CN112885130A - Method and device for presenting road information - Google Patents
Method and device for presenting road information Download PDFInfo
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- CN112885130A CN112885130A CN202110089277.6A CN202110089277A CN112885130A CN 112885130 A CN112885130 A CN 112885130A CN 202110089277 A CN202110089277 A CN 202110089277A CN 112885130 A CN112885130 A CN 112885130A
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- G—PHYSICS
- G08—SIGNALLING
- G08G—TRAFFIC CONTROL SYSTEMS
- G08G1/00—Traffic control systems for road vehicles
- G08G1/09—Arrangements for giving variable traffic instructions
- G08G1/0962—Arrangements for giving variable traffic instructions having an indicator mounted inside the vehicle, e.g. giving voice messages
- G08G1/0967—Systems involving transmission of highway information, e.g. weather, speed limits
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- G—PHYSICS
- G08—SIGNALLING
- G08G—TRAFFIC CONTROL SYSTEMS
- G08G1/00—Traffic control systems for road vehicles
- G08G1/09—Arrangements for giving variable traffic instructions
- G08G1/0962—Arrangements for giving variable traffic instructions having an indicator mounted inside the vehicle, e.g. giving voice messages
- G08G1/0968—Systems involving transmission of navigation instructions to the vehicle
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Abstract
According to an embodiment of the present disclosure, a method, an apparatus, a device, a storage medium, and a program product for presenting road information are provided. The method proposed herein comprises: presenting a road visual element associated with a target road; and in response to determining that the congestion level of the target road is above the predetermined threshold, presenting at least one road condition image in association with the road visual element, the at least one road condition image being selected from a set of road condition images used to determine the congestion level, the set of road condition images being acquired by an image acquisition device associated with a vehicle traveling on the target road. Based on the mode, more accurate road condition information can be provided for the user in a more intuitive mode.
Description
Technical Field
Embodiments of the present disclosure relate generally to the field of intelligent transportation and, more particularly, relate to a method, apparatus, device, storage medium, and program product for presenting road information
Background
With the progress of the times, riding a vehicle for traveling has become the majority of choices. Prior to travel, it may be desirable to know the road conditions of the roads to be traveled in order to route the trip. Alternatively, during travel, people may desire to know the road conditions of the remaining journey through the navigation application. Therefore, how to more effectively provide accurate traffic information to users has become a focus of attention.
Disclosure of Invention
According to some embodiments of the present disclosure, a scheme for presenting road information is provided.
In a first aspect of the disclosure, a method of presenting road information is provided. The method comprises the following steps: presenting a road visual element associated with a target road; and in response to determining that the congestion level of the target road is above the predetermined threshold, presenting at least one road condition image in association with the road visual element, the at least one road condition image being selected from a set of road condition images used to determine the congestion level, the set of road condition images being acquired by an image acquisition device associated with a vehicle traveling on the target road.
In a second aspect of the disclosure, an apparatus for presenting road information is provided. The device includes: a first presentation module configured to present a road visual element associated with a target road; and a second presentation module configured to present, in response to determining that the congestion level of the target road is above the predetermined threshold, at least one road condition image in association with the road visual element, the at least one road condition image selected from a set of road condition images used to determine the congestion level, the set of road condition images acquired by an image acquisition device associated with a vehicle traveling on the target road.
In a third aspect of the present disclosure, there is provided an electronic device comprising one or more processors and memory for storing computer-executable instructions for execution by the one or more processors to implement a method according to the first aspect of the present disclosure.
In a fourth aspect of the present disclosure, a computer-readable storage medium is provided having computer-executable instructions stored thereon, wherein the computer-executable instructions, when executed by a processor, implement a method according to the first aspect of the present disclosure.
In a fifth aspect of the present disclosure, a computer program product is provided comprising computer executable instructions, wherein the computer executable instructions, when executed by a processor, implement the method according to the first aspect of the present disclosure.
According to the embodiment of the disclosure, on one hand, the real-time road condition of the road can be more accurately determined through the road condition image acquired by the image acquisition device associated with the vehicle running on the road. On the other hand, by presenting such road condition images to the user, the user can be made to know the congestion situation more intuitively.
This summary is provided to introduce a selection of concepts in a simplified form that are further described below in the detailed description. This summary is not intended to identify key features or essential features of the disclosure, nor is it intended to be used to limit the scope of the disclosure.
Drawings
The above and other features, advantages and aspects of various embodiments of the present disclosure will become more apparent by referring to the following detailed description when taken in conjunction with the accompanying drawings. In the drawings, like or similar reference characters designate like or similar elements, and wherein:
FIG. 1 illustrates a block diagram of an example environment in which embodiments of the present disclosure can be implemented;
FIG. 2 shows a schematic diagram of an example interface for presenting road information, in accordance with an embodiment of the present disclosure;
FIG. 3 illustrates a schematic diagram of determining a level of road congestion in accordance with some embodiments of the present disclosure;
FIG. 4 illustrates a flow diagram of a process of presenting road information in accordance with some embodiments of the present disclosure;
FIG. 5 illustrates a block diagram of an apparatus for determining a location of a traffic violation, in accordance with some embodiments of the present disclosure; and
FIG. 6 illustrates a block diagram of an electronic device in which one or more embodiments of the disclosure may be implemented.
Detailed Description
Some example embodiments of the present disclosure will be described in more detail below with reference to the accompanying drawings. While certain embodiments of the present disclosure are shown in the drawings, it should be understood that the present disclosure may be embodied in various forms and should not be limited to the embodiments set forth herein. Rather, these embodiments are provided so that this disclosure will be thorough and complete, and will fully convey the scope of the disclosure to those skilled in the art.
The term "include" and variations thereof as used herein is meant to be inclusive in an open-ended manner, i.e., "including but not limited to". Unless specifically stated otherwise, the term "or" means "and/or". The term "based on" means "based at least in part on". The terms "one example embodiment" and "one embodiment" mean "at least one example embodiment". The term "another embodiment" means "at least one additional embodiment". The terms "first," "second," and the like may refer to different or the same object. Other explicit and implicit definitions are also possible below.
As discussed above, it is desirable to be able to understand the real-time road conditions of a road. On the one hand, some conventional solutions determine the real-time road conditions by the speed of the vehicles traveling on the road. For example, if the average speed of the vehicles on the road is fast, it may be determined that the road is currently clear. Conversely, in some cases, some vehicles may simply be traveling slower, resulting in a lower average speed, but the road is not actually congested. Alternatively, some vehicles may be driven in a bus lane or an emergency lane in violation of traffic so that the average speed is high, but the road is actually congested. Therefore, it may not be accurate to determine the degree of road congestion solely by the traveling speed of the vehicle.
On the other hand, conventional map or navigation applications represent the degree of congestion of a road by different colors. However, such a presentation makes it difficult for the user to intuitively know the actual congestion degree of the road.
In view of this, embodiments of the present disclosure provide a scheme for presenting road information. In this scheme, first, a road visual element associated with the target road is presented. When the congestion level of the target road is above a predetermined threshold, at least one road condition image is also presented in association with the road visual element, wherein the at least one road condition image is selected from a set of road condition images used to determine the congestion level, and the set of road condition images is acquired by an image acquisition device associated with a vehicle traveling on the target road.
According to such an aspect, according to the embodiments of the present disclosure, on one hand, the real-time road condition of the road can be more accurately determined through the road condition image collected by the image collecting device associated with the vehicle traveling on the road. On the other hand, by presenting such road condition images to the user, the user can be made to more intuitively understand the congestion situation and to determine the basis of the congestion situation.
Presentation of road condition images
Some example embodiments of the disclosure will now be described with continued reference to the accompanying drawings.
FIG. 1 illustrates a block diagram of an example environment 100 in which embodiments of the present disclosure can be implemented. As shown in fig. 1, the environment 100 includes a vehicle 120 traveling on a target road 110. Herein, the target road 110 is intended to mean a section of road having a predetermined length, and does not necessarily mean all parts of the road having the same name. For example, the target road 110 may be a segment of 200 meters in length having "XX road" that is one kilometer long.
In some implementations, the vehicle 120 may have an image capture device 130 mounted thereon to capture an image 140 of the road conditions of the target road 110. Illustratively, the image acquisition device 130 may be, for example, a tachograph on the vehicle 120.
Alternatively, the image acquisition device 130 may also be a smart device with a camera, such as a smartphone, a tablet, a smart watch, or smart glasses, etc. Such a smart device may be suitably installed, for example, for capturing road condition images 140 of the target road 110.
In some implementations, the image acquisition device 130 can capture the road condition image 140 at a predetermined frequency and transmit the road condition image 140 to the road condition analysis device 150 via the communication device. Illustratively, the traffic analyzing device 150 may be a server device with a relatively high computing power.
In some implementations, the image acquisition device 130 can also send the collected information associated with the traffic status image 140 to the traffic status analysis device 150. Such acquisition information may include, for example, the acquisition location of the road condition image 140, the acquisition time, and/or the travel speed of the vehicle 120 at the time the image was acquired, etc.
As shown in fig. 1, the traffic analysis device 150 may determine the congestion degree of the target road 110 according to one or more traffic images 140 of the received traffic images 140. Illustratively, the traffic status analyzing device 150 may determine the congestion level of the target road 110 by using all the received traffic status images 140, for example.
Alternatively, the traffic analysis device 150 may also determine the congestion level using a subset of the total traffic images 140 received. For example, the road condition analyzing device 150 may sample the received road condition images 140 to obtain a set of road condition images 140 for determining the congestion level.
In some implementations, the road condition analysis device 150 can select a set of road condition images 140 based on the collection location associated with the road condition images 140. For example, the collection locations of the selected set of road condition images 140 may be spaced apart by approximately the same distance.
Alternatively, the road condition analysis device 150 may also select a set of road condition images 140 based on the acquisition time associated with the road condition images 140. For example, the selected set of road condition images 140 may be collected at about the same time interval.
In still other implementations, the traffic analysis device 150 may also determine the congestion level of the target road 110 based only on the most recently received traffic image 140.
In some implementations, the congestion level may be represented as different levels, such as congestion, slowdown, and clear, for example. Alternatively, the congestion level may be expressed as a different value, such as 1-5, where a higher value may represent a higher congestion level. The determination process regarding the degree of congestion of the target road 110 will be described in detail below, and will not be described in detail here.
As shown in fig. 1, the congestion degree of the target road 110 determined by the road condition analyzing device 150 may be further provided to the terminal device 160. In some implementations, terminal device 160 may be, for example, a suitable computing device that may present a target interface 170 to a user. Examples of terminal device 160 include, but are not limited to: a smart phone, smart watch, smart glasses, laptop, tablet, desktop computer, personal digital assistant, or other suitable electronic device.
In some implementations, the target interface 170 can be generated, for example, by a mapping application or navigation application deployed on the terminal device 160. Alternatively, the target interface 170 may also be a map interface or a navigation interface provided by the terminal device 160 via a browser, for example.
As shown in FIG. 1, the target interface 170 may include, for example, a road visual element 184 associated with the target road 110. In some implementations, the road visual element 184 may be part of a map, for example, and presented similarly to other road segments.
Alternatively, in some implementations, the road visual element 184 may also be highlighted. For example, when the target road 110 is part of a navigation route recommended by a navigation application, the target road 110 may be highlighted in a map. Alternatively, the target road 110 may also be highlighted when the target road 110 is part of the remaining route of the trip being navigated by the navigation application.
In some implementations, the terminal device 160 may determine the congestion level of the target link 110. In some implementations, the terminal device 160 may obtain the congestion degree of the target road 110 from the road condition analysis device 150 when determining that the target road 110 is being viewed by the user. Alternatively, the terminal device 160 may also uniformly acquire the congestion degree of the associated road when loading the map data, even if a part of the road may not be currently viewed.
The terminal device 160 may also present at least one road condition image 182 of the set of road condition images 140 in association with the road visual element 184 when the congestion level of the target road 110 is above a predetermined threshold.
In some implementations, when the congestion level of the target road 110 is higher than the threshold value, the road condition analyzing device 150 may select at least one road condition image 182 from the set of road condition images 140 and transmit it to the terminal device 160 together with information indicating the congestion level of the target road 110.
In some implementations, the road condition analysis device 150 can select at least one road condition image 182 based on the travel speed of the vehicle 120 at the time the set of road condition images 140 was collected.
For example, the traffic analyzing device 150 may obtain the real-time speed of the vehicle 120 when the set of traffic images 140 are collected from the vehicle 120, and select one or more traffic images 140 with the real-time speed less than a predetermined threshold as the at least one traffic image 182. For example, the road condition analysis device 150 may select the road condition image 140 corresponding to the smallest real-time speed as the at least one road condition image 182 to be presented. In this way, the selected road condition image 182 can better reflect the road condition of the target road 110.
In some implementations, the road condition analysis device 150 can also determine the real-time speed of the vehicle 120 at the time the set of road condition images 140 were acquired based on the acquisition location and the acquisition time associated with the set of road condition images 140. For example, the traffic status analyzing device 150 may determine the real-time speed corresponding to the traffic status image 140 based on the distance and time difference between the adjacent 2 traffic status images 140. It should be appreciated that such real-time speed may correspond to one of the earlier collected road condition images 140 or the later collected road condition images 140.
In still other implementations, the road condition analysis device 150 may also transmit the entire set of road condition images 140 to the terminal device 160. Accordingly, the terminal device 160 may display all road condition images in the set of road condition images 140 in the target interface 170.
In some implementations, the terminal device 160 can also select at least one road condition image 182 from the received set of road condition images 140. Similarly, the terminal device 160 may also select at least one road condition image 182 to be presented, for example, based on the corresponding speed information or time information.
In some implementations, when multiple images are included in the set of road condition images 140 received by the terminal device 160, the terminal device 160 may display only one of the images in the target interface 170, but may present a visual element for guiding the user to view all of the road condition images 140. For example, the terminal device 160 may stack a plurality of road condition images and present only the uppermost road condition image.
In some implementations, the terminal device 160 may present the at least one road condition image 182 at a respective location 186, where the respective location 186 indicates an acquisition location of the at least one road condition image 182. As shown in fig. 1, the terminal device 160 may suggest an association of the presented at least one road condition image 182 with a corresponding location 186, for example, by using a visual element such as an arrow, to indicate that the at least one road condition image 182 is captured at the target location.
In some implementations, to reduce interference with the user's usage habits, the terminal device 160 may also first present the at least one road condition image 182 in a smaller first size. When receiving a viewing request of the user to view details of the road condition image, the terminal device 160 may present a user interface 200 as shown in fig. 2, for example. As shown in fig. 2, in the user interface 200, the terminal device 160 may present at least one road condition image 210 in a second, larger size, for example. In this way, the disturbance to the user can be reduced, and clearer road condition images can be provided for the user in a personalized manner.
In some implementations, the terminal device 160 may also display information 220 related to the congestion level of the target link 110 in the user interface 200, for example.
In some implementations, when the terminal device 160 receives all of the set of road condition images 140 and the set of road condition images 140 includes a plurality of images, the terminal device 160 may allow the user to view all of the set of road condition images 140, for example, in response to a viewing request.
As shown in fig. 2, the terminal device 160 may present, for example, a visual element 230 indicating that the user may view multiple road condition images. The user may view more road condition images by, for example, sliding the road condition image 210 presented in the second size left or right.
It should be appreciated that the terminal device 160 may also present the road condition image of the second size using a different form than the floating window shown in fig. 2. For example, the terminal device 160 may present a new interface covering the original target interface 170 for displaying the second-sized road condition image.
In some implementations, terminal device 160 may also present a road conditions visual element in association with the road visual element, the road conditions visual element indicating the congestion level of target road 110. Taking the target interface 170 as an example, the terminal device 160 may indicate the congestion level of the target road 110 through text or a specific color, for example.
In some implementations, to reduce the disturbance to the user and reduce the amount of rendering computation by the terminal device 160, the terminal device 160 may present the at least one road condition image 182 upon determining the user's intent to view the target road.
In one example, the target interface 170 may be a map viewing interface, and the terminal device 160 may, for example, only present the at least one road condition image 182 in association with the road visual element 184 when a map zoom associated with the map viewing interface is above a predetermined threshold.
Illustratively, the user may zoom the map, for example, in the map viewing interface, and when the user zooms the map to a predetermined level to focus on a predetermined area and the target road is located within the predetermined area, the terminal device 160 may present at least one road condition image 182 in the map viewing interface to indicate the degree of congestion of the target road 110.
In another example, goal interface 170 may be a route planning interface associated with goal road 110. In particular, when the recommended route presented by the routing interface is associated with the target road 110 and a road visual element 184 corresponding to the target road 110 is being presented, the terminal device 160 may present at least one road condition image 182 in association with the road visual element 184.
Illustratively, the user may plan a route from a starting point to an ending point via the navigation application, and when a particular route of the recommended one or more routes includes the target road 110 and the road visual element 184 corresponding to the target road 110 is within the display range, the terminal device 160 may present at least one road condition image 182 to indicate the degree of congestion of the target road 110.
In yet another example, the target interface 170 may be a real-time navigation interface associated with the target road 110. Specifically, when the remaining portion of the navigation route of the real-time navigation interface includes the target road 110 and the road visual element 184 corresponding to the target road 110 is within the display range, the terminal device 160 may present at least one road condition image 182 to indicate the degree of congestion of the target road 110.
Alternatively, when the remaining portion of the navigation route of the real-time navigation interface includes the target road 110 and the real-time location of the terminal device 160 is less than the predetermined threshold from the start of the target road 110 or the congested road segment, the terminal device 160 may also actively present the road visual element 184 and the at least one road condition image 182 in association if the road visual element 184 is temporarily not present.
Based on the scheme discussed above, the embodiment of the disclosure can more intuitively present the congestion degree of the target road to the user by acquiring and presenting the road condition image.
Determination of congestion level
As discussed above with reference to fig. 1, the traffic analysis device 150 may determine the congestion level of the target road 110 according to the set of traffic images 140.
In some implementations, the road condition analysis device 150 may apply the set of road condition images 140 to a time series model to determine congestion levels, where the time series model is trained based on a set of training images associated with a particular road and congestion level tagging information for the particular road.
In some implementations, the timing model may include, for example, any suitable timing machine learning model. Illustratively, the timing model may be, for example, a (2+1) D model. In training the time series model, a set of training images acquired by an acquiring vehicle on a specific road may be acquired, and input characteristics to the time series model may be determined based on the set of training images, and accordingly, a true value of the model may be determined based on annotation information for the degree of congestion. It should be appreciated that the congestion degree label information may be obtained in any suitable manner, and the disclosure is not intended to be limited thereto.
In this way, the congestion level of the target road 110 can be determined in an end-to-end manner, thereby simplifying the logic complexity.
In other implementations, the traffic analyzing device 150 may also determine the congestion level of the target road 110 based on a rule.
In some implementations, the traffic analysis device 150 may determine the current passable area in the target road 110 based on the set of traffic images 140. A process of determining the degree of congestion based on the rule will be described below with reference to fig. 3. For example, the road condition analyzing device 150 may first determine road boundary information of the target road 110, for example, the road boundary lines 310-1 and 310-2 in fig. 3. It should be appreciated that in some implementations, the target road 110 may not include a boundary line, and the road condition analysis device 150 may detect a hard boundary (hard boundary) as the road boundary information, for example.
In some implementations, the road condition analysis device 150 can also detect obstacle information in a set of road condition images 140, for example, dynamic obstacles such as vehicles and pedestrians, and/or static obstacles in the center of the road, etc., through the object recognition model.
For example, as shown in fig. 3, the road condition analyzing device 150 may generate a detection frame for each obstacle to indicate the position of the obstacle in the road condition image 140.
Accordingly, the road condition analysis device 150 may determine the current travelable region in the target road 110 based on the road boundary information and the obstacle information. Taking fig. 3 as an example, for example, the lateral area 325 represents a current travelable area in parallel with the vehicle corresponding to the detection frame 320 in the target road 110. The lateral regions 335-1 and 335-2 represent current drivable regions in parallel with the vehicle to which the detection box 330 corresponds.
Additionally, the traffic condition analyzing device 150 may determine the congestion degree of the target road 110 based on the current passable area.
In some implementations, the road condition analysis device 150 can first detect a current passable area within a predetermined distance from the vehicle 120 that captured the road condition image 140, and determine whether the vehicle 120 can pass through the current passable area based on the size of the current passable area.
For example, the road condition analyzing device 150 may first detect a current passable area within 30 meters from the vehicle. The traffic analysis device 150 further determines the width of the passable area according to the relationship between the image coordinates and the real world coordinates, thereby determining whether the vehicle 120 can pass through the current passable area.
Alternatively, in order to reduce the amount of calculation, the road condition analysis device 150 may also determine whether the vehicle 120 can pass through the current passable area based on the size of the vehicle detection box parallel to the current passable area, in consideration of the substantial proximity of the sizes of different vehicles in the real world. For example, the road condition analyzing device 150 may determine whether the width of the lateral area 325 is a predetermined multiple (for example, 1.5 times to allow for a safe distance) of the width of the detection block 320, thereby determining whether the vehicle 120 can pass through the lateral area 325.
For example, if the traffic analysis device 150 determines that the vehicle 120 cannot pass through the current passable area within 30 meters, the traffic analysis device 150 may determine the congestion degree of the target road 110 as "congested" or set a higher value to indicate a higher congestion degree, for example.
Taking fig. 3 as an example, road condition analysis device 150 may determine that vehicle 120 is able to pass through lateral zone 325 and lateral zone 335-2, but not able to pass through lateral zones 345-1 and 345-2, for example. If the vehicle corresponding to the detection block 340 is within 30 meters of the vehicle 120, the traffic condition analysis device 150 may determine that the vehicle 120 cannot pass through the current passable area within 10 meters, and thus determine the congestion degree of the target road 110 as "congestion".
In some implementations, if the traffic analysis device 150 determines that the vehicle 120 can pass through a first currently passable area within 10 meters, the traffic analysis device 150 may further detect a second currently passable area within 10 meters to 50 meters, for example, and determine whether the vehicle 120 can pass through the newly determined second currently passable area. For example, if the vehicle 120 cannot pass through the second currently passable area, the traffic condition analysis device 150 may determine the congestion degree of the target road 110 as "slow running", for example.
In some implementations, the traffic analysis device 150 may also determine, for example, the maximum distance that the vehicle 120 can travel based on the current navigable area and determine the congestion level of the target road 110 based on the maximum distance. For example, the road condition analyzing device 150 can compare the maximum distance with a predetermined plurality of sections to determine the corresponding congestion degree. For example, if the maximum distance that the vehicle 120 can travel is 5 meters, which falls within the section (0 meters, 10 meters), the road condition analysis device 150 may determine the congestion degree of the target road 110 as "congested", conversely, if the maximum distance that the vehicle 120 can travel is 60 meters, which falls within the section (50 meters, ∞), for example, the road condition analysis device 150 may determine the congestion degree of the target road 110 as "clear", for example.
In some implementations, when the target road 110 includes multiple lanes, the traffic analysis device 150 may also determine the number of lanes associated with the current navigable area and determine the congestion level based on the number of lanes.
For example, the road condition analyzing device 150 may detect a current passable area within 10 meters and determine the number of lanes available through the current passable area. For example, for a three lane scenario, if it is also determined that the vehicle 120 is able to pass through a current passable area within 10 meters, but if the vehicle 120 is, for example, only able to use one lane, while neither of the other two lanes is available, then the available travelable area ahead may be due to the vehicle 120 driving too slowly, and the congestion level of the target road 110 may be determined, for example, to be "slow travel". In contrast, if the vehicle 120 is able to pass through the currently passable area within 10 meters using, for example, two or more lanes, the road condition analysis device 150 may determine, for example, the degree of congestion of the target road 110 as "clear".
In still other examples, the traffic analysis device 150 may also sum the congestion level of the target road 110 into consideration, for example, by integrating the number of vehicles in the target road 110. For example, the traffic analysis device 150 may determine that the vehicle 120 is capable of traveling 40 meters based on the current passable area, and that the number of vehicles detected in the road area after a distance of 40 meters is less than a threshold, for example, the traffic analysis device 150 may determine the road congestion level as "clear" rather than "slow".
In some implementations, the traffic analysis device 150 may receive multiple sets of traffic images transmitted from different vehicles, and in order to reduce the calculation redundancy, the traffic analysis device 150 may only keep one set of traffic images for analysis for vehicles that all travel on the target road 110. Alternatively, the traffic analysis device 150 may analyze multiple sets of traffic images and weight the result to improve the accuracy of the congestion level.
In some implementations, the traffic analysis device 150 may also integrate the congestion level determined based on the set of traffic images 140 with the congestion level determined based on conventional means (e.g., average traveling speed) to determine a final congestion level. It should be appreciated that the final congestion level may be determined in any suitable manner, such as weighting, and the disclosure is not intended to be limiting.
It should be understood that the specific numerical values such as the distance ranges used above are merely exemplary, and appropriate values may be set as necessary, and the present disclosure is not intended to limit this.
Based on the above-discussed manner of determining the congestion degree, the embodiments of the present disclosure can determine the current congestion degree of the road more accurately by using the real-time road condition images collected by crowdsourced vehicles, and avoid inaccurate problems caused by some vehicles due to specific driving behaviors (e.g., abnormal slow driving, fast driving by using an irregular lane).
Example methods, apparatus, and devices
FIG. 4 shows a flowchart of an example process 400 of presenting road information, in accordance with an embodiment of the present disclosure. Process 400 may be performed, for example, by terminal device 160 in fig. 1.
As shown in fig. 4, at block 402, the terminal device 160 presents the road visual element 184 associated with the target road 110.
At block 404, in response to determining that the congestion level of the target road 110 is above the predetermined threshold, the terminal device 160 presents at least one road condition image 182 in association with the road visual element 184, the at least one road condition image 182 being selected from a set of road condition images 140 used to determine the congestion level, the set of road condition images 140 being acquired by the image acquisition device 130 associated with the vehicle 120 traveling on the target road 110.
In some implementations, presenting at least one road condition image includes: and presenting at least one road condition image at a corresponding position, wherein the corresponding position indicates the acquisition position of the at least one road condition image.
In some implementations, presenting at least one road condition image includes: presenting at least one road condition image in association with a road visual element in a first size; and presenting at least one road condition image in a second size in response to the viewing request for the road condition image, the second size being larger than the first size.
In some implementations, process 400 further includes: the traffic visual element is presented in association with the road visual element, the traffic visual element indicating a congestion level of the target road.
In some implementations, the at least one road condition image is selected based on a travel speed of the vehicle at a time the set of road condition images was acquired.
In some implementations, the travel speed is determined based on an acquisition location and an acquisition time associated with a set of road condition images.
In some implementations, the congestion level is determined by the traffic analysis device based on the following process: applying, by the traffic analysis device, the set of traffic images to a time series model to determine the congestion level, the time series model being trained based on a set of training images associated with the particular road and congestion level tagging information for the particular road.
In some implementations, the congestion level is determined by the traffic analysis device based on the following process: determining a current passable area in the target road by the road condition analysis equipment based on the group of road condition images; and determining the congestion degree of the target road by the road condition analysis equipment based on the current passable area.
In some implementations, the determining, by the traffic analysis device, the congestion degree of the target road based on the current passable area includes: determining, by a road condition analysis device, a maximum distance that a vehicle can travel based on a current passable area; and determining, by the road condition analysis device, a congestion degree based on the maximum distance.
In some implementations, the determining, by the traffic analysis device, the congestion degree of the target road based on the current passable area includes: determining, by a road condition analysis device, a number of lanes associated with a current passable area; and determining the congestion degree by the road condition analysis device based on the number of lanes.
In some implementations, presenting at least one road condition image used to determine congestion levels includes: presenting at least one road condition image in a target interface, the target interface comprising one of: a map viewing interface associated with the target road, wherein a map zoom level associated with the map viewing interface is above a predetermined threshold, a route planning interface associated with the target road, or a real-time navigation interface associated with the target road.
In some implementations, the image acquisition device includes a vehicle tachograph.
Fig. 5 illustrates a schematic block diagram of an apparatus 500 for presenting road information, according to some embodiments of the present disclosure. The apparatus 500 may be embodied as or included in the terminal device 160.
As shown in fig. 5, the apparatus 500 includes: a first rendering module 510 configured to render a road visual element associated with a target road. Additionally, the apparatus 500 further comprises a second presenting module 520 configured to present, in response to determining that the congestion level of the target road is above the predetermined threshold, at least one road condition image in association with the road visual element, the at least one road condition image being selected from a set of road condition images used to determine the congestion level, the set of road condition images being acquired by an image acquisition device associated with a vehicle traveling on the target road.
In some implementations, the second rendering module 520 includes: a third presenting module configured to present the at least one road condition image at a corresponding location, the corresponding location indicating an acquisition location of the at least one road condition image.
In some implementations, the second rendering module 520 includes: a fourth presentation module configured to present at least one road condition image in association with a road visual element in a first size; and a fifth presenting module configured to present the at least one road condition image in a second size in response to the viewing request for the road condition image, the second size being larger than the first size.
In some implementations, the apparatus 500 further includes: a sixth presentation module configured to present a traffic visual element in association with the road visual element, the traffic visual element indicating a congestion level of the target road.
In some implementations, the at least one road condition image is selected based on a travel speed of the vehicle at a time the set of road condition images was acquired.
In some implementations, the travel speed is determined based on an acquisition location and an acquisition time associated with a set of road condition images.
In some implementations, the congestion level is determined by the traffic analysis device based on the following process: applying, by the traffic analysis device, the set of traffic images to a time series model to determine the congestion level, the time series model being trained based on a set of training images associated with the particular road and congestion level tagging information for the particular road.
In some implementations, the congestion level is determined by the traffic analysis device based on the following process: determining a current passable area in the target road by the road condition analysis equipment based on the group of road condition images; and determining the congestion degree of the target road by the road condition analysis equipment based on the current passable area.
In some implementations, the determining, by the traffic analysis device, the congestion degree of the target road based on the current passable area includes: determining, by a road condition analysis device, a maximum distance that a vehicle can travel based on a current passable area; and determining, by the road condition analysis device, a congestion degree based on the maximum distance.
In some implementations, the determining, by the traffic analysis device, the congestion degree of the target road based on the current passable area includes: determining, by a road condition analysis device, a number of lanes associated with a current passable area; and determining the congestion degree by the road condition analysis device based on the number of lanes.
In some implementations, the second rendering module 520 includes: a sixth presentation module configured to present at least one road condition image in a target interface, the target interface including one of: a map viewing interface associated with the target road, wherein a map zoom level associated with the map viewing interface is above a predetermined threshold, a route planning interface associated with the target road, or a real-time navigation interface associated with the target road.
In some implementations, the image acquisition device includes a vehicle tachograph.
FIG. 6 illustrates a block diagram that shows an electronic device 600 in which one or more embodiments of the disclosure may be implemented. It should be understood that the electronic device 600 illustrated in FIG. 6 is merely exemplary and should not be construed as limiting in any way the functionality and scope of the embodiments described herein. The electronic device 600 shown in fig. 6 may be included in or implemented as the terminal device 160 of fig. 1 or other device for presenting road information that embodies the present disclosure.
As shown in fig. 6, the electronic device 600 is in the form of a general purpose computing device. The electronic device 600 may also be any type of computing device or server. The components of electronic device 600 may include, but are not limited to, one or more processors or processing units 610, memory 620, storage 630, one or more communication units 640, one or more input devices 650, and one or more output devices 660. The processing unit 610 may be a real or virtual processor and can perform various processes according to programs stored in the memory 620. In a multi-processor system, multiple processing units execute computer-executable instructions in parallel to improve the parallel processing capabilities of the electronic device 600.
The electronic device 600 may further include additional removable/non-removable, volatile/nonvolatile storage media. Although not shown in FIG. 6, a magnetic disk drive for reading from and writing to a removable, non-volatile magnetic disk (e.g., a "floppy disk") and an optical disk drive for reading from or writing to a removable, non-volatile optical disk may be provided. In these cases, each drive may be connected to a bus (not shown) by one or more data media interfaces. Memory 620 may include a computer program product 625 having one or more program modules configured to perform the various methods or acts of the various embodiments of the disclosure.
The communication unit 640 enables communication with other computing devices over a communication medium. Additionally, the functionality of the components of the electronic device 600 may be implemented in a single computing cluster or multiple computing machines, which are capable of communicating over a communications connection. Thus, the electronic device 600 may operate in a networked environment using logical connections to one or more other servers, network Personal Computers (PCs), or another network node.
The input device 650 may be one or more input devices such as a mouse, keyboard, trackball, or the like. Output device 660 may be one or more output devices such as a display, speakers, printer, or the like. Electronic device 600 may also communicate with one or more external devices (not shown), such as storage devices, display devices, etc., communication with one or more devices that enable a user to interact with electronic device 600, or communication with any devices (e.g., network cards, modems, etc.) that enable electronic device 600 to communicate with one or more other computing devices, as desired, via communication unit 640. Such communication may be performed via input/output (I/O) interfaces (not shown).
According to an exemplary implementation of the present disclosure, a computer-readable storage medium is provided, on which computer-executable instructions or a program are stored, wherein the computer-executable instructions or the program are executed by a processor to implement the above-described method or function. The computer-readable storage medium may include a non-transitory computer-readable medium. According to an exemplary implementation of the present disclosure, there is also provided a computer program product comprising computer executable instructions or a program, which are executed by a processor to implement the above described method or function. The computer program product may be tangibly embodied on a non-transitory computer-readable medium.
Various aspects of the present disclosure are described herein with reference to flowchart illustrations and/or block diagrams of methods, apparatus, devices and computer program products implemented in accordance with the disclosure. It will be understood that each block of the flowchart illustrations and/or block diagrams, and combinations of blocks in the flowchart illustrations and/or block diagrams, can be implemented by computer-executable instructions or programs.
These computer-executable instructions or programs may be provided to a processing unit of a general purpose computer, special purpose computer, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processing unit of the computer or other programmable data processing apparatus, create means for implementing the functions/acts specified in the flowchart and/or block diagram block or blocks. These computer-executable instructions or programs may also be stored in a computer-readable storage medium that can direct a computer, programmable data processing apparatus, and/or other devices to function in a particular manner, such that the computer-readable medium storing the instructions comprises an article of manufacture including instructions which implement the function/act specified in the flowchart and/or block diagram block or blocks.
The computer-executable instructions or programs may be loaded onto a computer, other programmable data processing apparatus, or other devices to cause a series of operational steps to be performed on the computer, other programmable apparatus or other devices to produce a computer-implemented process such that the instructions which execute on the computer, other programmable apparatus or other devices implement the functions/acts specified in the flowchart and/or block diagram block or blocks.
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 implementations of the present disclosure. In this regard, each block in the flowchart or block diagrams may represent a module, segment, or portion of instructions, which comprises one or more executable instructions for implementing the specified logical function(s). 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 which perform the specified functions or acts, or combinations of special purpose hardware and computer instructions.
The foregoing has described implementations of the present disclosure, and the above description is illustrative, not exhaustive, and not limited to the implementations disclosed. Many modifications and variations will be apparent to those of ordinary skill in the art without departing from the scope and spirit of the described implementations. The terminology used herein was chosen in order to best explain the principles of various implementations, the practical application, or improvements to the technology in the marketplace, or to enable others of ordinary skill in the art to understand various implementations disclosed herein.
Claims (10)
1. A method of presenting road information, comprising:
presenting a road visual element associated with a target road; and
in response to determining that the congestion level of the target road is above a predetermined threshold, presenting at least one road condition image in association with the road visual element, the at least one road condition image selected from a set of road condition images used to determine the congestion level, the set of road condition images acquired by an image acquisition device associated with a vehicle traveling on the target road.
2. The method of claim 1, wherein presenting the at least one road condition image comprises:
presenting the at least one road condition image at a corresponding location, the corresponding location indicating an acquisition location of the at least one road condition image.
3. The method of claim 1, wherein presenting the at least one road condition image comprises:
presenting the at least one road condition image in association with the road visual element in a first size; and
and presenting the at least one road condition image in a second size in response to a viewing request for the road condition image, wherein the second size is larger than the first size.
4. The method of claim 1, wherein the at least one road condition image is selected based on a travel speed of the vehicle at a time the set of road condition images was acquired.
5. The method of claim 1, wherein the congestion level is determined by a traffic analysis device based on:
applying, by the traffic analysis device, the set of traffic images to a time series model to determine the congestion level, the time series model being trained based on a set of training images associated with a particular road and congestion level tagging information for the particular road.
6. The method of claim 1, wherein the congestion level is determined by a traffic analysis device based on:
determining, by the road condition analysis device, a current passable area in the target road based on the set of road condition images; and
determining, by the road condition analysis device, the congestion degree of the target road based on the current passable area.
7. An apparatus for presenting road information, comprising:
a first presentation module configured to present a road visual element associated with a target road; and
a second presentation module configured to present, in response to determining that the congestion level of the target road is above a predetermined threshold, at least one road condition image in association with the road visual element, the at least one road condition image selected from a set of road condition images used to determine the congestion level, the set of road condition images acquired by an image acquisition device associated with a vehicle traveling on the target road.
8. An electronic device, comprising:
a memory and a processor;
wherein the memory is to store one or more computer instructions, wherein the one or more computer instructions are to be executed by the processor to implement the method of any one of claims 1 to 6.
9. A computer readable storage medium having one or more computer instructions stored thereon, wherein the one or more computer instructions are executed by a processor to implement the method of any one of claims 1 to 6.
10. A computer program product comprising computer executable instructions, wherein the computer executable instructions, when executed by a processor, implement the method of any one of claims 1 to 6.
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| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202110089277.6A CN112885130B (en) | 2021-01-22 | 2021-01-22 | Method and device for presenting road information |
| PCT/CN2022/071006 WO2022156553A1 (en) | 2021-01-22 | 2022-01-10 | Method and apparatus for presenting road information |
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| CN202110089277.6A CN112885130B (en) | 2021-01-22 | 2021-01-22 | Method and device for presenting road information |
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| CN112885130B CN112885130B (en) | 2023-03-31 |
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| WO2023125843A1 (en) * | 2021-12-30 | 2023-07-06 | 深圳云天励飞技术股份有限公司 | Road congestion detection method and apparatus, and electronic device and storage medium |
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| WO2022156553A1 (en) | 2022-07-28 |
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