CN113390429A - Navigation method and device - Google Patents
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Abstract
The application relates to the field of communication, in particular to a navigation method and a navigation device, which are used for solving the problem of inaccurate navigation results, and the method comprises the following steps: after the intelligent terminal determines each candidate driving path and each corresponding initial traffic flow density based on the obtained road positioning data and vehicle positioning data, the variance of statistical distribution is obtained based on a preset vehicle body length mean value and a preset vehicle distance mean value in combination with a preset vehicle positioning error, then a traffic flow density interval determined based on each initial traffic flow density and variance is adopted in combination with the variance to respectively determine the vehicle density mean value corresponding to each candidate driving path, and a candidate driving path corresponding to the vehicle density mean value which meets the set conditions is used as a navigation target driving path; therefore, the corresponding initial traffic density can be corrected in the traffic density interval corresponding to each candidate driving path, so that the obtained average value of the traffic density is more accurate, and the accuracy of the navigation result is improved.
Description
Technical Field
The present application relates to the field of communications, and in particular, to a navigation method and apparatus.
Background
In the related art, intelligent communication technology has been integrated into various aspects of life, including intelligent navigation systems. The intelligent navigation system collects road condition information, analyzes road traffic conditions and provides corresponding navigation suggestions through a communication means, and great convenience is brought to daily travel of people.
In practical application, the intelligent navigation system used on the vehicle-mounted computer can obtain each candidate driving path based on the road positioning data provided by the intelligent traffic control system, obtain the traffic density of driving on each candidate driving path based on the vehicle positioning data of other vehicles, and select the candidate driving path with the minimum traffic density as the target driving path for navigation.
In the above scheme, the obtained traffic density is an average value, and is generally obtained by calculating the number of vehicles on a unit path, so that the actual traffic density cannot be accurately reflected, and the accuracy of the navigation result is affected.
Therefore, there is a need to redesign a navigation method to overcome the above-mentioned drawbacks.
Disclosure of Invention
The application provides a navigation method and a navigation device, which are used for solving the problem that navigation results are inaccurate due to the fact that the traffic density cannot be accurately calculated in the related art.
The specific technical scheme provided by the application is as follows:
in a first aspect, a method of navigation, comprising:
determining N candidate running paths based on the obtained road positioning data, wherein N is a natural number, and respectively determining initial traffic flow density on each candidate running path based on the obtained vehicle positioning data;
obtaining a variance of statistical distribution based on a preset vehicle body length mean value and a preset vehicle distance mean value and in combination with a preset vehicle positioning error, and respectively determining traffic flow density intervals corresponding to each candidate driving path according to the initial traffic flow density and the variance of each candidate driving path;
respectively determining the vehicle density mean value corresponding to each candidate running path according to the traffic flow density interval corresponding to each candidate running path and the variance;
and selecting a candidate running path corresponding to the vehicle density mean value which meets the set conditions as a navigation target running path.
In a second aspect, an apparatus for navigation, comprises:
a first determining unit, configured to determine N candidate traveling paths based on the obtained road positioning data, where N is a natural number, and determine initial traffic density on each candidate traveling path based on the obtained vehicle positioning data, respectively;
the second determining unit is used for obtaining the variance of statistical distribution by combining a preset vehicle positioning error based on a preset vehicle body length mean value and a preset vehicle distance mean value, and respectively determining the traffic flow density intervals corresponding to the candidate running paths according to the initial traffic flow density and the variance of the candidate running paths;
a third determining unit, configured to determine a vehicle density mean value corresponding to each candidate travel path according to the traffic flow density interval and the variance corresponding to each candidate travel path;
and the selecting unit is used for selecting a candidate running path corresponding to the vehicle density mean value which meets the set conditions as a target running path for navigation.
In a third aspect, an electronic device includes:
a memory for storing executable instructions;
a processor configured to read and execute the executable instructions stored in the memory to implement the method according to the first aspect.
In a fourth aspect, a computer-readable storage medium is proposed, in which instructions that, when executed by a processor, enable the processor to perform the method of the first aspect.
In a fifth aspect, a computer program product or computer program comprises computer instructions stored in a computer-readable storage medium, which are read by a processor of a computer device from the computer-readable storage medium, and which are executed by the processor to enable the computer device to perform the method of the first aspect.
In the embodiment of the application, after the intelligent terminal determines each candidate driving path and each corresponding initial traffic density based on the obtained road positioning data and vehicle positioning data, the variance of statistical distribution is obtained based on the preset vehicle body length mean value and the preset vehicle distance mean value in combination with the preset vehicle positioning error, then the vehicle density mean value corresponding to each candidate driving path is respectively determined in combination with the variance according to the traffic density interval determined based on each initial traffic density and the variance, and a candidate driving path corresponding to the vehicle density mean value meeting the set conditions is selected to serve as the navigation target driving path; therefore, the corresponding initial traffic density is corrected in the traffic density interval corresponding to each candidate running path based on the preset vehicle body length mean value, the preset vehicle distance mean value and the preset vehicle positioning error, so that the finally obtained traffic density mean value of each candidate running path is more accurate, the accuracy of a navigation result is improved, and the service performance of the intelligent navigation system is improved.
Drawings
FIG. 1 is a schematic diagram of an application architecture of an intelligent navigation system according to an embodiment of the present application;
FIG. 2 is a diagram illustrating an application scenario in an embodiment of the present application;
FIG. 3 is a schematic navigation flow chart in the embodiment of the present application;
FIG. 4 is a schematic view of a first traffic density interval in the embodiment of the present application;
FIG. 5 is a schematic view of a second traffic density interval in the embodiment of the present application;
FIG. 6 is a schematic diagram of a logic architecture of an intelligent terminal according to an embodiment of the present application;
fig. 7 is a schematic diagram of an intelligent terminal entity architecture in the embodiment of the present application.
Detailed Description
In order to make the objects, technical solutions and advantages of the present application more apparent, the present application is described in further detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative of the present application and are not intended to limit the present application.
For the purpose of facilitating an understanding of the embodiments of the present application, a brief introduction of several concepts is provided below:
the intelligent terminal: various types of applications may be installed, and a device capable of displaying an object provided in the installed application may be mobile. For example, a smart phone, a vehicle-mounted computer, a tablet computer, a reader computer, a laptop computer, a PC, various wearable devices, a Personal Digital Assistant (PDA), a Point of Sales (POS), or other electronic devices capable of implementing the above functions.
A floating vehicle: the vehicle is a vehicle equipped with a Positioning module, for example, a taxi, a bus, a private car, etc. equipped with a Global Positioning System (GPS) module or a beidou module. In the following embodiments, for convenience of description, the floating vehicles are simply referred to as vehicles, but all the vehicles are assumed to be floating vehicles, and will not be described again.
Traffic density (traffic density): also known as traffic flow density. The number of vehicles in a lane or at some instant in a direction over a road segment of unit length (typically 1 km). To indicate how dense the vehicles are on a road.
Normal distribution: in the embodiment of the present application, it is assumed that the traffic density on the candidate travel path follows a normal distribution.
Credibility probability: the embodiment of the application refers to the probability that the value of the mean value of the traffic flow density is true.
Artificial Intelligence (AI): the method is a theory, method, technology and application system for simulating, extending and expanding human intelligence by using a digital computer or a machine controlled by the digital computer, sensing the environment, acquiring knowledge and obtaining the best result by using the knowledge. Artificial intelligence is a comprehensive technique of computer science that attempts to understand the essence of intelligence and produce a new intelligent machine that can react in a manner similar to human intelligence. Artificial intelligence is also a design principle and an implementation method for researching various intelligent machines, so that the machines have the functions of perception, reasoning and decision making.
The artificial intelligence technology is a comprehensive subject and relates to the field of extensive technology, namely the technology of a hardware level and the technology of a software level. The artificial intelligence base technologies generally include sensors, dedicated artificial intelligence chips, cloud computing, distributed storage, big data processing technologies, operation/interaction systems, mechatronics, and the like. The artificial intelligence software technology mainly comprises a computer vision technology, a voice processing technology, a natural language processing technology, machine learning, deep learning and the like.
As artificial intelligence technology has been researched and developed, it has been developed and applied in various fields, such as: intelligent house, intelligent wearing equipment, virtual assistant, intelligent audio amplifier, intelligent marketing, unmanned driving, autopilot, unmanned aerial vehicle, robot, intelligent medical treatment, intelligent customer service etc.. With the development of the technology, the artificial intelligence technology can be applied in more fields and can play more and more important value.
In order to solve the problem that navigation results are inaccurate due to the fact that traffic flow densities cannot be accurately calculated in the related art, in the embodiment of the application, automatic driving technologies (including technologies of high-precision maps, environment perception, behavior decision, path planning, motion control and the like) are combined, an intelligent terminal is enabled to determine each candidate driving path and each corresponding initial traffic flow density based on obtained road positioning data and vehicle positioning data, then variance of statistical distribution is obtained based on a preset vehicle body length mean value and a preset vehicle distance mean value and combined with a preset vehicle positioning error, then corresponding initial traffic flow densities are corrected in traffic flow density sections corresponding to each candidate driving path according to the traffic flow density sections determined based on each initial traffic flow density and variance and combined with the variance, and thus the traffic flow density mean values corresponding to each candidate driving path are obtained, and selecting a candidate running path corresponding to the vehicle density mean value which meets the set condition as a target running path for navigation.
Preferred embodiments of the present application will be described in further detail below with reference to the accompanying drawings.
Fig. 1 is a schematic view of an application scenario according to an embodiment of the present application. In this application scenario, the application architecture of the intelligent navigation system may include: the intelligent traffic control system comprises an intelligent traffic control system 100 and a plurality of intelligent terminals 101, wherein the plurality of intelligent terminals 101 may be vehicle-mounted computers on different vehicles, or smart phones, tablet computers, reader computers, portable computers, PCs, various wearable devices, Personal Digital Assistants (PDAs), Point of Sales (POS) or other electronic devices capable of realizing the above functions, and the like; each intelligent terminal 101 and the intelligent traffic control system 100 can communicate with each other through a communication network, each intelligent terminal 101 obtains road positioning data from the intelligent traffic control system, each intelligent terminal 101 can mutually transmit Vehicle positioning data between different vehicles through a Vehicle to Vehicle (V2V) protocol interface, and of course, each intelligent terminal 101 can also obtain Vehicle positioning data of other vehicles from the intelligent traffic control system 100, which is not limited herein.
The above example of fig. 1 is only one example of an application architecture for implementing the embodiment of the present application, and the embodiment of the present application is not limited to the application architecture described in fig. 1 above.
Referring to fig. 2, in the embodiment of the application, after a user logs in the intelligent terminal 101, the intelligent terminal 101 enters a navigation interface of the intelligent navigation system in response to a click operation of the user, and after an initial position and a destination position input by the user are obtained, N candidate driving routes can be obtained according to road positioning data provided by the intelligent traffic control system 100.
Based on the application architecture, referring to fig. 3, in the embodiment of the present application, a specific process of the intelligent terminal for navigation is as follows:
step 300: the intelligent terminal determines N candidate running paths based on the obtained road positioning data, wherein N is a natural number, and determines initial traffic flow density on each candidate running path respectively based on the obtained vehicle positioning data.
In the embodiment of the application, the intelligent terminal can acquire road positioning data within a specified range from the intelligent traffic control system, and plan out N candidate driving paths through the intelligent navigation system, as shown in fig. 2, which is not repeated.
On the other hand, the intelligent terminal can obtain the vehicle positioning data of each other vehicle within the specified range from the intelligent traffic control system, or can directly obtain the corresponding vehicle positioning data from each other vehicle within the specified range through the V2V protocol interface.
Further, as shown in fig. 2, the intelligent terminal provides the travel distance and the travel time of the candidate travel route in correspondence with each other in the interface of the N candidate travel routes planned by the intelligent navigation system, so that the intelligent terminal can calculate the initial traffic density on each candidate travel route based on the obtained travel distance of each candidate travel route and the obtained number of each vehicle based on the obtained vehicle positioning data of each other vehicle.
Optionally, the executing body for calculating the initial traffic density on each candidate driving path may be an intelligent terminal, or an intelligent traffic control system, and then the intelligent traffic control system sends the calculation result to the intelligent terminal, which is not limited herein.
Specifically, in the embodiment of the present application, the following formula may be adopted to calculate the initial traffic density:
wherein N is the serial number of the candidate driving path, and the value of N is [1, N]，gnA number of vehicles obtained based on the vehicle positioning data on the nth candidate running path; l isnThe driving distance of the nth candidate driving path is obtained.
For example, referring to fig. 2, a user logs in the intelligent terminal and enters a navigation interface, and it is assumed that a start position and a destination position input by the user are respectively shenzhen city and beijing city; then, the intelligent terminal obtains the road positioning data based on the intelligent traffic control system, and plans N candidate driving paths through the intelligent navigation system, where N is 3, and is the candidate driving path 1, the candidate driving path 2, and the candidate driving path 3, respectively.
The intelligent terminal obtains the driving distance and the driving time of the three candidate driving paths based on the intelligent navigation system; as shown in fig. 2, it is assumed that the travel distance of the candidate travel path 1 is 2300 km and the travel time is 25 hours and 45 minutes; the running distance of the candidate running route 2 is 2120 kilometers, and the running time is 23 hours; the travel distance of the candidate travel path 3 is 2220 km, and the travel time is 24 hours and 30 minutes.
Further, the intelligent terminal determines the number of vehicles on each candidate traveling path based on the vehicle positioning data on the three candidate traveling paths, respectively, assuming that the number of vehicles on the candidate traveling path 1 is 1500, the number of vehicles on the candidate traveling path 2 is 1000, and the number of vehicles on the candidate traveling path 3 is 1200.
Then, it can be derived by calculation,
the initial traffic density on the candidate travel path 1 is:
the initial traffic density on the candidate travel path 2 is:
the initial traffic density on the candidate travel path 3 is:
step 310: the intelligent terminal combines a preset vehicle positioning error to obtain the variance of statistical distribution based on a preset vehicle length mean value and a preset vehicle distance mean value.
In the embodiment of the present application, it is assumed that the traffic flow density on each candidate driving path is subject to a normal distribution, and in practical applications, the traffic flow density may also be subject to other statistical distributions, such as a rayleigh distribution.
Specifically, taking the nth candidate driving path as an example, in the embodiment of the present application, the following formula may be adopted to represent the normal distribution of the traffic flow density on the nth candidate driving path:
N(xn,α2)
wherein x isnRepresenting the initial traffic density on the nth candidate driving path, alpha representing the standard deviation of the normal distribution, alpha2The variance of a normal distribution is represented.
Optionally, the standard deviation α is calculated by using the following formula:
α＝[σ/(w+s)]
wherein w represents the vehicle body length mean value, s represents the vehicle distance mean value, and σ represents the preset vehicle positioning error.
The vehicle positioning error σ refers to an error of the obtained vehicle positioning data; in the embodiment of the application, because the positioning modes adopted by the intelligent navigation system and the intelligent traffic control system are known, for example, the positioning is carried out based on a GPS system or based on a Beidou system, the error of the vehicle positioning data is also known, namely sigma is also known.
The vehicle distance mean value s refers to a distance threshold between vehicles.
In the embodiment of the present application, the vehicle distance average s may be set as a distance between two vehicles, which is generally 2 meters, assuming that the driver of the vehicle can see the tires of the vehicles right in front.
The vehicle body length mean value w is a comprehensive evaluation value of the vehicle body length of each type of vehicle.
In practical applications, there may be a plurality of types of vehicles, such as miniature vehicles, small-sized vehicles, medium-sized vehicles, heavy-duty vehicles, on each candidate driving route, and the body lengths of different vehicles are different.
Then, the intelligent terminal or the intelligent traffic control system may count the vehicle types and the corresponding vehicle body lengths of the various vehicles passing through each road section based on the historical driving records, and then obtain the corresponding vehicle body length mean value by combining the vehicle body lengths corresponding to the various vehicle types based on the proportion of the number of the vehicles corresponding to the various vehicle types in the total number of passing vehicles on each road section.
After the intelligent terminal or the intelligent traffic control system obtains the vehicle body length mean value according to the above method, the vehicle body length mean value can be stored as a preset parameter and periodically updated, and if the intelligent traffic control system obtains the vehicle body length mean value, the intelligent terminal needs to be further informed of the vehicle body length mean value.
Further, there is no strict execution sequence limitation between the operation of obtaining the body length average value and steps 300 to 340, and the operation is not limited herein.
Specifically, taking the nth candidate driving route as an example, in the embodiment of the present application, the following formula may be used to represent the vehicle body length mean value w:
w＝a*l1+b*l2+…+d*lm
wherein a represents the proportion of the vehicles of the type a in the total number of passing vehicles of each road section, and l1Representing the body length of a vehicle of type a; b represents the ratio of the vehicles of type b to the total number of passing vehicles on each road section, l2Representing the body length of a vehicle of type b; d represents the ratio of the vehicles of type d to the total number of passing vehicles on each road section, lmThe body length of a vehicle of type d is indicated.
For example, referring to fig. 2, taking each candidate travel path as an example, it is assumed that the vehicle types of various vehicles passing through each link of each candidate travel path are 3 types including large-sized vehicles, medium-sized vehicles, and micro-sized vehicles according to the historical driving record, wherein it is assumed that the large-sized vehicles correspond to a vehicle body length of 6m, the medium-sized vehicles correspond to a vehicle body length of 4m, and the micro-sized vehicles correspond to a vehicle body length of 3m, and it is assumed that the ratio of the number of vehicles corresponding to the large-sized vehicles to the total number of passing vehicles is 10%, the ratio of the number of vehicles corresponding to the medium-sized vehicles to the total number of passing vehicles is 50%, and the ratio of the number of vehicles corresponding to the micro-sized vehicles to the total number of passing vehicles is 40%.
Then, the vehicle body length average w is calculated to be 6 × 10% +4 × 50% +3 × 40% — 3.8 m.
Based on the above average length of the car bodyAnd the mean value of the distance between the vehicles and the preset positioning error of the vehicles are combined to obtain the standard deviation alpha of normal distribution so as to obtain the corresponding variance alpha2。
Step 320: and the intelligent terminal respectively determines traffic density intervals corresponding to the candidate running paths according to the initial traffic density and the obtained variance of the candidate running paths.
In the embodiment of the application, the intelligent terminal can respectively generate corresponding statistical distribution random numbers based on the initial traffic density and the variance corresponding to each candidate driving path, and further respectively determine the traffic density interval corresponding to the corresponding candidate driving path based on each obtained statistical distribution random number.
Specifically, taking the candidate driving path N and the normal distribution as an example, N belongs to [1, N ]]And the intelligent terminal carries out the corresponding initial traffic density x on the basis of the candidate running path nnSum variance α2Generating corresponding normal distribution random number, noted as yn。
In practical application, the initial traffic density x on the candidate running path n is used as the basisnSum variance α2Normal distribution N (x) can be obtainedn,α2) Based on the normal distribution, the corresponding normal distribution random number y can be generated by adopting the existing tooln。
Then, based on xnAnd ynThe upper limit value and the lower limit value of the traffic density section corresponding to the candidate travel path n can be determined.
In particular, if yn>2xn-ynThen 2xn-ynAs a lower limit value of the traffic density section, y is setnAs the upper limit value of the traffic density section, that is, the traffic density section corresponding to the candidate travel path n is determined to be [2 × ]n-yn,yn]As shown in fig. 4.
In particular, if yn<2xn-ynThen y will benAs a lower limit value of the traffic density section, 2 ×, isn-ynAs the upper limit value of the traffic density section, that is, the traffic density zone corresponding to the candidate travel path n is determinedIs m between [ yn,2xn-yn]As shown in detail in fig. 5.
For example, for the application scenario shown in fig. 2, the normal distribution N (x) of each candidate travel path may be generated in the manner described above1,α2)、N(x2,α2)、N(x3,α2) Thereby generating a corresponding normally distributed random number y1、y2、y3Then the corresponding interval [2x ] is obtained1-y1,y1]、[2x2-y2,y2]、[2x3-y3,y3]Or [ y ] or1,2x1-y1]、[y2,2x2-y2]、[y3,2x3-y3]。
Step 330: and the intelligent terminal respectively determines the vehicle density mean value corresponding to each candidate running path according to the traffic flow density interval corresponding to each candidate running path and the obtained variance.
In the embodiment of the present application, still taking the candidate driving route N as an example, N ∈ [1, N]Assuming that the average of the traffic density on the candidate driving path n is xnBased on the traffic density x corresponding to the candidate driving path nnSum variance α2Generating a corresponding normally distributed random number ynThereby determining the traffic flow density section corresponding to the candidate running path n, and assuming ynIs an upper limit value, and is in the corresponding traffic density interval [2xn-yn,yn]For example.
Specifically, the following formula can be used to represent the average traffic density:
wherein N is the serial number of the candidate driving path, and the value of N is [1, N ]; x is an integral variable.
By adopting the mode, the average value of the traffic flow density of each candidate running path can be calculated and recorded as e1、e2、……、eN。
Step 340: and the intelligent terminal selects a candidate running path corresponding to the vehicle density mean value which meets the set conditions as a navigation target running path.
Optionally, when step 340 is executed, the following two ways may be adopted, but not limited to:
mode 1: and if the vehicle density mean value with the minimum value is one, obtaining the credible probability of the vehicle density mean value with the minimum value, and if the credible probability reaches a set threshold value, selecting the candidate running path corresponding to the vehicle density mean value with the minimum value as the target running path for navigation.
In the embodiment of the present application, the candidate driving route n and the normal distribution are taken as examples, and it is assumed that the random number y is based on the generated normal distributionnObtaining the traffic density interval [2x ] corresponding to the candidate driving path nn-yn,yn]。
Specifically, in the embodiment of the present application, the following formula may be adopted to represent the confidence probability based on the traffic density interval:
wherein N is the serial number of the candidate driving path, and the value of N is [1, N ]; x is an integral variable.
Based on the formula, the credibility probability p corresponding to the candidate driving path n is obtainednThen, the signal is compared with a set threshold value p, when p isnAnd when the number of the candidate driving paths n is larger than or equal to p, selecting the candidate driving paths n as the target driving paths of the navigation.
For example: referring to fig. 2, it is assumed that there are three candidate travel paths, i.e., a candidate travel path 1, a candidate travel path 2, and a candidate travel path 3; and assuming that the value of the vehicle density average of the candidate travel path 2 is minimum, that is:
then, when p is2And when the number p is larger than or equal to p, selecting the candidate driving path 2 as a target driving path.
On the other hand, if the confidence probability of the vehicle density mean value with the minimum value does not reach the set threshold value, deleting the candidate driving paths, and then re-selecting the candidate driving path corresponding to the vehicle density mean value with the minimum value from the rest candidate driving paths to be used as the target driving path for navigation.
For example, referring to fig. 2, still taking the minimum value of the vehicle density average of the candidate driving route 2 as an example, then when p is2If < p, the travel route candidate 2 is deleted and the remaining travel route candidates 1 and 3 are reselected.
If the value of the vehicle density average of the candidate running path 1 is the minimum after the candidate running path 2 is deleted, the formula is adopted to calculate to obtain p1Let p be1And if the number p is larger than or equal to p, selecting the candidate driving path 1 as a target driving path for navigation.
Mode 2: and if the vehicle density mean value with the minimum value is multiple, respectively obtaining the credible probability of each vehicle density mean value with the minimum value, comparing, and if the credible probability with the maximum value reaches a set threshold, selecting the candidate driving path associated with the vehicle density mean value corresponding to the credible probability with the maximum value as the target driving path for navigation.
For example, referring to fig. 2, it is assumed that there are three candidate travel paths, i.e., a candidate travel path 1, a candidate travel path 2, and a candidate travel path 3; then, it is assumed that there are two candidate travel paths corresponding to the minimum vehicle density mean, which are the candidate travel path 1 and the candidate travel path 2, respectively, so that the above formula can be used to calculate the confidence probability p of the candidate travel path 11The confidence probability of the candidate driving path 1 is p2。
Let p be1＞p2And p is1And if the running distance is more than p, selecting the candidate running path 1 as a target running path.
In the above embodiment, after the intelligent navigation system selects the minimum traffic density mean value, the confidence probability of the minimum traffic density mean value may be further calculated based on the corresponding traffic density interval and variance, and when it is determined that the confidence probability reaches the set threshold, the candidate driving path corresponding to the minimum traffic density mean value may be further selected as the target driving path.
Based on the same inventive concept, referring to fig. 6, in an embodiment of the present application, a navigation apparatus (e.g., an intelligent terminal) is provided, which at least includes: a first determining unit 61, a second determining unit 62, a third determining unit 63, a selecting unit 64,
a first determining unit 61, configured to determine N candidate traveling paths based on the obtained road positioning data, where N is a natural number, and determine initial traffic density on each candidate traveling path based on the obtained vehicle positioning data, respectively;
the second determining unit 62 is configured to obtain variances of statistical distribution by combining preset vehicle positioning errors based on preset vehicle length means and preset vehicle distance means, and determine traffic flow density sections corresponding to the candidate running paths according to initial traffic flow densities and the obtained variances of the candidate running paths, respectively;
a third determining unit 63, configured to determine a vehicle density mean value corresponding to each candidate driving route according to the traffic flow density interval corresponding to each candidate driving route and the obtained variance;
and the selecting unit 64 is configured to select a candidate driving route corresponding to one vehicle density average meeting the set condition as the target driving route.
Optionally, the preset vehicle body length average value is obtained by the second determining unit 62 according to the following manner:
counting the vehicle types and the corresponding vehicle body lengths of various vehicles passing through each road section based on historical driving records;
and obtaining the corresponding vehicle body length mean value by combining the vehicle body lengths corresponding to various vehicle types based on the vehicle number corresponding to various vehicle types on each road section and the proportion in the total number of passing vehicles.
Optionally, based on a preset vehicle length mean value and a preset vehicle distance mean value, a preset vehicle positioning error is combined to obtain a variance of statistical distribution, and the second determining unit 62 is configured to:
obtaining a standard deviation of statistical distribution based on a preset vehicle body length mean value and a preset vehicle distance mean value and in combination with a preset vehicle positioning error;
based on the obtained standard deviation, a corresponding variance is obtained.
Optionally, the second determining unit 62 is configured to determine traffic density sections corresponding to the candidate travel paths according to the initial traffic density and the obtained variance of each candidate travel path, respectively:
respectively generating corresponding statistical distribution random numbers based on the initial traffic density and the obtained variance corresponding to each candidate driving path;
and respectively determining the upper limit value and the lower limit value of the traffic density section corresponding to each candidate running path based on the initial traffic density and the statistical distribution random number corresponding to each candidate running path.
Optionally, a candidate driving route corresponding to one vehicle density average meeting the set condition is selected as a target driving route for navigation, and the selecting unit 64 is configured to:
if the vehicle density mean value with the minimum value is one, obtaining the credible probability of the vehicle density mean value with the minimum value, and if the credible probability reaches a set threshold value, selecting a candidate driving path corresponding to the vehicle density mean value with the minimum value as a navigation target driving path;
and if the vehicle density mean value with the minimum value is multiple, respectively obtaining the credible probability of each vehicle density mean value with the minimum value, comparing, and if the credible probability with the maximum value reaches a set threshold, selecting the candidate driving path associated with the vehicle density mean value corresponding to the credible probability with the maximum value as the target driving path for navigation.
Optionally, the confidence probability of the vehicle density mean value with a small value is determined, and the selecting unit 64 is configured to:
and obtaining the credible probability of the vehicle density mean value with the minimum value based on the traffic flow density interval of the candidate running path corresponding to the vehicle density mean value with the minimum value and the obtained variance.
Based on the same inventive concept, referring to fig. 7, an embodiment of the present application further provides an intelligent terminal 700, where the intelligent terminal 700 may be an electronic device such as a smart phone, a tablet computer, a laptop computer, or a PC. As shown in fig. 7, the smart terminal 700 includes a display unit 740, a processor 780, and a memory 720, wherein the display unit 740 includes a display panel 741 configured to display information input by a user or information provided to the user, and various object selection pages of the smart terminal 700, and the like, and in the embodiment of the present application, is mainly configured to display pages of applications installed in the smart terminal 700, shortcut windows, and the like. Alternatively, the Display panel 741 may be configured in the form of a Liquid Crystal Display (LCD) or an Organic Light-Emitting Diode (OLED).
The processor 780 is configured to read the computer program and then execute a method defined by the computer program, for example, the processor 780 reads the social application program, so as to run the application on the smart terminal 700 and display a page of the application on the display unit 740. The Processor 780 may include one or more general-purpose processors, and may further include one or more Digital Signal Processors (DSPs) for performing related operations to implement the technical solutions provided in the embodiments of the present application.
In addition, the smart terminal 700 may further include a display unit 740 for receiving input numerical information, character information, or contact touch operation/non-contact gesture, and generating signal input related to user setting and function control of the smart terminal 700, and the like. Specifically, in the embodiment of the present application, the display unit 740 may include a display panel 741. The display panel 741, for example, a touch screen, may collect touch operations (for example, operations of a player on the display panel 741 or on the display panel 741 by using any suitable object or accessory such as a finger or a stylus pen) by the user, and drive the corresponding connection device according to a preset program. Alternatively, the display panel 741 may include two portions of a touch detection device and a touch controller. The touch detection device comprises a touch controller, a touch detection device and a touch control unit, wherein the touch detection device is used for detecting the touch direction of a user, detecting a signal brought by touch operation and transmitting the signal to the touch controller; the touch controller receives touch information from the touch sensing device, converts it to touch point coordinates, and sends the touch point coordinates to the processor 780, and can receive and execute commands from the processor 780.
The display panel 741 can be implemented by various types, such as resistive, capacitive, infrared, and surface acoustic wave. The smart terminal 700 may further include an input unit 730 in addition to the display unit 740, and the input unit 730 may include, but is not limited to, one or more of a physical keyboard, function keys (such as volume control keys, switch keys, etc.), a trackball, a mouse, a joystick, and the like. In fig. 7, the input unit 730 includes an image input device 731 and another input device 732 as an example.
In addition to the above, the smart terminal 700 may also include a power supply 790 for powering other modules, an audio circuit 760, a near field communication module 770, and an RF circuit 710. The smart terminal 700 may also include one or more sensors 750, such as acceleration sensors, light sensors, pressure sensors, and the like. The audio circuit 760 specifically includes a speaker 761 and a microphone 762, for example, a user may use voice control, and the smart terminal 700 may collect a voice of the user through the microphone 762, may control the voice of the user, and when a prompt is required, plays a corresponding prompt sound through the speaker 761.
Based on the same inventive concept, embodiments of the present application provide a computer-readable medium, and when instructions in the computer-readable storage medium are executed by a processor, the processor is enabled to execute any one of the methods performed by the intelligent terminal in the above embodiments.
Alternatively, the computer readable medium may be a non-transitory computer readable storage medium, such as a ROM, a Random Access Memory (RAM), a CD-ROM, a magnetic tape, a floppy disk, an optical data storage device, and so forth.
Based on the same inventive concept, the embodiments of the present application provide a computer program product or a computer program, which includes computer instructions stored in a computer-readable storage medium. The processor of the computer device reads the computer instructions from the computer-readable storage medium, and the processor executes the computer instructions, so that the computer device executes any one of the methods executed by the intelligent terminal in the embodiments.
The program product may employ any combination of one or more readable media. The readable medium may be a readable signal medium or a readable storage medium. A readable signal medium may be, for example, but not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination of the foregoing. More specific examples (a non-exhaustive list) of the readable storage medium include: an electrical connection having one or more wires, a portable disk, a hard disk, a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.
The program product of embodiments of the present application may employ a portable compact disc read only memory (CD-ROM) and include program code, and may be run on a computing device. However, the program product of the present application is not limited thereto, and in this document, a readable storage medium may be any tangible medium that can contain, or store a program for use by or in connection with a command execution system, apparatus, or device.
A readable signal medium may include a propagated data signal with readable program code embodied therein, for example, in baseband or as part of a carrier wave. Such a propagated data signal may take many forms, including, but not limited to, electro-magnetic, optical, or any suitable combination thereof. A readable signal medium may also be any readable medium that is not a readable storage medium and that can communicate, propagate, or transport a program for use by or in connection with a command execution system, apparatus, or device.
Program code embodied on a readable medium may be transmitted using any appropriate medium, including but not limited to wireless, wireline, optical fiber cable, RF, etc., or any suitable combination of the foregoing.
While the preferred embodiments of the present application have been described, additional variations and modifications in those embodiments may occur to those skilled in the art once they learn of the basic inventive concepts. Therefore, it is intended that the appended claims be interpreted as including preferred embodiments and all alterations and modifications as fall within the scope of the application.
It will be apparent to those skilled in the art that various changes and modifications may be made in the present application without departing from the spirit and scope of the application. Thus, if such modifications and variations of the present application fall within the scope of the claims of the present application and their equivalents, the present application is intended to include such modifications and variations as well.
Claims (14)
1. A method of navigation, comprising:
determining N candidate running paths based on the obtained road positioning data, wherein N is a natural number, and respectively determining initial traffic flow density on each candidate running path based on the obtained vehicle positioning data;
obtaining a variance of statistical distribution based on a preset vehicle body length mean value and a preset vehicle distance mean value and in combination with a preset vehicle positioning error, and respectively determining traffic flow density intervals corresponding to each candidate driving path according to the initial traffic flow density and the variance of each candidate driving path;
respectively determining the vehicle density mean value corresponding to each candidate running path according to the traffic flow density interval corresponding to each candidate running path and the variance;
and selecting a candidate running path corresponding to the vehicle density mean value which meets the set conditions as a navigation target running path.
2. The method of claim 1, wherein the preset body length average is obtained by:
counting the vehicle types and the corresponding vehicle body lengths of various vehicles passing through each road section based on historical driving records;
and obtaining corresponding vehicle body length mean values by combining the vehicle body lengths corresponding to the various vehicle types based on the vehicle numbers corresponding to the various vehicle types on each road section and the ratio in the total number of passing vehicles.
3. The method of claim 1, wherein obtaining the variance of the statistical distribution based on a preset mean body length and a preset mean vehicle distance in combination with a preset vehicle positioning error comprises:
obtaining a standard deviation of statistical distribution based on a preset vehicle body length mean value and the preset vehicle distance mean value in combination with the preset vehicle positioning error;
based on the standard deviation, a corresponding variance is obtained.
4. The method of claim 1, wherein determining traffic density sections corresponding to the candidate travel paths according to the initial traffic density and the variance of the candidate travel paths respectively comprises:
respectively generating corresponding statistical distribution random numbers based on the initial traffic density and the variance corresponding to each candidate driving path;
and respectively determining the upper limit value and the lower limit value of the traffic density section corresponding to each candidate running path based on the initial traffic density and the statistical distribution random number corresponding to each candidate running path.
5. The method according to any one of claims 1 to 4, wherein selecting a candidate driving path corresponding to a vehicle density average meeting set conditions as a target driving path for navigation comprises:
if the vehicle density mean value with the minimum value is one, obtaining the credible probability of the vehicle density mean value with the minimum value, and if the credible probability reaches a set threshold value, selecting a candidate driving path corresponding to the vehicle density mean value with the minimum value as a navigation target driving path;
and if the vehicle density mean value with the minimum value is multiple, respectively obtaining the credible probability of each vehicle density mean value with the minimum value, comparing, and if the credible probability with the maximum value reaches a set threshold, selecting the candidate driving path associated with the vehicle density mean value corresponding to the credible probability with the maximum value as the target driving path for navigation.
6. The method of claim 5, wherein determining the confidence probability of the lowest-valued vehicle density mean comprises:
and obtaining the credible probability of the vehicle density mean value with the minimum value based on the traffic flow density interval of the candidate running path corresponding to the vehicle density mean value with the minimum value and the variance.
7. An apparatus for navigation, comprising:
a first determining unit, configured to determine N candidate traveling paths based on the obtained road positioning data, where N is a natural number, and determine initial traffic density on each candidate traveling path based on the obtained vehicle positioning data, respectively;
the second determining unit is used for obtaining the variance of statistical distribution by combining a preset vehicle positioning error based on a preset vehicle body length mean value and a preset vehicle distance mean value, and respectively determining the traffic flow density intervals corresponding to the candidate running paths according to the initial traffic flow density and the variance of the candidate running paths;
a third determining unit, configured to determine a vehicle density mean value corresponding to each candidate travel path according to the traffic flow density interval and the variance corresponding to each candidate travel path;
and the selecting unit is used for selecting a candidate running path corresponding to the vehicle density mean value which meets the set conditions as a target running path for navigation.
8. The apparatus according to claim 7, wherein the preset vehicle body length average value is obtained by the second determination unit in the following manner:
counting the vehicle types and the corresponding vehicle body lengths of various vehicles passing through each road section based on historical driving records;
and obtaining corresponding vehicle body length mean values by combining the vehicle body lengths corresponding to the various vehicle types based on the vehicle numbers corresponding to the various vehicle types on each road section and the ratio in the total number of passing vehicles.
9. The apparatus according to claim 7, wherein the variance of the statistical distribution is obtained based on a preset vehicle body length mean value and a preset vehicle distance mean value in combination with a preset vehicle positioning error, and the second determination unit is configured to:
obtaining a standard deviation of statistical distribution based on a preset vehicle body length mean value and the preset vehicle distance mean value in combination with the preset vehicle positioning error;
based on the standard deviation, a corresponding variance is obtained.
10. The apparatus according to claim 7, wherein the traffic density section corresponding to each candidate travel path is determined according to the initial traffic density and the variance of each candidate travel path, respectively, and the second determining unit is configured to:
respectively generating corresponding statistical distribution random numbers based on the initial traffic density and the variance corresponding to each candidate driving path;
and respectively determining the upper limit value and the lower limit value of the traffic density section corresponding to each candidate running path based on the initial traffic density and the statistical distribution random number corresponding to each candidate running path.
11. The apparatus according to any one of claims 7 to 10, wherein a candidate travel path corresponding to a vehicle density average meeting a set condition is selected as a target travel path for navigation, and the selecting unit is configured to:
if the vehicle density mean value with the minimum value is one, obtaining the credible probability of the vehicle density mean value with the minimum value, and if the credible probability reaches a set threshold value, selecting a candidate driving path corresponding to the vehicle density mean value with the minimum value as a navigation target driving path;
and if the vehicle density mean value with the minimum value is multiple, respectively obtaining the credible probability of each vehicle density mean value with the minimum value, comparing, and if the credible probability with the maximum value reaches a set threshold, selecting the candidate driving path associated with the vehicle density mean value corresponding to the credible probability with the maximum value as the target driving path for navigation.
12. The apparatus according to claim 11, wherein a confidence probability of a vehicle density mean with a minimum value is determined, and the selecting unit is configured to:
and obtaining the credible probability of the vehicle density mean value with the minimum value based on the traffic flow density interval of the candidate running path corresponding to the vehicle density mean value with the minimum value and the variance.
13. An electronic device, comprising:
a memory for storing executable instructions;
a processor for reading and executing executable instructions stored in the memory to implement the method of any one of claims 1-6.
14. A computer-readable storage medium, wherein instructions in the computer-readable storage medium, when executed by a processor, enable the processor to perform the method of any of claims 1-6.
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