CN111216737A - Method for predicting transportation path of freezing and refrigerating vehicle - Google Patents
Method for predicting transportation path of freezing and refrigerating vehicle Download PDFInfo
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- CN111216737A CN111216737A CN202010077132.XA CN202010077132A CN111216737A CN 111216737 A CN111216737 A CN 111216737A CN 202010077132 A CN202010077132 A CN 202010077132A CN 111216737 A CN111216737 A CN 111216737A
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- B—PERFORMING OPERATIONS; TRANSPORTING
- B60—VEHICLES IN GENERAL
- B60W—CONJOINT CONTROL OF VEHICLE SUB-UNITS OF DIFFERENT TYPE OR DIFFERENT FUNCTION; CONTROL SYSTEMS SPECIALLY ADAPTED FOR HYBRID VEHICLES; ROAD VEHICLE DRIVE CONTROL SYSTEMS FOR PURPOSES NOT RELATED TO THE CONTROL OF A PARTICULAR SUB-UNIT
- B60W50/00—Details of control systems for road vehicle drive control not related to the control of a particular sub-unit, e.g. process diagnostic or vehicle driver interfaces
- B60W50/0097—Predicting future conditions
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- B—PERFORMING OPERATIONS; TRANSPORTING
- B60—VEHICLES IN GENERAL
- B60W—CONJOINT CONTROL OF VEHICLE SUB-UNITS OF DIFFERENT TYPE OR DIFFERENT FUNCTION; CONTROL SYSTEMS SPECIALLY ADAPTED FOR HYBRID VEHICLES; ROAD VEHICLE DRIVE CONTROL SYSTEMS FOR PURPOSES NOT RELATED TO THE CONTROL OF A PARTICULAR SUB-UNIT
- B60W40/00—Estimation or calculation of non-directly measurable driving parameters for road vehicle drive control systems not related to the control of a particular sub unit, e.g. by using mathematical models
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- B—PERFORMING OPERATIONS; TRANSPORTING
- B60—VEHICLES IN GENERAL
- B60W—CONJOINT CONTROL OF VEHICLE SUB-UNITS OF DIFFERENT TYPE OR DIFFERENT FUNCTION; CONTROL SYSTEMS SPECIALLY ADAPTED FOR HYBRID VEHICLES; ROAD VEHICLE DRIVE CONTROL SYSTEMS FOR PURPOSES NOT RELATED TO THE CONTROL OF A PARTICULAR SUB-UNIT
- B60W2530/00—Input parameters relating to vehicle conditions or values, not covered by groups B60W2510/00 or B60W2520/00
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- B—PERFORMING OPERATIONS; TRANSPORTING
- B60—VEHICLES IN GENERAL
- B60W—CONJOINT CONTROL OF VEHICLE SUB-UNITS OF DIFFERENT TYPE OR DIFFERENT FUNCTION; CONTROL SYSTEMS SPECIALLY ADAPTED FOR HYBRID VEHICLES; ROAD VEHICLE DRIVE CONTROL SYSTEMS FOR PURPOSES NOT RELATED TO THE CONTROL OF A PARTICULAR SUB-UNIT
- B60W2540/00—Input parameters relating to occupants
Abstract
The invention relates to the technical field of path prediction, and particularly discloses a method for predicting a transportation path of a freezing and refrigerating vehicle, which comprises a step of detecting the current position of a target vehicle, a step of acquiring a plurality of transportation paths from the current position to a target position and real-time road conditions of each transportation path, a step of acquiring the driving history data of the target vehicle and the driving history data of other vehicles different from the target vehicle from a driving history storage device storing the driving history data of each vehicle, a step of calculating the predicted path of the target vehicle by combining the acquired real-time road conditions of each transportation path based on the driving history data of the target vehicle, the driving history data of other vehicles and the current position, and a step of outputting the calculated data of the predicted path; by adopting a more reasonable and effective method, the purpose of effectively predicting the path when the target vehicle runs in the non-running section is realized by utilizing the running history data of other vehicles.
Description
Technical Field
The invention relates to the technical field of path prediction, in particular to a method for predicting a transportation path of a freezing and refrigerating vehicle.
Background
When the refrigerated and frozen vehicle is transported, in order to ensure the freshness of the goods, the optimal transportation path needs to be selected as much as possible. By predicting the travel route of the vehicle in advance, various information on the travel route can be collected and provided to the driver, contributing to improvement of the transportation efficiency.
Since the driving habits and the travel history of the drivers are different from each other, the path prediction can be performed based on the determination of the past travel history of the vehicle. However, in the route prediction method based on the vehicle travel history data, although the route prediction can be performed with high accuracy in the section that has already traveled, it is difficult to perform effective prediction in the section that has not traveled.
Disclosure of Invention
Aiming at the defects of the prior art, the invention provides a method for predicting the transportation path of a freezing and refrigerating vehicle, which has the advantages of high prediction accuracy, capability of effectively predicting the non-driving section and the like, and solves the problem that the prior art is difficult to effectively predict the non-driving section.
The invention relates to a method for predicting the transportation path of a freezing and refrigerating vehicle, which comprises the following steps,
s1 detecting the current position of the subject vehicle;
s2, acquiring a plurality of transportation paths from the current position to the destination position and real-time road conditions of each transportation path;
s3 obtaining the travel history data of the target vehicle and the travel history data of another vehicle different from the target vehicle from the travel history storage device storing the travel history data of each vehicle, and obtaining the predicted route of the target vehicle based on the travel history data of the target vehicle, the travel history data of the another vehicle, and the current position by combining the obtained real-time road conditions of each transportation route;
a step of S4 outputting the data of the obtained predicted path;
in step S3, the driving history data of the subject vehicle acquired from the driving history storage device,
a, when the data quantity of a path from the current position to the target position is larger than or equal to a set threshold value, obtaining a predicted path of the target vehicle based on the traveling history data of the target vehicle;
b, when the data amount of the route from the current position to the target position is less than a set threshold value, acquiring the travel history data of other vehicles from the travel history storage device, and obtaining the predicted route of the target vehicle by using at least the travel history data of other vehicles.
In the case of step S3 b, the method for predicting a transportation route of a refrigerated or frozen vehicle obtains a predicted route of the target vehicle on the basis of travel history data obtained by summing up travel history data of the target vehicle to which a weight is given and travel history data of other vehicles to which a weight is given, by multiplying the travel history data of the target vehicle by a weight larger than the weight by which the travel history data of the other vehicles is multiplied.
The invention relates to a method for predicting a transportation path of a freezing and refrigerating vehicle, wherein a driving history storage device stores driving history data which are correlated with set conditions;
in the case of b in step S3, the travel history data of the other vehicle having the same conditions as the subject vehicle is acquired.
In a method for predicting a transportation route of a freezing and refrigerating vehicle, in step S1, a target vehicle is first identified by an identification device.
In step S2, the transportation path from the front position to the destination position at least includes the shortest travel distance path and the path with the least travel time under the current road condition.
According to the method for predicting the transportation path of the freezing and refrigerating vehicle, the road condition is reminded by combining the real-time road condition of the predicted path according to the data of the predicted path of the target vehicle output in the step S4.
Compared with the prior art, the invention has the following beneficial effects:
the invention predicts the running path of the target vehicle by adopting a more reasonable and effective method and utilizing the running history data of other vehicles, realizes the aim of effectively predicting the path when the target vehicle runs in the non-running section, and has more accurate prediction precision.
Drawings
The accompanying drawings, which are included to provide a further understanding of the application and are incorporated in and constitute a part of this application, illustrate embodiment(s) of the application and together with the description serve to explain the application and not to limit the application. In the drawings:
FIG. 1 is a schematic flow chart of an embodiment of the present invention;
FIG. 2 is a schematic flow chart of an embodiment of the present invention;
FIG. 3 is a schematic diagram of a three-flow process according to an embodiment of the present invention.
Detailed Description
In the following description, for purposes of explanation, numerous implementation details are set forth in order to provide a thorough understanding of the various embodiments of the present invention. It should be understood, however, that these implementation details are not to be interpreted as limiting the invention. That is, in some embodiments of the invention, such implementation details are not necessary.
Referring to fig. 1, in a first embodiment of the present invention:
a method for predicting the transportation path of a freezing and refrigerating vehicle comprises the following steps,
s1 detecting the current position of the subject vehicle;
s2, acquiring a plurality of transportation paths from the current position to the destination position and real-time road conditions of each transportation path;
s3 obtaining the travel history data of the target vehicle and the travel history data of another vehicle different from the target vehicle from the travel history storage device storing the travel history data of each vehicle, and obtaining the predicted route of the target vehicle based on the travel history data of the target vehicle, the travel history data of the another vehicle, and the current position by combining the obtained real-time road conditions of each transportation route;
a step of S4 outputting the data of the obtained predicted path;
in step S3, the driving history data of the subject vehicle acquired from the driving history storage device,
a, when the data quantity of a path from the current position to the target position is larger than or equal to a set threshold value, obtaining a predicted path of the target vehicle based on the traveling history data of the target vehicle;
b, when the data amount of the route from the current position to the target position is less than a set threshold value, acquiring the travel history data of other vehicles from the travel history storage device, and obtaining the predicted route of the target vehicle by using at least the travel history data of other vehicles.
Further, in step S1, the identification device first identifies the subject vehicle.
Further, in step S2, the transportation path from the front position to the destination position at least includes the shortest travel distance path and the path with the least travel time under the current road condition.
Further, according to the data of the predicted path of the target vehicle output in step S4, the road condition is prompted in combination with the real-time road condition of the predicted path.
The invention collects and analyzes the driving history of a plurality of transport vehicles, and the result shows that the selection of the target vehicle to the path reflects the preference of the target vehicle in the very familiar sections such as the frequently-driving transport route, namely the selection of the path reflects the personality; however, in a case where a new transportation route or a transportation route which is less traveled is traveled for the first time, it is common that a large number of drivers select the same route, for example, select a trunk road, a road with a wide road, or the like. Based on this finding, the present inventors have completed a method capable of predicting a route with high accuracy even in a section that has not been traveled in the past.
In step S3, the set threshold value of the data amount of the route from the current position referred by the travel history data of the subject vehicle to the destination position (i.e., the route library made up of the plurality of transportation routes acquired in step S2) is simply whether or not the past travel route of the subject vehicle includes a travel route in the route library, what the ratio thereof can be referred to is, or what the single similarity degree for a certain route in the library is geometric, or the like. In practical application, the path may be divided into a plurality of blocks, such as the number of trunk roads, the number of branch roads, the number of intersections, the number of curves, and the like, and the overall analysis and judgment may be performed according to the reference ratio of a plurality of segments.
When the data amount of the route library referenced by the travel history data of the target vehicle reaches a certain set threshold, it can be judged that the degree of similarity between a certain route or a combination of segments of certain routes in the route library and the travel history data of the target vehicle is high, that is, the condition a, so that the route prediction can be completed by using the travel history data of the target vehicle.
On the other hand, if the data amount of the route library referenced by the travel history data of the target vehicle is smaller than a predetermined threshold, this is the case b, and at this time, it is necessary to complete route prediction using the travel history data of another vehicle.
With the above-described flow, even in a region where the traveling experience of the target vehicle is little or no traveling experience, the target vehicle can be predicted appropriately using the traveling history of another vehicle.
Referring to fig. 2, the second embodiment of the present invention:
further, in the case of b in step S3, the travel history data of the target vehicle is multiplied by a weight larger than the weight by which the travel history data of the other vehicle is multiplied, and the predicted route of the target vehicle is found based on the travel history data obtained by summing up the travel history data of the target vehicle to which the weight is given and the travel history data of the other vehicle to which the weight is given.
The present embodiment is different from the first embodiment in that, in the case of b in step S3, the travel history data of the target vehicle is multiplied by a weight larger than the weight by which the travel history data of the other vehicle is multiplied, and the predicted route of the target vehicle is found based on the travel history data obtained by summing up the travel history data of the target vehicle to which the weight is given and the travel history data of the other vehicle to which the weight is given.
Specifically, the travel history data of the other vehicle is multiplied by a weight coefficient y, the travel history data of the subject vehicle is multiplied by a weight coefficient x, where x > y, and then the calculated values of both are summed up. This generates travel history data that can more accurately reflect the travel history data of the target vehicle. And the predicted path is calculated by using the driving history data given with the weight, so that the prediction accuracy is effectively improved.
Referring to fig. 3, the third embodiment of the present invention:
further, the driving history data stored in the driving history storage device is correlated with the set conditions; in the case of b in step S3, the travel history data of the other vehicle having the same conditions as the subject vehicle is acquired.
The present embodiment is different from the second embodiment in that, in the case of b in step S3, the travel history data of other vehicles under the same conditions as the subject vehicle is used.
According to the analysis of the inventor, the trends of the path selection of the vehicles with the same conditions are closer. Therefore, when the route prediction is performed by storing the travel history data for each vehicle, the accuracy of the route prediction based on the travel history data of other vehicles can be further improved by using the vehicle attributes such as the type of the vehicle, the price zone of the vehicle, and the size of the vehicle as the screening conditions and performing the route prediction using the travel history data of the vehicles having the same attributes.
Further, since the target vehicles for carrying out the transportation work may be driven by different drivers although they are the same, it is possible to store the travel history data for each target vehicle and also store the travel history data for each driver, which contributes to further improvement of the prediction accuracy when the route prediction is performed using the travel history data of the target vehicle. Therefore, when the route prediction process is started in step S1, the present invention first identifies the target vehicle, i.e., the driver thereof, using the identification device. The method can be carried out by adopting the existing common identification modes such as electronic identification codes.
Further, by storing the travel history data for each driver, when the route is predicted using the travel history data of other drivers, it is possible to improve the prediction accuracy by using the travel history data of the drivers having the same attribute to perform the route prediction using the personal attributes such as the driving history, age, and sex of the driver as the filtering conditions.
It should be noted that, when the refrigerated and frozen vehicle is transported, in order to ensure the freshness of the goods, it is necessary to select the optimal transportation path as much as possible. With the development of navigation technology, path navigation can intelligently quote a plurality of optimal driving paths to a vehicle as transportation path suggestions, wherein the optimal driving paths at least can comprise a shortest driving distance path and a path with the least driving time under the current road condition, and the quoted path suggestions also can comprise driving paths with secondary priority, so that the data volume of a quoted path library is expanded.
The above description is only an embodiment of the present invention, and is not intended to limit the present invention. Various modifications and alterations to this invention will become apparent to those skilled in the art. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention should be included in the scope of the claims of the present invention.
Claims (6)
1. A method for predicting a transportation path of a refrigerated vehicle is characterized by comprising the following steps: comprises the following steps of (a) carrying out,
s1 detecting the current position of the subject vehicle;
s2, acquiring a plurality of transportation paths from the current position to the destination position and real-time road conditions of the transportation paths;
s3 obtaining the travel history data of the target vehicle and the travel history data of another vehicle different from the target vehicle from a travel history storage device storing the travel history data of each vehicle, and obtaining the predicted route of the target vehicle based on the travel history data of the target vehicle, the travel history data of the another vehicle, and the current position in association with the real-time road condition of each of the obtained transportation routes;
a step of S4 outputting the data of the obtained predicted route;
wherein in step S3, the driving history data of the object vehicle obtained from the driving history storage device,
a, when the data quantity of the route from the current position to the target position is larger than or equal to a set threshold value, obtaining a predicted route of the target vehicle based on the traveling history data of the target vehicle;
b obtaining travel history data of another vehicle from the travel history storage device when the data amount of the route referencing the current position to the destination position is smaller than a set threshold value, and obtaining the predicted route of the target vehicle by using at least the travel history data of the another vehicle.
2. A frozen refrigerated vehicle transportation path prediction method as claimed in claim 1 wherein: in the case of step S3 b, the predicted route of the target vehicle is found based on the travel history data obtained by multiplying the travel history data of the target vehicle by a weight larger than the weight by which the travel history data of the other vehicle is multiplied, and summing up the travel history data of the target vehicle to which the weight is added and the travel history data of the other vehicle to which the weight is added.
3. A frozen refrigerated vehicle transportation path prediction method as claimed in claim 1 wherein: the driving history storage device stores driving history data which are correlated with set conditions;
in the case of b in step S3, the travel history data of the other vehicle having the same conditions as the subject vehicle is acquired.
4. A frozen refrigerated vehicle transportation path prediction method as claimed in claim 1 wherein: in step S1, the identification device is first used to identify the subject vehicle.
5. A frozen refrigerated vehicle transportation path prediction method as claimed in claim 1 wherein: in step S2, the transportation route from the front position to the destination position at least includes the shortest travel distance route and the route with the least travel time under the current road condition.
6. A frozen refrigerated vehicle transportation path prediction method as claimed in claim 1 wherein: and according to the data of the predicted path of the target vehicle output in the step S4, combining the real-time road condition of the predicted path to remind the road condition.
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| CN202010077132.XA CN111216737A (en) | 2020-01-23 | 2020-01-23 | Method for predicting transportation path of freezing and refrigerating vehicle |
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| CN202010077132.XA CN111216737A (en) | 2020-01-23 | 2020-01-23 | Method for predicting transportation path of freezing and refrigerating vehicle |
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Cited By (1)
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| CN113029174A (en) * | 2021-03-10 | 2021-06-25 | 西安主函数智能科技有限公司 | Path identification method and device under engineering transportation environment |
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Application publication date: 20200602 |