CN111651664B - Accident vehicle positioning method and device based on accident location point, storage medium and electronic equipment - Google Patents
Accident vehicle positioning method and device based on accident location point, storage medium and electronic equipment Download PDFInfo
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Abstract
The application discloses an accident vehicle positioning method and device based on accident location points, a storage medium and a terminal, wherein the method comprises the following steps: acquiring coordinate points of accident positions in an electronic map; matching historical vehicle driving data based on the coordinate points of the accident positions to generate a suspected vehicle set; sequencing each suspected vehicle in the suspected vehicle set according to a matching weight rule to generate a sequenced suspected vehicle set; acquiring historical driving data of each suspected vehicle in the sequenced suspected vehicle set; and determining the target suspected vehicle based on the historical driving data of each suspected vehicle. Therefore, by adopting the embodiment of the application, the accuracy of locating the accident vehicle can be improved.
Description
Technical Field
The application relates to the technical field of vehicle safety, in particular to an accident vehicle positioning method and device based on accident position points, a storage medium and electronic equipment.
Background
Along with the rapid development of social economy, vehicles become indispensable transportation means in people's life, and the occurrence rate of traffic accidents is inevitably increased while great convenience is brought to users, and after the occurrence of traffic accidents, the rapid finding of suspected vehicles is the key point of solving traffic accidents by law enforcement departments.
When currently searching for suspected vehicles that cause accidents, related law enforcement departments typically rely on a large number of hardware devices, such as a vehicle recorder, an early warning device, a traffic monitor, and a cloud server. The related law enforcement departments play back the historical driving video data through hardware equipment so as to locate the suspected vehicle. Because the hardware equipment cost is higher, a part of hardware equipment is not required to be installed by the national hardness, so that a part of vehicles do not have related hardware equipment, law enforcement departments may fail in acquiring driving video data, and therefore the accuracy of suspected vehicle positioning is reduced.
Disclosure of Invention
The embodiment of the application provides an accident vehicle positioning method and device based on accident location points, a storage medium and electronic equipment. The following presents a simplified summary in order to provide a basic understanding of some aspects of the disclosed embodiments. This summary is not an extensive overview and is intended to neither identify key/critical elements nor delineate the scope of such embodiments. Its sole purpose is to present some concepts in a simplified form as a prelude to the more detailed description that is presented later.
In a first aspect, an embodiment of the present application provides an accident vehicle positioning method based on an accident location point, where the method includes:
Acquiring coordinate points of accident positions in an electronic map;
matching historical vehicle driving data based on the coordinate points of the accident positions to generate a suspected vehicle set;
sequencing each suspected vehicle in the suspected vehicle set according to a matching weight rule to generate a sequenced suspected vehicle set;
acquiring historical driving data of each suspected vehicle in the sequenced suspected vehicle set;
and determining the target suspected vehicle based on the historical driving data of each suspected vehicle.
Optionally, after determining the target suspected vehicle based on the historical driving data of each suspected vehicle, the method further includes:
and drawing a 3D vehicle machine running model aiming at the target suspected vehicle at the position points with the preset time granularity, and sending the target suspected vehicle and the 3D vehicle machine running model to an accident monitoring platform.
Optionally, the matching the historical vehicle driving data based on the coordinate point of the accident position to generate a suspected vehicle set includes:
when a region division instruction aiming at the coordinate point is received, region division is carried out based on the region division instruction to generate an accident region;
and generating a suspected vehicle set according to the historical vehicle driving data in the accident area matching preset time interval.
Optionally, the sorting the suspected vehicles in the suspected vehicle set according to the matching weight rule, and generating the sorted suspected vehicle set includes:
calculating the suspicion probability of each suspicion vehicle in the suspicion vehicle set according to the multiple matching weights;
and descending order arrangement is carried out on the suspected vehicles according to the suspected probability, and a sorted suspected vehicle set is generated.
Optionally, the plurality of matching weights includes:
the first matching weight is determined according to whether the suspected vehicle has collision alarm or not;
the second matching weight is determined according to whether the suspected vehicle has negative acceleration or not;
and determining the third matching weight according to the number of the matching position points of the suspected vehicle.
Optionally, the determining the target suspected vehicle based on the historical driving data of each suspected vehicle includes:
acquiring the driving-in time and driving-in speed, the driving-out time and driving-out speed and the residence time of each suspected vehicle aiming at the accident area in the historical driving data of each suspected vehicle;
calculating corresponding acceleration of each suspected vehicle based on the driving-in time and driving-in speed, the driving-out time and driving-out speed and the residence time aiming at the accident area;
Calculating and generating the driving distance of each suspected vehicle according to the acceleration corresponding to each suspected vehicle;
calculating and generating a reference distance according to the diameter of the accident area and a preset road grade;
and when the driving distance is greater than the reference distance, determining a first target suspected vehicle set.
Optionally, after determining the first target suspected vehicle set, the method further includes:
acquiring a position point set aiming at the accident area in historical driving data of each suspected vehicle in the first target suspected vehicle set;
acquiring the running speed and running time of each position point in the position point set;
calculating and generating acceleration among the position points according to the running speed and the running time of the position points;
calculating and generating the driving distance of each suspected vehicle based on the acceleration among the position points;
calculating and generating a reference distance according to the diameter of the accident area and a preset road grade;
and when the driving distance is greater than the reference distance, determining the target suspected vehicle.
In a second aspect, an embodiment of the present application provides an accident vehicle positioning apparatus based on an accident location point, the apparatus comprising:
The coordinate point acquisition module is used for acquiring coordinate points of accident positions in the electronic map;
the first set generation module is used for matching the historical vehicle driving data based on the coordinate points of the accident positions to generate a suspected vehicle set;
the second set generation module is used for sequencing each suspected vehicle in the suspected vehicle set according to the matching weight rule to generate a sequenced suspected vehicle set;
the data acquisition module is used for acquiring historical driving data of each suspected vehicle in the sequenced suspected vehicle set;
and the vehicle determining module is used for determining the target suspected vehicle based on the historical driving data of each suspected vehicle.
In a third aspect, embodiments of the present application provide a computer storage medium storing a plurality of instructions adapted to be loaded by a processor and to perform the above-described method steps.
In a fourth aspect, an embodiment of the present application provides an electronic device, which may include: a processor and a memory; wherein the memory stores a computer program adapted to be loaded by the processor and to perform the above-mentioned method steps.
The technical scheme provided by the embodiment of the application can have the following beneficial effects:
In the embodiment of the application, user electronic equipment firstly acquires coordinate points of accident positions in an electronic map, then matches historical vehicle running data based on the coordinate points of the accident positions to generate a suspected vehicle set, ranks all suspected vehicles in the suspected vehicle set according to a matching weight rule to generate a ranked suspected vehicle set, acquires historical running data of all suspected vehicles in the ranked suspected vehicle set, and finally determines a target suspected vehicle based on the historical running data of all suspected vehicles. Because the scheme combines Beidou positioning service and map information (accident place), history position information of vehicles in the whole country is matched, then 3D model track backtracking is carried out on matched suspected vehicles, real driving track details of the vehicles at the preset millisecond level passing through the accident point are observed, accident scenes are restored, and finally the accident vehicles are positioned, so that the accuracy of positioning the accident vehicles is improved.
It is to be understood that both the foregoing general description and the following detailed description are exemplary and explanatory only and are not restrictive of the application as claimed.
Drawings
The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the application and together with the description, serve to explain the principles of the application.
FIG. 1 is a schematic flow chart of an accident vehicle positioning method based on accident location points according to an embodiment of the present application;
FIG. 2 is a schematic illustration of an implementation scenario of accident vehicle localization based on accident location points according to an embodiment of the present application;
FIG. 3 is a flow chart of another accident vehicle locating method based on accident location points according to an embodiment of the present application;
FIG. 4 is a schematic illustration of an accident vehicle locating apparatus based on accident location points according to an embodiment of the present application;
fig. 5 is a schematic structural diagram of an electronic device according to an embodiment of the present application.
Detailed Description
The following description and the drawings sufficiently illustrate specific embodiments of the application to enable those skilled in the art to practice them.
It should be understood that the described embodiments are merely some, but not all, embodiments of the application. 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.
When the following description refers to the accompanying drawings, the same numbers in different drawings refer to the same or similar elements, unless otherwise indicated. The implementations described in the following exemplary examples do not represent all implementations consistent with the application. Rather, they are merely examples of apparatus and methods consistent with certain aspects of the application as detailed in the accompanying claims.
In the description of the present application, it should be understood that the terms "first," "second," and the like are used for descriptive purposes only and are not to be construed as indicating or implying relative importance. The specific meaning of the above terms in the present application will be understood in specific cases by those of ordinary skill in the art. Furthermore, in the description of the present application, unless otherwise indicated, "a plurality" means two or more. "and/or", describes an association relationship of an association object, and indicates that there may be three relationships, for example, a and/or B, and may indicate: a exists alone, A and B exist together, and B exists alone. The character "/" generally indicates that the context-dependent object is an "or" relationship.
To date, for locating suspected vehicles of accidents, related law enforcement departments usually rely on a large number of hardware devices, such as a vehicle recorder, an early warning device, a traffic monitor and a cloud server, when searching for suspected vehicles of accidents. The related law enforcement departments play back the historical driving video data through hardware equipment so as to locate the suspected vehicle. Because the hardware equipment cost is higher, a part of hardware equipment is not required to be installed by the national hardness, so that a part of vehicles do not have related hardware equipment, law enforcement departments may fail in acquiring driving video data, and therefore the accuracy of suspected vehicle positioning is reduced. Therefore, the application provides an accident vehicle positioning method and device based on accident location points, a storage medium and electronic equipment, so as to solve the problems in the related technical problems. According to the technical scheme provided by the application, because the Beidou positioning service and map information (accident place) are combined, the historical position information of vehicles in the whole country is matched, then the matched suspected vehicles are subjected to 3D model track backtracking, the real running track details of the vehicles at the preset millisecond level passing through the accident point are observed and used for restoring the accident scene, and finally the accident vehicles are positioned, so that the accuracy of positioning the accident vehicles is improved, and the method is described in detail by adopting an exemplary embodiment.
The accident vehicle positioning method based on the accident location point according to the embodiment of the present application will be described in detail with reference to fig. 1 to 3. The method may be implemented in dependence on a computer program, and may be run on an accident location point based accident vehicle localization apparatus based on von neumann systems. The computer program may be integrated in the application or may run as a stand-alone tool class application. The accident vehicle positioning device based on the accident location point in the embodiment of the present application may be a user terminal, including but not limited to: personal computers, tablet computers, handheld devices, vehicle mounted devices, wearable devices, computing devices, or other processing devices connected to a wireless modem, and the like. User terminals may be called different names in different networks, for example: a user equipment, an access terminal, a subscriber unit, a subscriber station, a mobile station, a remote terminal, a mobile device, a user terminal, a wireless communication device, a user agent or user equipment, a cellular telephone, a cordless telephone, a personal digital assistant (personal digital assistant, PDA), a terminal device in a 5G network or a future evolution network, and the like.
Referring to fig. 1, a flow chart of an accident vehicle positioning method based on accident location points is provided in an embodiment of the present application. As shown in fig. 1, the method according to the embodiment of the present application may include the following steps:
s101, acquiring coordinate points of accident positions in an electronic map;
the electronic map is map data information provided by an electronic map manufacturer based on Beidou positioning service. The accident location point is the longitude and latitude point of the vehicle after the traffic accident. The coordinate points are longitude and latitude data used to describe the location point of the accident.
In general, the Beidou positioning service is equipment which is forcibly installed by the country when the vehicle leaves the factory, the running data of the vehicle can be acquired after being positioned by the Beidou positioning service, the vehicle position information reported by the Beidou positioning service has a uniform and definite protocol format, the difficulty is reduced for data analysis, and the consumption of service resources can be reduced due to less occupied space of data storage.
In one possible implementation manner, after the related departments receive the traffic accident early warning, the related departments search longitude and latitude coordinate points of the accident occurrence in the electronic map through the position points of the accident occurrence to check detailed data information near the position points of the accident.
S102, matching historical vehicle driving data based on the coordinate points of the accident positions to generate a suspected vehicle set;
the historical vehicle driving data is historical data information uploaded to a server by Beidou positioning service installed on a vehicle through real-time acquisition of driving data of the vehicle. The suspected vehicles are matched to a vehicle set corresponding to the accident location point according to the set time range value.
In the embodiment of the application, when a user performs historical vehicle running data matching, the user firstly needs to perform regional division according to the position points, a division instruction is generated after the user performs regional division through triggering a terminal key and is sent to the user terminal, when the user terminal receives the regional division instruction aiming at the coordinate points, the user terminal performs regional division on the basis of the regional division instruction to generate an accident region, and finally, historical vehicle running data in a preset time interval is matched according to the accident region to generate a suspected vehicle set.
In one possible implementation manner, the user terminal firstly obtains a coordinate point of the accident position, then divides the accident area based on an accident area division instruction sent by the user to generate an accident area, and finally matches historical vehicle driving data according to the accident area divided by the user and set time, and a plurality of suspected vehicles are generated after the matching is finished.
For example, after the accident happens, the small-scale receives traffic accident early warning information, the small-scale searches the accident occurrence position point coordinates and the nearby information of the position point coordinates in the electronic map according to the received early warning information, after the small-scale searches the accident position points, a circular fence (or polygon) with the radius of 1000 meters is planned according to the accident position points in the electronic map, when the nearby traffic terrains are complex and the traffic intersections are more, a plurality of fences can be drawn, then the approximate time that an accident vehicle may pass is marked on each fence, for example, 15:59:27 in the year 2020 and 17:59:41 in the month 4 and the month 2 are marked, when the fence planning and the time marking are finished, the small-scale sends a vehicle matching instruction, and when the user terminal receives the matching instruction sent by the small-scale, the user terminal matches the vehicle history position information within the appointed time according to the planned fences and marked time on the electronic map, so as to generate a plurality of suspected vehicle data.
Specifically, the function realized by the matching algorithm used in the history position information of the vehicle in the appointed time at least comprises the steps of obtaining the history position information of the vehicle in the appointed time and traversing whether the distance between each track point and the incident area is less than 1000 meters or not, and recording the matching position information.
S103, sequencing each suspected vehicle in the suspected vehicle set according to a matching weight rule, and generating a sequenced suspected vehicle set;
wherein the matching weight rule (the unit of the matching weight coefficient a-ij is W) comprises a first matching weight, a second matching weight and a third matching weight. And determining the first matching weight according to whether the suspected vehicle has collision alarm, determining the second matching weight according to whether the suspected vehicle has negative acceleration, and determining the third matching weight according to the number of matching position points of the suspected vehicle.
In the embodiment of the application, a user terminal calculates the suspicion probability of each suspicion vehicle in a suspicion vehicle set according to a first matching weight, a second matching weight and a third matching weight, and then performs descending arrangement on suspicion vehicles according to the suspicion probability to obtain a plurality of suspicion vehicles arranged from large to small.
In one possible implementation manner, the user terminal firstly judges whether each suspected vehicle in the suspected vehicle set has collision alarm, when the collision alarm exists, the weight of each suspected vehicle is matched according to the collision alarm of the vehicle, when the collision alarm exists, the vehicle indicates that the suspicion is larger, the matched weight can be 0.5W, then judges whether each suspected vehicle has negative acceleration, if the vehicle has the negative acceleration when the vehicle runs in an accident planning area, the vehicle indicates that the vehicle has deceleration behavior, according to the negative acceleration matched weight of each suspected vehicle, the matching weight is 0.3W as the negative acceleration is smaller and the suspicion is larger, finally, the number of the position points of each suspected vehicle in the accident planning area is judged, and according to the number of the position points of each suspected vehicle, the larger the number of the position points indicates that the stay time is larger, and the suspicion is larger. After the matching weights of the three conditions are finished, the suspicion probability of each suspicion vehicle is generated, and a plurality of suspicion vehicles which are arranged from large to small are obtained after the suspicion vehicles are arranged in descending order according to the generated suspicion probability.
It should be noted that, the weight coefficient in the embodiment of the present application may be determined according to the actual situation, which is not limited herein.
S104, acquiring historical driving data of each suspected vehicle in the sequenced suspected vehicle set;
in one possible implementation manner, when the ranked suspected vehicles are generated based on step S103, the user terminal sequentially obtains the historical driving data of each suspected vehicle from the vehicle with the largest suspicion. The historical driving data comprise driving-in time, driving-out time, positive and negative acceleration, driving direction, vehicle speed and number of position points of each suspected vehicle aiming at an accident area in the sequenced suspected vehicle set.
S105, determining a target suspected vehicle based on the historical driving data of each suspected vehicle.
In the embodiment of the application, a user terminal firstly acquires the driving-in time and driving-in speed, the driving-out time and driving-out speed and the stay time of each suspected vehicle in historical driving data of an accident area, then calculates the acceleration corresponding to each suspected vehicle based on the driving-in time and driving-in speed, the driving-out time and driving-out speed and the stay time of each suspected vehicle, then calculates the driving distance of each suspected vehicle according to the acceleration corresponding to each suspected vehicle, then calculates the generating reference distance according to the diameter of the accident area and the preset road grade, then determines a first target suspected vehicle set when the driving distance is larger than the reference distance, finally acquires the position point set of each suspected vehicle in the historical driving data of the accident area in the first target suspected vehicle set, then acquires the driving speed and driving time of each position point in the position point set, then calculates the acceleration between each position point according to the driving speed and the driving time of each position point, then calculates the driving distance of each suspected vehicle based on the acceleration between each position point, and then calculates the generating the reference distance according to the diameter of the accident area and the preset road grade, and finally determines the target suspected vehicle as the reference distance when the driving distance is larger than the target road.
In one possible implementation manner, the user terminal first obtains a time t for entering each suspected vehicle in the sorted suspected vehicle set for an accident area i Velocity v corresponding to the time of entry i Time t of driving out o Velocity v corresponding to the travel-out time o . According to the time t of entering i And entry and exit time t o The stay time delta t=t of the suspected vehicle in the accident area can be calculated o -t i According to Deltat and drive-in time t i Velocity v corresponding to the time of entry i Can calculate each suspected vehicleAcceleration a= (v) of vehicle o -v i )/(t o -t i ) Finally, according to the accelerations a, deltat and v of each suspected vehicle i 、a e Can be according to the formula s=v i Δt+1/2(a*a e )Δt 2 Obtaining the driving distance S of each suspected vehicle, if S>Sp is suspected. Wherein a is e The method is characterized in that the acceleration experience coefficient of the vehicle is accumulated in tens of millions, sp is a reference distance, the calculation formula of Sp is Sp= (rail diameter 2000+rail diameter 2000 x p) m, p is determined according to different road grades (high speed 20%, city 15% and urban and rural 10%), and a road grade obtaining mode is adopted at present.
According to the above-mentioned several vehicles whose running distance S is greater than the reference distance, all the position points of said several vehicles passing through accident area are obtained, the accelerations of front and rear two position points are respectively calculated, for example, the matched position has p 1 、p 2 、p 3 、p 4 Corresponding to the speed v of the car 1 、v 2 、v 3 、v 4 Corresponding to the running time t 1 、t 2 、t 3 、t, 4 Respectively calculate p 1 To p 2 Acceleration, p 2 To p 3 By such a way, the acceleration a of the passing accident area is calculated by referring to the formula of the previous step 12 、a 23 、a 34 . For example a 12 ＝(v 1 -v 1 )/(t 1 -t 1 ). Equally dividing time cutting (10-50 ms a time window, namely one second can cut 100-20 time equally dividing windows) according to the front and back position points, and calculating the driving distance S of the vehicle computer in each time window according to the respective acceleration 11 、S 12 ...S 1100 The calculation formula is as follows: s is S 11 ＝v 1 Δt+1/2(a 12 *a e )Δt 2 And so on, calculate the total distance S Total = S 11+ S 12+ …+S nn If S Total (S) >Sp is suspected, a 3D vehicle machine running model is drawn according to the 10-50 millisecond time granularity position points, and the suspected maximum vehicle after secondary screening and the corresponding 3D backtracking model are pushed to traffic law enforcement departments.
For example, as shown in fig. 2, in 2019, in the period from three points to three points and a half in the early morning at the 4 th month 5 th month, traffic accidents occur at the intersection of the sunny road and the national road, and through big data analysis, it is found that there are A, B, C vehicles passing through the intersection in the time range, the number of the matching track points is the same, but the track points of the negative acceleration are not found in the A and B, so that the suspicion of the A vehicle is larger than that of the B vehicle, and the suspicion of the C vehicle is the smallest.
In the embodiment of the application, a user terminal firstly acquires coordinate points of accident positions in an electronic map, then matches historical vehicle running data based on the coordinate points of the accident positions to generate a suspicious vehicle set, then ranks all suspicious vehicles in the suspicious vehicle set according to a matching weight rule to generate a ranked suspicious vehicle set, then acquires historical running data of all suspicious vehicles in the ranked suspicious vehicle set, and finally determines a target suspicious vehicle based on the historical running data of all suspicious vehicles. Because the scheme combines Beidou positioning service and map information (accident place), history position information of vehicles in the whole country is matched, then 3D model track backtracking is carried out on matched suspected vehicles, real driving track details of the vehicles at the preset millisecond level passing through the accident point are observed, accident scenes are restored, and finally the accident vehicles are positioned, so that the accuracy of positioning the accident vehicles is improved.
Fig. 3 is a schematic flow chart of an accident vehicle positioning method based on accident location points according to an embodiment of the present application. The present embodiment is exemplified by the accident vehicle locating method based on the accident location point being applied to the user terminal. The accident vehicle locating method based on the accident location point can comprise the following steps:
s201, acquiring coordinate points of accident positions in an electronic map;
s202, when a region division instruction aiming at the coordinate point is received, region division is carried out based on the region division instruction to generate an accident region;
s203, generating a suspected vehicle set according to the historical vehicle driving data in the accident area matching preset time interval;
s204, calculating the suspicion probability of each suspicion vehicle in the suspicion vehicle set according to the plurality of matching weights;
s205, descending order arrangement is carried out on the suspected vehicles according to the suspected probability, and a sequenced suspected vehicle set is generated;
s206, acquiring historical driving data of each suspected vehicle in the sequenced suspected vehicle set;
s207, determining a target suspected vehicle based on the historical driving data of each suspected vehicle;
and S208, drawing a 3D vehicle machine running model aiming at the target suspected vehicle at the position point of the preset time granularity, and sending the target suspected vehicle and the 3D vehicle machine running model to an accident monitoring platform.
In the embodiment of the application, a user terminal firstly acquires coordinate points of accident positions in an electronic map, then matches historical vehicle running data based on the coordinate points of the accident positions to generate a suspicious vehicle set, then ranks all suspicious vehicles in the suspicious vehicle set according to a matching weight rule to generate a ranked suspicious vehicle set, then acquires historical running data of all suspicious vehicles in the ranked suspicious vehicle set, and finally determines a target suspicious vehicle based on the historical running data of all suspicious vehicles. Because the scheme combines Beidou positioning service and map information (accident place), history position information of vehicles in the whole country is matched, then 3D model track backtracking is carried out on matched suspected vehicles, real driving track details of the vehicles at the preset millisecond level passing through the accident point are observed, accident scenes are restored, and finally the accident vehicles are positioned, so that the accuracy of positioning the accident vehicles is improved.
The following are examples of the apparatus of the present application that may be used to perform the method embodiments of the present application. For details not disclosed in the embodiments of the apparatus of the present application, please refer to the embodiments of the method of the present application.
Referring to fig. 4, a schematic structural diagram of an accident vehicle positioning apparatus based on accident location points according to an exemplary embodiment of the present invention is shown. The accident vehicle locating apparatus based on the accident location point may be implemented as all or a part of the terminal by software, hardware or a combination of both. The apparatus 1 includes a coordinate point acquisition module 10, a first set generation module 20, a second set generation module 30, a data acquisition module 40, and a vehicle determination module 50.
A coordinate point acquisition module 10, configured to acquire a coordinate point of an accident position in the electronic map;
a first set generating module 20, configured to match historical vehicle driving data based on the coordinate points of the accident location, and generate a suspected vehicle set;
the second set generating module 30 ranks each suspected vehicle in the suspected vehicle set according to the matching weight rule, and generates a ranked suspected vehicle set;
a data obtaining module 40, configured to obtain historical driving data of each suspected vehicle in the sorted suspected vehicle set;
the vehicle determination module 50 determines the vehicle as the target suspected vehicle based on the historical driving data of each suspected vehicle.
It should be noted that, when the accident vehicle positioning device based on the accident location point provided in the foregoing embodiment performs the accident vehicle positioning method based on the accident location point, only the division of the foregoing functional modules is used as an example, in practical application, the foregoing functional allocation may be completed by different functional modules according to needs, that is, the internal structure of the apparatus is divided into different functional modules, so as to complete all or part of the functions described above. In addition, the accident vehicle positioning device based on the accident location point and the accident vehicle positioning method based on the accident location point provided in the foregoing embodiments belong to the same concept, which embody detailed implementation procedures and are not described herein.
The foregoing embodiment numbers of the present application are merely for the purpose of description, and do not represent the advantages or disadvantages of the embodiments.
In the embodiment of the application, a user terminal firstly acquires coordinate points of accident positions in an electronic map, then matches historical vehicle running data based on the coordinate points of the accident positions to generate a suspicious vehicle set, then ranks all suspicious vehicles in the suspicious vehicle set according to a matching weight rule to generate a ranked suspicious vehicle set, then acquires historical running data of all suspicious vehicles in the ranked suspicious vehicle set, and finally determines a target suspicious vehicle based on the historical running data of all suspicious vehicles. Because the scheme combines Beidou positioning service and map information (accident place), history position information of vehicles in the whole country is matched, then 3D model track backtracking is carried out on matched suspected vehicles, real driving track details of the vehicles at the preset millisecond level passing through the accident point are observed, accident scenes are restored, and finally the accident vehicles are positioned, so that the accuracy of positioning the accident vehicles is improved.
The application also provides a computer readable medium on which program instructions are stored, which when executed by a processor implement the accident vehicle locating method based on accident location points provided by the above-mentioned respective method embodiments.
The application also provides a computer program product containing instructions which, when run on a computer, cause the computer to perform the accident location point-based accident vehicle locating method of the various method embodiments described above.
Referring to fig. 5, a schematic structural diagram of an electronic device is provided in an embodiment of the present application. As shown in fig. 5, the electronic device 1000 may include: at least one processor 1001, at least one network interface 1004, a user interface 1003, a memory 1005, at least one communication bus 1002.
Wherein the communication bus 1002 is used to enable connected communication between these components.
The user interface 1003 may include a Display screen (Display) and a Camera (Camera), and the optional user interface 1003 may further include a standard wired interface and a wireless interface.
The network interface 1004 may optionally include a standard wired interface, a wireless interface (e.g., WI-FI interface), among others.
Wherein the processor 1001 may include one or more processing cores. The processor 1001 connects various parts within the entire electronic device 1000 using various interfaces and lines, and performs various functions of the electronic device 1000 and processes data by executing or executing instructions, programs, code sets, or instruction sets stored in the memory 1005, and invoking data stored in the memory 1005. Alternatively, the processor 1001 may be implemented in at least one hardware form of digital signal processing (Digital Signal Processing, DSP), field programmable gate array (Field-Programmable Gate Array, FPGA), programmable logic array (Programmable Logic Array, PLA). The processor 1001 may integrate one or a combination of several of a central processing unit (Central Processing Unit, CPU), an image processor (Graphics Processing Unit, GPU), and a modem, etc. The CPU mainly processes an operating system, a user interface, an application program and the like; the GPU is used for rendering and drawing the content required to be displayed by the display screen; the modem is used to handle wireless communications. It will be appreciated that the modem may not be integrated into the processor 1001 and may be implemented by a single chip.
The Memory 1005 may include a random access Memory (Random Access Memory, RAM) or a Read-Only Memory (Read-Only Memory). Optionally, the memory 1005 includes a non-transitory computer readable medium (non-transitory computer-readable storage medium). The memory 1005 may be used to store instructions, programs, code, sets of codes, or sets of instructions. The memory 1005 may include a stored program area and a stored data area, wherein the stored program area may store instructions for implementing an operating system, instructions for at least one function (such as a touch function, a sound playing function, an image playing function, etc.), instructions for implementing the above-described respective method embodiments, etc.; the storage data area may store data or the like referred to in the above respective method embodiments. The memory 1005 may also optionally be at least one storage device located remotely from the processor 1001. As shown in FIG. 5, an operating system, a network communication module, a user interface module, and an accident vehicle locating application based on an accident location point may be included in memory 1005, which is a type of computer storage medium.
In the electronic device 1000 shown in fig. 5, the user interface 1003 is mainly used for providing an input interface for a user, and acquiring data input by the user; and the processor 1001 may be configured to invoke the accident-location-based accident-vehicle-localization application stored in the memory 1005 and to specifically perform the following operations:
Acquiring coordinate points of accident positions in an electronic map;
matching historical vehicle driving data based on the coordinate points of the accident positions to generate a suspected vehicle set;
sequencing each suspected vehicle in the suspected vehicle set according to a matching weight rule to generate a sequenced suspected vehicle set;
acquiring historical driving data of each suspected vehicle in the sequenced suspected vehicle set;
and determining the target suspected vehicle based on the historical driving data of each suspected vehicle.
In one embodiment, the processor 1001, after executing the determination of the target suspected vehicle based on the historical driving data of the suspected vehicles, further executes the following operations:
and drawing a 3D vehicle machine running model aiming at the target suspected vehicle at the position points with the preset time granularity, and sending the target suspected vehicle and the 3D vehicle machine running model to an accident monitoring platform.
In one embodiment, when the processor 1001 performs the matching of the historical vehicle driving data based on the coordinate point of the accident location to generate a suspected vehicle set, the processor specifically performs the following operations:
when a region division instruction aiming at the coordinate point is received, region division is carried out based on the region division instruction to generate an accident region;
And generating a suspected vehicle set according to the historical vehicle driving data in the accident area matching preset time interval.
In one embodiment, when the processor 1001 performs the ranking of the suspected vehicles in the suspected vehicle set according to the matching weight rule to generate a ranked suspected vehicle set, the processor specifically performs the following operations:
calculating the suspicion probability of each suspicion vehicle in the suspicion vehicle set according to the multiple matching weights;
and descending order arrangement is carried out on the suspected vehicles according to the suspected probability, and a sorted suspected vehicle set is generated.
In one embodiment, when executing the determination of the target suspected vehicle based on the historical driving data of each suspected vehicle, the processor 1001 specifically executes the following operations:
acquiring the driving-in time and driving-in speed, the driving-out time and driving-out speed and the residence time of each suspected vehicle aiming at the accident area in the historical driving data of each suspected vehicle;
calculating corresponding acceleration of each suspected vehicle based on the driving-in time and driving-in speed, the driving-out time and driving-out speed and the residence time aiming at the accident area;
calculating and generating the driving distance of each suspected vehicle according to the acceleration corresponding to each suspected vehicle;
Calculating and generating a reference distance according to the diameter of the accident area and a preset road grade;
and when the driving distance is greater than the reference distance, determining a first target suspected vehicle set.
In one embodiment, the processor 1001, after executing the determining the first set of target suspected vehicles, further performs the following:
acquiring a position point set aiming at the accident area in historical driving data of each suspected vehicle in the first target suspected vehicle set;
acquiring the running speed and running time of each position point in the position point set;
calculating and generating acceleration among the position points according to the running speed and the running time of the position points;
calculating and generating the driving distance of each suspected vehicle based on the acceleration among the position points;
calculating and generating a reference distance according to the diameter of the accident area and a preset road grade;
and when the driving distance is greater than the reference distance, determining the target suspected vehicle.
In the embodiment of the application, user electronic equipment firstly acquires coordinate points of accident positions in an electronic map, then matches historical vehicle running data based on the coordinate points of the accident positions to generate a suspected vehicle set, ranks all suspected vehicles in the suspected vehicle set according to a matching weight rule to generate a ranked suspected vehicle set, acquires historical running data of all suspected vehicles in the ranked suspected vehicle set, and finally determines a target suspected vehicle based on the historical running data of all suspected vehicles. Because the scheme combines Beidou positioning service and map information (accident place), history position information of vehicles in the whole country is matched, then 3D model track backtracking is carried out on matched suspected vehicles, real driving track details of the vehicles at the preset millisecond level passing through the accident point are observed, accident scenes are restored, and finally the accident vehicles are positioned, so that the accuracy of positioning the accident vehicles is improved.
Those skilled in the art will appreciate that implementing all or part of the above-described methods in the embodiments may be accomplished by computer programs stored in a computer-readable storage medium, which when executed, may include the steps of the embodiments of the methods described above. The storage medium may be a magnetic disk, an optical disk, a read-only memory, a random access memory, or the like.
The foregoing disclosure is illustrative of the present application and is not to be construed as limiting the scope of the application, which is defined by the appended claims.
Claims (7)
1. A method of accident vehicle localization based on accident location points, the method comprising:
acquiring coordinate points of accident positions in an electronic map;
matching historical vehicle driving data based on the coordinate points of the accident positions to generate a suspected vehicle set; wherein,
the matching of the historical vehicle driving data based on the coordinate points of the accident positions is performed to generate a suspected vehicle set, and the matching comprises the following steps:
when a region division instruction aiming at the coordinate point is received, region division is carried out based on the region division instruction to generate an accident region;
According to the historical vehicle driving data in the accident area matching preset time interval, a suspected vehicle set is generated;
sequencing each suspected vehicle in the suspected vehicle set according to a matching weight rule to generate a sequenced suspected vehicle set; wherein,
the matching weight rule is determined according to whether the suspected vehicle has collision alarm or not, whether the suspected vehicle has negative acceleration or not, and the number of matching position points of the suspected vehicle;
acquiring historical driving data of each suspected vehicle in the sequenced suspected vehicle set;
determining a target suspected vehicle based on the historical driving data of each suspected vehicle; wherein,
the determining the target suspected vehicle based on the historical driving data of each suspected vehicle comprises the following steps:
acquiring the driving-in time and driving-in speed, the driving-out time and driving-out speed and the residence time of each suspected vehicle aiming at the accident area in the historical driving data of each suspected vehicle;
calculating corresponding acceleration of each suspected vehicle based on the driving-in time and driving-in speed, the driving-out time and driving-out speed and the residence time aiming at the accident area;
calculating and generating the driving distance of each suspected vehicle according to the acceleration corresponding to each suspected vehicle;
Calculating and generating a reference distance according to the diameter of the accident area and a preset road grade;
when the driving distance is greater than the reference distance, determining a first target suspected vehicle set; wherein after the first target suspected vehicle set is determined, the method further comprises:
acquiring a position point set aiming at the accident area in historical driving data of each suspected vehicle in the first target suspected vehicle set;
acquiring the running speed and running time of each position point in the position point set;
calculating and generating acceleration among the position points according to the running speed and the running time of the position points;
calculating and generating the driving distance of each suspected vehicle based on the acceleration among the position points;
calculating and generating a reference distance according to the diameter of the accident area and a preset road grade;
and when the driving distance is greater than the reference distance, determining the target suspected vehicle.
2. The method of claim 1, wherein after determining the target suspect vehicle based on the historical travel data for each suspect vehicle, further comprising:
and drawing a 3D vehicle machine running model aiming at the target suspected vehicle at the position points with the preset time granularity, and sending the target suspected vehicle and the 3D vehicle machine running model to an accident monitoring platform.
3. The method of claim 1, wherein the ranking each suspect vehicle in the set of suspect vehicles according to the matching weight rule to generate the ranked set of suspect vehicles comprises:
calculating the suspicion probability of each suspicion vehicle in the suspicion vehicle set according to the multiple matching weights;
and descending order arrangement is carried out on the suspected vehicles according to the suspected probability, and a sorted suspected vehicle set is generated.
4. The method of claim 3, wherein the plurality of matching weights comprises:
the first matching weight is determined according to whether the suspected vehicle has collision alarm or not;
the second matching weight is determined according to whether the suspected vehicle has negative acceleration or not;
and determining the third matching weight according to the number of the matching position points of the suspected vehicle.
5. An accident vehicle locating apparatus based on accident location points, the apparatus comprising:
the coordinate point acquisition module is used for acquiring coordinate points of accident positions in the electronic map;
the first set generation module is used for matching the historical vehicle driving data based on the coordinate points of the accident positions to generate a suspected vehicle set; wherein,
The first set generating module is specifically configured to:
when a region division instruction aiming at the coordinate point is received, region division is carried out based on the region division instruction to generate an accident region;
according to the historical vehicle driving data in the accident area matching preset time interval, a suspected vehicle set is generated;
the second set generation module is used for sequencing each suspected vehicle in the suspected vehicle set according to the matching weight rule to generate a sequenced suspected vehicle set; wherein,
the matching weight rule is determined according to whether the suspected vehicle has collision alarm or not, whether the suspected vehicle has negative acceleration or not, and the number of matching position points of the suspected vehicle;
the data acquisition module is used for acquiring historical driving data of each suspected vehicle in the sequenced suspected vehicle set;
the vehicle determining module is used for determining the vehicle to be a target suspected vehicle based on the historical driving data of each suspected vehicle; wherein,
the determining the target suspected vehicle based on the historical driving data of each suspected vehicle comprises the following steps:
acquiring the driving-in time and driving-in speed, the driving-out time and driving-out speed and the residence time of each suspected vehicle aiming at the accident area in the historical driving data of each suspected vehicle;
Calculating corresponding acceleration of each suspected vehicle based on the driving-in time and driving-in speed, the driving-out time and driving-out speed and the residence time aiming at the accident area;
calculating and generating the driving distance of each suspected vehicle according to the acceleration corresponding to each suspected vehicle;
calculating and generating a reference distance according to the diameter of the accident area and a preset road grade;
when the driving distance is greater than the reference distance, determining a first target suspected vehicle set; wherein after the first target suspected vehicle set is determined, the method further comprises:
acquiring a position point set aiming at the accident area in historical driving data of each suspected vehicle in the first target suspected vehicle set;
acquiring the running speed and running time of each position point in the position point set;
calculating and generating acceleration among the position points according to the running speed and the running time of the position points;
calculating and generating the driving distance of each suspected vehicle based on the acceleration among the position points;
calculating and generating a reference distance according to the diameter of the accident area and a preset road grade;
and when the driving distance is greater than the reference distance, determining the target suspected vehicle.
6. A computer storage medium storing a plurality of instructions adapted to be loaded by a processor and to perform the method of any of claims 1-4.
7. An electronic device, comprising: a processor and a memory; wherein the memory stores a computer program adapted to be loaded by the processor and to perform the method according to any of claims 1-4.
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| CN114743373B (en) * | 2022-03-29 | 2023-10-13 | 北京万集科技股份有限公司 | Traffic accident handling method, device, equipment and storage medium |
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Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US6246933B1 (en) * | 1999-11-04 | 2001-06-12 | BAGUé ADOLFO VAEZA | Traffic accident data recorder and traffic accident reproduction system and method |
| CN105788271A (en) * | 2016-05-17 | 2016-07-20 | 厦门市美亚柏科信息股份有限公司 | Method and apparatus for identifying target moving object through trajectory matching |
| CN107798856A (en) * | 2017-09-14 | 2018-03-13 | 王淑芳 | A kind of emphasis commerial vehicle analysis on accident cause method and system |
| CN110459052A (en) * | 2019-07-05 | 2019-11-15 | 华为技术有限公司 | A kind of car accident recording method, device and vehicle |
-
2020
- 2020-04-24 CN CN202010334012.3A patent/CN111651664B/en active Active
Patent Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US6246933B1 (en) * | 1999-11-04 | 2001-06-12 | BAGUé ADOLFO VAEZA | Traffic accident data recorder and traffic accident reproduction system and method |
| CN105788271A (en) * | 2016-05-17 | 2016-07-20 | 厦门市美亚柏科信息股份有限公司 | Method and apparatus for identifying target moving object through trajectory matching |
| CN107798856A (en) * | 2017-09-14 | 2018-03-13 | 王淑芳 | A kind of emphasis commerial vehicle analysis on accident cause method and system |
| CN110459052A (en) * | 2019-07-05 | 2019-11-15 | 华为技术有限公司 | A kind of car accident recording method, device and vehicle |
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