CN114676178A - Accident detection method and device and electronic equipment - Google Patents
Accident detection method and device and electronic equipment Download PDFInfo
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Abstract
The disclosure provides an accident detection method, an accident detection device and electronic equipment, relates to the technical field of artificial intelligence, and particularly relates to an intelligent traffic technology. The method comprises the following steps: extracting a target road section from the congested road section, wherein the congestion degree of the target road section is higher than that of other road sections in the congested road section; determining whether the target road section is an accident road section according to the road section information of the target road section and the road section information of the upstream and downstream road sections of the target road section; and if the target road section is the accident road section, determining the position of the accident point according to the speed information and the track information of the target road section. The method improves the coverage rate of accident detection.
Description
Technical Field
The present disclosure relates to intelligent traffic technologies in the field of artificial intelligence technologies, and in particular, to an accident detection method and apparatus, and an electronic device.
Background
In recent years, the number of automobiles kept in cities is increasing, the road traffic efficiency is improved, and the management of traffic jam is an important subject. One important cause of traffic congestion is traffic accidents. Traffic accidents can cause the phenomenon of road occupation, and the traffic accidents depend on manual handling, so that long-time long-distance congestion is easy to cause, and great inconvenience is brought to the traffic. If the traffic accident occurrence place can be detected in time, the accident point is marked by combining a map application program, so that the vehicle can be guided to go out, and lane change or blockage can be avoided in advance; on the other hand, the emergency handling response speed of the accident can be improved, and the traffic jam condition can be relieved.
Besides manual reporting, the traditional method for accident detection also has a detection method based on image recognition, and the method depends on returning and recognizing field images, so that the coverage rate of accident detection is low.
Disclosure of Invention
The disclosure provides an accident detection method and device and electronic equipment, wherein the accident detection coverage rate is improved.
According to a first aspect of the present disclosure, there is provided an accident detection method, comprising:
extracting a target road section from congested road sections, wherein the congestion degree of the target road section is higher than that of other road sections in the congested road sections;
determining whether the target road section is an accident road section according to the road section information of the target road section and the road section information of the upstream and downstream road sections of the target road section;
and if the target road section is an accident road section, determining the position of an accident point according to the speed information and the track information of the target road section.
According to a second aspect of the present disclosure, there is provided an accident detection apparatus comprising:
the extracting module is used for extracting a target road section from congested road sections, wherein the congestion degree of the target road section is higher than that of other road sections in the congested road sections;
the first determining module is used for determining whether the target road section is an accident road section according to the target road section and road section information of an upstream road section and a downstream road section of the target road section;
and the second determining module is used for determining the position of an accident point according to the speed information and the track information of the target road section if the target road section is the accident road section.
According to a third aspect of the present disclosure, there is provided an electronic device comprising:
at least one processor; and
a memory communicatively coupled to the at least one processor; wherein,
the memory stores instructions executable by the at least one processor to enable the at least one processor to perform the method of the first aspect.
According to a fourth aspect of the present disclosure, there is provided a non-transitory computer readable storage medium having stored thereon computer instructions for causing the computer to perform the method of the first aspect described above.
According to a fifth aspect of the present disclosure, there is provided a computer program product, the program product comprising: a computer program, stored in a readable storage medium, from which at least one processor of an electronic device can read the computer program, execution of the computer program by the at least one processor causing the electronic device to perform the method of the first aspect.
According to the technical scheme of the disclosure, the coverage rate of accident detection is improved.
It should be understood that the statements in this section do not necessarily identify key or critical features of the embodiments of the present disclosure, nor do they limit the scope of the present disclosure. Other features of the present disclosure will become apparent from the following description.
Drawings
The drawings are included to provide a better understanding of the present solution and are not to be construed as limiting the present disclosure. Wherein:
FIG. 1 is a schematic flow chart diagram of a method for accident detection provided in accordance with an embodiment of the present disclosure;
FIG. 2 is a schematic flow chart diagram of another accident detection method provided in accordance with an embodiment of the present disclosure;
fig. 3 is a schematic structural diagram of an accident detection apparatus provided according to an embodiment of the present disclosure;
fig. 4 is a schematic block diagram of an electronic device for implementing the incident detection method of an embodiment of the present disclosure.
Detailed Description
Exemplary embodiments of the present disclosure are described below with reference to the accompanying drawings, in which various details of the embodiments of the disclosure are included to assist understanding, and which are to be considered as merely exemplary. Accordingly, those of ordinary skill in the art will recognize that various changes and modifications of the embodiments described herein can be made without departing from the scope and spirit of the present disclosure. Also, descriptions of well-known functions and constructions are omitted in the following description for clarity and conciseness.
Considering that detection of a road accident in the related art needs to depend on a road field image, and thus, accident detection cannot be realized for a road without road side equipment, the embodiment of the disclosure provides an accident detection method which does not depend on a field image, and the accident position is marked by combining real-time track and speed characteristics based on traffic big data information, so as to realize accident detection of a whole road network coverage surface.
The disclosure provides an accident detection method, an accident detection device and electronic equipment, which are applied to the field of intelligent transportation in the technical field of artificial intelligence, and particularly can be applied to an accident detection scene to improve the coverage rate of accident detection.
Hereinafter, the accident detection method provided by the present disclosure will be described in detail by specific examples. It is to be understood that the following detailed description may be combined with other embodiments, and that the same or similar concepts or processes may not be repeated in some embodiments.
Fig. 1 is a schematic flowchart of an accident detection method according to an embodiment of the present disclosure. The execution subject of the method is an accident detection device, and the device can be realized by software and/or hardware. As shown in fig. 1, the method includes:
s101, extracting a target road section from the congested road section, wherein the congestion degree of the target road section is higher than that of other road sections in the congested road section.
The congested road segments in this step may be obtained by real-time determination of the states of the road segments in the road network, or may be directly obtained from other data sources. Because the proportion of the accident road sections in the whole road network is very low, if the accident detection is carried out on all the jammed road sections in the whole road network, a large amount of computing resources are wasted, and the problems of long computing time consumption and low efficiency exist, therefore, in the embodiment of the disclosure, the jammed road sections are firstly filtered, and in the large amount of jammed road sections, the extremely jammed target road sections are more easily caused by the accident, the detection value is higher, the computing efficiency can be improved, and the computing cost is reduced. The target link refers to a link with a higher congestion degree in congested links, and may be referred to as an extremely congested link or an extremely congested link.
S102, determining whether the target road section is an accident road section according to the road section information of the target road section and the road section information of the upstream and downstream road sections of the target road section.
Therefore, in the embodiment of the disclosure, the upstream and downstream road segments of the target road segment are obtained according to the road network topological relation, if the target road segment has an accident, the road segment information of the upstream and downstream road segments can reflect the difference from the conventional congestion in the performance of the upstream and downstream road segments when the accident occurs, whether the target road segment is the accident road segment is determined based on the target road segment and the road segment information of the upstream and downstream road segments of the target road segment, the extension range of the upstream and downstream road segments can affect the detection accuracy, and the detection accuracy is improved.
And S103, if the target road section is the accident road section, determining the position of the accident point according to the speed information and the track information of the target road section.
After the target road section is determined to be the accident road section, the position of the accident point needs to be determined so as to be conveniently and accurately identified. The congested road section has certain regularity in speed characteristics and track characteristics when an accident occurs, for example, the speed of an upstream of an accident point is low, the speed of a downstream of the accident point is high, and a track point of a target road section deviates to a certain extent near the accident point, so that when the accident point is located, speed information and track information are used as input, and the position of the accident point is determined based on the two types of information.
The method comprises the steps of excavating an extremely congested road section in all congested road sections to serve as a target road section, and acquiring road section information of the target road section and upstream and downstream associated road sections of the target road section; and performing accident detection on the target road section according to the road section information to obtain a detection result of whether the target road section is an accident road section, and determining the position of an accident point on the basis of the speed information and the track information of the target road section detected as the accident road section.
To give the reader a more profound understanding of the principles underlying the present disclosure, the embodiment shown in fig. 1 will now be further refined in conjunction with fig. 2 below. As shown in fig. 2, the method of the embodiment of the present application includes:
s201, inputting attribute information and real-time information of the congested road section into a congestion mining model to obtain a target road section.
Optionally, the road segments in the road network with the speed lower than the speed threshold are determined as congested road segments. Optionally, the congested road segments are obtained from other data sources, such as a mapping application. The attribute information of the congested road section comprises at least one item of road grade, road type, road length, maximum traffic track quantity, number of lanes and road events; the real-time information of the congested road section comprises at least one item of road condition state, congestion length, road section speed and road section track. The congestion mining model is obtained by adopting congestion road section samples to train in advance.
S202, inputting the road section information of the target road section and the road section information of the upstream and downstream road sections of the target road section into the accident road section detection model to obtain the probability that the target road section is the accident road section.
The road information of the target road section and the road sections on the upstream and downstream of the target road section reflects the space-time characteristics of the target road section, and the road section information comprises attribute information, real-time information, historical accidents and/or jam probability of the road sections. The attribute information comprises at least one item of road grade, road type, maximum traffic track quantity, lane number and road event; the real-time information comprises real-time speed information and real-time track information; the historical accident and/or congestion probability comprises historical accident probability, historical congestion probability, historical accident and congestion simultaneous occurrence probability, wherein each probability can be divided into historical period overall occurrence probability and time-sharing probability, and the time-sharing probability is used for representing different occurrence probabilities of accidents and/or congestion in different time periods. Meanwhile, historical congestion probability of the target road section is obtained, and by combining the accident probability, the difference of the probability of occurrence of two different types of congestion, namely accident congestion and conventional congestion, of the target road section in different time periods can be represented.
Due to the fact that roads with poor traffic capacity and roads with events such as road closure, traffic control and lane change are prone to be misdetected as accident road sections, the difference between the events and accidents cannot be well distinguished only through real-time track information. For example, roads with poor traffic capacity are prone to generate more low-speed tracks, or road sections with changing lanes generate speed laws similar to accidents, and the accidents on the roads can be continuously detected by a model depending on the real-time tracks, so that the result is inaccurate. In the embodiment of the disclosure, besides the real-time information of the target road section, the self-attribute information of the target road section is introduced to solve the problems, that is, the self-traffic capacity and the special road events of each road section are considered to ensure the accuracy of detection. Meanwhile, historical information is introduced, and congestion and accident probability expression of a target road section in a historical period are comprehensively considered, for example, a road section which is congested in historical expression in the same period and has low accident occurrence frequency tends to have no accident, and a road section which is smooth in historical expression in the same period and has high accident occurrence frequency tends to have an accident, so that the accuracy of accident detection can be further improved.
The accident road section detection model is obtained by adopting an extreme congestion road section sample for training in advance.
S203, if the probability that the target road section is the accident road section is larger than the probability threshold value, determining that the target road section is the accident road section.
The accident road section detection model outputs the probability that the target road section is the accident road section, and the probability threshold value in the step can be set according to needs.
Optionally, in the embodiment of the present disclosure, duplicate removal may be performed on the detection result of the accident road segment in a spatial dimension, for example, in a long-distance congestion, if all of a plurality of continuous target road segments are determined as the accident road segment, a road segment with the highest probability among the plurality of continuous target road segments is determined as the accident road segment, and other road segments except the road segment with the highest probability are determined as non-accident road segments, so that repeated accident false reports are avoided.
And S204, inputting the speed information and the track information of the target road section into the accident point positioning model to obtain the position percentile of the accident point in the target road section.
When the position of the accident point is determined, based on the current speed information and track information of the target road section, the position percentile output by the accident point positioning model is the position percentage of the accident point in the target road section, or is called position quantile, and the accident point positioning model is obtained by adopting an accident road section sample training in advance. The speed information and the track information can also comprise all derivable characteristics of the speed and the track, such as the number of tracks in different speed ranges, the number of tracks in different types and the like, besides the speed and the track, and the addition of the derived characteristics can effectively improve the coordinate accuracy of detection.
And S205, determining the position of the accident point according to the position percentile.
And determining the position of the accident point according to the position percentile, and the coordinates of the starting point and the ending point of the target road section. And subtracting the coordinate of the starting point from the coordinate of the ending point, multiplying the obtained difference by the position percentile, and adding the result of the multiplication to the coordinate of the starting point to obtain the coordinate of the accident point, namely the position of the accident point, so that the accuracy of the accident point is improved. In addition, the position of the accident point can be marked on the map so as to guide the user to go out, the user can conveniently plan and bypass the accident point in advance, and congestion is avoided.
The training process of each model in the above-described embodiment is explained below.
And aiming at the congestion mining model, acquiring a congestion road section sample, marking a label of the congestion road section sample as congestion or extreme congestion, training a classification model, using the model for real-time classification of the congestion road section, and outputting an extreme congestion road section extracted from the congestion road section, thereby performing accident detection by using the extreme congestion road section as a target road section.
The method comprises the steps of obtaining extreme congestion road section samples aiming at an accident road section detection model, wherein each sample comprises road section information of an extreme congestion road section and an upstream road section and a downstream road section, identifying whether the sample is a label of the accident road section or not aiming at each training sample, carrying out data cleaning and weight removal on the sample during marking, selecting a sample with high reliability, removing a repeatedly marked sample, training an initial accident road section detection model by using a sample set, outputting the accident road section detection model after training is completed, taking a classification machine learning model as a basis for the accident road section detection model, improving the generalization capability of the model by adopting a boosting aggregation (Bagging) mode in the training process, and solving the problem of unbalanced samples by adopting a mode of upper-lower sampling and weighting loss.
And aiming at the accident point positioning model, acquiring accident section samples, wherein each sample comprises the speed information and the track information, the labels of the samples are the position percentiles of the accident points in the actual recording section, and training the initial accident point positioning model by using the samples to obtain the trained accident point positioning model.
Fig. 3 is a schematic structural diagram of an accident detection device provided according to an embodiment of the present disclosure. As shown in fig. 3, the accident detection apparatus 300 includes:
the extracting module 301 is configured to extract a target road segment from the congested road segment, where a congestion degree of the target road segment is higher than congestion degrees of other road segments in the congested road segment;
a first determining module 302, configured to determine whether a target road segment is an accident road segment according to road segment information of the target road segment and road segments upstream and downstream of the target road segment;
and a second determining module 303, configured to determine, if the target road segment is an accident road segment, a position of the accident point according to the speed information and the track information of the target road segment.
In one embodiment, the second determining module 303 comprises:
the first input unit is used for inputting the speed information and the track information of the target road section into the accident point positioning model to obtain the position percentile of an accident point in the target road section;
and the first determining unit is used for determining the position of the accident point according to the position percentile.
In one embodiment, the first determination unit includes:
and the first determining subunit is used for determining the position of the accident point according to the position percentile, and the coordinates of the starting point and the ending point of the target road section.
In one embodiment, the extraction module 301 comprises:
and the second input unit is used for inputting the attribute information and the real-time information of the congested road section into a congestion mining model to obtain a target road section, wherein the congestion mining model is obtained by adopting a congestion road section sample to train in advance.
In one embodiment, the first determining module 302 includes:
the third input unit is used for inputting the road section information of the target road section and the road sections on the upstream and downstream of the target road section into the accident road section detection model to obtain the probability that the target road section is the accident road section, wherein the road section information comprises the attribute information, the real-time information, the historical accident and/or the jam probability of the route;
and the second determining unit is used for determining that the target road section is the accident road section if the probability that the target road section is the accident road section is greater than the probability threshold.
In one embodiment, the accident detection apparatus 300 further comprises:
and a third determining unit, configured to determine, if all of the plurality of consecutive target links are determined as accident links, a link with a highest probability among the plurality of consecutive target links as an accident link, and determine, as a non-accident link, a link other than the link with the highest probability.
In one embodiment, the accident detection apparatus 300 further comprises:
and a fourth determination unit for determining the road section with the road section speed lower than the speed threshold value as the congested road section.
The device of the embodiment of the present disclosure may be used to execute the accident detection method in the above method embodiments, and the implementation principle and the technical effect are similar, which are not described herein again.
The present disclosure also provides an electronic device and a non-transitory computer-readable storage medium storing computer instructions, according to embodiments of the present disclosure.
According to an embodiment of the present disclosure, the present disclosure also provides a computer program product comprising: a computer program, stored in a readable storage medium, from which at least one processor of the electronic device can read the computer program, the at least one processor executing the computer program causing the electronic device to perform the solution provided by any of the embodiments described above.
Fig. 4 is a schematic block diagram of an electronic device for implementing the incident detection method of an embodiment of the present disclosure. Electronic devices are intended to represent various forms of digital computers, such as laptops, desktops, workstations, personal digital assistants, servers, blade servers, mainframes, and other appropriate computers. The electronic device may also represent various forms of mobile devices, such as personal digital processing, cellular phones, smart phones, wearable devices, and other similar computing devices. The components shown herein, their connections and relationships, and their functions, are meant to be examples only, and are not meant to limit implementations of the disclosure described and/or claimed herein.
As shown in fig. 4, the electronic device 400 includes a computing unit 401 that can perform various appropriate actions and processes according to a computer program stored in a Read Only Memory (ROM)402 or a computer program loaded from a storage unit 408 into a Random Access Memory (RAM) 403. In the RAM 403, various programs and data necessary for the operation of the device 400 can also be stored. The computing unit 401, ROM 402, and RAM 403 are connected to each other via a bus 404. An input/output (I/O) interface 405 is also connected to bus 404.
A number of components in the electronic device 400 are connected to the I/O interface 405, including: an input unit 406 such as a keyboard, a mouse, or the like; an output unit 407 such as various types of displays, speakers, and the like; a storage unit 408 such as a magnetic disk, optical disk, or the like; and a communication unit 409 such as a network card, modem, wireless communication transceiver, etc. The communication unit 409 allows the device 400 to exchange information/data with other devices via a computer network, such as the internet, and/or various telecommunication networks.
Various implementations of the systems and techniques described here above may be implemented in digital electronic circuitry, integrated circuitry, Field Programmable Gate Arrays (FPGAs), Application Specific Integrated Circuits (ASICs), Application Specific Standard Products (ASSPs), system on a chip (SOCs), Complex Programmable Logic Devices (CPLDs), computer hardware, firmware, software, and/or combinations thereof. These various embodiments may include: implemented in one or more computer programs that are executable and/or interpretable on a programmable system including at least one programmable processor, which may be special or general purpose, receiving data and instructions from, and transmitting data and instructions to, a storage system, at least one input device, and at least one output device.
Program code for implementing the methods of the present disclosure may be written in any combination of one or more programming languages. These program codes may be provided to a processor or controller of a general purpose computer, special purpose computer, or other programmable data processing apparatus, such that the program codes, when executed by the processor or controller, cause the functions/operations specified in the flowchart and/or block diagram to be performed. The program code may execute entirely on the machine, partly on the machine, as a stand-alone software package partly on the machine and partly on a remote machine or entirely on the remote machine or server.
In the context of this disclosure, a machine-readable medium may be a tangible medium that can contain, or store a program for use by or in connection with an instruction execution system, apparatus, or device. The machine-readable medium may be a machine-readable signal medium or a machine-readable storage medium. A machine-readable medium may include, but is not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any suitable combination of the foregoing. More specific examples of a machine-readable storage medium would include an electrical connection based on one or more wires, a portable computer diskette, a hard disk, a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.
To provide for interaction with a user, the systems and techniques described here can be implemented on a computer having: a display device (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to a user; and a keyboard and a pointing device (e.g., a mouse or a trackball) by which a user can provide input to the computer. Other kinds of devices may also be used to provide for interaction with a user; for example, feedback provided to the user can be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user may be received in any form, including acoustic, speech, or tactile input.
The systems and techniques described here can be implemented in a computing system that includes a back-end component (e.g., as a data server), or that includes a middleware component (e.g., an application server), or that includes a front-end component (e.g., a user computer having a graphical user interface or a web browser through which a user can interact with an implementation of the systems and techniques described here), or any combination of such back-end, middleware, or front-end components. The components of the system can be interconnected by any form or medium of digital data communication (e.g., a communication network). Examples of communication networks include: local Area Networks (LANs), Wide Area Networks (WANs), and the Internet.
The computer system may include clients and servers. A client and server are generally remote from each other and typically interact through a communication network. The relationship of client and server arises by virtue of computer programs running on the respective computers and having a client-server relationship to each other. The Server can be a cloud Server, also called a cloud computing Server or a cloud host, and is a host product in a cloud computing service system, so as to solve the defects of high management difficulty and weak service expansibility in the traditional physical host and VPS service ("Virtual Private Server", or simply "VPS"). The server may also be a server of a distributed system, or a server incorporating a blockchain.
It should be understood that various forms of the flows shown above may be used, with steps reordered, added, or deleted. For example, the steps described in the present application may be executed in parallel, sequentially, or in different orders, and are not limited herein as long as the desired results of the technical solutions disclosed in the present disclosure can be achieved.
The above detailed description should not be construed as limiting the scope of the disclosure. It should be understood by those skilled in the art that various modifications, combinations, sub-combinations and substitutions may be made in accordance with design requirements and other factors. Any modification, equivalent replacement, and improvement made within the spirit and principle of the present disclosure should be included in the scope of protection of the present disclosure.
Claims (17)
1. A method of accident detection, comprising:
extracting a target road section from congested road sections, wherein the congestion degree of the target road section is higher than that of other road sections in the congested road sections;
determining whether the target road section is an accident road section according to the road section information of the target road section and the road section information of the upstream and downstream road sections of the target road section;
and if the target road section is an accident road section, determining the position of an accident point according to the speed information and the track information of the target road section.
2. The method of claim 1, wherein the determining the location of the accident point from the speed information and trajectory information of the target road segment comprises:
inputting the speed information and the track information of the target road section into an accident point positioning model to obtain the position percentile of an accident point in the target road section;
and determining the position of the accident point according to the position percentile.
3. The method of claim 2, wherein the determining the location of the incident point from the location percentile comprises:
and determining the position of the accident point according to the position percentile, and the coordinates of the starting point and the ending point of the target road section.
4. The method according to any one of claims 1-3, wherein the extracting the target road segment from the congested road segment comprises:
and inputting the attribute information and the real-time information of the congested road section into a congestion mining model to obtain the target road section, wherein the congestion mining model is obtained by adopting a congestion road section sample to train in advance.
5. The method according to any one of claims 1-4, wherein the determining whether the target road segment is an accident road segment according to the road segment information of the target road segment and road segments upstream and downstream of the target road segment comprises:
inputting the road section information of the target road section and the road section information of the upstream and downstream road sections of the target road section into an accident road section detection model to obtain the probability that the target road section is an accident road section, wherein the road section information comprises attribute information, real-time information, historical accident and/or congestion probability of a route;
and if the probability that the target road section is the accident road section is greater than the probability threshold value, determining that the target road section is the accident road section.
6. The method of claim 5, further comprising:
and if the plurality of continuous target road sections are all determined as accident road sections, determining the road section with the highest probability in the plurality of continuous target road sections as an accident road section, and determining other road sections except the road section with the highest probability as non-accident road sections.
7. The method according to any one of claims 1-6, before extracting the target road segment from the congested road segment, the method further comprising:
and determining the road sections with the road section speed lower than the speed threshold value as the congested road sections.
8. An accident detection apparatus, comprising:
the extracting module is used for extracting a target road section from the congested road section, wherein the congestion degree of the target road section is higher than that of other road sections in the congested road section;
the first determining module is used for determining whether the target road section is an accident road section according to the target road section and road section information of an upstream road section and a downstream road section of the target road section;
and the second determining module is used for determining the position of an accident point according to the speed information and the track information of the target road section if the target road section is the accident road section.
9. The apparatus of claim 8, wherein the second determining means comprises:
the first input unit is used for inputting the speed information and the track information of the target road section into an accident point positioning model to obtain the position percentile of an accident point in the target road section;
and the first determining unit is used for determining the position of the accident point according to the position percentile.
10. The apparatus of claim 9, wherein the first determining unit comprises:
and the first determining subunit is used for determining the position of the accident point according to the position percentile, and the coordinates of the starting point and the ending point of the target road section.
11. The apparatus of any one of claims 8-10, wherein the extraction module comprises:
and the second input unit is used for inputting the attribute information and the real-time information of the congested road section into a congestion mining model to obtain the target road section, wherein the congestion mining model is obtained by adopting a congestion road section sample to train in advance.
12. The apparatus of any of claims 8-11, wherein the first determining means comprises:
a third input unit, configured to input road segment information of the target road segment and road segments upstream and downstream of the target road segment into an accident road segment detection model, to obtain a probability that the target road segment is an accident road segment, where the road segment information includes attribute information of a route, real-time information, a historical accident and/or a probability of congestion;
and the second determining unit is used for determining that the target road section is the accident road section if the probability that the target road section is the accident road section is greater than the probability threshold.
13. The apparatus of claim 12, further comprising:
and a third determining unit, configured to determine, if all of a plurality of consecutive target links are determined as accident links, a link with a highest probability among the plurality of consecutive target links as an accident link, and determine, as a non-accident link, a link other than the link with the highest probability.
14. The apparatus of any of claims 8-13, further comprising:
and a fourth determination unit for determining the road section with the road section speed lower than the speed threshold value as the congested road section.
15. An electronic device, comprising:
at least one processor; and a memory communicatively coupled to the at least one processor;
wherein the memory stores instructions executable by the at least one processor to enable the at least one processor to perform the method of any one of claims 1-7.
16. A non-transitory computer readable storage medium having stored thereon computer instructions for causing the computer to perform the method of any one of claims 1-7.
17. A computer program product comprising a computer program which, when executed by a processor, implements the method of any one of claims 1-7.
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Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN116189134A (en) * | 2023-04-26 | 2023-05-30 | 宜宾闪马智通科技有限公司 | Region identification method and device based on image identification and radar |
| CN116863708A (en) * | 2023-09-04 | 2023-10-10 | 成都市青羊大数据有限责任公司 | Smart city scheduling distribution system |
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- 2022-03-30 CN CN202210327333.XA patent/CN114676178A/en active Pending
Cited By (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN116189134A (en) * | 2023-04-26 | 2023-05-30 | 宜宾闪马智通科技有限公司 | Region identification method and device based on image identification and radar |
| CN116863708A (en) * | 2023-09-04 | 2023-10-10 | 成都市青羊大数据有限责任公司 | Smart city scheduling distribution system |
| CN116863708B (en) * | 2023-09-04 | 2024-01-12 | 成都市青羊大数据有限责任公司 | Smart city scheduling distribution system |
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