CN115249406A

CN115249406A - Road condition data acquisition method and device, electronic equipment and readable storage medium

Info

Publication number: CN115249406A
Application number: CN202110469800.8A
Authority: CN
Inventors: 左帆; 王雪松
Original assignee: Alibaba Singapore Holdings Pte Ltd
Current assignee: Alibaba Innovation Co
Priority date: 2021-04-28
Filing date: 2021-04-28
Publication date: 2022-10-28
Anticipated expiration: 2041-04-28
Also published as: CN115249406B

Abstract

The embodiment of the disclosure discloses a road condition data acquisition method, a road condition data acquisition device, electronic equipment and a readable storage medium, wherein the method comprises the following steps: acquiring track positions of a vehicle sample, and matching the track positions of the vehicle sample with preset road network data to acquire an out-degree path of the vehicle sample passing through an intersection, wherein the out-degree path consists of an in-degree target road section and a corresponding out-degree target road section; calculating to obtain the passing parameters on the corresponding entry degree target road section and the exit degree target road section of the vehicle sample based on the track position data of the vehicle sample; for one intersection, the traffic parameters of the vehicle samples with the same entrance target road section and the exit direction are fused to obtain the road condition data of the corresponding entrance target road section in the exit direction. The technical scheme provides directional fine road condition information for the same road section, and provides a finer data basis for road condition calculation, navigation planning, ahead driving change, congestion avoidance and the like, so that the travel cost of a user is greatly reduced.

Description

Road condition data acquisition method and device, electronic equipment and readable storage medium

Technical Field

The embodiment of the disclosure relates to the technical field of traffic data processing, in particular to a road condition data acquisition method and device, electronic equipment and a readable storage medium.

Background

With the development and progress of the society, vehicles on roads are more and more, and the condition of road congestion is more and more common, so that the travel of many users depends on the distribution of traffic real-time road condition data. However, the current traffic real-time traffic data is traffic state information obtained by taking a road section as a unit, and when an intersection exists at the end of a certain road section, the traffic conditions going to different directions are in a chaotic fusion state, and the difference of the traffic conditions in different directions cannot be reflected.

Disclosure of Invention

The embodiment of the disclosure provides a road condition data acquisition method, a road condition data acquisition device, electronic equipment and a readable storage medium.

In a first aspect, an embodiment of the present disclosure provides a road condition data obtaining method.

Specifically, the road condition data acquiring method includes:

acquiring the track position of a vehicle sample, and matching the track position of the vehicle sample with preset road network data to acquire an out-of-service route of the vehicle sample passing through an intersection, wherein the out-of-service route consists of an in-service target road section and a corresponding out-of-service target road section;

calculating to obtain the passing parameters on the corresponding entry degree target road section and the exit degree target road section of the vehicle sample based on the track position data of the vehicle sample;

for one intersection, the traffic parameters of the vehicle samples with the same entrance target road section and the exit direction are fused to obtain the road condition data of the corresponding entrance target road section in the exit direction.

In a second aspect, an embodiment of the present disclosure provides a road condition data acquiring device.

Specifically, the road condition data acquiring device includes:

the system comprises an acquisition module, a processing module and a display module, wherein the acquisition module is configured to acquire the track positions of a vehicle sample, match the track positions of the vehicle sample with preset road network data and acquire the outgoing routes of the vehicle sample passing through an intersection, and the outgoing routes are one or more and consist of an incoming target road section and a corresponding outgoing target road section which the vehicle sample passes through;

the calculation module is configured to calculate and obtain the passing parameters on the corresponding in-degree target road section and out-degree target road section of the vehicle sample based on the track position data of the vehicle sample;

and the fusion module is configured to fuse the traffic parameters of the vehicle samples with the same entrance target road section and the same exit direction for one intersection to obtain the road condition data of the corresponding entrance target road section in the exit direction.

In a third aspect, an embodiment of the present disclosure provides an electronic device, including a memory and a processor, where the memory is configured to store one or more computer instructions that support a traffic data acquisition device to execute the traffic data acquisition method, and the processor is configured to execute the computer instructions stored in the memory. The road condition data acquisition device may further include a communication interface for the road condition data acquisition device to communicate with other devices or a communication network.

In a fourth aspect, an embodiment of the present disclosure provides a computer-readable storage medium for storing computer instructions for a traffic data acquiring device, where the computer instructions are used to execute the traffic data acquiring method to a traffic data acquiring device.

The technical scheme provided by the embodiment of the disclosure can have the following beneficial effects:

according to the technical scheme, the passing parameters of the vehicle samples are calculated by obtaining the position data of the vehicle samples running on the road section, and the road condition data in different passing directions are obtained by combining the passing paths of the vehicle samples passing through the intersection. The technical scheme provides the precise road condition information with directionality for the same road section, and provides a more precise data base for road condition calculation, navigation planning, ahead-time changing, congestion avoidance and the like, so that the travel cost of a user is greatly reduced.

It is to be understood that both the foregoing general description and the following detailed description are exemplary and explanatory only and are not restrictive of embodiments of the disclosure.

Drawings

Other features, objects, and advantages of embodiments of the disclosure will become apparent from the following detailed description of non-limiting embodiments, which proceeds with reference to the accompanying drawings. In the drawings:

fig. 1 is a flowchart illustrating a road condition data acquisition method according to an embodiment of the present disclosure;

2A-2F illustrate a target road segment segmentation schematic according to an embodiment of the present disclosure;

FIG. 3 illustrates a target sub-segment segmentation schematic according to an embodiment of the present disclosure;

FIG. 4 illustrates a road out section acquisition schematic according to an embodiment of the disclosure;

FIG. 5 illustrates a outbound path acquisition diagram according to an embodiment of the present disclosure;

fig. 6 is a block diagram illustrating a road condition data acquiring apparatus according to an embodiment of the present disclosure;

fig. 7 is a schematic structural diagram of a computer system suitable for implementing the road condition data acquisition method according to an embodiment of the present disclosure.

Detailed Description

Hereinafter, exemplary embodiments of the disclosed embodiments will be described in detail with reference to the accompanying drawings so that they can be easily implemented by those skilled in the art. Also, for the sake of clarity, parts not relevant to the description of the exemplary embodiments are omitted in the drawings.

In the disclosed embodiments, it is to be understood that terms such as "including" or "having," etc., are intended to indicate the presence of the disclosed features, numbers, steps, behaviors, components, parts, or combinations thereof, and are not intended to preclude the possibility that one or more other features, numbers, steps, behaviors, components, parts, or combinations thereof may be present or added.

It should be further noted that the embodiments and features of the embodiments in the present disclosure may be combined with each other without conflict. The embodiments of the present disclosure will be described in detail below with reference to the accompanying drawings in conjunction with embodiments.

According to the technical scheme provided by the embodiment of the disclosure, the passing parameters of the vehicle samples are calculated by acquiring the position data of the vehicle samples running on the road section, and the road condition data in different outgoing directions are obtained by combining the outgoing paths of the vehicle samples passing through the intersection. The technical scheme provides the precise road condition information with directionality for the same road section, and provides a more precise data base for road condition calculation, navigation planning, ahead-time changing, congestion avoidance and the like, so that the travel cost of a user is greatly reduced.

Fig. 1 shows a flow chart of a traffic data acquiring method according to an embodiment of the present disclosure, and as shown in fig. 1, the traffic data acquiring method includes the following steps S101 to S103:

in step S101, obtaining a track position of a vehicle sample, and matching the track position of the vehicle sample with preset road network data to obtain an out-degree path of the vehicle sample passing through an intersection, where the out-degree path is composed of an in-degree target road segment and a corresponding out-degree target road segment;

in step S102, based on the track position data of the vehicle sample, calculating to obtain the passing parameters on the corresponding entry degree target road section and the exit degree target road section of the vehicle sample;

in step S103, for an intersection, the traffic parameters of the vehicle samples having the same entry target road segment and the same exit direction are fused to obtain the road condition data of the corresponding entry target road segment in the exit direction.

As mentioned above, with the development and progress of society, vehicles on roads are more and more, and road congestion is more and more common, so that many users travel by relying on the distribution of traffic real-time road condition data. However, the current traffic real-time traffic data is traffic state information obtained by taking a road section as a unit, and when an intersection exists at the end of a certain road section, the traffic conditions going to different directions are in a chaotic fusion state, and the difference of the traffic conditions in different directions cannot be reflected.

In view of the above problem, in this embodiment, a traffic data acquiring method is provided, which calculates traffic parameters of vehicle samples by acquiring position data of the vehicle samples traveling on a road segment, and acquires traffic data in different traffic directions in combination with traffic routes of the vehicle samples passing through an intersection. The technical scheme provides the precise road condition information with directionality for the same road section, and provides a more precise data base for road condition calculation, navigation planning, ahead-time changing, congestion avoidance and the like, so that the travel cost of a user is greatly reduced.

In an embodiment of the present disclosure, the road condition data acquiring method may be applied to a terminal computer, a computing device, an electronic device, a server, a service cluster, and the like, which may perform road condition data acquisition and calculation.

In an embodiment of the present disclosure, the vehicle sample refers to a vehicle traveling on a target road segment at a certain traveling speed, and the vehicle is loaded with a GNSS device, such as a GPS positioning device, a beidou positioning device, and the like, which can acquire GNSS track position data, wherein the GNSS track position data can be sent out by means of a data communication device loaded on the vehicle.

In an embodiment of the present disclosure, the road network data refers to road network data obtained based on geographic data and/or divided road division data, where the road network data includes one or more pieces of road information, intersection information between roads, lengths of roads, attributes of roads, levels of roads, and the like. The attributes of the roads can be, for example, high expressway and non-high expressway, the high expressway includes expressway, urban expressway and the like, and the non-high expressway includes urban main road, national road, provincial road, county road and the like; the level of the road refers to a level to which the road belongs, for example, the level of the high expressway is higher than the level of the non-high expressway, and the level of the main road, the national road and the provincial road in the city is higher than the level of the prefectural road.

In an embodiment of the present disclosure, the out-of-service route refers to a route obtained by connecting a current road segment and a next road segment, i.e., an exit road, connected to the current road segment after passing through a certain intersection, that is, the out-of-service route includes an in-service target road segment and a corresponding out-of-service target road segment that the vehicle sample passes through. In many cases, there is more than one exit road, and thus more than one exit route.

In one embodiment of the present disclosure, the target link refers to a road or a part of the road in a certain direction between two intersections for calculating road condition data in the certain direction, that is, the road condition data is calculated in units of the target link, and the road may be divided into two or more target links if the road in the certain direction between two intersections is too long or meets a preset dividing condition.

In an embodiment of the present disclosure, the traffic parameter includes a traffic speed and/or a traffic time. The passing speed can be calculated based on the track position data of the vehicle sample and the length of the target road section where the vehicle sample is located, and the passing time can be directly obtained based on the track position data of the vehicle sample.

In an embodiment of the present disclosure, the out-degree direction refers to a direction to which the out-degree path points, and if the out-degree paths are the same, the direction to which the out-degree paths point is also considered to be the same.

In the above embodiment, the departure route of the vehicle sample passing through the intersection is obtained based on the track position data of the vehicle sample, then the traffic parameters of the vehicle sample on the corresponding departure target road section and the corresponding entry target road section are obtained by calculation based on the track position data of the vehicle sample, and then for one intersection, the traffic parameters of the vehicle sample with the same departure direction and the same entry target road section are fused, so that the road condition data of the corresponding departure target road section in the departure direction can be obtained. Therefore, the road condition information in different directions can be provided for the same entrance road section, a more detailed data basis is provided for road condition calculation, navigation planning, ahead-of-time driving change, congestion avoidance and the like, and the driving cost of a user is greatly reduced. Meanwhile, as can be seen from the above, the above embodiment does not need to use detailed information of each road at lane level, and also does not need to use traffic light timing information, and can calculate road condition data in different directions only based on position data of a vehicle sample running on a road section, so that not only can calculation resources be saved and calculation efficiency be improved, but also the embodiment can be used in various scenes including frequent changes of road or lane information.

In an embodiment of the present disclosure, the method further comprises the steps of:

and determining a target road section based on the road network data.

As mentioned above, the calculation of the traffic data uses the target road segment as a unit, so the target road segment needs to be determined before calculating the traffic data.

In an embodiment of the present disclosure, the step of determining the target road segment based on the road network data may include the steps of:

determining an initial road segment with a first preset length along the reverse driving direction of the vehicle by taking an intersection as a starting point based on the road network data;

and detecting whether a division point exists in the initial road section, if so, dividing the initial road section into one or more target road sections according to the division point, and if not, determining the initial road section as the target road section.

When the target road section is determined, firstly, an initial road section with a first preset length is determined along the opposite direction of vehicle driving, namely the direction of vehicle coming, by taking an intersection as a starting point based on the road network data, wherein the first preset length can be set according to the requirements of practical application and the characteristics of roads, for example, the first preset length can be set to 400 meters or 500 meters, and specific values of the first preset length are not specifically limited in the disclosure; and then detecting whether a segmentation point exists in the initial road section, if so, segmenting the initial road section into one or more target road sections according to the segmentation point, and if not, determining the initial road section as the target road section.

The division point may be set differently according to the road attribute, for example, for a high-speed road, the division point may be set as a non-high-speed road intersection, a single lane intersection, a toll gate, and the like, that is, in a high-speed road scene, for the initial road segment, a determination is made in a reverse direction of vehicle travel, the division of the road segment may be performed when the non-high-speed road is encountered, the division of the road segment may be performed when a certain road segment is determined to be a single lane, the division of the road segment may be performed when the toll gate is encountered, the division of the road segment may be performed when a continuous exit and an entrance are encountered, and the like. For another example, for a non-high expressway, the division point may be set as a traffic light, a turn point, or the like, that is, in a non-high expressway scene, the initial road segment is determined along the opposite direction of vehicle travel, and the division of the road segment may be performed if the traffic light, the turn point, or the turn point is encountered.

Fig. 2A to 2F are schematic diagrams illustrating target segment division according to an embodiment of the present disclosure, and in fig. 2A to 2F, the initial segment is divided into one or more target segments by the division points as described above, as shown by the positions of arrows in fig. 2A to 2F.

In an embodiment of the present disclosure, the step of determining the target road segment based on the road network data may further include the steps of:

and if the target road section obtained after the segmentation meets the preset condition, determining the target road section as a non-target road section.

In view of the fact that the reference value of the target road segment obtained by dividing through the dividing point is small when the direction-dividing road condition data is calculated, in this embodiment, a preset condition is set to distinguish the target road segment, that is, whether the target road segment obtained by dividing meets the preset condition is judged, and if the preset condition is met, the target road segment is determined to be a non-target road segment and does not participate in the subsequent direction-dividing road condition data calculation, or even participate in the subsequent direction-dividing road condition data, the issuing operation is not executed.

The preset condition may be, for example: the target road segment is an entrance road segment, as shown by the road segment indicated by the dotted arrow in fig. 2C; the target road segment is a single lane, as indicated by the road segment indicated by the dashed arrow in fig. 2E; the length of the target road section is smaller than a preset length threshold; the road level of the target road section is higher than a preset level threshold, for example, the target road section is a main urban road, a national road or a provincial road, and the number of the exit directions of the target road section is greater than a preset number threshold, for example, greater than 8; and so on.

dividing the target road segment into one or more target sub-road segments by a second preset length along the opposite direction of vehicle driving by taking the intersection as a starting point based on the road network data;

for each target sub-road section, calculating vehicle passing parameter difference values in different out-of-degree directions;

if the vehicle passing parameter difference value exceeds a preset parameter threshold value, confirming that the target sub-road section is a target sub-road section with directional road condition difference;

fusing continuous target sub-road sections with directional road condition difference into a target sub-road section;

and taking the target sub-road section as the target road section.

In order to improve the fineness of the calculation of the direction-specific road condition data, in this embodiment, the target link is divided again. Specifically, firstly, based on the road network data, taking an intersection as a starting point, dividing the target road segment into one or more target sub-road segments by a second preset length along a reverse direction of vehicle driving, where the second preset length is smaller than the first preset length, and the second preset length may be set according to a requirement of practical application and characteristics of a road, for example, 10 meters or 20 meters, and a specific value of the second preset length is not specifically limited by the present disclosure; then, calculating the difference value of the vehicle passing parameters in different out-of-degree directions for each target sub-road section; if the vehicle passing parameter difference value exceeds a preset parameter threshold value, the target sub-road section can be confirmed to be the target sub-road section with the directional road condition difference; then, fusing continuous target sub-road sections with directional road condition differences into a target sub-road section; and finally, taking the target sub-road section as the target road section, namely taking the target sub-road section as a calculation unit of the traffic data of different directions.

Fig. 3 is a schematic diagram illustrating a target sub-link division according to an embodiment of the present disclosure, as shown in fig. 3, a target link is divided into 4 target sub-links by a second preset length along a reverse direction of vehicle driving with an intersection as a starting point: the target sub-section 1, the target sub-section 2, the target sub-section 3 and the target sub-section 4 may determine that there is a difference in the directional road conditions corresponding to the target sub-section 1, the target sub-section 2 and the target sub-section 3 based on GNSS location data of a vehicle sample a, a vehicle sample b, a vehicle sample c and a vehicle sample d driven on the target sub-section, and there is no difference in the directional road conditions corresponding to the target sub-section 4, and thus, the target sub-section 1, the target sub-section 2 and the target sub-section 3 may be integrated into one target sub-section as one target section, and the target sub-section 4 may be used as another target section.

In an embodiment of the present disclosure, the step S101 of matching the track position of the vehicle sample with the preset road network data to obtain the outbound route of the vehicle sample passing through the intersection may include the following steps:

for an intersection, matching the track position of a vehicle sample with preset road network data to obtain an in-degree target road section and an out-degree target road section through which the vehicle sample passes;

and taking an incoming degree target section where the vehicle sample is located before passing through the intersection as a starting point section, taking an outgoing degree target section where the vehicle sample is located after passing through the intersection as an end point section, and generating an outgoing degree path where the vehicle sample passes through the intersection.

In this embodiment, in order to obtain a departure path of a vehicle sample passing through an intersection, for a certain intersection, first, a track position of the vehicle sample is matched with preset road network data to obtain an incoming target road segment and a departure target road segment through which the vehicle sample passes, then, an incoming target road segment where the vehicle sample is located before passing through the intersection is taken as a starting point road segment, and a departure target road segment where the vehicle sample is located after passing through the intersection is taken as an ending point road segment, so that the departure path of the vehicle sample passing through the intersection can be generated.

Fig. 4 is a schematic diagram illustrating an out-of-service road section acquisition according to an embodiment of the disclosure, where a plurality of in-service target road sections exist in fig. 4, and taking an in-service target road section L1, an in-service target road section L2, an in-service target road section L3, and an in-service target road section L4 which are located on the same side of a traffic light, have the same driving direction, and have a distance from an intersection smaller than the first preset length as an example, as shown in fig. 4, right turning-out degrees of the in-service target road section L1 after passing through an intersection are road sections L5 and L6, straight turning-out degrees after passing through an intersection are road sections L7 and L8, left turning-out degrees after passing through an intersection are road sections L9, and turn-around turning-out degrees are road sections L10 and L11; the approach target road section L2 is similar to the approach target road section L1, the right turn-out degree after passing through the intersection is road sections L5 and L6, the straight-going out degree after passing through the intersection is road sections L7 and L8, the left turn-out degree after passing through the intersection is road section L9, and the turn-around out degree is road sections L10 and L11; the approach target section L3 is similar to the approach target section L1, the right turn-out after passing through the intersection is sections L5 and L6, the straight-going out after passing through the intersection is sections L7 and L8, and the left turn-out after passing through the intersection is section L9, except that the turn-around out of the approach target section L3 is only section L10; the right turn-out degree of the approach target section L4 after passing through the intersection is only the section L6, the straight-ahead turn-out degrees after passing through the intersection are the sections L7 and L8, the left turn-out degree after passing through the intersection is the section L9, and the turn-around turn-out degree is the section L10, as shown in the following table.

	Degree of right turn-out	Straight going out degree	Left turn-out degree	Turning over the head
					L1	L5，L6	L7，L8	L9	L10，L11
L2	L5，L6	L7，L8	L9	L10，L11
					L3	L5，L6	L7，L8	L9	L10
L4	L6	L7，L8	L9	L10

After the out-degree section of the in-degree target section is determined, an in-degree target section where the vehicle sample is located before passing through the intersection can be used as a starting point, the out-degree section where the vehicle sample is located after passing through the intersection can be used as an end point, and an out-degree path where the vehicle sample passes through the intersection is generated. Fig. 5 shows a outbound path acquisition schematic according to an embodiment of the present disclosure, in fig. 5, there are 6 vehicle samples: the vehicle sample 1, the vehicle sample 2, the vehicle sample 3, the vehicle sample 4, the vehicle sample 5, and the vehicle sample 6, wherein the travel tracks of the 6 vehicle samples are indicated in fig. 5 by using symbols with different shapes, as shown in fig. 5, the vehicle sample 1 and the vehicle sample 2 do not pass through an intersection, and therefore there is no corresponding outbound route, and the outbound route of the vehicle sample 3 includes three: l1- > L3, L2- > L3, L3- > L7, the out-of-service route of the vehicle sample 4 also includes three: l1- > L3, L2- > L3, L3- > L4, the out-of-service route of the vehicle sample 5 includes two: l1- > L5, L2- > L5, the out-of-service route of the vehicle sample 6 also includes two: l1- > L6, L2- > L6.

In an embodiment of the present disclosure, the step S103 of fusing the traffic parameters of the vehicle samples having the same incoming target road segment and the outgoing direction for one intersection to obtain the road condition data of the corresponding incoming target road segment in the outgoing direction may include the following steps:

determining fusion weight elements and corresponding fusion element weight values;

grouping the vehicle samples according to an incoming degree target road section and an outgoing degree direction to obtain one or more outgoing degree groups;

and for one out-degree group, calculating the road condition data of the corresponding in-degree target road section in the corresponding out-degree direction based on the fusion element weight value and the traffic parameters of the vehicle sample.

In this embodiment, a fusion weight element and a corresponding fusion element weight value are first determined, and in an embodiment of the present disclosure, the fusion weight element may include, for example: sample time freshness weight, sample velocity distribution weight, sample operation state weight, sample coverage weight, sample position data return interval weight, sample position data drift weight, sample velocity fluctuation weight, sample abnormal behavior weight, and the like.

The sample time freshness weight is used for representing the influence of the early and late of the vehicle sample data acquisition time on the calculation of the current road condition data, and the earlier the vehicle sample data acquisition time is, namely the smaller the time difference between the vehicle sample data acquisition time and the current time is, the more referential significance is considered to the calculation of the vehicle sample data on the current road condition data, so the weight is larger, and vice versa.

The sample speed distribution weight is used for representing the influence of the distribution of the vehicle sample passing speed on the calculation of the current road condition data, and the closer the vehicle sample passing speed is to the average passing speed or the smaller the difference with the other vehicle sample passing speeds is, the more referential significance is considered to the calculation of the current road condition data by the vehicle sample data, so the weight is larger, and vice versa.

The sample operation state weight is used for representing the influence of the operation state of the vehicle sample on the calculation of the current road condition data, for example, for a taxi sample, the purpose of driving the passenger-carrying taxi sample relative to an unloaded taxi sample is stronger, and the passenger-carrying taxi sample data is considered to have more reference significance on the calculation of the current road condition data, so that the weight is larger, for example, for a common vehicle, the purpose of driving the vehicle sample in a navigation state relative to the vehicle sample in a non-navigation state is stronger, and the weight is considered to be larger because the vehicle sample data in the navigation state has more reference significance on the calculation of the current road condition data, and vice versa.

The sample coverage rate weight is used for representing the influence of the mileage coverage rate of the vehicle sample on the road section on the calculation of the current road condition data, and the higher the mileage coverage rate of the vehicle sample on the road section is, namely the longer the distance the vehicle sample travels on the road section is, the more referential significance is considered to the calculation of the vehicle sample data on the current road condition data, so the weight is larger, and vice versa.

The sample position data returning interval weight is used for representing the influence of the vehicle sample position data returning interval on the calculation of the current road condition data, and the shorter the vehicle sample position data returning interval is, namely the more frequent the vehicle sample position data is returned, the more referential significance is considered to the calculation of the current road condition data by the vehicle sample data, so the greater the weight of the vehicle sample data is, and vice versa.

The sample position data drift weight is used for representing the influence of the drift of the vehicle sample position data on the calculation of the current road condition data, and the smaller the drift of the vehicle sample position data is, the more referential significance is considered to be brought to the calculation of the vehicle sample data on the current road condition data, so the larger the weight is, and vice versa.

The sample speed fluctuation weight is used for representing the influence of the fluctuation of the vehicle sample speed on the calculation of the current road condition data, and the smaller the fluctuation of the vehicle sample passing speed, the more meaningful the calculation of the vehicle sample data on the current road condition data is considered, so the weight is larger, and vice versa.

The sample abnormal behavior weight is used for representing the influence of the abnormal behavior of the vehicle sample on the calculation of the current road condition data, the smaller the possibility that the vehicle sample is identified as the abnormal behavior is, the more referential the vehicle sample is considered to be in the calculation of the current road condition data, so the weight of the vehicle sample is larger, and vice versa, wherein the abnormal behavior can comprise temporary stopping of the vehicle sample, the passing speed of the vehicle sample is lower than a preset speed threshold value in a preset time period, the acceleration of the vehicle sample is lower than a preset acceleration threshold value in the preset time period, and the like.

The fusion element weight value corresponding to the fusion weight element can be set according to the requirement of practical application and the importance degree of the fusion weight element.

And then grouping the vehicle samples according to the out-degree direction to obtain one or more out-degree groups, namely each out-degree group has a consistent out-degree direction. Taking the outbound route shown in fig. 5 as an example, if the currently processed inbound route target segment is L1, the 6 vehicle samples can be divided into four groups according to the outbound direction: the vehicle sample 1 and the vehicle sample 2 can be divided into one group because both do not pass through the road junction; the outgoing directions of the vehicle sample 3 and the vehicle sample 4 are both L3 and can be divided into a group; the outgoing direction of the vehicle sample 5 is L5 and can be divided into a group; the direction of departure of the vehicle sample 6 is L6, and can be divided into one group.

And for each out-of-service group, calculating road condition data of a corresponding in-service target road section in a corresponding out-of-service direction based on the fusion element weight value and the traffic parameters of the vehicle sample.

After the output groups with different output directions are obtained, the road condition data of the corresponding input target road section in the corresponding output direction can be calculated for each output group by integrating the weight values of the fusion elements and the traffic parameters of the vehicle samples.

In an embodiment of the present disclosure, for each outbound packet, the step of calculating road condition data of a corresponding inbound target road segment in a corresponding outbound direction based on the fusion element weight value and the traffic parameter of the vehicle sample may be implemented as:

calculating a fusion weight value corresponding to a vehicle sample based on a fusion element weight value of the vehicle sample;

for each first-out-degree group, calculating the product of the traffic parameter of the vehicle sample and the corresponding fusion weight value to obtain the weighting speed of the vehicle sample;

adding the weighted speeds of all the vehicle samples to obtain a weighted speed sum;

adding the fusion weight values of all the vehicle samples to obtain a weight sum;

dividing the weighted speed sum by the weighted sum to obtain vehicle passing parameters in the corresponding out-of-degree direction;

and determining road condition data of the corresponding entry target road section in the corresponding exit direction according to the vehicle passing parameters.

In this embodiment, a fusion weight value corresponding to each vehicle sample is first calculated based on the fusion element weight value of each vehicle sample, for example, each vehicle sample and the fusion element weight value corresponding to the fusion weight element may be added to obtain a fusion weight value corresponding to the vehicle sample; and then, for each out-of-degree group, calculating the product of the traffic parameter of each vehicle sample and the corresponding fusion weight value to obtain the weighting speed of the vehicle sample. Assuming that the traffic parameter is a traffic speed, three vehicle samples exist in a certain outbound packet a, the corresponding traffic speeds are respectively represented as V1, V2 and V3, and the corresponding fusion weight values are respectively represented as W1, W2 and W3, so that the weighted speeds of the three vehicle samples can be respectively represented as V1 × W1, V2 × W2 and V3 × W3; the weighted velocities for all vehicle samples are then summed to give a weighted velocity sum: v1 xW 1+ V2 xW 2+ V3 xW 3; adding the fusion weight values of all vehicle samples to obtain a weight sum: w1+ W2+ W3; dividing the weighted speed sum by the weighted sum to obtain a vehicle passing parameter in the corresponding out-of-degree direction, where in the above example, the out-of-degree group a may be represented as: (V1 xW 1+ V2 xW 2+ V3 xW 3)/(W1 + W2+ W3); finally, determining road condition data of the corresponding entry target road section in the corresponding departure direction according to the vehicle passing parameters, wherein the road condition data can comprise smooth road, slow road, road congestion, severe road congestion and the like, and for example, if the passing speed VA is greater than a first preset speed threshold value, the road condition of the corresponding entry target road section in the corresponding departure direction can be considered as smooth; if the passing speed VA is greater than a second preset speed threshold but less than a first preset speed threshold, the road condition of the corresponding entry target road section in the corresponding exit direction can be considered as slow-going; if the passing speed VA is greater than the third preset speed threshold but less than the second preset speed threshold, the road condition of the corresponding entry target road section in the corresponding exit direction may be considered as congestion; if the passing speed VA is less than the third preset speed threshold, the road condition of the corresponding entry target road section in the corresponding departure direction may be considered as a heavy congestion.

Further, taking the example of fig. 5 as an example, assuming that the traffic parameter is still the traffic speed, there are two vehicle samples in the first out-of-degree group a: the corresponding traffic speeds of the vehicle sample 1 and the vehicle sample 2 are respectively represented as V1 and V2, and the corresponding fusion weight values are respectively represented as W1 and W2, so that the weighting speeds of the two vehicle samples can be respectively represented as V1 × W1 and V2 × W2; the weighted velocities for all vehicle samples are then summed to give a weighted velocity sum: v1 xW 1+ V2 xW 2; adding the fusion weight values of all vehicle samples to obtain a weight sum: w1+ W2; dividing the weighted speed sum by the weighted sum to obtain a passing speed VA in the outgoing direction corresponding to the outgoing degree group a, which can be expressed as: (V1 × W1+ V2 × W2)/(W1 + W2); there are two vehicle samples in the second out-of-degree grouping B: the corresponding traffic speeds of the vehicle sample 3 and the vehicle sample 4 are respectively represented as V3 and V4, and the corresponding fusion weight values are respectively represented as W3 and W4, and based on the same reason, the traffic speed VB in the outgoing direction corresponding to the outgoing group B can be represented as: (V3 × W3+ V4 × W4)/(W3 + W4); there is only one vehicle sample in each of the third and fourth out-of-degree groupings C and D: the corresponding passing speeds of the vehicle sample 5 and the vehicle sample 6 are respectively represented as V5 and V6, the passing speed VC in the out-degree direction corresponding to the out-degree group C is the passing speed V5 of the vehicle sample 5, and the passing speed VD in the out-degree direction corresponding to the out-degree group D is the passing speed V6 of the vehicle sample 6. And subsequently, determining the road condition in the corresponding departure direction according to the comparison between VA, VB, VC and VD and the preset speed threshold.

In an embodiment of the present disclosure, the method may further include the steps of:

and correcting the road condition data in the corresponding out-degree direction of the corresponding in-degree target road section according to the current scene information, the road condition data of the next in-degree target road section in the driving direction of the vehicle or the unmodified historical road condition data of the same in-degree target road section.

In view of the fact that the calculated traffic data may have a certain deviation, in this embodiment, the traffic data in the corresponding departure direction of the corresponding entry target road segment is further modified according to the current scene information, the traffic data of the next entry target road segment in the vehicle driving direction, or the unmodified historical traffic data of the same entry target road segment.

For example, when the road condition data in the corresponding departure direction of the corresponding entry target road section is corrected according to the road condition data of the next entry target road section in the vehicle traveling direction, it is assumed that the entry target road sections L1 and L2 obtained by calculation are both slowly traveled by turning right and smoothly traveled straight, but the entry target road section L3 in the straight traveling direction is calculated as straight traffic jam, and the straight traffic jam of L2 is corrected to be straight traffic jam.

For example, when the traffic data in the corresponding departure direction of the corresponding entry target segment is modified according to the uncorrected historical traffic data of the same entry target segment, assuming that the traffic data calculated at points L2 and 12 of a certain entry target segment is straight traffic congestion, but the traffic data calculated at points L56 to L59 of a certain time period before point L12 is straight traffic, it is considered that the traffic data calculated at points L12 may have a deviation, and the traffic data with straight traffic congestion needs to be corrected, but it needs to be noted that the historical traffic data refers to the uncorrected historical traffic data rather than the corrected historical traffic data, for example, if the traffic data calculated at point L01 is straight traffic congestion, the traffic data calculated at point L12 is considered as straight traffic congestion, and the traffic data calculated at point L01 does not need to be corrected.

and issuing the road condition data.

After the road condition data is obtained, the road condition data can be published on a traffic medium, a navigation application or other platforms, so that a user can know the current reliable and more precise road condition data, more accurate and precise data bases are provided for strategies of changing rows in advance, avoiding congestion and the like, and the travel cost of the user is greatly reduced.

When the traffic data is published, the traffic data may be published corresponding to corresponding visual or auditory data, for example, in a navigation application, a green section may be used to fill a green road section to represent a smooth traffic, a yellow section may be used to fill a yellow road section to represent a slow traffic, a red section may be used to fill a red road section to represent a congested traffic, a deep red section may be used to represent a heavily congested traffic, and the like.

The following are embodiments of the disclosed apparatus that may be used to perform embodiments of the disclosed methods.

Fig. 6 is a block diagram illustrating a traffic data acquiring apparatus according to an embodiment of the present disclosure, which may be implemented as part or all of an electronic device through software, hardware or a combination of both. As shown in fig. 6, the road condition data acquiring device includes:

the acquisition module 601 is configured to acquire track positions of vehicle samples, and match the track positions of the vehicle samples with preset road network data to acquire a departure path of the vehicle samples passing through an intersection, wherein the departure path is one or more and consists of an entrance target road segment and a corresponding departure target road segment;

the calculation module 602 is configured to calculate, based on the trajectory position data of the vehicle sample, a traffic parameter on an in-degree target road section and an out-degree target road section corresponding to the vehicle sample;

the fusion module 603 is configured to fuse, for an intersection, the traffic parameters of the vehicle samples having the same entrance target road segment and the same exit direction to obtain the road condition data of the corresponding entrance target road segment in the exit direction.

As mentioned above, with the development and progress of society, vehicles on roads are more and more, and road congestion is more and more common, so that many users travel by relying on the distribution of traffic real-time road condition data. However, the current traffic real-time traffic data is non-directional traffic information obtained by taking a road section as a unit, that is, when an intersection exists at the end of a certain road section, the traffic to different directions is in a chaotic fusion state, and the difference of the traffic conditions cannot be reflected.

In view of the above problem, in this embodiment, a traffic data acquiring device is provided, which calculates the passing parameters of the vehicle samples by acquiring the position data of the vehicle samples traveling on the road section, and obtains the traffic data in different outgoing directions by combining the outgoing paths of the vehicle samples passing through the intersection. The technical scheme provides the directional fine road condition information for the same road section, and provides a finer data basis for road condition calculation, navigation planning, ahead-of-time changing, congestion avoidance and the like, so that the travel cost of a user is greatly reduced.

In an embodiment of the present disclosure, the road condition data acquiring device may be implemented as a terminal computer, a computing device, an electronic device, a server, a service cluster, and the like, which can perform road condition data acquisition and calculation.

In one embodiment of the present disclosure, the vehicle sample refers to a vehicle traveling on a target road segment at a certain traveling speed, and the vehicle is loaded with a GNSS device, such as a GPS positioning device, a beidou positioning device, and the like, which can acquire GNSS track position data, wherein the GNSS track position data can be sent out by means of a data communication device loaded on the vehicle.

In an embodiment of the present disclosure, the outbound path refers to a path obtained by connecting a current road segment and a next road segment, i.e., an exit road, connected to the current road segment after passing through a certain intersection, that is, the outbound path is composed of an inbound target road segment and a corresponding outbound target road segment, through which the vehicle sample passes. In many cases, there is more than one exit road, and thus more than one exit route.

In an embodiment of the present disclosure, the target link refers to a road or a part of the road in a certain direction between two intersections for calculating road condition data in the certain direction, that is, the road condition data is calculated in units of the target link, and the road may be divided into two or more target links if the road in the certain direction between the two intersections is too long or meets a preset division condition.

In the above embodiment, the departure route of the vehicle sample passing through the intersection is obtained based on the track position data of the vehicle sample, then the traffic parameters of the vehicle sample on the corresponding departure target road section and the corresponding entry target road section are obtained by calculation based on the track position data of the vehicle sample, and then for one intersection, the traffic parameters of the vehicle sample with the same departure direction and the same entry target road section are fused, so that the road condition data of the corresponding departure target road section in the departure direction can be obtained. Therefore, road condition information in different directions can be provided for the same entrance road section, a more precise data base is provided for road condition calculation, navigation planning, ahead driving change, congestion avoidance and the like, and the traveling cost of a user is greatly reduced. Meanwhile, as can be seen from the above, the above embodiment does not need to use detailed information of each road at lane level, nor traffic light timing information, and can calculate road condition data in different directions only based on position data of a vehicle sample running on a road section, which not only can save calculation resources and improve calculation efficiency, but also can be used in various scenes including frequent changes of road or lane information.

In an embodiment of the present disclosure, the apparatus may further include:

a determination module configured to determine a target road segment based on the road network data.

In an embodiment of the present disclosure, the determining module may be configured to:

determining an initial road segment with a first preset length along the opposite direction of vehicle driving by taking an intersection as a starting point based on the road network data;

The division point may be set differently according to the road attribute, for example, for a high-speed road, the division point may be set as a non-high-speed road intersection, a single lane intersection, a toll gate, and the like, that is, in a high-speed road scene, for the initial road segment, a determination is made in a reverse direction of vehicle travel, the division of the road segment may be performed when the non-high-speed road is encountered, the division of the road segment may be performed when a certain road segment is determined to be a single lane, the division of the road segment may be performed when the toll gate is encountered, the division of the road segment may be performed when a continuous exit and an entrance are encountered, and the like. For another example, for a non-high expressway, the division point may be set as a traffic light, a turn point, etc., that is, in a non-high expressway scene, the initial road segment is determined along the opposite direction of vehicle driving, and the division of the road segment may be performed in the case of a traffic light, a turn point, or a turn point.

Fig. 2A-2F illustrate a target segment division diagram according to an embodiment of the present disclosure, and in fig. 2A-2F, the initial segment is divided into one or more target segments by the division points as described above, as shown by the positions of arrows in fig. 2A-2F.

In an embodiment of the present disclosure, the determining module may be further configured to:

In view of the fact that the target road segment obtained by dividing through the division points has a small reference value when calculating the direction-dividing road condition data, in this embodiment, a preset condition is set to distinguish the target road segment, that is, it is determined whether the target road segment obtained by dividing meets the preset condition, and if so, the target road segment is determined as a non-target road segment and does not participate in the subsequent calculation of the direction-dividing road condition data, or even participate in the subsequent direction-dividing road condition data, the issuing operation is not executed.

The preset condition may be, for example: the target road segment is an entrance road segment, as shown by the road segment indicated by the dotted arrow in fig. 2C; the target road segment is a single lane, as indicated by the road segment indicated by the dashed arrow in fig. 2E; the length of the target road section is smaller than a preset length threshold value; the road level of the target road section is higher than a preset level threshold, for example, the target road section is a main urban road, a national road or a provincial road, and the number of the exit directions of the target road section is greater than a preset number threshold, for example, greater than 8; and so on.

if the vehicle passing parameter difference value exceeds a preset parameter threshold value, confirming that the target sub-road section is the target sub-road section with the directional road condition difference;

fusing continuous target sub-sections with directional road condition difference into a target sub-section;

and taking the target sub-road section as the target road section.

In order to improve the fineness of the calculation of the direction-specific road condition data, in this embodiment, the target link is divided again. Specifically, firstly, based on the road network data, taking an intersection as a starting point, dividing the target road segment into one or more target sub-road segments by a second preset length along a reverse direction of vehicle driving, where the second preset length is smaller than the first preset length, and the second preset length may be set according to the needs of practical application and the characteristics of roads, such as 10 meters and 20 meters, and the specific value of the second preset length is not specifically limited in the present disclosure; then, for each target sub-road section, calculating the difference value of vehicle passing parameters in different out-of-degree directions; if the vehicle passing parameter difference value exceeds a preset parameter threshold value, the target sub-road section can be confirmed to be the target sub-road section with the directional road condition difference; then, fusing continuous target sub-road sections with directional road condition differences into a target sub-road section; and finally, taking the target sub-road section as the target road section, namely taking the target sub-road section as a calculation unit of the traffic data of different directions.

Fig. 3 is a schematic diagram illustrating target sub-link division according to an embodiment of the present disclosure, as shown in fig. 3, a target link is divided into 4 target sub-links by a second preset length along a reverse direction of vehicle driving, with an intersection as a starting point: the target sub-section 1, the target sub-section 2, the target sub-section 3 and the target sub-section 4 may determine that there is a difference in the directional road conditions corresponding to the target sub-section 1, the target sub-section 2 and the target sub-section 3 based on GNSS position data of a vehicle sample a, a vehicle sample b, a vehicle sample c and a vehicle sample d driven on the target sub-section, and there is no difference in the directional road conditions corresponding to the target sub-section 4, and thus, the target sub-section 1, the target sub-section 2 and the target sub-section 3 may be integrated into one target sub-section as one target section, and the target sub-section 4 may be used as another target section.

In an embodiment of the present disclosure, the obtaining module may be configured to match the track position of the vehicle sample with preset road network data to obtain a portion of the outbound route of the vehicle sample passing through the intersection, and configured to:

for an intersection, matching the track position of a vehicle sample with preset road network data to obtain an incoming degree target road section and an outgoing degree target road section through which the vehicle sample passes;

In this embodiment, in order to obtain an out-route of a vehicle sample passing through an intersection, for a certain intersection, first, a track position of the vehicle sample is matched with preset road network data to obtain an in-route target road segment and an out-route target road segment through which the vehicle sample passes, then, an in-route target road segment where the vehicle sample is located before passing through the intersection is used as a starting point road segment, and an out-route target road segment where the vehicle sample is located after passing through the intersection is used as an ending point road segment, so that the out-route of the vehicle sample passing through the intersection can be generated.

Fig. 4 is a schematic diagram illustrating an out-of-service route acquisition according to an embodiment of the present disclosure, where a plurality of in-service target routes exist in fig. 4, and taking an in-service target route L1, an in-service target route L2, an in-service target route L3, and an in-service target route L4 which are located on the same side of a traffic light, have the same driving direction, and have a distance from an intersection smaller than the first preset length as an example, as shown in fig. 4, right turn-out degrees of the in-service target route L1 after passing through the intersection are routes L5 and L6, straight turn-out degrees of the in-service target route L1 after passing through the intersection are routes L7 and L8, left turn-out degrees of the in-service route after passing through the intersection are routes L9, and turn-around degrees are routes L10 and L11; the approach target road section L2 is similar to the approach target road section L1, the right turn-out degree after passing through the intersection is road sections L5 and L6, the straight-going out degree after passing through the intersection is road sections L7 and L8, the left turn-out degree after passing through the intersection is road section L9, and the turn-around out degree is road sections L10 and L11; the approach target section L3 is similar to the approach target section L1, the right turn-out after passing through the intersection is sections L5 and L6, the straight-going out after passing through the intersection is sections L7 and L8, and the left turn-out after passing through the intersection is section L9, except that the turn-around out of the approach target section L3 is only section L10; the right turn-out degree of the approach target section L4 after passing through the intersection is only the section L6, the straight-ahead degree after passing through the intersection is the sections L7 and L8, the left turn-out degree after passing through the intersection is the section L9, and the turn-around degree is the section L10, as shown in the following table.

	Degree of right turn out	Straight going out degree	Left turn out	Turning over the head
					L1	L5，L6	L7，L8	L9	L10，L11
L2	L5，L6	L7，L8	L9	L10，L11
					L3	L5，L6	L7，L8	L9	L10
L4	L6	L7，L8	L9	L10

After the out-degree section of the in-degree target section is determined, the in-degree target section where the vehicle sample is located before passing through the intersection is used as a starting point, the out-degree section where the vehicle sample is located after passing through the intersection is used as an end point, and an out-degree path where the vehicle sample passes through the intersection is generated. Fig. 5 shows a outbound path acquisition schematic according to an embodiment of the present disclosure, in fig. 5, there are 6 vehicle samples: the vehicle sample 1, the vehicle sample 2, the vehicle sample 3, the vehicle sample 4, the vehicle sample 5, and the vehicle sample 6, wherein the travel tracks of the 6 vehicle samples are indicated in fig. 5 by using symbols with different shapes, as shown in fig. 5, the vehicle sample 1 and the vehicle sample 2 do not pass through an intersection, and therefore there is no corresponding outbound route, and the outbound route of the vehicle sample 3 includes three: l1- > L3, L2- > L3, L3- > L7, the out-of-service route of the vehicle sample 4 also includes three: l1- > L3, L2- > L3, L3- > L4, the out-of-service route of the vehicle sample 5 includes two: l1- > L5, L2- > L5, the out-of-service route of the vehicle sample 6 also includes two: l1- > L6, L2- > L6.

In an embodiment of the present disclosure, the fusion module may be configured to:

grouping the vehicle samples according to an incoming target road section and an outgoing direction to obtain one or more outgoing groups;

In this embodiment, first, a fusion weight element and a corresponding fusion element weight value are determined, and in an embodiment of the present disclosure, the fusion weight element may include, for example: sample time freshness weight, sample velocity distribution weight, sample operation state weight, sample coverage weight, sample position data return interval weight, sample position data drift weight, sample velocity fluctuation weight, sample abnormal behavior weight, and the like.

The sample time freshness weight is used for representing the influence of the morning and evening of the vehicle sample data acquisition time on the current road condition data calculation, and the earlier the vehicle sample data acquisition time is, namely the smaller the time difference between the vehicle sample data acquisition time and the current time is, the more referential significance is considered to the calculation of the vehicle sample data on the current road condition data, so the greater the weight is, and vice versa.

The sample speed fluctuation weight is used for representing the influence of the fluctuation of the vehicle sample speed on the calculation of the current road condition data, and the smaller the fluctuation of the vehicle sample passing speed, the more referential significance is considered to be provided for the calculation of the current road condition data by the vehicle sample data, so the larger the weight is, and vice versa.

The fusion element weight value corresponding to the fusion weight element can be set according to the requirements of practical application and the importance degree of the fusion weight element.

And then grouping the vehicle samples according to the output direction to obtain one or more output groups, namely each output group has a consistent output direction. Taking the outbound route shown in fig. 5 as an example, if the currently processed inbound route target segment is L1, the 6 vehicle samples can be divided into four groups according to the outbound direction: the vehicle sample 1 and the vehicle sample 2 can be divided into one group because both do not pass through the road junction; the outgoing directions of the vehicle sample 3 and the vehicle sample 4 are both L3 and can be divided into a group; the out-of-degree direction of the vehicle sample 5 is L5 and can be divided into a group; the direction of departure of the vehicle sample 6 is L6, and can be divided into one group.

And for each out-degree group, calculating the road condition data of the corresponding in-degree target road section in the corresponding out-degree direction based on the fusion element weight value and the traffic parameters of the vehicle sample.

After the output groups with different output directions are obtained, the road condition data of the corresponding input target road section in the corresponding output direction can be calculated for each output group by combining the fusion element weight value and the traffic parameters of the vehicle sample.

In this embodiment, a fusion weight value corresponding to each vehicle sample is first calculated based on the fusion element weight value of each vehicle sample, for example, each vehicle sample and the fusion element weight value corresponding to the fusion weight element may be added to obtain a fusion weight value corresponding to the vehicle sample; and then, for each out-of-degree group, calculating the product of the traffic parameter of each vehicle sample and the corresponding fusion weight value to obtain the weighting speed of the vehicle sample. Assuming that the traffic parameter is a traffic speed, three vehicle samples exist in a certain out-of-range group a, the corresponding traffic speeds of the three vehicle samples are respectively represented as V1, V2 and V3, and the corresponding fusion weight values are respectively represented as W1, W2 and W3, so that the weighting speeds of the three vehicle samples can be respectively represented as V1 × W1, V2 × W2 and V3 × W3; the weighted velocities for all vehicle samples are then summed to give a weighted velocity sum: v1 xW 1+ V2 xW 2+ V3 xW 3; and adding the fusion weight values of all the vehicle samples to obtain a weight sum: w1+ W2+ W3; dividing the weighted speed sum by the weighted sum to obtain a vehicle passing parameter in the corresponding out-of-degree direction, where in the above example, the out-of-degree group a may be represented as: (V1 xW 1+ V2 xW 2+ V3 xW 3)/(W1 + W2+ W3); finally, determining road condition data of the corresponding entry target road section in the corresponding departure direction according to the vehicle passing parameters, wherein the road condition data can comprise smooth road, slow road, road congestion, severe road congestion and the like, and for example, if the passing speed VA is greater than a first preset speed threshold value, the road condition of the corresponding entry target road section in the corresponding departure direction can be considered as smooth; if the passing speed VA is greater than a second preset speed threshold but less than a first preset speed threshold, the road condition of the corresponding in-degree target road section in the corresponding out-degree direction can be considered as slow running; if the passing speed VA is greater than the third preset speed threshold but less than the second preset speed threshold, the road condition of the corresponding entry target road section in the corresponding exit direction may be considered as congestion; if the passing speed VA is less than the third preset speed threshold, the road condition of the corresponding entry target road section in the corresponding departure direction may be considered as a heavy congestion.

Further, taking the example of fig. 5 as an example, assuming that the traffic parameter is still the traffic speed, there are two vehicle samples in the first out-of-degree group a: the corresponding traffic speeds of the vehicle sample 1 and the vehicle sample 2 are respectively represented as V1 and V2, and the corresponding fusion weight values are respectively represented as W1 and W2, so that the weighted speeds of the two vehicle samples can be respectively represented as V1 × W1 and V2 × W2; the weighted velocities for all vehicle samples are then summed to give a weighted velocity sum: v1 XW 1+ V2 XW 2; and adding the fusion weight values of all the vehicle samples to obtain a weight sum: w1+ W2; dividing the weighted speed sum by the weighted sum to obtain a passing speed VA in a corresponding out-degree direction of the out-degree group a, which can be represented as: (V1 × W1+ V2 × W2)/(W1 + W2); there are two vehicle samples in the second out-of-degree grouping B: the corresponding traffic speeds of the vehicle sample 3 and the vehicle sample 4 are respectively represented as V3 and V4, the corresponding fusion weight values are respectively represented as W3 and W4, and based on the same reason, the traffic speed VB in the corresponding outgoing direction of the outgoing group B can be represented as: (V3 × W3+ V4 × W4)/(W3 + W4); there is only one vehicle sample in each of the third out-of-degree grouping C and the fourth out-of-degree grouping D: the corresponding passing speeds of the vehicle sample 5 and the vehicle sample 6 are respectively represented as V5 and V6, the passing speed VC in the out-degree direction corresponding to the out-degree group C is the passing speed V5 of the vehicle sample 5, and the passing speed VD in the out-degree direction corresponding to the out-degree group D is the passing speed V6 of the vehicle sample 6. And subsequently, determining the road condition in the corresponding outgoing direction according to the comparison between VA, VB, VC and VD and the preset speed threshold.

In an embodiment of the present disclosure, the apparatus may further include:

and the correction module is configured to correct the road condition data in the corresponding outgoing direction of the corresponding incoming target road section according to the current scene information, the road condition data of the next incoming target road section in the driving direction of the vehicle or the unmodified historical road condition data of the same incoming target road section.

In view of the fact that the calculated traffic data may have a certain deviation, in this embodiment, the traffic data in the corresponding departure direction of the corresponding departure target segment is further modified according to the current scene information, the traffic data of the next departure target segment in the vehicle driving direction, or the unmodified historical traffic data of the same departure target segment.

In an embodiment of the present disclosure, the apparatus may further include:

and the issuing module is configured to issue the road condition data.

The embodiment of the disclosure also discloses a navigation service, wherein the road condition data acquisition result of the navigated object is acquired based on the road condition data acquisition method, and the navigation guidance service of the corresponding scene is provided for the navigated object based on the road condition data acquisition result. Wherein, the corresponding scene is one or a combination of more of AR navigation, overhead navigation or main and auxiliary road navigation.

The embodiment of the disclosure also discloses an electronic device, which comprises a memory and a processor; wherein, the first and the second end of the pipe are connected with each other,

the memory is configured to store one or more computer instructions, wherein the one or more computer instructions are executable by the processor to perform any of the method steps described above.

Fig. 7 is a schematic structural diagram of a computer system suitable for implementing a road condition data acquiring method according to an embodiment of the present disclosure.

As shown in fig. 7, the computer system 700 includes a processing unit 701 that can execute various processes in the above-described embodiments according to a program stored in a Read Only Memory (ROM) 702 or a program loaded from a storage section 708 into a Random Access Memory (RAM) 703. In the RAM703, various programs and data necessary for the operation of the computer system 700 are also stored. The processing unit 701, the ROM702, and the RAM703 are connected to each other by a bus 704. An input/output (I/O) interface 705 is also connected to bus 704.

The following components are connected to the I/O interface 705: an input portion 706 including a keyboard, a mouse, and the like; an output section 707 including a display such as a Cathode Ray Tube (CRT), a Liquid Crystal Display (LCD), and the like, and a speaker; a storage section 708 including a hard disk and the like; and a communication section 709 including a network interface card such as a LAN card, a modem, or the like. The communication section 709 performs communication processing via a network such as the internet. A drive 710 is also connected to the I/O interface 705 as needed. A removable medium 711 such as a magnetic disk, an optical disk, a magneto-optical disk, a semiconductor memory, or the like is mounted on the drive 710 as necessary, so that a computer program read out therefrom is mounted into the storage section 708 as necessary. The processing unit 701 may be implemented as a CPU, a GPU, a TPU, an FPGA, an NPU, or other processing units.

In particular, the above described methods may be implemented as computer software programs, according to embodiments of the present disclosure. For example, embodiments of the present disclosure include a computer program product comprising a computer program tangibly embodied on a medium readable thereby, the computer program comprising program code for executing the road condition data acquisition method. In such an embodiment, the computer program may be downloaded and installed from a network through the communication section 709, and/or installed from the removable medium 711.

The flowchart and block diagrams in the figures illustrate the architecture, functionality, and operation of possible implementations of systems, methods and computer program products according to various embodiments of the present disclosure. In this regard, each block in the flowcharts or block diagrams may represent a module, a program segment, or a portion of code, which comprises one or more executable instructions for implementing the specified logical function(s). It should also be noted that, in some alternative implementations, the functions noted in the block may occur out of the order noted in the figures. For example, two blocks shown in succession may, in fact, be executed substantially concurrently, or the blocks may sometimes be executed in the reverse order, depending upon the functionality involved. It will also be noted that each block of the block diagrams and/or flowchart illustration, and combinations of blocks in the block diagrams and/or flowchart illustration, can be implemented by special purpose hardware-based systems that perform the specified functions or acts, or combinations of special purpose hardware and computer instructions.

The units or modules described in the embodiments of the present disclosure may be implemented by software or hardware. The units or modules described may also be provided in a processor, and the names of the units or modules do not in some cases constitute a limitation on the units or modules themselves.

As another aspect, the disclosed embodiment also provides a computer-readable storage medium, which may be the computer-readable storage medium included in the apparatus in the foregoing embodiment; or it may be a separate computer readable storage medium not incorporated into the device. The computer readable storage medium stores one or more programs for use by one or more processors in performing the methods described in the embodiments of the present disclosure.

The foregoing description is only exemplary of the preferred embodiments of the disclosure and is illustrative of the principles of the technology employed. It will be appreciated by those skilled in the art that the scope of the invention in the embodiments of the present disclosure is not limited to the specific combination of the above-mentioned features, but also encompasses other embodiments in which any combination of the above-mentioned features or their equivalents is made without departing from the inventive concept. For example, the above features and (but not limited to) the features with similar functions disclosed in the embodiments of the present disclosure are mutually replaced to form the technical solution.

Claims

1. A road condition data acquisition method comprises the following steps:

acquiring track positions of a vehicle sample, and matching the track positions of the vehicle sample with preset road network data to acquire an out-degree path of the vehicle sample passing through an intersection, wherein the out-degree path consists of an in-degree target road section and a corresponding out-degree target road section;

2. The method of claim 1, wherein matching the track positions of the vehicle sample with the preset road network data to obtain the outbound route of the vehicle sample passing through the intersection comprises:

and taking an in-degree target section where the vehicle sample is located before the vehicle sample passes through the intersection as a starting point section, taking an out-degree target section where the vehicle sample is located after the vehicle sample passes through the intersection as an end point section, and generating an out-degree path where the vehicle sample passes through the intersection.

3. The method according to claim 1 or 2, wherein for an intersection, the step of fusing the traffic parameters of the vehicle samples having the same incoming target road segment and the same outgoing direction to obtain the road condition data of the corresponding incoming target road segment in the outgoing direction comprises:

4. The method of claim 3, wherein for one outbound packet, calculating road condition data for a corresponding inbound target segment in a corresponding outbound direction based on the fusion element weight value and the traffic parameter of the vehicle sample is implemented as:

for one out-of-class group, calculating the product of the traffic parameter of the vehicle sample and the corresponding fusion weight value to obtain the weighting speed of the vehicle sample;

5. The method of any of claims 1-4, further comprising:

and determining a target road section based on the road network data.

6. The method of claim 5, said determining target road segments based on said road network data comprising:

7. The method of claim 6, said determining target road segments based on said road network data, further comprising:

8. The method of claim 7, further comprising:

and taking the target sub-road section as the target road section.

9. The method of any of claims 1-8, further comprising:

and correcting the road condition data in the corresponding departure direction of the corresponding entrance target road section according to the current scene information, the road condition data of the next entrance target road section in the driving direction of the vehicle or the unmodified historical road condition data of the same entrance target road section.

10. A road condition data acquisition device comprises: the system comprises an acquisition module, a processing module and a display module, wherein the acquisition module is configured to acquire the track positions of a vehicle sample, match the track positions of the vehicle sample with preset road network data and acquire the outgoing routes of the vehicle sample passing through an intersection, and the outgoing routes are one or more and consist of an incoming target road section and a corresponding outgoing target road section which the vehicle sample passes through;

and the fusion module is configured to fuse the traffic parameters of the vehicle samples with the same entrance target road section and the exit direction for one intersection to obtain the road condition data of the corresponding entrance target road section in the exit direction.

11. A computer readable storage medium having computer instructions stored thereon, wherein the computer instructions, when executed by a processor, implement the method steps of any of claims 1-9.

12. A navigation service, wherein based on the method of claims 1-9, road condition data of a navigated object is obtained, and based on the road condition data, a navigation guidance service of a corresponding scene is provided for the navigated object.