CN109993969B - Road condition judgment information acquisition method, device and equipment - Google Patents
Road condition judgment information acquisition method, device and equipment Download PDFInfo
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- CN109993969B CN109993969B CN201910176262.6A CN201910176262A CN109993969B CN 109993969 B CN109993969 B CN 109993969B CN 201910176262 A CN201910176262 A CN 201910176262A CN 109993969 B CN109993969 B CN 109993969B
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
- G06V20/00—Scenes; Scene-specific elements
- G06V20/50—Context or environment of the image
- G06V20/56—Context or environment of the image exterior to a vehicle by using sensors mounted on the vehicle
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- G08G1/00—Traffic control systems for road vehicles
- G08G1/01—Detecting movement of traffic to be counted or controlled
- G08G1/0104—Measuring and analyzing of parameters relative to traffic conditions
- G08G1/0108—Measuring and analyzing of parameters relative to traffic conditions based on the source of data
- G08G1/0112—Measuring and analyzing of parameters relative to traffic conditions based on the source of data from the vehicle, e.g. floating car data [FCD]
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- G—PHYSICS
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- G08G—TRAFFIC CONTROL SYSTEMS
- G08G1/00—Traffic control systems for road vehicles
- G08G1/01—Detecting movement of traffic to be counted or controlled
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- G08G1/0125—Traffic data processing
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Abstract
The invention discloses a road condition judgment information acquisition method, a device and equipment, wherein the method comprises the following steps: acquiring real-time track information of a vehicle in a driving road section; determining a suspected congestion road section in the driving road section according to the real-time track information; acquiring a target image, wherein the target image is an image in front of the vehicle, which is acquired at the suspected congestion road section; obtaining road section key information of the suspected congested road section according to the target image; the road section key information comprises vehicle density information and road section identification information; and sending real-time track information corresponding to the suspected congested road section and the key information of the road section to obtain road condition judgment information. The method and the device can effectively identify abnormal driving behaviors, reasonably judge road conditions and effectively avoid misjudgment.
Description
Technical Field
The invention relates to the technical field of vehicle information processing, in particular to a road condition judgment information acquisition method, a road condition judgment information acquisition device and road condition judgment information acquisition equipment.
Background
The current technologies for real-time road condition determination include a real-time road condition processing technology based on a floating car track and a fixed-point road condition distinguishing technology based on a monitoring video.
The method comprises the steps that a real-time road condition production technology based on a floating car track specifically takes real-time track data as input, and the road condition state is judged by calculating a single-car speed sample and a multi-car average speed; however, this technique is likely to cause erroneous determination when the vehicle is in an abnormal driving behavior.
The fixed-point road condition discrimination technology based on the monitoring video generally includes that a traffic management department erects camera equipment above an important intersection or road section, then, terminal equipment or a background server identifies vehicles in an acquired image to obtain density information, and then, judgment of whether a road is congested is given by combining road information. However, due to the limitations of authority, cost, and the like required for deploying the device, the device can only be used for acquiring traffic information of a relatively small number of fixed road segments, and cannot support a large-scale traffic information service.
Therefore, it is necessary to provide a technical solution that can efficiently determine traffic information without being limited to the support range.
Disclosure of Invention
In order to solve the problems in the prior art, the invention provides a road condition judgment information acquisition method, a road condition judgment information acquisition device and road condition judgment equipment, and specifically comprises the following steps:
on one hand, the method for acquiring the road condition judgment information is provided, and the method comprises the following steps:
acquiring real-time track information of a vehicle in a driving road section;
determining a suspected congestion road section in the driving road section according to the real-time track information;
acquiring a target image, wherein the target image is an image in front of the vehicle, which is acquired at the suspected congestion road section;
obtaining road section key information of the suspected congested road section according to the target image; the road section key information comprises vehicle density information and road section identification information;
sending real-time track information corresponding to the suspected congested road section and the key information of the road section to obtain road condition judgment information
In another aspect, a method for acquiring traffic condition determination information is provided, where the method includes:
acquiring real-time track information of a vehicle in a driving road section;
the real-time track information is sent, so that a suspected congestion road section in the driving road section is determined according to the real-time track information, and a target image is obtained; the target image is a vehicle front image collected at the suspected congestion road section;
sending real-time track information corresponding to the suspected congested road section and the target image so as to obtain road condition judgment information according to the real-time track information corresponding to the suspected congested road section and road section key information; the key information of the road section is obtained according to the image in front of the vehicle; the road section key information includes vehicle density information and road section identification information.
Another aspect provides a traffic condition determination information acquiring apparatus, including:
the real-time information acquisition module is used for acquiring real-time track information of the vehicle in a driving road section;
the suspected congestion road section determining module is used for determining a suspected congestion road section in the driving road section according to the real-time track information;
a target image acquisition module, configured to acquire a target image, where the target image is an image of a vehicle ahead collected at the suspected congested road segment;
the road section key information obtaining module is used for obtaining road section key information of the suspected congestion road section according to the target image; the road section key information comprises vehicle density information and road section identification information;
a first information sending module, configured to send real-time track information corresponding to the suspected congested road segment and the key information of the road segment to obtain road condition determination information
Another aspect provides a traffic condition determination information acquiring apparatus, including:
the track information acquisition module is used for acquiring real-time track information of the vehicle in a driving road section;
the track information sending module is used for sending the real-time track information so as to determine a suspected congestion road section in the driving road section according to the real-time track information and obtain a target image; the target image is a vehicle front image collected at the suspected congestion road section;
the second information sending module is used for sending the real-time track information corresponding to the suspected congested road section and the target image so as to obtain road condition judgment information according to the real-time track information corresponding to the suspected congested road section and the road section key information; the key information of the road section is obtained according to the image in front of the vehicle; the road section key information comprises vehicle density information and road section identification information
Another aspect provides a device comprising a processor and a memory, wherein the memory stores at least one instruction, at least one program, a set of codes, or a set of instructions, and the at least one instruction, the at least one program, the set of codes, or the set of instructions is loaded and executed by the processor to implement the traffic condition determination information acquisition method according to the above one aspect, or loaded and executed to implement the traffic condition determination information acquisition method according to the above another aspect.
The road condition judgment information acquisition method, the road condition judgment information acquisition device and the road condition judgment information acquisition equipment have the beneficial effects that:
the method comprises the steps of acquiring real-time track information of a vehicle in a driving road section; further determining a suspected congestion road section in the driving road section according to the real-time track information; acquiring a target image, wherein the target image is an image in front of the vehicle, which is acquired at the suspected congestion road section; obtaining road section key information of the suspected congested road section according to the target image; and sending real-time track information corresponding to the suspected congested road section and the key information of the road section to obtain road condition judgment information. According to the invention, the real vehicle density information and the road section identification information on the road can be effectively obtained by identifying the target image, and the road condition state is comprehensively judged by further utilizing the real-time track information and the key information of the road section, so that the abnormal driving behavior is effectively identified, the road condition judgment is reasonably carried out, and the occurrence of misjudgment is effectively avoided.
Drawings
In order to more clearly illustrate the embodiments of the present disclosure or the technical solutions and advantages of the prior art, the drawings used in the description of the embodiments or the prior art will be briefly described below, it is obvious that the drawings in the following description are only some embodiments of the present disclosure, and other drawings can be obtained by those skilled in the art without creative efforts.
Fig. 1 is a scene diagram provided in an embodiment of the present specification;
fig. 2 is a flowchart of a method for acquiring traffic information according to an embodiment of the present disclosure;
FIG. 3 is a flowchart illustrating steps provided by embodiments of the present disclosure to determine a target track segment in the travel path segment;
FIG. 4 is a flowchart of steps provided by an embodiment of the present description after determining a target track segment in the travel segment;
FIG. 5 is a flowchart illustrating an implementation of acquiring a target image according to an embodiment of the present disclosure;
FIG. 6 is an information interaction diagram in a synchronous on-demand screenshot mode provided in an embodiment of the present specification;
fig. 7 is a flowchart of another road condition determination information acquisition method provided in the embodiments of the present specification;
FIG. 8 is an information interaction diagram in an asynchronous on-demand screenshot mode provided by an embodiment of the present specification;
fig. 9 is a block diagram illustrating a traffic condition determination information acquiring apparatus according to an embodiment of the present disclosure;
fig. 10 is a block diagram illustrating another traffic information acquiring apparatus according to an embodiment of the present disclosure;
fig. 11 is a schematic structural diagram of a server device provided in an embodiment of the present specification.
Detailed Description
The technical solutions in the embodiments of the present disclosure will be clearly and completely described below with reference to the drawings in the embodiments of the present disclosure, and it is obvious that the described embodiments are only a part of the embodiments of the present disclosure, and not all of the embodiments. All other embodiments, which can be obtained by a person skilled in the art without any inventive step based on the embodiments of the present invention, are within the scope of the present invention.
It should be noted that the terms "first," "second," and the like in the description and claims of the present invention and in the drawings described above are used for distinguishing between similar elements and not necessarily for describing a particular sequential or chronological order. It is to be understood that the data so used is interchangeable under appropriate circumstances such that the embodiments of the invention described herein are capable of operation in sequences other than those illustrated or described herein. Furthermore, the terms "comprises," "comprising," and "having," and any variations thereof, are intended to cover a non-exclusive inclusion, such that a process, method, system, article, or server that comprises a list of steps or elements is not necessarily limited to those steps or elements expressly listed, but may include other steps or elements not expressly listed or inherent to such process, method, article, or apparatus.
In the current technical scheme of road condition information judgment, misjudgment is easy to occur when a vehicle belongs to abnormal driving behaviors; and because the road is monitored by the road test facilities which are installed too much, the road condition information service in a large range cannot be supported, and certain limitation is realized.
Therefore, the present specification provides a technical solution for efficiently determining a road condition to obtain road condition determination information, and can be oriented to an application scenario of performing a road condition information service in a large range; in addition, the technical scheme can be independently implemented, and can also be combined with the road condition production technology of the existing floating car so as to improve the quality of real-time road condition information; the technical scheme can be applied to the road sections with rare traffic flows or special road sections with frequent abnormal driving behaviors, and state misjudgment caused by track data of the abnormal driving behaviors can be effectively avoided.
Wherein the abnormal driving behavior is: this refers to a behavior in which the vehicle does not travel with a normal traffic flow because of a violation of traffic control measures or for other purposes. For example: driving on an emergency lane, running on a red light, intentionally driving at a slow speed, intentionally stopping at a roadside, and the like. Under these behavior states, the speed of the vehicle cannot reflect the true road congestion state; for example, when a road is really congested, the vehicle is high in speed because of passing through an emergency lane; when the road is clear, the vehicle exhibits a low speed due to intentional roadside parking.
Correspondingly, normal driving behavior: the vehicle is a behavior that the vehicle can go to a destination as quickly as possible on the premise of complying with the traffic regulations. In this behavior state, the vehicle running speed is only subjected to: the running speed can well reflect the real congestion state of the road under the influence of factors such as the quality of road facilities, the speed limit of the road, traffic management measures such as signal lamps and the like, the type of vehicles, the congestion degree of traffic flow and the like.
Fig. 1 is a scene diagram corresponding to a technical solution for acquiring traffic information according to an embodiment of the present disclosure; in this specification, two different implementation modes can be selected according to the difference in the capabilities of the devices mounted on the actual vehicle:
the first mode is as follows: carrying out a synchronous on-demand screenshot mode of image recognition in real time on the terminal equipment;
and a second mode: and carrying out an asynchronous on-demand screenshot mode of image recognition in real time at the server side.
Specifically, an embodiment of the present specification provides a method for acquiring traffic condition determination information, which belongs to a technical scheme in a synchronous on-demand screenshot mode, and as shown in fig. 2, the method includes:
s202, acquiring real-time track information of a vehicle in a driving road section;
in the embodiment, the terminal equipment on the vehicle acquires real-time track information of the vehicle on a driving road section; the real-time track information comprises vehicle positioning related information and vehicle speed related information; in detail, the vehicle positioning related information includes longitude coordinates, latitude coordinates, azimuth angles, positioning accuracy, and the like of the vehicle; the vehicle speed-related information includes an instantaneous speed of the vehicle and the like.
The method comprises the following steps that an execution cycle can be set, real-time track information is obtained in the execution cycle, and road condition information of the real-time track information is judged; for example, the following settings can be selected: 10 seconds, 30 seconds, or 1 minute, etc.; generally, more than 1 minute is not preferred. Further, the determination results of the traffic information in the multiple execution cycles can be fused to obtain the traffic determination result in the expected target time period.
S204, determining a suspected congestion road section in the driving road section according to the real-time track information;
s206, acquiring a target image, wherein the target image is an image in front of the vehicle, which is acquired at the suspected congestion road section;
in a specific embodiment, the step S204 of determining the suspected congested road segment in the traveling road segment may include:
S2A, processing the real-time track information to determine a target track segment in the driving road section;
S2B, obtaining the suspected congestion road section from the target track segment;
specifically, the terminal device performs information processing on the real-time track information to determine a target track segment from the driving road segment, and further obtains the suspected congestion road segment from the target track segment.
Step S2A determines the target track segment in the driving road segment, as shown in fig. 3, which may include:
s402, traversing each track point in the driving road section;
s404, obtaining the running speed corresponding to each track point according to the real-time track information of each track point;
and S406, obtaining the target track segment by using a plurality of continuous track points with the running speed less than the preset speed.
Specifically, each track point has corresponding real-time track information, and the running speed of the vehicle at the track point can be obtained according to the real-time track information of each track point, and can be regarded as the instantaneous speed of the vehicle at the track point; further comparing the instantaneous speed with a preset speed, in case the instantaneous speed with a plurality of continuous track points is lower than the preset speed, a target track segment consisting of the plurality of continuous track points can be obtained.
For example, traversing each GPS point of the travel route if the instantaneous speed of the GPS point is less than the predetermined speed VjamIf the GPS is in the congestion state, the GPS points continuously in the congestion state form a congestion time interval, and the track corresponding to the congestion time interval is the target track segment.
It should be noted that, in this embodiment, a preset distance (for example, 200 to 500 meters, which may also be set by referring to the length of a conventional road segment) for the vehicle to travel may also be selected as a determination object, and further, an average speed of each preset distance may be calculated according to the time used by the preset distance; setting a speed threshold value, and comparing each obtained average speed with the speed threshold value to be used as a judgment basis of the congestion state; for example, the speed threshold value is 20km/h, and the preset distance is in a congestion state under the condition that the obtained average speed is lower than 20 km/h.
In a specific embodiment, the step S2A may determine the target track segment in the driving route, as shown in fig. 4, and then include:
S4A, judging the number of the target track fragments;
S4B, acquiring the duration corresponding to the target track segment;
S4C, when the number of the target track fragments is larger than a preset number, selecting the target track fragments with the preset number; wherein the duration of the selected target track segment is greater than the duration of any unselected target track segment.
Wherein, the number of the target track segments may be one or more; when the number of the target track segments is multiple and is greater than the preset number N, sequencing the multiple target track segments according to time length, only reserving or only selecting the preset number N of the target track segments, and collecting the images in front of the vehicle for the selected preset number N of the target track segments; and when the number of the target track segments is less than a preset number N, directly acquiring the intercepted image corresponding to the target track segments.
In one possible implementation, the step S2B determines a suspected congestion road segment in the driving road segment, and then includes:
S2C, acquiring an image interception request;
specifically, after the suspected congestion road section is determined, the terminal device generates and acquires an image interception request.
Step S206, acquiring a target image, including:
s602, acquiring an intercepted image corresponding to the target track segment according to the image intercepting request;
and S604, obtaining the target image by the intercepted image corresponding to the target track segment.
Specifically, the target image is obtained from the captured image obtained by the image capture request.
In detail, the step S602 of acquiring the captured image corresponding to the target track segment may include:
S6A, determining the starting moment of the target track segment;
and S6B, acquiring an image of the starting moment of the target track segment to obtain a captured image corresponding to the target track segment.
Specifically, the starting moment of the selected target track segment is a screenshot requirement moment, and is used for requesting to capture an image from a corresponding moment of video information acquisition of the image acquisition equipment; and obtaining the target image from a set of N intercepted images with preset number. For example, when only 3 target track segments with the longest duration are retained, the starting time of the 3 target track segments is used as the screenshot requirement time, and an image set consisting of 3 captured images, namely the target image, can be further obtained; an execution flow chart for acquiring a target image is given as shown in fig. 5.
It should be noted that the device for acquiring the image in front of the vehicle in the driving road section may be an image acquisition device such as a vehicle data recorder on the vehicle; or other intelligent vehicle-mounted equipment with the camera shooting function can be developed through a necessary terminal and has the capability of shooting in real time and delaying screenshot in a local video,
s208, obtaining road section key information of the suspected congested road section according to the target image; the road section key information comprises vehicle density information and road section identification information;
in this embodiment, the target image is identified by using an image identification technology based on a deep neural network, and the key information of the road section is obtained. In order to reduce the complexity of the recognition problem and the complexity of the recognition result being used, the vehicle density information is described by three discrete levels of "more vehicles", "general", "less vehicles". In many cases, only the vehicle density information is considered, and an effective road condition judgment result cannot be obtained; therefore, in this embodiment, the road condition is judged by considering the road section identification information at the same time; the road section identification information may be special lane identification information on a road, such as identification information of whether the road section is on a bus lane, whether the road section is on an emergency lane, and the like; the information has important decision value for eliminating the influence of other types of abnormal driving behaviors.
Under the condition that the target image comprises a plurality of intercepted images, vehicles in each intercepted image are identified through an image identification process based on a deep neural network, the number of the vehicles in the image is obtained, and each intercepted image is further converted into discrete label information of one of three, namely 'more vehicles', 'general' and 'less vehicles'. And meanwhile, obtaining the road section identification information in the intercepted image based on the process of image recognition.
And S210, sending real-time track information corresponding to the suspected congestion road section and the key information of the road section to obtain road condition judgment information.
In this embodiment, the sending of the key information of the road section is specifically to send vehicle density information and road section identification information of all captured images in the target image to a server; in this embodiment, the obtaining of the road condition determination information specifically is that the server performs comprehensive determination on the road condition information by using the vehicle density information and the road section identification information of the captured image and the real-time track information corresponding to the captured image, so as to obtain the road condition determination information of the driving road section.
Specifically, if the vehicle is a non-public transport vehicle, and the obtained road section identification information of one of the captured images is special lane identification information, such as whether the current vehicle is located in a public transport lane or an emergency lane; if so, the vehicle at the moment is considered to be in abnormal driving behavior, and the information of the intercepted image object is discarded. If the vehicle is a non-public transport vehicle, the obtained road section identification information is a normal motor lane, and if the speed information in the corresponding real-time track information is lower than a set speed threshold value at the moment, and the corresponding vehicle density information is 'more vehicles', the road condition judgment result of the running road section can be obtained to be 'blocked' under the condition; the embodiment effectively judges the road condition information by fusing the information of the captured images under all normal driving behaviors.
In the embodiment, the road condition can be judged through the machine learning model to obtain a road condition judgment result; specifically, vehicle density information, road section identification information and real-time track information corresponding to the captured image are converted into input vectors, and the input vectors are input into a neural network model (such as a convolutional neural network model CNN) to obtain a road condition judgment result of the driving road section.
Fig. 6 is an information interaction diagram in the synchronous on-demand screenshot mode. In the implementation, the road condition information is judged by integrating the real-time track information and the key information of the road section, so that the influence of abnormal driving behaviors can be eliminated in a larger range, and the quality of the comprehensive judgment result of the road condition is improved.
An embodiment of the present specification provides a method for acquiring traffic condition determination information, which belongs to a technical scheme in an asynchronous on-demand screenshot mode, and as shown in fig. 7, the method includes:
s802, acquiring real-time track information of a vehicle in a driving road section;
specifically, terminal equipment on a vehicle acquires real-time track information of the vehicle on a driving road section; the real-time track information comprises vehicle positioning related information and vehicle speed related information; in detail, the vehicle positioning related information includes longitude coordinates, latitude coordinates, azimuth angles, positioning accuracy information, and the like of the vehicle; the vehicle speed-related information includes an instantaneous speed of the vehicle and the like.
S804, sending the real-time track information to determine a suspected congestion road section in the driving road section according to the real-time track information and obtain a target image; the target image is a vehicle front image collected at the suspected congestion road section;
in a possible implementation manner, the step S804 of sending the real-time track information to determine a suspected congestion section of the travel sections according to the real-time track information may include:
S8A, sending the real-time track information to determine a target track segment in the driving road section according to the real-time track information;
S8B, obtaining the suspected congestion road section from the target track segment;
specifically, the terminal device sends the real-time track information to a server, and the server processes the real-time track information to determine the suspected congested road section.
Further, the server generates a screenshot request corresponding to the suspected congestion road section, and sends the screenshot request to the terminal device; and after receiving the screenshot request, the terminal equipment acquires a corresponding screenshot image from the corresponding moment of the image information acquired by the image acquisition equipment so as to obtain an image in front of the vehicle corresponding to the suspected congestion road section, and sends the image in front of the vehicle to a server. Specifically, an image of the start time of each target track segment is obtained, and the target image is obtained.
Wherein the step S8A of determining the target track segment in the driving road segment includes:
s1002, traversing each track point in the driving road section;
s1004, obtaining the running speed corresponding to each track point according to the real-time track information of each track point;
s1006, acquiring road attribute information of the driving road section;
and S1008, determining the target track segment according to the running speed of the track points in the running road section and the road attribute information of the running road section.
Specifically, the embodiment can perform road matching on the track through the server, and identify the road attribute information based on the accurate position of the vehicle on the driving road section; the road attribute information of the driving road section is inherent information of a road corresponding to the driving road section, and comprises a road type and a lane number, wherein the lane number comprises the number of lanes in the same direction; for example, the road is a high-grade road and 2 lanes are arranged in the same direction; or a high-grade road and 3 lanes in the same direction, etc.
The server determines a target track segment according to the road attribute information and the running speed of the track point, specifically obtains the limited speed under the road attribute information, takes the limited speed under the road attribute information as a speed threshold value, and compares the speed threshold value with the running speed of the track point to determine the target track segment.
For example, when the high-grade road has 2 lanes in the same direction, the lowest speed limit of the left lane is 100 km/h; if the driving speed of the track point is lower than 100 km/h when the vehicle runs on the lane, and a plurality of continuous track points are further obtained, the plurality of continuous track points can be considered to form a target track segment.
It should be noted that the preset parameters or the threshold values of some parameters in the present specification are not limited to specific values, and may be set according to requirements.
According to the embodiment, the congestion condition of the track points can be accurately judged by combining the corresponding speed threshold according to the road attribute information; and then the suspected congested road section is judged with high reliability, so that the validity of the road condition information judgment is ensured.
S806, sending real-time track information corresponding to the suspected congested road section and the target image to obtain road condition judgment information according to the real-time track information corresponding to the suspected congested road section and road section key information; the key information of the road section is obtained according to the image in front of the vehicle; the road section key information includes vehicle density information and road section identification information.
Specifically, the terminal device sends the target image to a server, and the server performs identification processing on the target image to obtain key road section information of the suspected congested road section; and further, the server obtains road condition judgment information according to the real-time track information and the road section key information corresponding to the suspected congestion road section.
Fig. 8 is an information interaction diagram in the asynchronous on-demand screenshot mode. According to the technical scheme for acquiring the road condition judgment information, the screenshot requirement is generated under the condition that the suspected congestion road section is detected, the image of the front of the suspected congestion road section is acquired, the burden of processing all image information is reduced, and the efficiency of judging the road condition information is improved.
It should be noted that the embodiments in the present description may be implemented independently, or may be implemented in combination with the existing road condition obtaining technology based on a floating car; especially in the case where the available equipment with photographing capability is not yet large in scale, it is more reasonable to: a large amount of real-time road condition data are generated by using a technology based on a floating car, then a high-reliability road condition data subset is obtained by using the embodiment of the specification, and the road condition data of the two subsets are fused to obtain final road condition judgment information.
In addition, traffic signal information (such as signal lights and traffic signs) can be acquired in the embodiment and comprehensively judged; or in this embodiment, the instant weather information may also be obtained to obtain an accurate speed threshold (for example, if the speed limit in foggy or rainy weather is 60km/h, 60km/h may be updated to a preset speed threshold).
It is further described that the traffic condition determination information obtained in the present specification has higher quality, and can further improve the performance of the traffic condition display function, the Estimated Time of Arrival (ETA) estimation of the trip, the route planning, and other functions in various service channels.
An embodiment of the present specification provides a traffic condition determination information obtaining apparatus, as shown in fig. 9, the apparatus includes:
the real-time information acquisition module 202 is used for acquiring real-time track information of a vehicle in a driving road section;
a suspected congestion road section determining module 204, configured to determine a suspected congestion road section in the driving road section according to the real-time track information;
a target image obtaining module 206, configured to obtain a target image, where the target image is a vehicle front image collected at the suspected congested road segment;
a road section key information obtaining module 208, configured to obtain road section key information of the suspected congested road section according to the target image; the road section key information comprises vehicle density information and road section identification information;
the first information sending module 210 is configured to send real-time track information corresponding to the suspected congested road segment and the key information of the road segment, so as to obtain road condition determination information.
In one possible embodiment, the suspected congested road segment determination module includes:
the target track segment determining unit is used for processing the real-time track information to determine a target track segment in the driving road section;
a suspected congestion road section determining unit, configured to obtain the suspected congestion road section from the target track segment;
wherein the target track segment determining unit includes:
the first track point traversing subunit is used for traversing each track point in the driving road section;
the first track point speed obtaining subunit is used for obtaining the running speed corresponding to each track point according to the real-time track information of each track point;
and the first target track segment obtaining subunit is used for obtaining the target track segment from a plurality of continuous track points with the running speed less than the preset speed.
In a possible implementation manner, the suspected congested road segment determination module further includes:
an image interception request acquisition unit for acquiring an image interception request;
wherein, the target image acquisition module comprises:
the intercepted image acquisition unit is used for acquiring an intercepted image corresponding to the target track fragment according to the image interception request;
and the target image obtaining unit is used for obtaining the target image from the intercepted image corresponding to the target track fragment.
In a possible implementation manner, the suspected congested road segment determination module further includes:
a track segment judging unit, configured to judge the number of the target track segments;
the track segment duration acquisition unit is used for acquiring duration corresponding to the target track segment;
the track segment selection unit is used for selecting a preset number of target track segments when the number of the target track segments is greater than a preset number; wherein the duration of the selected target track segment is greater than the duration of any unselected target track segment.
In a possible implementation, the truncated image obtaining unit includes:
a start time determining subunit, configured to determine a start time of the target track segment;
and the intercepted image obtaining subunit is used for obtaining the image of the starting moment of the target track segment so as to obtain the intercepted image corresponding to the target track segment.
It should be noted that the device embodiments have the same inventive concept as the method embodiments described above, and for concrete content, reference is made to the corresponding method embodiments.
This specification also provides a traffic condition determination information acquisition device, as shown in fig. 10, the device includes:
a track information obtaining module 402, configured to obtain real-time track information of a vehicle in a driving road section;
a track information sending module 404, configured to send the real-time track information, so as to determine a suspected congested road segment in the driving road segment according to the real-time track information, and obtain a target image; the target image is a vehicle front image collected at the suspected congestion road section;
a second information sending module 406, configured to send real-time track information corresponding to the suspected congested road segment and the target image, so as to obtain road condition determination information according to the real-time track information corresponding to the suspected congested road segment and road segment key information; the key information of the road section is obtained according to the image in front of the vehicle; the road section key information includes vehicle density information and road section identification information.
In a possible implementation manner, the track information sending module includes:
the track information sending unit is used for sending the real-time track information so as to determine a target track segment in the driving road section according to the real-time track information;
a suspected congestion segment obtaining unit, configured to obtain the suspected congestion road segment from the target track segment;
wherein, the track information sending unit includes:
the second track point traversing subunit is used for traversing each track point in the driving road section;
the second track point speed obtaining subunit is used for obtaining the running speed corresponding to each track point according to the real-time track information of each track point;
a road attribute information acquiring subunit, configured to acquire road attribute information of the travel section;
and the second track segment determining subunit is used for determining the target track segment according to the running speed of the track point in the running road segment and the road attribute information of the running road segment.
It should be noted that the device embodiments have the same inventive concept as the method embodiments described above, and for concrete content, reference is made to the corresponding method embodiments.
An embodiment of the present specification provides an apparatus, which includes a processor and a memory, where the memory stores at least one instruction, at least one program, a code set, or an instruction set, and the at least one instruction, the at least one program, the code set, or the instruction set is loaded and executed by the processor to implement the road condition decision information obtaining method according to the above method embodiment.
Specifically, please refer to fig. 11 for a schematic structural diagram of a server device provided in an embodiment of the present specification. The server is used for implementing the road condition judgment information acquisition method provided in the above embodiment. Specifically, the method comprises the following steps:
the server 2000 includes a Central Processing Unit (CPU)2001, a system memory 2004 including a Random Access Memory (RAM)2002 and a Read Only Memory (ROM)2003, and a system bus 2005 connecting the system memory 2004 and the central processing unit 2001. The server 2000 also includes a basic input/output system (I/O system) 2006 to facilitate transfer of information between devices within the computer, and a mass storage device 2007 to store an operating system 2013, application programs 2014, and other program modules 2015.
The basic input/output system 2006 includes a display 2008 for displaying information and an input device 2009 such as a mouse, keyboard, etc. for a user to input information. Wherein the display 2008 and the input devices 2009 are coupled to the central processing unit 2001 through an input-output controller 2010 coupled to the system bus 2005. The basic input/output system 2006 may also include an input/output controller 2010 for receiving and processing input from a number of other devices, such as a keyboard, mouse, or electronic stylus. Similarly, the input-output controller 2010 also provides output to a display screen, a printer, or other type of output device.
The mass storage device 2007 is connected to the central processing unit 2001 through a mass storage controller (not shown) connected to the system bus 2005. The mass storage device 2007 and its associated computer-readable media provide non-volatile storage for the server 2000. That is, the mass storage device 2007 may include a computer-readable medium (not shown) such as a hard disk or CD-ROM drive.
Without loss of generality, the computer-readable media may comprise computer storage media and communication media. Computer storage media includes volatile and nonvolatile, removable and non-removable media implemented in any method or technology for storage of information such as computer readable instructions, data structures, program modules or other data. Computer storage media includes RAM, ROM, EPROM, EEPROM, flash memory or other solid state memory technology, CD-ROM, DVD, or other optical storage, magnetic cassettes, magnetic tape, magnetic disk storage or other magnetic storage devices. Of course, those skilled in the art will appreciate that the computer storage media is not limited to the foregoing. The system memory 2004 and mass storage device 2007 described above may be collectively referred to as memory.
The server 2000 may also operate as a remote computer connected to a network via a network, such as the internet, according to various embodiments of the present invention. That is, the server 2000 may be connected to the network 2012 through a network interface unit 2011 that is coupled to the system bus 2005, or the network interface unit 2011 may be utilized to connect to other types of networks or remote computer systems (not shown).
The memory also includes one or more programs stored in the memory and configured to be executed by one or more processors; the one or more programs include instructions for performing the method of the backend server side.
The embodiment of the present invention further provides a computer storage medium, where the storage medium may be disposed in a client to store at least one instruction, at least one program, a code set, or an instruction set related to implementing a method for acquiring traffic condition determination information in the method embodiments, and the at least one instruction, the at least one program, the code set, or the instruction set is loaded and executed by the processor to implement the method for acquiring traffic condition determination information provided in the method embodiments.
Optionally, in this embodiment, the storage medium may be located in at least one network device of a plurality of network devices of a computer network. Optionally, in this embodiment, the storage medium may include, but is not limited to: a U-disk, a Read-Only Memory (ROM), a Random Access Memory (RAM), a removable hard disk, a magnetic disk, or an optical disk, which can store program codes.
It should be noted that: the sequence of the embodiments in this specification is merely for description, and does not represent the advantages or disadvantages of the embodiments. And specific embodiments thereof have been described above. Other embodiments are within the scope of the following claims. In some cases, the acts or steps loaded in the claims may be performed in a different order than in the embodiments and still achieve desirable results. In addition, the processes depicted in the accompanying figures do not necessarily require the particular order shown, or sequential order, to achieve desirable results. In some embodiments, multitasking and parallel processing may also be possible or may be advantageous.
The embodiments in the present specification are described in a progressive manner, and the same and similar parts among the embodiments are referred to each other, and each embodiment focuses on the differences from the other embodiments. In particular, as for the device and server embodiments, since they are substantially similar to the method embodiments, the description is simple, and the relevant points can be referred to the partial description of the method embodiments.
It will be understood by those skilled in the art that all or part of the steps for implementing the above embodiments may be implemented by hardware, or may be implemented by a program instructing relevant hardware, where the program may be stored in a computer-readable storage medium, and the above-mentioned storage medium may be a read-only memory, a magnetic disk or an optical disk, etc.
The above description is only for the purpose of illustrating the preferred embodiments of the present invention and is not to be construed as limiting the invention, and any modifications, equivalents, improvements and the like that fall within the spirit and principle of the present invention are intended to be included therein.
Claims (9)
1. A road condition judgment information acquisition method is characterized by comprising the following steps:
acquiring real-time track information of a vehicle in a driving road section;
processing the real-time track information to determine a target track segment in the driving road section; obtaining a suspected congestion road section in the driving road section according to the target track segment;
acquiring an image interception request;
determining the starting moment of the target track segment according to the image capturing request, wherein the starting moment of the target track segment is the moment of screenshot requirement and is used for requesting to capture an image from the corresponding moment of video information acquired by image acquisition equipment;
acquiring an image of the starting moment of the target track segment to obtain an intercepted image corresponding to the target track segment;
an image set consisting of a plurality of intercepted images corresponding to the target track segment forms a target image, and the target image is a vehicle front image acquired at the suspected congestion road segment;
obtaining road section key information of the suspected congested road section according to the target image; the road section key information comprises vehicle density information and road section identification information; the road section identification information is obtained based on a target image recognition process; the road section identification information is used for eliminating road section key information corresponding to the vehicle with abnormal driving behaviors; and sending real-time track information corresponding to the suspected congested road section and the key information of the road section to obtain road condition judgment information.
2. The traffic condition decision information acquisition method according to claim 1,
the determining the target track segment in the driving road section comprises the following steps:
traversing each track point in the driving road section;
obtaining the running speed corresponding to each track point according to the real-time track information of each track point;
and obtaining the target track segment by a plurality of continuous track points with the running speed less than the preset speed.
3. The method according to claim 2, wherein the determining the target track segment in the travel section comprises:
judging the number of the target track segments;
acquiring the time length corresponding to the target track segment;
when the number of the target track segments is larger than the preset number, selecting the preset number of target track segments; wherein the duration of the selected target track segment is greater than the duration of any unselected target track segment.
4. A road condition judgment information acquisition method is characterized by comprising the following steps:
acquiring real-time track information of a vehicle in a driving road section;
sending the real-time track information, and processing the real-time track information to determine a target track segment in the driving road section; obtaining a suspected congestion road section in the driving road section from the target track segment, and obtaining an image interception request; determining the starting moment of the target track segment according to the image capturing request, wherein the starting moment of the target track segment is the moment of screenshot requirement and is used for requesting to capture an image from the corresponding moment of video information acquired by image acquisition equipment;
acquiring an image of the starting moment of the target track segment to obtain an intercepted image corresponding to the target track segment; an image set formed by a plurality of intercepted images corresponding to the target track segment forms a target image; the target image is a vehicle front image collected at the suspected congestion road section;
sending real-time track information corresponding to the suspected congested road section and the target image so as to obtain road condition judgment information according to the real-time track information corresponding to the suspected congested road section and road section key information; the key information of the road section is obtained according to the image in front of the vehicle; the road section key information comprises vehicle density information and road section identification information, wherein the road section identification information is obtained based on a target image recognition process; the road section identification information is used for eliminating road section key information corresponding to the vehicle with abnormal driving behaviors.
5. The method according to claim 4, wherein the determining the target track segment in the travel section comprises:
traversing each track point in the driving road section;
obtaining the running speed corresponding to each track point according to the real-time track information of each track point;
acquiring road attribute information of the driving road section;
and determining the target track segment according to the running speed of the track points in the running road section and the road attribute information of the running road section.
6. A traffic condition determination information acquisition apparatus, comprising:
the real-time information acquisition module is used for acquiring real-time track information of the vehicle in a driving road section;
the suspected congestion road section determining module is used for processing the real-time track information to determine a target track segment in the driving road section; obtaining a suspected congestion road section in the driving road section according to the target track segment;
the target image acquisition module is used for determining the starting moment of the target track segment, wherein the starting moment of the target track segment is the moment required for screenshot, and is used for requesting to capture an image from the corresponding moment of video information acquired by the image acquisition equipment;
acquiring an image of the starting moment of the target track segment to obtain an intercepted image corresponding to the target track segment;
an image set consisting of a plurality of intercepted images corresponding to the target track segment forms a target image, and the target image is a vehicle front image acquired at the suspected congestion road segment; the road section key information obtaining module is used for obtaining road section key information of the suspected congestion road section according to the target image; the road section key information comprises vehicle density information and road section identification information; the road section identification information is obtained based on a target image recognition process; the road section identification information is used for eliminating road section key information corresponding to the vehicle with abnormal driving behaviors;
and the first information sending module is used for sending real-time track information corresponding to the suspected congestion road section and the key information of the road section so as to obtain road condition judgment information.
7. A traffic condition determination information acquisition apparatus, comprising:
the track information acquisition module is used for acquiring real-time track information of the vehicle in a driving road section;
the track information sending module is used for sending the real-time track information so as to process the real-time track information to determine a target track segment in the driving road section; obtaining a suspected congestion road section in the driving road section from the target track segment, and obtaining an image interception request; determining the starting moment of the target track segment according to the image capturing request, wherein the starting moment of the target track segment is the moment of screenshot requirement and is used for requesting to capture an image from the corresponding moment of video information acquired by image acquisition equipment;
acquiring an image of the starting moment of the target track segment to obtain an intercepted image corresponding to the target track segment;
an image set formed by a plurality of intercepted images corresponding to the target track segment forms a target image; the target image is a vehicle front image collected at the suspected congestion road section; the second information sending module is used for sending the real-time track information corresponding to the suspected congested road section and the target image so as to obtain road condition judgment information according to the real-time track information corresponding to the suspected congested road section and the road section key information; the key information of the road section is obtained according to the image in front of the vehicle; the road section key information comprises vehicle density information and road section identification information, wherein the road section identification information is obtained based on a target image recognition process; the road section identification information is used for eliminating road section key information corresponding to the vehicle with abnormal driving behaviors.
8. An apparatus, comprising a processor and a memory, wherein the memory stores at least one instruction, at least one program, a code set, or a set of instructions, and the at least one instruction, the at least one program, the code set, or the set of instructions is loaded and executed by the processor to implement the method for acquiring traffic condition determination information according to any one of claims 1 to 3, or loaded and executed to implement the method for acquiring traffic condition determination information according to any one of claims 4 to 5.
9. A computer storage medium, characterized in that the storage medium stores at least one instruction, at least one program, code set or instruction set, and the at least one instruction, the at least one program, the code set or instruction set is loaded and executed by a processor to implement the method for acquiring road condition determination information according to any one of claims 1 to 3, or loaded and executed to implement the method for acquiring road condition determination information according to any one of claims 4 to 5.
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Families Citing this family (19)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN108417029B (en) * | 2018-02-11 | 2020-11-06 | 东南大学 | Urban road network travel time estimation method based on self-adaptive multitask deep learning |
| CN111768629B (en) * | 2019-11-04 | 2022-04-12 | 北京京东乾石科技有限公司 | Vehicle scheduling method, device and system |
| CN112857381A (en) * | 2019-11-28 | 2021-05-28 | 北京搜狗科技发展有限公司 | Path recommendation method and device and readable medium |
| CN111815945B (en) * | 2019-12-17 | 2022-05-06 | 北京嘀嘀无限科技发展有限公司 | Image acquisition method and device for congested road section, storage medium and electronic equipment |
| CN113221602B (en) * | 2020-01-21 | 2023-09-29 | 百度在线网络技术（北京）有限公司 | Road surface condition determining method, device, equipment and medium |
| CN113283272B (en) * | 2020-02-20 | 2022-09-27 | 百度在线网络技术（北京）有限公司 | Real-time image information prompting method and device for road congestion and electronic equipment |
| CN111523482A (en) * | 2020-04-24 | 2020-08-11 | 深圳市商汤科技有限公司 | Lane congestion detection method and apparatus, electronic device, and storage medium |
| CN111561945B (en) * | 2020-05-26 | 2022-01-04 | 中咨数据有限公司 | Navigation path generation method and device based on Internet of vehicles |
| CN111649752B (en) * | 2020-05-29 | 2021-09-21 | 北京四维图新科技股份有限公司 | Map data processing method, device and equipment for congested road section |
| CN111739291B (en) * | 2020-06-05 | 2023-01-13 | 腾讯科技（深圳）有限公司 | Interference identification method and device in road condition calculation |
| CN112132315A (en) * | 2020-08-18 | 2020-12-25 | 华为技术有限公司 | Escape route prediction method and deployment and control platform of target object |
| CN111932891B (en) * | 2020-08-20 | 2022-03-25 | 腾讯科技（深圳）有限公司 | Road condition identification method and related device |
| CN112150666A (en) * | 2020-08-28 | 2020-12-29 | 汉海信息技术(上海)有限公司 | Method, device, electronic equipment and storage medium for determining road congestion reason |
| CN112435472A (en) * | 2020-11-12 | 2021-03-02 | 北京嘀嘀无限科技发展有限公司 | Congestion analysis method, device, equipment and storage medium |
| CN112885087A (en) * | 2021-01-22 | 2021-06-01 | 北京嘀嘀无限科技发展有限公司 | Method, apparatus, device and medium for determining road condition information and program product |
| CN112926425A (en) * | 2021-02-10 | 2021-06-08 | 北京嘀嘀无限科技发展有限公司 | Road state detection method, device and equipment |
| CN113066285B (en) * | 2021-03-15 | 2022-12-09 | 北京百度网讯科技有限公司 | Road condition information determining method and device, electronic equipment and storage medium |
| CN113343905B (en) * | 2021-06-28 | 2022-06-14 | 山东理工大学 | Method and system for training road abnormity intelligent recognition model and recognizing road abnormity |
| CN114037963B (en) * | 2021-11-26 | 2022-08-16 | 中关村科学城城市大脑股份有限公司 | Road sign state monitoring method and device, storage medium and equipment |
Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN102768801A (en) * | 2012-07-12 | 2012-11-07 | 复旦大学 | Method for detecting motor vehicle green light follow-up traffic violation based on video |
| CN205943076U (en) * | 2016-05-27 | 2017-02-08 | 武汉万集信息技术有限公司 | Detection car -mounted device blocks up |
| CN107038861A (en) * | 2017-04-07 | 2017-08-11 | 北京易华录信息技术股份有限公司 | A kind of traffic congestion detecting system, method and device based on vehicle electron identifying |
Family Cites Families (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN105654417A (en) * | 2016-01-13 | 2016-06-08 | 北京中交兴路信息科技有限公司 | Lorry parking point information obtaining method and system |
| CN107204113A (en) * | 2016-03-17 | 2017-09-26 | 高德软件有限公司 | Determine the methods, devices and systems of congestion in road state |
| CN106056910A (en) * | 2016-07-13 | 2016-10-26 | 乐视控股（北京）有限公司 | Method and device for traffic state detection |
| CN106023593B (en) * | 2016-07-26 | 2018-04-03 | 深圳市喜悦智慧数据有限公司 | A kind of traffic jam detection method and device |
| JP2019036012A (en) * | 2017-08-10 | 2019-03-07 | トヨタ自動車株式会社 | Information notification device, information notification system, information notification method, and information notification program |
| CN109360416A (en) * | 2018-10-11 | 2019-02-19 | 平安科技（深圳）有限公司 | Road traffic prediction technique and server |
-
2019
- 2019-03-08 CN CN201910176262.6A patent/CN109993969B/en active Active
Patent Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN102768801A (en) * | 2012-07-12 | 2012-11-07 | 复旦大学 | Method for detecting motor vehicle green light follow-up traffic violation based on video |
| CN205943076U (en) * | 2016-05-27 | 2017-02-08 | 武汉万集信息技术有限公司 | Detection car -mounted device blocks up |
| CN107038861A (en) * | 2017-04-07 | 2017-08-11 | 北京易华录信息技术股份有限公司 | A kind of traffic congestion detecting system, method and device based on vehicle electron identifying |
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