CN117436821B - Method, device and storage medium for generating traffic accident diagnosis report - Google Patents
Method, device and storage medium for generating traffic accident diagnosis report Download PDFInfo
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Abstract
The invention discloses a method, equipment and a storage medium for generating a traffic accident diagnosis report, and belongs to the technical field of traffic supervision. The method for generating the traffic accident diagnosis report comprises the following steps: acquiring space data and behavior data of an accident vehicle in a target period; obtaining a city/building information model corresponding to an accident scene and traffic regulation data associated with the city/building information model, and restoring a dynamic accident scene according to the city/building information model, the space data and the behavior data; and generating a traffic accident diagnosis report according to the traffic regulation data and the dynamic accident scene. By the method, the accident scene can be accurately restored, the accident diagnosis efficiency is improved, and meanwhile, the accident responsibility is clearly judged.
Description
Technical Field
The present invention relates to the field of traffic supervision, and in particular, to a method, an apparatus, and a storage medium for generating a traffic accident diagnosis report.
Background
At present, an accident scene is shot by means of an unmanned aerial vehicle in a complex road traffic accident identification mode, meanwhile, data such as scene topography, vehicle positions and the like are collected by means of sensors equipped by the unmanned aerial vehicle, and the traffic accident identification work is assisted by combining images and sensor data to analyze. However, the image and data acquisition mode of the unmanned aerial vehicle is easily interfered by external environmental factors, so that the accuracy of the finally acquired image and data is not high, and the accuracy of the accident scene diagnosis result is affected.
The foregoing is provided merely for the purpose of facilitating understanding of the technical solutions of the present invention and is not intended to represent an admission that the foregoing is prior art.
Disclosure of Invention
The invention mainly aims to provide a method, equipment and a storage medium for generating a traffic accident diagnosis report, and aims to solve the technical problems that the conventional diagnosis mode for complex road traffic accidents is easy to be interfered by external environment factors and the accuracy of accident diagnosis results is difficult to ensure.
In order to achieve the above object, the present invention provides a method for generating a traffic accident diagnosis report, the method comprising:
acquiring space data and behavior data of an accident vehicle in a target period;
obtaining a city/building information model corresponding to an accident scene and traffic regulation data associated with the city/building information model, and restoring a dynamic accident scene according to the city/building information model, the space data and the behavior data;
and generating a traffic accident diagnosis report according to the traffic regulation data and the dynamic accident scene.
Optionally, the step of acquiring the spatial data and the behavior data of the accident vehicle in the target period includes:
determining space data acquired by a positioning system of the accident vehicle in the target period and behavior data of a vehicle-mounted electronic control unit of the accident vehicle in the target period based on terminal access equipment;
and uploading the space data, the behavior data and the vehicle model corresponding to the accident vehicle to an information processing system.
Optionally, the step of restoring the dynamic accident scene according to the city/building information model, the spatial data and the behavior data includes:
determining road segment data of an accident location based on the matching of the location data in the spatial data with the city/building information model;
constructing a vehicle kinematics model based on the information processing system according to the space data, the road section data and the vehicle model so as to simulate the movement track of the accident vehicle;
according to the behavior data and the vehicle model, constructing a vehicle dynamics model based on the information processing system so as to simulate the stress condition of the accident vehicle in the collision process;
and coupling the vehicle kinematic model and the vehicle dynamic model to obtain a corresponding vehicle collision model so as to restore the dynamic accident scene.
Optionally, the step of generating a traffic accident diagnosis report according to the traffic regulation data and the dynamic accident scene includes:
determining road index data corresponding to an accident road section in the dynamic accident scene according to the traffic regulation data;
comparing the road index data with the vehicle running data in the dynamic accident scene to diagnose whether the accident vehicle has illegal behaviors or not;
and generating the traffic accident diagnosis report according to the comparison result.
Optionally, the road index data includes road segment speed limit data, the vehicle driving data includes displacement, accelerator operation data and tire rotation speed data, and the step of comparing the road index data with the vehicle driving data in the dynamic accident scene to diagnose whether the accident vehicle has an illegal action includes:
determining the running speed of the accident vehicle on the current road section according to the displacement, the accelerator operation data and the tire rotation speed data;
and when the running speed is greater than the road section speed limit data, judging that overspeed behavior exists in the accident vehicle, and intercepting specific overspeed moment of the accident vehicle based on the dynamic accident scene.
Optionally, the road index data further includes a solid line driving range, the vehicle driving data further includes vehicle displacement coordinates, and the step of comparing the road index data with the vehicle driving data in the dynamic accident scene to diagnose whether the accident vehicle has the illegal action further includes:
positioning the displacement track of the accident vehicle according to the vehicle displacement coordinates;
and when the displacement track exceeds the solid line driving range and is biased to another vehicle, determining that the accident vehicle has illegal lane change behavior.
Optionally, after the step of generating a traffic accident diagnosis report according to the traffic regulation data and the dynamic accident scene, the method further includes:
outputting the traffic accident diagnosis report and scene simulation video of the accident process;
confirming an accident responsible party based on the traffic accident diagnosis report and the scene simulation video;
and sending the traffic accident diagnosis report and the scene simulation video to accident related parties including the accident responsible party.
Optionally, after the step of generating a traffic accident diagnosis report according to the traffic regulation data and the dynamic accident scene, the method further includes:
acquiring an accident road section corresponding to the dynamic accident scene based on the city/building information model;
determining the to-be-broadcasted range and traffic condition information of the accident road section;
planning a corresponding detour path based on the traffic condition information;
and pushing the detour path and the accident information to the vehicles on the accident road section based on the range to be broadcasted.
In addition, in order to achieve the above object, the present invention also provides a traffic accident diagnosis report generating apparatus, including: the system comprises a memory, a processor and a traffic accident diagnosis report generation program stored on the memory and capable of running on the processor, wherein the traffic accident diagnosis report generation program is configured to realize the steps of the traffic accident diagnosis report generation method.
In addition, in order to achieve the above object, the present invention also provides a storage medium having stored thereon a generation program of a traffic accident diagnosis report, which when executed by a processor, implements the steps of the generation method of a traffic accident diagnosis report as described above.
The embodiment of the invention provides a method for generating a traffic accident diagnosis report, which comprises the steps of acquiring space data and behavior data of an accident vehicle in a target period, acquiring a city/building information model corresponding to an accident scene and traffic regulation data associated with the city/building information model, restoring a dynamic accident scene according to the city/building information model, the space data and the behavior data, and finally generating the traffic accident diagnosis report of the current accident vehicle according to the traffic regulation data and the dynamic accident scene. By the method, the accident scene can be accurately restored, the accident diagnosis efficiency is improved, and meanwhile, the accident responsibility is clearly judged.
Drawings
FIG. 1 is a flow chart of a first embodiment of a method for generating a traffic accident diagnosis report according to the present invention;
FIG. 2 is a flow chart of a second embodiment of a method for generating a traffic accident diagnosis report according to the present invention;
FIG. 3 is a schematic diagram of the overall execution flow of the method for generating traffic accident diagnosis report according to the present invention;
fig. 4 is a schematic diagram of a terminal structure of a hardware running environment according to an embodiment of the present invention.
The achievement of the objects, functional features and advantages of the present invention will be further described with reference to the accompanying drawings, in conjunction with the embodiments.
Detailed Description
It should be understood that the specific embodiments described herein are for purposes of illustration only and are not intended to limit the scope of the invention.
The embodiment of the invention provides a method for generating a traffic accident diagnosis report, and referring to fig. 1, fig. 1 is a schematic flow chart of a first embodiment of the method for generating a traffic accident diagnosis report.
In this embodiment, the method for generating a traffic accident diagnosis report includes:
and step S10, acquiring space data and behavior data of the accident vehicle in a target period.
In this embodiment, the target period refers to a period in which an accident vehicle has a near term of the accident, and the period includes three phases of driving of the accident vehicle, a pre-accident phase, an accident occurrence phase and an accident end phase, and the specific value of the target period may be set according to factors such as occurrence time of the accident, driving state of the vehicle and traffic condition of the road. The space data refers to geographical position data of the accident vehicle, at least comprises position data of a road section where the accident vehicle is located, traffic information of the road section, space data of the accident vehicle, displacement data of the accident vehicle and the like, and the behavior data refers to some operation data of the accident vehicle, including brake, steering, speed per hour, vehicle power data and the like. The invention requires that three stages of running of the accident vehicle are required to be covered for the acquisition range of the space data and the behavior data of the accident vehicle in the target period, and optionally, the space data and the behavior data of the accident vehicle in the period can be acquired by taking the accident occurrence time as a reference point, selecting the target period which is 30 minutes before the accident occurrence time and 30 minutes after the accident occurrence time. Therefore, even if a plurality of accident vehicles exist in the same traffic accident, the data acquisition range of the accident vehicles in the whole traffic accident can be covered by the same time point.
Further, accident vehicle space data and behavior data may be obtained through a terminal access device of the vehicle. The terminal access device of the vehicle is an electronic device for connecting the vehicle-mounted device and the mobile communication network, and generally comprises an embedded computer, a wireless module and a group of sensors, so that various functions of vehicle position tracking, data acquisition, voice communication, remote control and the like can be realized, space data of an accident vehicle in a target period can be acquired through a positioning system of the terminal access device, and particularly position data of a road section where the vehicle is located, coordinate data (such as represented by longitude and latitude) of the vehicle on the road section and traffic data (such as road conditions, other vehicles, pedestrians and the like on the road section) of the road section are acquired through a GPS (global positioning system) receiver. The acquisition mode of the accident vehicle behavior data can be obtained through the data of the vehicle-mounted electronic control unit acquired by each sensor in the terminal access equipment, and specifically comprises the steps of acquiring operation parameters such as braking, steering, displacement speed per hour, vehicle physical displacement data, vehicle type and vehicle power information in a target period of the accident vehicle, and acquiring real-time operation log data of the accident vehicle through access equipment such as a vehicle recorder and the like. In automobiles, various on-board Electronic Control Units (ECU) are often installed, such as an engine control unit, a brake control unit, a steering control unit, etc., which record operation logs of the vehicle, key parameters such as vehicle speed, acceleration, brake status, steering angle, etc., and fault codes and diagnostic information, which are typically stored in binary form and processed and compressed by equipment in real time. The collected behavior data and space data can be sent to the information processing system through Bluetooth, 5G and other communication modes. The information processing system is a digital processing system constructed based on a city/building information model. In addition, the data uploaded to the information processing system further comprises a vehicle model of the accident vehicle, the information processing system can process the collected data by using a big data analysis technology, the processed data can be displayed by a visualization tool, and the vehicle model is used for further analysis and research, so that the vehicle driving state can be conveniently simulated subsequently, and the potential risk is analyzed.
In the embodiment, the space data and the behavior data of the accident vehicle in the target period are collected, so that the accident vehicle is conveniently uploaded to the information processing system, and the driving data of the accident vehicle are dynamically simulated through the data.
And step S20, acquiring a city/building information model corresponding to the accident scene and traffic regulation data associated with the city/building information model, and restoring a dynamic accident scene according to the city/building information model, the space data and the behavior data.
In this embodiment, the spatial data and the behavior data of all accident vehicles collected previously are accessed to the data processing system, and the accident process of the current accident vehicle in the digital environment can be dynamically simulated based on the environmental data of the city/building information model corresponding to the accident scene. The city/building information model comprises a city information model (City Information Modeling, CIM) and a building information model (Building Information Modeling, BIM), wherein the CIM is an organic complex for building a three-dimensional city space model and city information by taking city information data as a base, and the BIM is a virtual model containing buildings and related information, and integrates the geometric shape, spatial relationship, construction, materials, equipment and other data of the buildings. The method comprises the steps of constructing a digital system through the two virtual models, accessing space data and behavior data of an accident vehicle into an environment digital system constructed based on BIM/CIM, constructing accident roads and surrounding environment information of roads in a one-to-one mode through the BIM/CIM, digitally representing static building data of an actual scene to construct a static model of an urban road, wherein the static road model comprises static road models such as highway traffic lights, highway signs, highway barriers, highway indication lines, greening buildings and the like, and the system further comprises a conversion 1:1 physical length, width and height characteristic data. Therefore, the data simulation of the accident vehicle is carried out by combining the dynamic accident vehicle data with the static urban building road model. In addition, the system can also store traffic regulation data associated with the city/building information model in an associated mode, when an accident is identified on the simulated traffic accident, the constraint behavior of the traffic regulation data on the road and the vehicle is converted into digital data, for example, the speed limit guideboard of the speed limit road section can be input into the corresponding road section of the city road model, and the running speed of the vehicle when the vehicle passes through the road section can be analyzed through the simulated accident process through the space data and the behavior data of the accident vehicle uploaded into the system, so that whether the accident vehicle overspeed is judged.
Further, in the process of restoring the dynamic accident scene, besides the behavior data and the space data of the accident vehicle, the vehicle model of the accident vehicle is required to be connected into the system, the vehicle model can gradually draw the outline line of the vehicle by using a basic geometrical body in modeling software or by the operation of a dotted line surface based on the picture, the design drawing, the specification parameters and the like of the accident vehicle, and the outline line is adjusted and corrected according to the curved surface characteristics of the vehicle until the actual vehicle body appearance is obtained. In addition, according to the detailed information in the reference materials, the detailed parts of the vehicle, such as lamps, windows, wheels, windscreen wipers and the like, can be added, and the vehicle model is proportionally adjusted so as to ensure the accuracy and the sense of reality of the vehicle model.
Referring to fig. 3, a vehicle model of an accident vehicle, and its own behavior space data and behavior data, are matched with a city information model and a building information model of an accident section to determine a specific location and time of an accident. Optionally, in a possible implementation manner, the specific position of the accident is determined by matching the position information in the spatial data of the accident vehicle with the road network data in the city information model. According to the behavior data of the accident vehicle, including speed, acceleration, braking, steering and the like, combining the information of gradient, curvature and the like of an accident road section, establishing a vehicle kinematic model, simulating the motion trail of the vehicle, determining the time information when the accident happens by utilizing the video data of an accident vehicle driving recorder or the accident road section video monitoring, matching the time information with facilities such as traffic lights in an urban information model, establishing a vehicle kinematic model, and simulating the stress condition of the vehicle in the collision process. The vehicle kinematic model and the vehicle dynamics model are coupled to obtain a complete vehicle collision model, and the accident occurrence process is simulated, wherein the accident occurrence process comprises the motion trail, collision force and the like of the vehicle. Through the steps, the dynamic accident process can be restored, the subsequent combination of the dynamic accident process is facilitated, the accident diagnosis is carried out, and a corresponding accident diagnosis report is provided.
In the embodiment, by combining dynamic vehicle data with a static city building model, accident vehicle information data simulation is performed, an accident model is restored, the accident model is converted into a digital model, detailed data of the beginning and the end of the accident and the inside of the accident can be recorded, the follow-up analysis of converting the behavior information of the vehicle in the accident into a data model of whether or not is facilitated, the process of actual survey data of a real scene is omitted, the judging efficiency of traffic accidents is greatly improved, and the misjudging rate of the traffic accidents is also reduced.
And step S30, generating a traffic accident diagnosis report according to the traffic regulation data and the dynamic accident scene.
In the present market embodiment, the real scene of the accident has been simulated by the data model and the digital scene restoration, including the accident picture from the occurrence of the accident to the complete stop of the vehicle, and related accident data information is also derived. Referring to fig. 3, the accident responsibility party can be given and a corresponding traffic accident diagnosis report can be given by intelligently judging the accident data information in combination with traffic regulations, and the data information can be archived and reserved as the evidence of subsequent disputes. In the accident determination process, the constraint behaviors of traffic regulation data on roads and vehicles are converted into digital data, the digital data comprise road index data corresponding to each road section, and the vehicle running data in the restored dynamic accident scene are compared with the road index data of the traffic regulation, so that the identification of the accident vehicle illegal behaviors is realized. To facilitate an understanding of the present solution, the decision logic and process are described below by way of several examples:
example one, solid line lane change scene determination: in this scenario, the calculation is performed mainly by combining the vehicle displacement coordinates in the vehicle running data and the solid line area range in the road index data, with the solid line position of the road information in the city/building information model. The displacement track of the accident vehicle can be positioned through the displacement coordinates of the vehicle, and when the displacement track is displayed to exceed a delimited solid line driving area and the displacement difference value is changed to display that the displacement track is gradually deviated to drive into another vehicle, the condition that the vehicle is illegal and changed in track is judged. If traffic accidents are caused and other vehicles have no abnormal problems, the vehicle is judged to be caused to occur due to illegal lane change, and the vehicle owner takes on main responsibility.
Example two, overspeed determination: in combination with the throttle operation data and displacement of the accident vehicle, the tire rotation speed log data can determine the real-time running speed of the accident vehicle in the road section. And acquiring speed limit sign data of the current road section based on road index data associated with the city/building information model, judging that an overspeed condition exists in the accident vehicle if the running speed of the accident vehicle in the road section exceeds the speed limit data corresponding to the sign, and intercepting specific overspeed moment in detail based on the previously restored accident dynamic scene.
Example three, not following traffic signs: and acquiring a corresponding rule of the accident road section through the urban building model by combining the displacement data and the steering data of the accident vehicle, if the running data of the running vehicle does not accord with the rule data of the road section, judging that the vehicle violates the traffic regulations, and giving out the violations of the traffic regulations according to specific conditions.
Example four, other intelligent decision logic: and converting the traffic rules into urban road model index data, and comparing the urban road model index data with the vehicle simulation data, and judging that the traffic rules are corresponding non-conforming rule items if the traffic rules are not conforming to the vehicle simulation data.
The above examples are merely illustrative for ease of understanding the decision logic, and in the actual decision process, a fusion decision of multiple violations may be made on the accident vehicle, and all violations that exist on the accident vehicle during the accident are output. And for the situation that a plurality of illegal behaviors exist in a plurality of accident vehicles, the main responsibility, the equivalent responsibility and the secondary responsibility can be respectively admitted by combining the action and the severity of the illegal behaviors on the occurrence of the accidents. The scoring mechanism can be set in the system in advance, corresponding weight values are set for various illegal behaviors, corresponding basic scores are given according to the sequence of the illegal behaviors related to the accident vehicles in the accident, if the accident vehicle with the illegal behaviors first gives out the basic score with the highest value, the basic score of other illegal behaviors is lower than the basic score with the highest value, the weight values of all illegal behaviors are combined for accumulated calculation, the score of each accident vehicle is obtained, the vehicle with the highest score is judged to be the main responsibility, the party with the lower score is the secondary responsibility, and the two parties with the same score are equally responsible.
After the judgment result is obtained, a corresponding accident diagnosis report is provided, data can be sent to mobile phone software of an accident related party so as to play back and confirm the accident responsibility party, the accident related party can also comprise law enforcement personnel such as traffic police and the like besides the accident responsibility party, the accident related party can clearly see an accident scene 3D model based on BIM/CIM simulation, and the accident related party can check the driving state of any vehicle before and after the accident, and can basically confirm the related responsibility party based on the driving state, so that subjective factor disputes are reduced.
In the embodiment, intelligent diagnosis of traffic accidents is performed and corresponding diagnosis reports are provided through the dynamic accident process based on simulation, and behavior analysis in the accidents is converted into data model analysis after the accidents, so that the diagnosis efficiency of the accidents is greatly improved, and meanwhile, the subjective factor disputes are reduced, and the accident misjudgment rate is also reduced. In addition, through the steps, a large amount of accident derivative data exists in the simulated accident process, and the scene of effectively improving the safety of automobile products can be realized by collecting the derivative data.
Further, referring to fig. 2, in a second embodiment of the method for generating a traffic accident diagnosis report according to the present invention, step S30 further includes the following steps:
and step S40, acquiring an accident road section corresponding to the dynamic accident scene based on the city/building information model.
And S50, determining the range to be broadcasted and the traffic condition information of the accident road section.
And step S60, planning a corresponding detour path based on the traffic condition information.
And step S70, pushing the detour path and the accident information to the vehicles on the accident road section based on the range to be broadcasted.
In this embodiment, road accident information may also be broadcast to vehicles passing through the road based on the BIM/CIM for the road section where the accident occurs. And the data analysis is used for occupying the track range, so that an optimal bypass mode is provided, and early warning is performed in advance to avoid congestion. Referring to fig. 3, alternatively, in a possible embodiment, a location of a road accident, that is, a corresponding accident section, is determined according to a BIM/CIM model, a vehicle driving track adjacent to the location is extracted, and a vehicle to be broadcasted is limited to a vehicle within a fixed distance near the accident site by using vehicle track data and a vehicle identification identifier (such as a license plate), so that warning information including the accident site, traffic control conditions, road congestion degree, etc. can be conveniently transmitted to vehicles meeting the broadcasting conditions through a vehicle-mounted communication system. For vehicles that fail to receive the alert information, the broadcast may be performed again by other means, such as television broadcast, cell phone application software, etc. It should be noted that in practical application, the BIM/CIM technology needs to integrate and cooperate with other data sources and communication devices, and when carrying out road accident broadcasting work based on BIM/CIM, the problems of multiple aspects such as data extraction, communication protocols, hardware devices and the like need to be considered, and factors such as communication efficiency, information security and the like are comprehensively considered so as to realize efficient, accurate and reliable broadcasting service.
Further, in addition to broadcasting accident information to the accident road section, reasonable detour information can be recommended to vehicles on the accident road section, road traffic condition information can be obtained in real time by using a traffic monitoring system, GPS data and the like, the traffic condition around the accident road section is determined by analyzing road congestion and traffic flow, and a detour path is calculated by adopting a path planning algorithm such as Dijkstra algorithm, A-type algorithm, ant colony algorithm and the like according to the connection relation between the position of the accident road section and a surrounding road network. In addition, a traffic model can be constructed based on historical traffic data and machine learning technology, and traffic conditions including congestion degree, traffic flow and the like in a future period can be predicted through the model so as to plan a detour path in advance. In other feasible implementation modes, the method can be combined with various data sources such as traffic real-time information, mobile network data, social media data and the like to perform data fusion analysis, and provides more accurate and comprehensive detour suggestions for vehicles by comprehensively considering various data information. In addition to traffic conditions, other factors such as road conditions, road capacity, traffic lights and the like should be considered, and the detour path should avoid other congestion points and bottleneck sections as much as possible, so as to provide the fastest and safest detour scheme.
In this embodiment, by combining with the CIM/BIM system, the traffic information of the accident road is broadcast to other vehicles on the accident road section, and a reasonable detour path is recommended for the other vehicles, so that the other vehicles can be helped to avoid the accident road section, and the best detour path is selected, thereby improving traffic efficiency and driving safety.
Referring to fig. 4, fig. 4 is a schematic structural diagram of a device for generating a traffic accident diagnosis report of a hardware operation environment according to an embodiment of the present invention.
As shown in fig. 4, the traffic accident diagnosis report generating apparatus may include: a processor 1001, such as a central processing unit (Central Processing Unit, CPU), a communication bus 1002, a user interface 1003, a network interface 1004, a memory 1005. Wherein the communication bus 1002 is used to enable connected communication between these components. The user interface 1003 may include a Display, an input unit such as a Keyboard (Keyboard), and the optional user interface 1003 may further include a standard wired interface, a wireless interface. The network interface 1004 may optionally include a standard wired interface, a WIreless interface (e.g., a WIreless-FIdelity (WI-FI) interface). The Memory 1005 may be a high-speed random access Memory (Random Access Memory, RAM) Memory or a stable nonvolatile Memory (NVM), such as a disk Memory. The memory 1005 may also optionally be a storage device separate from the processor 1001 described above.
It will be appreciated by those skilled in the art that the structure shown in fig. 4 does not constitute a limitation of the traffic accident diagnosis report generating apparatus, and may include more or less components than those illustrated, or may combine certain components, or may be a different arrangement of components.
As shown in fig. 4, an operating system, a data storage module, a network communication module, a user interface module, and a generation program of a traffic accident diagnosis report may be included in the memory 1005 as one type of storage medium.
In the traffic accident diagnosis report generating apparatus shown in fig. 4, the network interface 1004 is mainly used for data communication with other apparatuses; the user interface 1003 is mainly used for data interaction with a user; the processor 1001 and the memory 1005 in the traffic accident diagnosis report generating apparatus of the present invention may be provided in the traffic accident diagnosis report generating apparatus, which invokes a traffic accident diagnosis report generating program stored in the memory 1005 through the processor 1001 and performs the steps of:
acquiring space data and behavior data of an accident vehicle in a target period;
obtaining a city/building information model corresponding to an accident scene and traffic regulation data associated with the city/building information model, and restoring a dynamic accident scene according to the city/building information model, the space data and the behavior data;
and generating a traffic accident diagnosis report according to the traffic regulation data and the dynamic accident scene.
Further, the traffic accident diagnosis report generating apparatus calls a traffic accident diagnosis report generating program stored in the memory 1005 through the processor 1001, and performs the following steps:
determining space data acquired by a positioning system of the accident vehicle in the target period and behavior data of a vehicle-mounted electronic control unit of the accident vehicle in the target period based on terminal access equipment;
and uploading the space data, the behavior data and the vehicle model corresponding to the accident vehicle to an information processing system.
Further, the traffic accident diagnosis report generating apparatus calls a traffic accident diagnosis report generating program stored in the memory 1005 through the processor 1001, and performs the following steps:
determining road segment data of an accident location based on the matching of the location data in the spatial data with the city/building information model;
constructing a vehicle kinematics model based on the information processing system according to the space data, the road section data and the vehicle model so as to simulate the movement track of the accident vehicle;
according to the behavior data and the vehicle model, constructing a vehicle dynamics model based on the information processing system so as to simulate the stress condition of the accident vehicle in the collision process;
and coupling the vehicle kinematic model and the vehicle dynamic model to obtain a corresponding vehicle collision model so as to restore the dynamic accident scene.
Further, the traffic accident diagnosis report generating apparatus calls a traffic accident diagnosis report generating program stored in the memory 1005 through the processor 1001, and performs the following steps:
determining road index data corresponding to an accident road section in the dynamic accident scene according to the traffic regulation data;
comparing the road index data with the vehicle running data in the dynamic accident scene to diagnose whether the accident vehicle has illegal behaviors or not;
and generating the traffic accident diagnosis report according to the comparison result.
Further, the traffic accident diagnosis report generating apparatus calls a traffic accident diagnosis report generating program stored in the memory 1005 through the processor 1001, and performs the following steps:
determining the running speed of the accident vehicle on the current road section according to the displacement, the accelerator operation data and the tire rotation speed data;
and when the running speed is greater than the road section speed limit data, judging that overspeed behavior exists in the accident vehicle, and intercepting specific overspeed moment of the accident vehicle based on the dynamic accident scene.
Further, the traffic accident diagnosis report generating apparatus calls a traffic accident diagnosis report generating program stored in the memory 1005 through the processor 1001, and performs the following steps:
positioning the displacement track of the accident vehicle according to the vehicle displacement coordinates;
and when the displacement track exceeds the solid line driving range and is biased to another vehicle, determining that the accident vehicle has illegal lane change behavior.
Further, the traffic accident diagnosis report generating apparatus calls a traffic accident diagnosis report generating program stored in the memory 1005 through the processor 1001, and performs the following steps:
outputting the traffic accident diagnosis report and scene simulation video of the accident process;
confirming an accident responsible party based on the traffic accident diagnosis report and the scene simulation video;
and sending the traffic accident diagnosis report and the scene simulation video to accident related parties including the accident responsible party.
Further, the traffic accident diagnosis report generating apparatus calls a traffic accident diagnosis report generating program stored in the memory 1005 through the processor 1001, and performs the following steps:
acquiring an accident road section corresponding to the dynamic accident scene based on the city/building information model;
determining the to-be-broadcasted range and traffic condition information of the accident road section;
planning a corresponding detour path based on the traffic condition information;
and pushing the detour path and the accident information to the vehicles on the accident road section based on the range to be broadcasted.
It should be noted that, in this document, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or system 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 system. 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 system that comprises the element.
The foregoing embodiment numbers of the present invention are merely for the purpose of description, and do not represent the advantages or disadvantages of the embodiments.
From the above description of embodiments, it will be clear to a person skilled in the art that the above embodiment method may be implemented by means of software plus a necessary general hardware platform, but may of course also be implemented by means of hardware, but in many cases the former is a preferred embodiment. Based on this understanding, the technical solution of the present invention may be embodied essentially or in a part contributing to the prior art in the form of a software product stored in a storage medium (e.g. ROM/RAM, magnetic disk, optical disk) as described above, comprising instructions for causing a terminal device (which may be a mobile phone, a computer, etc.) to perform the method according to the embodiments of the present invention.
The foregoing description is only of the preferred embodiments of the present invention, and is not intended to limit the scope of the invention, but rather is intended to cover any equivalents of the structures or equivalent processes disclosed herein or in the alternative, which may be employed directly or indirectly in other related arts.
Claims (7)
1. The method for generating the traffic accident diagnosis report is characterized by comprising the following steps of:
based on terminal access equipment, determining space data acquired by a positioning system of an accident vehicle in a target period and behavior data of a vehicle-mounted electronic control unit of the accident vehicle in the target period, wherein the target period comprises 30 minutes before the accident occurrence time, 30 minutes after the accident occurrence time;
uploading the space data, the behavior data and the vehicle model corresponding to the accident vehicle to an information processing system;
determining road section data of an accident position based on matching of position data in the space data with a city/building information model, wherein the city/building information model is used for digitally representing static building data of an actual scene so as to construct a static model of an urban road, the static model comprises a highway traffic light, a highway indication board, a highway barrier, a highway indication line and a greening building static road model, and the city/building information model comprises an urban information model and a building information model;
constructing a vehicle kinematics model based on the information processing system according to the space data, the behavior data, the road section data and the vehicle model so as to simulate the movement track of the accident vehicle;
according to the behavior data and the vehicle model, constructing a vehicle dynamics model based on the information processing system so as to simulate the stress condition of the accident vehicle in the collision process;
coupling the vehicle kinematic model and the vehicle dynamic model to obtain a corresponding vehicle collision model so as to restore a dynamic accident scene;
determining road index data corresponding to an accident road section in the dynamic accident scene according to traffic regulation data associated with the city/building information model;
comparing the road index data with the vehicle running data in the dynamic accident scene to diagnose whether the accident vehicle has illegal behaviors or not;
and generating the traffic accident diagnosis report according to the comparison result.
2. The method of generating a traffic accident diagnosis report according to claim 1, wherein the road index data includes road segment speed limit data, the vehicle running data includes displacement, throttle operation data, and tire rotation speed data, and the step of comparing the road index data with the vehicle running data in the dynamic accident scene to diagnose whether the accident vehicle has an offence comprises:
determining the running speed of the accident vehicle on the current road section according to the displacement, the accelerator operation data and the tire rotation speed data;
and when the running speed is greater than the road section speed limit data, judging that overspeed behavior exists in the accident vehicle, and intercepting specific overspeed moment of the accident vehicle based on the dynamic accident scene.
3. The method of generating a traffic accident diagnosis report according to claim 1, wherein the road index data further includes a solid line travel range, the vehicle travel data further includes vehicle displacement coordinates, and the step of comparing the road index data with the vehicle travel data in the dynamic accident scene to diagnose whether the accident vehicle has an offence further includes:
positioning the displacement track of the accident vehicle according to the vehicle displacement coordinates;
and when the displacement track exceeds the solid line driving range and is biased to another vehicle, determining that the accident vehicle has illegal lane change behavior.
4. The method for generating a traffic accident diagnosis report according to claim 1, wherein after the step of generating the traffic accident diagnosis report according to the comparison result, further comprises:
outputting the traffic accident diagnosis report and scene simulation video of the accident process;
confirming an accident responsible party based on the traffic accident diagnosis report and the scene simulation video;
and sending the traffic accident diagnosis report and the scene simulation video to accident related parties including the accident responsible party.
5. The method for generating a traffic accident diagnosis report according to claim 1, wherein after the step of generating the traffic accident diagnosis report according to the comparison result, further comprises:
acquiring an accident road section corresponding to the dynamic accident scene based on the city/building information model;
determining the to-be-broadcasted range and traffic condition information of the accident road section;
planning a corresponding detour path based on the traffic condition information;
and pushing the detour path and the accident information to the vehicles on the accident road section based on the range to be broadcasted.
6. A traffic accident diagnosis report generating apparatus, characterized in that the traffic accident diagnosis report generating apparatus includes: a memory, a processor, and a traffic accident diagnosis report generation program stored on the memory and executable on the processor, the traffic accident diagnosis report generation program configured to implement the steps of the traffic accident diagnosis report generation method according to any one of claims 1 to 5.
7. A storage medium, wherein a program for generating a traffic accident diagnosis report is stored on the storage medium, and the program for generating a traffic accident diagnosis report, when executed by a processor, realizes the steps of the method for generating a traffic accident diagnosis report according to any one of claims 1 to 5.
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Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| WO2018032295A1 (en) * | 2016-08-16 | 2018-02-22 | 华为技术有限公司 | Accident scene reconstruction method and device, and moving monitoring apparatus |
| CN110738842A (en) * | 2018-07-18 | 2020-01-31 | 阿里巴巴集团控股有限公司 | Accident responsibility division and behavior analysis method, device, equipment and storage medium |
| CN111400423A (en) * | 2020-03-16 | 2020-07-10 | 郑州航空工业管理学院 | Smart city CIM three-dimensional vehicle pose modeling system based on multi-view geometry |
| CN115547029A (en) * | 2022-06-30 | 2022-12-30 | 公安部道路交通安全研究中心 | Traffic accident analysis method, system, electronic device and storage medium |
| US11654904B1 (en) * | 2021-12-17 | 2023-05-23 | International Business Machines Corporation | Vehicular traffic adverse event monitoring mechanism |
-
2023
- 2023-12-19 CN CN202311747140.0A patent/CN117436821B/en active Active
Patent Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| WO2018032295A1 (en) * | 2016-08-16 | 2018-02-22 | 华为技术有限公司 | Accident scene reconstruction method and device, and moving monitoring apparatus |
| CN110738842A (en) * | 2018-07-18 | 2020-01-31 | 阿里巴巴集团控股有限公司 | Accident responsibility division and behavior analysis method, device, equipment and storage medium |
| CN111400423A (en) * | 2020-03-16 | 2020-07-10 | 郑州航空工业管理学院 | Smart city CIM three-dimensional vehicle pose modeling system based on multi-view geometry |
| US11654904B1 (en) * | 2021-12-17 | 2023-05-23 | International Business Machines Corporation | Vehicular traffic adverse event monitoring mechanism |
| CN115547029A (en) * | 2022-06-30 | 2022-12-30 | 公安部道路交通安全研究中心 | Traffic accident analysis method, system, electronic device and storage medium |
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