CN115371698B - Navigation method and system for full-scene intelligent auxiliary driving - Google Patents
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Abstract
The invention discloses a navigation method and a system for full-scene intelligent auxiliary driving, which relate to the field of arrangement of general vehicle lighting or signal devices and solve the problems that the conventional navigation system cannot screen a planned route, so that the selected route has an inaccurate condition, the selected route cannot be analyzed in real time, the selected route is seriously congested, and the use experience of a user is influenced; the navigation system utilizes a plurality of routes to carry out static measurement, selects the route with the best comprehensive condition, ensures the accuracy of the selected route, and then carries out dynamic measurement on the selected route and the analyzed route after driving, thereby carrying out real-time analysis on the selected route, ensuring that the selected route is continuously the best route, avoiding the condition of serious congestion of the selected route, and improving the user experience.
Description
Technical Field
The invention relates to the field of arrangement of general vehicle lighting or signal devices, in particular to a navigation method and a navigation system for full-scene intelligent auxiliary driving.
Background
Navigation is a process of guiding people or objects to move from one place to another place, and the purpose of quickly reaching a specified position is achieved mainly by positioning coordinates of an initial position and a key position and reasonably planning a driving path. Therefore, when navigating in a spatial position, the situation that the positioning is inaccurate and the navigation is inaccurate is easy to occur.
Patent with application number CN202010897219.1 discloses a live-action aided navigation method and a navigation system, belonging to the technical field of navigation. Framing the scenes inside and outside the building and setting reference marks to form a pre-stored image which is stored in a database; secondly, recording a destination; then framing through a camera of the mobile phone, calling a prestored image in the system for comparison, and positioning according to a marker identified in the framed image to determine the initial position of the user; and finally planning a user traveling path and performing live-action aided navigation. In the process, a positioning system such as a GPS is not needed, the position of the user can be determined according to the comparison between the framing image and the pre-stored image and navigation can be carried out even if the signal is shielded and interrupted, the real-scene aided navigation system can meet the navigation requirement of the user without the need of a positioning system such as a GPS, accurate navigation can be carried out on the user even in a signal shielding area, and the user can quickly reach a designated destination, but the following defects still exist: the navigation system cannot screen the planned route, so that the selected route has an inaccurate condition, the selected route cannot be analyzed in real time, and the condition that the selected route is seriously jammed exists, so that the use experience of a user is influenced.
Disclosure of Invention
In order to overcome the technical problems, the invention aims to provide a navigation method and a system for full-scene intelligent auxiliary driving, wherein the navigation method comprises the following steps: the problems that an existing navigation system cannot screen planned routes, so that the selected routes are inaccurate, the selected routes cannot be analyzed in real time, the selected routes are seriously jammed, and the use experience of users is influenced are solved.
The purpose of the invention can be realized by the following technical scheme:
full scene intelligence driver assistance's navigation includes:
the route generating module is used for generating a plurality of routes according to the starting point and the end point, screening a preselected route from the generated routes and sending the preselected route to the route analyzing module;
the route analysis module is used for acquiring analysis parameters of a preselected route and sending the analysis parameters to the intelligent auxiliary platform, wherein the analysis parameters comprise a route distance LJ, a mean driving time JS and an accident time ratio CS;
the intelligent auxiliary platform is used for obtaining an analysis value FX according to the analysis parameters, obtaining a real division coefficient SF according to the real-time parameters, obtaining a selected route according to the analysis value FX and the real division coefficient SF, sending the selected route to the route display module, simultaneously generating a real-time analysis instruction and sending the real-time analysis instruction to the real-time analysis module;
the route display module is used for carrying out map display on the selected route according to the received selected route and carrying out voice navigation;
and the real-time analysis module is used for receiving the real-time analysis instruction to obtain an analysis route according to the selected route, respectively obtaining real-time parameters of the selected route and the analysis route, and sending the real-time parameters to the intelligent auxiliary platform, wherein the real-time parameters comprise a route distance LJ, a route vehicle number LC, an average vehicle speed PS, a red light number HS and a vehicle distance CL.
As a further scheme of the invention: the specific process of screening out the preselected route by the route generation module is as follows:
generating a plurality of routes according to the starting point and the end point, marking the route with the shortest distance as a reference route, marking the distance of the reference route as a reference distance CJ, substituting the reference distance CJ into a formula
Obtaining a preselected distance YJ, wherein gamma is a preset multiple and is greater than 1, and taking gamma =1.482;
acquiring route distances of all routes, sequentially comparing each route distance with a preselected distance YJ, and marking the route with the route distance smaller than the preselected distance YJ as a preselected route;
the preselected route is sent to a route analysis module.
As a further scheme of the invention: the specific process of the route analysis module for obtaining the analysis parameters is as follows:
obtaining a route distance LJ of a preselected route;
obtaining the driving time in the pre-selection route historical data, summing the driving time and solving an average value to obtain an average driving time JS;
acquiring the total number of accidents occurring in unit time of a preselected route and the time difference between two adjacent accidents occurring in unit time of the preselected route, respectively marking the total number of accidents as SC and the time difference of accidents SS, acquiring the ratio of the total number of accidents and the time difference of accidents SS, and marking the ratio as the time ratio of accidents CS;
and sending the route distance LJ, the average driving time JS and the accident time ratio CS to the intelligent auxiliary platform.
As a further scheme of the invention: the specific process of obtaining the analysis value FX by the intelligent auxiliary platform is as follows:
substituting the route distance LJ, the average driving time JS and the accident time ratio CS into a formula
Obtaining an analysis value FX, wherein theta 1, theta 2 and theta 3 are preset weight coefficients of the route distance LJ, the average driving time JS and the accident time ratio CS respectively, and theta 1+ theta 2+ theta 3=1, theta 1 is more than 0 and more than theta 2 and more than theta 3 and less than 1;
and sequencing the preselected routes according to the sequence of the analysis values FX from small to large, marking the preselected route at the head as a selected route, sending the selected route to a route display module, generating a real-time analysis instruction at the same time, and sending the real-time analysis instruction to a real-time analysis module.
As a further scheme of the invention: the specific process of the intelligent auxiliary platform for obtaining the real partition coefficient SF is as follows:
substituting the route distance LJ, the route vehicle number LC, the average vehicle speed PS, the red light number HS and the vehicle distance CL into a formula
Obtaining an actual division number SF, wherein the actual division number SF comprises an actual division coefficient SF1 of a selected route and an actual division coefficient SF2 of an analyzed route, Q1, Q2, Q3, Q4 and Q5 are preset weight factors of a route distance LJ, a route vehicle number LC, an average vehicle speed PS, a red light number HS and a vehicle distance CL, and Q3 is more than Q5 is more than Q2 is more than Q1 is more than Q4 is more than 1.239;
comparing the real division coefficient SF1 of the selected route with the real division coefficient SF2 of the analyzed route:
and if the real division coefficient SF2 of the analyzed route is larger than the real division coefficient SF1 of the selected route, marking the analyzed route as the selected route and sending the selected route to the route display module.
As a further scheme of the invention: the specific process of the real-time analysis module for obtaining the real-time parameters is as follows:
after receiving a real-time analysis instruction, marking a pre-selected route with intersection points existing between the selected route and a starting point and an end point as a pre-analysis route, acquiring a current position, and marking the pre-analysis route with the current position closest to the intersection points as an analysis route;
obtaining a selected route and analyzing the route distance LJ of the route;
acquiring the total number of vehicles entering the selected route, analyzing the route and the total number of vehicles exiting the preselected route in real time, acquiring the difference value of the two, and marking the difference value as the number LC of the vehicles on the route;
acquiring a selected route in real time, analyzing the speed of all vehicles in the route, summing and calculating an average value to obtain an average speed PS;
obtaining a selected route, analyzing the number of traffic lights going straight and turning left in the route, obtaining the number of traffic lights with a right turn sign, obtaining the sum of the two, and marking the sum as the number HS of the red lights;
obtaining a selected route, analyzing a closest distance value between a vehicle and a front vehicle in the route, marking the closest distance value as a front distance value QZ, obtaining a closest distance value between the vehicle and a side vehicle, marking the closest distance value as a side distance value CZ, and substituting the front distance value QZ and the side distance value CZ into a formula
Obtaining a vehicle distance CL, wherein a front vehicle represents a vehicle in the same lane, a side vehicle represents a vehicle in a non-same lane, q1 and q2 are preset proportionality coefficients of a front distance value QZ and a side distance value CZ respectively, and q1+ q2=1, q1=0.72, and q2=0.28 are taken;
and sending the route distance LJ, the route vehicle number LC, the average vehicle speed PS, the red light number HS and the vehicle distance CL to the intelligent auxiliary platform.
As a further scheme of the invention: the navigation method of full-scene intelligent auxiliary driving comprises the following steps:
the method comprises the following steps: the route generation module generates a plurality of routes according to the starting point and the end point and marks the route with the shortest distanceFor the reference route, the distance of the reference route is marked as a reference distance CJ, and the reference distance CJ is substituted into the formula
Obtaining a preselected distance YJ, wherein gamma is a preset multiple and is greater than 1, and taking gamma =1.482;
step two: the route generation module acquires route distances of all routes, compares each route distance with a preselected distance YJ in sequence, and marks the route with the route distance smaller than the preselected distance YJ as a preselected route;
step three: the route generation module sends the preselected route to the route analysis module;
step four: the route analysis module acquires a route distance LJ of a preselected route;
step five: the route analysis module acquires the driving time in the pre-selection route historical data, and sums the driving time to obtain an average value to obtain an average driving time JS;
step six: the route analysis module acquires the total times of accidents occurring in unit time of a preselected route and the time difference of two adjacent accidents, respectively marks the total times of accidents SC and the time difference SS of accidents, acquires the ratio of the total times of accidents SC and the time difference SS of accidents, and marks the ratio as the time ratio of accidents CS;
step seven: the route analysis module sends the route distance LJ, the average driving time JS and the accident time ratio CS to the intelligent auxiliary platform;
step eight: the intelligent auxiliary platform substitutes the route distance LJ, the average driving time JS and the accident time ratio CS into a formula
Obtaining an analysis value FX, wherein theta 1, theta 2 and theta 3 are preset weight coefficients of the route distance LJ, the average driving time JS and the accident time ratio CS respectively, and theta 1+ theta 2+ theta 3=1, theta 1 is more than 0 and more than theta 2 and more than theta 3 and less than 1;
step nine: the intelligent auxiliary platform sorts the preselected routes from small to large according to the analysis value FX, marks the preselected route at the head as a selected route, sends the selected route to a route display module, generates a real-time analysis instruction at the same time, sends the real-time analysis instruction to a real-time analysis module, and the route display module performs map display on the selected route according to the received selected route and performs voice navigation;
step ten: the real-time analysis module receives a real-time analysis instruction, and then marks a pre-selected route which has intersection points with a selected route except a starting point and an end point as a pre-analysis route, acquires a current position, and marks the pre-analysis route which is closest to the intersection point at the current position as an analysis route;
step eleven: the real-time analysis module acquires a selected route and analyzes the route distance LJ of the route;
step twelve: the real-time analysis module acquires the total number of vehicles entering the selected route, analyzing the route and the total number of vehicles exiting the preselected route in real time, acquires the difference value of the two, and marks the difference value as the number LC of the vehicles on the route;
step thirteen: the real-time analysis module acquires the selected route in real time, analyzes the speed of all vehicles in the route, and sums up to obtain an average value to obtain an average speed PS;
fourteen steps: the real-time analysis module acquires the selected route, analyzes the number of traffic lights going straight and turning left in the route, acquires the number of traffic lights with a right-turn sign, acquires the sum of the two, and marks the sum as the number HS of the red lights;
a fifteenth step: the real-time analysis module obtains the selected route, analyzes the closest distance value between the vehicle and the front vehicle in the route, marks the closest distance value as a front distance value QZ, obtains the closest distance value between the vehicle and the side vehicle, marks the closest distance value as a side distance value CZ, and substitutes the front distance value QZ and the side distance value CZ into a formula
Obtaining a vehicle distance CL, wherein a front vehicle represents a vehicle in the same lane, a side vehicle represents a vehicle in a non-same lane, q1 and q2 are preset proportionality coefficients of a front distance value QZ and a side distance value CZ respectively, and q1+ q2=1, q1=0.72 and q2=0.28 are taken;
sixthly, the steps are as follows: the real-time analysis module sends the route distance LJ, the route vehicle number LC, the average vehicle speed PS, the red light number HS and the vehicle distance CL to the intelligent auxiliary platform;
seventeen steps: the intelligent auxiliary platform substitutes the route distance LJ, the route vehicle number LC, the average vehicle speed PS, the red light number HS and the vehicle distance CL into a formula
Obtaining a real-division number SF, wherein the real-division number SF comprises a real-division coefficient SF1 of a selected route and a real-division coefficient SF2 of an analyzed route, Q1, Q2, Q3, Q4 and Q5 are preset weight factors of a route distance LJ, a route vehicle number LC, an average vehicle speed PS, a red light number HS and a vehicle distance CL, and Q3 is more than Q5 and more than Q2 is more than Q1 and more than Q4 is more than 1.239;
eighteen steps: the intelligent auxiliary platform compares the real division coefficient SF1 of the selected route with the real division coefficient SF2 of the analyzed route:
and if the real division coefficient SF2 of the analyzed route is larger than the real division coefficient SF1 of the selected route, marking the analyzed route as the selected route and sending the selected route to the route display module, and the route display module performs map display on the selected route according to the received selected route and performs voice navigation.
The invention has the beneficial effects that:
the invention relates to a navigation method and a system for full-scene intelligent assistant driving, wherein a plurality of routes are generated through a route generation module according to a starting point and an end point, a preselected route is screened out from the generated routes, analysis parameters of the preselected route are obtained through a route analysis module, an intelligent assistant platform obtains analysis values according to the analysis parameters, the analysis values are used for carrying out static measurement on the preselected route, so that the optimal preselected route is selected and set as a selected route, when a user drives along with the selected navigation, the real-time analysis module is used for obtaining the analysis routes according to the selected route, and real-time parameters of the selected route and the analysis routes are respectively obtained, the intelligent assistant platform obtains real division coefficients according to the real-time parameters, the real division coefficients are used for carrying out dynamic measurement on the analysis routes, the real division coefficients are used for measuring the traffic smoothness degree of the routes, the larger the real division numbers represent that the routes are smoother, the smaller the real division numbers represent that the routes are more obstructed, the real division numbers are the smaller the routes are, so that the optimal selected routes are selected in real division coefficients are blocked, and the selected routes are updated for navigation; the navigation system utilizes a plurality of routes to carry out static measurement, selects the route with the best comprehensive condition, ensures the accuracy of the selected route, and then carries out dynamic measurement on the selected route and the analyzed route after driving, thereby carrying out real-time analysis on the selected route, ensuring that the selected route is continuously the best route, avoiding the occurrence of serious congestion of the selected route and improving the use experience of a user.
Drawings
The invention will be further described with reference to the accompanying drawings.
Fig. 1 is a schematic block diagram of a full-scene intelligent driving-assisted navigation system according to the present invention.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
Example 1:
referring to fig. 1, the navigation system for full-scene intelligent auxiliary driving in the present embodiment includes a route generating module, a route analyzing module, an intelligent auxiliary platform, a route displaying module, and a real-time analyzing module;
the route generation module generates a plurality of routes according to the starting point and the end point, screens out a preselected route from the generated routes, and sends the preselected route to the route analysis module;
the route analysis module acquires analysis parameters of a preselected route and sends the analysis parameters to the intelligent auxiliary platform, wherein the analysis parameters comprise a route distance LJ, a mean driving time JS and an accident time ratio CS;
the intelligent auxiliary platform obtains an analysis value FX according to the analysis parameter, is also used for obtaining a real-time division coefficient SF according to the real-time parameter, obtains a selected route according to the analysis value FX and the real-time division coefficient SF, sends the selected route to the route display module, simultaneously generates a real-time analysis instruction and sends the real-time analysis instruction to the real-time analysis module;
the route display module displays a map according to the received selected route and performs voice navigation;
the real-time analysis module receives the real-time analysis instruction, obtains an analysis route according to the selected route, respectively obtains real-time parameters of the selected route and the analysis route, and sends the real-time parameters to the intelligent auxiliary platform, wherein the real-time parameters comprise a route distance LJ, a route vehicle number LC, an average vehicle speed PS, a red light number HS and a vehicle distance CL.
Example 2:
referring to fig. 1, the navigation method for full-scene intelligent auxiliary driving in the present embodiment includes the following steps:
the method comprises the following steps: the route generation module generates a plurality of routes according to the starting point and the end point, marks the route with the shortest distance as a reference route, marks the distance of the reference route as a reference distance CJ, and substitutes the reference distance CJ into a formula
Obtaining a preselected distance YJ, wherein gamma is a preset multiple and is greater than 1, and taking gamma =1.482;
step two: the route generation module acquires route distances of all routes, compares each route distance with a preselected distance YJ in sequence, and marks the route with the route distance smaller than the preselected distance YJ as a preselected route;
step three: the route generation module sends the preselected route to the route analysis module;
step four: the route analysis module acquires a route distance LJ of a preselected route;
step five: the route analysis module acquires the driving time in the history data of the pre-selected route, and sums the driving time to obtain an average value to obtain the average driving time JS;
step six: the route analysis module acquires the total times of accidents occurring in unit time of a preselected route and the time difference of two adjacent accidents, respectively marks the total times of accidents SC and the time difference SS of accidents, acquires the ratio of the total times of accidents SC and the time difference SS of accidents, and marks the ratio as the time ratio of accidents CS;
step seven: the route analysis module sends the route distance LJ, the average driving time JS and the accident time ratio CS to the intelligent auxiliary platform;
step eight: the intelligent auxiliary platform substitutes the route distance LJ, the average driving time JS and the accident time ratio CS into a formula
Obtaining an analysis value FX, wherein theta 1, theta 2 and theta 3 are preset weight coefficients of the route distance LJ, the average driving time JS and the accident time ratio CS respectively, and theta 1+ theta 2+ theta 3=1, theta 1 is more than 0 and more than theta 2 and more than theta 3 and less than 1;
step nine: the intelligent auxiliary platform sorts the preselected routes from small to large according to the analysis value FX, marks the preselected route at the head as a selected route, sends the selected route to a route display module, generates a real-time analysis instruction at the same time, sends the real-time analysis instruction to a real-time analysis module, and the route display module performs map display on the selected route according to the received selected route and performs voice navigation;
step ten: the real-time analysis module receives a real-time analysis instruction, and then marks a pre-selected route which has intersection points with a selected route except a starting point and an end point as a pre-analysis route, acquires a current position, and marks the pre-analysis route which is closest to the intersection point at the current position as an analysis route;
step eleven: the real-time analysis module acquires a selected route and analyzes the route distance LJ of the route;
step twelve: the real-time analysis module acquires the total number of vehicles entering the selected route, analyzing the route and the total number of vehicles exiting the preselected route in real time, acquires the difference value of the two, and marks the difference value as the number LC of the vehicles on the route;
step thirteen: the real-time analysis module acquires the selected route in real time, analyzes the speed of all vehicles in the route, and sums to obtain an average value to obtain an average speed PS;
fourteen steps: the real-time analysis module acquires the selected route, analyzes the number of traffic lights going straight and turning left in the route, acquires the number of traffic lights with a right-turn sign, acquires the sum of the two, and marks the sum as the number HS of the red lights;
step fifteen: the real-time analysis module obtains the selected route, analyzes the closest distance value between the vehicle and the front vehicle in the route, marks the closest distance value as a front distance value QZ, obtains the closest distance value between the vehicle and the side vehicle, marks the closest distance value as a side distance value CZ, and substitutes the front distance value QZ and the side distance value CZ into a formula
Obtaining a vehicle distance CL, wherein a front vehicle represents a vehicle in the same lane, a side vehicle represents a vehicle in a non-same lane, q1 and q2 are preset proportionality coefficients of a front distance value QZ and a side distance value CZ respectively, and q1+ q2=1, q1=0.72 and q2=0.28 are taken;
sixthly, the steps are as follows: the real-time analysis module sends the route distance LJ, the route vehicle number LC, the average vehicle speed PS, the red light number HS and the vehicle distance CL to the intelligent auxiliary platform;
seventeen steps: the intelligent auxiliary platform substitutes the route distance LJ, the route vehicle number LC, the average vehicle speed PS, the red light number HS and the vehicle distance CL into a formula
Obtaining an actual division number SF, wherein the actual division number SF comprises an actual division coefficient SF1 of a selected route and an actual division coefficient SF2 of an analyzed route, Q1, Q2, Q3, Q4 and Q5 are preset weight factors of a route distance LJ, a route vehicle number LC, an average vehicle speed PS, a red light number HS and a vehicle distance CL, and Q3 is more than Q5 is more than Q2 is more than Q1 is more than Q4 is more than 1.239;
eighteen steps: the intelligent auxiliary platform compares the real score coefficient SF1 of the selected route with the real score coefficient SF2 of the analyzed route:
and if the real division coefficient SF2 of the analyzed route is larger than the real division coefficient SF1 of the selected route, marking the analyzed route as the selected route and sending the selected route to the route display module, and the route display module performs map display on the selected route according to the received selected route and performs voice navigation.
In the description herein, references to the description of "one embodiment," "an example," "a specific example" or the like are intended to mean that a particular feature, structure, material, or characteristic described in connection with the embodiment or example is included in at least one embodiment or example of the invention. In this specification, the schematic representations of the terms used above do not necessarily refer to the same embodiment or example. Furthermore, the particular features, structures, materials, or characteristics described may be combined in any suitable manner in any one or more embodiments or examples.
The foregoing is illustrative and explanatory only and is not intended to be exhaustive or to limit the invention to the precise embodiments described, and various modifications, additions, and substitutions may be made by those skilled in the art without departing from the scope of the invention or exceeding the scope of the claims.
Claims (2)
1. Full scene intelligence driver assistance's navigation, its characterized in that includes:
the route generation module is used for generating a plurality of routes according to the starting point and the end point, screening a preselected route from the generated routes and sending the preselected route to the route analysis module;
the specific process of screening out the preselected route by the route generation module is as follows:
generating a plurality of routes according to the starting point and the end point, marking the route with the shortest distance as a reference route, marking the distance of the reference route as a reference distance CJ, substituting the reference distance CJ into a formula
Obtaining a preselected distance YJ, wherein gamma is a preset multiple and is larger than 1, and taking gamma =1.482;
acquiring route distances of all routes, sequentially comparing each route distance with a preselected distance YJ, and marking the route with the route distance smaller than the preselected distance YJ as a preselected route;
sending the preselected route to a route analysis module;
the route analysis module is used for acquiring analysis parameters of a preselected route and sending the analysis parameters to the intelligent auxiliary platform;
the specific process of the route analysis module for obtaining the analysis parameters is as follows:
obtaining a route distance LJ of a preselected route;
obtaining the driving time in the pre-selection route historical data, summing the driving time and solving an average value to obtain an average driving time JS;
acquiring the total number of accidents occurring in unit time of a preselected route and the time difference of two adjacent accidents occurring in unit time of the preselected route, respectively marking the total number of accidents SC and the time difference SS of the accidents, acquiring the ratio of the total number of accidents SC and the time difference SS of the accidents, and marking the ratio as the time ratio CS of the accidents;
the route distance LJ, the average driving time JS and the accident time ratio CS are sent to an intelligent auxiliary platform;
the intelligent auxiliary platform is used for obtaining an analysis value according to the analysis parameter, obtaining a real-time coefficient according to the real-time parameter, obtaining a selected route according to the analysis value and the real-time coefficient, sending the selected route to the route display module, simultaneously generating a real-time analysis instruction and sending the real-time analysis instruction to the real-time analysis module;
the specific process of obtaining the analysis value FX by the intelligent auxiliary platform is as follows:
substituting the route distance LJ, the average driving time JS and the accident time ratio CS into a formula
Obtaining an analysis value FX, wherein theta 1, theta 2 and theta 3 are preset weight coefficients of the route distance LJ, the average driving time JS and the accident time ratio CS respectively, and theta 1+ theta 2+ theta 3=1, theta 1 is more than 0 and more than theta 2 and more than theta 3 and less than 1;
sequencing the preselected routes according to the sequence of the analysis values FX from small to large, marking the preselected route at the head as a selected route, sending the selected route to a route display module, simultaneously generating a real-time analysis instruction, and sending the real-time analysis instruction to a real-time analysis module;
the specific process of the intelligent auxiliary platform for obtaining the real division coefficient SF is as follows:
the distance LJ of the route, the number LC of the vehicles on the route, the average speed PS and redFormula substituting number of lamps HS and vehicle distance CL into
Obtaining a real-division number SF, wherein the real-division number SF comprises a real-division coefficient SF1 of a selected route and a real-division coefficient SF2 of an analyzed route, Q1, Q2, Q3, Q4 and Q5 are preset weight factors of a route distance LJ, a route vehicle number LC, an average vehicle speed PS, a red light number HS and a vehicle distance CL, and Q3 is more than Q5 and more than Q2 is more than Q1 and more than Q4 is more than 1.239;
comparing the real division coefficient SF1 of the selected route with the real division coefficient SF2 of the analyzed route:
if the real division coefficient SF2 of the analyzed route is larger than the real division coefficient SF1 of the selected route, the analyzed route is marked as the selected route and is sent to a route display module;
the route display module is used for carrying out map display on the selected route according to the received selected route and carrying out voice navigation;
the real-time analysis module is used for receiving the real-time analysis instruction, obtaining an analysis route according to the selected route, respectively obtaining real-time parameters of the selected route and the analysis route, and sending the real-time parameters to the intelligent auxiliary platform;
the specific process of the real-time analysis module for obtaining the real-time parameters is as follows:
after receiving a real-time analysis instruction, marking a preselected route which has intersection points with the selected route except a starting point and an end point as a pre-analysis route, acquiring a current position, and marking the pre-analysis route which is closest to the intersection points at the current position as an analysis route;
obtaining a selected route and analyzing the route distance LJ of the route;
acquiring the total number of vehicles entering the selected route, analyzing the route and the total number of vehicles exiting the preselected route in real time, acquiring a difference value of the two, and marking the difference value as the number LC of the vehicles on the route;
obtaining a selected route in real time, analyzing the speed of all vehicles in the route, summing and solving an average value to obtain an average vehicle speed PS;
obtaining a selected route, analyzing the number of traffic lights going straight and turning left in the route, obtaining the number of traffic lights with a right turn sign, obtaining the sum of the two, and marking the sum as the number HS of the red lights;
obtaining a selected route, analyzing a closest distance value between a vehicle and a front vehicle in the route, marking the closest distance value as a front distance value QZ, obtaining a closest distance value between the vehicle and a side vehicle, marking the closest distance value as a side distance value CZ, and substituting the front distance value QZ and the side distance value CZ into a formula
Obtaining a vehicle distance CL, wherein a front vehicle represents a vehicle in the same lane, a side vehicle represents a vehicle in a non-same lane, q1 and q2 are preset proportionality coefficients of a front distance value QZ and a side distance value CZ respectively, and q1+ q2=1, q1=0.72 and q2=0.28 are taken;
and sending the route distance LJ, the route vehicle number LC, the average vehicle speed PS, the red light number HS and the vehicle distance CL to the intelligent auxiliary platform.
2. The navigation method of the full-scene intelligent auxiliary driving is characterized by comprising the following steps:
the method comprises the following steps: the route generation module generates a plurality of routes according to the starting point and the end point, marks the route with the shortest distance as a reference route, marks the distance of the reference route as a reference distance, and analyzes the reference distance to obtain a preselected distance;
step two: the route generation module acquires route distances of all routes, compares each route distance with a preselected distance in sequence, and marks the route with the route distance smaller than the preselected distance as the preselected route;
step three: the route generation module sends the preselected route to the route analysis module;
step four: the route analysis module acquires route distances of the preselected route;
step five: the route analysis module acquires the running time in the historical data of the preselected route, and sums the running time to obtain an average value to obtain the average running time;
step six: the route analysis module acquires the total times of accidents occurring in unit time of a preselected route and the time difference of two adjacent accidents occurring in unit time of the preselected route, respectively marks the total times of the accidents and the time difference of the accidents, acquires the ratio of the total times of the accidents and the time difference of the accidents, and marks the ratio as the accident time ratio;
step seven: the route analysis module sends the route distance, the average driving time length and the accident time ratio to the intelligent auxiliary platform;
step eight: the intelligent auxiliary platform analyzes the route distance, the average driving time length and the accident time ratio to obtain analysis values;
step nine: the intelligent auxiliary platform sorts the preselected routes from small to large according to analysis values, marks the preselected route at the head as a selected route, sends the selected route to a route display module, generates a real-time analysis instruction at the same time, sends the real-time analysis instruction to a real-time analysis module, and the route display module performs map display on the selected route according to the received selected route and performs voice navigation;
step ten: the real-time analysis module receives a real-time analysis instruction, and then marks a pre-selected route which has intersection points with a selected route except a starting point and an end point as a pre-analysis route, acquires a current position, and marks the pre-analysis route which is closest to the intersection point at the current position as an analysis route;
step eleven: the real-time analysis module acquires a selected route and analyzes the route distance of the route;
step twelve: the real-time analysis module acquires the total number of vehicles entering the selected route, analyzing the route and the total number of vehicles exiting the preselected route in real time, acquires the difference value of the two, and marks the difference value as the number of vehicles on the route;
step thirteen: the real-time analysis module acquires the speed of all vehicles in the selected route and the analyzed route in real time, and sums the speed to obtain an average value to obtain an average vehicle speed;
fourteen steps: the real-time analysis module acquires the selected route, analyzes the number of traffic lights going straight and turning left in the route, acquires the number of traffic lights with a right-turn sign, acquires the sum of the two, and marks the sum as the number HS of the red lights;
step fifteen: the real-time analysis module acquires the selected route, analyzes the closest distance value between the vehicle and the front vehicle in the route, marks the closest distance value as a front distance value, acquires the closest distance value between the vehicle and the side vehicle, marks the closest distance value as a side distance value, and analyzes the front distance value and the side distance value to obtain the vehicle distance;
sixthly, the step of: the real-time analysis module sends the route distance, the route vehicle number, the average vehicle speed, the red light number and the vehicle distance to the intelligent auxiliary platform
Seventeen steps: the intelligent auxiliary platform analyzes the route distance, the route vehicle number, the average vehicle speed, the red light number and the vehicle distance to obtain a real division number, wherein the real division number comprises a real division coefficient of the selected route and a real division coefficient of the analyzed route;
eighteen steps: the intelligent auxiliary platform compares the real division coefficient of the selected route with the real division coefficient of the analyzed route:
and if the real score coefficient of the analyzed route is larger than the real score coefficient of the selected route, marking the analyzed route as the selected route and sending the selected route to the route display module.
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