CN114291111A - Target path determination method, target path determination device, vehicle and storage medium - Google Patents
Target path determination method, target path determination device, vehicle and storage medium Download PDFInfo
- Publication number
- CN114291111A CN114291111A CN202111645047.XA CN202111645047A CN114291111A CN 114291111 A CN114291111 A CN 114291111A CN 202111645047 A CN202111645047 A CN 202111645047A CN 114291111 A CN114291111 A CN 114291111A
- Authority
- CN
- China
- Prior art keywords
- parameter
- driving
- predicted
- path
- vehicle
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Granted
Links
- 238000000034 method Methods 0.000 title claims abstract description 75
- 230000001133 acceleration Effects 0.000 claims description 69
- 238000013507 mapping Methods 0.000 claims description 67
- 238000004590 computer program Methods 0.000 claims description 12
- 238000004364 calculation method Methods 0.000 claims description 9
- 238000012545 processing Methods 0.000 description 7
- 238000004891 communication Methods 0.000 description 6
- 238000010586 diagram Methods 0.000 description 6
- 230000006870 function Effects 0.000 description 5
- 238000005516 engineering process Methods 0.000 description 4
- 230000003287 optical effect Effects 0.000 description 2
- 239000007787 solid Substances 0.000 description 2
- 230000001174 ascending effect Effects 0.000 description 1
- 230000009286 beneficial effect Effects 0.000 description 1
- 238000013500 data storage Methods 0.000 description 1
- 238000011161 development Methods 0.000 description 1
- 230000000694 effects Effects 0.000 description 1
- 238000011156 evaluation Methods 0.000 description 1
- 239000000835 fiber Substances 0.000 description 1
- 238000012423 maintenance Methods 0.000 description 1
- 239000004065 semiconductor Substances 0.000 description 1
- 238000012546 transfer Methods 0.000 description 1
Images
Landscapes
- Traffic Control Systems (AREA)
- Navigation (AREA)
Abstract
The application discloses a method and a device for determining a target path, a vehicle and a storage medium, and belongs to the technical field of vehicle control. Applied to a vehicle, the method comprising: for each predicted driving path in the at least two predicted driving paths, acquiring at least two driving related parameters corresponding to the vehicle on each predicted driving path; for each predicted driving path, determining the parameter types of at least two driving related parameters from preset parameter types; obtaining respective scoring results of the predicted driving paths according to the parameter types of the at least two driving related parameters; and determining a target path from at least two predicted driving paths according to the respective grading results of the predicted driving paths. According to the method and the device, the parameter types of the driving related parameters are determined, the driving paths are scored based on the parameter types, the target paths are selected from the predicted driving paths, and the accuracy of scoring results of the multiple driving paths is improved.
Description
Technical Field
The present disclosure relates to the field of vehicle control technologies, and in particular, to a method and an apparatus for determining a target route, a vehicle, and a storage medium.
Background
With the continuous development of scientific technology, various vehicles in real life become indispensable vehicles for users to go out, and it is very important to plan the driving path of the vehicle in the driving process.
Currently, in various vehicles, the vehicle-mounted terminal generally has an automatic driving function, and in route planning of automatic driving, the vehicle-mounted terminal can automatically generate a plurality of different driving paths and set different vehicle control parameters, such as speed, acceleration and the like, in the different driving paths so as to control the vehicle to drive. When the vehicle-mounted terminal selects a proper path from all target paths, the whole path is often directly scored according to parameter values of control parameters in different paths, and the parameters adopted in the process of scoring the driving paths are single, so that the problem that the accuracy of the result of scoring a plurality of driving paths is low is caused.
Disclosure of Invention
The embodiment of the application provides a method and a device for determining a target path, a vehicle and a storage medium, which can improve the efficiency of pushing vehicle related information to a vehicle unpurchased user.
In one aspect, 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 running path of at least two predicted running paths, acquiring at least two running related parameters corresponding to the vehicle on each predicted running path;
for each predicted driving path, determining a parameter category to which the at least two driving related parameters belong from preset parameter categories;
obtaining respective scoring results of the predicted driving paths according to the parameter types of the at least two driving related parameters;
and determining a target path from the at least two predicted driving paths according to the respective grading result of each predicted driving path.
Optionally, the obtaining, according to the parameter category to which the at least two driving related parameters belong, a scoring result of each of the predicted driving paths includes:
determining a grading mapping relation of each driving related parameter according to the parameter type to which each of the at least two driving related parameters belongs;
calculating the target score of each driving related parameter according to the score mapping relation of each driving related parameter;
and obtaining respective scoring results of the predicted driving paths according to the target scores of the driving related parameters.
Optionally, before obtaining the scoring result of each predicted travel path according to the parameter category to which each of the at least two travel related parameters belongs, the method further includes:
calculating the 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 relevant parameter according to the score mapping relation of each driving relevant parameter comprises the following steps:
and mapping 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.
Optionally, for each predicted travel path, determining a parameter category to which each of the at least two travel-related parameters belongs from preset parameter categories, including:
determining a range interval of the parameter score according to the respective parameter score of each driving related parameter;
and determining the parameter types to which the at least two driving related parameters belong from the preset parameter types according to the range interval of the parameter scores.
Optionally, the preset parameter categories correspond to respective score mapping relationships, and mapping intervals of the respective score mapping relationships of the preset parameter categories are different.
Optionally, the driving related parameter includes any one or more of a following distance parameter, an acceleration parameter, and an acceleration variation parameter;
the preset parameter types corresponding to the following distance parameters comprise a first distance type and a second distance type;
the preset parameter types corresponding to the acceleration parameters comprise a first acceleration type and a second acceleration type;
the preset parameter types corresponding to the acceleration variation parameters comprise a first variation type and a second variation type.
Optionally, before determining the target route from the at least two predicted travel routes according to the respective scoring results of the predicted travel routes, the method further includes:
for each predicted driving path, obtaining a respective stability coefficient of each predicted driving path, wherein the stability coefficient is used for indicating the stability degree of each predicted driving path;
the determining a target route from the at least two predicted travel routes according to the respective scoring results of the predicted travel routes includes:
and determining a target path from the at least two predicted driving paths according to the respective stability coefficient of each predicted driving path and the respective grading result of each driving path.
In another aspect, an embodiment of the present application provides an apparatus for determining a target path, where the apparatus is applied to a vehicle, and the apparatus includes:
the system comprises a parameter acquisition module, a parameter calculation module and a parameter calculation module, wherein the parameter acquisition module is used for acquiring at least two driving related parameters corresponding to a vehicle on each predicted driving path for each predicted driving path in at least two predicted driving paths;
the category determination module is used for determining the parameter categories to which the at least two driving related parameters belong from preset parameter categories for each predicted driving path;
the score obtaining module is used for obtaining a score result of each predicted driving path according to the parameter type to which the at least two driving related parameters belong;
and the path determining module is used for determining a target path from the at least two predicted running paths according to the respective grading result of each predicted running path.
In another aspect, an embodiment of the present application provides a vehicle, where the vehicle includes a vehicle-mounted terminal, where the vehicle-mounted terminal includes a memory and a processor, where the memory stores a computer program, and the computer program, when executed by the processor, causes the processor to implement the method for determining a target path according to the above aspect and any one of the optional implementations.
In another aspect, the present application provides a computer-readable storage medium, on which a computer program is stored, and the computer program, when executed by a processor, implements the method for determining a target path according to the another aspect and the optional embodiments thereof.
The technical scheme provided by the embodiment of the application can at least comprise the following beneficial effects:
for each predicted driving path in the at least two predicted driving paths, acquiring at least two driving related parameters corresponding to the vehicle on each predicted driving path; for each predicted driving path, determining the parameter types of at least two driving related parameters from preset parameter types; obtaining respective scoring results of the predicted driving paths according to the parameter types of the at least two driving related parameters; and determining a target path from at least two predicted driving paths according to the respective grading results of the predicted driving paths. According to the method and the device, the driving related parameters of each predicted driving path are obtained, the parameter types to which the driving related parameters belong are determined, the driving paths are scored based on the parameter types, and the target path is selected from the predicted driving paths, so that path scoring is more detailed, and the result accuracy of scoring a plurality of driving paths is improved.
Drawings
In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the drawings needed to be used 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 it is obvious for those skilled in the art that other drawings can be obtained according to these drawings without creative efforts.
FIG. 1 is an exemplary illustration of different travel paths generated by a vehicle in accordance with an exemplary embodiment of the present application;
FIG. 2 is a flowchart of a method for determining a target path according to an exemplary embodiment of the present application;
FIG. 3 is a flowchart of a method for determining a target path according to an exemplary embodiment of the present application;
FIG. 4 is a graphical illustration of a parameter score and a target score according to an exemplary embodiment of the present application;
fig. 5 is a block diagram illustrating a structure of a target path determining apparatus 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 the exemplary embodiments, examples of which are illustrated in the accompanying drawings. When the following description refers to the accompanying drawings, like numbers in different drawings represent the same or similar elements unless otherwise indicated. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with the present application. Rather, they are merely examples of apparatus and methods consistent with certain aspects of the present application, as detailed in the appended claims.
Reference herein to "a plurality" means two or more. "and/or" describes the association relationship of the associated objects, meaning that there may be three relationships, e.g., a and/or B, which may mean: a exists alone, A and B exist simultaneously, and B exists alone. The character "/" generally indicates that the former and latter associated objects are in 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 different objects, and are not used for describing a specific order. The terms "comprises," "comprising," and "having," and any variations thereof, of the embodiments of the present application, 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, but may include other steps or elements not expressly listed or inherent to such process, method, article, or apparatus.
The scheme provided by the application can be used in a real scene of viewing some vehicle related information by using the terminal in daily life, and for convenience of understanding, the application architecture related to the embodiment of the application is first briefly introduced below.
In daily life, vehicles have been widely used as indispensable vehicles. The vehicle is required to select a running path in the running process. At present, various vehicles have an automatic driving function, and in the automatic driving process, the vehicles need to plan a driving path by themselves and select the driving path to drive. For example, please refer to fig. 1, which illustrates an exemplary diagram of different travel paths generated by a vehicle according to an exemplary embodiment of the present application. As shown in fig. 1, the current position 101, the other positions 102, and the generated respective travel paths 103 are included therein. In a vehicle having an automatic driving function, the in-vehicle terminal may generate each travel path 103 based on the vehicle current position 101 and other positions 102.
Optionally, the vehicle-mounted terminal may be connected to the server through a communication network during the automatic driving process. Optionally, the communication network may be a wired network or a wireless network, optionally, the wireless network or wired network using standard communication techniques and/or protocols. The Network is typically the internet, but may be any Network including, but not limited to, any combination of Local Area Networks (LANs), Metropolitan Area Networks (MANs), Wide Area networks (MANs), mobile, wireline or wireless networks, private networks, or virtual private networks. In some embodiments, data exchanged over a network is represented using techniques and/or formats including Hypertext Mark-up Language (HTML), 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 (SSL), Transport Layer Security (TLS), Virtual Private Network (VPN), Internet Protocol Security (IPsec). 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 a service for an application installed in the vehicle. The server can be a server, or a plurality of 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.
Currently, the driving path generated by the vehicle-mounted terminal in the above manner is planned based on different positions, and when the vehicle moves from the position a to the position B, the vehicle usually adjusts control parameters of the vehicle, such as speed, acceleration and the like, based on the planned driving path. In general, the driving conditions of the vehicle are definitely different when the vehicle drives according to different planned driving paths, and it is important to determine a best driving path from the various planned driving paths. For example, the vehicle will generally score the entire route according to the parameter values of the control parameters in different routes, and select the route with the best driving effect (e.g., the best comfort level, etc.). In the process, each generated driving path is often directly scored based on the parameter value of the control parameter, the adopted parameter is single, the criterion of evaluation is not comprehensive enough, and the problem that the accuracy of scoring the driving path is low exists.
In order to improve the accuracy of a vehicle in scoring a generated driving path and improve the determination efficiency of the driving path, the application provides a solution, the parameter types of at least two driving related parameters corresponding to each predicted driving path are preset, the parameter types of the driving related parameters are respectively determined, and the scoring results of the predicted driving paths are obtained according to the parameter types of the driving related parameters, so that the driving related parameters are classified, the driving related parameters and the respective types are combined for scoring, and the flexibility of scoring the driving paths is improved.
Referring to fig. 2, a 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 scene shown in fig. 1, and is executed by the vehicle-mounted terminal of the vehicle in the scene. As shown in fig. 2, the method for determining the target path may include the following steps.
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 a navigation system, a vehicle generates a driving route according to a current position and a destination, and during driving of the vehicle, the vehicle can further continue to generate at least two predicted driving paths according to the current position of the vehicle and a position to be driven. Optionally, the position to be traveled may be a position 2 kilometers before the current position of the vehicle in the driving route, or may be a road segment within 10 seconds to be traveled by the vehicle calculated according to the current vehicle speed of the vehicle.
Optionally, the vehicle acquires the driving related parameter on each predicted driving path of the at least two predicted driving paths according to the at least two generated predicted driving paths. The driving-related parameter may be any one or more of a following distance parameter, an acceleration variation parameter, a driving speed parameter, and the like. In the present application, the vehicle may obtain at least two driving related parameters for each predicted driving path. Alternatively, the vehicle may obtain at least two travel related parameters for each position in the predicted travel path. For example, for a predicted travel path, data such as a following distance parameter, an acceleration parameter, and an acceleration variation parameter are provided at each position on the predicted travel path, and the vehicle-mounted terminal can obtain a travel-related parameter at each position. Each position on the predicted travel path is related to the coordinate scale generated by the in-vehicle terminal, for example, if the coordinate scale in the coordinate system generated by the in-vehicle terminal is 1 cm, the predicted travel path is a position every 1 cm on each coordinate axis in the coordinate system.
Optionally, in the present application, each preset parameter category includes at least two parameter categories. For example, the preset parameter categories respectively include a parameter category one and a parameter category two, 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 the parameter categories may be more, for example, three or four, and the specific number may be designed based on actual requirements, and is not described herein again.
In one possible implementation, the on-board terminals may determine the respective parameter category according to the respective parameter value of each driving-related parameter. For example, in the prediction of the first driving route, each position acquires a following distance parameter, the vehicle-mounted terminal compares a parameter value (for example, the following distance is 30 meters) of the following distance parameter with a preset following threshold (for example, 40 meters), 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 a second parameter type, 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 a first parameter type.
Alternatively, the in-vehicle terminal may determine the parameter score of each travel-related parameter according to the parameter value of each travel-related parameter. For example, in the first predicted driving path, the following distance parameter is acquired 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. Wherein the preset formula can be preset in the vehicle-mounted terminal by a developer. According to the comparison of the parameter score and the preset score threshold, when the parameter score of the vehicle following distance parameter is larger than the preset score threshold, the vehicle-mounted terminal determines that the parameter category of the vehicle following distance parameter at the position is a second parameter category, and when the parameter score of the vehicle following distance parameter is not larger than the preset score threshold, the vehicle-mounted terminal determines that the parameter category of the vehicle following distance parameter at the position is a first parameter category.
The vehicle-mounted terminal calculates a path score for each predicted driving path based on the parameter type of each of the at least two driving related parameters, and obtains a score result of each predicted driving path.
And step 204, determining a target route from at least two predicted driving routes according to the respective scoring results of the predicted driving routes.
Optionally, after the vehicle-mounted terminal scores the generated predicted travel paths, the vehicle-mounted terminal may also sort the generated predicted travel paths according to their respective path scores; taking the predicted driving path with the path score ranked first in each predicted driving path as a target path; and controlling the vehicle to run according to 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 types of at least two driving related parameters from preset parameter types; obtaining respective scoring results of the predicted driving paths according to the parameter types of the at least two driving related parameters; and determining a target path from at least two predicted driving paths according to the respective grading results of the predicted driving paths. According to the method and the device, the driving related parameters of each predicted driving path are obtained, the parameter types to which the driving related parameters belong are determined, the driving paths are scored based on the parameter types, and the target path is selected from the predicted driving paths, so that path scoring is more detailed, and the result accuracy of scoring a plurality of driving paths is improved.
In a possible implementation manner, each of the preset parameter categories further corresponds to a respective score mapping relationship, and the score mapping relationship is used for mapping the obtained parameter scores into a corresponding numerical range, so that the diversity of scoring the driving paths is improved.
Referring to fig. 3, a 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 scene shown in fig. 1, and is executed by the vehicle-mounted terminal of the vehicle in the scene. As shown in fig. 3, the method for determining the target path may include the following steps.
Optionally, the vehicle-mounted terminal may generate at least two predicted travel paths based on a current position of the vehicle and a target position during the vehicle traveling, where the target position is any one position in the vehicle traveling direction. For example, a navigation system in the vehicle may be started during the driving of the vehicle, and after the user inputs the destination location, a navigation route may be generated, and the user may control the vehicle to drive in the navigation route. During the running process 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 the target position according to a preset distance threshold, and the vehicle-mounted terminal determines a position in the driving direction, which is the distance threshold from the current position, 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 also determine the target position according to a preset time period to be traveled during the traveling process, for example, the preset time period set by the developer or the operation and maintenance worker is 10 seconds, and the vehicle-mounted terminal may obtain the position to which the vehicle travels after 10 seconds during the traveling process of the vehicle based on the current speed of the vehicle, and take the position as the target position. For example, if the current vehicle speed is 20 meters per second, the vehicle-mounted terminal calculates that the target position is a position 200 meters away from the current position in the vehicle traveling direction based on the current vehicle speed and a preset time period, and takes 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 control the vehicle to run according to one of the running paths.
Optionally, the vehicle-mounted terminal obtains at least two driving related parameters corresponding to 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 a following distance parameter, an acceleration variation parameter, a driving speed parameter, and the like.
Optionally, each preset parameter category includes at least two parameter categories. For example, the driving-related parameter includes any one or more of a following distance parameter, an acceleration parameter, and an acceleration variation parameter; in the preset parameter categories, the preset parameter categories corresponding to the following distance parameters comprise a first distance category and a second distance category; the preset parameter types corresponding to the acceleration parameters comprise a first acceleration type and a second acceleration type; the preset parameter types corresponding to the acceleration variation parameters include a first variation type and a second variation type. 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 variation category is a comfort variation category, and the second variation category is a non-comfort variation category.
In one possible implementation, the vehicle-mounted terminal may determine a parameter class to which each of the at least two driving-related parameters belongs from preset parameter classes as follows. For example, the vehicle-mounted terminal calculates the parameter score of each driving related parameter according to a preset formula; the vehicle-mounted terminal determines a parameter scoring range interval according to the respective parameter scoring of each driving related parameter; and determining the parameter types of the at least two driving related parameters from the preset parameter types according to the range interval of the parameter scores.
Alternatively, the preset formula of each of the driving-related parameters is different. For example, for the following distance parameter, the acceleration parameter, and the acceleration variation parameter, the following distance parameter may be calculated as follows: the following distance cost is C1 (a1/a2), wherein a1 is a preset distance threshold of the following distance, and a2 is the actually acquired following distance; the calculation formula of the acceleration parameter may be as follows: acceleration cost ═ C2 (B1-B2)2Wherein, B1 is a preset acceleration threshold, and B2 is an actually acquired acceleration; the calculation formula of the first variation parameter may be as follows: first variation cost ═ C3 ═ D1-D22Where D1 is a preset first variation threshold, and D2 is a variation of acceleration derived from an actually acquired acceleration. Wherein C1, C2 and C3 are constants in respective formulas and are detected by developers according to experienceAnd (4) obtaining.
Optionally, the vehicle-mounted terminal may obtain at least two driving related parameters for each position in the predicted driving path. For example, for any one predicted travel path, data such as a following distance parameter, an acceleration parameter, and an acceleration variation parameter are provided at each position on the predicted travel path, and the vehicle-mounted terminal can acquire the travel-related parameters at each position. Each position on the predicted travel path is related to the coordinate scale generated by the in-vehicle terminal, for example, if the coordinate scale in the coordinate system generated by the in-vehicle terminal is 1 cm, 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 estimation and calculation. That is, in the present application, the in-vehicle terminal estimates the following distance, the acceleration, the variation of the acceleration, and the like of the vehicle at each position on the predicted travel path, thereby obtaining the following distance parameter, the acceleration parameter, and the acceleration variation parameter for each position, calculates the parameter score of the travel-related parameter for each position according to the above-mentioned formulas, and determines each parameter type.
Optionally, after the vehicle-mounted terminal obtains the respective parameter score of each driving related parameter, the vehicle-mounted terminal may determine the range interval of the parameter score according to the respective parameter score of each driving related parameter; and determining the parameter types of the at least two driving related parameters from the preset parameter types according to the range interval of the parameter scores. For example, for any one of the driving-related parameters, the present application may provide a table of correspondence between the parameter score of the driving-related parameter and the preset parameter category, please refer to table 1, which shows a table of correspondence between the parameter score and the preset parameter category according to an exemplary embodiment of the present application.
| Parameter scoring | Class of preset parameters |
| Range interval one | Parameter class one |
| Range interval two | Parameter class two |
| Interval of range three | Parameter class three |
| …… | …… |
TABLE 1
As shown in table 1, each parameter score range corresponds to a preset parameter category, and the parameter score 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 the range interval of the parameter score based on the parameter score obtained through calculation, query the corresponding relation table and obtain the corresponding parameter category from the corresponding relation table.
In one possible implementation, the vehicle-mounted terminal may determine a parameter class to which each of the at least two driving-related parameters belongs from preset parameter classes as follows. For example, the in-vehicle terminal divides each of the travel-related parameters according to the parameter value of the travel-related parameter. 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 type of the following distance parameter at the position is the second parameter type, 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 first parameter type. Other driving-related parameters are similar and will not be described in detail herein.
Optionally, in the application, the preset parameter categories correspond to respective score mapping relationships, and the vehicle-mounted terminal may determine the score mapping relationships of the respective driving related parameters based on the acquired parameter categories to which the driving related parameters belong. The score mapping relationship is used for mapping the parameter scores of the driving related parameters to obtain the corresponding numerical value range.
Optionally, the mapping intervals of the score 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 variation category is a comfort variation category, and the second variation category is a non-comfort variation category. For example, the score mapping relationship corresponding to the emergency following distance is a score first mapping relationship, the score mapping relationship corresponding to the non-emergency following distance is a score second mapping relationship, the score first mapping relationship may be a value between 2 and 10, and the score second mapping relationship may be a value between 0 and 1. For example, the score mapping relationship corresponding to the comfortable acceleration is a score mapping relationship three, the score mapping relationship corresponding to the non-comfortable acceleration is a score mapping relationship four, the score mapping relationship three may be a value between 0 and 1, and the score mapping relationship four may be a value between 1 and 2. For example, the score mapping relationship corresponding to the comfortable acceleration variation is a score mapping relationship five, the score mapping relationship corresponding to the non-comfortable acceleration variation is a score mapping relationship six, the score mapping relationship five may be a value between 0 and 1, and the score mapping relationship six may be a value between 1 and 2.
And step 304, calculating target scores of the driving related parameters according to the score mapping relation of the driving related parameters.
Optionally, after determining the score mapping relationship 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 relationship of each driving related parameter, so as to obtain the target score of each driving related parameter. For example, please refer to fig. 4, which shows 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, a 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 and the device, 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 expanded, and the influence degree of the driving related parameter on the total score of the predicted driving path is improved.
For example, taking the example that the driving related parameter is the following distance parameter, the vehicle-mounted terminal determines that the parameter class to which the following distance parameter belongs is the emergency following distance according to the following distance parameter at a certain position, determines that the scoring mapping relationship is the scoring mapping relationship one, and the vehicle-mounted terminal performs mapping by taking a value from 2 to 10 (for example, taking 2) according to the scoring mapping relationship, and multiplies the parameter score by the value taken from 2 to 10, thereby obtaining the target score of the parameter score of the following distance parameter after mapping. And the rest parameters are analogized in turn, and are not described in detail herein.
Optionally, the score mapping relationship may also be used to indicate the importance degree of the driving related parameter at a position corresponding to the driving related parameter in the predicted driving path. For example, in predicting the driving path, the vehicle-mounted terminal may determine a value in the score mapping relationship based on different traffic information. For example, the vehicle-mounted terminal acquires the generated road condition information of each predicted driving path, and determines a value in the score mapping relationship based on the road condition information. Alternatively, the road condition information may be at least one of a path curvature on the predicted travel path, a number of vehicles on the predicted travel path, a number of curves on the predicted travel path, and a number of holes on the predicted travel path. For example, the value of the on-vehicle terminal taken from 2 to 10 on the predicted travel path with a large number of curves and dimples is larger than the value of the on-vehicle terminal taken from 2 to 10 on the predicted travel path with a small number of curves and dimples.
For example, for the first predicted driving path, after the vehicle-mounted terminal acquires the parameter scores of the following distance parameter, the acceleration parameter and the acceleration variation parameter at each position, and determines the following distance parameter, the acceleration parameter, the parameter category of the acceleration variation parameter, such as, the parameter type of the vehicle following distance parameter at the first position is the emergency vehicle following distance, the parameter type of the acceleration parameter is the comfortable acceleration, the parameter type of the acceleration variation parameter is the comfortable acceleration variation, the vehicle-mounted terminal can determine the corresponding grading mapping relation, and acquiring a value of a score mapping relation based on the road condition information of the predicted driving path I, and acquiring a target score of a following distance parameter, an acceleration parameter and an acceleration variation parameter at the first position after mapping according to the value of the score mapping relation and the parameter score. And the rest positions are analogized in turn, and the description is omitted here.
And 305, acquiring respective scoring results of the predicted driving paths according to the target scores of the driving related parameters.
Optionally, the vehicle-mounted terminal obtains respective scoring results of each predicted driving path according to the obtained target scoring of each driving related parameter. In one possible implementation manner, the vehicle-mounted terminal sums the target scores at each position on each predicted driving path to obtain the path score on the predicted driving 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 travel path. For example, the predicted travel path includes 200 positions, the vehicle-mounted terminal finally acquires 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 travel path.
And step 306, determining a target path from at least two predicted driving paths according to the respective scoring results of the predicted driving paths.
In a possible implementation manner, after the vehicle-mounted terminal obtains the respective scoring results of each predicted travel path according to each generated predicted travel path, the vehicle-mounted terminal may also perform sorting (for example, ascending sorting) according to the respective scoring results of each predicted travel path; taking the predicted driving path with the score ranked first in each predicted driving path as a target path; and controlling the vehicle to run according to 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, the vehicle-mounted terminal respectively acquires the final scoring results of the 3 predicted travel paths, then sorts the 3 predicted travel paths in the descending order, takes the predicted travel path with the lowest path score as the target path, and controls the vehicle to travel according to the target path.
In a possible implementation manner, for each predicted driving path, obtaining a respective stability coefficient of each predicted driving path, wherein the stability coefficient is used for indicating the stability degree of each predicted driving path; the target path can be determined in combination with the stationary coefficient in the step. That is, the vehicle-mounted terminal determines the target route from the at least two predicted travel routes according to the respective stability coefficients of the respective predicted travel routes and the respective scoring results of each travel route.
Optionally, after obtaining the road condition information of each predicted travel path, the vehicle-mounted terminal may obtain a stability coefficient of the predicted travel path according to the road condition information of the predicted travel path, for example, the larger the curvature of the path on the predicted travel path is, the smaller the stability coefficient is, the larger the number of vehicles on the predicted travel path is, the larger the stability coefficient is, the larger the number of curves on the predicted travel path is, the smaller the stability coefficient is, the larger the number of potholes on the predicted travel path is, and the smaller the stability coefficient is. The vehicle-mounted terminal determines the stability coefficient of the predicted driving path based on the road condition information, and can multiply the stability coefficient of each predicted driving path with the grading result of each predicted driving path to obtain the smallest predicted driving path in the multiplying results 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 types of at least two driving related parameters from preset parameter types; obtaining respective scoring results of the predicted driving paths according to the parameter types of the at least two driving related parameters; and determining a target path from at least two predicted driving paths according to the respective grading results of the predicted driving paths. According to the method and the device, the driving related parameters of each predicted driving path are obtained, the parameter types to which the driving related parameters belong are determined, the driving paths are scored based on the parameter types, and the target path is selected from the predicted driving paths, so that path scoring is more detailed, and the result accuracy of scoring a plurality of driving paths is improved.
The following are embodiments of the apparatus of the present application that may be used to perform embodiments of the method of the present application. For details which are not disclosed in the embodiments of the apparatus of the present application, reference is made to the embodiments of the method of the present application.
Referring to fig. 5, a block diagram of a target route determining apparatus 500 according to an exemplary embodiment of the present application is shown, where the target route determining apparatus 500 may be applied to a vehicle, and the target route determining apparatus 500 includes:
a parameter obtaining module 501, configured to obtain, for each predicted travel path of at least two predicted travel paths, at least two travel related parameters corresponding to the vehicle on each predicted travel path;
a category determining module 502, configured to determine, for each predicted driving path, a parameter category to which each of the at least two driving related parameters belongs from preset parameter categories;
a score obtaining module 503, configured to obtain a score result of each predicted driving path according to a parameter type to which each of the at least two driving related parameters belongs;
a path determining module 504, configured to determine a target path from the at least two predicted travel paths according to respective scoring results of the predicted travel paths.
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 types of at least two driving related parameters from preset parameter types; obtaining respective scoring results of the predicted driving paths according to the parameter types of the at least two driving related parameters; and determining a target path from at least two predicted driving paths according to the respective grading results of the predicted driving paths. According to the method and the device, the driving related parameters of each predicted driving path are obtained, the parameter types to which the driving related parameters belong are determined, the driving paths are scored based on the parameter types, and the target path is selected from the predicted driving paths, so that path scoring is more detailed, and the result accuracy of scoring a plurality of driving paths is improved.
Optionally, the score obtaining module 503 includes: a first determining unit, a first calculating unit and a first obtaining unit;
the first determining unit is used for determining the grade mapping relation of each driving related parameter according to the parameter category to which each of the at least two driving related parameters belongs;
the first calculating unit is used for calculating the target scores of the driving related parameters according to the score mapping relation of the driving related parameters;
the first obtaining unit is configured to obtain a scoring result of each predicted travel path according to a target score of each travel-related parameter.
Optionally, the apparatus further comprises:
the score calculation module is used for calculating the parameter score of each driving related parameter according to a preset formula before obtaining the score result of each predicted driving path according to the parameter category to which each driving related parameter belongs;
the first computing unit is used for
And mapping 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.
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 score according to the respective parameter score of each driving related parameter;
the third determining unit is configured to determine, according to the range interval of the parameter score, a parameter category to which each of the at least two driving related parameters belongs from the preset parameter categories.
Optionally, the preset parameter categories correspond to respective score mapping relationships, and mapping intervals of the respective score mapping relationships of the preset parameter categories are different.
Optionally, the driving related parameter includes any one or more of a following distance parameter, an acceleration parameter, and an acceleration variation parameter;
the preset parameter types corresponding to the following distance parameters comprise a first distance type and a second distance type;
the preset parameter types corresponding to the acceleration parameters comprise a first acceleration type and a second acceleration type;
the preset parameter types corresponding to the acceleration variation parameters comprise a first variation type and a second variation type.
Optionally, the apparatus further comprises:
a coefficient obtaining module, configured to obtain a stationary coefficient for each of the predicted travel paths before determining a target path from the at least two predicted travel paths according to the respective scoring result of each of the predicted travel paths, where the stationary coefficient is used to indicate a stationary degree of each of the predicted travel paths;
the path determination module 504 is further configured to
And determining a target path from the at least two predicted driving paths according to the respective stability coefficient of each predicted driving path and the respective grading result 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, in-vehicle terminal 600 includes a Central Processing Unit (CPU) 601, a system Memory 604 including a Random Access Memory (RAM) 602 and a Read Only Memory (ROM) 603, and a system bus 605 connecting system Memory 604 and Central Processing Unit 601. The in-vehicle terminal 600 further includes a basic Input/Output System (I/O System) 608 that facilitates 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, input/output controller 610 may also provide 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 media 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 a CD-ROM (Compact disk Read-Only Memory) drive.
The computer readable media 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) or other optical, magnetic, tape, magnetic disk storage or other magnetic storage devices. Of course, those skilled in the art will appreciate that the computer storage media is not limited to the foregoing. 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 devices through a network interface unit 611 connected to the system bus 605.
The memory further includes one or more programs, the one or more programs are stored in the memory, and the central processing unit 601 implements all or part of the steps executed by the vehicle-mounted terminal in the methods provided by the above-mentioned embodiments of the present application by executing the one or more programs. Alternatively, the vehicle-mounted terminal may be mounted in the vehicle to execute the method for determining the target path according to the embodiments.
In the above embodiments, the implementation may be wholly or partially realized 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, cause the processes or functions described in accordance with the embodiments of the application to occur, in whole or in part. The computer may be a general purpose computer, a special purpose computer, a network of computers, or other programmable device. The computer instructions may be stored on a computer readable storage medium or transmitted from one computer readable storage medium to another, for example, from one website, computer, server, or data center to another website, computer, server, or data center via wire (e.g., coaxial cable, fiber optic, Digital Subscriber Line (DSL)) or wireless (e.g., infrared, wireless, microwave, etc.). The computer-readable storage medium can be any available medium that a computer can store or a data storage device, such as a server, a data center, etc., that is integrated with one or more available media. The usable medium may be a magnetic medium (e.g., floppy Disk, hard Disk, magnetic tape), an optical medium (e.g., Digital Video Disk (DVD)), or a semiconductor medium (e.g., Solid State Disk (SSD)), among others.
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. Optionally, the terminal may be a vehicle-mounted terminal in this embodiment.
The embodiment of the application also discloses a computer readable storage medium which stores a computer program, wherein the computer program realizes the method in the embodiment of the method 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 should also appreciate that the embodiments described in this specification are all alternative embodiments and that the acts and modules involved are not necessarily required for this application.
In various embodiments of the present application, it should be understood that the size of the serial number of each process described above does not mean that the execution sequence is necessarily sequential, and the execution sequence of each process should be determined by its function and inherent logic, and should not constitute any limitation on the implementation process of the embodiments of the present application.
The units described as separate parts may or may not be physically separate, and parts displayed as units may or may not be physical units, may be located in one place, or may be distributed on a plurality of network units. Some or all of the units can be selected according to actual needs to achieve the purpose of the solution of the embodiment.
In addition, functional units in the embodiments of the present application may be integrated into one processing unit, or each unit may exist alone physically, or two or more units are integrated into one unit. The integrated unit can be realized in a form of hardware, and can also be realized in a form of a software functional unit.
The integrated units, if implemented as software functional units and sold or used as a stand-alone product, may be stored in a computer accessible memory. Based on such understanding, the technical solution of the present application, which is a part of or contributes to the prior art in essence, 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 causing a computer device (which may be a personal computer, a server, a network device, or the like, and may specifically be a processor in the computer device) to execute part or all of the steps of the above-described method of the embodiments of the present application.
It will be understood by those skilled in the art that all or part of the steps in the methods of the embodiments described above may be implemented by hardware instructions of a program, and the program may be stored in a computer-readable storage medium, where the storage medium includes Read-Only Memory (ROM), Random Access Memory (RAM), Programmable Read-Only Memory (PROM), 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 (CD-ROM), or other Memory, such as a magnetic disk, or a combination thereof, A tape memory, or any other medium readable by a computer that can be used to carry or store data.
The foregoing describes a method, an apparatus, a vehicle, and a storage medium for determining a target path, which are disclosed in the embodiments of the present application, and the present application uses an example to explain the principles and embodiments of the present application, and the description of the foregoing embodiments is only used to help understand the method and the core idea of the present application; meanwhile, for a person skilled in the art, according to the idea of the present application, there may be variations in the embodiments and the application scope, and in summary, the content of the present specification should not be construed as a limitation to the present application.
Claims (10)
1. A method for determining a target path, the method being applied to a vehicle, the method comprising:
for each predicted running path of at least two predicted running paths, acquiring at least two running related parameters corresponding to the vehicle on each predicted running path;
for each predicted driving path, determining a parameter category to which the at least two driving related parameters belong from preset parameter categories;
obtaining respective scoring results of the predicted driving paths according to the parameter types of the at least two driving related parameters;
and determining a target path from the at least two predicted driving paths according to the respective grading result of each predicted driving path.
2. The method according to claim 1, wherein the obtaining of the scoring result of each of the predicted travel paths according to the parameter category to which each of the at least two travel-related parameters belongs comprises:
determining a grading mapping relation of each driving related parameter according to the parameter type to which each of the at least two driving related parameters belongs;
calculating the target score of each driving related parameter according to the score mapping relation of each driving related parameter;
and obtaining respective scoring results of the predicted driving paths according to the target scores of the driving related parameters.
3. The method according to claim 2, before the obtaining of the scoring result of each of the predicted travel paths according to the parameter category to which each of the at least two travel-related parameters belongs, further comprising:
calculating the 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 relevant parameter according to the score mapping relation of each driving relevant parameter comprises the following steps:
and mapping 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.
4. The 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 preset parameter classes comprises:
determining a range interval of the parameter score according to the respective parameter score of each driving related parameter;
and determining the parameter types to which the at least two driving related parameters belong from the preset parameter types according to the range interval of the parameter scores.
5. The method according to claim 2, wherein the preset parameter categories correspond to respective score mapping relationships, and mapping intervals of the score mapping relationships of the preset parameter categories are different.
6. The method according to any one of claims 1 to 5, wherein the driving-related parameters include any one or more of a following distance parameter, an acceleration parameter, and an acceleration variation parameter;
the preset parameter types corresponding to the following distance parameters comprise a first distance type and a second distance type;
the preset parameter types corresponding to the acceleration parameters comprise a first acceleration type and a second acceleration type;
the preset parameter types corresponding to the acceleration variation parameters comprise a first variation type and a second variation type.
7. The method according to any one of claims 1 to 5, further comprising, before said determining a target route from the at least two predicted travel routes based on the respective scoring results of the respective predicted travel routes:
for each predicted driving path, obtaining a respective stability coefficient of each predicted driving path, wherein the stability coefficient is used for indicating the stability degree of each predicted driving path;
the determining a target route from the at least two predicted travel routes according to the respective scoring results of the predicted travel routes includes:
and determining a target path from the at least two predicted driving paths according to the respective stability coefficient of each predicted driving path and the respective grading result of each driving path.
8. An apparatus for determining a target path, the apparatus being applied to a vehicle, the apparatus comprising:
the system comprises a parameter acquisition module, a parameter calculation module and a parameter calculation module, wherein the parameter acquisition module is used for acquiring at least two driving related parameters corresponding to a vehicle on each predicted driving path for each predicted driving path in at least two predicted driving paths;
the category determination module is used for determining the parameter categories to which the at least two driving related parameters belong from preset parameter categories for each predicted driving path;
the score obtaining module is used for obtaining a score result of each predicted driving path according to the parameter type to which the at least two driving related parameters belong;
and the path determining module is used for determining a target path from the at least two predicted running paths according to the respective grading result of each predicted running path.
9. A vehicle, characterized in that the vehicle comprises a vehicle-mounted terminal including a memory and a processor, the memory having stored therein a computer program, which, when executed by the processor, causes the processor to implement the method of determining a target path according to any one of claims 1 to 7.
10. A computer-readable storage medium, on which a computer program is stored, which, when being executed by a processor, carries out the method for determining a target path according to any one of claims 1 to 7.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202111645047.XA CN114291111B (en) | 2021-12-30 | 2021-12-30 | Target path determination method, device, vehicle and storage medium |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202111645047.XA CN114291111B (en) | 2021-12-30 | 2021-12-30 | Target path determination method, device, vehicle and storage medium |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN114291111A true CN114291111A (en) | 2022-04-08 |
| CN114291111B CN114291111B (en) | 2024-03-08 |
Family
ID=80972559
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202111645047.XA Active CN114291111B (en) | 2021-12-30 | 2021-12-30 | Target path determination method, device, vehicle and storage medium |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN114291111B (en) |
Citations (11)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN105599764A (en) * | 2014-11-12 | 2016-05-25 | 现代自动车株式会社 | Driving path planning apparatus and method for autonomous vehicle |
| US20190122543A1 (en) * | 2017-10-20 | 2019-04-25 | Zendrive, Inc. | Method and system for vehicular-related communications |
| CN110045734A (en) * | 2019-04-04 | 2019-07-23 | 百度在线网络技术（北京）有限公司 | Method, apparatus and computer equipment are determined based on the parameters weighting of path planning |
| WO2020124438A1 (en) * | 2018-12-18 | 2020-06-25 | Beijing Voyager Technology Co., Ltd. | Systems and methods for determining driving path in autonomous driving |
| CN111724599A (en) * | 2020-06-30 | 2020-09-29 | 暨南大学 | Method, device, equipment and medium for acquiring safe driving behavior evaluation data |
| CN112249032A (en) * | 2020-10-29 | 2021-01-22 | 浪潮(北京)电子信息产业有限公司 | Automatic driving decision method, system, equipment and computer storage medium |
| US20210114617A1 (en) * | 2019-10-18 | 2021-04-22 | Uatc, Llc | Method for Using Lateral Motion to Optimize Trajectories for Autonomous Vehicles |
| US20210171063A1 (en) * | 2019-12-10 | 2021-06-10 | Rideflux Inc. | Method, apparatus, and computer program for avoiding collision of autonomous vehicle |
| CN113200054A (en) * | 2021-06-21 | 2021-08-03 | 清华大学 | Path planning method and system for automatic driving takeover |
| CN113204920A (en) * | 2021-05-12 | 2021-08-03 | 紫清智行科技(北京)有限公司 | Intelligent vehicle lane change comfort evaluation and track planning method and device based on support vector machine |
| US20210269059A1 (en) * | 2020-02-29 | 2021-09-02 | Uatc, Llc | Trajectory Prediction for Autonomous Devices |
-
2021
- 2021-12-30 CN CN202111645047.XA patent/CN114291111B/en active Active
Patent Citations (11)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN105599764A (en) * | 2014-11-12 | 2016-05-25 | 现代自动车株式会社 | Driving path planning apparatus and method for autonomous vehicle |
| US20190122543A1 (en) * | 2017-10-20 | 2019-04-25 | Zendrive, Inc. | Method and system for vehicular-related communications |
| WO2020124438A1 (en) * | 2018-12-18 | 2020-06-25 | Beijing Voyager Technology Co., Ltd. | Systems and methods for determining driving path in autonomous driving |
| CN110045734A (en) * | 2019-04-04 | 2019-07-23 | 百度在线网络技术（北京）有限公司 | Method, apparatus and computer equipment are determined based on the parameters weighting of path planning |
| US20210114617A1 (en) * | 2019-10-18 | 2021-04-22 | Uatc, Llc | Method for Using Lateral Motion to Optimize Trajectories for Autonomous Vehicles |
| US20210171063A1 (en) * | 2019-12-10 | 2021-06-10 | Rideflux Inc. | Method, apparatus, and computer program for avoiding collision of autonomous vehicle |
| US20210269059A1 (en) * | 2020-02-29 | 2021-09-02 | Uatc, Llc | Trajectory Prediction for Autonomous Devices |
| CN111724599A (en) * | 2020-06-30 | 2020-09-29 | 暨南大学 | Method, device, equipment and medium for acquiring safe driving behavior evaluation data |
| CN112249032A (en) * | 2020-10-29 | 2021-01-22 | 浪潮(北京)电子信息产业有限公司 | Automatic driving decision method, system, equipment and computer storage medium |
| CN113204920A (en) * | 2021-05-12 | 2021-08-03 | 紫清智行科技(北京)有限公司 | Intelligent vehicle lane change comfort evaluation and track planning method and device based on support vector machine |
| CN113200054A (en) * | 2021-06-21 | 2021-08-03 | 清华大学 | Path planning method and system for automatic driving takeover |
Also Published As
| Publication number | Publication date |
|---|---|
| CN114291111B (en) | 2024-03-08 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN110909096B (en) | Method and device for determining recommended boarding point, storage medium and electronic equipment | |
| CN110816549B (en) | Energy management device, model management method, and computer program | |
| CN109978075B (en) | Vehicle false position information identification method and device, electronic equipment and storage medium | |
| US20170010111A1 (en) | Management of events and moving objects | |
| CN111366160A (en) | Path planning method, path planning device and terminal equipment | |
| CN112037518B (en) | Calculation method and system for predicting traffic state and vehicle | |
| CN102243811A (en) | Vehicle navigation system and recommended path searching method | |
| CN111861643A (en) | Riding position recommendation method and device, electronic equipment and storage medium | |
| CN112556717B (en) | Travel mode screening method and travel route recommending method and device | |
| CN112629524A (en) | Travel path recommendation management method and device and electronic equipment | |
| JP2019028526A (en) | Congestion prediction device | |
| CN114954129A (en) | Charging station information recommendation method and device, electronic terminal and storage medium | |
| CN115164922A (en) | Path planning method, system, equipment and storage medium | |
| CN108921360A (en) | A kind of social interaction mode, device and electronic equipment based on unmanned vehicle | |
| JP6633372B2 (en) | Route search device and route search method | |
| CN111476389A (en) | Method and device for pre-estimating order receiving waiting time | |
| CN113139139A (en) | Method, apparatus, electronic device, and medium for determining boarding point | |
| CN116088538B (en) | Vehicle track information generation method, device, equipment and computer readable medium | |
| JP2023155476A (en) | Information processing system, information processing apparatus, information processing method, and information processing program | |
| CN114291111A (en) | Target path determination method, target path determination device, vehicle and storage medium | |
| CN111861538A (en) | Information pushing method and device, electronic equipment and storage medium | |
| CN114413923B (en) | Driving route recommendation method, device, storage medium and system | |
| CN111339468B (en) | Information pushing method, device, electronic equipment and storage medium | |
| CN114419758A (en) | Vehicle following distance calculation method and device, vehicle and storage medium | |
| CN114162142B (en) | Method and device for scoring driving path, vehicle and storage medium |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant |