CN114291111B - Target path determination method, device, vehicle and storage medium - Google Patents
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Abstract
The application discloses a target path determining method, a target path determining device, a vehicle and a storage medium, and belongs to the technical field of vehicle control. Applied to a vehicle, the method comprises: for each predicted travel path of the at least two predicted travel paths, acquiring at least two travel-related parameters corresponding to the vehicle on each predicted travel path; for each predicted driving path, determining the parameter category to which at least two driving related parameters belong from preset parameter categories; obtaining respective scoring results of each predicted driving path according to the parameter category to which at least two driving related parameters belong; and determining a target path from at least two predicted travel paths according to the scoring results of each predicted travel path. According to the method and the device, the parameter category of each running related parameter is determined, the running path is scored based on the parameter category, the target path is selected from each predicted running path, and the accuracy of the scoring result of the multiple running paths is improved.
Description
Technical Field
The present disclosure relates to the field of vehicle control technologies, and in particular, to a method and apparatus for determining a target path, a vehicle, and a storage medium.
Background
With the continuous development of science and technology, various vehicles in real life have become indispensable vehicles for users to travel, and it is very important to plan the travel path of the vehicles during the traveling process.
Currently, in various vehicles, an on-board terminal generally has an automatic driving function, and in an automatic driving route planning, the on-board terminal may automatically generate a plurality of different driving paths, and set different vehicle control parameters, such as speed, acceleration, etc., in the different driving paths, so as to control the vehicle to run. When the vehicle-mounted terminal selects a proper path from all target paths, the whole path is often scored directly according to parameter values of control parameters in different paths, and parameters adopted in the scoring process of the driving paths are single, so that the problem of low accuracy of scoring results of multiple driving paths is solved.
Disclosure of Invention
The embodiment of the application provides a target path determining method, a target path determining device, a vehicle and a storage medium, which can improve the efficiency of pushing vehicle related information to a non-purchased vehicle user.
In one aspect, an embodiment of the present application provides a method for determining a target path, where the method is applied to a vehicle, and the method includes:
for each predicted travel path of at least two predicted travel paths, acquiring at least two travel related parameters corresponding to the vehicle on each predicted travel path;
for each predicted driving path, determining the parameter category to which each of the at least two driving related parameters belongs from preset parameter categories;
obtaining respective scoring results of the predicted driving paths according to the parameter categories to which the at least two driving related parameters belong;
and determining a target path from the at least two predicted travel paths according to the scoring results of the predicted travel paths.
Optionally, the obtaining, according to the parameter category to which the at least two driving related parameters belong, a scoring result of each predicted driving path includes:
determining a scoring mapping relation of each driving related parameter according to the parameter category of each driving related parameter;
calculating target scores of the running related parameters according to the score mapping relation of the running related parameters;
And obtaining respective scoring results of the predicted driving paths according to the target scoring of each driving related parameter.
Optionally, before the scoring result of each predicted driving path is obtained according to the parameter category to which each of the at least two driving related parameters belongs, the method further includes:
calculating a parameter score of each driving related parameter according to a preset formula for each driving related parameter;
the calculating the target score of each driving related parameter according to the score mapping relation of each driving related parameter comprises the following steps:
and mapping the parameter scores of each driving related parameter according to the score mapping relation of each driving related parameter to obtain the target score of each driving related parameter.
Optionally, for each of the predicted driving paths, determining, from preset parameter categories, a parameter category to which each of the at least two driving related parameters belongs, including:
determining a range interval of the parameter score according to the respective parameter score of each driving related parameter;
and determining the parameter category to which each of the at least two driving related parameters belongs from the preset parameter categories according to the range interval of the parameter score.
Optionally, the preset parameter categories correspond to respective scoring mapping relationships, and mapping intervals of the scoring mapping relationships of the preset parameter categories are different.
Optionally, the driving related parameter includes any one or more of following distance parameter, acceleration parameter and acceleration variation parameter;
the preset parameter categories corresponding to the following distance parameters comprise a first distance category and a second distance category;
the preset parameter categories corresponding to the acceleration parameters comprise a first acceleration category and a second acceleration category;
the preset parameter categories corresponding to the acceleration variation parameters comprise a first variation category and a second variation category.
Optionally, before determining the target path from the at least two predicted travel paths according to the scoring result of each predicted travel path, the method further includes:
for each predicted travel path, acquiring a respective stability coefficient of each predicted travel path, wherein the stability coefficient is used for indicating the stability degree of each predicted travel path;
the determining a target path from the at least two predicted driving paths according to the scoring results of the respective predicted driving paths comprises the following steps:
And determining a target path from the at least two predicted driving paths according to the respective stability coefficients of the predicted driving paths and the respective scoring results of each driving path.
In another aspect, an embodiment of the present application provides a target path determining apparatus, where the apparatus is applied to a vehicle, and the apparatus includes:
the parameter acquisition module is used for acquiring at least two corresponding driving related parameters of the vehicle on each predicted driving path for each predicted driving path in the at least two predicted driving paths;
the category determining module is used for determining the parameter category to which each of the at least two driving related parameters belongs from preset parameter categories for each predicted driving path;
the scoring acquisition module is used for acquiring the scoring result of each predicted driving path according to the parameter category to which each of the at least two driving related parameters belongs;
and the path determining module is used for determining a target path from the at least two predicted driving paths according to the scoring results of each predicted driving path.
In another aspect, an embodiment of the present application provides a vehicle, where the vehicle includes a vehicle-mounted terminal, and the vehicle-mounted terminal includes a memory and a processor, where the memory stores a computer program, and when the computer program is executed by the processor, the processor implements a method for determining a target path according to one of the above aspects and any one of optional implementations of the above aspect.
In another aspect, embodiments of the present application provide a computer readable storage medium having a computer program stored thereon, the computer program when executed by a processor implementing a method for determining a target path according to the other aspect and alternatives thereof.
The technical scheme provided by the embodiment of the application at least comprises the following beneficial effects:
for each predicted travel path of the at least two predicted travel paths, acquiring at least two travel-related parameters corresponding to the vehicle on each predicted travel path; for each predicted driving path, determining the parameter category to which at least two driving related parameters belong from preset parameter categories; obtaining respective scoring results of each predicted driving path according to the parameter category to which at least two driving related parameters belong; and determining a target path from at least two predicted travel paths according to the scoring results of each predicted travel path. According to the method and the device, the running related parameters of each predicted running path are obtained, the parameter category of each running related parameter is determined, the running path is scored based on the parameter category, and the target path is selected from each predicted running path, so that the path scoring is finer, and the accuracy of the scoring result of a plurality of running paths is improved.
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In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings that are needed in the embodiments will be briefly described below, and it is obvious that the drawings in the following description are only some embodiments of the present invention, and other drawings may be obtained according to these drawings without inventive effort for a person skilled in the art.
FIG. 1 is an exemplary diagram of a vehicle-generated different travel path in accordance with an exemplary embodiment of the present application;
FIG. 2 is a method flow diagram of a method for determining a target path according to an exemplary embodiment of the present application;
FIG. 3 is a method flow diagram of a method for determining a target path according to an exemplary embodiment of the present application;
FIG. 4 is a schematic diagram of a parameter score and a target score according to an exemplary embodiment of the present application;
FIG. 5 is a block diagram of a target path determination device according to an exemplary embodiment of the present application;
fig. 6 is a schematic structural diagram of a vehicle-mounted terminal according to an exemplary embodiment of the present application.
Detailed Description
Reference will now be made in detail to exemplary embodiments, examples of which are illustrated in the accompanying drawings. When the following description refers to the accompanying drawings, the same numbers in different drawings refer to the same or similar elements, unless otherwise indicated. The implementations described in the following exemplary examples are not representative of all implementations consistent with the present application. Rather, they are merely examples of apparatus and methods consistent with some aspects of the present application as detailed in the accompanying claims.
References herein to "a plurality" means two or more. "and/or", describes an association relationship of an association object, and indicates that there may be three relationships, for example, a and/or B, and may indicate: a exists alone, A and B exist together, and B exists alone. The character "/" generally indicates that the context-dependent object is an "or" relationship.
It should be noted that the terms "first," "second," "third," and "fourth," etc. in the description and claims of the present application are used for distinguishing between different objects and not for describing a particular sequential order. The terms "comprises," "comprising," and "having," and any variations thereof, are intended to cover a non-exclusive inclusion, such that a process, method, system, article, or apparatus that comprises a list of steps or elements is not necessarily limited to those steps or elements expressly listed or inherent to such process, method, article, or apparatus.
The scheme provided by the application can be used for viewing real scenes of some vehicle related information in daily life through the terminal, and for facilitating understanding, the application architecture related to the embodiment of the application is briefly introduced.
In daily life, vehicles have been widely used as an indispensable vehicle. Wherein, the selection of the driving path is indispensable to the vehicle during driving. Currently, various vehicles have an autopilot function, and during autopilot, the vehicle needs to plan a travel path by itself and select the travel path to travel. For example, referring to FIG. 1, an exemplary diagram of a different travel path generated by a vehicle in accordance with an exemplary embodiment of the present application is shown. As shown in fig. 1, the current location 101, other locations 102, and the generated travel paths 103 are included. In a vehicle having an autopilot function, the in-vehicle terminal may generate respective travel paths 103 based on the vehicle current location 101 and other locations 102.
Optionally, the vehicle-mounted terminal may be further connected to a server through a communication network during an automatic driving process. Alternatively, the communication network may be a wired network or a wireless network, optionally using standard communication techniques and/or protocols. The network is typically the internet, but may be any network including, but not limited to, a Local area network (Local AreaNetwork, LAN), metropolitan area network (Metropolitan Area Network, MAN), wide area network (Wide Area Network, MAN), mobile, wired or wireless network, a private network, or any combination of virtual private networks. In some embodiments, data exchanged over the network is represented using techniques and/or formats including HyperText Mark-up Language (HTML), extensible markup Language (Extensible Markup Language, XML), and the like. All or some of the links may also be encrypted using conventional encryption techniques such as secure socket layer (Secure Socket Layer, SSL), transport layer security (Transport Layer Security, TLS), virtual private network (Virtual Private Network, VPN), internet protocol security (Internet Protocol Security, IPsec), and the like. In other embodiments, custom and/or dedicated data communication techniques may also be used in place of or in addition to the data communication techniques described above.
Alternatively, the server may be a server that provides services for an application installed in a vehicle. The server may be a server, or several servers, or a virtualization platform, or a cloud computing service center. Alternatively, the server is a server provided by the company that produces the vehicle.
At present, the travel path generated by the vehicle-mounted terminal in the manner is planned based on different positions, and during the running of the vehicle, when the vehicle moves from the position A to the position B, the vehicle generally adjusts the control parameters of the vehicle, such as speed, acceleration and the like, based on the planned travel path. In general, it is important to determine a best travel path from among various planned travel paths, in which the travel conditions of the vehicle must be different when the vehicle travels along different planned paths. For example, a vehicle will generally score the entire path through parameter values of control parameters in different paths, and select a driving path with the best driving effect (for example, the best comfort, etc.). In the process, each generated driving path is often scored directly based on the parameter value of the control parameter, the adopted parameter is single, the judgment basis is not comprehensive enough, and the problem of low accuracy of scoring the driving path exists.
In order to improve the accuracy of scoring the generated driving paths and improve the determination efficiency of the driving paths, the application provides a solution, wherein the parameter categories are preset for at least two corresponding driving related parameters on each predicted driving path, the parameter categories to which the driving related parameters belong are respectively determined, and the scoring results of the predicted driving paths are obtained according to the parameter categories to which the driving related parameters belong, so that the classification of the driving related parameters is achieved, the driving related parameters and the scoring of the categories are combined, and the flexibility of scoring the driving paths is improved.
Referring to fig. 2, a method flowchart of a method for determining a target path according to an exemplary embodiment of the present application is shown. The method for determining the target path can be applied to the scenario shown in fig. 1 and executed by the vehicle-mounted terminal of the vehicle in the scenario. As shown in fig. 2, the method of determining the target path may include the following steps.
Step 201, for each of at least two predicted travel paths, obtaining at least two travel related parameters corresponding to the vehicle on each predicted travel path.
The at least two predicted driving paths are paths for planning any two position points in the current driving route in the driving process of the vehicle. For example, in the navigation system, the vehicle generates a driving route according to the current position and the destination, and during the driving of the vehicle, the vehicle may further generate at least two predicted driving paths according to the current position of the vehicle during the driving and a position to be driven. Alternatively, the certain position to be driven may be a position 2 km before the current position of the vehicle in the driving route, or may be a section of road within 10 seconds to be driven by the vehicle calculated from the current speed of the vehicle.
Optionally, the vehicle obtains a driving related parameter on each of the at least two predicted driving paths according to the generated at least two predicted driving paths. The driving related parameter may be any one or more of following distance parameter, acceleration variation parameter, driving speed parameter, and the like. In the present application, the vehicle may acquire at least two travel-related parameters for each predicted travel path. Alternatively, the vehicle may acquire at least two travel-related parameters for each location in the predicted travel path. For example, for a predicted travel path, there are data such as a following distance parameter, an acceleration variation parameter, etc. at each position on the predicted travel path, and the vehicle-mounted terminal can acquire a travel-related parameter at each position. Each position on the predicted travel path is related to a coordinate scale generated by the vehicle-mounted terminal, for example, the coordinate scale in the coordinate system generated by the vehicle-mounted terminal is 1 cm, and then the predicted travel path is a position every 1 cm on each coordinate axis in the coordinate system.
Step 202, for each predicted driving path, determining a parameter class to which at least two driving related parameters belong from preset parameter classes.
Optionally, in the present application, each preset parameter class includes at least two parameter classes. For example, the preset parameter categories include a first parameter category and a second parameter category, the vehicle-mounted terminal determines the category of each driving related parameter acquired by each predicted driving path, and determines the parameter category of each driving related parameter from the preset parameter categories. It should be noted that the number of parameter categories may be greater, for example, three or four, and the specific number may be designed based on actual requirements, which will not be described herein.
In one possible implementation manner, the vehicle-mounted terminal may determine the parameter class to which each of the driving-related parameters belongs according to the respective parameter value of each of the driving-related parameters. For example, in the first predicted driving path, the following distance parameter is obtained at each position, the vehicle-mounted terminal compares the following distance parameter with a preset following threshold (for example, 40 meters) according to the parameter value (for example, the following distance is 30 meters) of the following distance parameter, when the parameter value of the following distance parameter is greater than the preset following threshold, the vehicle-mounted terminal determines that the parameter type of the following distance parameter at the position is the parameter type two, and when the parameter value of the following distance parameter is not greater than the preset following threshold, the vehicle-mounted terminal determines that the parameter type of the following distance parameter at the position is the parameter type one.
Alternatively, the vehicle-mounted terminal may determine the parameter score of each of the travel-related parameters according to the parameter value of each of the travel-related parameters. For example, in the first predicted driving path, the following distance parameter is obtained at each position, and the vehicle-mounted terminal calculates the parameter score of the following distance parameter according to the parameter value of the following distance parameter (for example, the following distance is 30 meters) and a preset formula. The preset formula may be preset in the vehicle-mounted terminal by a developer. And comparing the parameter score with a preset score threshold, when the parameter score of the following distance parameter is larger than the preset score threshold, determining that the parameter class of the following distance parameter at the position is a parameter class II by the vehicle-mounted terminal, and when the parameter score of the following distance parameter is not larger than the preset score threshold, determining that the parameter class of the following distance parameter at the position is a parameter class I by the vehicle-mounted terminal.
And 203, obtaining the scoring result of each predicted driving path according to the parameter category to which each of the at least two driving related parameters belongs.
The vehicle-mounted terminal calculates path scores for all the predicted driving paths based on the parameter types of at least two driving related parameters, and obtains the scoring results of all the predicted driving paths.
And 204, determining a target path from at least two predicted travel paths according to the scoring results of each predicted travel path.
Optionally, after scoring each of the generated predicted travel paths, the vehicle-mounted terminal may further rank according to the respective path scores of each of the predicted travel paths; taking the predicted travel path with the path score arranged at the first position in each predicted travel path as a target path; the vehicle is controlled to travel along the target path.
In summary, for each predicted travel path of the at least two predicted travel paths, at least two travel-related parameters corresponding to the vehicle on each predicted travel path are obtained; for each predicted driving path, determining the parameter category to which at least two driving related parameters belong from preset parameter categories; obtaining respective scoring results of each predicted driving path according to the parameter category to which at least two driving related parameters belong; and determining a target path from at least two predicted travel paths according to the scoring results of each predicted travel path. According to the method and the device, the running related parameters of each predicted running path are obtained, the parameter category of each running related parameter is determined, the running path is scored based on the parameter category, and the target path is selected from each predicted running path, so that the path scoring is finer, and the accuracy of the scoring result of a plurality of running paths is improved.
In one possible implementation manner, each parameter category in the preset parameter categories further corresponds to a respective scoring mapping relationship, and the scoring mapping relationship is used for mapping the obtained parameter scores to a corresponding numerical range, so that the diversity of scoring the driving paths is improved.
Referring to fig. 3, a method flowchart of a method for determining a target path according to an exemplary embodiment of the present application is shown. The method for determining the target path can be applied to the scenario shown in fig. 1 and executed by the vehicle-mounted terminal of the vehicle in the scenario. As shown in fig. 3, the method of determining the target path may include the following steps.
Step 301, for each of at least two predicted travel paths, obtaining at least two travel related parameters corresponding to the vehicle on each predicted travel path.
Optionally, the vehicle-mounted terminal may generate at least two predicted travel paths based on the current position of the vehicle and a target position during the travel of the vehicle, where the target position is any one position in the travel direction of the vehicle. For example, the vehicle may start a navigation system in the vehicle during traveling, and after the user inputs the end position, a navigation route may be generated, in which the user may control the vehicle to travel. During the running of the vehicle, the vehicle-mounted terminal can determine a target position in the running direction of the vehicle. For example, the vehicle-mounted terminal obtains a target position according to a preset distance threshold, determines that the current position is a position at the distance threshold from the current position in the driving direction according to the current position of the vehicle in the navigation system, and takes the position as the target position.
Or, the vehicle-mounted terminal may determine the target position according to a preset time period to be driven in the driving process, for example, the preset time period set by a developer or an operation and maintenance person is 10 seconds, and in the driving process of the vehicle, the vehicle-mounted terminal may acquire the position to which the vehicle is driven after 10 seconds based on the current speed of the vehicle, and take the position as the target position. For example, the current vehicle speed is 20 m/s, and then the in-vehicle terminal calculates, based on the current vehicle speed and the preset time period, that the target position is a position 200 m away from the current position in the vehicle traveling direction, and the in-vehicle terminal regards the position as the target position. After the vehicle-mounted terminal acquires the current position and the target position, at least two predicted running paths can be generated according to the current position and the target position so as to be selected by the vehicle-mounted terminal, and the vehicle is controlled to run according to one of the running paths.
Optionally, the vehicle-mounted terminal obtains at least two corresponding driving related parameters on each predicted driving path. For example, the predicted travel path generated by the vehicle-mounted terminal includes a first predicted travel path and a second predicted travel path, and then the vehicle-mounted terminal may acquire at least two travel-related parameters on the first predicted travel path and at least two travel-related parameters on the second predicted travel path.
The driving related parameter may be any one or more of following distance parameter, acceleration variation parameter, driving speed parameter, etc.
Step 302, for each predicted driving path, determining a parameter class to which at least two driving related parameters belong from preset parameter classes.
Optionally, each preset parameter class includes at least two parameter classes. For example, the driving related parameters include any one or more of following distance parameters, acceleration parameters and acceleration variation parameters; among the preset parameter categories, the preset parameter category corresponding to the following distance parameter comprises a first distance category and a second distance category; the preset parameter categories corresponding to the acceleration parameters comprise a first acceleration category and a second acceleration category; the preset parameter categories corresponding to the acceleration variation parameters comprise a first variation category and a second variation category. For example, the first distance category is an emergency following distance and the second distance category is a non-emergency following distance; the first acceleration category is a comfort acceleration category, the second acceleration category is a non-comfort acceleration category, the first variance category is a comfort variance category, and the second variance category is a non-comfort variance category.
In one possible implementation manner, the vehicle terminal may determine, from the preset parameter categories, the parameter category to which each of the at least two driving related parameters belongs, as follows. For example, the vehicle-mounted terminal calculates a parameter score of each driving related parameter according to a preset formula for each driving related parameter; the vehicle-mounted terminal determines a range interval of the parameter score according to the respective parameter score of each driving related parameter; and determining the parameter category to which at least two driving related parameters belong from preset parameter categories according to the range interval of the parameter score.
Optionally, the preset formula for each driving related parameter is different. For example, for the following distance parameter, the following distance parameter may be calculated according to the following calculation formulas: following distance cost=c1 x (A1/A2), where A1 is a preset distance threshold of the following distance, and A2 is the actual following distance acquired; the calculation formula of the acceleration parameter may be as follows: acceleration cost=c2 (B1-B2) 2 Wherein B1 is a preset acceleration threshold value, and B2 is the acceleration actually acquired; the calculation formula of the first variation parameter may be as follows: first variation cost=c3 (D1-D2) 2 Wherein, D1 is a preset first variation threshold, and D2 is the variation of the acceleration obtained by deriving according to the acceleration actually collected. Wherein, C1, C2 and C3 are constants in the respective formulas, and are detected by a developer according to experience.
Alternatively, the vehicle-mounted terminal may acquire at least two driving-related parameters for each position in the predicted driving path. For example, for any predicted travel path, there are data such as a following distance parameter, an acceleration variation parameter, and the like at each position on the predicted travel path, and the vehicle-mounted terminal can acquire a travel-related parameter at each position. Each position on the predicted travel path is related to a coordinate scale generated by the vehicle-mounted terminal, for example, the coordinate scale in the coordinate system generated by the vehicle-mounted terminal is 1 cm, and then the predicted travel path is a position every 1 cm on each coordinate axis in the coordinate system. The following distance parameter, the acceleration variation parameter and the like of each position can be obtained through pre-estimation calculation. In other words, in the present application, the vehicle-mounted terminal budgets the following distance, acceleration, and variation of acceleration of each position on the predicted travel path of the vehicle, so as to obtain the following distance parameter, acceleration parameter, and acceleration variation parameter of each position, calculate the parameter score of the travel related parameter of each position according to the above formulas, and determine each parameter type.
Optionally, after obtaining the respective parameter scores of the driving related parameters, the vehicle-mounted terminal may further determine a range interval of the parameter scores according to the respective parameter scores of the driving related parameters; and determining the parameter category to which at least two driving related parameters belong from preset parameter categories according to the range interval of the parameter score. For example, for any one of the driving related parameters, the present application may provide a table of correspondence between the parameter scores of the driving related parameters and the preset parameter categories, please refer to table 1, which illustrates a table of correspondence between one of the parameter scores and the preset parameter categories according to an exemplary embodiment of the present application.
| Parameter scoring | Preset parameter categories |
| Range interval one | Parameter class one |
| Range interval two | Parameter class II |
| Range interval three | Parameter class III |
| …… | …… |
TABLE 1
As shown in table 1, each range of the parameter scores corresponds to a preset parameter category, and the parameter scores may be a parameter score of a following distance parameter, a parameter score of an acceleration parameter, and a parameter score of a variation parameter of acceleration. The vehicle-mounted terminal can obtain a range interval of the parameter score based on the calculated parameter score, inquire the corresponding relation table and obtain a corresponding parameter category from the corresponding relation table.
In one possible implementation manner, the vehicle terminal may determine, from the preset parameter categories, the parameter category to which each of the at least two driving related parameters belongs, as follows. For example, the in-vehicle terminal divides each of the travel-related parameters according to the parameter values of the travel-related parameters. For example, for the following distance parameter, when the parameter value of the following distance parameter is greater than the preset distance threshold, the vehicle-mounted terminal determines that the parameter class of the following distance parameter at the position is the parameter class two, and when the parameter value of the following distance parameter is not greater than the preset following threshold, the vehicle-mounted terminal determines that the parameter class of the following distance parameter at the position is the parameter class one. Other driving related parameters are similar, and will not be described again here.
Step 303, determining the scoring mapping relation of each driving related parameter according to the parameter category to which each of the at least two driving related parameters belongs.
Optionally, in the present application, the preset parameter categories correspond to respective score mapping relationships, and the vehicle terminal may determine the score mapping relationship of each driving related parameter based on the acquired parameter category to which each driving related parameter belongs. The scoring mapping relation is used for mapping the parameter scores of the driving related parameters, and the corresponding numerical value ranges are obtained.
Optionally, the mapping intervals of the scoring mapping relationships of the preset parameter categories are different. For example, the first distance category is an emergency following distance and the second distance category is a non-emergency following distance; the first acceleration category is a comfort acceleration category, the second acceleration category is a non-comfort acceleration category, the first variance category is a comfort variance category, and the second variance category is a non-comfort variance category. For example, the scoring mapping relationship corresponding to the emergency following distance is a scoring mapping relationship one, the scoring mapping relationship corresponding to the non-emergency following distance is a scoring mapping relationship two, the scoring mapping relationship one can take a value of 2-10, and the scoring mapping relationship two can take a value of 0-1. For example, the score map corresponding to the comfort acceleration is a score map three, the score map corresponding to the non-comfort acceleration is a score map four, the score map three may be a value of 0-1, and the score map four may be a value of 1-2. For example, the score map corresponding to the comfort acceleration variation is a score map five, the score map corresponding to the non-comfort acceleration variation is a score map six, the score map five may take a value of 0-1, and the score map six may take a value of 1-2.
Step 304, calculating the target score of each driving related parameter according to the score mapping relation of each driving related parameter.
Optionally, after determining the score mapping relation of each driving related parameter according to the parameter category, the vehicle-mounted terminal may map the parameter score of each driving related parameter according to the score mapping relation of each driving related parameter to obtain the target score of each driving related parameter. For example, please refer to fig. 4, which illustrates a schematic diagram of a parameter score and a target score according to an exemplary embodiment of the present application. As shown in fig. 4, the first parameter scoring interval 401, a target scoring interval 402 corresponding to the first parameter scoring interval 401, a second parameter scoring interval 403, a target scoring interval 404 corresponding to the second parameter scoring interval 403, a first coordinate axis 405, and a second coordinate axis 406 are included. Each parameter score is represented on a first axis 401 and each target score is represented on a second axis 402. In the method, after the parameter score is obtained for each driving related parameter, the target score corresponding to the driving related parameter is obtained through the corresponding mapping relation, so that the scoring result of the driving related parameter is enlarged, and the influence degree of the driving related parameter on the total score of the predicted driving path is improved.
For example, taking the driving related parameter as an example of the following distance parameter, the vehicle-mounted terminal determines that the parameter class of the following distance parameter belongs to the emergency following distance according to the following distance parameter at a certain position, determines that the scoring mapping relation is a scoring mapping relation I, maps the parameter score according to the scoring mapping relation by taking a value of 2-10 (for example, taking a value of 2), and multiplies the parameter score by the value taken in 2-10, thereby obtaining the target score of the parameter score of the following distance parameter after mapping. The other parameters are analogized in order and are not described in detail here.
Alternatively, the above-described score map may be used to indicate the importance degree of the travel-related parameter at the position corresponding to the travel-related parameter in the predicted travel path. For example, in the predicted driving path, the vehicle-mounted terminal may determine the value in the scoring mapping relationship based on different road condition information. For example, the vehicle-mounted terminal acquires the generated road condition information of each predicted driving path, and determines the value in the scoring mapping relation based on the road condition information. Alternatively, the road condition information may be at least one of a path curvature on a predicted travel path, a number of travels on the predicted travel path, a number of curves on the predicted travel path, and a number of pits on the predicted travel path. For example, the value of the vehicle terminal from 2 to 10 on the predicted travel path with a large number of curves and a large number of pits is larger than the value of the vehicle terminal from 2 to 10 on the predicted travel path with a small number of curves and a large number of pits.
For example, for the first predicted driving path, after the vehicle-mounted terminal obtains the following distance parameters, the acceleration parameters and the parameter scores of the acceleration variation parameters at each position, the following distance parameters, the acceleration parameters and the parameter categories of the acceleration variation parameters are determined, for example, the following distance parameters at the first position are emergency following distances, the parameter categories of the acceleration parameters are comfortable acceleration, the parameter categories of the acceleration variation parameters are comfortable acceleration variation, the vehicle-mounted terminal can determine the corresponding scoring mapping relation, obtain the value of the scoring mapping relation based on the road condition information of the first predicted driving path, and obtain the target scores of the following distance parameters, the acceleration parameters and the acceleration variation parameters at the first position after mapping according to the value of the scoring mapping relation and the parameter scores. The other positions are analogized in order and are not described in detail here.
And 305, obtaining respective scoring results of each predicted driving path according to the target scores of each driving related parameter.
Optionally, the vehicle-mounted terminal obtains respective scoring results of each predicted driving path according to the obtained target scores of each driving related parameter. In one possible implementation, the vehicle-mounted terminal sums the target scores at each location on each predicted travel path to obtain a path score on the predicted travel path. For example, the vehicle-mounted terminal sums the target scores of each position, and the final sum result is used as the path score of the predicted driving path. For example, the predicted driving path includes 200 positions, the vehicle terminal finally obtains the target scores of the 200 positions, sums the target scores of the 200 positions, and uses the sum result as the path score of the predicted driving path.
And 306, determining a target path from at least two predicted travel paths according to the scoring results of each predicted travel path.
In one possible implementation manner, after obtaining respective scoring results of each predicted travel path according to each generated predicted travel path, the vehicle-mounted terminal may further perform ranking (such as ascending ranking) according to the respective scoring results of each predicted travel path; taking the predicted travel path with the score arranged at the first position in each predicted travel path as a target path; the vehicle is controlled to travel along the target path. For example, the vehicle-mounted terminal generates 3 predicted travel paths according to the first position and the second position in the navigation route, and after the vehicle-mounted terminal obtains the final scoring results for the 3 predicted travel paths, the vehicle-mounted terminal ranks the predicted travel paths in a sequence from low to high, takes the predicted travel path with the lowest path scoring as a target path, and controls the vehicle to travel according to the target path.
In one possible implementation manner, for each predicted travel path, a respective stability coefficient of each predicted travel path is obtained, the stability coefficient being used to indicate a degree of stability of each predicted travel path; the target path may also be determined jointly in this step in combination with the stationary coefficients. That is, the in-vehicle terminal determines the target path from at least two predicted travel paths based on the respective stability coefficients of the respective predicted travel paths and the respective scoring results of each travel path.
Optionally, after the vehicle-mounted terminal obtains the road condition information of each predicted driving path, the vehicle-mounted terminal may obtain the stability coefficient of the predicted driving path according to the road condition information of the predicted driving path, for example, the larger the path curvature on the predicted driving path is, the smaller the stability coefficient is, the larger the number of driving on the predicted driving path is, the larger the stability coefficient is, the larger the number of curves on the predicted driving path is, the smaller the stability coefficient is, the larger the number of pits on the predicted driving path is, and the smaller the stability coefficient is. The vehicle-mounted terminal determines the stability coefficient of the predicted travel path based on the road condition information, and can multiply the stability coefficient of each predicted travel path with the scoring result of each predicted travel path to obtain the smallest predicted travel path in the multiplication result as the target path.
In summary, for each predicted travel path of the at least two predicted travel paths, at least two travel-related parameters corresponding to the vehicle on each predicted travel path are obtained; for each predicted driving path, determining the parameter category to which at least two driving related parameters belong from preset parameter categories; obtaining respective scoring results of each predicted driving path according to the parameter category to which at least two driving related parameters belong; and determining a target path from at least two predicted travel paths according to the scoring results of each predicted travel path. According to the method and the device, the running related parameters of each predicted running path are obtained, the parameter category of each running related parameter is determined, the running path is scored based on the parameter category, and the target path is selected from each predicted running path, so that the path scoring is finer, and the accuracy of the scoring result of a plurality of running paths is improved.
The following are device embodiments of the present application, which may be used to perform method embodiments of the present application. For details not disclosed in the device embodiments of the present application, please refer to the method embodiments of the present application.
Referring to fig. 5, which shows a block diagram of a target path determining apparatus provided in an exemplary embodiment of the present application, the target path determining apparatus 500 may be applied to a vehicle, and the target path determining apparatus 500 includes:
a parameter obtaining module 501, configured to obtain, for each of at least two predicted travel paths, at least two travel related parameters corresponding to the vehicle on each of the predicted travel paths;
a category determining module 502, configured to determine, for each of the predicted driving paths, a parameter category to which each of the at least two driving related parameters belongs from preset parameter categories;
a scoring module 503, configured to obtain a scoring result of each predicted driving path according to a parameter class to which each of the at least two driving related parameters belongs;
and a path determining module 504, configured to determine a target path from the at least two predicted driving paths according to the scoring result of each predicted driving path.
In summary, for each predicted travel path of the at least two predicted travel paths, at least two travel-related parameters corresponding to the vehicle on each predicted travel path are obtained; for each predicted driving path, determining the parameter category to which at least two driving related parameters belong from preset parameter categories; obtaining respective scoring results of each predicted driving path according to the parameter category to which at least two driving related parameters belong; and determining a target path from at least two predicted travel paths according to the scoring results of each predicted travel path. According to the method and the device, the running related parameters of each predicted running path are obtained, the parameter category of each running related parameter is determined, the running path is scored based on the parameter category, and the target path is selected from each predicted running path, so that the path scoring is finer, and the accuracy of the scoring result of a plurality of running paths is improved.
Optionally, the score acquisition module 503 includes: a first determination unit, a first calculation unit and a first acquisition unit;
the first determining unit is configured to determine a scoring mapping relationship of each driving related parameter according to a parameter class to which each of the at least two driving related parameters belongs;
The first calculation unit is used for calculating the target score of each driving related parameter according to the score mapping relation of each driving related parameter;
the first obtaining unit is configured to obtain respective scoring results of the predicted driving paths according to target scores of the driving related parameters.
Optionally, the apparatus further includes:
the scoring calculation module is used for calculating the parameter score of each driving related parameter according to a preset formula for each driving related parameter before the scoring result of each predicted driving path is obtained according to the parameter category to which each of the at least two driving related parameters belongs;
the first computing unit is used for
And mapping the parameter scores of each driving related parameter according to the score mapping relation of each driving related parameter to obtain the target score of each driving related parameter.
Optionally, the category determining module 502 includes: a second determination unit and a third determination unit;
the second determining unit is used for determining a range interval of the parameter scores according to the parameter scores of the driving related parameters;
the third determining unit is configured to determine, from the preset parameter categories, a parameter category to which each of the at least two driving related parameters belongs, according to the range interval of the parameter score.
Optionally, the preset parameter categories correspond to respective scoring mapping relationships, and mapping intervals of the scoring mapping relationships of the preset parameter categories are different.
Optionally, the driving related parameter includes any one or more of following distance parameter, acceleration parameter and acceleration variation parameter;
the preset parameter categories corresponding to the following distance parameters comprise a first distance category and a second distance category;
the preset parameter categories corresponding to the acceleration parameters comprise a first acceleration category and a second acceleration category;
the preset parameter categories corresponding to the acceleration variation parameters comprise a first variation category and a second variation category.
Optionally, the apparatus further includes:
the coefficient acquisition module is used for acquiring a stability coefficient of each predicted running path for each predicted running path before determining a target path from the at least two predicted running paths according to the scoring result of each predicted running path, wherein the stability coefficient is used for indicating the stability degree of each predicted running path;
the path determining module 504 is further configured to
And determining a target path from the at least two predicted driving paths according to the respective stability coefficients of the predicted driving paths and the respective scoring results of each driving path.
Fig. 6 is a schematic structural diagram of a vehicle-mounted terminal according to an exemplary embodiment of the present application. As shown in fig. 6, the in-vehicle terminal 600 includes a central processing unit (Central Processing Unit, CPU) 601, a system Memory 604 including a random access Memory (Random Access Memory, RAM) 602 and a Read Only Memory (ROM) 603, and a system bus 605 connecting the system Memory 604 and the central processing unit 601. The in-vehicle terminal 600 also includes a basic Input/Output System (I/O) 608, which facilitates the transfer of information between various devices within the computer, and a mass storage device 607 for storing an operating System 612, application programs 613 and other program modules 614.
The basic input/output system 606 includes a display 608 for displaying information and an input device 609, such as a mouse, keyboard, etc., for a user to input information. Wherein the display 608 and the input device 609 are connected to the central processing unit 601 through an input output controller 610 connected to the system bus 605. The basic input/output system 606 may also include an input/output controller 610 for receiving and processing input from a number of other devices, such as a keyboard, mouse, or electronic stylus. Similarly, the input output controller 610 also provides output to a display screen, a printer, or other type of output device.
The mass storage device 607 is connected to the central processing unit 601 through a mass storage controller (not shown) connected to the system bus 605. The mass storage device 607 and its associated computer-readable medium provide non-volatile storage for the in-vehicle terminal 600. That is, the mass storage device 607 may include a computer readable medium (not shown) such as a hard disk or CD-ROM (Compact Disc Read-Only Memory) drive.
The computer readable medium may include computer storage media and communication media. Computer storage media includes volatile and nonvolatile, removable and non-removable media implemented in any method or technology for storage of information such as computer readable instructions, data structures, program modules or other data. Computer storage media includes RAM, ROM, EPROM (Erasable Programmable Read Only Memory), EEPROM (Electrically Erasable Programmable Read-Only Memory), flash Memory or other solid state Memory technology, CD-ROM, DVD (Digital Video Disc, high density digital video disc) or other optical storage, magnetic cassettes, magnetic tape, magnetic disk storage or other magnetic storage devices. Of course, those skilled in the art will recognize that the computer storage medium is not limited to the one described above. The system memory 604 and mass storage device 607 described above may be collectively referred to as memory.
The in-vehicle terminal 600 may be connected to the internet or other network device through a network interface unit 611 connected to the system bus 605.
The memory further includes one or more programs stored in the memory, and the central processing unit 601 implements all or part of the steps of the method provided in the various embodiments of the present application by executing the one or more programs. Alternatively, the vehicle-mounted terminal may be mounted in the vehicle to perform the target path determination method according to the above embodiments.
In the above embodiments, it may be implemented in whole or in part by software, hardware, firmware, or any combination thereof. When implemented in software, may be implemented in whole or in part in the form of a computer program product.
The computer program product includes one or more computer instructions. When loaded and executed on a computer, produces a flow or function in accordance with embodiments of the present application, in whole or in part. The computer may be a general purpose computer, a special purpose computer, a computer network, or other programmable apparatus. The computer instructions may be stored in a computer-readable storage medium or transmitted from one computer-readable storage medium to another computer-readable storage medium, for example, the computer instructions may be transmitted from one website, computer, server, or data center to another website, computer, server, or data center by a wired (e.g., coaxial cable, fiber optic, digital subscriber line (Digital Subscriber Line, DSL)) or wireless (e.g., infrared, wireless, microwave, etc.). The computer readable storage medium may be any available medium that can be stored by a computer or a data storage device such as a server, data center, etc. that contains an integration of one or more available media. The usable medium may be a magnetic medium (e.g., a floppy Disk, a hard Disk, a magnetic tape), an optical medium (e.g., a high-density digital video disc (Digital Video Disc, DVD)), or a semiconductor medium (e.g., a Solid State Disk (SSD)), or the like.
The embodiment of the application also discloses a vehicle, which comprises a vehicle-mounted terminal, wherein the vehicle-mounted terminal comprises a memory and a processor, the memory stores a computer program, and when the computer program is executed by the processor, the processor is enabled to realize the method for determining the target path in the method embodiment. Alternatively, the terminal may be a vehicle-mounted terminal in this embodiment.
The application embodiment also discloses a computer readable storage medium storing a computer program, wherein the computer program realizes the method in the method embodiment when being executed by a processor.
It should be appreciated that reference throughout this specification to "one embodiment" or "an embodiment" means that a particular feature, structure or characteristic described in connection with the embodiment is included in at least one embodiment of the present application. Thus, the appearances of the phrases "in one embodiment" or "in an embodiment" in various places throughout this specification are not necessarily all referring to the same embodiment. Furthermore, the particular features, structures, or characteristics may be combined in any suitable manner in one or more embodiments. Those skilled in the art will also appreciate that the embodiments described in the specification are all alternative embodiments and that the acts and modules referred to are not necessarily required in the present application.
In various embodiments of the present application, it should be understood that the size of the sequence numbers of the above processes does not mean that the execution sequence of the processes is necessarily sequential, and the execution sequence of the processes should be determined by the functions and internal logic thereof, and should not constitute any limitation on the implementation process of the embodiments of the present application.
The units described above as separate components may or may not be physically separate, and components shown as units may or may not be physical units, may be located in one place, or may be distributed over a plurality of network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the embodiment.
In addition, each functional unit in the embodiments of the present application may be integrated in one processing unit, or each unit may exist alone physically, or two or more units may be integrated in one unit. The integrated units may be implemented in hardware or in software functional units.
The integrated units described above, if implemented in the form of software functional units and sold or used as stand-alone products, may be stored in a computer-accessible memory. Based on such understanding, the technical solution of the present application, or a part contributing to the prior art or all or part of the technical solution, may be embodied in the form of a software product stored in a memory, including several requests for a computer device (which may be a personal computer, a server or a network device, etc., in particular may be a processor in the computer device) to perform part or all of the steps of the above-mentioned method of the various embodiments of the present application.
Those of ordinary skill in the art will appreciate that all or part of the steps of the various methods of the above embodiments may be implemented by a program that instructs associated hardware, the program may be stored in a computer readable storage medium including Read-Only Memory (ROM), random access Memory (Random Access Memory, RAM), programmable Read-Only Memory (Programmable Read-Only Memory, PROM), erasable programmable Read-Only Memory (Erasable Programmable Read Only Memory, EPROM), one-time programmable Read-Only Memory (OTPROM), electrically erasable programmable Read-Only Memory (EEPROM), compact disc Read-Only Memory (Compact Disc Read-Only Memory, CD-ROM) or other optical disk Memory, magnetic disk Memory, tape Memory, or any other medium that can be used for carrying or storing data that is readable by a computer.
The above description has been given by way of example to a method, apparatus, vehicle and storage medium for determining a target path disclosed in the embodiments of the present application, and examples are applied herein to illustrate the principles and embodiments of the present application, where the above description of the embodiments is only for helping to understand the method and core ideas of the present application; meanwhile, as those skilled in the art will have variations in embodiments and application ranges based on the ideas of the present application, the present disclosure should not be construed as limiting the present application in view of the above.
Claims (10)
1. A method of determining a target path, the method being applied to a vehicle, the method comprising:
for each predicted travel path in at least two predicted travel paths, acquiring at least two travel-related parameters corresponding to each position of the vehicle on each predicted travel path; the respective positions are related to coordinate scales of a predicted travel path generated by the vehicle;
for each predicted driving path, determining the parameter category to which each of the at least two driving related parameters belongs from preset parameter categories, wherein each preset parameter category comprises at least two parameter categories;
obtaining respective scoring results of the predicted driving paths according to the parameter categories to which the at least two driving related parameters belong;
and determining a target path from the at least two predicted travel paths according to the scoring results of the predicted travel paths.
2. The method according to claim 1, wherein the obtaining the scoring result of each predicted travel path according to the parameter class to which each of the at least two travel-related parameters belongs includes:
Determining a scoring mapping relation of each driving related parameter according to the parameter category of each driving related parameter;
calculating target scores of the running related parameters according to the score mapping relation of the running related parameters;
and obtaining respective scoring results of the predicted driving paths according to the target scoring of each driving related parameter.
3. The method according to claim 2, further comprising, before the obtaining the scoring result of each of the predicted travel paths according to the parameter class to which each of the at least two travel-related parameters belongs:
calculating a parameter score of each driving related parameter according to a preset formula for each driving related parameter;
the calculating the target score of each driving related parameter according to the score mapping relation of each driving related parameter comprises the following steps:
and mapping the parameter scores of each driving related parameter according to the score mapping relation of each driving related parameter to obtain the target score of each driving related parameter.
4. A method according to claim 3, wherein said determining, for each of said predicted travel paths, a parameter class to which each of said at least two travel-related parameters belongs from among preset parameter classes, comprises:
Determining a range interval of the parameter scores according to the respective parameter scores of the driving related parameters;
and determining the parameter category to which each of the at least two driving related parameters belongs from the preset parameter categories according to the range interval of the parameter score.
5. The method of claim 2, wherein the preset parameter classes correspond to respective scoring mappings, and wherein the mapping intervals of the respective scoring mappings of the preset parameter classes are different.
6. The method according to any one of claims 1 to 5, wherein the travel-related parameter includes any one or more of a following distance parameter, an acceleration parameter, and an acceleration variation parameter;
the preset parameter categories corresponding to the following distance parameters comprise a first distance category and a second distance category;
the preset parameter categories corresponding to the acceleration parameters comprise a first acceleration category and a second acceleration category;
the preset parameter categories corresponding to the acceleration variation parameters comprise a first variation category and a second variation category.
7. The method according to any one of claims 1 to 5, further comprising, before said determining a target path from said at least two predicted travel paths based on the respective scoring results for each of said predicted travel paths:
For each predicted travel path, acquiring a respective stability coefficient of each predicted travel path, wherein the stability coefficient is used for indicating the stability degree of each predicted travel path;
the determining a target path from the at least two predicted driving paths according to the scoring results of the respective predicted driving paths comprises the following steps:
and determining a target path from the at least two predicted driving paths according to the respective stability coefficients of the predicted driving paths and the respective scoring results of each driving path.
8. A target path determination apparatus, the apparatus being applied to a vehicle, the apparatus comprising:
the parameter acquisition module is used for acquiring at least two running related parameters corresponding to each position of the vehicle on each predicted running path for each predicted running path in at least two predicted running paths; the respective positions are related to coordinate scales of a predicted travel path generated by the vehicle;
the category determining module is used for determining, for each predicted driving path, a parameter category to which each of the at least two driving related parameters belongs from preset parameter categories, wherein each preset parameter category respectively comprises at least two parameter categories;
The scoring acquisition module is used for acquiring the scoring result of each predicted driving path according to the parameter category to which each of the at least two driving related parameters belongs;
and the path determining module is used for determining a target path from the at least two predicted driving paths according to the scoring results of each predicted driving path.
9. A vehicle comprising a vehicle-mounted terminal, the vehicle-mounted terminal comprising a memory and a processor, the memory storing a computer program which, when executed by the processor, causes the processor to implement the method of determining a target path as claimed in any one of claims 1 to 7.
10. A computer readable storage medium, on which a computer program is stored, characterized in that the computer program, when being executed by a processor, implements a method of determining a target path according to any one of claims 1 to 7.
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