CN117808437A - Traffic management method, equipment and medium based on virtual simulation technology - Google Patents
Traffic management method, equipment and medium based on virtual simulation technology Download PDFInfo
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Abstract
The application relates to the field of traffic model generation, and particularly discloses a traffic management method, equipment and medium based on a virtual simulation technology, wherein the method comprises the following steps: acquiring a target traffic accident video, and determining each target vehicle corresponding to the target traffic accident based on the target traffic accident video; acquiring a preset vehicle model corresponding to each target vehicle, and determining vehicle operation data and road surface information of each target vehicle based on the target traffic accident video; generating a three-dimensional collision model corresponding to the target traffic accident based on a preset vehicle model, vehicle operation data and road surface information; and managing the target traffic accident based on the three-dimensional collision model, wherein the management mode comprises at least one of responsibility fixing and driving simulation. The three-dimensional collision model of the target traffic accident is generated on the basis of the monitoring video through the virtual simulation technology, so that the traffic accident can be managed conveniently, and objection of both sides of the accident to the traffic accident responsibility fixing result is reduced.
Description
Technical Field
The application relates to the field of traffic model generation, in particular to a traffic management method, equipment and medium based on a virtual simulation technology.
Background
In real life, traffic accidents are common events, and monitoring videos shot by a vehicle recorder and a traffic camera can serve as an important evidence source and play a vital role in accident investigation and responsibility identification.
Traffic managers can deal with the responsibility-defining cases of many traffic accidents every day, and although the monitoring video can provide a certain objective record, in some cases, there may still be some limitations and disadvantages, which make it difficult to accurately judge the accident responsibility. Only by means of the monitoring video, particularly the vehicle-mounted video shot by the automobile data recorder, the accident parties can usually carry out the words, responsibility attribution cannot be determined efficiently, the workload of traffic management personnel is definitely increased, and the management efficiency is reduced.
Disclosure of Invention
In order to solve the above problems, the present application provides a traffic management method, device and medium based on virtual simulation technology, wherein the method includes:
acquiring a target traffic accident video, and determining each target vehicle corresponding to the target traffic accident based on the target traffic accident video; acquiring a preset vehicle model corresponding to each target vehicle, and determining vehicle operation data and road surface information of each target vehicle based on the target traffic accident video; generating a three-dimensional collision model corresponding to the target traffic accident based on the preset vehicle model, the vehicle operation data and the road surface information; and managing the target traffic accident based on the three-dimensional collision model, wherein the management mode comprises at least one of responsibility fixing and driving simulation.
In one example, the determining, based on the target traffic accident video, each target vehicle corresponding to the target traffic accident specifically includes: determining an occurrence time interval of the target traffic accident based on the target traffic accident video; taking all passing vehicles in the occurrence time interval and the preset range of the target traffic accident scene as initial target vehicles; determining the relative distance, the relative speed and the matched motion track of the initial target vehicle and the accident vehicle respectively based on the traffic accident video; determining a correlation coefficient of the initial target vehicle and the accident vehicle based on the relative distance, the relative speed and the trajectory of the coordinated movement; and taking the initial target vehicles with the correlation coefficients higher than a preset threshold value as the target vehicles corresponding to the target traffic accidents.
In one example, the determining the correlation coefficient between the initial target vehicle and the accident vehicle based on the relative distance, the relative speed, and the trajectory of the coordinated motion specifically includes: determining a first impact factor of different initial target vehicles and accident vehicles before the accident occurrence time by the following formula:
wherein,for the first influencing factor, +.>For the preset time point before accident occurrence, +.>For accident time, < > the->Expressed as the time period before the time of the accident, < > and>is any time point in a time end before the accident occurrence time; />Is->The length of the corresponding matching motion trail of the moment, < >>Is->Relative speed corresponding to time, < >>Is->Relative distance corresponding to time, < >>Is a first correction constant;
determining a second influence factor of different initial target vehicles and accident vehicles after the accident occurrence time through the following formula;
wherein,for the second influencing factor, +.>For the preset time point after accident occurrence, +.>Expressed as the time period after the time of the accident, < > and>is any point in time within the time period after the time of the accident; />Is->The length of the corresponding matching motion trail of the moment, < >>Is->Relative speed corresponding to time, < >>Is->Relative distance corresponding to time, < >>Is a second correction constant;
and determining a correlation coefficient of the initial target vehicle and the accident vehicle based on the first influence factor and the second influence factor.
In one example, the managing the target traffic accident based on the three-dimensional collision model specifically includes: determining the accident type of the target traffic accident based on the target traffic accident video; selecting a preset responsibility model from a preset database based on the accident type; and performing auxiliary responsibility fixing on each target vehicle in the three-dimensional collision model based on the responsibility fixing model.
In one example, after the generating the three-dimensional collision model corresponding to the target traffic accident, the method further includes: acquiring field data of the target traffic accident, wherein the field data at least comprises vehicle collision data, congestion information and road surface information; determining a similar historical collision model of the target traffic accident in a plurality of historical collision models based on the field data; and acquiring historical evacuation suggestions corresponding to the similar historical collision model, and managing the target traffic accident scene based on the historical evacuation suggestions.
In one example, the managing the target traffic accident scene based on the history evacuation advice specifically includes: determining an evacuation action and an evacuation time of the history evacuation suggestion; acquiring historical field data corresponding to the similar historical collision model; determining a congestion information gap interval between the historical site data and the site data of the target traffic accident; correcting the evacuation actions and the evacuation time in the history evacuation suggestions based on the congestion information gap interval to obtain target evacuation suggestions; and managing the target traffic accident scene based on the target evacuation suggestion.
In one example, the managing the target traffic accident based on the three-dimensional collision model specifically includes: determining the accident type and the accident reason of the target traffic accident; modifying the three-dimensional collision model based on the accident type and the accident cause to obtain a safe driving visual model; storing the safe driving visual model and the three-dimensional collision model corresponding to the target traffic accident into a preset database; and assisting a target driver to perform driving simulation through the safe driving visual model and the three-dimensional collision model.
In one example, the traffic accident video includes at least one of an in-vehicle video and a surveillance video.
The application also provides traffic management equipment based on the virtual simulation technology, which comprises: at least one processor; and a memory communicatively coupled to the at least one processor; wherein the memory stores instructions executable by the at least one processor to enable the at least one processor to perform: acquiring a target traffic accident video, and determining each target vehicle corresponding to the target traffic accident based on the target traffic accident video; acquiring a preset vehicle model corresponding to each target vehicle, and determining vehicle operation data and road surface information of each target vehicle based on the target traffic accident video; generating a three-dimensional collision model corresponding to the target traffic accident based on the preset vehicle model, the vehicle operation data and the road surface information; and managing the target traffic accident based on the three-dimensional collision model, wherein the management mode comprises at least one of responsibility fixing and driving simulation.
The present application also provides a non-volatile computer storage medium storing computer executable instructions, characterized in that the computer executable instructions are configured to: acquiring a target traffic accident video, and determining each target vehicle corresponding to the target traffic accident based on the target traffic accident video; acquiring a preset vehicle model corresponding to each target vehicle, and determining vehicle operation data and road surface information of each target vehicle based on the target traffic accident video; generating a three-dimensional collision model corresponding to the target traffic accident based on the preset vehicle model, the vehicle operation data and the road surface information; and managing the target traffic accident based on the three-dimensional collision model, wherein the management mode comprises at least one of responsibility fixing and driving simulation.
The method provided by the application has the following beneficial effects: three-dimensional models of vehicles, pedestrians and road surfaces of different types are stored in advance, and a three-dimensional collision model of a target traffic accident is rapidly generated on the basis of a monitoring video through a virtual simulation technology, so that traffic accidents are conveniently managed, objections of both sides of the accident to the traffic accident responsibility determining result are reduced, and traffic management efficiency is improved through the three-dimensional collision model.
Drawings
The accompanying drawings, which are included to provide a further understanding of the application and are incorporated in and constitute a part of this application, illustrate embodiments of the application and together with the description serve to explain the application and do not constitute an undue limitation to the application. In the drawings:
fig. 1 is a schematic flow chart of a traffic management method based on a virtual simulation technology in an embodiment of the application;
fig. 2 is a schematic structural diagram of a traffic management device based on a virtual simulation technology in an embodiment of the present application.
Detailed Description
For the purposes, technical solutions and advantages of the present application, the technical solutions of the present application will be clearly and completely described below with reference to specific embodiments of the present application and corresponding drawings. It will be apparent that the described embodiments are only some, but not all, of the embodiments of the present application. All other embodiments, which can be made by one of ordinary skill in the art without undue burden from the present disclosure, are within the scope of the present disclosure.
The following describes in detail the technical solutions provided by the embodiments of the present application with reference to the accompanying drawings.
Fig. 1 is a schematic flow chart of a traffic management method based on virtual simulation technology according to one or more embodiments of the present disclosure. The method can be applied to traffic accident management of different types, such as rear-end collision scraping of vehicles, pedestrian collision, rollover accidents, major congestion accidents and the like. Some input parameters or intermediate results in the flow allow for manual intervention adjustments to help improve accuracy.
The implementation of the analysis method according to the embodiment of the present application may be a terminal device or a server, which is not particularly limited in this application. For ease of understanding and description, the following embodiments are described in detail with reference to a server.
It should be noted that the server may be a single device, or may be a system composed of a plurality of devices, that is, a distributed server, which is not specifically limited in this application.
As shown in fig. 1, an embodiment of the present application provides a traffic management method based on a virtual simulation technology, including:
s101: and acquiring a target traffic accident video, and determining each target vehicle corresponding to the target traffic accident based on the target traffic accident video.
Firstly, a target traffic accident video needs to be acquired, and in general, the traffic accident video may come from a monitoring camera, a vehicle recorder, intersection monitoring equipment, or a channel for shooting by a live witness or the like. These videos record the process and the scene situation of the accident, and are important basis for accident investigation. When acquiring the video, the integrity and the authenticity of the video need to be ensured, and the condition of omission or tampering is avoided, so that the accuracy and the fairness of investigation are ensured. After the target traffic accident video is acquired, each target vehicle associated with the target traffic accident may be determined. The target vehicle herein refers to a vehicle associated with a target traffic accident or having a larger influence. Taking a collision accident as an example, if the vehicle B suddenly stops due to improper lane changing operation of the vehicle a, and the vehicle B collides with the vehicle C at the tail, it is needless to say that the vehicle B and the vehicle C are target vehicles at this time, and the vehicle a is not an accident vehicle but still belongs to the target vehicles.
Specifically, when determining a target vehicle corresponding to a target traffic accident, it is necessary to determine an occurrence time interval of the target traffic accident based on the target traffic accident video. Still taking a collision accident as an example, the occurrence time interval refers to a time interval obtained by taking a period of time before and after the collision time point and performing splicing. And then taking all passing vehicles in the occurrence time interval and the preset range of the target traffic accident scene as initial target vehicles, and determining the target vehicles from the initial target vehicles. Determining the relative distance, the relative speed and the matched motion track of different initial target vehicles and accident vehicles respectively based on traffic accident videos; based on the relative distance, the relative speed and the matched motion trail, determining the correlation coefficients of different initial target vehicles and the accident vehicles; and finally, taking the initial target vehicles with the correlation coefficients higher than the preset threshold value as the target vehicles corresponding to the target traffic accidents. When determining the relative distance through the traffic accident video, the relative distance between the initial target vehicle and the accident vehicle of the video frame can be determined through frame extraction to the traffic accident video to obtain different video frames and the pixel scale of the preset monitoring equipment, and on the basis, the relative speed can be determined through the video duration and the relative distance change value corresponding to a plurality of video frames. The matched motion track refers to similar running tracks of an initial target vehicle and an accident vehicle at different time points, if the A vehicle and the B vehicle pass through the C road section, the C road section is the matched motion track of the A vehicle and the B vehicle, and when the size of the matched motion track is determined, the method is similar to the determination of the relative distance, and the determination can be carried out through traffic accident videos. The preset threshold of the correlation coefficient is set in advance by a worker, and can be modified according to actual conditions. For example, the preset threshold may be adjusted according to the remaining computing resources, when the remaining computing resources are smaller, the number of target vehicles in the three-dimensional collision model is required to be smaller at this time, and the preset threshold of the correlation number may be adjusted to be higher, so as to reduce the number of target vehicles, and further reduce the computing resource occupation when the three-dimensional collision model is generated.
Further, in determining the correlation coefficient, consideration needs to be given to both before occurrence of an accident and after occurrence of an accident. When the accident is a collision accident, the first influencing factors of different initial target vehicles and accident vehicles before the accident occurrence time can be determined by the following formula:
wherein,for the first influencing factor, +.>For the preset time point before accident occurrence, +.>For accident time, < > the->Expressed as the time period before the time of the accident, < > and>is any time point in a time end before the accident occurrence time; />Is->The length of the corresponding matching motion trail of the moment, < >>Is->Relative speed corresponding to time, < >>Is->Relative distance corresponding to time, < >>Is the first correction constant.
Determining a second influence factor of different initial target vehicles and accident vehicles after the accident occurrence time through the following formula;
wherein,for the second influencing factor, +.>For the preset time point after accident occurrence, +.>Expressed as the time period after the time of the accident, < > and>is any point in time within the time period after the time of the accident; />Is->The length of the corresponding matching motion trail of the moment, < >>Is->Relative speed corresponding to time, < >>Is->Relative distance corresponding to time, < >>Is the second correction constant.
In the above formula, the first correction constantSecond correction constant->The magnitude of (2) is set in advance by the staff, and in general, the first correction constant +.>Default value of 1, second correction constant +.>Is 0.3; since the magnitude of the first influence factor and the second influence factor is related to the number of target vehicles, when the number of target vehicles is large, the first correction constant can be reduced>Second correction constant->To reduce the number of target vehicles in the three-dimensional collision model, thereby reducing the amount of computation in generating the model.
The correlation coefficient of the initial target vehicle and the accident vehicle can then be determined based on the first influence factor and the second influence factor, and the larger the first influence factor and the second influence factor, the deeper the influence of the accident vehicle on the initial target vehicle is indicated, so that the sum or the product between the first influence factor and the second influence factor can be calculated as the correlation coefficient of the initial target vehicle and the accident vehicle when the correlation coefficient is determined.
S102: and acquiring a preset vehicle model corresponding to each target vehicle, and determining vehicle operation data and road surface information of each target vehicle based on the target traffic accident video.
After the target vehicles are determined, model information of the target vehicles can be obtained through the target traffic accident video, and then preset vehicle models of the target vehicles are obtained in a preset database. Meanwhile, the target traffic accident video is analyzed to determine vehicle operation data such as running speed, acceleration and the like and road surface information. The road surface information here should include, in addition to basic lane information, whether there is a depression in the road surface or a blurred marking.
S103: and generating a three-dimensional collision model corresponding to the target traffic accident based on the preset vehicle model, the vehicle running data and the road surface information.
After the data are acquired, a three-dimensional collision model corresponding to the target traffic accident can be quickly generated based on a preset vehicle model, the vehicle running data and the road surface information. It should be noted that, for road sections with frequent traffic accidents, a road section model may be generated in advance for storage, after the traffic accident occurs, the target traffic accident video is not required to be analyzed, and after the target vehicle is determined, the road section model and the road section model may be spliced based on a preset vehicle model, so as to generate the three-dimensional collision model more rapidly. When the three-dimensional collision model is generated, the preset vehicle model corresponding to the target vehicle can be displayed in the road section model according to the vehicle running data so as to simulate the running process of different target vehicles before and after the accident vehicle collides. It is to be noted that, the three-dimensional collision model generated by the virtual simulation technology belongs to the prior art, and is widely applied to the fields of digital city, real estate roaming, travel teaching and the like.
S104: and managing the target traffic accident based on the three-dimensional collision model, wherein the management mode comprises at least one of responsibility fixing and driving simulation.
After the three-dimensional collision model is generated, the target traffic accident can be managed through the three-dimensional collision model, wherein the management comprises management modes of responsibility determination by law enforcement personnel, driving simulation learning by drivers with less driving experience, accident evacuation by law enforcement personnel and the like.
Specifically, when auxiliary responsibility is determined through the three-dimensional collision model, the accident type of the target traffic accident needs to be determined based on the target traffic accident video, and a preset responsibility model is selected from a preset database based on the accident type, so that the accuracy of auxiliary responsibility determination is improved. And finally, auxiliary responsibility fixing can be carried out on each target vehicle in the three-dimensional collision model through the responsibility fixing model. The responsibility-defining model is a recognition model which is trained in advance and used for defining the responsibility of the three-dimensional collision model. During pre-training, traffic management staff is needed to assist, and responsibility of different three-dimensional collision models is determined so as to obtain training data. After the responsibility-defining model is trained through the training data, the responsibility-defining model can be started when the responsibility-defining accuracy is higher than a preset threshold value. When a preset responsibility model is selected, a responsibility-defining model under the corresponding accident type of the target traffic accident needs to be selected. Taking a rear-end collision accident as an example, the accident type is a vehicle collision accident, and a preset responsibility model under the vehicle collision accident type should be selected. The responsibility-defining model is an identification model for identifying the three-dimensional collision model to determine responsibility attribution, the model can be a neural network model, and the training data of the responsibility-defining model is different types of example three-dimensional models and corresponding responsibility-defining results.
When the auxiliary evacuation is performed through the three-dimensional collision model, field data of the target traffic accident needs to be acquired, wherein the field data at least comprises vehicle collision data, congestion information and road surface information, and then a similar historical collision model of the target traffic accident can be determined from a plurality of historical collision models based on the field data. And acquiring historical evacuation suggestions corresponding to the similar historical collision model, and managing the target traffic accident scene based on the historical evacuation suggestions. When the similar historical collision model is determined, the field data can be compared to select a historical collision model with highest similarity with the target traffic accident field data from a plurality of historical collision models as the similar historical collision model. The process of determining the similarity of data is the prior art and will not be described in detail herein.
During management, historical evacuation suggestions cannot be carried as required, and evacuation actions and evacuation time of the historical evacuation suggestions need to be determined; and acquiring historical field data corresponding to the similar historical collision model. And then determining a congestion information gap interval between the historical site data and the site data of the target traffic accident. The congestion information gap interval can be directly obtained by making a difference between historical site data and site data of a target traffic accident. And then correcting the evacuation actions and the evacuation time in the history evacuation suggestions based on the congestion information gap interval so as to obtain target evacuation suggestions. When correcting, the historical evacuation suggestion can be corrected through the congestion information gap interval, if the historical field data is lower than the field data of the target traffic accident, the evacuation action force and the evacuation time can be properly reduced, and in one example, the larger the congestion information gap interval is, the larger the correction amplitude of the historical evacuation suggestion is. And finally, the traffic manager can manage the target traffic accident scene based on the target evacuation suggestion, so that the management efficiency is improved.
When driving simulation teaching is performed through a three-dimensional collision model, determining the accident type and the accident cause of a target traffic accident; and then, based on the accident type and the accident cause, modifying the three-dimensional collision model to obtain a safe driving visual model. When the three-dimensional collision model is modified, traffic management personnel are required to indicate correct traffic driving rules, the driving mode of the accident vehicle is modified, and the accident vehicle is displayed in the same road section model based on the modified driving mode, so that the accident vehicle in the safe driving visual model cannot collide. The process of generating the safe driving visual model is similar to that of a three-dimensional collision model. And storing the safe driving visual model and the three-dimensional collision model corresponding to the target traffic accident into a preset database, and assisting the target driver in improving the safe driving experience through comparison of the safe driving visual model and the three-dimensional collision model.
As shown in fig. 2, the embodiment of the present application further provides a traffic management device based on a virtual simulation technology, including: at least one processor; and a memory communicatively coupled to the at least one processor; wherein the memory stores instructions executable by the at least one processor to enable the at least one processor to:
acquiring a target traffic accident video, and determining each target vehicle corresponding to the target traffic accident based on the target traffic accident video; acquiring a preset vehicle model corresponding to each target vehicle, and determining vehicle operation data and road surface information of each target vehicle based on the target traffic accident video; generating a three-dimensional collision model corresponding to the target traffic accident based on the preset vehicle model, the vehicle operation data and the road surface information; and managing the target traffic accident based on the three-dimensional collision model, wherein the management mode comprises at least one of responsibility fixing and driving simulation.
The embodiments also provide a non-volatile computer storage medium storing computer executable instructions configured to:
acquiring a target traffic accident video, and determining each target vehicle corresponding to the target traffic accident based on the target traffic accident video; acquiring a preset vehicle model corresponding to each target vehicle, and determining vehicle operation data and road surface information of each target vehicle based on the target traffic accident video; generating a three-dimensional collision model corresponding to the target traffic accident based on the preset vehicle model, the vehicle operation data and the road surface information; and managing the target traffic accident based on the three-dimensional collision model, wherein the management mode comprises at least one of responsibility fixing and driving simulation.
All embodiments in the application are described in a progressive manner, and identical and similar parts of all embodiments are mutually referred, so that each embodiment mainly describes differences from other embodiments. In particular, for the apparatus and medium embodiments, the description is relatively simple, as it is substantially similar to the method embodiments, with reference to the section of the method embodiments being relevant.
The devices and media provided in the embodiments of the present application are in one-to-one correspondence with the methods, so that the devices and media also have similar beneficial technical effects as the corresponding methods, and since the beneficial technical effects of the methods have been described in detail above, the beneficial technical effects of the devices and media are not described in detail herein.
It will be appreciated by those skilled in the art that embodiments of the present application may be provided as a method, system, or computer program product. Accordingly, the present application may take the form of an entirely hardware embodiment, an entirely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the present application may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, and the like) having computer-usable program code embodied therein.
The present application is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems) and computer program products according to embodiments of the application. It will be understood that each flow and/or block of the flowchart illustrations and/or block diagrams, and combinations of flows and/or blocks in the flowchart illustrations and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
In one typical configuration, a computing device includes one or more processors (CPUs), input/output interfaces, network interfaces, and memory.
The memory may include volatile memory in a computer-readable medium, random Access Memory (RAM) and/or nonvolatile memory, such as Read Only Memory (ROM) or flash memory (flash RAM). Memory is an example of computer-readable media.
Computer readable media, including both non-transitory and non-transitory, removable and non-removable media, may implement information storage by any method or technology. The information may be computer readable instructions, data structures, modules of a program, or other data. Examples of storage media for a computer include, but are not limited to, phase change memory (PRAM), static Random Access Memory (SRAM), dynamic Random Access Memory (DRAM), other types of Random Access Memory (RAM), read Only Memory (ROM), electrically Erasable Programmable Read Only Memory (EEPROM), flash memory or other memory technology, compact disc read only memory (CD-ROM), digital Versatile Discs (DVD) or other optical storage, magnetic cassettes, magnetic tape magnetic disk storage or other magnetic storage devices, or any other non-transmission medium, which can be used to store information that can be accessed by a computing device. Computer-readable media, as defined herein, does not include transitory computer-readable media (transmission media), such as modulated data signals and carrier waves.
It should also be noted that the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising one … …" does not exclude the presence of other like elements in a process, method, article or apparatus that comprises the element.
The foregoing is merely exemplary of the present application and is not intended to limit the present application. Various modifications and changes may be made to the present application by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc. which are within the spirit and principles of the present application are intended to be included within the scope of the claims of the present application.
Claims (10)
1. The traffic management method based on the virtual simulation technology is characterized by comprising the following steps of:
acquiring a target traffic accident video, and determining each target vehicle corresponding to the target traffic accident based on the target traffic accident video;
acquiring a preset vehicle model corresponding to each target vehicle, and determining vehicle operation data and road surface information of each target vehicle based on the target traffic accident video;
generating a three-dimensional collision model corresponding to the target traffic accident based on the preset vehicle model, the vehicle operation data and the road surface information;
and managing the target traffic accident based on the three-dimensional collision model, wherein the management mode comprises at least one of responsibility fixing and driving simulation.
2. The method according to claim 1, wherein the determining each target vehicle corresponding to the target traffic accident based on the target traffic accident video specifically comprises:
determining an occurrence time interval of the target traffic accident based on the target traffic accident video;
taking all passing vehicles in the occurrence time interval and the preset range of the target traffic accident scene as initial target vehicles;
determining the relative distance, the relative speed and the matched motion track of the initial target vehicle and the accident vehicle respectively based on the traffic accident video;
determining a correlation coefficient of the initial target vehicle and the accident vehicle based on the relative distance, the relative speed and the trajectory of the coordinated movement;
and taking the initial target vehicles with the correlation coefficients higher than a preset threshold value as the target vehicles corresponding to the target traffic accidents.
3. The method according to claim 2, wherein the determining the correlation coefficient of the initial target vehicle and the accident vehicle based on the relative distance, the relative speed and the trajectory of the cooperative motion specifically includes:
determining a first impact factor of different initial target vehicles and accident vehicles before the accident occurrence time by the following formula:
wherein,for the first influencing factor, +.>For the preset time point before accident occurrence, +.>Is the accident occurrence time，/>Expressed as the time period before the time of the accident, < > and>is any time point in a time end before the accident occurrence time; />Is->The length of the corresponding matching motion trail of the moment, < >>Is->Relative speed corresponding to time, < >>Is->Relative distance corresponding to time, < >>Is a first correction constant;
determining a second influence factor of different initial target vehicles and accident vehicles after the accident occurrence time through the following formula;
wherein,for the second influencing factor, +.>For the preset time point after accident occurrence, +.>Expressed as the time period after the time of the accident, < > and>is any point in time within the time period after the time of the accident; />Is->The length of the corresponding matching motion trail of the moment, < >>Is->Relative speed corresponding to time, < >>Is->Relative distance corresponding to time, < >>Is a second correction constant;
and determining a correlation coefficient of the initial target vehicle and the accident vehicle based on the first influence factor and the second influence factor.
4. The method according to claim 1, wherein the managing the target traffic accident based on the three-dimensional collision model specifically comprises:
determining the accident type of the target traffic accident based on the target traffic accident video;
selecting a preset responsibility model from a preset database based on the accident type;
and performing auxiliary responsibility fixing on each target vehicle in the three-dimensional collision model based on the responsibility fixing model.
5. The method of claim 1, wherein after the generating the three-dimensional collision model corresponding to the target traffic accident, the method further comprises:
acquiring field data of the target traffic accident, wherein the field data at least comprises vehicle collision data, congestion information and road surface information;
determining a similar historical collision model of the target traffic accident in a plurality of historical collision models based on the field data;
and acquiring historical evacuation suggestions corresponding to the similar historical collision model, and managing the target traffic accident scene based on the historical evacuation suggestions.
6. The method according to claim 5, wherein the managing the target traffic accident scene based on the historical evacuation advice specifically comprises:
determining an evacuation action and an evacuation time of the history evacuation suggestion;
acquiring historical field data corresponding to the similar historical collision model;
determining a congestion information gap interval between the historical site data and the site data of the target traffic accident;
correcting the evacuation actions and the evacuation time in the history evacuation suggestions based on the congestion information gap interval to obtain target evacuation suggestions;
and managing the target traffic accident scene based on the target evacuation suggestion.
7. The method according to claim 1, wherein the managing the target traffic accident based on the three-dimensional collision model specifically comprises:
determining the accident type and the accident reason of the target traffic accident;
modifying the three-dimensional collision model based on the accident type and the accident cause to obtain a safe driving visual model;
storing the safe driving visual model and the three-dimensional collision model corresponding to the target traffic accident into a preset database;
and assisting a target driver to perform driving simulation through the safe driving visual model and the three-dimensional collision model.
8. The method of claim 1, wherein the traffic accident video comprises at least one of an on-board video and a surveillance video.
9. A traffic management device based on virtual simulation technology, comprising:
at least one processor; and a memory communicatively coupled to the at least one processor; wherein,
the memory stores instructions executable by the at least one processor to enable the at least one processor to perform:
acquiring a target traffic accident video, and determining each target vehicle corresponding to the target traffic accident based on the target traffic accident video;
acquiring a preset vehicle model corresponding to each target vehicle, and determining vehicle operation data and road surface information of each target vehicle based on the target traffic accident video;
generating a three-dimensional collision model corresponding to the target traffic accident based on the preset vehicle model, the vehicle operation data and the road surface information;
and managing the target traffic accident based on the three-dimensional collision model, wherein the management mode comprises at least one of responsibility fixing and driving simulation.
10. A non-transitory computer storage medium storing computer-executable instructions, the computer-executable instructions configured to:
acquiring a target traffic accident video, and determining each target vehicle corresponding to the target traffic accident based on the target traffic accident video;
acquiring a preset vehicle model corresponding to each target vehicle, and determining vehicle operation data and road surface information of each target vehicle based on the target traffic accident video;
generating a three-dimensional collision model corresponding to the target traffic accident based on the preset vehicle model, the vehicle operation data and the road surface information;
and managing the target traffic accident based on the three-dimensional collision model, wherein the management mode comprises at least one of responsibility fixing and driving simulation.
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Citations (9)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN108154681A (en) * | 2016-12-06 | 2018-06-12 | 杭州海康威视数字技术股份有限公司 | Risk Forecast Method, the apparatus and system of traffic accident occurs |
| CN114550053A (en) * | 2022-02-24 | 2022-05-27 | 深圳壹账通科技服务有限公司 | Traffic accident responsibility determination method, device, computer equipment and storage medium |
| CN114898569A (en) * | 2022-07-15 | 2022-08-12 | 山东金宇信息科技集团有限公司 | Tunnel traffic emergency processing method and device |
| US20220340146A1 (en) * | 2021-04-26 | 2022-10-27 | Kookmin University Industry Academy Cooperation Foundation | Apparatus and method for calculating ratio of negligence based on 3d simulator |
| CN115376319A (en) * | 2022-08-22 | 2022-11-22 | 深圳市神州路路通网络科技有限公司 | Traffic accident intelligent processing method, device, equipment and storage medium |
| CN117033562A (en) * | 2023-10-09 | 2023-11-10 | 北京中机车辆司法鉴定中心 | Dangerous prediction method and system based on scene element matching |
| CN117152945A (en) * | 2022-05-24 | 2023-12-01 | 博泰车联网(武汉)有限公司 | Method and system for handling traffic accidents and storage medium |
| CN117216666A (en) * | 2023-09-28 | 2023-12-12 | 浙江大学嘉兴研究院 | Traffic accident prediction method and system based on big data of computer |
| CN117436821A (en) * | 2023-12-19 | 2024-01-23 | 深圳市智慧城市科技发展集团有限公司 | Method, device and storage medium for generating traffic accident diagnosis report |
-
2024
- 2024-02-28 CN CN202410217039.2A patent/CN117808437B/en active Active
Patent Citations (9)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN108154681A (en) * | 2016-12-06 | 2018-06-12 | 杭州海康威视数字技术股份有限公司 | Risk Forecast Method, the apparatus and system of traffic accident occurs |
| US20220340146A1 (en) * | 2021-04-26 | 2022-10-27 | Kookmin University Industry Academy Cooperation Foundation | Apparatus and method for calculating ratio of negligence based on 3d simulator |
| CN114550053A (en) * | 2022-02-24 | 2022-05-27 | 深圳壹账通科技服务有限公司 | Traffic accident responsibility determination method, device, computer equipment and storage medium |
| CN117152945A (en) * | 2022-05-24 | 2023-12-01 | 博泰车联网(武汉)有限公司 | Method and system for handling traffic accidents and storage medium |
| CN114898569A (en) * | 2022-07-15 | 2022-08-12 | 山东金宇信息科技集团有限公司 | Tunnel traffic emergency processing method and device |
| CN115376319A (en) * | 2022-08-22 | 2022-11-22 | 深圳市神州路路通网络科技有限公司 | Traffic accident intelligent processing method, device, equipment and storage medium |
| CN117216666A (en) * | 2023-09-28 | 2023-12-12 | 浙江大学嘉兴研究院 | Traffic accident prediction method and system based on big data of computer |
| CN117033562A (en) * | 2023-10-09 | 2023-11-10 | 北京中机车辆司法鉴定中心 | Dangerous prediction method and system based on scene element matching |
| CN117436821A (en) * | 2023-12-19 | 2024-01-23 | 深圳市智慧城市科技发展集团有限公司 | Method, device and storage medium for generating traffic accident diagnosis report |
Non-Patent Citations (2)
| Title |
|---|
| YUAN Q ET AL: "Investigating crash-related injuries between animal-related and motor vehicle in Rural China: Bayesian random parameter probit model considering endogenous variables", COGENT ENGINEERING, vol. 10, 5 June 2023 (2023-06-05), pages 1 - 9 * |
| 赵晨 等: "基于事故逆向再现的车辆追尾事故仿真分析研究", 中国安全生产科学技术, vol. 16, no. 03, 31 March 2020 (2020-03-31), pages 157 - 163 * |
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