehicle in all lanes in the preset area of the target taxi at the current time.

[0072] Preferably, in an embodiment of the present application, the method for obtaining the following time difference degree of the preset area of the target taxi at the current time specifically comprises:

[0073] The dispersion degree of the following time of all vehicles in all lanes in the preset area of the target taxi at the current time is analyzed to obtain the following time chaos degree of the preset area of the target taxi at the current time. The greater the following time chaos degree, the more inconsistent the distribution of the following time of each vehicle in the preset area of the target taxi.

[0074] Any two vehicles in all lanes in the preset area of the target taxi are taken as a vehicle control group. The absolute value of the difference between the following time of the two vehicles in each vehicle control group at the current time is taken as the following time difference value of each vehicle control group at the current time. The average value of the following time difference values of all vehicle control groups at the current time is taken as the overall difference value of the preset area of the target taxi at the current time. The greater the overall difference value, the greater the overall difference level of the following time of each vehicle in the preset area of the target taxi.

[0075] The greater the following time chaos degree and the overall difference value of the preset area of the target taxi at the current time, the more obvious the difference between the following time of the vehicles around the target taxi, so the following time chaos degree and the overall difference value can be integrated to obtain the following time difference degree of the preset area of the target taxi at the current time.

[0076] As an example, in an embodiment of the present application, the expression of the following car time length difference degree of the preset area of the target taxi at the current time can be specifically, for example,

[0077]

[0078] wherein, ΔT represents the following car time length difference degree of the preset area of the target taxi at the current time; p represents the standard deviation of the following car time length of all vehicles in all lanes in the preset area of the target taxi at the current time, that is, the following car time length chaos degree of the preset area of the target taxi at the current time; T m and T ′ m respectively represent the following car time length of the two vehicles in the mth vehicle control group at the current time; |T m -T ′ m | represents the following car time length difference value of the mth vehicle control group at the current time; M represents the number of vehicle control groups; represents the overall difference value of the preset area of the target taxi at the current time.

[0079] Further, in the target lane, the difference value of the vehicle speed data between each vehicle and the adjacent previous vehicle at the current time is taken as the numerator, the vehicle distance parameter of each vehicle at the current time is taken as the denominator, and the ratio is taken as the vehicle speed gradient value of each vehicle in the target lane at the current time. The greater the vehicle speed gradient value, the more likely it is that the vehicle will cause congestion by rear-end collision, so the vehicle with a vehicle speed gradient value greater than a preset gradient value threshold in the target lane at the current time can be taken as a second risk vehicle of the target lane at the current time, wherein the preset gradient value threshold is in the range of [0.1, 0.2], and in an embodiment of the present application, the preset gradient value threshold is set to 0.1. The specific value of the preset gradient value threshold can also be set by the implementer according to the specific implementation scene, which is not limited here.

[0080] The greater the proportion of the number of first risk vehicles and second risk vehicles in each lane in the preset area of the target taxi, the more likely there is a greater congestion risk around the target taxi, so the congestion risk assessment value of the preset area of the target taxi at the current time can be obtained according to the number of first risk vehicles and the number of second risk vehicles of each lane in the preset area of the target taxi at the current time.

[0081] Preferably, in an embodiment of the present application, the method for obtaining the congestion risk assessment value of the preset area of the target taxi at the current time specifically comprises:

[0082] The number of the first risk vehicles of the target lane at the current time is the numerator, the number of all vehicles of the target lane at the current time is the denominator, and the ratio is the first risk vehicle proportion of the target lane at the current time. The number of the second risk vehicles of the target lane at the current time is the numerator, the number of all vehicles of the target lane at the current time is the denominator, and the ratio is the second risk vehicle proportion of the target lane at the current time.

[0083] The first risk vehicle proportion and the second risk vehicle proportion are integrated to obtain the comprehensive risk vehicle proportion of the target lane at the current time. The greater the comprehensive risk vehicle proportion, the more the vehicles that cause congestion risk by rear-end collision in the target lane, and the greater the possibility of congestion risk in the target lane. The comprehensive risk vehicle proportion of each lane in the preset area of the target taxi at the current time can be obtained by the same method, and the average of the comprehensive risk vehicle proportions of all lanes in the preset area of the target taxi at the current time can be used as the congestion risk evaluation value of the preset area of the target taxi at the current time.

[0084] As an example, in an embodiment of the application, the expression of the congestion risk evaluation value of the preset area of the target taxi at the current time can be specifically as follows:

[0085]

[0086] Wherein, A represents the congestion risk evaluation value of the preset area of the target taxi at the current time; K (n,1) represents the number of the first risk vehicles of the nth lane in the preset area of the target taxi at the current time; K (n,2) represents the number of the second risk vehicles of the nth lane in the preset area of the target taxi at the current time; C n represents the number of all vehicles of the nth lane at the current time; represents the first risk vehicle proportion of the nth lane at the current time; represents the second risk vehicle proportion of the nth lane at the current time; represents the comprehensive risk vehicle proportion of the nth lane at the current time; and N represents the number of lanes in the preset area of the target taxi.

[0087] Finally, the greater the following distance time difference and the congestion risk evaluation value of the preset area of the target taxi at the current time, the more likely the congestion phenomenon around the target taxi. Therefore, the following distance time difference and the congestion risk evaluation value can be integrated to obtain the second congestion performance of the target taxi at the current time.

[0088] As an example, in an embodiment of the present application, the expression of the second congestion performance degree of the target taxi at the current time can be specifically, for example:

[0089] S2 = AT x A

[0090] wherein S2 represents the second congestion performance degree of the target taxi at the current time; AT represents the follow-up time difference degree of the preset area of the target taxi at the current time; and A represents the congestion risk assessment value of the preset area of the target taxi at the current time.

[0091] The greater the first congestion performance degree and the second congestion performance degree of the target taxi at the current time, the greater the congestion degree shown by the traffic condition around the target taxi, and there is a greater risk of congestion. Therefore, the congestion degree of the target taxi at the current time can be obtained based on the first congestion performance degree and the second congestion performance degree, and the communication priority of the target taxi can be accurately analyzed based on the congestion degree subsequently.

[0092] Preferably, in an embodiment of the present application, the method for obtaining the congestion degree of the target taxi at the current time specifically comprises:

[0093] After the first congestion performance degree and the second congestion performance degree are comprehensively processed and normalized, the calculation result is limited in the range of [0, 1], so as to obtain the congestion degree of the target taxi at the current time.

[0094] In an embodiment of the present application, the normalization processing can be specifically, for example, the maximum-minimum value normalization processing, and the normalization in the subsequent steps can also adopt the maximum-minimum value normalization processing. In other embodiments of the present application, other normalization methods can be selected according to the specific range of values, or the normalization processing can be realized by using an activation function and a hyperbolic tangent function, and no further description and limitation is made.

[0095] As an example, in an embodiment of the present application, the expression of the congestion degree of the target taxi at the current time can be specifically, for example:

[0096] S = norm (S1 x S2)

[0097] wherein S represents the congestion degree of the target taxi at the current time; S1 represents the first congestion performance degree of the target taxi at the current time; S2 represents the second congestion performance degree of the target taxi at the current time; and norm() represents a normalization function, which is used for normalization processing.

[0098] At this point, the analysis of the congestion risk of the traffic condition around the target taxi is completed.

[0099] Step S3: obtaining a trajectory disorder index of each vehicle in the preset area of the target taxi at the current time according to the change of the position point of each vehicle in the preset area of the target taxi at each time within a preset time period before the current time; and obtaining a communication transmission weight of the target taxi at the current time according to the trajectory disorder index of each vehicle in the preset area of the target taxi at the current time, the distance between each vehicle at the current time, and the congestion degree of the target taxi at the current time.

[0100] In addition to the congestion risk, the vehicle conditions around the target taxi can also have a collision risk. For example, if the driving trajectory of a part of the vehicles changes greatly, there is a great collision risk. The taxis with a great collision risk around the vehicle conditions also need to transmit information in time. Therefore, the change of the position point of each vehicle in the preset area of the target taxi at each time within a preset time period before the current time is analyzed, and the trajectory disorder index obtained reflects the possibility of collision risk of each vehicle due to the chaotic driving trajectory. The length of the preset time period is 20-30 seconds. In an embodiment of the present application, the length of the preset time period is set to 20 seconds. The length of the preset time period can also be set by the implementer according to the specific implementation scene, which is not limited herein. It should be noted that a sufficient length of time before the current time is needed to ensure smooth analysis.

[0101] Preferably, in an embodiment of the present application, the method for obtaining the trajectory disorder index of each vehicle in the preset area of the target taxi at the current time specifically comprises:

[0102] Any vehicle in the preset area of the target taxi is taken as a target vehicle, the line between the position point of the target vehicle at each time within a preset time period before the current time and the position point of the adjacent next time is taken as the driving direction line of the target vehicle at each time, the dispersion degree of the included angle between the driving direction lines of all adjacent two times within the preset time period is analyzed and normalized, and the calculation result is limited in the range of [0, 1], so as to obtain the trajectory disorder index of the target vehicle at the current time.

[0103] It should be noted that the included angle between the driving direction lines of the adjacent two times refers to the angle swept when the driving direction line of the previous time is rotated to the driving direction line of the next time in the same direction (for example, clockwise or counterclockwise).

[0104] As an example, in an embodiment of the present application, the expression of the trajectory disorder index of the target vehicle at the current time can be specifically, for example:

[0105] G=tanh(β)

[0106] Wherein, G represents the trajectory disorder index of the target vehicle at the current moment; β represents the standard deviation of the included angle between the driving direction lines of all adjacent two moments within the preset time period; tanh() represents the hyperbolic tangent function, used for normalization processing.

[0107] The trajectory disorder index of each vehicle in the preset area of the target taxi at the current moment can be obtained by the same method as described above. The greater the trajectory disorder index of each vehicle in the preset area of the target taxi at the current moment, and the closer the distance between each vehicle in the preset area, the more likely it is that the vehicles in the preset area have a collision risk. Therefore, the trajectory disorder index of each vehicle in the preset area of the target taxi at the current moment and the distance between each vehicle at the current moment can be analyzed, and the degree of information transmission priority in the local area of the target taxi due to the existence of vehicle congestion or collision risk can be reflected by the communication transmission weight obtained above. The greater the communication transmission weight, the more it is necessary to prioritize the transmission of information of the target taxi. Subsequently, different priority information transmission can be performed on different taxis based on the communication transmission weight, so that taxis in local areas with congestion or collision risks can transmit communication data in time, thereby improving the timeliness of taxi communication.

[0108] Preferably, in an embodiment of the present application, the method for obtaining the communication transmission weight of the target taxi at the current moment specifically comprises:

[0109] The greater the trajectory disorder index of a certain vehicle at the current moment, the greater the collision risk of the vehicle. Therefore, in the preset area of the target taxi, the vehicles with a trajectory disorder index greater than a preset trajectory disorder threshold are taken as the collision risk vehicles in the preset area, wherein the preset trajectory disorder threshold has a value range of (0.5, 1). In an embodiment of the present application, the preset trajectory disorder threshold is set to 0.7. The preset trajectory disorder threshold can also be set by the implementer according to the specific implementation scenario, which is not limited herein.

[0110] The average value of the trajectory disorder index of all collision risk vehicles in the preset area of the target taxi at the current moment is taken as the overall trajectory disorder value of the preset area of the target taxi at the current moment. The greater the overall trajectory disorder value, the greater the collision risk of the vehicle condition around the target taxi.

[0111] selecting a reference collision risk vehicle for each collision risk vehicle from other collision risk vehicles in the preset area of the target taxi, wherein the distance between each collision risk vehicle and the corresponding reference collision risk vehicle is the closest, and performing negative correlation mapping on the average value of the distances between all collision risk vehicles and the corresponding reference collision risk vehicles to obtain the collision vehicle aggregation degree of the preset area of the target taxi at the current time. The greater the collision vehicle aggregation degree is, the stronger the aggregation of the collision risk vehicles is, and the more likely the collision phenomenon is.

[0112] Further, the congestion degree of the target taxi at the current time, the overall trajectory disorder value, the collision vehicle aggregation degree, and the number of collision risk vehicles of the target taxi in the preset area are comprehensively processed and normalized, and the calculation result is limited in the range of [0, 1], so as to obtain the communication transmission weight of the target taxi at the current time.

[0113] As an example, in an embodiment of the present application, the expression of the communication transmission weight of the target taxi at the current time can be specifically, for example:

[0114]

[0115] wherein U represents the communication transmission weight of the target taxi at the current time; S represents the congestion degree of the target taxi at the current time; G r represents the trajectory disorder index of the rth collision risk vehicle in the preset area of the target taxi at the current time; R represents the number of all collision risk vehicles in the preset area of the target taxi; represents the overall trajectory disorder value of the preset area of the target taxi at the current time; D r represents the distance between the rth collision risk vehicle and the corresponding reference collision risk vehicle. In actual scenarios, the distance between the two vehicles is not 0, so D r ≠0; represents the collision vehicle aggregation degree of the preset area of the target taxi at the current time; norm() represents a normalization function for normalization processing.

[0116] The communication transmission weight of each taxi at the current time can be obtained by the same method.

[0117] Step S4: performing communication transmission with different priorities for each taxi based on the communication transmission weight of each taxi at the current time.

[0118] The greater the communication transmission weight of a certain taxi at the current time, the greater the risk of congestion or collision of the taxi at the current time, and thus the taxi needs to be communicated preferentially, and thus the communication transmission of the taxis can be performed with different priorities based on the communication transmission weight of each taxi at the current time, so as to ensure that the taxi with a risk of congestion or collision in a local range can transmit communication data in time, and improve the timeliness of taxi communication.

[0119] Preferably, in one embodiment of the present application, the method of performing communication transmission of the taxis with different priorities specifically comprises:

[0120] The communication data of the taxis is transmitted in descending order of the communication transmission weight, that is, the greater the communication transmission weight of the taxi, the higher the priority of the communication data transmission of the taxi.

[0121] One embodiment of the present application provides a computer device, which comprises a memory and a processor, wherein the memory stores a computer program, and the processor can implement the method described in steps S1-S4 when executing the computer program.

[0122] It should be noted that the above-mentioned sequence of the embodiments of the present application is only for description, and does not represent the advantages and disadvantages of the embodiments. The processes depicted in the drawings do not necessarily require the specific order or continuous order shown to achieve the desired results. In some embodiments, multitasking and parallel processing are possible or can be advantageous.

[0123] Each of the embodiments in the specification is described in a progressive manner, and the same or similar parts of each of the embodiments can be referred to each other. Each embodiment mainly describes the difference from other embodiments.

Claims

1. A car-to-car communication data transmission control method for a taxi, characterized by, The method comprises: Real-time acquisition of vehicle speed data and position points of each vehicle in different lanes; Taking any one taxi in the lane as a target taxi, obtaining a first congestion performance of the target taxi at the current time according to the distribution of distances between adjacent vehicles in the same lane within a preset area of the target taxi at the current time, obtaining a second congestion performance of the target taxi at the current time according to the distance between each vehicle and the adjacent previous vehicle in the same lane within the preset area of the target taxi at the current time, the vehicle speed data of each vehicle at the current time, and the difference between the vehicle speed data between each vehicle and the adjacent previous vehicle at the current time, and obtaining the congestion degree of the target taxi at the current time based on the first congestion performance and the second congestion performance; Obtaining a trajectory disorder index of each vehicle in the preset area of the target taxi at the current time according to the change of the position points of each vehicle in the preset area of the target taxi at each time within a preset time period before the current time, obtaining a communication transmission weight of the target taxi at the current time according to the trajectory disorder index of each vehicle in the preset area of the target taxi at the current time, the distance between each vehicle at the current time, and the congestion degree of the target taxi at the current time; Based on the communication transmission weight of each taxi at the current time, different priority communication transmission is performed on each taxi; The determination method of the first congestion performance comprises: Taking any one lane in the preset area of the target taxi as a target lane, and taking the distance between each vehicle and the adjacent previous vehicle in the target lane at the current time as a vehicle distance parameter of each vehicle in the target lane at the current time; Analyzing the discrete degree of the vehicle distance parameter of all vehicles in the target lane at the current time to obtain a vehicle distance distribution disorder degree of the target lane at the current time; Performing negative correlation mapping on the average value of the vehicle distance parameter of all vehicles in the target lane at the current time to obtain a vehicle distance proximity degree of the target lane at the current time; Comprehensively obtaining a congestion risk coefficient of the target lane at the current time by comprehensively obtaining the vehicle distance distribution disorder degree and the vehicle distance proximity degree; Taking the average value of the congestion risk coefficient of all lanes in the preset area of the target taxi at the current time as the first congestion performance of the target taxi at the current time; The determination method of the second congestion performance comprises: In the target lane, taking the vehicle distance parameter of each vehicle at the current time as the numerator, taking the vehicle speed data of each vehicle at the current time as the denominator, taking the ratio as the following time length of each vehicle at the current time, and taking the vehicle with a following time length less than a preset safety time threshold as a first risk vehicle of the target lane at the current time; Obtaining a following time length difference degree of the preset area of the target taxi at the current time according to the distribution of the following time length of each vehicle in all lanes within the preset area of the target taxi at the current time. In the target lane, the difference value of the vehicle speed data between each vehicle and the previous vehicle at the current time is the numerator, the vehicle distance parameter of each vehicle at the current time is the denominator, and the ratio is the vehicle speed gradient value of each vehicle in the target lane at the current time. The vehicle with a vehicle speed gradient value greater than a preset gradient value threshold in the target lane is the second risk vehicle of the target lane at the current time; According to the number of the first risk vehicle and the number of the second risk vehicle of each lane in the preset area of the target taxi at the current time, the congestion risk evaluation value of the preset area of the target taxi at the current time is obtained; The follow-up time length difference degree and the congestion risk evaluation value are comprehensively obtained, and the second congestion performance degree of the target taxi at the current time is obtained; The determination method of the follow-up time length difference degree includes: The dispersion degree of the follow-up time length of all vehicles in all lanes in the preset area of the target taxi at the current time is analyzed, and the follow-up time length chaos degree of the preset area of the target taxi at the current time is obtained; Any two vehicles in all lanes in the preset area of the target taxi are taken as a vehicle control group, the absolute value of the difference value of the follow-up time length between the two vehicles in each vehicle control group at the current time is taken as the follow-up time length difference value of each vehicle control group at the current time, and the average value of the follow-up time length difference value of all vehicle control groups at the current time is taken as the overall difference value of the preset area of the target taxi at the current time; The follow-up time length chaos degree and the overall difference value are comprehensively obtained, and the follow-up time length difference degree of the preset area of the target taxi at the current time is obtained; The determination method of the congestion risk evaluation value includes: The number of the first risk vehicle of the target lane at the current time is the numerator, the number of all vehicles of the target lane at the current time is the denominator, and the ratio is the first risk vehicle proportion of the target lane at the current time; The number of the second risk vehicle of the target lane at the current time is the numerator, the number of all vehicles of the target lane at the current time is the denominator, and the ratio is the second risk vehicle proportion of the target lane at the current time; The first risk vehicle proportion and the second risk vehicle proportion are comprehensively obtained, and the comprehensive risk vehicle proportion of the target lane at the current time is obtained; The average value of the comprehensive risk vehicle proportion of all lanes in the preset area of the target taxi at the current time is taken as the congestion risk evaluation value of the preset area of the target taxi at the current time; The determination method of the congestion degree includes: After the first congestion performance degree and the second congestion performance degree are comprehensively obtained and normalized, the congestion degree of the target taxi at the current time is obtained; The determination method of the trajectory disorder index includes: The trajectory disorder index of the target vehicle at the current time is obtained by analyzing the dispersion degree of the included angle between the driving direction lines of the target vehicle at each time point in a preset time period before the current time and performing normalization processing. The method for determining the communication transmission weight comprises: In the preset area of the target taxi, the vehicle with the trajectory disorder index greater than a preset trajectory disorder threshold is regarded as a collision risk vehicle in the preset area. The average value of the trajectory disorder index of all collision risk vehicles in the preset area of the target taxi at the current time is regarded as the overall trajectory disorder value of the preset area of the target taxi at the current time. Reference collision risk vehicles are selected from other collision risk vehicles in the preset area of the target taxi, wherein the distance between each collision risk vehicle and the corresponding reference collision risk vehicle is the closest, and the average value of the distance between all collision risk vehicles and the corresponding reference collision risk vehicles is negatively correlated to obtain the collision vehicle aggregation degree of the preset area of the target taxi at the current time. The communication transmission weight of the target taxi at the current time is obtained by comprehensively processing the congestion degree, the overall trajectory disorder value, the collision vehicle aggregation degree and the number of collision risk vehicles in the preset area of the target taxi at the current time and performing normalization processing. The communication transmission of each taxi with different priorities comprises: The communication data of each taxi is transmitted in the order from large to small according to the communication transmission weight.

2. A car-to-car communication data transmission control method for a taxi according to claim 1, characterized by, The method for calculating the car distance distribution disorder degree comprises: The standard deviation of the car distance parameter of all vehicles in the target lane at the current time is regarded as the car distance distribution disorder degree of the target lane at the current time.

3. The car-to-car communication data transmission control method for a taxi according to Claim 1, wherein The method for calculating the car distance proximity degree comprises: The average value of the car distance parameter of all vehicles in the target lane at the current time is calculated, and the reciprocal of the average value is regarded as the car distance proximity degree.

4. The car-to-car communication data transmission control method for a taxi according to Claim 1, wherein The method for determining the congestion risk coefficient comprises: The product value of the car distance distribution disorder degree and the car distance proximity degree is calculated as the congestion risk coefficient.

5. The car-to-car communication data transmission control method for a taxi according to Claim 1, wherein The method for determining the following time length disorder degree comprises: The standard deviation of the following time length of all vehicles in all lanes in the preset area of the target taxi at the current time is calculated as the following time length disorder degree.

6. A car-to-car communication data transmission control method for a taxi according to claim 1, wherein, The calculation formula of the communication transmission weight is: ;in, This indicates the communication transmission weight of the target taxi at the current moment; This indicates the current level of congestion for the target taxi. Indicates the first [unit] within the preset area of ​​the target taxi The trajectory turbulence index of a vehicle at risk of collision at the current moment; This indicates the number of all vehicles at risk of collision within the preset area of ​​the target taxi; Indicates the first The distance between a vehicle at risk of collision and its corresponding reference vehicle at risk of collision. This indicates the collision vehicle concentration in the target taxi's preset area at the current moment; This represents the normalization function.

7. A computer device, the device comprising a memory and a processor, the memory storing a computer program, characterized in that, The processor executes the computer program to realize the steps of the method of any one of claims 1-6. The processor executes the computer program to realize the steps of the method of any one of claims 1-6.

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