Vehicle driving panoramic collision warning method and system

Abstract

The application relates to the field of vehicle early warning technology, and particularly discloses a vehicle driving panoramic collision early warning method and system, which comprises the following steps: acquiring vehicle data at regular time intervals, determining a vehicle risk value according to the vehicle data, determining a position acquisition frequency according to the vehicle risk value, acquiring a position containing a time label based on the position acquisition frequency, determining a speed change amount containing a probability according to the position containing the time label, receiving an early warning request sent by any vehicle, acquiring a vehicle position, acquiring the speed change amount containing the probability of other vehicles in a preset space range based on the vehicle position, and determining a monitoring parameter of a vehicle-mounted radar based on the speed change amount containing the probability. The application performs safety analysis on surrounding vehicles, thereby reducing the monitoring frequency of some normal vehicles, saving resources, and providing a relatively high-quality early warning service under the condition of limited resources.

Description

Technical Field

[0001] This invention relates to the field of vehicle warning technology, specifically a method and system for panoramic collision warning of vehicles. Background Technology

[0002] Safety has always been a key focus in automotive development. If a driver could be alerted even one second earlier than a potential hazard, many collisions could be avoided. Driving risk warning systems can detect the surrounding environment to avoid collisions, a crucial element in ensuring safe vehicle operation. The key challenge of driving risk warning lies in real-time identification of potential hazards and accurate quantitative assessment of future risks to improve the accuracy and timeliness of warnings. While existing warning devices and their built-in algorithms are sufficiently sophisticated and still under development, a problem exists: vehicle energy is limited. Overly precise warnings require significant resources, and in resource-constrained situations, the warning process is almost completely shut down, posing a certain risk. Therefore, this invention aims to address the technical problem of providing a collision warning solution under low-resource conditions, offering a degree of collision warning service even with limited resources. Summary of the Invention

[0003] The purpose of this invention is to provide a vehicle driving panoramic collision warning method and system to solve the problems mentioned in the background art.

[0004] To achieve the above objectives, the present invention provides the following technical solution:

[0005] A vehicle driving panoramic collision warning method, the method comprising:

[0006] Vehicle data is acquired periodically based on preset permissions, and a vehicle risk value is determined based on the vehicle data; wherein, the vehicle risk value is used to characterize the stability of the vehicle's operating status.

[0007] The location acquisition frequency is determined based on the vehicle risk value. Locations with time tags are acquired based on the location acquisition frequency. The speed change with probability is determined based on the location with time tags.

[0008] For any vehicle, receive the warning request sent by the vehicle, send the information sharing permission acquisition request to the vehicle, obtain the vehicle's location based on the obtained sharing permission, and obtain the probability-based speed change of other vehicles within a preset spatial range based on the vehicle's location in real time.

[0009] The monitoring parameters of the vehicle radar are determined based on the probabilistic velocity change.

[0010] Furthermore, the step of periodically acquiring vehicle data based on preset permissions and determining the vehicle risk value based on the vehicle data includes:

[0011] Based on the Bluetooth terminal, a vehicle data acquisition request is broadcast to the vehicle, and a data stream containing vehicle tag, type tag and time tag is received from the vehicle;

[0012] The data stream is regularized based on type labels and time labels to obtain a data matrix containing time ranges;

[0013] For any type of data in the data matrix, calculate the data volatility, statistically analyze the data volatility of each type, and determine the vehicle risk value.

[0014] Determine the data acquisition ratio for each type of label based on the data volatility and the amount of empty data for each type of data, and insert a vehicle data acquisition request.

[0015] In the initial vehicle data acquisition request, the data acquisition ratio for each type of label is the same. The data acquisition ratio is used to represent the proportion of data acquisition for a certain type of label in the amount acquired per unit time.

[0016] Furthermore, the step of calculating the data volatility for any type of data in the data matrix, statistically analyzing the data volatility for each type, and determining the vehicle risk value includes:

[0017] Normalize the data of any type in the data matrix to obtain a normalized value;

[0018] Using time labels as the horizontal axis and normalized values ​​as the vertical axis, coordinate points are constructed, and data curves and their data functions are fitted synchronously.

[0019] The data function is periodically identified and peak values ​​are identified. The data volatility is determined based on the periodicity identification results and peak value identification results.

[0020] The vehicle risk value is obtained by summing the volatility of each type of data using an exponential function.

[0021] The step of determining the data acquisition ratio for each type of label based on the data volatility and the amount of empty data for each type includes:

[0022] The data acquisition score is determined based on the direct proportionality between the data volatility and the amount of empty data. The data acquisition scores for all types are then normalized to determine the data acquisition ratio.

[0023] Furthermore, the steps of determining the location acquisition frequency based on the vehicle risk value, acquiring locations with time tags based on the location acquisition frequency, and determining the probability-based speed change based on the locations with time tags include:

[0024] The time when the vehicle risk value is generated is obtained, and the location acquisition frequency is determined based on the vehicle risk value, which is used as the location acquisition frequency after the generation time.

[0025] Locations with time tags are obtained based on location acquisition frequency;

[0026] The movement speed is determined based on the location containing the time tag, and the change in movement speed within a preset time span is determined simultaneously.

[0027] The change amount is classified according to a preset change amount gradient, the frequency of each level of change amount within a preset time period is determined, and the probability of each level of change amount is determined according to the frequency.

[0028] The changes at each level and their probabilities are statistically analyzed and used as probabilistic velocity changes.

[0029] Furthermore, the steps of receiving a warning request from any vehicle, sending an information sharing permission acquisition request to the vehicle, acquiring the vehicle's location based on the acquired sharing permission, and acquiring the probabilistic speed changes of other vehicles within a preset spatial range based on the vehicle's location in real time include:

[0030] For any vehicle, receive the warning request sent by the vehicle and send a request to the vehicle to obtain information sharing permission;

[0031] Receive information sharing permissions granted by vehicles and build a permission library;

[0032] Based on the obtained shared permissions, the location of each vehicle in the permission database is obtained;

[0033] When the distance between any two vehicles is less than a preset distance threshold, the positions of both vehicles and the probabilistic speed changes are shared.

[0034] Furthermore, the step of determining the monitoring parameters of the vehicle-mounted radar based on the probabilistic speed change includes:

[0035] For any vehicle, query the positions of other vehicles it receives and their probabilistic speed changes;

[0036] Vehicle stability is determined based on probabilistic speed changes.

[0037] The monitoring frequency of the vehicle radar pointing to the vehicle's position is determined based on the inverse ratio of vehicle stability.

[0038] For vehicles whose speed changes with probability are not acquired within the preset space range, their vehicle stability is set to the default minimum value.

[0039] The present invention also provides a vehicle driving panoramic collision warning system, the system comprising:

[0040] The risk value determination module is used to periodically acquire vehicle data based on preset permissions and determine the vehicle risk value based on the vehicle data; wherein, the vehicle risk value is used to characterize the stability of the vehicle's operating status.

[0041] The speed analysis module is used to determine the location acquisition frequency based on the vehicle risk value, acquire the location with time tag based on the location acquisition frequency, and determine the speed change with probability based on the location with time tag.

[0042] The information sharing module is used to receive warning requests from any vehicle, send information sharing permission requests to the vehicle, obtain the vehicle's location based on the obtained sharing permission, and obtain the probability-based speed changes of other vehicles within a preset spatial range based on the vehicle's location in real time.

[0043] The monitoring parameter adjustment module is used to determine the monitoring parameters of the vehicle radar based on the probabilistic speed change.

[0044] Furthermore, the risk value determination module includes:

[0045] The data stream acquisition unit is used to broadcast a vehicle data acquisition request to the vehicle via Bluetooth and receive a data stream containing vehicle tag, type tag and time tag from the vehicle.

[0046] The data shaping unit is used to shape the data stream based on type labels and time labels to obtain a data matrix containing time ranges;

[0047] The data fluctuation analysis unit is used to calculate the data fluctuation degree for any type of data in the data matrix, statistically analyze the data fluctuation degree of each type, and determine the vehicle risk value.

[0048] The proportion determination application unit is used to determine the data acquisition proportion of each type of label based on the data volatility and the amount of empty data of each type of data, and to insert vehicle data acquisition requests.

[0049] In the initial vehicle data acquisition request, the data acquisition ratio for each type of label is the same. The data acquisition ratio is used to represent the proportion of data acquisition for a certain type of label in the amount acquired per unit time.

[0050] Furthermore, the speed analysis module includes:

[0051] The frequency determination unit is used to obtain the generation time of the vehicle risk value and determine the location acquisition frequency based on the vehicle risk value, which is used as the location acquisition frequency after the generation time.

[0052] The location acquisition unit is used to acquire locations with time tags based on the location acquisition frequency.

[0053] The change determination unit is used to determine the motion speed based on the position containing the time label, and simultaneously determine the change in motion speed within a preset time span;

[0054] The hierarchical evaluation unit is used to classify the change amount based on a preset change amount gradient, determine the frequency of each level of change amount within a preset time period, and determine the probability of each level of change amount based on the frequency.

[0055] The change statistics unit is used to count the changes at each level and their probabilities, as a rate change quantity containing probabilities.

[0056] Furthermore, the information sharing module includes:

[0057] The permission acquisition unit is used to receive warning requests sent by any vehicle and send information sharing permission acquisition requests to any vehicle.

[0058] The permission library construction unit is used to receive information sharing permissions granted by vehicles and build the permission library.

[0059] The permission library application unit is used to obtain the location of each vehicle in the permission library based on the acquired shared permissions;

[0060] The shared execution unit is used to share the positions and probabilistic speed changes of any two vehicles when the distance between them is less than a preset distance threshold.

[0061] Compared with the prior art, the beneficial effects of the present invention are:

[0062] This invention provides a superior simplified solution under the premise of insufficient resources. It performs safety analysis on surrounding vehicles, thereby reducing the monitoring frequency of some normal vehicles to save resources. This allows for greater attention to abnormal vehicles under limited resources, and enables the provision of relatively high-quality early warning services even with limited resources. Attached Figure Description

[0063] To more clearly illustrate the technical solutions in the embodiments of the present invention, the accompanying drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention.

[0064] Figure 1 The overall flowchart of the vehicle driving panoramic collision warning method is shown.

[0065] Figure 2 The first sub-flowchart of the vehicle driving panoramic collision warning method is shown.

[0066] Figure 3The second sub-flowchart of the vehicle driving panoramic collision warning method is shown.

[0067] Figure 4 The third sub-flowchart of the vehicle driving panoramic collision warning method is shown.

[0068] Figure 5 The fourth sub-flowchart of the vehicle driving panoramic collision warning method is shown.

[0069] Figure 6 A structural diagram of a vehicle driving panoramic collision warning system is shown. Detailed Implementation

[0070] To make the technical problems, solutions, and beneficial effects of this invention clearer, the invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the invention.

[0071] Figure 1 The diagram shows the overall flowchart of a vehicle driving panoramic collision warning method. In this embodiment of the invention, a vehicle driving panoramic collision warning method includes:

[0072] Step S100: Based on preset permissions, acquire vehicle data periodically, and determine the vehicle risk value based on the vehicle data; wherein, the vehicle risk value is used to characterize the stability of the vehicle's operating status.

[0073] Vehicle data is the data generated during the operation of the vehicle itself. It is the detection data of the vehicle's own system, including signals measured by various sensors and operating signals of various modules. It can be obtained as long as the user has the necessary permissions. By analyzing the acquired vehicle data, the stability of the vehicle can be determined, which is represented by the parameter of vehicle risk value.

[0074] Step S200: Determine the location acquisition frequency based on the vehicle risk value, acquire the location with time tag based on the location acquisition frequency, and determine the speed change with probability based on the location with time tag;

[0075] The vehicle risk value represents the vehicle's operating status. The location acquisition frequency is determined based on the vehicle risk value. The higher the vehicle risk value, the lower the vehicle's stability, and the higher the location acquisition frequency needs to be. The location with time tags is obtained based on the location acquisition frequency. This process involves positioning. With pre-authorized access, it can be obtained through positioning devices. In the current technological context, navigation services are very common, and the location acquisition process is not difficult. By analyzing the obtained locations with time tags, the speed at different times (or time periods) can be determined, the speed changes can be calculated, and the distribution of speed changes over a longer time range can be determined. The distribution is represented by probability, and the speed change with probability is obtained. To simplify the processing, the absolute value of the speed change can be taken. In this case, the speed change or decrease is reflected in the numerical value.

[0076] Step S300: For any vehicle, receive the warning request sent by the vehicle, send the information sharing permission acquisition request to the vehicle, obtain the vehicle's location based on the acquired sharing permission, and obtain the probability-based speed change of other vehicles within the preset spatial range based on the vehicle's location in real time.

[0077] In practical application, for any vehicle, receiving a warning request from another vehicle and sending a request to obtain information sharing permission means that when a vehicle wants to use the warning service provided by this method, it needs to first be granted information sharing permission. At this time, other vehicles can also obtain the vehicle's information, including the vehicle's position and a probability-based speed change, thereby assisting other vehicles in completing the warning process. Of course, for the current vehicle, it can also obtain information from other vehicles (authorized vehicles) and then use the warning service.

[0078] Step S400: Determine the monitoring parameters of the vehicle radar based on the probabilistic speed change;

[0079] Assuming all vehicles surrounding a given vehicle are authorized, a particular vehicle can obtain probabilistic speed changes of the surrounding vehicles. Based on these probabilistic speed changes, the monitoring parameters for the vehicle's radar can be determined. The core of this process is to reduce the application frequency of the vehicle's radar, thereby mitigating the vehicle's resource consumption. Specifically, for vehicles whose speed changes are likely to be low, the frequency of radar monitoring can be slightly lower. Of course, this is not a solution that completely conflicts with existing early warning technologies; it can be combined with existing visual early warning systems. In fact, it simply provides a better early warning solution when resources are limited.

[0080] Figure 2The diagram illustrates the first sub-flowchart of a vehicle driving panoramic collision warning method. The step of periodically acquiring vehicle data based on preset permissions and determining the vehicle risk value based on the vehicle data includes:

[0081] Step S101: Broadcast a vehicle data acquisition request to the vehicle via Bluetooth and receive a data stream containing vehicle tag, type tag and time tag from the vehicle;

[0082] Step S102: Regulate the data stream based on type labels and time labels to obtain a data matrix containing time ranges;

[0083] Step S103: For any type of data in the data matrix, calculate the data volatility, statistically analyze the data volatility of each type, and determine the vehicle risk value;

[0084] Step S104: Determine the data acquisition ratio for each type of label based on the data volatility and the amount of empty data for each type, and insert a vehicle data acquisition request;

[0085] In the initial vehicle data acquisition request, the data acquisition ratio for each type of label is the same. The data acquisition ratio is used to represent the proportion of data acquisition for a certain type of label in the amount acquired per unit time.

[0086] The above describes the process of assessing vehicle risk, which is essentially a vehicle data processing process. It includes vehicle data acquisition and vehicle data identification. Regarding the vehicle data acquisition process, this invention employs an intermittent acquisition method via Bluetooth. Bluetooth devices are set up on the road segment, and when a vehicle approaches the Bluetooth device, it pairs with the device, interacts with it, and uploads vehicle data. This process does not require the vehicle to upload vehicle data in real time, resulting in minimal transmission pressure. However, the downside is that the upload process itself is intermittent. When the vehicle speed is high or highly volatile, a single upload cycle may only last a few seconds, resulting in extremely limited data uploads. Therefore, the vehicle data upload process itself involves a selection process.

[0087] The system broadcasts a vehicle data acquisition request to the vehicle via Bluetooth, receives a data stream containing vehicle tags, type tags, and time tags from the vehicle, and organizes the data stream based on the type and time tags to obtain a data matrix containing a time range. The vehicle tag is used as the index of the data matrix to indicate which vehicle the data corresponds to. The rows and columns in the data matrix correspond to the type and time, respectively. For any data (one row or one column) corresponding to any type in the data matrix, the data fluctuation is calculated and reflected by the data fluctuation degree. After the data fluctuation degree of each type is calculated, the data fluctuation degree of each type is statistically analyzed to determine the vehicle risk value that reflects the overall situation of the vehicle.

[0088] Building upon the above, additional processing is required for data volatility. The data acquisition ratio for each type of label is determined based on its volatility and the amount of empty data. Vehicle data acquisition requests are then inserted. This addresses the scenario where, since an upload cycle might only last a few seconds, the amount of data corresponding to different types of labels may vary over a period of time. This results in different amounts of data for each type in the data matrix. Subsequently, a parameter called "data acquisition ratio" is introduced to represent the proportion of data acquired for a specific type of label within the unit of time acquisition. The more empty data, the greater the data volatility, the more data needs to be acquired, and the higher the data acquisition ratio. In the initial vehicle data acquisition request, the data acquisition ratio for each type of label is the same.

[0089] As a preferred embodiment of the technical solution of the present invention, the step of calculating the data volatility for any type of data in the data matrix, statistically analyzing the data volatility for each type, and determining the vehicle risk value includes:

[0090] Normalize the data of any type in the data matrix to obtain a normalized value;

[0091] Using time labels as the horizontal axis and normalized values ​​as the vertical axis, coordinate points are constructed, and data curves and their data functions are fitted synchronously.

[0092] The data function is periodically identified and peak values ​​are identified. The data volatility is determined based on the periodicity identification results and peak value identification results.

[0093] The vehicle risk value is obtained by summing the volatility of each type of data using an exponential function.

[0094] The above content defines the process for determining vehicle risk values. Essentially, it involves acquiring and applying data volatility. Normalization is performed on data of any type in the data matrix to obtain a normalized value. This normalization process is essentially the process of positiveizing the indicator. The simplest method is to calculate the ratio of the normalized value to its maximum value. As long as the value has a defined range and is dimensionless, it can be used as the normalized value. Using time labels as the x-axis and the normalized value as the y-axis, coordinate points are constructed. Simultaneously, data curves and their functions are fitted. Periodicity and peak values ​​are identified within the data function. Based on the periodicity and peak value identification results, data volatility is determined. Periodicity identification determines whether there is a significant period, and peak value identification determines whether the peak value is dangerous. Combining these two results yields the data volatility. The vehicle risk value is obtained by accumulating the data volatility of each type using an exponential function. The implication of this process is that the greater the volatility of a data type, the greater its impact on the vehicle risk value (the derivative of the exponential function is also an increasing function).

[0095] Regarding the periodicity identification process, one feasible approach is to perform a Fourier transform on the data function to obtain the frequency domain amplitude spectrum; determine whether there is a dominant frequency peak in the amplitude spectrum that is greater than a preset threshold; if a dominant frequency peak exists, obtain the amplitude proportion of the dominant frequency peak and use the amplitude proportion as the periodicity identification result. Regarding the peak identification process, one feasible approach is to mark the corresponding data segment when the marked peak (the peak of the data function, not the peak in the spectrum) reaches a preset value, and use the length of the marked data segment as the peak identification result. Finally, the determined data volatility is inversely proportional to the amplitude proportion, indicating that the larger the amplitude proportion, the more obvious the periodicity and the smaller the data volatility; the determined data volatility is directly proportional to the data segment length, indicating that the larger the data segment length, the more data exceeds the limit, and the greater the data volatility.

[0096] The step of determining the data acquisition ratio for each type of label based on the data volatility and the amount of empty data for each type includes:

[0097] The data acquisition score is determined based on the direct proportionality between the data volatility and the amount of empty data. The data acquisition scores for all types are then normalized to determine the data acquisition ratio.

[0098] In one example of the technical solution of the present invention, a recursive adjustment scheme for the data acquisition process is introduced. The data acquisition score is determined according to the direct proportion of data fluctuation and the direct proportion of empty data. The sum of all data acquisition scores is calculated, and then the ratio of each data acquisition score to the sum is calculated to obtain the data acquisition ratio.

[0099] Figure 3 The second sub-flow flowchart of the vehicle driving panoramic collision warning method is shown. The steps of determining the location acquisition frequency based on the vehicle risk value, acquiring the location with time label based on the location acquisition frequency, and determining the velocity change amount with probability based on the location with time label include:

[0100] Step S201: Obtain the generation time of the vehicle risk value, and determine the location acquisition frequency based on the vehicle risk value, which will be used as the location acquisition frequency after the generation time;

[0101] Step S202: Obtain locations containing time tags based on location acquisition frequency;

[0102] Step S203: Determine the motion speed based on the location containing the time tag, and simultaneously determine the change in motion speed within a preset time span;

[0103] Step S204: Classify the change amount based on the preset change amount gradient, determine the frequency of each level of change amount within the preset time period, and determine the probability of each level of change amount based on the frequency;

[0104] Step S205: Statistically analyze the changes at each level and their probabilities, as probabilistic velocity changes.

[0105] In one example of the technical solution of this invention, the location analysis process is described. First, the location acquisition process is different from the vehicle data acquisition process. Vehicle data itself has a large amount of data, and the resource requirements for real-time transmission are very large. However, the location is different. Real-time location acquisition technology is very mature and can be acquired in real time. However, in the technical solution of this invention, the location acquisition process is still selectively down-frequency. Each time a vehicle risk value is obtained, its generation time is obtained. The location acquisition frequency is determined based on the vehicle risk value and used as the location acquisition frequency after the generation time. The location acquisition frequency is proportional to the vehicle risk value. Based on the location acquisition frequency, the location containing the time tag is obtained. The movement speed is determined based on the location containing the time tag. The change in movement speed within a preset time span is determined simultaneously. The time span is generally a few seconds. The change is graded based on a preset change gradient (for example, the speed change from 0 to 20 is divided into 5 or 10 levels). The frequency of each level of change within the preset time period is determined. The probability of each level of change is determined based on the frequency. This process is simply the frequency divided by the sum of frequencies.

[0106] It should be noted that the change in motion speed is complicated to process because it is signed. Therefore, in step S203, the absolute value of the change in motion speed can be calculated before proceeding with the subsequent processing. In this case, the grading process only needs to set some non-negative thresholds.

[0107] Figure 4 The diagram illustrates the third sub-flow flowchart of the vehicle driving panoramic collision warning method. The steps of receiving a warning request from any vehicle, sending an information sharing permission acquisition request to the vehicle, acquiring the vehicle's position based on the acquired sharing permission, and acquiring the probabilistic speed changes of other vehicles within a preset spatial range based on the vehicle's position include:

[0108] Step S301: For any vehicle, receive the warning request sent by the vehicle and send an information sharing permission acquisition request to the vehicle;

[0109] Step S302: Receive information sharing permissions granted by the vehicle and build a permission library;

[0110] Step S303: Obtain the location of each vehicle in the permission database based on the acquired shared permissions;

[0111] Step S304: When the distance between any two vehicles is less than a preset distance threshold, share the positions of both vehicles and the probabilistic speed changes.

[0112] In the practical application phase, the data sharing process was explained. The core of this process is the issue of permissions. For any vehicle, the system receives a warning request from the vehicle, indicating that the vehicle needs the service provided by the method executor. At this time, the system sends an information sharing permission acquisition request to the vehicle, receives the information sharing permission granted by the vehicle, and builds a permission library. The obtained permission library contains all vehicles that have been granted permissions. Of course, permissions can be revoked, which is entirely up to the user to decide, and will not be elaborated here.

[0113] For each vehicle in the permission library, the location of each vehicle is obtained based on the acquired shared permissions. When the distance between any two vehicles is less than a preset distance threshold, it means that they are close enough. At this time, the positions of both parties and the probability of speed change are shared. In fact, for the position, if the positions of both parties are close enough, the vehicle detection equipment can actually obtain a more accurate position. The role of shared position is that it can provide a general range for the vehicle detection equipment, which can assist the vehicle detection equipment in both the preprocessing stage and the subsequent result verification stage.

[0114] Figure 5 The fourth sub-flowchart of the vehicle driving panoramic collision warning method is shown. The step of determining the monitoring parameters of the vehicle radar based on the probabilistic speed change includes:

[0115] Step S401: For any vehicle, query the positions of other vehicles it receives and their probabilistic speed changes;

[0116] Step S402: Determine vehicle stability based on the probabilistic speed change;

[0117] Step S403: Determine the monitoring frequency of the vehicle radar pointing to the vehicle's position based on the inverse ratio of vehicle stability;

[0118] For vehicles whose speed changes with probability are not acquired within the preset space range, their vehicle stability is set to the default minimum value.

[0119] For any given vehicle, query the positions of other vehicles it receives and their probabilistic speed changes. Determine the vehicle's stability based on these probabilistic speed changes. One method is to calculate the product of the probability and the speed change, then sum them up. The vehicle's stability is determined by the inverse ratio of the summation result. The sum represents the vehicle's speed change and its probability. A larger absolute value of speed change indicates a higher probability, a less stable vehicle, and a lower vehicle stability. The monitoring frequency of the vehicle radar pointing to the vehicle's position is determined by the inverse ratio of the vehicle stability. The lower the vehicle stability, the higher the monitoring frequency should be.

[0120] It is worth mentioning that the above sharing process only occurs between vehicles in the permission library. In real-world scenarios, there are certainly many vehicles that are not in the permission library. In this case, the original on-board detection equipment and its working process are applied. That is, for vehicles that have not acquired probabilistic speed changes within the preset space range, their vehicle stability is set to the default minimum value, which corresponds to the maximum monitoring frequency of the on-board detection equipment.

[0121] It should be noted that the above solution is actually a simplified solution under the premise of insufficient resources. The maximum monitoring frequency is actually the normal frequency. However, if resources are insufficient, this method architecture can reduce the monitoring frequency of some normal vehicles to save resources. If resources are sufficient, the maximum monitoring frequency (or a normal larger value monitoring frequency) can be used for monitoring.

[0122] Figure 6 A structural diagram of a vehicle driving panoramic collision warning system is shown. In a preferred embodiment of the technical solution of the present invention, a vehicle driving panoramic collision warning system is also provided, the system 10 comprising:

[0123] The risk value determination module 11 is used to periodically acquire vehicle data based on preset permissions and determine the vehicle risk value based on the vehicle data; wherein, the vehicle risk value is used to characterize the stability of the vehicle's operating status.

[0124] Speed ​​analysis module 12 is used to determine the location acquisition frequency based on the vehicle risk value, acquire the location with time tag based on the location acquisition frequency, and determine the speed change with probability based on the location with time tag.

[0125] Information sharing module 13 is used to receive warning requests sent by any vehicle, send information sharing permission acquisition requests to the vehicle, obtain the vehicle's location based on the acquired sharing permission, and obtain the probability-based speed change of other vehicles within a preset spatial range based on the vehicle's location in real time.

[0126] The monitoring parameter adjustment module 14 is used to determine the monitoring parameters of the vehicle radar based on the probabilistic speed change.

[0127] Furthermore, the risk value determination module 11 includes:

[0128] The data stream acquisition unit is used to broadcast a vehicle data acquisition request to the vehicle via Bluetooth and receive a data stream containing vehicle tag, type tag and time tag from the vehicle.

[0129] The data shaping unit is used to shape the data stream based on type labels and time labels to obtain a data matrix containing time ranges;

[0130] The data fluctuation analysis unit is used to calculate the data fluctuation degree for any type of data in the data matrix, statistically analyze the data fluctuation degree of each type, and determine the vehicle risk value.

[0131] The proportion determination application unit is used to determine the data acquisition proportion of each type of label based on the data volatility and the amount of empty data of each type of data, and to insert vehicle data acquisition requests.

[0132] In the initial vehicle data acquisition request, the data acquisition ratio for each type of label is the same. The data acquisition ratio is used to represent the proportion of data acquisition for a certain type of label in the amount acquired per unit time.

[0133] Specifically, the speed analysis module 12 includes:

[0134] The frequency determination unit is used to obtain the generation time of the vehicle risk value and determine the location acquisition frequency based on the vehicle risk value, which is used as the location acquisition frequency after the generation time.

[0135] The location acquisition unit is used to acquire locations with time tags based on the location acquisition frequency.

[0136] The change determination unit is used to determine the motion speed based on the position containing the time label, and simultaneously determine the change in motion speed within a preset time span;

[0137] The hierarchical evaluation unit is used to classify the change amount based on a preset change amount gradient, determine the frequency of each level of change amount within a preset time period, and determine the probability of each level of change amount based on the frequency.

[0138] The change statistics unit is used to count the changes at each level and their probabilities, as a rate change quantity containing probabilities.

[0139] Furthermore, the information sharing module 13 includes:

[0140] The permission acquisition unit is used to receive warning requests sent by any vehicle and send information sharing permission acquisition requests to any vehicle.

[0141] The permission library construction unit is used to receive information sharing permissions granted by vehicles and build the permission library.

[0142] The permission library application unit is used to obtain the location of each vehicle in the permission library based on the acquired shared permissions;

[0143] The shared execution unit is used to share the positions and probabilistic speed changes of any two vehicles when the distance between them is less than a preset distance threshold.

[0144] The above are merely preferred embodiments of the present invention and do not limit the patent scope of the present invention. Any equivalent structural or procedural transformations made based on the content of the present invention's specification and drawings, or direct or indirect applications in other related technical fields, are similarly included within the patent protection scope of the present invention.

Claims

1. A vehicle running panoramic collision warning method characterized by, The method comprises: acquiring vehicle data based on a preset permission timing, determining a vehicle risk value based on the vehicle data; wherein the vehicle risk value is used to represent the stability of the vehicle operating state; determining a position acquisition frequency based on the vehicle risk value, acquiring a position with a time label based on the position acquisition frequency, and determining a speed change amount with a probability based on the position with the time label; for any vehicle, receiving a warning request sent by the vehicle, sending an information sharing permission acquisition request to the vehicle, acquiring the position of the vehicle based on the acquired sharing permission, and acquiring the speed change amount with a probability of other vehicles in a preset spatial range based on the position of the vehicle; wherein the any vehicle refers to a vehicle that wants to use the warning service provided by the method; determining the monitoring parameters of the vehicle-mounted radar based on the speed change amount with a probability; the step of determining the position acquisition frequency based on the vehicle risk value, acquiring the position with a time label based on the position acquisition frequency, and determining the speed change amount with a probability based on the position with the time label comprises: acquiring the generation time of the vehicle risk value, determining the position acquisition frequency after the generation time based on the vehicle risk value as the position acquisition frequency; acquiring the position with a time label based on the position acquisition frequency; determining the motion speed based on the position with the time label, and synchronously determining the change amount of the motion speed within a preset time span; grading the change amount based on a preset change amount gradient, determining the frequency of each level of change amount within a preset time period, and determining the probability of each level of change amount based on the frequency; statistically determining each level of change amount and its probability as the speed change amount with a probability; the step of, for any vehicle, receiving a warning request sent by the vehicle, sending an information sharing permission acquisition request to the vehicle, acquiring the position of the vehicle based on the acquired sharing permission, and acquiring the speed change amount with a probability of other vehicles in a preset spatial range based on the position of the vehicle comprises: for any vehicle, receiving a warning request sent by the vehicle, sending an information sharing permission acquisition request to the vehicle; receiving the information sharing permission granted by the vehicle, and constructing a permission library; acquiring the positions of each vehicle in the permission library based on the acquired sharing permission; when the distance between any two vehicles is less than a preset distance threshold, sharing the positions and the speed change amount with a probability of both parties; the step of determining the monitoring parameters of the vehicle-mounted radar based on the speed change amount with a probability comprises: for any vehicle, querying the positions of other vehicles received by the vehicle and the speed change amount with a probability thereof; determining the vehicle smoothness based on the speed change amount with a probability; determining the monitoring frequency of the vehicle-mounted radar pointing to the position of the vehicle based on the inverse of the vehicle smoothness; wherein for a vehicle in a preset spatial range for which the speed change amount with a probability is not acquired, the vehicle smoothness of the vehicle is set to a default minimum value.

2. The vehicle travel panoramic collision warning method according to claim 1, characterized by, the step of acquiring vehicle data based on a preset permission timing, and determining a vehicle risk value based on the vehicle data comprises: broadcasting a vehicle data acquisition request to the vehicle based on a Bluetooth terminal, and receiving a data stream with a vehicle label, a type label and a time label fed back by the vehicle; The data stream is regularized based on type labels and time labels to obtain a data matrix containing time ranges; For any type of data in the data matrix, calculate the data volatility, statistically analyze the data volatility of each type, and determine the vehicle risk value. Determine the data acquisition ratio for each type of label based on the data volatility and the amount of empty data for each type of data, and insert a vehicle data acquisition request. In the initial vehicle data acquisition request, the data acquisition ratio for each type of label is the same. The data acquisition ratio is used to represent the proportion of data acquisition for a certain type of label in the amount acquired per unit time.

3. The vehicle travel panoramic collision warning method according to claim 2, characterized by, The steps of calculating data volatility for any type of data in the data matrix, statistically analyzing the data volatility for each type, and determining the vehicle risk value include: Normalize the data of any type in the data matrix to obtain a normalized value; Using time labels as the horizontal axis and normalized values ​​as the vertical axis, coordinate points are constructed, and data curves and their data functions are fitted synchronously. The data function is periodically identified and peak values ​​are identified. The data volatility is determined based on the periodicity identification results and peak value identification results. The vehicle risk value is obtained by summing the volatility of each type of data using an exponential function. The step of determining the data acquisition ratio for each type of label based on the data volatility and the amount of empty data for each type includes: The data acquisition score is determined based on the direct proportionality between the data volatility and the amount of empty data. The data acquisition scores for all types are then normalized to determine the data acquisition ratio.

4. A vehicle travel panoramic collision warning system characterized by comprising: The system includes: The risk value determination module is used to periodically acquire vehicle data based on preset permissions and determine the vehicle risk value based on the vehicle data; wherein, the vehicle risk value is used to characterize the stability of the vehicle's operating status. The speed analysis module is used to determine the location acquisition frequency based on the vehicle risk value, acquire the location with time tag based on the location acquisition frequency, and determine the speed change with probability based on the location with time tag. The information sharing module is used to receive warning requests sent by any vehicle, send information sharing permission acquisition requests to the vehicle, obtain the vehicle's location based on the acquired sharing permission, and obtain the probability-based speed changes of other vehicles within a preset spatial range based on the vehicle's location in real time; wherein, any vehicle refers to a vehicle that wants to use the warning service provided by this method. The monitoring parameter adjustment module is used to determine the monitoring parameters of the vehicle radar based on the probabilistic speed change. The velocity analysis module includes: The frequency determination unit is used to obtain the generation time of the vehicle risk value and determine the location acquisition frequency based on the vehicle risk value, which is used as the location acquisition frequency after the generation time. The location acquisition unit is used to acquire locations with time tags based on the location acquisition frequency. The change determination unit is used to determine the motion speed based on the position containing the time label, and simultaneously determine the change in motion speed within a preset time span; The hierarchical evaluation unit is used to classify the change amount based on a preset change amount gradient, determine the frequency of each level of change amount within a preset time period, and determine the probability of each level of change amount based on the frequency. The change statistics unit is used to count the changes at each level and their probabilities, as a rate change quantity containing probabilities; The information sharing module includes: The permission acquisition unit is used to receive warning requests sent by any vehicle and send information sharing permission acquisition requests to any vehicle. The permission library construction unit is used to receive information sharing permissions granted by vehicles and build the permission library. The permission library application unit is used to obtain the location of each vehicle in the permission library based on the acquired shared permissions; The shared execution unit is used to share the positions and probabilistic speed changes of both vehicles when the distance between any two vehicles is less than a preset distance threshold. The steps for determining the monitoring parameters of the vehicle-mounted radar based on probabilistic speed changes include: For any vehicle, query the positions of other vehicles it receives and their probabilistic speed changes; Vehicle stability is determined based on probabilistic speed changes. The monitoring frequency of the vehicle radar pointing to the vehicle's position is determined based on the inverse ratio of vehicle stability. For vehicles whose speed changes with probability are not acquired within the preset space range, their vehicle stability is set to the default minimum value.

5. The vehicle travel panoramic collision warning system according to claim 4, characterized by, The risk value determination module includes: The data stream acquisition unit is used to broadcast a vehicle data acquisition request to the vehicle via Bluetooth and receive a data stream containing vehicle tag, type tag and time tag from the vehicle. The data shaping unit is used to shape the data stream based on type labels and time labels to obtain a data matrix containing time ranges; The data fluctuation analysis unit is used to calculate the data fluctuation degree for any type of data in the data matrix, statistically analyze the data fluctuation degree of each type, and determine the vehicle risk value. The proportion determination application unit is used to determine the data acquisition proportion of each type of label based on the data volatility and the amount of empty data of each type of data, and to insert vehicle data acquisition requests. In the initial vehicle data acquisition request, the data acquisition ratio for each type of label is the same. The data acquisition ratio is used to represent the proportion of data acquisition for a certain type of label in the amount acquired per unit time.

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