Method for determining confidence and / or priority of vehicle reported event data

Abstract

The present invention relates to the field of vehicle networking, and in particular to a vehicle back-end data monitoring platform for a vehicle networking system. A method for determining the confidence and / or priority of reported event data reported by a vehicle in a vehicle networking system (1) is provided, comprising at least: obtaining an event danger parameter corresponding to the reported event type, obtaining the historical traffic speed at the corresponding geographical location based on the reported geographical location, and then obtaining a speed deviation parameter based on the deviation between the vehicle speed at the reporting time and the historical traffic speed; determining the confidence and / or priority of the reported event data based at least on the event danger parameter and the speed deviation parameter. The method may also comprise: determining a frequency parameter based at least on the reporting frequency of the same or similar events at the corresponding geographical location, and further considering the frequency parameter when determining the confidence and / or priority of the reported event data. The reliability of the calculation of the confidence and / or priority of the vehicle reported event can be improved.

Description

Technical Field

[0001] The present invention relates to a method for determining the confidence and / or priority of event data reported by a vehicle in a connected vehicle system, a vehicle back-end data monitoring platform for the connected vehicle system, and a computer-readable program carrier for the vehicle back-end data monitoring platform of the connected vehicle system and / or a networked vehicle. Background Art

[0002] The Internet of Vehicles industry relies on information and communication technology to provide comprehensive information services through all-round connection and data interaction within the car, between cars, between cars and roads, between cars and people, and between cars and service platforms, forming a new industrial form with deep integration of industries such as automobiles, electronics, information and communications, and road transportation (Guidelines for the Construction of a National Internet of Vehicles Industry Standard System). The Internet of Vehicles system uses advanced sensing technology, network technology, computing technology, control technology, and intelligent technology to perceive roads and traffic, and realize large-scale and large-capacity data interaction between multiple systems. For example, the Internet of Vehicles system can perform traffic control on the corresponding cars and on the corresponding roads to provide networks and applications that focus on traffic efficiency and traffic safety.

[0003] Vehicle to Everything (V2X) wireless communication technology includes vehicle-to-vehicle (V2V), vehicle-to-infrastructure (V2I), vehicle-to-network (V2N), vehicle-to-pedestrian (V2P), etc., and C2X and Car2X have been commercialized or are about to be commercialized. Cloud-based interactive systems such as Car2X distribute information passively and actively reported by other vehicles equipped with Car2X through a cloud platform to achieve real-time information interaction between vehicles. More specifically, the cloud platform aggregates data, checks its rationality, and forwards it to related, nearby, and other vehicles equipped with the Car2X system. It expands the detection range limits of traditional on-board sensors, communication equipment and systems, such as cameras and radars, to achieve early warning of dangerous events. In the future, this system is also envisioned to be connected to traffic lights, on-board diagnostics, road warnings, and other emergency services and commercial services.

[0004] Looking at the development of the entire industry, the future trend is to apply the information of the Internet of Vehicles to the field of autonomous driving through vehicle-to-vehicle communication technology, wireless communication and remote sensing technology. The emergence of autonomous driving has brought rapidly growing new demands and challenges to C-V2X. Simply put, models equipped with this system can directly read and monitor key data predefined by the system. Through a secure communication network (such as 5G and eLTE), the data can be sent to the data center closest to the vehicle and then spread all over the world. Therefore, the autonomous driving system can receive relevant information through C-V2X, and in the autonomous driving mode, it can synchronously process such information and adjust the output results of the path planning and vehicle control of the autonomous driving vehicle in real time.

[0005] However, if the events reported by vehicles in the connected vehicle system are used for the planning and control of the autonomous driving system without screening, the vehicles will be excessively disturbed. How the data center or the vehicle receiving the information confirms the data confidence and / or priority is the key difficulty in whether this technology can be widely used.

[0006] For example, in China's urban environment, there is a problem of abuse of hazard warning flashers (double flash lights). Hazard warning flashers are a signal light that alerts other vehicles and pedestrians to special situations that have occurred in the vehicle. Article 52 of the Road Traffic Safety Law of the People's Republic of China describes one of the applications of hazard warning flashers as follows: "When a motor vehicle breaks down on the road and needs to stop to repair the fault, the driver shall immediately turn on the hazard warning flashers and move the motor vehicle to a place where it does not hinder traffic. If it is difficult to move, the hazard warning flashers shall be kept on, and warning signs and other measures shall be set up in the direction of oncoming vehicles to expand the warning distance, and the police shall be called promptly when necessary." Hazard warning flashers should be turned on when a motor vehicle breaks down on the highway, when driving at high speed, when temporarily stopping on the road, when towing a faulty motor vehicle, when an emergency failure occurs during driving, when a vehicle is involved in a traffic accident, and when performing special tasks. However, due to geographical and cultural reasons, it is difficult to sort out a fixed and quantitative standard for the application of hazard warning flashers. Many drivers turn on hazard warning flashers when it is not urgent.

[0007] The Internet of Vehicles system defines information such as hazard warning flashers as high-risk warning information, which should be transmitted to the surrounding affected vehicles. For drivers, if such frequent pop-up warning messages do not have a substantial impact on driving, drivers will have a certain degree of annoyance with such information, which will affect their trust and favorability towards the system; and for vehicles with automatic driving systems turned on, vehicle safety is always the top priority of system design. If such information is received at close range, the system should re-plan the route or even stop the automatic driving mode. If the automatic driving system frequently receives warning information on the scheduled driving route, it will be difficult for the system to provide reasonable and comfortable planning and control.

[0008] Some methods have been proposed in the prior art to verify the reliability and / or priority of data and perform some subsequent processing based on the verification results.

[0009] For example, CN107959943A discloses a V2X communication device and system and a method for verifying the reliability of V2X data, wherein a reliability verification parameter for verifying the reliability of V2X data is calculated by a parameter calculator, and the reliability level is determined by comparing the reliability verification parameter with a predetermined reference value, and then V2X data including the determined reliability level is generated. The article also gives some specific calculation and judgment methods in detail, for example, the reliability level determiner can set the first reference value to the third reference value based on the speed of the vehicle and the distance between the vehicle and the surrounding vehicles, and the first reference value to the third reference value can be set to become smaller as the speed of the vehicle decreases and the distance between the vehicle and the surrounding vehicles becomes farther, and the reliability level can include a first reliability level corresponding to the guidance level, a second reliability level corresponding to the warning level, and a third reliability level corresponding to the control level.

[0010] CN103918242A also discusses determining the reliability of information received by a vehicle, for example, based on the signal strength of the received peer transmission, a comparison of the signal strength and the distance to the sender based on the message content, the timeliness of the received message, the signal quality of the received message, and other parameters.

[0011] CN109672628A also relates to the field of Internet of Vehicles, and discloses determining the priority of a V2X data packet based on, for example, filtering rules including the correspondence between attribute information of different V2X data packets and their priorities, wherein the priority of the V2X data packet is related to a frequency of data reception.

[0012] CN105684397A also relates to the field of Internet of Vehicles, and discloses how to filter messages, for example, filtering can be performed based on parameters such as the type of vehicle-to-X message, the sender's location, etc.

[0013] CN101916514A discloses a method for using a V2X-based in-network message protocol to support road hazard condition warnings and using a wireless communication network to provide messages indicating potentially dangerous road conditions, wherein the vehicle's sensors can detect various potentially dangerous road conditions, such as rain, fog, icy road conditions, traffic congestion, etc., and multiple vehicles that detect a specific road condition provide a confidence value for the existence of the condition, and the confidence value is then aggregated by the vehicle with the confidence values ​​of the detected conditions from other vehicles to provide an aggregated result, which identifies the probability of the detected road condition occurring. The aggregated result may then be transmitted in a multi-hop manner to other vehicles approaching the road condition. As an alternative, the confidence values ​​from all vehicles that detect the condition can be transmitted to the approaching vehicle, and the approaching vehicle will provide an aggregated result identifying the possibility of the condition existing.

[0014] CN109756897A discloses an authentication and authorization mechanism and system for a vehicle network, which describes how to authenticate the credibility of V2X information.

[0015] However, the prior art still has many deficiencies. In particular, the confidence and / or priority of data are related to many factors, among which the correlation between certain factors and the confidence and / or priority of data and how they specifically affect the confidence and / or priority have not been discussed in the prior art, which will also affect the application of the Internet of Vehicles. Summary of the invention

[0016] The object of the present invention is to provide an improved method for determining the confidence and / or priority of reported event data reported by a vehicle in a connected vehicle system, an improved vehicle back-end data monitoring platform for a connected vehicle system, and an improved vehicle back-end data monitoring platform for a connected vehicle system and / or a computer-readable program carrier for a connected vehicle, so as to at least solve some of the problems in the prior art.

[0017] According to a first aspect of the present invention, a method for determining the confidence and / or priority of reported event data reported by a vehicle in a vehicle networking system is provided, the method comprising at least the following steps: obtaining an event danger parameter corresponding to an event type based on the event type reported by the vehicle, obtaining the historical traffic speed at the corresponding geographical location based on the reported geographical location, and then obtaining a speed deviation parameter based on the deviation between the vehicle speed of the vehicle at the reporting time and the historical traffic speed; and determining the confidence and / or priority of the reported event data based at least on the event danger parameter and the speed deviation parameter.

[0018] According to an optional embodiment of the present invention, the method further includes: receiving event data reported by a vehicle, wherein the reported event data includes the type of the reported event, the driving speed of the event reporting vehicle at the time of reporting the event, and the geographical location information of the event occurring.

[0019] According to an optional embodiment of the present invention, the method also includes the following steps: determining a frequency parameter based at least on the reporting frequency of the same or similar events at the corresponding geographical location within a predetermined time period; and additionally determining the confidence and / or priority of the reported event data based on the frequency parameter.

[0020] According to an optional embodiment of the present invention, the confidence and / or priority is determined by the following function:

[0021]

[0022] Where C represents confidence and / or priority;

[0023] e is the event risk parameter corresponding to the event type;

[0024] f is the frequency parameter; and

[0025] v is the speed deviation parameter.

[0026] According to an optional embodiment of the present invention, the data center fits the historical traffic speed of each area block into a normal distribution, and stores the position parameters and scale parameters of the normal distribution curve of the traffic speed of each area block after statistics, and determines the speed deviation parameter based on the relationship between the difference between the vehicle speed of the vehicle at the reporting time and the average value of the historical traffic speed and the scale parameter of the historical traffic speed; and / or the historical traffic speed includes the historical traffic speed of the corresponding geographical location in the time period near the reporting time within a period of time in the past relative to the reporting time; and / or the historical traffic speed also includes the historical traffic speed of vehicles in a predetermined area near the reported geographical location.

[0027] According to an optional embodiment of the present invention, the historical traffic speed and its normal distribution curve are obtained by integrating the vehicle back-end data monitoring platform of the Internet of Vehicles system according to the historical traffic speeds of vehicles monitored by the vehicle back-end data monitoring platform; or the historical traffic speed and its normal distribution curve are jointly determined by the vehicle back-end data monitoring platform of the Internet of Vehicles system in collaboration with other monitoring platforms different from the vehicle back-end data monitoring platform.

[0028] According to an optional embodiment of the present invention, the frequency parameter is determined based on the reporting frequency of the vehicle reporting event within a certain period of time; or the frequency parameter is determined based on the reporting frequency of the vehicle reporting event and the total number of vehicles passing through the corresponding geographical location; and / or the predetermined reference value range of the frequency parameter is (0,1].

[0029] According to an optional embodiment of the present invention, the confidence level and / or priority level is determined based on a big data machine learning method; and / or according to the determined confidence level and / or priority level, the reported event data is divided into three categories: invalid information, prompt information, and warning information.

[0030] According to an optional embodiment of the present invention, when it is a warning message, the corresponding information is dispatched to the relevant vehicle for corresponding operation, and the corresponding operation displays an icon on the screen of the relevant vehicle and / or makes a voice. In the case that the dispatched vehicle is an autonomous driving vehicle with a network security interface, the corresponding information is used to control the relevant vehicle; and / or the analysis results of the confidence and / or priority are archived as data for insurance and / or accident and / or municipal purposes.

[0031] According to a second aspect of the present invention, a vehicle back-end data monitoring platform for a vehicle networking system is provided, wherein the vehicle back-end data monitoring platform is configured to be able to execute the above method.

[0032] According to a third aspect of the present invention, a computer-readable program carrier for a vehicle back-end data monitoring platform of a vehicle networking system is provided, wherein the computer-readable program carrier stores program instructions, and the program instructions can execute the above method when executed by a processor.

[0033] The method of the present invention can improve the calculation reliability of the confidence and / or priority of vehicle-reported events. BRIEF DESCRIPTION OF THE DRAWINGS

[0034] The present invention will be described in more detail below with reference to the accompanying drawings, so that the principles, features and advantages of the present invention can be better understood. The accompanying drawings include:

[0035] Figure 1 An application scenario diagram of a vehicle networking system according to an exemplary embodiment of the present invention is shown; and

[0036] Figure 2 A method for calculating the confidence and / or priority of reported event data is shown. DETAILED DESCRIPTION

[0037] In order to make the technical problems, technical solutions and beneficial technical effects to be solved by the present invention more clearly understood, the present invention will be further described in detail below in conjunction with the accompanying drawings and multiple exemplary embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention, rather than to limit the scope of protection of the present invention.

[0038] The present invention generally relates to a technical solution for analyzing and determining the confidence and / or priority of reported event data by a vehicle back-end data monitoring platform based at least on event risk parameters corresponding to the reported event type, the deviation between the vehicle speed of the reporting vehicle at the time of the reporting event and the historical traffic speed, and / or the reporting frequency of the same or similar events, so as to further improve the reliability and user-friendliness of the actual application of the Internet of Vehicles. The reported event data can be V2X, C2X or Car2X data.

[0039] Figure 1 An application scenario diagram of a vehicle networking system according to an exemplary embodiment of the present invention is shown.

[0040] like Figure 1 As shown, the exemplary Internet of Vehicles system 1 includes: an on-board Internet of Vehicles module 11 installed on the vehicle, a road side unit 12, and a vehicle back-end data monitoring platform 13 that can communicate with the on-board Internet of Vehicles module 11 and the road side unit 12. The vehicle back-end data monitoring platform 13 can be a cloud server, and the connected vehicle can report the event information sensed by the vehicle or the road test equipment and / or the vehicle's own state to the vehicle back-end data monitoring platform 13 through the on-board Internet of Vehicles module 11 and the road side unit 12 through its communication module in a wireless communication manner (for example, through 5G, eLTE network, etc.) for storage and / or analysis and processing. The vehicle back-end data monitoring platform 13 can also transmit the stored information or the results of the analysis and processing to related vehicles (for example, other vehicles near the location of the traffic event) to provide information to the related vehicles and / or control the driving of the related vehicles.

[0041] The cloud server can be a server provided by a third party or a server provided by the vehicle manufacturer itself.

[0042] You can also Figure 1As can be seen from the figure, in the vehicle networking system 1, in addition to the vehicle being able to report information to the vehicle back-end data monitoring platform 13, vehicles can also directly transmit information to each other through the vehicle-mounted vehicle networking module 11 (for example, V2V-Vehicle to Vehicle), as shown by reference numeral 14. In addition, the vehicle can also transmit information to the roadside equipment 12 (for example, V2I-Vehicle to Infrastructure), as shown by reference numeral 15; the vehicle can also transmit information to other facilities, such as buildings, as shown by reference numeral 16; the vehicle can also provide information to pedestrians, as shown by reference numeral 17.

[0043] According to an exemplary embodiment of the present invention, the reported event data received by the vehicle back-end data monitoring platform 13 from the vehicle may at least include specific events, the driving speed of the event reporting vehicle at the time of reporting, and the geographical location information of the event. For example, in the case of a collision accident at a certain road location, the reported event data may include: the collision event, the driving speed of the collision event reporting vehicle at the time of reporting the collision accident event, and the geographical location information of the road location; in the case of a vehicle detecting and reporting that a vehicle at a certain road location has turned on the hazard warning flasher, the reported event data may include: the hazard warning flasher warning event, the driving speed of the event reporting vehicle at the time of detecting the hazard warning flasher, and the geographical location information of the road location. The vehicle back-end data monitoring platform 13 processes and analyzes the received reported event data, and according to the processing results, it may provide warning information to the relevant vehicles (e.g., vehicles traveling toward the road location), and if the relevant vehicles are in an automatic driving state, it may even change the automatic driving mode or parameters.

[0044] According to an embodiment of the present invention, the reliability and / or priority of the reported event data need to be ensured so that a more reasonable and effective early warning and / or control adjustment can be given later. To this end, the confidence and / or priority analysis of the reported event data is required.

[0045] Confidence can be used to characterize the reliability of reported event data. Priority can be used to characterize the importance, degree of danger and / or degree of urgency of reported event data. According to an exemplary embodiment of the present invention, confidence and / or priority can be calculated based on at least the specific event type reported and / or the deviation between the driving speed of the event reporting vehicle at the time of the reported event and the historical traffic speed at the corresponding geographical location (the deviation can be directly expressed as an absolute difference, or as a relative difference or other form). It can be understood that the greater the deviation between the driving speed of the event reporting vehicle at the time of the reported event and the historical traffic speed at the corresponding geographical location, the greater the deviation between the driving state of the vehicle currently reporting the event and the normal driving state at the geographical location, and therefore, the greater the possibility of the occurrence of the traffic event, and the greater the impact of the traffic event on the traffic and surrounding vehicles, and accordingly, the higher the confidence and / or priority of the reported event data.

[0046] According to a further exemplary embodiment of the present invention, the calculation of the confidence and / or priority may also additionally consider the reporting frequency of the same or similar events at the corresponding geographical location within a predetermined period. Those skilled in the art will understand that if a certain event is prone to occur at a specific geographical location, and the basic external driving environment does not change, for example, in a certain period of time, other vehicles will also be in the same driving environment. If other influencing factors are not considered, other vehicles are also likely to have the same or similar events at the geographical location. Detection. According to an embodiment of the present invention, when calculating the reliability and / or priority of reported event data, the three parameters of the specific event type, the deviation between the driving speed of the event reporting vehicle at the time of reporting the event and the historical traffic speed at the corresponding geographical location, and the reporting frequency of the same or similar events at the geographical location within a predetermined period of time can be used alone or in combination (including two-by-two combinations or all combinations) to better reflect the reliability and / or priority of the reported event data.

[0047] The predetermined area around the geographic location is likely to have a similar driving environment, so that the same or similar events may also occur or be detected by nearby vehicles. Therefore, according to an exemplary embodiment of the present invention, the reporting frequency of the same or similar events in the predetermined area around the corresponding geographic location during the predetermined period may also be considered when calculating the confidence and / or priority.

[0048] For example, after the vehicle back-end data monitoring platform 13 receives the event type manually or automatically reported by the vehicle equipped with the on-board Internet of Vehicles module 11, the driving speed of the event reporting vehicle at the time of reporting the event and the geographical location at the time of reporting, the confidence and / or priority of the reported event data are calculated based at least on the event type and the deviation between the driving speed of the event reporting vehicle at the time of reporting the event and the historical traffic speed at the corresponding geographical location. In addition, the number of times (frequency) of the same or similar events reported by other vehicles in a predetermined area around the geographical location, such as a road section of 1 km before and after, or the corresponding frequency parameters within a predetermined period, such as 1 minute, can also be used as a parameter for calculating the confidence and / or priority of the reported event data.

[0049] According to an exemplary embodiment of the present invention, if the vehicle back-end data monitoring platform 13 decides to distribute corresponding information to other related vehicles based on the calculation results of confidence and / or priority, it can decide which vehicles to distribute the information to based on the geographical location where the reported event occurred. For example, the corresponding information can be distributed to vehicles equipped with the on-board Internet of Vehicles module 11 within a section of road within 3 km before and after the geographical location.

[0050] Next, a more specific exemplary embodiment will be used to describe how the vehicle back-end data monitoring platform 13 performs confidence and / or priority analysis on the reported event data.

[0051] The confidence and / or priority C can be calculated by, for example, the following function (1):

[0052]

[0053] Where C represents confidence and / or priority;

[0054] e is the event risk parameter that characterizes the impact weight of the event type;

[0055] f is a frequency parameter representing the reporting frequency of the same or similar events at the corresponding geographical location within a predetermined period of time; and

[0056] v is a speed deviation parameter that represents the deviation between the driving speed of the event reporting vehicle at the time of reporting the event and the historical traffic speed at the corresponding geographical location.

[0057] 1) Event risk parameters corresponding to event types

[0058] Different event types determine the degree of impact of the event on the vehicle. For example, information such as an irregular oversized vehicle ahead or a slow-moving vehicle ahead has a smaller potential threat and impact on the vehicle behind, while information such as an accident ahead or a fire ahead has a greater potential threat and impact on the vehicle behind. Therefore, different event risk parameters e can be assigned to different events defined in the event library. The higher the event risk parameter e of the reported event, the higher the reference value of the calculation result of the confidence and / or priority C.

[0059] It can be seen that the event risk parameter e corresponding to the event type can be classified and preset according to the influence of the event. According to an exemplary embodiment, the interval range of the value of the event risk parameter e can be [0.1, 1]. The greater the influence of the event, the higher the event risk parameter e. For example, the e value corresponding to the detection of the hazard warning flasher is 0.5, the e value corresponding to the vehicle failure ahead is 0.55, the e value corresponding to the slippery ground ahead is 0.7, the e value corresponding to the rain, snow and fog is 0.75, the e value corresponding to the detection of a motorcycle on the highway is 0.8, and the e value corresponding to the danger of the road ahead is 0.9.

[0060] 2) The deviation between the speed of the vehicle reporting the event at the time of reporting the event and the historical traffic speed at the corresponding geographical location

[0061] The current driving status of the reporting vehicle can be judged based on the driving speed x of the reporting vehicle at the time of the reporting event and the historical traffic speed z at the corresponding geographical location, which can also reflect the real-time traffic status of the road at the current geographical location.

[0062] The vehicle back-end data monitoring platform 13 can record and store the historical traffic speed z of the time period near the reporting time in the past period of time and the corresponding geographical location (which may also include a predetermined nearby area), or record and store the statistical data of the traffic speed of the time period near the reporting time in the past period of time at the corresponding geographical location (which may also include a predetermined nearby area) The position parameters and scale parameters of the normal distribution curve after fitting. For example, the traffic speed or statistical data parameter value within 15 minutes before and after the reporting time of the past 7 days and within 1km before and after the reported geographical location can be used. It can be understood by those skilled in the art that the time interval, time period, and predetermined nearby area can be adjusted according to the situation. The present invention does not impose any restrictions on this. It can be understood by those skilled in the art that the normal distribution curve is a universal speed distribution curve. If other probability statistical distribution models, such as Poisson distribution, Bernoulli distribution, uniform distribution, exponential distribution, etc. can more accurately characterize the historical traffic speed of the actual road environment, the present invention does not impose any restrictions on this.

[0063] The greater the deviation between the vehicle speed x of the reporting vehicle and the historical traffic speed z, the greater the degree of influence of the vehicle speed x, and the higher the value of the calculation result of the confidence and / or priority C.

[0064] It can be understood that the historical traffic speed z is a statistical value, for example, it may conform to a normal distribution. In this case, the historical traffic speed z can be characterized by its average value μ (location parameter) and scale parameter σ, that is, z(μ,σ 2 ), where the scale parameter σ reflects the change range of the historical traffic speed z. Different z(μ,σ 2 ) parameter value.

[0065] In practice, the real-time driving status of the current reporting vehicle can be judged and the speed deviation parameter v can be assigned based on the difference Δx between the driving speed x of the event reporting vehicle at the time of reporting the event and the average value μ of the historical traffic speed z. As mentioned above, this can also indirectly reflect the real-time traffic status of the current road.

[0066] According to an exemplary embodiment of the present invention, the speed deviation parameter v can be assigned based on the difference Δx between the driving speed x of the event reporting vehicle at the time of reporting the event and the average value μ of the historical traffic speed z, and with reference to the scale parameter σ that characterizes the amplitude of the change of the historical traffic speed z.

[0067] For example, Δx=x-μ, when -1.65σ≤Δx≤0, v=0.7; when -1.96σ≤Δx<-1.65σ, v=0.8; when -2.58σ≤Δx<-1.96σ, v=0.9; when Δx>0, it means that the speed x of the vehicle reporting the event at the time of reporting the event is higher than the average value μ of the historical traffic speed z. At this time, it can be considered that the current driving of the vehicle is not affected or the impact is small and negligible, and v=1. Therefore, for this case, the value of v can be expressed by the mathematical formula ν∈{0.7,0.8,0.9,1}. Or for the case when Δx>0, v can also be made close to 1, that is, v→1.

[0068] Those skilled in the art will appreciate that the above-mentioned interval division is only exemplary, and any other suitable interval division method can be adopted according to the actual situation, for example, it can also be divided by numerical points -3σ, -2σ, -σ and 0. Preferably, for the normal distribution method listed here as an example, no matter how μ is taken and how σ is defined, this interval division follows the principle of unchanged area ratio, or further, no matter how μ is taken and how σ is defined, the value of v is associated with the following probability density function formula (2).

[0069]

[0070] Of course, the relationship between the driving speed x of the event reporting vehicle at the time of reporting the event and the historical traffic speed z can also be described and obtained in other ways. For example, according to an exemplary embodiment of the present invention, the relationship between the driving speed x of the event reporting vehicle at the time of reporting the event and the historical traffic speed z can be obtained by any suitable multivariate function relationship, probability distribution relationship or machine learning method based on big data.

[0071] Those skilled in the art will appreciate that other representation methods may be used for the speed deviation parameter v, as long as they are conducive to determining a reliable confidence level and / or priority, and the present invention is not limited thereto.

[0072] There may be many ways to obtain the data of the historical vehicle flow speed z, and the present invention does not limit this.

[0073] For example, according to an exemplary embodiment of the present invention, the historical traffic speed z can be obtained by the vehicle back-end data monitoring platform 13 based on the historical data integration of the vehicles monitored by the vehicle back-end data monitoring platform 13 .

[0074] According to another exemplary embodiment of the present invention, the historical vehicle flow speed z can be determined by the vehicle back-end data monitoring platform 13 in collaboration with other monitoring platforms, which can further improve the accuracy of subsequent calculations.

[0075] 3) The reporting frequency of the same or similar events in the corresponding geographical location within the predetermined time period

[0076] The reporting frequency of the same or similar events can indicate the credibility of the events and the number of vehicles affected. Therefore, the frequency parameter f is defined according to the reporting frequency of the same or similar events at the corresponding geographical location (which can be the total number of reports within a predetermined distance before and after the reporting geographical location and a predetermined time period). The relationship between the frequency and the frequency parameter is determined by a calculation formula. For example, for the formula described in the present invention, the value of the frequency parameter f can be the reciprocal of the number of reports. For example, if the number of reports is 10, the frequency parameter f is 1 / 10, that is, 0.1.

[0077] Preferably, the frequency parameter f can also be determined by the reporting frequency of the same or similar events and the total number of vehicles equipped with the on-board Internet of Vehicles module 11 passing through the corresponding geographical location (which may include a predetermined nearby area). For example, the total number of vehicles equipped with the on-board Internet of Vehicles module 11 passing through the corresponding geographical location is 24, and if the number of reports is 10, for example, the frequency parameter f = [(24-10) + 1] / 25 = 0.6; if the number of reports is 15, the frequency parameter f = [(24-15) + 1] / 25 = 0.4.

[0078] Therefore, the value range of the frequency parameter f can be (0,1].

[0079] The more times the same or similar events are reported or the greater the proportion relative to the total number of vehicles involved, the higher the value of the calculated result of the confidence and / or priority C.

[0080] For those skilled in the art, the frequency parameter f may also be represented by other calculation methods, and the present invention does not impose any limitation on this.

[0081] In addition, those skilled in the art will appreciate that the above function (1) is only an exemplary function for calculating confidence and / or priority C, and any other suitable function may be used. For example, multiple types of functions such as those shown in (3) and (4) may be used.

[0082] C=e f ·v a (3)

[0083] C=e a (1 / f) b ·v c (4)

[0084] According to an exemplary embodiment of the present invention, the confidence level and / or the priority level C may also be calculated based on a machine learning method of big data under the condition that the input parameters remain unchanged.

[0085] According to an exemplary embodiment of the present invention, the present invention also relates to a method for determining the confidence and / or priority C of reported event data, such as Figure 2 As shown, the method may include the following steps:

[0086] S1: The vehicle back-end data monitoring platform 13 receives reported event data from the vehicle. The reported event data may at least include the specific event type, the driving speed of the event reporting vehicle at the time of reporting the event, and the geographical location information of the event occurrence.

[0087] S2: After receiving the reported event data, the vehicle back-end data monitoring platform 13 obtains information required for calculating the confidence and / or priority of the reported event data; which may include:

[0088] S21: Obtaining an event risk parameter e corresponding to the event type based on the reported event type;

[0089] S22: obtaining a historical traffic speed z at the corresponding geographical location based on the reported geographical location, and then obtaining a speed deviation parameter ν based on a deviation between a driving speed x of the event reporting vehicle at the time of the event reporting and the historical traffic speed z;

[0090] S23: Optionally, obtaining a frequency parameter f based at least on the reporting frequency of the same or similar events at the corresponding geographical location within a predetermined period; and

[0091] S3: Calculate the confidence and / or priority C of the reported event data according to the event danger parameter e corresponding to the event type, the speed deviation parameter ν between the driving speed x of the event reporting vehicle at the time of reporting the event and the historical traffic speed z, and / or the reporting frequency parameter f of the same or similar events at the corresponding geographical location within a predetermined time period;

[0092] S4: Based on the calculated results / values ​​of the confidence and / or priority C of the event data, reliable event information (optionally, as well as the calculated results / values ​​of the confidence and / or priority of the event information) is distributed to relevant vehicles, such as vehicles that may be affected near the geographical location where the event occurred.

[0093] According to an exemplary embodiment of the present invention, the confidence and / or priority C is usually expressed as a percentage, ranging from [0, 1]. When the confidence and / or priority C is 0, the event information is considered to be completely unreliable and / or has the lowest priority; when the confidence and / or priority C is 1, the event information is considered to be completely reliable and / or has the highest priority.

[0094] According to an exemplary embodiment of the present invention, according to the confidence and / or priority results / values ​​determined by the vehicle back-end data monitoring platform 13, the reported event data can be divided into three categories: invalid information, prompt information, and warning information.

[0095] According to an exemplary embodiment of the present invention, different processing methods are used for different information classifications, and the following is an example of a classification threshold of {0.5, 0.9}:

[0096] 1. Invalid information

[0097] The confidence and / or priority calculation result interval is [0, 0.5], and the event information is stored in the back-end data monitoring platform 13 and is not distributed to related vehicles.

[0098] 2. Prompt information

[0099] The confidence and / or priority calculation result interval is [0.5, 0.9], and the information is distributed to related vehicles, such as vehicles that may be affected near the geographical location where the incident occurred, for prompting. Preferably, the prompting method can be to display an icon on the screen of the related vehicle, issue a voice, etc.

[0100] 3. Warning Information

[0101] The confidence and / or priority calculation results are in the interval [0.9, 1], and the information is distributed to relevant vehicles, such as vehicles that may be affected near the geographical location where the incident occurred, for corresponding operations. Preferably, the corresponding operation may be displaying an icon on the screen of the relevant vehicle, issuing a voice, etc. Preferably, if the dispatched vehicle is an autonomous driving vehicle with a network security interface, the information can also be used to control the relevant vehicle.

[0102] Of course, the above only gives exemplary classification intervals, and different classification intervals can also be divided by changing the classification threshold.

[0103] It can be understood by those skilled in the art that the technical solution described above is also applicable to the confidence and / or priority analysis of event data collected, reported or sent by the roadside equipment of the vehicle networking system 1. In this case, the roadside equipment can be understood logically corresponding to the vehicle. No further details will be given here.

[0104] According to other exemplary embodiments of the present invention, the method for determining the confidence and / or priority C of reported event data according to the present invention can also be used by an information receiving vehicle to determine the confidence and / or priority of event information (e.g., vehicle-to-vehicle direct communication, V2V, sent / received information) received by the information receiving vehicle and sent by other vehicles. Accordingly, the method may include the following steps:

[0105] S1': The information receiving vehicle receives the reported event data from other vehicles. The reported event data may at least include the specific event type, the driving speed of the event reporting vehicle at the time of reporting the event, and the geographical location information of the event occurrence.

[0106] S2': After receiving the reported event data, the information receiving vehicle obtains the information required to calculate the confidence and / or priority of the reported event data; which may include:

[0107] S21': obtaining an event risk parameter e corresponding to the event type based on the reported event type;

[0108] S22': obtaining the historical traffic speed z at the corresponding geographical location based on the reported geographical location, and obtaining the speed deviation parameter ν based on the deviation between the driving speed x of the event reporting vehicle at the time of reporting the event and the historical traffic speed z;

[0109] S23': Optionally, obtaining a frequency parameter f based at least on the reporting frequency of the same or similar events at the corresponding geographical location within a predetermined period; and

[0110] S3': the information receiving vehicle calculates the confidence and / or priority C of the reported event data according to the event danger parameter e corresponding to the event type, the speed deviation parameter ν between the driving speed x of the event reporting vehicle at the time of reporting the event and the historical traffic speed z, and / or the reporting frequency parameter f of the same or similar events at the corresponding geographical location within a predetermined time period;

[0111] S4': Based on the calculated results / values ​​of the confidence and / or priority C of the event data, the information receiving vehicle distributes the reliable event information (optionally, as well as the calculated results / values ​​of the confidence and / or priority of the event information) to other relevant vehicles, such as vehicles that may be affected near the geographical location where the event occurred or around the information receiving vehicle.

[0112] In addition, the confidence and / or priority analysis results can also be archived as data, for example, for insurance, accidents, municipal administration, etc. Of course, other application scenarios of confidence and / or priority analysis results can also be imagined, and the present invention is not limited to this.

[0113] Table 1 below exemplifies a data archive for a confidence and / or priority analysis process.

[0114] Table 1

[0115]

[0116] Those skilled in the art will appreciate that the present invention also relates to a computer-readable program carrier storing program instructions capable of executing the above method.

[0117] Although specific embodiments of the present invention are described in detail herein, they are provided for the purpose of explanation only and should not be considered to limit the scope of the present invention. Various substitutions, changes and modifications may be conceived without departing from the spirit and scope of the present invention.

Claims

1. A method for determining the confidence and / or priority of reported event data reported by a vehicle in a vehicle networking system (1), the method comprising at least the following steps: Obtaining an event risk parameter corresponding to the event type based on the event type reported by the vehicle, obtaining a historical traffic speed at the corresponding geographical location based on the reported geographical location, and obtaining a speed deviation parameter based on a deviation between a driving speed of the event reporting vehicle at the time of reporting the event and the historical traffic speed; and determining the confidence and / or priority of the reported event data based at least on the event risk parameter and the speed deviation parameter, The historical traffic speed includes the historical traffic speed at the corresponding geographical location in the period near the reporting time within a period of time in the past relative to the reporting time.

2. The method according to claim 1, wherein: The method further comprises the following steps: Receiving event data reported by a vehicle, wherein the reported event data includes the type of the reported event, the speed of the vehicle reporting the event at the time of reporting the event, and the geographical location information where the event occurred; and / or The frequency parameter is determined based at least on the reporting frequency of the same or similar events at the corresponding geographical location within a predetermined period, and the confidence and / or priority of the reported event data is additionally determined based on the frequency parameter.

3. The method according to claim 2, wherein: The confidence and / or priority is determined by the following function (1): Where C represents confidence and / or priority; e is the event risk parameter corresponding to the event type; f is the frequency parameter; and ν is the speed deviation parameter.

4. The method according to any one of claims 1 to 3, wherein: The method further comprises: Fitting the historical traffic speed of each area block to a normal distribution, storing the position parameters and scale parameters of the normal distribution curve of the traffic speed of each area block after statistics, and determining the speed deviation parameter based on the relationship between the difference between the driving speed of the event reporting vehicle at the time of reporting the event and the average value of the historical traffic speed and the scale parameter of the historical traffic speed; and / or The historical traffic speed also includes the historical traffic speed of vehicles in a predetermined area near the reported geographical location.

5. The method according to any one of claims 1 to 3, wherein: The historical traffic speed is obtained by integrating the historical traffic speeds of vehicles monitored by the vehicle back-end data monitoring platform (13) of the vehicle networking system (1); or The historical vehicle flow speed is determined by the collaboration between the vehicle back-end data monitoring platform (13) of the vehicle networking system (1) and other monitoring platforms different from the vehicle back-end data monitoring platform (13).

6. The method according to claim 4, wherein: The historical traffic speed is obtained by integrating the historical traffic speeds of vehicles monitored by the vehicle back-end data monitoring platform (13) of the vehicle networking system (1); or The historical vehicle flow speed is determined by the collaboration between the vehicle back-end data monitoring platform (13) of the vehicle networking system (1) and other monitoring platforms different from the vehicle back-end data monitoring platform (13).

7. The method according to claim 2 or 3, wherein: The frequency parameter is determined based on the reporting frequency and the total number of vehicles passing the corresponding geographical location; and / or The frequency parameter ranges from (0,1].

8. The method according to any one of claims 1 to 3 and 6, wherein: The confidence and / or priority are determined based on a big data machine learning method; and / or According to the determined confidence level and / or priority, the reported event data is divided into three categories: invalid information, prompt information, and warning information.

9. The method according to claim 4, wherein: The confidence and / or priority are determined based on a big data machine learning method; and / or According to the determined confidence level and / or priority, the reported event data is divided into three categories: invalid information, prompt information, and warning information.

10. The method according to claim 5, wherein: The confidence and / or priority are determined based on a big data machine learning method; and / or According to the determined confidence level and / or priority, the reported event data is divided into three categories: invalid information, prompt information, and warning information.

11. The method according to claim 7, wherein: The confidence and / or priority are determined based on a big data machine learning method; and / or According to the determined confidence level and / or priority, the reported event data is divided into three categories: invalid information, prompt information, and warning information.

12. The method according to any one of claims 1-3, 6, 9-11, wherein: When it is a warning message, the corresponding information is dispatched to the relevant vehicle for corresponding operation, and the corresponding operation displays an icon on the screen of the relevant vehicle and / or issues a voice. In the case where the dispatched vehicle is an autonomous driving vehicle with a network security interface, the corresponding information is used to control the relevant vehicle; and / or The confidence and / or priority analysis results are archived as data for insurance and / or accident and / or municipal purposes.

13. The method according to claim 4, wherein: When it is a warning message, the corresponding information is dispatched to the relevant vehicle for corresponding operation, and the corresponding operation displays an icon on the screen of the relevant vehicle and / or issues a voice. In the case where the dispatched vehicle is an autonomous driving vehicle with a network security interface, the corresponding information is used to control the relevant vehicle; and / or The confidence and / or priority analysis results are archived as data for insurance and / or accident and / or municipal purposes.

14. The method according to claim 5, wherein: When it is a warning message, the corresponding information is dispatched to the relevant vehicle for corresponding operation, and the corresponding operation displays an icon on the screen of the relevant vehicle and / or issues a voice. In the case where the dispatched vehicle is an autonomous driving vehicle with a network security interface, the corresponding information is used to control the relevant vehicle; and / or The confidence and / or priority analysis results are archived as data for insurance and / or accident and / or municipal purposes.

15. The method according to claim 7, wherein: When it is a warning message, the corresponding information is dispatched to the relevant vehicle for corresponding operation, and the corresponding operation displays an icon on the screen of the relevant vehicle and / or issues a voice. In the case where the dispatched vehicle is an autonomous driving vehicle with a network security interface, the corresponding information is used to control the relevant vehicle; and / or The confidence and / or priority analysis results are archived as data for insurance and / or accident and / or municipal purposes.

16. The method according to claim 8, wherein: When it is a warning message, the corresponding information is dispatched to the relevant vehicle for corresponding operation, and the corresponding operation displays an icon on the screen of the relevant vehicle and / or issues a voice. In the case where the dispatched vehicle is an autonomous driving vehicle with a network security interface, the corresponding information is used to control the relevant vehicle; and / or The confidence and / or priority analysis results are archived as data for insurance and / or accident and / or municipal purposes.

17. A vehicle back-end data monitoring platform (13) for a vehicle networking system (1), wherein: The vehicle back-end data monitoring platform (13) is configured to be able to execute the method according to any one of claims 1-16.

18. A computer-readable program carrier for a vehicle back-end data monitoring platform (13) of a vehicle networking system (1), wherein the computer-readable program carrier stores program instructions, and when the program instructions are executed by a processor, the method according to any one of claims 1 to 16 can be executed.

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