CN107449435B - Navigation method and device - Google Patents
Navigation method and device Download PDFInfo
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- CN107449435B CN107449435B CN201710523648.0A CN201710523648A CN107449435B CN 107449435 B CN107449435 B CN 107449435B CN 201710523648 A CN201710523648 A CN 201710523648A CN 107449435 B CN107449435 B CN 107449435B
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- G01—MEASURING; TESTING
- G01C—MEASURING DISTANCES, LEVELS OR BEARINGS; SURVEYING; NAVIGATION; GYROSCOPIC INSTRUMENTS; PHOTOGRAMMETRY OR VIDEOGRAMMETRY
- G01C21/00—Navigation; Navigational instruments not provided for in groups G01C1/00 - G01C19/00
- G01C21/26—Navigation; Navigational instruments not provided for in groups G01C1/00 - G01C19/00 specially adapted for navigation in a road network
- G01C21/34—Route searching; Route guidance
- G01C21/3453—Special cost functions, i.e. other than distance or default speed limit of road segments
- G01C21/3492—Special cost functions, i.e. other than distance or default speed limit of road segments employing speed data or traffic data, e.g. real-time or historical
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Abstract
The application discloses a navigation method and a navigation device, wherein the method comprises the following steps: receiving a path planning request sent by user equipment, wherein the path planning request comprises a starting position and an end position; acquiring a navigation path range of the user equipment according to the starting position and the end position; acquiring all road traffic information within the navigation path range to determine the road traffic capacity, wherein the road traffic information at least comprises real-time traffic signal lamp information and real-time road conditions; performing path planning based at least in part on road traffic capacity; and feeding back the path planning result to the user equipment. According to the technical scheme of the embodiment of the application, the influence of the real-time traffic signal lamp information and the real-time road condition on the road traffic capacity is brought into path planning, so that the global planning of the path based on the actual road condition can be realized, and more real-time and accurate navigation is realized.
Description
Technical Field
The disclosure relates generally to the field of internet of things, particularly to the field of internet of vehicles, and particularly relates to a navigation method, a navigation server and a vehicle-mounted system.
Background
With the gradual development of communication technology and GPS technology, the car navigation system plays an increasingly important role in vehicle driving, and brings great convenience for people to accurately reach a destination through path planning in an unfamiliar and complicated road environment.
With the advent of the artificial intelligence era, more and more users want to obtain more real-time and more accurate navigation, and how to make the next generation of navigation more artificial intelligence is a great challenge.
Disclosure of Invention
In view of the above-mentioned drawbacks and deficiencies of the prior art, it is desirable to provide an intelligent navigation method based on traffic signal information.
In a first aspect, an embodiment of the present application provides a navigation method, including:
receiving a path planning request sent by user equipment, wherein the path planning request comprises a starting position and an end position;
acquiring a navigation path range of the user equipment according to the starting position and the end position;
acquiring all road traffic information within the navigation path range to determine the road traffic capacity, wherein the road traffic information at least comprises real-time traffic signal lamp information and real-time road conditions;
performing path planning based at least in part on road traffic capacity; and
and feeding back the path planning result to the user equipment.
In some embodiments, the method wherein obtaining all of the road traffic information within the navigation path to determine the road traffic capacity comprises: in the navigation path range, a pre-established model of influencing the road traffic capacity by using traffic lights is utilized to simulate the road traffic capacity in one or more directions at the intersection. In some implementations, the model of the traffic light affecting road traffic capacity may be pre-established as follows: constructing an initial model based on the influence factors; training the initial model to simulate the road traffic capacity of one or more directions at the intersection; wherein the influencing factors include one or more of: number of vehicles waiting for a traffic light, traffic light status information, vehicle status information, date, time, and weather conditions.
In a second aspect, an embodiment of the present application further provides a navigation server, including:
the device comprises a receiving unit, a processing unit and a processing unit, wherein the receiving unit is used for receiving a path planning request sent by user equipment, and the path planning request comprises a starting position and an end position;
the navigation path range determining unit is used for obtaining the navigation path range of the user equipment according to the starting point position and the end point position;
the traffic capacity determining unit is used for obtaining all road traffic information in the navigation path range to determine the road traffic capacity, and the road traffic information at least comprises real-time traffic signal lamp information and real-time road conditions;
a path planning unit for planning a path based at least in part on road traffic capacity; and
and the feedback unit is used for feeding back the path planning result to the user equipment.
In a third aspect, an embodiment of the present application further provides an apparatus, including:
one or more processors and memory;
wherein the memory contains instructions executable by the one or more processors to cause the one or more processors to perform a navigation method provided according to embodiments of the present application.
In a fourth aspect, the present application further provides a computer-readable storage medium storing a computer program, where the computer program causes a computer to execute the navigation method provided in accordance with the embodiments of the present application.
In a fifth aspect, an embodiment of the present application further provides an on-vehicle system, including:
the V2X communication equipment is used for acquiring traffic signal light information;
the sending equipment is used for sending a path planning request to the navigation server, wherein the path planning request comprises a starting position and an end position;
the uploading equipment is used for uploading road traffic information to the navigation server, and the road traffic information at least comprises real-time traffic signal lamp information;
and the receiving equipment is used for receiving a path planning result from the navigation server, wherein the path planning result is obtained by obtaining all road traffic information in the determined navigation path range by the navigation server based on the path planning request to determine the road traffic capacity, and performing path planning at least partially based on the road traffic capacity.
In some embodiments, the vehicle-mounted system further comprises a voice broadcasting device for broadcasting in real time in navigation according to the information related to the road traffic capacity.
According to the navigation method provided by the embodiment of the application, the influence of the real-time traffic signal lamp information and the real-time road condition on the road traffic capacity is brought into the path planning, the global planning of the path based on the actual road condition can be realized, and therefore more real-time and accurate navigation is realized.
The navigation method provided by some embodiments of the application further builds a model of the influence of a plurality of influence factors and the influence on the traffic capacity of one or more directions of the intersection through machine learning, so that the navigation is timely and accurate, and the navigation is intelligentized.
The navigation method provided by some embodiments of the application can further remind a driver of the road condition in front and the corresponding route optimization in advance through voice broadcast, and is helpful for the driver to know the real-time situation more accurately, so that the optimal driving route is obtained better, and the navigation experience is improved.
Drawings
Other features, objects and advantages of the present application will become more apparent upon reading of the following detailed description of non-limiting embodiments thereof, made with reference to the accompanying drawings in which:
FIG. 1 illustrates an exemplary flow chart of a navigation method according to an embodiment of the present application;
FIG. 2 illustrates an exemplary block diagram of a navigation server according to an embodiment of the present application;
FIG. 3 shows an exemplary block diagram of an apparatus according to an embodiment of the present application;
FIG. 4 illustrates an exemplary block diagram of an in-vehicle system according to an embodiment of the present application; and
FIG. 5 shows an exemplary block diagram of an in-vehicle system according to another embodiment of the present application.
Detailed Description
The present application will be described in further detail with reference to the following drawings and examples. It is to be understood that the specific embodiments described herein are merely illustrative of the relevant invention and not restrictive of the invention. It should be noted that, for convenience of description, only the portions related to the present invention are shown in the drawings.
It should be noted that the embodiments and features of the embodiments in the present application may be combined with each other without conflict. The present application will be described in detail below with reference to the embodiments with reference to the attached drawings.
Fig. 1 shows an exemplary flowchart of a navigation method according to an embodiment of the present application.
As shown in fig. 1, the navigation method includes:
step S10, receiving a path planning request sent by the user equipment, where the path planning request includes a start position and an end position.
Specifically, in this embodiment, the starting point position of the path planning request may be automatically obtained by the vehicle positioning and positioning device, or may be obtained by user input, and the starting point position may be specifically determined to the lane where the vehicle is located; the end position may be obtained by user input.
In step S20, a navigation path range of the user equipment is obtained according to the start position and the end position.
In this embodiment, the navigation route range may be a range included in all the traversable routes from the starting position to the ending position, and is not limited to a certain route.
And step S30, acquiring all road traffic information in the navigation path range to determine the road traffic capacity, wherein the road traffic information at least comprises real-time traffic signal light information and real-time road conditions.
In the embodiment, the real-time traffic light information can be acquired by the user equipment based on the technology of interconnecting the vehicle and other equipment V2X. The real-time traffic signal information may include: traffic light state information and traffic light duration information of one or more directions at the intersection. Specifically, the traffic signal light status information of one or more directions at the intersection may be the color of a traffic signal light in a certain driving direction at the intersection, such as a straight red light and a right turn green light; the time length of the traffic signal lamp can be the waiting time of the traffic signal lamp with the corresponding color, such as 3 minutes for waiting for a straight red light and 2 minutes for right-turn green light passing.
The real-time road condition can be automatically acquired by the server, and specifically, the real-time road condition can be other information related to the same road condition besides traffic signal lamp information, such as road condition congestion condition, whether to restrict driving, whether to have an accident, whether to construct and the like.
Specifically, step S30 may be implemented, but is not limited to, as follows:
in the navigation path range, a pre-established model of influencing the road traffic capacity by using traffic lights is utilized to simulate the road traffic capacity in one or more directions at the intersection.
Further, the model of the traffic signal lamp influencing the road traffic capacity can be established in advance as follows:
constructing an initial model based on the influence factors;
training the initial model to simulate the road traffic capacity of one or more directions at the intersection;
wherein the influencing factors include one or more of: number of vehicles waiting for a traffic light, traffic light status information, vehicle status information, date, time, and weather conditions.
Specifically, among the influencing factors, the number of vehicles waiting for the traffic signal light may be the number of vehicles waiting for the traffic signal light ahead of the current passing vehicle, and may be obtained from the received vehicle position uploaded by each vehicle. The date, the time and the weather conditions can be captured through the internet, wherein the date can be a working day, a common rest day and a major holiday, the time can be a morning and evening peak and a common time, and the weather conditions can be heavy rainstorm, snowfall and heavy fog. The road capacity in one or more directions at an intersection can be expressed in terms of the number of vehicles that the intersection can pass through per unit time. It is understood that the influence factor can be extended to other factors having influence on the road traffic capacity, and is not limited to the listed factors in the embodiment.
In the embodiment, a model is constructed by machine learning according to a plurality of influence factors and influence on the traffic capacity of one or more directions of the intersection, so that the navigation is timely and accurate, and the navigation is intelligentized.
In more embodiments, the method provided by the application is not limited to machine learning model building, and more other methods for building a model of traffic signal lamps influencing road traffic capacity can be configured, so long as the model can simulate the road traffic capacity in one or more directions of the intersection, the same technical effect can be achieved.
And step S40, planning the path at least partially based on the road traffic capacity.
Specifically, in the present embodiment, step S40 can be implemented, but is not limited to, as follows:
simulating the road traffic capacity of all road intersections in the navigation path range based on a model of the traffic signal lamp influencing the road traffic capacity;
evaluating the rationality of the path based on the road traffic capacity of all the road intersections;
and planning the path according to the path evaluation result.
Specifically, the road traffic information of the road intersections of all the passable paths in the navigation path range obtained in the step S20 is brought into a model in which traffic lights affect the road traffic capacity, so that the road traffic capacity of all the road intersections is simulated, whether each path scheme is reasonable or not is evaluated based on the road traffic capacity, intersections with weak traffic capacity can be avoided, and one or more path schemes are obtained as evaluation results to perform path planning.
And step S50, feeding back the path planning result to the user equipment.
Specifically, the path planning result may be one or more path plans, and the user may select the path planning result according to the result fed back to the user equipment, so as to determine an appropriate path plan for navigation.
The embodiment provides a navigation method, and by incorporating the influence of real-time traffic signal lamp information and real-time road conditions on road traffic capacity into path planning, global planning of a path based on actual road conditions can be realized, so that more real-time and accurate navigation is realized.
It should be noted that while the operations of the method of the present invention are depicted in the drawings in a particular order, this does not require or imply that the operations must be performed in this particular order, or that all of the illustrated operations must be performed, to achieve desirable results. Rather, the steps depicted in the flowcharts may change the order of execution. Additionally or alternatively, certain steps may be omitted, multiple steps combined into one step execution, and/or one step broken down into multiple step executions.
Fig. 2 shows an exemplary structural block diagram of a navigation server according to an embodiment of the present application. The navigation server shown in fig. 2 may correspondingly perform any of the methods described above in connection with fig. 1.
As shown in fig. 2, in the present embodiment, the present application provides a navigation server, including:
a receiving unit 11, configured to receive a path planning request sent by user equipment, where the path planning request includes a starting position and an ending position;
a navigation path range determining unit 12, configured to obtain a navigation path range of the user equipment according to the starting point position and the ending point position;
a traffic capacity determining unit 13, configured to obtain all road traffic information within the navigation path range to determine road traffic capacity, where the road traffic information at least includes real-time traffic signal light information and real-time road conditions;
a path planning unit 14 for performing path planning based at least in part on road traffic capacity; and
and the feedback unit 15 is configured to feed back the path planning result to the user equipment.
Specifically, in this embodiment, the starting point position of the path planning request received by the receiving unit 11 may be obtained automatically by the vehicle positioning and positioning device, or may be obtained by user input, and the starting point position may be specifically the lane where the vehicle is located; the end position may be obtained by user input. The navigation route range obtained by the navigation route range determination unit 12 may be a range included in all of the traversable routes from the start position to the end position, and is not limited to only a certain route.
In some embodiments, the traffic signal information may include: traffic light state information and traffic light duration information of one or more directions at the intersection. The traffic signal lamp state information of one or more directions at the intersection can be the color of a traffic signal lamp in a certain driving direction at the intersection, such as a straight red lamp and a right-turn green lamp; the time length of the traffic signal lamp can be the waiting time of the traffic signal lamp with the corresponding color, such as 3 minutes for waiting for a straight red light and 2 minutes for right-turn green light passing.
In some embodiments, the traffic capacity determining unit 13 may be further configured to: and in the navigation path range, simulating the road traffic capacity in one or more directions of the intersection by utilizing a pre-established model of influencing the road traffic capacity by using traffic signal lamps.
Specifically, the model of the traffic light influencing the road traffic capacity may be, but is not limited to, pre-established as follows:
constructing an initial model based on the influence factors;
training the initial model to simulate the road traffic capacity of one or more directions at the intersection;
wherein the influencing factors include one or more of: number of vehicles waiting for a traffic light, traffic light status information, vehicle status information, date, time, and weather conditions.
Specifically, among the influencing factors, the number of vehicles waiting for the traffic signal light may be the number of vehicles waiting for the traffic signal light ahead of the current passing vehicle, and may be obtained from the received vehicle position uploaded by each vehicle. The date, the time and the weather conditions can be captured through the internet, wherein the date can be a working day, a common rest day and a major holiday, the time can be a morning and evening peak and a common time, and the weather conditions can be heavy rainstorm, snowfall and heavy fog. The road capacity in one or more directions at an intersection can be expressed in terms of the number of vehicles that the intersection can pass through per unit time. It is understood that the influence factor can be extended to other factors having influence on the road traffic capacity, and is not limited to the listed factors in the embodiment.
In the embodiment, a model is constructed by machine learning according to a plurality of influence factors and influence on the traffic capacity of one or more directions of the intersection, so that the navigation is timely and accurate, and the navigation is intelligentized.
In more embodiments, the method provided by the application is not limited to machine learning model building, and more other methods for building a model of traffic signal lamps influencing road traffic capacity can be configured, so long as the model can simulate the road traffic capacity in one or more directions of the intersection, the same technical effect can be achieved.
In some embodiments, the path planning unit 14 is further configured to:
simulating the road traffic capacity of all road intersections in the navigation path range based on a model of the traffic signal lamp influencing the road traffic capacity;
evaluating the rationality of the path based on the road traffic capacity of all the road intersections;
and planning the path according to the path evaluation result.
Specifically, the road traffic information of the road intersections of all passable paths in the navigation path range obtained by the navigation path range determining unit 12 is brought into a model in which traffic lights influence the road traffic capacity, so that the road traffic capacity of all the road intersections is simulated, whether each path scheme is reasonable or not is evaluated based on the road traffic capacity, intersections with weak traffic capacity can be avoided, and one or more path schemes are obtained as evaluation results to perform path planning.
In some embodiments, the path planning result fed back to the user equipment by the feedback unit 15 may be one or more path plans, and the user may select the path plan according to the requirement, so as to determine a suitable path plan for navigation.
FIG. 3 shows an exemplary block diagram of an apparatus according to one embodiment of the present application.
As shown in fig. 3, the apparatus 300 includes a Central Processing Unit (CPU)301 that can perform various appropriate actions and processes in accordance with a program stored in a Read Only Memory (ROM)302 or a program loaded from a storage section 308 into a Random Access Memory (RAM) 303. In the RAM 303, various programs and data necessary for the operation of the apparatus 300 are also stored. The CPU301, ROM 302, and RAM 303 are connected to each other via a bus 304. An input/output (I/O) interface 305 is also connected to bus 304.
The following components are connected to the I/O interface 305: an input portion 306 including a keyboard, a mouse, and the like; an output section 307 including a display such as a Cathode Ray Tube (CRT), a Liquid Crystal Display (LCD), and the like, and a speaker; a storage section 308 including a hard disk and the like; and a communication section 309 including a network interface card such as a LAN card, a modem, or the like. The communication section 309 performs communication processing via a network such as the internet. A drive 310 is also connected to the I/O interface 305 as needed. A removable medium 311 such as a magnetic disk, an optical disk, a magneto-optical disk, a semiconductor memory, or the like is mounted on the drive 310 as necessary, so that a computer program read out therefrom is mounted into the storage section 308 as necessary.
In particular, the process described above with reference to fig. 1 may be implemented as a computer software program, according to an embodiment of the present disclosure. For example, embodiments of the present disclosure include a computer program product comprising a computer program tangibly embodied on a machine-readable medium, the computer program comprising program code for performing the method of fig. 1. In such an embodiment, the computer program may be downloaded and installed from a network through the communication section 309, and/or installed from the removable medium 311.
As another aspect, the present application also provides a computer-readable storage medium, which may be the computer-readable storage medium included in the apparatus in the above-described embodiments; 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 formula input methods described herein.
FIG. 4 shows an exemplary block diagram of an in-vehicle system according to an embodiment of the present application.
As shown in fig. 4, in the present embodiment, the present application provides an in-vehicle system, including:
the V2X communication equipment 21 is used for acquiring traffic signal light information;
the sending device 22 is configured to send a path planning request to the navigation server so that the navigation server obtains a navigation path range, where the path planning request includes a start position and an end position;
the uploading device 23 is used for uploading road traffic information to the navigation server, wherein the road traffic information at least comprises real-time traffic signal lamp information and real-time road conditions;
and the receiving device 24 is used for receiving a path planning result from the navigation server, wherein the path planning result is obtained by the navigation server through determining the road traffic capacity based on all the road traffic information obtained in the received navigation path range and performing path planning based at least in part on the road traffic capacity.
Specifically, in this embodiment, the starting point position of the path planning request may be automatically obtained by the vehicle positioning and positioning device, or may be obtained by user input, and the starting point position may be specifically determined to the lane where the vehicle is located; the end position may be obtained by user input. The navigation route range may be a range included in all of the possible routes from the start position to the end position, and is not limited to a certain route.
The real-time traffic signal information may include: traffic light state information and traffic light duration information of one or more directions at the intersection. The real-time road condition can be automatically acquired by the server, and specifically, the real-time road condition can be other information related to the same road condition besides traffic signal lamp information, such as road condition congestion condition, whether to restrict driving, whether to have an accident, whether to construct and the like.
The path planning result can be one or more path plans, and the user can select according to the result fed back to the user equipment, so that the appropriate path plan is determined for navigation.
FIG. 5 shows an exemplary block diagram of an in-vehicle system according to another embodiment of the present application.
As shown in fig. 5, in another embodiment, the present application provides an in-vehicle system, further comprising:
the reception device also receives information related to road traffic capacity from the navigation server, and,
and the voice broadcasting device 25 is used for broadcasting in real time in navigation according to the information related to the road traffic capacity.
Specifically, the broadcasted content includes at least one of the following: the number of vehicles waiting for the traffic signal lamp, the state information of the traffic signal lamp, the road traffic capacity of the current intersection and the driving suggestion. For example, the red light at the front intersection waits for 5 minutes to please decelerate in advance, and the front intersection moves straight slowly to suggest that the right turn please merge ahead.
In the above embodiment, through voice broadcast, can remind driver place ahead road conditions and corresponding route optimization in advance, help the user to know the real-time situation more accurately to make driving decision better, select the optimum driving route, promote navigation experience.
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 invention. In this regard, each block in the flowchart or block diagrams may represent a module, segment, or 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 which 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 application may be implemented by software or hardware. The described units or modules may also be provided in a processor. The names of these units or modules do not in some cases constitute a limitation of the unit or module itself.
The above description is only a preferred embodiment of the application and is illustrative of the principles of the technology employed. It will be appreciated by a person skilled in the art that the scope of the invention as referred to in the present application is not limited to the embodiments with a specific combination of the above-mentioned features, but also covers other embodiments with any combination of the above-mentioned features or their equivalents without departing from the inventive concept. For example, the above features may be replaced with (but not limited to) features having similar functions disclosed in the present application.
Claims (13)
1. A method of navigation, the method comprising:
receiving a path planning request sent by user equipment, wherein the path planning request comprises a starting position and an end position;
acquiring a navigation path range of the user equipment according to the starting position and the end position;
acquiring all road traffic information within the navigation path range to determine the road traffic capacity, wherein the road traffic information at least comprises real-time traffic signal lamp information and real-time road conditions;
performing path planning based at least in part on the road traffic capacity; and
feeding back the path planning result to the user equipment;
the obtaining all road traffic information within the navigation path range to determine road traffic capacity comprises:
and in the navigation path range, simulating the road traffic capacity in one or more directions of the intersection by utilizing a pre-established model of influencing the road traffic capacity by using traffic signal lamps.
2. The method of claim 1, wherein the traffic signal information is obtained by the user equipment based on vehicle and other equipment V2X interconnection technology;
the traffic signal light information includes: traffic light state information and traffic light duration information of one or more directions at the intersection.
3. The method according to claim 1, characterized in that the model of the traffic light influencing the road traffic capacity is pre-established in the following way:
constructing an initial model based on the influence factors;
training the initial model to simulate the road traffic capacity of one or more directions of the intersection;
wherein the influencing factors include one or more of: number of vehicles waiting for a traffic light, traffic light information, vehicle status information, date, time, and weather conditions.
4. The method of claim 1 or 3, wherein the path planning based at least in part on the road traffic capacity comprises:
simulating the road traffic capacity of all road intersections in the navigation path range based on the model of the traffic signal lamp influencing the road traffic capacity;
evaluating the rationality of the path based on the road traffic capacity of all the road intersections;
and planning the path according to the evaluation path result.
5. A navigation server, comprising:
the device comprises a receiving unit, a processing unit and a processing unit, wherein the receiving unit is used for receiving a path planning request sent by user equipment, and the path planning request comprises a starting position and an end position;
the navigation path range determining unit is used for obtaining the navigation path range of the user equipment according to the starting point position and the end point position;
the traffic capacity determining unit is used for obtaining all road traffic information in the navigation path range to determine the road traffic capacity, wherein the road traffic information at least comprises real-time traffic signal lamp information and real-time road conditions;
a path planning unit for planning a path based at least in part on the road traffic capacity; and
a feedback unit, configured to feed back the path planning result to the user equipment;
the capacity determination unit is configured to:
and in the navigation path range, simulating the road traffic capacity in one or more directions of the intersection by utilizing a pre-established model of influencing the road traffic capacity by using traffic signal lamps.
6. The server according to claim 5,
the traffic signal light information is obtained by the user equipment based on the interconnection technology of the vehicle and other equipment V2X;
the traffic signal light information includes: traffic light state information and traffic light duration information of one or more directions at the intersection.
7. The server according to claim 5, wherein the traffic signal lamp model affecting road traffic capacity is pre-established as follows:
constructing an initial model based on the influence factors;
training the initial model to simulate the road traffic capacity of one or more directions of the intersection;
wherein the influencing factors include one or more of: number of vehicles waiting for a traffic light, traffic light information, vehicle status information, date, time, and weather conditions.
8. The server according to claim 5 or 7, wherein the path planning unit is configured to:
simulating the road traffic capacity of all road intersections in the navigation path range based on the model of the traffic signal lamp influencing the road traffic capacity;
evaluating the rationality of the path based on the road traffic capacity of all the road intersections;
and planning the path according to the evaluation path result.
9. An apparatus, characterized in that the apparatus comprises:
one or more processors;
a storage device for storing one or more programs,
when executed by the one or more processors, cause the one or more processors to implement the method of any one of claims 1-4.
10. A computer-readable storage medium, on which a computer program is stored which, when being executed by a processor, carries out the method according to any one of claims 1-4.
11. An in-vehicle system, comprising:
the V2X communication equipment is used for acquiring traffic signal light information;
the system comprises a sending device, a navigation server and a processing device, wherein the sending device is used for sending a path planning request to the navigation server, and the path planning request comprises a starting position and an end position;
the uploading equipment is used for uploading road traffic information to the navigation server, and the road traffic information at least comprises real-time traffic signal lamp information;
the receiving device is used for receiving a path planning result from the navigation server, wherein the path planning result is obtained by the navigation server through obtaining all road traffic information in the determined navigation path range based on the path planning request to determine road traffic capacity and performing path planning based at least in part on the road traffic capacity;
the obtaining all road traffic information within the determined navigation path to determine road traffic capacity comprises:
and in the navigation path range, simulating the road traffic capacity in one or more directions of the intersection by utilizing a pre-established model of influencing the road traffic capacity by using traffic signal lamps.
12. The system of claim 11, wherein:
the reception device further receives information related to road traffic capacity from the navigation server, and,
the system also comprises a voice broadcasting device which is used for broadcasting in real time in navigation according to the information related to the road traffic capacity.
13. The system of claim 12, wherein the announced content comprises at least one of: the number of vehicles waiting for the traffic signal lamp, the state information of the traffic signal lamp, the road traffic capacity of the current intersection and the driving suggestion.
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|---|---|---|---|---|
| CN109959380A (en) * | 2017-12-22 | 2019-07-02 | 中南大学 | A kind of determination method of the passage door based on vehicle mounted guidance track characteristic |
| CN108286981B (en) * | 2017-12-29 | 2020-12-22 | 广州斯沃德科技有限公司 | Vehicle path planning method and device of Internet of vehicles and computer equipment |
| CN110470314B (en) * | 2018-05-09 | 2024-05-24 | 阿里巴巴（中国）有限公司 | ETA model generation method and device, ETA prediction method and device and electronic equipment |
| CN108665716A (en) * | 2018-05-28 | 2018-10-16 | 于挺进 | A kind of method that the automatic anticipation red light crossing of automobile auxiliary is passed through |
| CN108801282A (en) * | 2018-06-13 | 2018-11-13 | 新华网股份有限公司 | Air navigation aid, device and the computing device of vehicle traveling |
| CN109300308B (en) * | 2018-10-23 | 2021-08-20 | 杭州翔毅科技有限公司 | Intelligent management platform based on dynamic traffic map analysis |
| CN112556706B (en) * | 2019-09-10 | 2023-12-26 | 菜鸟智能物流控股有限公司 | Navigation method, device, equipment and machine-readable medium |
| CN112577503B (en) * | 2019-09-30 | 2024-04-09 | 北京百度网讯科技有限公司 | Path planning method, device and equipment for vehicle starting point area |
| CN111397631A (en) * | 2020-04-10 | 2020-07-10 | 上海安吉星信息服务有限公司 | Navigation path planning method and device and navigation equipment |
| CN111522348A (en) * | 2020-05-27 | 2020-08-11 | 杭州野乐科技有限公司 | Self-walking control method and system for scooter |
| CN112256983B (en) * | 2020-11-13 | 2022-02-08 | 腾讯科技（深圳）有限公司 | Navigation information processing method and device, electronic equipment and storage medium |
| CN112489460A (en) * | 2020-12-03 | 2021-03-12 | 百度国际科技（深圳）有限公司 | Signal lamp information output method and device |
| CN112798003B (en) * | 2020-12-30 | 2023-03-21 | 腾讯科技（深圳）有限公司 | Navigation prompt information generation method, prompting method, device and equipment |
| CN113514074B (en) * | 2021-07-09 | 2023-11-17 | 北京航空航天大学 | Intersection path planning method, intersection path planning device and storage medium |
Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN103134508A (en) * | 2012-12-25 | 2013-06-05 | 上海博泰悦臻电子设备制造有限公司 | Navigation method, navigation device and navigation system |
| CN105387864A (en) * | 2015-10-15 | 2016-03-09 | 深圳市城市交通规划设计研究中心有限公司 | Route planning device and method |
| CN206056604U (en) * | 2016-06-22 | 2017-03-29 | 西安工业大学 | A kind of intelligent vehicle path planning system based on vehicle condition and road conditions |
| CN106679685A (en) * | 2016-12-29 | 2017-05-17 | 鄂尔多斯市普渡科技有限公司 | Driving path planning method for vehicle navigation |
| CN106871913A (en) * | 2015-12-14 | 2017-06-20 | 北京奇虎科技有限公司 | Path analysis method and device |
Family Cites Families (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN102128629A (en) * | 2010-01-13 | 2011-07-20 | 华为技术有限公司 | Path planning method, navigation system and geographical information system |
| US9501929B2 (en) * | 2013-01-25 | 2016-11-22 | Mitsubishi Electric Corporation | Movement assistance device and movement assistance method |
| CN104154926A (en) * | 2013-05-14 | 2014-11-19 | 腾讯科技（深圳）有限公司 | Navigation method and device |
| KR20160106444A (en) * | 2015-03-02 | 2016-09-12 | 한남대학교 산학협력단 | Safe driving guidance system that provides the current signal information and scheduled time information change. |
| CN105571605A (en) * | 2016-01-29 | 2016-05-11 | 宇龙计算机通信科技(深圳)有限公司 | Navigation route planning method and device, terminal and server |
| CN106289293B (en) * | 2016-08-11 | 2019-02-15 | 浪潮电子信息产业股份有限公司 | Urban content positioning navigation system based on intelligent learning algorithm |
-
2017
- 2017-06-30 CN CN201710523648.0A patent/CN107449435B/en active Active
Patent Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN103134508A (en) * | 2012-12-25 | 2013-06-05 | 上海博泰悦臻电子设备制造有限公司 | Navigation method, navigation device and navigation system |
| CN105387864A (en) * | 2015-10-15 | 2016-03-09 | 深圳市城市交通规划设计研究中心有限公司 | Route planning device and method |
| CN106871913A (en) * | 2015-12-14 | 2017-06-20 | 北京奇虎科技有限公司 | Path analysis method and device |
| CN206056604U (en) * | 2016-06-22 | 2017-03-29 | 西安工业大学 | A kind of intelligent vehicle path planning system based on vehicle condition and road conditions |
| CN106679685A (en) * | 2016-12-29 | 2017-05-17 | 鄂尔多斯市普渡科技有限公司 | Driving path planning method for vehicle navigation |
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