CN116740660A - Vehicle data processing method and system based on AI technology - Google Patents
Vehicle data processing method and system based on AI technology Download PDFInfo
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Abstract
The application discloses a vehicle data processing method and system based on an AI technology, which belong to the technical field of traffic management, and comprise the steps of monitoring road surface conditions by a camera and acquiring road monitoring videos and road basic information; extracting images among each group of key frames to obtain a driving track and first driving information of the vehicle among each group of key frames; integrating the first driving information and the road basic information based on an AI technology to obtain first description data, and inserting the first description data into a corresponding key frame; analyzing the running tracks of different vehicles on the road at the same moment, if the running tracks coincide, indicating that the vehicles have accidents, otherwise, indicating that the vehicles normally run; analyzing second driving information of the coincident point based on the coincidence of the driving tracks, and integrating the second driving information and the first description data based on an AI technology to obtain second description data; and determining the position of the key frame according to the coincidence point of the running track, and overlaying the second description data to replace the first description data.
Description
Technical Field
The application relates to the technical field of traffic management, in particular to a vehicle data processing method and system based on an AI technology.
Background
The traffic accident responsibility identification refers to the behavior of the public security authority to identify the traffic accident responsibility of the accident according to the causal relationship between the illegal behaviors of the parties and the traffic accident and the role of the illegal behaviors in the traffic accident after the traffic accident reason is ascertained.
The current traffic accident treatment and responsibility determination mainly depend on the field investigation evidence collection and inquiry of the related management departments to assist in taking road monitoring videos, and the accident reconstruction is carried out by manual analysis and judgment, so that after the 'people-vehicles-roads (objects)' involved in the occurrence of traffic injury cases are comprehensively investigated, the process of the whole case is simulated and reconstructed, and a scientific basis is provided for distinguishing the accident responsibility of the accident-causing escape cases. When the traffic accident is manually analyzed and judged, the efficiency is lower, the treatment period is relatively longer, and traffic jam is easy to cause. And after the traffic accident happens, the damage of the vehicle needs to be evaluated, the obligation born by accident liability people is determined, the process is long, the vehicle cannot immediately withdraw from the accident site, and traffic jam is easy to cause.
Therefore, how to provide a vehicle data processing method, so that the accident responsibility party can be conveniently distinguished and the loss of compensation can be determined, is a technical problem to be solved by the person skilled in the art.
Disclosure of Invention
Therefore, the application provides a vehicle data processing method and system based on an AI technology, which are used for solving the problems that vehicles cannot be immediately evacuated from an accident scene and traffic jam is easy to cause because the responsibility of accident vehicles is unclear in the prior art.
In order to achieve the above object, the present application provides the following technical solutions:
according to a first aspect of the present application, there is provided a vehicle data processing method based on AI technology, comprising the steps of:
s1: the camera monitors road surface conditions and acquires road monitoring videos and road basic information;
s2: extracting images among each group of key frames based on the road monitoring video;
s3: obtaining a driving track and first driving information of the vehicle between each group of key frames based on the images between each group of key frames;
s4: integrating the first driving information and the road basic information based on an AI technology to obtain first description data, and inserting the first description data into a corresponding key frame;
s5: analyzing the running tracks of different vehicles on the road at the same moment, if the running tracks coincide, indicating that the vehicles have accidents, otherwise, indicating that the vehicles normally run;
s6: analyzing second driving information of the coincident point based on the coincidence of the driving tracks, and integrating the second driving information and the first description data based on an AI technology to obtain second description data;
s7: and determining the position of the key frame according to the coincidence point of the driving track, and overlaying the second description data to replace the first description data.
Further, the specific process of obtaining the driving track of the vehicle between each group of key frames based on the images between each group of key frames in S3 includes:
s301: acquiring a vehicle image between each group of key frames, and cutting the vehicle image;
s302: intercepting vehicle images of a start frame, an intermediate frame and an end frame in each group of key frames;
s303: and drawing a running track of the vehicle based on the vehicle images of the starting frame, the middle frame and the ending frame.
Further, the first travel information includes at least a speed of the vehicle at steady travel and a speed of the vehicle at lane change.
Further, the road basic information at least comprises road traffic light conditions and road congestion conditions.
Further, the second traveling information includes at least a distance between the crashed vehicles, vehicle crash information, and a vehicle crash position.
Further, S8: responsibility is assigned to the accident vehicle based on the second description data and the loss is evaluated.
Further, the specific process of evaluating the loss in S8 is as follows:
s801: determining a vehicle responsible party, a vehicle accident party, vehicle collision information and a vehicle collision position based on the second description data, and analyzing internal operation data of the accident vehicle;
s802: determining a vehicle damaged component type based on the internal operating data of the accident vehicle and a vehicle collision location;
s803: based on the type of the damaged part of the vehicle and the collision information of the vehicle, according to a maintenance evaluation formula, the irreversible loss of the vehicle accident side after maintenance is evaluated;
s804: and claiming compensation from the vehicle responsible party based on the irreversible loss after the maintenance of the vehicle accident party.
Further, the maintenance evaluation formula is:
；
wherein W is an irreversible loss value after maintenance of a vehicle accident side, K i The weight corresponding to the ith vehicle damaged part type of the vehicle accident side is that S is the collision damage area of the vehicle accident side, n represents the number of the vehicle damaged part types, the value range of i is an integer which is more than or equal to 1 and less than or equal to n, and P 1 Bearing pressure for collision of responsible party of vehicle, P 2 The collision of the accident side of the vehicle is stressed.
Further, the road monitoring video is provided with a plurality of key frames, and two adjacent key frames are in a group.
According to a second aspect of the present application, there is provided an AI-technology-based vehicle data processing system for implementing any one of the above-described AI-technology-based vehicle data processing methods, including:
the information acquisition unit is used for acquiring road monitoring video and road basic information monitored by the camera;
an information extraction unit for extracting images between each group of key frames;
the track generation unit is used for obtaining the driving track and the first driving information of the vehicle between each group of key frames based on the images between each group of key frames;
the first information processing unit is used for integrating the first driving information and the road basic information to obtain first description data and inserting the first description data into a corresponding key frame;
the track processing unit is used for analyzing the running tracks of different vehicles on the road at the same moment, if the running tracks coincide, the track processing unit indicates that the vehicles have accidents, otherwise, the track processing unit indicates that the vehicles normally run;
the second information processing unit is used for analyzing second driving information of the coincident point based on the coincidence of the driving tracks and integrating the second driving information and the first description data based on an AI technology to obtain second description data;
and the third information processing unit is used for determining the position of the key frame according to the coincident point of the running track and overlaying the second description data to replace the first description data.
The application has the following advantages:
the camera monitors road surface conditions and acquires road monitoring videos and road basic information. Based on the road monitoring video, images between each group of key frames are extracted. And obtaining the driving track and the first driving information of the vehicle between each group of key frames based on the images between each group of key frames. And integrating the first driving information and the road basic information to obtain first description data, and inserting the first description data into the corresponding key frame. And analyzing the running tracks of different vehicles on the road at the same moment, if the running tracks coincide, indicating that the vehicles have accidents, otherwise, indicating that the vehicles normally run. And analyzing second driving information of the coincident point based on the coincidence of the driving tracks, and integrating the second driving information with the first description data to obtain second description data. And determining the position of the key frame according to the coincidence point of the running track, and overlaying the second description data to replace the first description data.
According to the application, the description data is inserted into the key frames in the road monitoring video through monitoring the road surface condition, so that a processor can call out the description data by a method for calling out the key frames when calling out the monitoring video, and the processor can conveniently obtain evidence. The description data is divided into first description data, which is basic information of a vehicle traveling on a road surface, and second description data, which adds state information between vehicles at the time of occurrence of an accident on the basis of the first description data. According to the description of the second description data, the responsible party for the accident is determined, compensation which should be borne by the responsible party of the vehicle is evaluated, the processing speed is high, and traffic jam is not easy to cause.
Drawings
In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly described below. It will be apparent to those of ordinary skill in the art that the drawings in the following description are exemplary only and that other implementations can be obtained from the extensions of the drawings provided without inventive effort.
The structures, proportions, sizes, etc. shown in the present specification are shown only for the purposes of illustration and description, and are not intended to limit the scope of the application, which is defined by the claims, so that any structural modifications, changes in proportions, or adjustments of sizes, which do not affect the efficacy or the achievement of the present application, should fall within the ambit of the technical disclosure.
FIG. 1 is a flow chart of a vehicle data processing method based on AI technology provided by the application;
FIG. 2 is a flowchart showing a step S3 in the vehicle data processing method according to the present application;
FIG. 3 is a flowchart showing a step S8 in the vehicle data processing method according to the present application;
fig. 4 is a connection block diagram of a vehicle data processing system based on AI technology provided by the present application.
Detailed Description
Other advantages and advantages of the present application will become apparent to those skilled in the art from the following detailed description, which, by way of illustration, is to be read in connection with certain specific embodiments, but not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the application without making any inventive effort, are intended to be within the scope of the application.
According to a first aspect of the present application, as shown in fig. 1, there is provided a vehicle data processing method based on AI technology, including the steps of:
s1: the method comprises the steps that a camera monitors road surface conditions and acquires a road monitoring video and road basic information, wherein the road monitoring video is provided with a plurality of key frames, and two adjacent key frames are in a group;
s2: extracting images among each group of key frames based on the road monitoring video;
s3: obtaining a driving track and first driving information of the vehicle between each group of key frames based on the images between each group of key frames;
s4: integrating the first driving information and the road basic information based on an AI technology to obtain first description data, and inserting the first description data into a corresponding key frame;
s5: analyzing the running tracks of different vehicles on the road at the same moment, if the running tracks coincide, indicating that the vehicles have accidents, otherwise, indicating that the vehicles normally run;
s6: analyzing second driving information of the coincident point based on the coincidence of the driving tracks, and integrating the second driving information and the first description data based on an AI technology to obtain second description data;
s7: and determining the position of the key frame according to the coincidence point of the running track, and overlaying the second description data to replace the first description data.
The road monitoring video is provided with a plurality of key frames, two adjacent key frames are in a group, and a complete moving image is arranged between each group of key frames of the road monitoring video. And obtaining the driving track of the vehicle between each group of key frames by acquiring the complete image between each group of key frames. And the running tracks of all vehicles appearing on the video are stored in the memory in an overlapping way, so that whether the passing vehicles release collision accidents or not can be conveniently analyzed.
The first travel information includes at least a speed of the vehicle at steady travel and a speed of the vehicle at lane change. The road basic information at least comprises road traffic light conditions and road congestion conditions. And integrating the first driving information and the road basic information to obtain first description data. If the vehicle runs on a forestation road and is in peak period, road conditions are crowded, a plurality of vehicles run on a lane in parallel, the average speed of stable running is 60km/h, the speed of variable-road running is 50km/h, and the vehicle is braked when meeting a front red light. And inserting the first description data into the end frame of the corresponding key frame, so that the processing personnel can conveniently call and check the first description data.
As shown in fig. 2, step S3 is a specific process of obtaining a driving track of the vehicle between each group of key frames based on the images between each group of key frames, including:
s301: acquiring a vehicle image between each group of key frames, and cutting the vehicle image;
s302: intercepting vehicle images of a start frame, an intermediate frame and an end frame in each group of key frames;
s303: and drawing a running track of the vehicle based on the vehicle images of the start frame, the middle frame and the end frame.
And drawing the running tracks of all vehicles according to the vehicle images of the monitoring video. When the tracks are mutually overlapped, the collision of the vehicles is indicated, otherwise, the normal running of the vehicles is indicated. And acquiring second driving information of the coincident point according to the coincident point of the driving track, and integrating the second driving information and the first description data to obtain second description data. From the coincidence of the driving trajectories, the group position of the key frames can be determined, so that it can be determined on which key frame the second description data is inserted.
The second traveling information includes at least a distance between the crashed vehicles, vehicle crash information, and a vehicle crash position. And integrating the second driving information and the first description data to obtain second description data. If the vehicle runs on the forestation road, the vehicle is in peak period and the road condition is crowded, a plurality of vehicles are parallel on the lane, and the vehicle stably runs on the average speed of the first laneThe degree is 60km/h, the rear vehicle runs on the second lane at the average speed of 70km/h and changes lanes at the position 15m away from the vehicle, so that the two vehicles collide, the front and rear vehicles are sunken, and the sunken area is 50cm 2 The collision pressure was 60 pa. And replacing the first description data with the second description data, and inserting the second description data into an end frame of a corresponding key frame, so that a processor can conveniently call and check the second description data, and the operator can clearly know that the vehicle responsibility is the rear vehicle.
Further comprising step S8: responsibility is assigned to the accident vehicle based on the second description data and the loss is evaluated.
As shown in fig. 3, the specific procedure for evaluating the loss in step S8 is:
s801: determining a vehicle responsible party, a vehicle accident party, vehicle collision information and a vehicle collision position based on the second description data, and analyzing internal operation data of the accident vehicle;
s802: determining a vehicle damaged component type based on the internal operating data of the accident vehicle and the vehicle collision location;
s803: based on the type of the damaged part of the vehicle and the collision information of the vehicle, according to a maintenance evaluation formula, the irreversible loss of the vehicle accident side after maintenance is evaluated;
s804: and claiming compensation to the vehicle responsible party based on the irreversible loss after the maintenance of the vehicle accident party.
The maintenance evaluation formula is:
；
wherein W is an irreversible loss value after maintenance of a vehicle accident side, K i The weight corresponding to the ith vehicle damaged part type of the vehicle accident side is that S is the collision damage area of the vehicle accident side, n represents the number of the vehicle damaged part types, the value range of i is an integer which is more than or equal to 1 and less than or equal to n, and P 1 Bearing pressure for collision of responsible party of vehicle, P 2 The collision of the accident side of the vehicle is stressed.
And determining the type of the damaged part of the vehicle based on the internal operation data of the accident vehicle and the collision position of the vehicle, and intensively analyzing the internal operation data at the collision position of the vehicle to screen out the abnormal damaged part of the vehicle.
The accident side of the vehicle is the side with no responsibility or lighter responsibility. The damaged parts of different vehicles have different values, such as an airbag, a brake pad and the like, and some parts can be repaired, but the damaged parts cannot completely reach the state before an accident after the repair, and the damaged parts cause loss when the vehicle accident side performs secondary buying and selling. Therefore, it is necessary to evaluate the irreversible loss after the repair of the vehicle accident side and claim the vehicle responsible side based on the irreversible loss.
According to the description of the second description data, determining the responsible party for accident occurrence, and the irreversible loss of the repaired vehicle accident party, and simultaneously, combining the vehicle repair loss of the vehicle accident party, claim is carried out on the vehicle responsible party, so that the processing speed is high, and traffic jam is not easy to cause.
According to a second aspect of the present application, there is provided a vehicle data processing system based on AI technology for implementing a vehicle data processing method based on AI technology, as shown in fig. 4, including:
the information acquisition unit is used for acquiring road monitoring video and road basic information monitored by the camera;
an information extraction unit for extracting images between each group of key frames;
the track generation unit is used for obtaining the driving track and the first driving information of the vehicle between each group of key frames based on the images between each group of key frames;
the first information processing unit is used for integrating the first driving information and the road basic information to obtain first description data and inserting the first description data into the corresponding key frame;
the track processing unit is used for analyzing the running tracks of different vehicles on the road at the same moment, if the running tracks coincide, the track processing unit indicates that the vehicles have accidents, otherwise, the track processing unit indicates that the vehicles normally run;
the second information processing unit is used for analyzing second driving information of the coincident point based on the coincidence of the driving tracks, and integrating the second driving information and the first description data based on an AI technology to obtain second description data;
and the third information processing unit is used for determining the position of the key frame according to the coincidence point of the running track and overlaying the second description data to replace the first description data.
The camera monitors road conditions, and the information acquisition unit acquires road monitoring videos and road basic information. Based on the road monitoring video, the information extraction unit extracts images between each group of key frames. The track generation unit obtains a travel track of the vehicle between each group of key frames and first travel information based on the images between each group of key frames. The first information processing unit integrates the first driving information and the road basic information to obtain first description data, and the first description data is inserted into a corresponding key frame. The track processing unit analyzes the running tracks of different vehicles on the road at the same moment, if the running tracks coincide, the track processing unit indicates that the vehicles have accidents, otherwise, the track processing unit indicates that the vehicles normally run. And based on the superposition of the running tracks, the second information processing unit analyzes the second running information of the superposition point, and integrates the second running information and the first description data to obtain second description data. And the third information processing unit determines the position of the key frame according to the coincidence point of the running track, and overlays the second description data to replace the first description data.
While the application has been described in detail in the foregoing general description and specific examples, it will be apparent to those skilled in the art that modifications and improvements can be made thereto. Accordingly, such modifications or improvements may be made without departing from the spirit of the application and are intended to be within the scope of the application as claimed.
Claims (10)
1. A vehicle data processing method based on AI technology, comprising the steps of:
s1: the camera monitors road surface conditions and acquires road monitoring videos and road basic information;
s2: extracting images among each group of key frames based on the road monitoring video;
s3: obtaining a driving track and first driving information of the vehicle between each group of key frames based on the images between each group of key frames;
s4: integrating the first driving information and the road basic information based on an AI technology to obtain first description data, and inserting the first description data into a corresponding key frame;
s5: analyzing the running tracks of different vehicles on the road at the same moment, if the running tracks coincide, indicating that the vehicles have accidents, otherwise, indicating that the vehicles normally run;
s6: analyzing second driving information of the coincident point based on the coincidence of the driving tracks, and integrating the second driving information and the first description data based on an AI technology to obtain second description data;
s7: and determining the position of the key frame according to the coincidence point of the driving track, and overlaying the second description data to replace the first description data.
2. The AI-technology-based vehicle data processing method of claim 1, wherein the specific process of obtaining the travel track of the vehicle between each group of key frames based on the image between each group of key frames in S3 includes:
s301: acquiring a vehicle image between each group of key frames, and cutting the vehicle image;
s302: intercepting vehicle images of a start frame, an intermediate frame and an end frame in each group of key frames;
s303: and drawing a running track of the vehicle based on the vehicle images of the starting frame, the middle frame and the ending frame.
3. The AI-technology-based vehicle data processing method according to claim 1, wherein the first travel information includes at least a speed of the vehicle at steady running and a speed of the vehicle at lane change.
4. The AI-technology-based vehicle data processing method of claim 1, wherein the road base information includes at least a road traffic light condition and a road congestion condition.
5. The AI-technology-based vehicle data processing method of claim 1, wherein the second travel information includes at least a vehicle distance between colliding vehicles, vehicle collision information, and a vehicle collision position.
6. The AI-technology-based vehicle data processing method according to claim 1, characterized by further comprising S8: responsibility is assigned to the accident vehicle based on the second description data and the loss is evaluated.
7. The AI-technology-based vehicle data processing method according to claim 6, wherein the specific procedure of evaluating the loss in S8 is:
s801: determining a vehicle responsible party, a vehicle accident party, vehicle collision information and a vehicle collision position based on the second description data, and analyzing internal operation data of the accident vehicle;
s802: determining a vehicle damaged component type based on the internal operating data of the accident vehicle and a vehicle collision location;
s803: based on the type of the damaged part of the vehicle and the collision information of the vehicle, according to a maintenance evaluation formula, the irreversible loss of the vehicle accident side after maintenance is evaluated;
s804: and claiming compensation from the vehicle responsible party based on the irreversible loss after the maintenance of the vehicle accident party.
8. The AI-technology-based vehicle data processing method of claim 7, wherein the maintenance evaluation formula is:
；
wherein W is an irreversible loss value after maintenance of a vehicle accident side, K i The weight corresponding to the ith vehicle damaged part type of the vehicle accident side is that S is the collision damage area of the vehicle accident side, n represents the number of the vehicle damaged part types, and the value range of i is more than or equal to 1 and less thanIntegers equal to n, P 1 Bearing pressure for collision of responsible party of vehicle, P 2 The collision of the accident side of the vehicle is stressed.
9. The AI-technology-based vehicle data processing method of claim 1, wherein the road monitoring video has a plurality of key frames, and two adjacent key frames are a group.
10. An AI technology-based vehicle data processing system for implementing the AI technology-based vehicle data processing method according to any one of claims 1 to 9, characterized by comprising:
the information acquisition unit is used for acquiring road monitoring video and road basic information monitored by the camera;
an information extraction unit for extracting images between each group of key frames;
the track generation unit is used for obtaining the driving track and the first driving information of the vehicle between each group of key frames based on the images between each group of key frames;
the first information processing unit is used for integrating the first driving information and the road basic information to obtain first description data and inserting the first description data into a corresponding key frame;
the track processing unit is used for analyzing the running tracks of different vehicles on the road at the same moment, if the running tracks coincide, the track processing unit indicates that the vehicles have accidents, otherwise, the track processing unit indicates that the vehicles normally run;
the second information processing unit is used for analyzing second driving information of the coincident point based on the coincidence of the driving tracks and integrating the second driving information and the first description data based on an AI technology to obtain second description data;
and the third information processing unit is used for determining the position of the key frame according to the coincident point of the running track and overlaying the second description data to replace the first description data.
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| Publication number | Priority date | Publication date | Assignee | Title |
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