CN111896021B - Intelligent navigation method for signal-free road - Google Patents
Intelligent navigation method for signal-free road Download PDFInfo
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- G—PHYSICS
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- 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
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- G01C21/28—Navigation; Navigational instruments not provided for in groups G01C1/00 - G01C19/00 specially adapted for navigation in a road network with correlation of data from several navigational instruments
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Abstract
The invention discloses a signal-free road intelligent navigation method which comprises the steps of monitoring whether a road with poor signal transmission exists on a navigation route in real time, if so, intercepting a circuit diagram of the road before entering, and acquiring an actual distance value represented by the circuit diagram; establishing a topological structure of a characteristic layer, and modeling the circuit diagram according to the actual distance value; counting and calibrating the branch nodes on the circuit diagram; monitoring the traveling distance of the vehicle after entering the road in real time, and converting the real-time position of the vehicle in the virtual model of the road map by combining the actual distance value; the invention provides a method for navigating before entering a no-signal road and in the road and provides a route selection scheme when a driver misses an original fork in the no-signal road.
Description
Technical Field
The invention relates to the technical field of artificial intelligence, in particular to a signal-free road intelligent navigation method.
Background
In recent years, the rise of navigation greatly facilitates the passage of people. At present, with the continuous development of intelligent science and technology, the application of navigation is more and more perfect.
The navigation positioning system in the existing market is generally provided with a GPS, and due to the limitation of the GPS, when a vehicle passes through a tunnel and other long-distance non-signal roads, the specific position of the vehicle cannot be positioned, so that a driver cannot be prompted when needing to select the road, and the driver deviates from a route. Considering that China's Beidou positioning is superior to GPS in terms of signal degree and accuracy, but the Beidou positioning is not popularized yet, and the positioning fluency of a Beidou positioning system on a long-time signal-free road can not be ensured; meanwhile, the technical accuracy of the off-line navigation configured in the existing navigation system has larger deviation, and the actual use is not beneficial. Therefore, based on the development of the existing intelligent technology, the intelligent navigation method for the signal-free road has longer practical significance and market benefit.
Disclosure of Invention
This section is for the purpose of summarizing some aspects of embodiments of the invention and to briefly introduce some preferred embodiments. In this section, as well as in the abstract and the title of the invention of this application, simplifications or omissions may be made to avoid obscuring the purpose of the section, the abstract and the title, and such simplifications or omissions are not intended to limit the scope of the invention.
The present invention has been made in view of the above-mentioned problems with the conventional navigation positioning system.
Therefore, the technical problem solved by the invention is as follows: the problem of current navigation positioning system can't fix a position the concrete position of vehicle when the vehicle passes through the no signal road of longer distance such as tunnel, leads to the vehicle can't fix a position when needing to carry out road selection to deviate from the route is solved.
In order to solve the technical problems, the invention provides the following technical scheme: a no-signal road intelligent navigation method comprises the steps of monitoring whether a road with poor signal transmission exists on a navigation route in real time, if so, intercepting a line graph of the road before entering, and acquiring an actual distance value represented by the line graph; establishing a topological structure of a characteristic layer, and modeling the circuit diagram according to the actual distance value; counting and calibrating the branch nodes on the circuit diagram; monitoring the traveling distance of the vehicle after the vehicle enters the road in real time, and converting the real-time position of the vehicle in the virtual model of the circuit diagram by combining the actual distance value; and giving a navigation prompt when the vehicle reaches the turnout node.
As a preferred scheme of the intelligent navigation method for the signal-free road, the method comprises the following steps: intercepting the road map before implementing the method, wherein the method comprises the steps of acquiring the speed v and the instantaneous acceleration a of the vehicle and the actual distance S' of the vehicle from the road in real time; when the difference value between the operation distance value S obtained according to the formula and the actual distance S' is within the threshold value for the first time, the method is started to operate;
wherein,
wherein S represents a running distance value; a represents the instantaneous acceleration of the vehicle; v represents the speed of the vehicle.
As a preferred scheme of the intelligent navigation method for the signal-free road, the method comprises the following steps: the threshold is a2。
As a preferred scheme of the intelligent navigation method for the signal-free road, the method comprises the following steps: when the vehicle misses the turnout node in the road according to the original navigation prompt, the optimal route to the destination is obtained again; acquiring a corresponding route map represented by the optimal route in a no-signal stage and an actual distance of the corresponding route map; establishing a topological structure of a characteristic layer, and modeling the circuit diagram according to the actual distance value; counting and calibrating the branch nodes on the circuit diagram; monitoring the traveling distance of the vehicle after entering the road in real time, and converting the real-time position of the vehicle in the virtual model of the circuit diagram by combining the actual distance value; and giving a navigation prompt when the vehicle reaches the turnout node.
As a preferred scheme of the intelligent navigation method for the signal-free road, the method comprises the following steps: the step of re-acquiring the optimal route to the destination comprises the steps of counting all the remaining branch nodes and corresponding exit positions in the road according to the overall navigation route, and listing the remaining sub-routes in the road in parallel; obtaining routes of the different exits to the destination in combination with the navigated overall route; acquiring the coincidence ratio theta between the destination route and the original route from different outlets and the distance P between the first coincidence position and each outlet; acquiring the distances P' between different exits and the exit of the road original route; establishing an optimization training model, inputting the corresponding parameters for optimization training, and acquiring an output correction parameter ratio delta; and selecting the line with the minimum correction parameter ratio delta as an optimal alternative route.
As a preferred scheme of the intelligent navigation method for the signal-free road, the method comprises the following steps: the optimized training model is established by the following steps of,
δ(P，P′，θ)＝logθ(x)logp(x)min(p·p′)max(θ)
wherein δ is the ratio of the correction parameters, θ is the coincidence ratio between the route from the different outlets to the destination and the original route, P is the distance from the first coincident position to the respective outlet, min is a minimum function model, and max is a maximum function model.
As a preferred scheme of the intelligent navigation method for the signal-free road, the method comprises the following steps: the optimization training model performs optimization training according to the following formula,
in the formula, δ is the ratio of the correction parameters, θ is the coincidence ratio between the route from the different outlets to the destination and the original route, P is the distance from the first coincidence position to the respective outlet, and min is a minimum function model.
The invention has the beneficial effects that: the intelligent navigation method for the no-signal road provided by the invention gives out the navigation method before entering the no-signal road and in the road, better considers the actual situation, simultaneously gives out the route selection scheme when the driver misses the original turnout in the no-signal road, and solves the problem that the existing navigation positioning system can not position the specific position of the vehicle when the vehicle passes through the no-signal road with a longer distance such as a tunnel, and the vehicle can not be positioned when the road needs to be selected, thereby deviating the route.
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In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings needed to be used in the description of the embodiments will be briefly introduced below, and it is obvious that the drawings in the following description are only some embodiments of the present invention, and it is obvious for those skilled in the art to obtain other drawings based on these drawings without inventive exercise. Wherein:
FIG. 1 is a flow chart of a method of the intelligent navigation method for a signal-free road according to the present invention;
fig. 2 is a topology of a modeled wiring diagram provided by the present invention.
Detailed Description
In order to make the aforementioned objects, features and advantages of the present invention comprehensible, specific embodiments accompanied with figures are described in detail below, and it is apparent that the described embodiments are a part of the embodiments of the present invention, not all of the embodiments. All other embodiments, which can be obtained by a person skilled in the art without making creative efforts based on the embodiments of the present invention, shall fall within the protection scope of the present invention.
In the following description, numerous specific details are set forth in order to provide a thorough understanding of the present invention, but the present invention may be practiced in other ways than those specifically described and will be readily apparent to those of ordinary skill in the art without departing from the spirit of the present invention, and therefore the present invention is not limited to the specific embodiments disclosed below.
Furthermore, reference herein to "one embodiment" or "an embodiment" means that a particular feature, structure, or characteristic described in connection with the embodiment is included in at least one implementation of the invention. The appearances of the phrase "in one embodiment" in various places in the specification are not necessarily all referring to the same embodiment, nor are separate or alternative embodiments mutually exclusive of other embodiments.
The present invention will be described in detail with reference to the drawings, wherein the cross-sectional views illustrating the structure of the device are not enlarged partially in general scale for convenience of illustration, and the drawings are only exemplary and should not be construed as limiting the scope of the present invention. In addition, the three-dimensional dimensions of length, width and depth should be included in the actual fabrication.
Meanwhile, in the description of the present invention, it should be noted that the terms "upper, lower, inner and outer" and the like indicate orientations or positional relationships based on the orientations or positional relationships shown in the drawings, and are only for convenience of describing the present invention and simplifying the description, but do not indicate or imply that the referred device or element must have a specific orientation, be constructed in a specific orientation and operate, and thus, cannot be construed as limiting the present invention. Furthermore, the terms first, second, or third are used for descriptive purposes only and are not to be construed as indicating or implying relative importance.
The terms "mounted, connected and connected" in the present invention are to be understood broadly, unless otherwise explicitly specified or limited, for example: can be fixedly connected, detachably connected or integrally connected; they may be mechanically, electrically, or directly connected, or indirectly connected through intervening media, or may be interconnected between two elements. The specific meanings of the above terms in the present invention can be understood in specific cases to those skilled in the art.
Example 1
Roads similar to long-distance tunnels and on which navigation signals cannot be transmitted or even cannot be transmitted often appear in the actual driving process, and when a fork appears in the roads, a driver cannot obtain navigation prompts in time, so that route deviation occurs. Considering that China's Beidou positioning is superior to GPS in terms of signal degree and accuracy, but the Beidou positioning is not popularized yet, and the positioning fluency of a Beidou positioning system on a long-time signal-free road can not be ensured; meanwhile, the technical accuracy of the off-line navigation configured in the existing navigation system has larger deviation, and the actual use is not beneficial.
Therefore, referring to fig. 1 and fig. 2, the present invention provides a method for signal-free road navigation, comprising:
monitoring whether a road with poor signal transmission exists on a navigation route in real time, if so, intercepting a road line graph before entering, and acquiring an actual distance value represented by the line graph;
establishing a topological structure of a characteristic layer, and modeling a circuit diagram according to an actual distance value;
counting and calibrating branch nodes on the circuit diagram;
monitoring the traveling distance of the vehicle after entering the road in real time, and converting the real-time position of the vehicle in the virtual model of the circuit diagram by combining the actual distance value;
and giving a navigation prompt when the vehicle reaches the turnout node.
It should be noted that:
firstly, before the driving navigation system is operated, the summary of road conditions in front of a selected route exists in the navigation system, and the situation that the original driving route can be changed continuously in the actual driving process is considered, so that the method adopts real-time monitoring to determine whether a road with poor signal transmission, such as a long-distance tunnel, exists on the navigation route.
Secondly, roads with poor signal transmission can be artificially defined in advance, for example, tunnels and specific remote zones are set as sensitive monitoring keywords, and the method is operated when the roads appear;
what is additionally needed is: considering that when the driving vehicle appears on a strange no-signal road, the method can intelligently supplement and store the signal condition of the road to be passed, and the method is operated in advance when the vehicle passes next time, so that the calculation amount is reduced, and the intelligence is improved.
Considering that the target circuit diagram is generally obtained by identifying and selecting the circuit diagram after downloading the circuit diagram and then splitting the circuit diagram, the method has large operation amount and longer operation result, therefore, the invention creatively adopts an interception mode to obtain the circuit diagram, and the comparison is shown in the following table 1:
table 1: circuit diagram acquisition performance comparison table
| When obtaining the circuit diagram onceWorkshop(s) | Fluency of operation (%) | |
| Prior Art | 2.441 | 85.556 |
| The invention | 0.830 | 92.143 |
As shown in table 1 above, when the method and the prior art are performed in a simulworks simulation environment, the time for obtaining the circuit diagram once and the smoothness of multiple operations (preferably 20 times, and performance comparison can be obtained on the basis of saving the operation times to the maximum) are selected for comparison, so that the method and the system are obviously superior to the prior art in the time for obtaining the circuit diagram once, and the smoothness of obtaining the circuit diagram is higher than that of the prior art.
Specifically, the specific operation code for intercepting the road map is as follows:
from selenium import webdriver
driver＝webdriver.Chrome()
driver.get(′url′)
driver.get_screenshot_as_png()
driver.save_screenshot(′file_path′)
image. open ('screenshot. png')
code＝pytesseract.image_to_string(image)
print(code)。
And fourthly, as shown in the figure 2, establishing a topological structure of the characteristic layer, and modeling the circuit diagram according to the actual distance value.
And adopting a three-dimensional modeling technology to carry out virtual modeling on the circuit diagram, and establishing a digital world twinning with the physical world. The digital world is used for digitizing the physical world, and a scene virtual model consistent with a circuit diagram is established by adopting a three-dimensional modeling technology.
The digital world construction mode specifically comprises the following steps: firstly, creating a G I S (Geographic Information System) feature layer; and then processing the GIS characteristic layer through ArcGIS, creating a three-dimensional model and a topological structure through ArcScene, and establishing a corresponding digital world.
The actual distance value can be obtained through the original inclusion technology in navigation, the distance of the vehicle after entering the road is monitored in real time, the real-time position of the vehicle in the virtual model of the road map is calculated by combining the actual distance value, and the operation formula is as follows:
and acquiring the corresponding position in real time to obtain the real-time position of the vehicle on the no-signal road.
Further, the intercepting of the circuit diagram before the implementation method comprises:
acquiring the speed v and the instantaneous acceleration a of a vehicle and the actual distance S' of the vehicle from a road in real time;
when the difference value between the first running distance value S and the actual distance S' obtained according to the formula is within the threshold value, starting the running method;
wherein,
wherein S represents a running distance value; a represents the instantaneous acceleration of the vehicle; v represents the speed of the vehicle.
In order to reduce the operation amount of the system operating the method and give reaction time in advance, the invention intercepts the embedded circuit diagram in advance. Considering that the running distance value S is in a variable state influenced by the speed v and the instantaneous acceleration a of the vehicle, and the actual distance S' of the vehicle from the road is in a state of becoming smaller, the method starts to run when the first difference between the two variables is within the threshold value.
The following table 2 shows a comparison of the reaction conditions according to the invention with those without prior measures:
table 2: comparison table of reaction state by adopting the invention and not adopting an advance mode
As shown in Table 2 above, the present invention was performed in a SimuWorks simulation environment in comparison to the reaction state without an advance approach. The steps are finished by adopting the method of the invention 0.0014s after entering the road, and the steps can be finished by 2.983s without adopting an advance mode. And when the vehicle enters the road at a constant speed of 60km/h to run, the first fork is respectively placed at 10m, 20m, 30m and 40m to carry out a plurality of simulation experiments, and the influence of the driver is considered, so that the method which does not adopt an advance mode has a high probability of missing the first fork, but the method hardly misses the fork.
Further, the threshold is a2。
Preferably, considering that even if there is no navigation prompt on the signal road, the vehicle misses the route of the original navigation prompt due to too fast speed and inattention, and so on, this also includes when the vehicle misses the branch node in the road according to the original navigation prompt,
re-acquiring an optimal route to the destination;
acquiring a corresponding route map represented by the optimal route in a signal-free stage and an actual distance of the optimal route;
establishing a topological structure of a characteristic layer, and modeling a circuit diagram according to an actual distance value;
counting and calibrating branch nodes on the circuit diagram;
monitoring the traveling distance of the vehicle after entering the road in real time, and converting the real-time position of the vehicle in the virtual model of the road map by combining the actual distance value;
and giving a navigation prompt when the vehicle reaches the turnout node.
Further, the retrieving of the optimal route to the destination includes:
counting all the remaining branch nodes and corresponding exit positions in the road according to the whole navigation route, and listing the remaining sub-routes in the road in parallel;
obtaining routes from different exits to a destination by combining the integral route of navigation;
acquiring the coincidence ratio theta between the route from different outlets to the destination and the original route and the distance P between the first coincidence position and each outlet;
acquiring the distances P' between different exits and an exit of an original road route;
establishing an optimization training model, inputting the corresponding parameters for optimization training, and acquiring an output correction parameter ratio delta;
and selecting the line with the least correction parameter ratio delta as the optimal alternative line.
It is understood that the overall navigation route can provide a specific overview of the no-signal road stage, count all the remaining branch nodes and their corresponding exit positions, and list the remaining different sub-routes. And considering that different routes from different road exits to the destination are selected, and considering that a driver possibly has a stopping point on the original route in actual driving, the new route is overlapped with the original route as much as possible, so that the overlap ratio theta is improved.
After the contact ratio theta is taken as a reference quantity, the distance P between the first coincident position and each outlet and the distance P' between different outlets and the road original route outlet are obtained, optimization training is carried out through an optimization training model, the output correction parameter ratio delta is obtained, the route with the minimum correction parameter ratio delta is selected as the optimal alternative route, and the contact ratio is improved to the maximum extent.
The established optimization training model is as follows:
δ(P，P′，θ)＝logθ(x)logp(x)min(p·p′)max(θ)
wherein, δ is a ratio of the correction parameters, θ is a coincidence ratio between routes from different outlets to a destination and an original route, P is a distance from a first coincidence position to respective outlets, min is a minimum function model, and max is a maximum function model.
Further, the optimization training model performs optimization training according to the following formula:
in the formula, δ is a correction parameter ratio, θ is the coincidence ratio of the routes from different outlets to the destination and the original route, P is the distance from the first coincidence position to the respective outlet, and min is a minimum function model.
The intelligent navigation method for the no-signal road provided by the invention gives out the navigation method before entering the no-signal road and in the road, better considers the actual situation, simultaneously gives out the route selection scheme when the driver misses the original turnout in the no-signal road, and solves the problem that the existing navigation positioning system can not position the specific position of the vehicle when the vehicle passes through the no-signal road with a longer distance such as a tunnel, and the vehicle can not be positioned when the road needs to be selected, thereby deviating the route.
It should be recognized that embodiments of the present invention can be realized and implemented by computer hardware, a combination of hardware and software, or by computer instructions stored in a non-transitory computer readable memory. The methods may be implemented in a computer program using standard programming techniques, including a non-transitory computer-readable storage medium configured with the computer program, where the storage medium so configured causes a computer to operate in a specific and predefined manner, according to the methods and figures described in the detailed description. Each program may be implemented in a high level procedural or object oriented programming language to communicate with a computer system. However, the program(s) can be implemented in assembly or machine language, if desired. In any case, the language may be a compiled or interpreted language. Furthermore, the program can be run on a programmed application specific integrated circuit for this purpose.
Further, the operations of processes described herein can be performed in any suitable order unless otherwise indicated herein or otherwise clearly contradicted by context. The processes described herein (or variations and/or combinations thereof) may be performed under the control of one or more computer systems configured with executable instructions, and may be implemented as code (e.g., executable instructions, one or more computer programs, or one or more applications) collectively executed on one or more processors, by hardware, or combinations thereof. The computer program includes a plurality of instructions executable by one or more processors.
Further, the method may be implemented in any type of computing platform operatively connected to a suitable interface, including but not limited to a personal computer, mini computer, mainframe, workstation, networked or distributed computing environment, separate or integrated computer platform, or in communication with a charged particle tool or other imaging device, and the like. Aspects of the invention may be implemented in machine-readable code stored on a non-transitory storage medium or device, whether removable or integrated onto a computing platform, such as a hard disk, optically read and/or write storage media, RAM, ROM, etc., so that it is readable by a programmable computer, which when read by the computer can be used to configure and operate the computer to perform the procedures described herein. Further, the machine-readable code, or portions thereof, may be transmitted over a wired or wireless network. The invention described herein includes these and other different types of non-transitory computer-readable storage media when such media include instructions or programs that implement the steps described above in conjunction with a microprocessor or other data processor. The invention also includes the computer itself when programmed according to the methods and techniques described herein. A computer program can be applied to input data to perform the functions described herein to transform the input data to generate output data that is stored to non-volatile memory. The output information may also be applied to one or more output devices, such as a display. In a preferred embodiment of the invention, the transformed data represents physical and tangible objects, including particular visual depictions of physical and tangible objects produced on a display.
As used in this application, the terms "component," "module," "system," and the like are intended to refer to a computer-related entity, either hardware, firmware, a combination of hardware and software, or software in execution. For example, a component may be, but is not limited to being: a process running on a processor, an object, an executable, a thread of execution, a program, and/or a computer. By way of example, both an application running on a computing device and the computing device can be a component. One or more components can reside within a process and/or thread of execution and a component can be localized on one computer and/or distributed between two or more computers. In addition, these components can execute from various computer readable media having various data structures thereon. The components may communicate by way of local and/or remote processes such as in accordance with a signal having one or more data packets (e.g., data from one component interacting with another component in a local system, distributed system, and/or across a network such as the internet with other systems by way of the signal).
It should be noted that the above-mentioned embodiments are only for illustrating the technical solutions of the present invention and not for limiting, and although the present invention has been described in detail with reference to the preferred embodiments, it should be understood by those skilled in the art that modifications or equivalent substitutions may be made on the technical solutions of the present invention without departing from the spirit and scope of the technical solutions of the present invention, which should be covered by the claims of the present invention.
Claims (1)
1. A no-signal road intelligent navigation method is characterized in that: the method comprises the steps of monitoring whether a road with poor signal transmission exists on a navigation route in real time, if so, intercepting a line graph of the road before entering, and acquiring an actual distance value represented by the line graph; establishing a topological structure of a characteristic layer, and modeling the circuit diagram according to the actual distance value; counting and calibrating the branch nodes on the circuit diagram; monitoring the traveling distance of the vehicle after the vehicle enters the road in real time, and converting the real-time position of the vehicle in the virtual model of the circuit diagram by combining the actual distance value; giving a navigation prompt when the vehicle reaches the turnout node;
when the vehicle misses the turnout node in the road according to the original navigation prompt, the optimal route to the destination is obtained again; acquiring a corresponding route map represented by the optimal route in a signal-free stage and an actual distance of the route map; establishing a topological structure of a characteristic layer, and modeling the circuit diagram according to the actual distance value; counting and calibrating the branch nodes on the circuit diagram; monitoring the traveling distance of the vehicle after entering the road in real time, and converting the real-time position of the vehicle in the virtual model of the circuit diagram by combining the actual distance value; giving a navigation prompt when the vehicle reaches the turnout node;
the step of re-acquiring the optimal route to the destination comprises the steps of counting all the remaining branch nodes and corresponding exit positions in the road according to the overall navigation route, and listing the remaining sub-routes in the road in parallel; obtaining routes from different exits to the destination in combination with the navigated overall route; acquiring the coincidence ratio theta between the destination route and the original route from different outlets and the distance P between the first coincidence position and each outlet; acquiring the distances P' between different exits and the exit of the road original route; establishing an optimization training model, inputting the corresponding parameters for optimization training, and acquiring an output correction parameter ratio delta; and selecting the line with the minimum correction parameter ratio delta as an optimal alternative route.
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