Traffic accident prediction and identification method and system, and medium

Abstract

The invention relates to the technical field of intelligent traffic, and discloses a traffic accident prediction identification method and system, and a medium, and the method comprises the steps: receiving an accident query request and a query word corresponding to the accident query request; determining a corresponding target area based on the accident query request, and obtaining roadside sensing data of the target area; determining a traffic participant identification result based on the roadside sensing data; and based on the traffic participant identification result and the query word, accident prediction text information is generated and sent by using a generative pre-training model. According to the scheme of the invention, the roadside sensing data is used as model input data, the roadside sensing data is used for reflecting the real traffic flow of the target area, a data basis is provided for subsequently ensuring the accuracy of prediction, the query word is used as the query keyword, and the accident prediction text information of the area related to the vehicle is calculated. Therefore, the data calculation amount is reduced, and the response time of traffic accident prediction is prolonged.

Description

Technical Field

[0001] The present invention relates to the field of intelligent transportation technology, and in particular to a traffic accident prediction and identification method, system, and medium. Background Art

[0002] With the acceleration of urbanization, vehicles have become an indispensable means of transportation. With the rapid increase in the number of vehicles, traffic accidents have also increased significantly. In the case of many vehicles and complex road conditions, the prediction of traffic accidents is of great significance. On the one hand, it can give early warning to car owners to avoid accidents, and on the other hand, it can help traffic management departments screen core areas of concern and improve labor efficiency.

[0003] In related technologies, in order to predict traffic accidents, traffic flow statistics are usually collected by using historical data or by relying on vehicle license plates and vehicle logos for vehicle identification, and on this basis, calculations are performed using the full amount of data. In this way, traffic statistics based on vehicle identification may not accurately reflect the actual situation of traffic flow due to occlusion or loss, thereby affecting the accuracy of traffic accident predictions. In addition, since the full amount of data needs to be calculated, the calculation of some data that is not related to the target vehicle or cannot affect the target vehicle causes a waste of system computing resources, further affecting the response time of traffic accident predictions related to the target vehicle. Summary of the invention

[0004] In view of this, the present invention provides a traffic accident prediction and identification method, system, and medium to solve the technical problems in the related art that the response time of traffic accident prediction is slow and the accuracy of the prediction cannot be guaranteed.

[0005] In the first aspect, the present invention provides a traffic accident prediction and identification method, which is applied to the server side, and the method includes: receiving an accident query request and a query word corresponding to the accident query request; determining the corresponding target area based on the accident query request, and obtaining roadside perception data of the target area; determining a traffic participant identification result based on the roadside perception data; based on the traffic participant identification result and the query word, using a generative pre-trained model, generating and sending accident prediction text information.

[0006] In combination with the first aspect, in a possible implementation method of the first aspect, the corresponding target area is determined based on the accident query request, and roadside perception data of the target area is obtained, including: based on the accident query request, determining the vehicle information corresponding to the accident query request; based on the vehicle information, determining the target area, and obtaining roadside perception data corresponding to the target area.

[0007] In combination with the first aspect, in a possible implementation of the first aspect, a target area is determined based on vehicle information, and roadside perception data corresponding to the target area is obtained, including: determining the target area corresponding to the vehicle based on location information in the vehicle information; and obtaining roadside perception data corresponding to the target area based on a correspondence between preset area blocks and the target area.

[0008] In combination with the first aspect, in a possible implementation of the first aspect, based on the traffic participant recognition results and query words, a generative pre-trained model is used to generate and send accident prediction text information, including: based on the traffic participant recognition results and roadside perception data, determining matching image data; based on the matching image data, using a scene description recognition sub-model to determine a natural language scene description; based on the scene description and query words, using a text recognition sub-model to generate accident prediction text information, and sending the accident prediction text information.

[0009] In the second aspect, the present invention provides a traffic accident prediction and identification method, which is applied to the vehicle side, and the method includes: in response to a trigger query word, sending an accident query request and a query word corresponding to the accident query request to the server side; receiving the accident prediction text information returned by the server side based on the accident query request and the corresponding query word; based on the accident prediction text information, generating early warning information.

[0010] In combination with the second aspect, in a possible implementation method of the second aspect, in response to a triggering query word, an accident query request and a query word corresponding to the accident query request are sent to the server side, including: in response to a periodically triggered preset query word, an accident query request and a preset query word corresponding to the accident query request are sent to the server side; or, in response to an input query word triggered by a user, an accident query request and an input query word corresponding to the accident query request are sent to the server side.

[0011] In combination with the second aspect, in a possible implementation method of the second aspect, before the step of sending an accident query request and a query word corresponding to the accident query request to the server side, it also includes: calculating the time interval between the preset query word triggered periodically for the last time and the input query word triggered by the user; in response to the time interval being less than the preset interval, based on the order of triggering the query words, selecting the first accident prediction text information corresponding to the preset query word as the accident prediction text information, or selecting the second accident prediction text information corresponding to the input query word as the accident prediction text information, and generating early warning information based on the accident prediction text information; in response to the time interval being greater than the preset interval, proceeding to the step of sending an accident query request and a query word corresponding to the accident query request to the server side.

[0012] In combination with the second aspect, in a possible implementation of the second aspect, warning information is generated based on the accident prediction text information, including: warning the vehicle owner based on the accident prediction text information; and / or reporting information based on the accident prediction text information using the location of the accident.

[0013] In the third aspect, the present invention provides a traffic accident prediction and identification system, comprising: a server side and a vehicle side, the server side and the vehicle side are communicatively connected, wherein the server side is used to execute the method for traffic accident prediction and identification of the above-mentioned first aspect or any corresponding embodiment thereof; the vehicle side is used to execute the method for traffic accident prediction and identification of the above-mentioned second aspect or any corresponding embodiment thereof.

[0014] In a fourth aspect, the present invention provides a computer-readable storage medium having computer instructions stored thereon, the computer instructions being used to enable a computer to execute the method for predicting and identifying traffic accidents according to the first aspect or any corresponding embodiment thereof, or to execute the method for predicting and identifying traffic accidents according to the second aspect or any corresponding embodiment thereof.

[0015] The technical solution of the present invention has the following advantages: The present invention provides a method, system, and medium for predicting and identifying traffic accidents. The method receives an accident query request and a query word, uses the accident query request to determine a target area related to a vehicle, determines the roadside sensing data corresponding to the target area, and uses an image detection network to determine the traffic participant identification result, thereby using a generative pre-trained model to generate and send accident prediction text information corresponding to the vehicle. In this process, by determining the target area corresponding to the vehicle, the roadside sensing data is used as the model input data, and the roadside sensing data is used to reflect the actual traffic flow in the target area, providing a data basis for subsequent guarantee of the accuracy of the prediction, and using the query word as the query keyword to clarify the type of query required, and by calculating the accident prediction text information of the target area corresponding to the vehicle, the calculation of the full amount of data is avoided, thereby reducing the amount of data calculation and improving the response time of traffic accident prediction. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] In order to more clearly illustrate the specific implementation methods of the present invention or the technical solutions in the prior art, the drawings required for use in the specific implementation methods or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are some implementation methods of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0017] Figure 1 is a flow chart of a traffic accident prediction and identification method provided according to an embodiment of the present invention; Figure 2 is a flow chart of another method for predicting and identifying traffic accidents provided according to an embodiment of the present invention; Figure 3 is a flow chart of another method for predicting and identifying traffic accidents provided according to an embodiment of the present invention; Figure 4 The figure is a schematic structural block diagram of a traffic accident prediction and recognition system provided according to an embodiment of the present invention. DETAILED DESCRIPTION

[0018] In order to make the purpose, technical solution and advantages of the embodiments of the present invention clearer, the technical solution in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative work are within the scope of protection of the present invention.

[0019] According to an embodiment of the present invention, an embodiment of a traffic accident prediction and identification method is provided. It should be noted that the steps shown in the flowchart of the accompanying drawings can be executed in a computer system such as a set of computer executable instructions, and although a logical order is shown in the flowchart, in some cases, the steps shown or described can be executed in an order different from that shown here.

[0020] This embodiment provides a traffic accident prediction and identification method, which is applied to a server side, such as Figure 1 As shown, the method comprises the following steps: S101: Receive an accident query request and a query word corresponding to the accident query request.

[0021] Specifically, the accident query request refers to the request information sent by each vehicle terminal, including vehicle information such as vehicle latitude and longitude, elevation, speed, acceleration, etc. The query term refers to the vocabulary used to clarify the type of query, such as speeding, traffic accidents, congestion, alternative routes, etc. The query term includes preset query terms and input query terms, wherein the preset query term refers to the vocabulary preset for periodic query at the vehicle terminal, and the input query term refers to the vocabulary for query in response to the user's input at the vehicle segment.

[0022] S102: Determine a corresponding target area based on the accident query request, and obtain roadside perception data of the target area.

[0023] Specifically, determining the corresponding target area based on the accident query request means determining the vehicle information corresponding to the accident query request through the accident query request, thereby utilizing the location information contained in the vehicle information, such as latitude and longitude, elevation and other information, to clarify the area where the relevant vehicle is located, thereby providing a data basis for the subsequent calculation of the accident prediction text information of the target area corresponding to the vehicle.

[0024] Specifically, roadside perception data is collected by high-definition cameras, lidar, millimeter-wave radar and other equipment installed on the side of the road, including timestamp, vehicle ID, vehicle coordinates, vehicle outline, instantaneous speed and other information. By acquiring the roadside perception data of the target area, the actual traffic flow in the target area can be reflected, avoiding data missing due to occlusion or loss, and providing a data basis for the subsequent calculation of accident prediction text information in the target area corresponding to the vehicle.

[0025] S103: Determine a traffic participant recognition result based on the roadside perception data, for example, by using an image detection network.

[0026] Specifically, determining the traffic participant identification result based on the roadside perception data means taking the roadside perception data as the input of the image detection network and outputting the traffic participant identification result, wherein the traffic participant identification result includes the detection frame corresponding to the traffic participant, the trajectory, the category of the traffic participant and other information, and the category of the traffic participant includes buses, cars, bicycles, pedestrians and the like. The image detection network includes an image processing module, a view converter module, a fusion module and a multi-head module, wherein the image processing module is used to perform image preprocessing on the input roadside perception data to unify the size, size and other information of the input roadside perception data; the view converter module is used to perform posture conversion on the image processed by the image processing module to meet the requirements of the fusion module; the fusion module is used for fusion BEV (Bird's Eye View) of multi-scene fusion perception; the multi-head module is used for target detection, BEV perception and trajectory prediction. Generally, the image detection network adopts the BEVerse class model, thereby generating accurate 3D detection frames, perception results and trajectory prediction results from the BEV perspective for traffic participants such as vehicles and pedestrians.

[0027] S104. Based on the traffic participant recognition results and the query words, the generative pre-training model is used to generate and send accident prediction text information.

[0028] Specifically, based on the traffic participant recognition results and query words, using the generative pre-trained model, generating and sending accident prediction text information means using the traffic participant recognition results to match the roadside perception data, so that the images collected by the roadside perception data contain the traffic participant recognition results, thereby determining the matching image data, using the matching image data and the query words as the input of the generative pre-trained model, outputting the accident prediction text information, and sending the accident prediction text information to the vehicle end corresponding to the accident query request. Among them, the generative pre-trained model includes a scene description recognition sub-model and a text recognition sub-model, the scene description recognition sub-model is used to output the scene description in natural language, and the text recognition sub-model is used to output the accident prediction text information.

[0029] The present invention provides a method, system and medium for predicting and identifying traffic accidents. The method receives an accident query request and a query word, uses the accident query request to determine a target area related to a vehicle, determines the roadside sensing data corresponding to the target area, uses an image detection network to determine the traffic participant identification result, and uses a generative pre-trained model to generate and send accident prediction text information corresponding to the vehicle. In this process, by determining the target area corresponding to the vehicle, the roadside sensing data is used as the model input data, and the roadside sensing data is used to reflect the real traffic flow of the target area, providing a data basis for subsequent guarantee of the accuracy of the prediction, and using the query word as the query keyword to clarify the type of query required, and by calculating the accident prediction text information of the target area corresponding to the vehicle, the calculation of the full amount of data is avoided, thereby reducing the amount of data calculation and improving the response time of traffic accident prediction.

[0030] In an optional implementation, determining a corresponding target area based on an accident query request and acquiring roadside perception data of the target area includes: Based on the accident query request, determine the vehicle information corresponding to the accident query request; based on the vehicle information, determine the target area, and obtain the roadside perception data corresponding to the target area.

[0031] Specifically, based on the accident query request, determining the vehicle information corresponding to the accident query request means determining the vehicle information corresponding to the accident query request through the identification information corresponding to the accident query request. For example, by encapsulating the vehicle end ID into the accident query request, the vehicle information corresponding to the accident query request is determined by using the accident query request carrying the vehicle end ID; or by assigning a corresponding request ID to the accident query request, the vehicle information corresponding to the accident query request is determined by the request ID.

[0032] Specifically, determining the target area based on vehicle information refers to determining the target area through location information contained in the vehicle information, wherein the location information is determined through vehicle information such as vehicle latitude and longitude, elevation, speed, acceleration, etc.

[0033] Specifically, obtaining the roadside perception data corresponding to the target area refers to obtaining the roadside perception data in the corresponding area through the correspondence between the preset area blocks and the target area.

[0034] In an optional implementation, determining a target area based on vehicle information and acquiring roadside perception data corresponding to the target area includes: Based on the position information in the vehicle information, the target area corresponding to the vehicle is determined; based on the correspondence between the preset area blocks and the target area, the roadside perception data corresponding to the target area is obtained.

[0035] Specifically, determining the target area corresponding to the vehicle based on the position information in the vehicle information means determining the target area corresponding to the vehicle through the position information in the vehicle information. For example, determining the area where the vehicle is located through the longitude, latitude and elevation in the vehicle information; or determining the area where the vehicle is located after the target time through the longitude, latitude, elevation, speed and acceleration in the vehicle information.

[0036] Specifically, determining the area where the vehicle is located through the latitude, longitude and elevation in the vehicle information means using the latitude, longitude and elevation reported by the vehicle end at the current moment to determine the area where the vehicle is located at the current moment.

[0037] Specifically, the target time includes a preset time or a calculated response time. The preset time is used to indicate the time selected or preset by the user, which may be ten minutes, twenty minutes, one hour or other time. The calculated response time refers to the time required to calculate the position of the vehicle using the longitude, latitude and elevation. Determining the area where the vehicle is located after the target time by using the longitude, latitude, elevation, speed and acceleration in the vehicle information means determining the position of the vehicle at the corresponding moment by using the speed and acceleration of the vehicle, by the preset time or the calculated response time, and determining the area where the vehicle is located at the corresponding moment by using the longitude, latitude and elevation of the corresponding position. Thus, the position of the vehicle after the preset time is determined, and based on this, a data basis is provided for obtaining the roadside perception data corresponding to the target area and determining the accident prediction text information corresponding to the roadside perception data. In addition, by determining the calculated response time, the calculation delay caused by insufficient computing resources can be avoided to cause the vehicle position to shift, thereby providing a data basis for subsequently ensuring the accuracy of the accident prediction text information.

[0038] Specifically, based on the correspondence between the preset regional blocks and the target area, obtaining the roadside perception data corresponding to the target area means dividing the map according to the blocks in advance, establishing the correspondence between the regional blocks and the target area, and thus determining the roadside perception data corresponding to the target area through the correspondence. Generally, obtaining the roadside perception data corresponding to the target area means obtaining the roadside perception data within a preset range of the target area, and the preset range can be set according to the actual working conditions, and this embodiment does not specifically limit this.

[0039] In an optional implementation, based on the traffic participant recognition results and the query words, a generative pre-trained model is used to generate and send accident prediction text information, including: Based on the traffic participant recognition results and roadside perception data, matching image data is determined; based on the matching image data, a scene description recognition sub-model is used to determine a natural language scene description; based on the scene description and query terms, a text recognition sub-model is used to generate accident prediction text information, and the accident prediction text information is sent.

[0040] Specifically, based on the traffic participant recognition results and roadside perception data, determining the matching image data refers to introducing the original roadside perception data of the target area, such as video data or picture data, matching the corresponding traffic participant recognition results with the roadside perception data to form matching image data.

[0041] Specifically, based on the matching image data, using the scene description recognition sub-model, determining the scene description in natural language means taking the matching image data as input and outputting the scene description in natural language. Among them, the scene description recognition sub-model usually adopts the GPT-4V model.

[0042] Specifically, based on the scene description and query words, using the text recognition sub-model, generating accident prediction text information, and sending the accident prediction text information means taking the natural language scene description and keywords as input, outputting the accident prediction text information, and sending the accident prediction text information to the vehicle end corresponding to the accident query request. Among them, the text recognition sub-model usually adopts the GPT model.

[0043] For example, a traffic accident is detected at two intersections ahead of the vehicle. The camera images and point cloud data in the roadside perception data are used to generate text descriptions through GPT-4V. The query word is the current traffic situation. After the two pass through GPT, the accident prediction text information is generated: a traffic accident has occurred ahead, and it is recommended to slow down and drive carefully. The user can further query the location of the accident by entering the query word: alternative route, and GPT can give a new recommended route based on the context.

[0044] For example, if it detects dangerous driving behaviors such as speeding or serpentine driving behind the vehicle, GPT-4V will generate a scene description, and the query term is the abnormal behavior of the surrounding vehicles. GPT will then generate accident prediction text information: There is a vehicle overtaking at high speed about 100 meters behind, please pay attention to vehicles coming from the side.

[0045] This embodiment provides a traffic accident prediction and identification method, which is applied to a vehicle side, such as Figure 2 As shown, the method comprises the following steps: S201. In response to a triggering query word, an accident query request and a query word corresponding to the accident query request are sent to a server.

[0046] Specifically, the query words include preset query words and input query words, wherein the preset query words refer to words preset for periodic query at the vehicle end, and the input query words refer to words queried in response to the user's input at the vehicle section. In response to the triggering query words, sending the accident query request and the query words corresponding to the accident query request means generating the corresponding accident query request when the triggering period of the preset query words is reached or when the user inputs the query words, and sending the generated accident query request and the corresponding query words to the server end.

[0047] S202: Receive accident prediction text information returned by the server based on the accident query request and the corresponding query words.

[0048] Specifically, receiving the accident prediction text information returned by the server based on the accident query request and the corresponding query word means that the vehicle receives the accident prediction text information sent by the server corresponding to the accident query request occurring at the vehicle.

[0049] S203: Generate warning information based on the accident prediction text information.

[0050] Specifically, generating warning information based on the accident prediction text information means issuing a warning to the vehicle owner based on the accident prediction text information or reporting a location warning corresponding to the accident location.

[0051] The present invention provides a traffic accident prediction and identification method, system, and medium. The method generates early warning information by triggering a query word, sending an accident query request and a query word, and receiving accident prediction text information corresponding to the accident query request. In this process, in response to the triggering query word, the accident query request and the query word are sent to the server side for calculation, thereby providing a data basis for the subsequent reception of the accident prediction text information generated by the server side. And the early warning information is generated by the accident prediction text information sent by the server side, which depends on the accuracy of the accident prediction text information, so as to warn the vehicle in advance to avoid the occurrence of accidents, improve driving safety, or report the location of the accident to facilitate relevant personnel to screen the core focus area and improve the efficiency of accident handling response.

[0052] In an optional implementation, in response to a triggering query word, sending an accident query request and a query word corresponding to the accident query request to a server side includes: In response to a periodically triggered preset query word, an accident query request and the preset query word corresponding to the accident query request are sent to the server; or, in response to an input query word triggered by a user, an accident query request and the input query word corresponding to the accident query request are sent to the server.

[0053] Specifically, the query term refers to a term used to clarify the type of query, such as speeding, traffic accident, congestion, alternative routes, etc. The trigger period of the preset query term can be set according to actual working conditions, and this embodiment does not specifically limit this.

[0054] In an optional implementation manner, before the step of sending the accident query request and the query words corresponding to the accident query request to the server, the method further includes: Calculate the time interval between the most recent periodically triggered preset query word and the input query word triggered by the user; in response to the time interval being less than the preset interval, based on the order of the triggered query words, select the first accident prediction text information corresponding to the preset query word as the accident prediction text information, or select the second accident prediction text information corresponding to the input query word as the accident prediction text information, and generate warning information based on the accident prediction text information; in response to the time interval being greater than the preset interval, proceed to the step of sending an accident query request and the query word corresponding to the accident query request to the server.

[0055] Specifically, calculating the time interval between the last periodically triggered preset query and the input query triggered by the user means calculating the time interval between the first triggering moment of the last periodically triggered preset query and the second triggering moment of the last user-triggered input query. For example, if the triggering moment of the current cycle of the preset query is 3:00 p.m. and the triggering moment of the input query is 3:10 p.m., the time interval between the two is ten minutes.

[0056] Specifically, in response to the time interval being less than the preset interval, using the order of the triggering query words, taking the first accident prediction text information corresponding to the preset query words as the accident prediction text information, or taking the second accident prediction text information corresponding to the input query words as the accident prediction text information means that if the time interval between the two triggers is less than the preset interval, the accident prediction text information corresponding to the first triggered query words is taken as the accident prediction text information corresponding to the later triggered query words. For example, if the triggering time of the preset query words in the current cycle is 3:00 pm, the triggering time of the input query words is 3:10 pm, and the preset interval is 15 minutes, the first accident prediction text information is taken as the accident prediction text information corresponding to the second accident prediction text information; similarly, if the triggering time corresponding to the first accident prediction text information is 3:10 pm, the triggering time corresponding to the first accident prediction text information is 3:00 pm, and the preset interval is 20 minutes, the second accident prediction text information is taken as the accident prediction text information corresponding to the first accident prediction text information. It should be understood that the preset interval can be set according to the actual working conditions, and this embodiment does not specifically limit this. Thus, by calculating the time interval between the query words, the waste of computing resources caused by frequent queries is avoided.

[0057] In an optional implementation, generating warning information based on accident prediction text information includes: Based on the accident prediction text information, the vehicle owner is warned; and / or, based on the accident prediction text information, the location of the accident is used to report the information.

[0058] Specifically, based on the accident prediction text information, warning the car owner means warning the car owner of a traffic accident through a vehicle-side screen display or voice broadcast.

[0059] Specifically, based on the accident prediction text information and using the location of the accident, reporting the information means reporting the location of the accident in the accident prediction text information to, for example, the traffic control department as early warning information.

[0060] This embodiment provides a traffic accident prediction and identification method. Figure 3 As shown, the method comprises the following steps: S301, sending an accident query request and query words.

[0061] Specifically, refer to the relevant description about step S201 in the above embodiment, which will not be repeated here.

[0062] S302: Acquire roadside perception data of the target area.

[0063] Specifically, refer to the relevant description about step S102 in the above embodiment, which will not be repeated here.

[0064] S303: Determine the traffic participant identification result.

[0065] Specifically, refer to the relevant description about step S103 in the above embodiment, which will not be repeated here.

[0066] S304: Generate accident prediction text information.

[0067] Specifically, refer to the relevant description about step S104 in the above embodiment, which will not be repeated here.

[0068] S305: Send accident prediction text information.

[0069] Specifically, refer to the relevant description about step S104 in the above embodiment, which will not be repeated here.

[0070] S306: Generate warning information.

[0071] Specifically, refer to the relevant description about step S203 in the above embodiment, which will not be repeated here.

[0072] In this embodiment, a traffic accident prediction and identification system is also provided, which is used to implement the above embodiments and preferred implementations, and the descriptions that have been made will not be repeated. Although the system described in the following embodiments is preferably implemented in software, the implementation in hardware, or a combination of software and hardware, is also possible and conceivable.

[0073] This embodiment provides a traffic accident prediction and recognition system. Figure 4 As shown, including: The server 10 is connected to the vehicle 20, and the server 10 is connected to the vehicle 20 for communication, wherein the server 10 is used to execute the method shown in the above embodiment. The specific process can be referred to the relevant description of step 301 in the above embodiment, which will not be repeated here.

[0074] The vehicle end 20 is used to execute the method shown in the above embodiment. The specific process can be found in the description of step S305 in the above embodiment, which will not be repeated here.

[0075] The traffic accident prediction and identification system in this embodiment is presented in the form of a functional unit, where the unit refers to an ASIC (Application Specific Integrated Circuit) circuit, a processor and memory that executes one or more software or fixed programs, and / or other devices that can provide the above functions.

[0076] The embodiment of the present invention also provides a computer-readable storage medium. The method according to the embodiment of the present invention can be implemented in hardware, firmware, or can be implemented as a computer code that can be recorded in a storage medium, or can be implemented as a computer code that is originally stored in a remote storage medium or a non-temporary machine-readable storage medium and will be stored in a local storage medium through a network download, so that the method described herein can be stored in such software processing on a storage medium using a general-purpose computer, a dedicated processor, or programmable or dedicated hardware. Among them, the storage medium can be a magnetic disk, an optical disk, a read-only storage memory, a random access memory, a flash memory, a hard disk or a solid-state hard disk, etc.; further, the storage medium can also include a combination of the above types of memories. It can be understood that a computer, a processor, a microprocessor controller, or programmable hardware includes a storage component that can store or receive software or computer code. When the software or computer code is accessed and executed by a computer, a processor, or hardware, the method shown in the above embodiment is implemented.

[0077] Although the embodiments of the present invention have been described in conjunction with the accompanying drawings, those skilled in the art may make various modifications and variations without departing from the spirit and scope of the present invention, and such modifications and variations are all within the scope defined by the appended claims.

Claims

1. A traffic accident prediction and identification method, characterized in that: Applied to the server side, the method includes: Receiving an accident query request and a query word corresponding to the accident query request; Determine a corresponding target area based on the accident query request, and obtain roadside perception data of the target area; Determining a traffic participant identification result based on the roadside sensing data; Based on the traffic participant recognition results and the query words, a generative pre-training model is used to generate and send accident prediction text information.

2. The method according to claim 1, characterized in that The determining a corresponding target area based on the accident query request and acquiring roadside perception data of the target area includes: Based on the accident query request, determining vehicle information corresponding to the accident query request; Based on the vehicle information, a target area is determined, and roadside perception data corresponding to the target area is acquired.

3. The method according to claim 2, characterized in that The determining a target area based on the vehicle information and acquiring roadside sensing data corresponding to the target area includes: Determining a target area corresponding to the vehicle based on the position information in the vehicle information; Based on the correspondence between the preset area blocks and the target area, roadside perception data corresponding to the target area is acquired.

4. The method according to claim 1, characterized in that The method of generating and sending accident prediction text information based on the traffic participant recognition result and the query word using a generative pre-training model includes: Determining matching image data based on the traffic participant recognition result and the roadside perception data; Based on the matching image data, using a scene description recognition sub-model, determining a scene description in natural language; Based on the scene description and the query word, the accident prediction text information is generated using a text recognition sub-model, and the accident prediction text information is sent.

5. A traffic accident prediction and identification method, characterized in that: Applied to the vehicle side, the method includes: In response to a triggering query word, sending an accident query request and a query word corresponding to the accident query request to a server; Receiving accident prediction text information returned by the server based on the accident query request and the corresponding query word; Based on the accident prediction text information, early warning information is generated.

6. The method according to claim 5, characterized in that The step of sending an accident query request and a query word corresponding to the accident query request to a server in response to a triggering query word includes: In response to the periodically triggered preset query words, sending an accident query request and the preset query words corresponding to the accident query request to the server; or, In response to an input query word triggered by a user, an accident query request and an input query word corresponding to the accident query request are sent to a server.

7. The method according to claim 6, characterized in that Before the step of sending the accident query request and the query words corresponding to the accident query request to the server, the method further includes: Calculate the time interval between the last periodically triggered preset query word and the input query word triggered by the user; In response to the time interval being less than a preset interval, based on the sequence of the triggering query words, selecting first accident prediction text information corresponding to the preset query words as the accident prediction text information, or selecting second accident prediction text information corresponding to the input query words as the accident prediction text information, and generating warning information based on the accident prediction text information; In response to the time interval being greater than the preset interval, the process proceeds to the step of sending an accident query request and a query word corresponding to the accident query request to a server.

8. The method according to claim 5, characterized in that The generating of warning information based on the accident prediction text information includes: Based on the accident prediction text information, a warning is given to the vehicle owner; and / or, Based on the accident prediction text information, the location of the accident is used to report the information.

9. A traffic accident prediction and recognition system, characterized in that: include: The server side and the vehicle side are connected in communication with each other, wherein: The server is used to execute the traffic accident prediction and identification method according to any one of claims 1 to 4; The vehicle end is used to execute the traffic accident prediction and identification method as described in any one of claims 5 to 8.

10. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores computer instructions, and the computer instructions are used to enable a computer to execute the traffic accident prediction and identification method according to any one of claims 1 to 4 or 5 to 8.

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