CN112050824A - Route planning method, device and system for vehicle navigation and electronic equipment - Google Patents
Route planning method, device and system for vehicle navigation and electronic equipment Download PDFInfo
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Abstract
The application discloses a route planning method, a route planning device, a route planning system, electronic equipment and a storage medium, which are applied to vehicle navigation, and relates to the technical field of cloud platforms and technical driving. The specific implementation scheme is as follows: the method comprises the steps of responding to a received route planning request, determining a set of paths to be recommended, wherein the set of paths to be recommended comprises at least one path to be recommended, determining an automatic driving function based on a vehicle, driving automatic driving operation information of each path to be recommended in the set of paths to be recommended, selecting and outputting at least one navigation path from the paths to be recommended according to the automatic driving operation information, determining the automatic driving operation information based on the automatic driving function, determining the navigation path based on the automatic driving operation information, and improving the utilization rate of resources of the automatic driving function, so that the automation and the intellectualization of vehicle traveling are improved, and the automatic driving experience of a user is enhanced.
Description
Technical Field
The present disclosure relates to automatic driving technologies in computer and cloud platform technologies, and in particular, to a method, an apparatus, a system, an electronic device, and a storage medium for vehicle navigation route planning.
Background
With the increase of the number of vehicles and the development of automatic driving technology, how to realize the route planning applied to vehicle navigation becomes an urgent problem to be solved.
In the prior art, after a server determines a departure place and a destination for route planning, a navigation route is generally determined from each section from the departure place to the destination by adopting a mechanism of time priority, shortest route, least charge, high-speed priority and congestion avoidance.
However, the navigation route determined by the above-mentioned method may not be fully utilized, which may result in a disadvantage of resource idle.
Disclosure of Invention
The application provides a route planning method, a route planning device, a route planning system, electronic equipment and a storage medium for improving resource utilization rate and used for vehicle navigation.
According to an aspect of the present application, there is provided a route planning method for vehicle navigation, comprising:
in response to a received route planning request, determining a set of paths to be recommended, wherein the set of paths to be recommended comprises at least one path to be recommended;
determining automatic driving operation information of each path to be recommended in the set of paths to be recommended based on the automatic driving function of the vehicle;
and selecting and outputting at least one navigation route from each path to be recommended according to the automatic driving operation information.
In this embodiment, the technical effect of improving the utilization rate of the resources of the automatic driving function can be achieved by determining the automatic driving operation information based on the automatic driving function and selecting the navigation route based on the automatic driving operation information.
According to another aspect of the present application, there is provided a route planning apparatus for vehicle navigation, comprising:
the generating module is used for responding to the received route planning request and determining a path set to be recommended, wherein the path set to be recommended comprises at least one path to be recommended;
the first determination module is used for determining the automatic driving operation information of each path to be recommended in the set of paths to be recommended based on the automatic driving function of the vehicle;
the selection module is used for selecting at least one navigation route from each path to be recommended according to the automatic driving operation information;
and the output module is used for outputting the navigation route.
According to another aspect of the present application, there is provided a navigation system including: a cloud platform and a vehicle, wherein,
the cloud platform comprises a route planning device applied to vehicle navigation in any embodiment;
the vehicle is used for sending the acquired route planning request to the route planning device applied to vehicle navigation and outputting the navigation route fed back by the route planning device applied to vehicle navigation.
According to another aspect of the present application, there is provided an electronic device including:
at least one processor; and
a memory communicatively coupled to the at least one processor; wherein the content of the first and second substances,
the memory stores instructions executable by the at least one processor to enable the at least one processor to perform a method as in any one of the embodiments above.
According to another aspect of the present application, there is provided a non-transitory computer readable storage medium having stored thereon computer instructions for causing the computer to perform the method of any of the above embodiments.
The application provides a route planning method, a device, a system, an electronic device and a storage medium for vehicle navigation, which comprise the following steps: the method comprises the steps of responding to a received route planning request, determining a set of paths to be recommended, wherein the set of paths to be recommended comprises at least one path to be recommended, determining automatic driving operation information of each path to be recommended in the set of paths to be recommended based on the automatic driving function of a vehicle, selecting and outputting at least one navigation path from the paths to be recommended according to the automatic driving operation information, determining the automatic driving operation information based on the automatic driving function, determining the navigation path based on the automatic driving operation information, realizing high association between the navigation path and the automatic driving function, realizing full utilization of the automatic driving function, improving the utilization rate of resources of the automatic driving function, and when the utilization rate of the resources of the automatic driving function is improved, relatively reducing manual driving operation of the vehicle, thereby improving automation and intellectualization of vehicle traveling, enhancing the user's experience of automatic driving.
It should be understood that the statements in this section do not necessarily identify key or critical features of the embodiments of the present application, nor do they limit the scope of the present application. Other features of the present application will become apparent from the following description.
Drawings
The drawings are included to provide a better understanding of the present solution and are not intended to limit the present application. Wherein:
FIG. 1 is a schematic diagram of an application scenario according to an embodiment of the present application;
FIG. 2 is a schematic interface diagram of a vehicle navigation system according to an embodiment of the present application;
FIG. 3 is a schematic interface diagram of a car navigator according to another embodiment of the present application
FIG. 4 is a schematic flow chart diagram of a route planning method for vehicle navigation according to an embodiment of the present application;
FIG. 5 is a schematic flow chart diagram illustrating a route planning method for vehicle navigation according to another embodiment of the present application;
FIG. 6 is a schematic flow chart diagram illustrating a route planning method for vehicle navigation according to another embodiment of the present application;
FIG. 7 is a schematic diagram of a route planning apparatus for vehicle navigation according to an embodiment of the present application;
FIG. 8 is a schematic diagram of a route planning apparatus for vehicle navigation according to another embodiment of the present application;
fig. 9 is a block diagram of an electronic device according to an embodiment of the present application.
Detailed Description
The following description of the exemplary embodiments of the present application, taken in conjunction with the accompanying drawings, includes various details of the embodiments of the application for the understanding of the same, which are to be considered exemplary only. Accordingly, those of ordinary skill in the art will recognize that various changes and modifications of the embodiments described herein can be made without departing from the scope and spirit of the present application. Also, descriptions of well-known functions and constructions are omitted in the following description for clarity and conciseness.
With the continuous development of internet technology and automobile technology, the automatic driving technology has entered into the lives of people, and with the continuous deepening of the automatic driving technology, intelligent traveling is gradually possible.
The embodiment of the application provides a route planning method for vehicle navigation, which can be applied to vehicles so as to realize intelligent travel of users. Referring to fig. 1, fig. 1 is a schematic view of an application scenario according to an embodiment of the present application.
As shown in fig. 1, the application scenario includes a vehicle 100 and a cloud platform 200, and the vehicle 100 and the cloud platform 200 are connected through a network.
The vehicle 100 may include the car navigator 110 shown in fig. 1, and specifically, the car navigator 110 is connected to the cloud platform 200 through a network, and implements interaction with the cloud platform 200 based on the network.
In one specific application, a user may initiate a route planning request to the cloud platform 200 through the vehicle-mounted navigator 110, and the route planning request may be used to enable the user to select a navigation route. The cloud platform 200 may generate one or more paths to be recommended based on the route planning request, and feed back the paths to be recommended to the car navigator 110. The vehicle-mounted navigator 110 may display the paths to be recommended for the user to select the navigation routes, and when the user selects one of the paths to be recommended, the vehicle-mounted navigator 100 determines the route as the navigation route, and performs a navigation task corresponding to the navigation route in combination with a controller (not shown in the figure) of the vehicle 100, thereby implementing travel intelligence.
In another specific application, the user may initiate a route planning request to the cloud platform 200 through the car navigator 110, and the route planning request may be used to enable the user to select a navigation route. The cloud platform 200 may generate one or more paths to be recommended based on the route planning request, select one or more paths to be recommended as a navigation route based on a preset route selection rule, and feed the navigation route back to the vehicle-mounted navigator 110. The vehicle-mounted navigator 110 may display the navigation routes for the user to select a target navigation route, and when the user selects one of the navigation routes, the vehicle-mounted navigator 100 determines the route as the target navigation route, and performs a navigation task corresponding to the target navigation route in combination with a controller (not shown in the figure) of the vehicle 100, thereby implementing travel intelligence.
The route selection rule may be set in advance by the car navigator 110, such as shortest distance, shortest time, fewest traffic lights, priority of an automatic driving function, and the like; the route selection rule may also be preset for the cloud platform 200 based on a historical navigation route, and this embodiment is not limited.
For example, as shown in fig. 2 (fig. 2 is an interface schematic diagram of a car navigator in an embodiment of the present application), a user may select a navigation function by sliding a display screen of the car navigator 110. As shown in fig. 2, when the user slides to the left on the display screen, that is, when the vehicle-mounted navigator acquires a sliding operation of the user, the vehicle-mounted navigator 110 may switch the interface including the function of the voice assistant to the interface including the function of navigation, and when the user clicks "navigation", that is, when the vehicle-mounted navigator 110 acquires a selected operation of the user, the function of navigation may be turned on and an interface supporting the user to initiate a route planning request, that is, an interface supporting the user to input a departure place and a destination, may be output.
Continuing with fig. 2, the car navigator 110 can set the departure place as the current position of the user, i.e., "my position" as shown in fig. 2, and of course, the user can input the name of the departure place based on the requirement, or the departure place can be selected from an interface (not shown in the figure). Similarly, the user may enter the name of the destination or select the destination from an interface. When the user inputs (or selects) the origin and destination on the interface of the car navigator 110, a virtual key "confirm" as shown in fig. 2 may be clicked, which may be used to trigger a route planning request.
Based on the above analysis, the car navigator 110 can send a route planning request to the cloud platform 200. The cloud platform 200 may generate one or more paths to be recommended based on the route planning request, and may select at least a part of the paths to be recommended as a navigation route to be sent to the vehicle-mounted navigator 110. The in-vehicle navigator 110 may display the navigation route for the user to select a target navigation route. For an interface diagram of the navigation route, reference may be made to fig. 3.
As shown in fig. 3, the navigation route may include routes generated by the cloud platform 200 from multiple dimensions, such as a route generated from a dimension that is prioritized for autopilot (e.g., "autopilot-prioritized route" as shown in fig. 3), a route generated from a dimension that is a regular route (e.g., regular route as shown in fig. 3), or a route generated from a dimension that is less chargeable (not shown in the figure).
It should be noted that fig. 3 is only exemplary to illustrate the dimensions that the navigation route may cover, and is not to be understood as a definition of the content of the navigation route generated by the cloud platform 200, and a definition of the content of the navigation route.
As can be seen from fig. 3, the cloud platform 200 may further generate relevant information of the navigation route, and send the relevant information to the car navigator 110, and the car navigator 110 displays the relevant information.
For example, if the navigation route is a regular route, the related information of the regular route may include a transit time of the vehicle 100 from the departure point to the destination, a manual/automatic driving ratio, a total distance, traffic lights (number), a manual driving distance, an automatic driving distance, and the like.
Similarly, if the navigation route is a route with priority for automatic driving or a route with less charges, the navigation route may also include the related information, which is not described herein again.
The user can select the navigation route of a certain dimension in a touch screen mode, and can continue to select one of the navigation routes of a certain dimension as a target navigation route.
It should be understood that the above description of the application scenario is only used for exemplarily illustrating an application scenario to which the route planning method applied to vehicle navigation according to the embodiment of the present application may be applied, and is not to be construed as a specific limitation to the application scenario, and the description of the interface described above and the illustration of the interface in the drawings are only used for exemplarily illustrating and are not to be construed as a specific limitation to the display content of the interface.
In the related art, when a navigation route is planned for a user by a cloud platform, the navigation route is mainly realized from the dimensions of time priority, shortest route and the like.
However, the cloud platform implements route planning applied to vehicle navigation through implementation manners in the related art, which may cause a drawback that an automatic driving function of a vehicle cannot be fully utilized.
The inventor of the application obtains the inventive concept of the application through creative work: route planning is performed based on the automatic driving function of the vehicle, and the utilization rate of resources of the automatic driving function is improved.
The following describes the technical solutions of the present application and how to solve the above technical problems with specific embodiments. The following several specific embodiments may be combined with each other, and details of the same or similar concepts or processes may not be repeated in some embodiments. Embodiments of the present application will be described below with reference to the accompanying drawings.
The application provides a route planning method applied to vehicle navigation, which is applied to the field of automatic driving in the technical field of computers so as to fully utilize the resources of the automatic driving function of a vehicle and improve the automation and the intellectualization of vehicle driving.
Referring to fig. 4, fig. 4 is a flowchart illustrating a route planning method for vehicle navigation according to an embodiment of the present application.
As shown in fig. 4, the method includes:
s101: in response to the received route planning request, determining a set of paths to be recommended, wherein the set of paths to be recommended comprises at least one path to be recommended.
The executing subject of the present application may be a route planning device applied to vehicle navigation, and the route planning device applied to vehicle navigation may be a server, a computer, a terminal device, and the like, and the present application is not limited.
For example, when the route planning method for vehicle navigation according to the embodiment of the present application is applied to the application scenario shown in fig. 1, the route planning device for vehicle navigation may be a cloud platform as shown in fig. 1, and specifically may be a server, a processor, a computer, and the like arranged in the cloud platform; alternatively, the route planning device for vehicle navigation may also be a vehicle-mounted navigator as shown in fig. 1; alternatively, the route planning apparatus for vehicle navigation may be other devices (not shown in the figure) On the vehicle as shown in fig. 1, such as a server, a computer, a vehicle BOX (T-BOX), a Domain Controller (DC), a Multi-Domain Controller (MDC), an On Board Unit (OBU), a car networking chip, and the like, which are provided On the vehicle.
It is worth noting that when the route planning method for vehicle navigation according to the embodiment of the present application is applied to the application scenario shown in fig. 1, the route planning device for vehicle navigation may be preferably deployed on a cloud platform, so as to improve the operation efficiency of the route planning device for vehicle navigation, reduce the calculation amount of the vehicle, and realize safe and reliable driving of the vehicle.
In the embodiment of the present application, to avoid redundant description, taking an execution subject as a cloud platform as an example, a route planning method for vehicle navigation in the embodiment of the present application is exemplarily described.
In connection with the application scenario as shown in fig. 1, the description of this step is as follows:
the vehicle-mounted navigator arranged on the vehicle can generate a route planning request based on touch screen operation of a user and send the route planning request to the cloud platform. The cloud platform receives a route planning request sent by the vehicle-mounted navigator, and generates a path set to be recommended based on the route planning request, wherein the path set to be recommended comprises at least one path to be recommended.
S102: and determining the automatic driving operation information of each path to be recommended in the set of paths to be recommended based on the automatic driving function of the vehicle.
The automatic driving function is understood to be a function for automatically controlling the vehicle to run without manual intervention.
The automatic driving operation information may be understood as related information generated when the vehicle travels based on the automatic driving function, such as at least one of time, distance, and related parameters of each device on the vehicle.
That is, the cloud platform may determine relevant information that may include time, distance, and related parameters generated by the vehicle while driving each of the routes to be recommended based on the automatic driving function of the vehicle.
S103: and selecting and outputting at least one navigation route from each path to be recommended according to the automatic driving operation information.
It is worth to be noted that, in this embodiment, the cloud platform is provided to determine the automatic driving operation information, and the navigation route is determined based on the automatic driving operation information, so that the problem that the utilization rate of resources of the automatic driving function is low due to the fact that the navigation route is determined from a short-time dimension and a low-cost dimension in the related art is solved, the utilization rate of the resources of the automatic driving function is improved, and the technical effects of intelligence and automation of vehicle traveling are improved.
Based on the above analysis, an embodiment of the present application provides a route planning method for vehicle navigation, which may be deployed on a cloud platform, and includes: the method comprises the steps of responding to a received route planning request, determining a set of paths to be recommended, wherein the set of paths to be recommended comprises at least one path to be recommended, determining automatic driving operation information of each path to be recommended in the set of paths to be recommended based on the automatic driving function of a vehicle, selecting and outputting at least one navigation path from the paths to be recommended according to the automatic driving operation information, determining the automatic driving operation information based on the automatic driving function, determining the navigation path based on the automatic driving operation information, realizing high association between the navigation path and the automatic driving function, realizing full utilization of the automatic driving function, improving the utilization rate of resources of the automatic driving function, and when the utilization rate of the resources of the automatic driving function is improved, relatively reducing manual driving operation of the vehicle, thereby improving automation and intellectualization of vehicle traveling, enhancing the user's experience of automatic driving.
For the reader to more deeply understand the route planning method for vehicle navigation of the present application, the route planning method for vehicle navigation of the present application is now explained in detail from the dimension of the set of paths to be recommended in conjunction with fig. 5. Fig. 5 is a schematic flow chart of a route planning method for vehicle navigation according to another embodiment of the present application.
As shown in fig. 5, the method includes:
s201: and responding to the received route planning request, and acquiring attribute information of each road section from the starting place to the destination, wherein the route planning request comprises the starting place and the destination.
It is understood that, in general, a plurality of road segments are included between the departure point and the destination, and each road segment has its own characteristic, which can be understood as attribute information, such as that some road segments are relatively flat and some road segments are relatively tortuous; some road sections are wider, and some road sections are narrower; there are more crossroads in some road sections, fewer crossroads in some road sections, and so on.
As can be known from fig. 3, a user may select a corresponding dimension to obtain a navigation route based on a requirement, and therefore, in some embodiments, the route planning request may carry an intention to obtain the navigation route, the intention is used to characterize the dimension of the user to obtain the navigation route, such as a conventional route, an automatic driving priority, and a low charge, and the cloud platform analyzes the route planning request, and if the acquired intention is the intention of the automatic driving priority, the method of this embodiment is executed.
In some embodiments, the attribute information includes: at least one of historical track data, real-time dynamic data, and user-generated content data.
Wherein, the description about the historical track data is as follows:
the historical trajectory data may be understood from two dimensions, one being historical trajectory data for manual driving and the other being historical trajectory data for automated driving. That is, the historical trajectory data includes both the historical trajectory data of the respective road segments traveled by the own vehicle and the other vehicles and the historical trajectory data of the respective road segments traveled by the own vehicle and the other vehicles based on the automatic driving function.
It is worth to be noted that, in this embodiment, by introducing the historical trajectory data, the attribute information can be described from two dimensions of manual driving and automatic driving, so that the integrity and comprehensiveness of the attribute information are realized, and further, the flexibility of subsequently determining the set of the path to be recommended based on the attribute information is realized.
The description about the real-time dynamic data is as follows:
the real-time dynamic data can also be understood from two dimensions, wherein one dimension is vehicle real-time dynamic data, and the other dimension is external real-time dynamic data.
For example, a sensor such as a radar system and an image capture device (e.g., a camera device) may be disposed on the vehicle, and the vehicle real-time dynamic data may be understood as sensor data.
As another example, the ambient real-time dynamic data may include weather data.
It should be noted that, in this embodiment, by introducing the real-time dynamic data, the influence of the real-time dynamic data on the driving of the vehicle can be fully considered, so as to achieve the technical effects of safety and reliability of the driving of the vehicle.
In addition, the real-time dynamic data are described from two dimensions of the vehicle and external factors, so that the completeness and comprehensiveness of the description of the real-time dynamic data can be realized, the comprehensiveness and reliability of the generated path set to be recommended are realized, the accuracy and reliability of the determined navigation route are realized, and the travel experience of a user is improved.
Among them, the User-generated content (UGC) data is described as follows:
the user-generated content data may be understood as data generated by a user operating the vehicle while driving the vehicle. For example, the user switches the automatic driving mode to the manual driving mode, and the like.
And in one possible implementation, the user-generated content data may be further derived as: a degree of preference of the user for the automatic driving function determined based on the operation data of the user, and the like.
It should be noted that, in this embodiment, the attribute information is described in combination with the user-generated content data, so that the integrity and comprehensiveness of the attribute information can be achieved, and the user-generated content data includes data of the operation of the user on the vehicle, and therefore, based on the attribute information, the use preference of the user on the automatic driving function can be determined, and further, the reasonable utilization of the resource of the automatic driving function is achieved, and when the navigation route is determined based on the attribute information, the flexibility of determining the navigation route and the personalized technical effect can be improved.
Among them, the description about the high-precision map data is as follows:
it should be noted that the automatic driving function may be affected by the high-precision map data, for example, if the probability that the automatic driving function is used is relatively low on a road section without the high-precision map data, and the probability that the automatic driving function is used is relatively high on a road section with the high-precision map data, and therefore, in the present embodiment, the high-precision map data is introduced into the attribute information.
It should be noted that, in this embodiment, the attribute information is described by introducing the high-precision map data, which is equivalent to elaborating the attribute information in more detail on the resource support, so that the integrity and comprehensiveness of the attribute information are realized, and further, the diversity and comprehensiveness of the determined set of paths to be recommended are realized, so that various factors that may affect the automatic driving function are fully considered, and the technical effects of improving the safety and reliability of vehicle driving while improving the utilization rate of resources of the automatic driving function are achieved.
S202: and determining the running information of the automatic driving function of the vehicle when the vehicle runs on each road section based on the automatic driving function according to the attribute information.
The operation information may be, for example, a use time length, a use distance, a use position, and a performance of the vehicle in use of the automatic driving function.
Based on the above analysis, it can be known that the attribute information can be understood from multiple dimensions, and therefore, in this embodiment, if the attribute information includes historical trajectory data, the operation information may be operation information corresponding to the historical trajectory data; if the attribute information includes real-time dynamic data, the operation information may be operation information corresponding to the real-time dynamic data, and so on, which are not listed here.
In some embodiments, if the attribute information includes historical track data, real-time dynamic data, user-generated content data, and high-precision map data, the operational information includes operational information corresponding to each of the historical track data, the real-time dynamic data, the user-generated content data, and the high-precision map data.
That is, if the attribute information includes data of four dimensions, the operation information may include operation information generated for each dimension.
It should be noted that, in the present embodiment, the operation information corresponding to each of the data of the respective dimensions is generated so as to understand and grasp the operation of the automatic driving function from a plurality of dimensions, thereby achieving a technical effect of improving the reliability of the use of the resource of the automatic driving function.
In some embodiments, the operation information may be obtained by constructing a prediction model and performing prediction in combination with the prediction model, for example, a method of constructing the prediction model may include:
s2021: collecting sample information, the sample information comprising: the sample attribute information of each road section, and the calibration operation information of the automatic driving function applied when the vehicle runs on each road section based on the sample attribute information.
The amount of the sample information may be set by the cloud platform based on a requirement, a history, a test, and the like, which is not limited in this embodiment.
For the description of the sample attribute information, reference may be made to the description of the attribute information in the above embodiment, and for the description of the calibration operation information, reference may be made to the description of the operation information in the above embodiment, which is not described herein again.
S2022: and training a preset basic network model according to the sample information to generate a prediction model.
The basic network model may be any one of a convolutional neural network model, a cyclic neural network model, a long-short term memory neural network model, and an antagonistic neural network model, and this embodiment is not limited.
In some embodiments, the step may specifically include: inputting the sample information into a basic network model, generating predicted operation information, comparing the predicted operation information with the calibrated operation information, calculating a loss value between the predicted operation information and the calibrated operation information, adjusting parameters of the basic network model according to the loss value until the loss value between the predicted operation information and the calibrated operation information is smaller than a preset loss threshold value or the iteration number reaches an iteration threshold value, and determining the basic network model under the condition as the predicted model.
Similarly, the loss threshold and the iteration may be set by the cloud platform based on the requirement, the history, the experiment, and the like, and this embodiment is not limited.
Then, in the case of constructing a prediction model and generating operation information by combining the prediction model, S202 may specifically be: and inputting the attribute information into the prediction model to generate operation information.
It should be noted that, in the present embodiment, by constructing the prediction model so as to generate the operation information based on the prediction model, the technical effect of improving the efficiency of generating the operation information can be achieved.
S203: and combining the sections according to the operation information to generate each path to be recommended in the path set to be recommended.
Wherein the step can be understood as: the cloud platform splices the road sections based on the operation information to obtain a path set to be recommended, wherein the path set to be recommended comprises at least one path to be recommended.
It should be noted that, in this embodiment, the operation information is related to the automatic driving function, and therefore, when each path to be recommended generated based on the operation information is also related to the automatic driving function, when one or more paths to be recommended are selected from the set of paths to be recommended as the navigation route, the relevance between the navigation route and the automatic driving function may be improved, thereby achieving the technical effects of improving the utilization rate of resources of the automatic driving function and improving automation and intelligence of vehicle driving.
Based on the above analysis, when the attribute information includes information of multiple dimensions, the operation information may also include operation information of multiple dimensions generated based on the attribute information of multiple dimensions, and when the operation information includes operation information of multiple dimensions, each path to be recommended in the set to be recommended may be generated based on the operation information of multiple dimensions of each road segment.
That is, in some embodiments, S203 may include:
s2031: and determining respective corresponding application weights of the automatic driving functions when the automatic driving functions travel on the road sections according to the respective corresponding operation information.
Wherein, the application weight can be understood as: the proportion of the autopilot function, or the score, during the travel of the vehicle.
With reference to the above example, if the operation information is four-dimensional operation information, for each road segment, it is determined that the corresponding application weight when the automatic driving function drives the road segment based on the operation information of each dimension, so as to obtain four application weights.
In some embodiments, the application weights may be determined by constructing a weight model. For example, for a certain dimension of operation information, a training sample of the dimension may be collected and a standard result may be calibrated. And inputting the training samples into a preset neural network model to generate a training result, and adjusting parameters of the neural network model based on the training result and the standard result to generate a weight model.
And in conjunction with the above analysis, in determining operational information, may be implemented based on a predictive model, and thus, in some embodiments, a model may be generated that includes functionality with a predictive model and a weight model for generating application weights based on attribute information. The training principle of the model may refer to the above embodiments, which are not described herein again.
S2031: and combining the sections according to the application weight to generate each path to be recommended in the path set to be recommended.
After the application weight of each road section is determined by the cloud platform, each road section can be combined based on the application weight, and the application weight is the weight corresponding to the automatic driving function, so that each path to be recommended obtained by combination can be highly attached to the automatic driving function when each road section is combined based on the application weight, the path to be recommended is the path which fully utilizes the automatic driving function, and the technical effect of improving the utilization rate of resources of the automatic driving function is achieved.
In some embodiments, S2031 may comprise: determining the road section weight of each road section according to the application weight, performing ascending or descending arrangement on the road section weight of each road section, and sequentially selecting the path with the highest path weight as the path in the set to be recommended based on the arrangement until the selected path weight is equal to a preset weight threshold.
The link weight may be an average value of application weights corresponding to the link, for example, if the application weights of a certain link are a, b, c, and d (i.e., the link includes application weights of four dimensions), the link weight of the link is (a + b + c + d)/4.
A path weight may be understood as the sum of the segment weights of the segments that make up the path.
That is, in the present embodiment, the path weight of each path to be recommended in the set of paths to be recommended is larger than the weight threshold value, so as to provide the resource utilization rate of the automatic driving function.
Similarly, the weight threshold may be set by the cloud platform based on a requirement, a history, a test, and the like, which is not limited in this embodiment.
S204: and determining the automatic driving operation information for driving each path to be recommended in the set of paths to be recommended based on the automatic driving function.
For a description of S204, refer to S102, which is not described herein again.
S205: and selecting and outputting at least one navigation route from each path to be recommended according to the automatic driving operation information.
For a description of S205, reference may be made to S103, which is not described herein again.
As can be seen from the above analysis, in the above embodiments, the route planning method for vehicle navigation is described in combination with the method for determining the set of paths to be recommended by the cloud platform, and in order to make the reader more fully understand the solution of the present application, the present application is now described in detail from the dimension of the selected navigation route in combination with fig. 6. Fig. 6 is a schematic flow chart of a route planning method for vehicle navigation according to another embodiment of the present application.
As shown in fig. 6, the method includes:
s301: in response to the received route planning request, determining a set of paths to be recommended, wherein the set of paths to be recommended comprises at least one path to be recommended.
For the description of S301, reference may be made to S101, or refer to S201 to S203, which is not described herein again.
S302: determining the use duration of the automatic driving function and/or the use distance of the automatic driving function when the automatic driving function based on the vehicle runs on each path to be recommended in the set of paths to be recommended.
Similarly, in one possible implementation, a duration and/or distance model may be pre-constructed for predicting a duration of use of the autonomous driving function and/or predicting a distance of use of the autonomous driving function. For the principle of constructing the duration and/or distance model, reference may be made to the principle of constructing the prediction model in the above embodiments, which is not described herein again.
In some embodiments, the cloud platform may further determine, on the basis of determining the usage duration and/or the usage distance, related information derived based on the usage duration and/or the usage distance, such as a time ratio and/or a distance ratio, and the like, which is not limited in this embodiment.
Based on the above analysis, the set of paths to be recommended may be determined by the cloud platform according to the operation information, where the operation information is related to information generated when the vehicle travels through each road segment based on the automatic driving function, and therefore, in some embodiments, S302 may include: and determining the use time length and/or the use distance according to the operation information.
It is worth mentioning that, in the present embodiment, since the use duration and/or the use distance is related to the automatic driving function, when the navigation route is determined based on the use duration and/or the use distance, a technical effect of improving the utilization rate of the resource of the automatic driving function is achieved.
S303: and determining manual driving operation information according to the attribute information.
Based on the above analysis, the operation information may be determined based on the attribute information, and then the step may include: and determining manual driving operation information according to the running information.
Here, the manual driving operation information may be understood as related information generated by manually intervening the automatic driving function, for example, the frequency of manual intervening operations, that is, the number of times the driver switches or confirms the driving mode (including the manual driving mode performed by the driver, the automatic driving mode based on the automatic driving function), and the like.
S304: and determining the success rate of driving on each road section based on the automatic driving function according to the attribute information.
It should be noted that, each road segment has its own characteristics (such as the historical track data, the user generated content data, and the high-precision map data in the foregoing embodiments), and external factors (such as the real-time dynamic data in the foregoing embodiments) have an influence on its own characteristics, so that the probability that each road segment supports the vehicle to successfully complete the automatic driving function may be different.
It is worth mentioning that in one possible implementation, an information model may be constructed for predicting the duration and/or distance of use, manual driving operation information, and success rate based on the operational information and/or the attribute information. For the method for constructing the information model, reference may be made to the method for constructing the prediction model in the above example, and details are not repeated here.
S305: and selecting and outputting the navigation route from each path to be recommended according to the automatic driving operation information, the manual driving operation information and the success rate.
In a possible implementation scheme, the cloud platform may set priorities of the automatic driving operation information and the success rate, and if the priority of the success rate is higher than the priority of the automatic driving operation information, the routes to be recommended are sorted in an ascending order or a descending order based on the success rate, and the route to be recommended with the highest success rate is sequentially selected and determined as the navigation route until the success rate is equal to a preset success rate threshold. That is, the success rate of the navigation route is greater than the success rate threshold, thereby ensuring reliable operation of the vehicle based on the autonomous driving function.
On the contrary, if the priority of the automatic driving operation information is higher than the priority of the success rate, the routes to be recommended may be sorted in an ascending order or a descending order based on the usage duration and/or the usage distance, and the implementation principle of the method may be referred to the above example, which is not described herein again.
In another possible implementation scheme, the cloud platform may select the navigation route by combining the success rate and the automatic driving operation information, for example, the success rate of the selected navigation route is greater than a success rate threshold, and the use duration and/or the use duration of the navigation route is greater than a preset threshold.
In another possible implementation scheme, the cloud platform may further select the navigation route by combining the success rate, the automatic driving operation information, and the manual driving operation information, for example, on the basis of the above example, the number of mode switching times of the navigation route is less than a preset number threshold.
It is worth explaining that when the navigation route is selected from three dimensions of automatic driving operation information, manual driving operation information and success rate, the utilization rate of resources of the automatic driving function is considered, and the safety and the convenience of the automatic driving function are also considered, so that the technical effects of the resource utilization rate, the flexibility, the convenience and the reliability of the navigation route are realized, and further the technical effects of intelligent travel and safe travel are realized.
According to another aspect of the embodiments of the present application, there is also provided a route planning device for vehicle navigation, configured to perform the method according to any one of the above embodiments, such as the route planning method for vehicle navigation shown in any one of fig. 4, fig. 5 and fig. 6.
Referring to fig. 7, fig. 7 is a schematic diagram of a route planning device for vehicle navigation according to an embodiment of the present application.
As shown in fig. 7, the apparatus includes:
the generating module 11 is configured to determine a set of paths to be recommended in response to a received route planning request, where the set of paths to be recommended includes at least one path to be recommended;
the first determination module 12 is configured to determine automatic driving operation information for driving each to-be-recommended route in the to-be-recommended route set based on an automatic driving function of a vehicle;
the selecting module 13 is configured to select at least one navigation route from each of the to-be-recommended routes according to the automatic driving operation information;
and the output module 14 is used for outputting the navigation route.
In some embodiments, the route planning request includes a start location and a destination; the generating module 11 is configured to obtain attribute information of each road segment from the departure location to the destination, determine, according to the attribute information, operation information of an automatic driving function of the vehicle when the vehicle travels through each road segment based on the automatic driving function, combine each road segment according to the operation information, and generate each to-be-recommended route in the to-be-recommended route set.
In some embodiments, the attribute information includes: at least one of historical track data, real-time dynamic data, user-generated content data, and high-precision map data.
In some embodiments, if the attribute information includes the historical track data, the real-time dynamic data, the user-generated content data, and high-precision map data, the operation information includes operation information corresponding to each of the historical track data, the real-time dynamic data, the user-generated content data, and the high-precision map data.
In some embodiments, the generating module 11 is configured to determine, according to the respective corresponding operation information, respective corresponding application weights of an automatic driving function when the vehicle runs on the road segments, and combine the road segments according to the application weights to generate each to-be-recommended route in the to-be-recommended route set.
As can be seen in fig. 8, in some embodiments, the method further includes:
an acquisition module 15, configured to acquire sample information, where the sample information includes: sample attribute information of each road section, and calibration operation information of an automatic driving function applied when the vehicle runs on each road section on the basis of the sample attribute information;
the training module 16 is used for training a preset basic network model according to the sample information to generate a prediction model;
and the generating module 11 is configured to input the attribute information to the prediction model, and generate the operation information.
In some embodiments, the automatic driving operation information includes: the service time of the automatic driving function and/or the service distance of the automatic driving function; the first determining module 12 is configured to determine the usage duration and/or the usage distance according to the operation information.
As can be seen in fig. 8, in some embodiments, the method further includes:
a second determining module 17, configured to determine manual driving operation information according to the operation information, and determine, according to the attribute information, a success rate of the vehicle when the vehicle travels on each road segment based on an automatic driving function;
and the selecting module 13 is configured to select the navigation route from each of the to-be-recommended routes according to the automatic driving operation information, the manual driving operation information, and the success rate.
In some embodiments, the real-time dynamic data includes weather data and sensor data.
According to another aspect of an embodiment of the present application, there is also provided a navigation system including a cloud platform and a vehicle, such as the cloud platform and the vehicle shown in fig. 1, wherein,
the cloud platform comprises a route planning device for vehicle navigation according to any one of the above embodiments, such as a route planning device for vehicle navigation shown in fig. 7 or fig. 8;
the vehicle is used for sending the acquired route planning request to the route planning device for vehicle navigation and outputting the navigation route fed back by the route planning device for vehicle navigation.
In combination with the above analysis, in some embodiments, the vehicle may include the vehicle-mounted navigator described in the above embodiments, and the vehicle-mounted navigator sends the acquired route planning request to the route planning device applied to vehicle navigation, and outputs the navigation route fed back by the route planning device applied to vehicle navigation.
According to an embodiment of the present application, an electronic device and a readable storage medium are also provided.
Fig. 9 is a block diagram of an electronic device applied to a route planning method for vehicle navigation according to an embodiment of the present application. Electronic devices are intended to represent various forms of digital computers, such as laptops, desktops, workstations, personal digital assistants, servers, blade servers, mainframes, and other appropriate computers. The electronic device may also represent various forms of mobile devices, such as personal digital processing, cellular phones, smart phones, wearable devices, and other similar computing devices. The components shown herein, their connections and relationships, and their functions, are meant to be examples only, and are not meant to limit implementations of the present application that are described and/or claimed herein.
As shown in fig. 9, the electronic apparatus includes: one or more processors 901, memory 902, and interfaces for connecting the various components, including a high-speed interface and a low-speed interface. The various components are interconnected using different buses and may be mounted on a common motherboard or in other manners as desired. The processor may process instructions for execution within the electronic device, including instructions stored in or on the memory to display graphical information of a GUI on an external input/output apparatus (such as a display device coupled to the interface). In other embodiments, multiple processors and/or multiple buses may be used, along with multiple memories, as desired. Also, multiple electronic devices may be connected, with each device providing portions of the necessary operations (e.g., as a server array, a group of blade servers, or a multi-processor system). Fig. 9 illustrates an example of a processor 901.
The memory 902, which is a non-transitory computer readable storage medium, may be used to store non-transitory software programs, non-transitory computer executable programs, and modules, such as program instructions/modules corresponding to the route planning method applied to vehicle navigation in the embodiments of the present application. The processor 901 executes various functional applications of the server and data processing by running non-transitory software programs, instructions, and modules stored in the memory 902, that is, implements the route planning method for vehicle navigation in the above method embodiments.
The memory 902 may include a program storage area and a data storage area, wherein the program storage area may store an operating system, an application program required for at least one function; the storage data area may store data created according to use of an electronic device applied to a route planning method for vehicle navigation, and the like. Further, the memory 902 may include high speed random access memory, and may also include non-transitory memory, such as at least one magnetic disk storage device, flash memory device, or other non-transitory solid state storage device. In some embodiments, the memory 902 may optionally include memory remotely located from the processor 901, which may be connected over a network to the electronics of the route planning method applied to vehicle navigation. Examples of such networks include, but are not limited to, the internet, intranets, local area networks, mobile communication networks, and combinations thereof.
The electronic device applied to the route planning method for vehicle navigation may further include: an input device 903 and an output device 904. The processor 901, the memory 902, the input device 903 and the output device 904 may be connected by a bus or other means, and fig. 9 illustrates the connection by a bus as an example.
The input device 903 may receive input numeric or character information and generate key signal inputs related to user settings and function control of an electronic apparatus applied to a route planning method for vehicle navigation, such as an input device of a touch screen, a keypad, a mouse, a track pad, a touch pad, a pointing stick, one or more mouse buttons, a track ball, a joystick, or the like. The output devices 904 may include a display device, auxiliary lighting devices (e.g., LEDs), tactile feedback devices (e.g., vibrating motors), and the like. The display device may include, but is not limited to, a Liquid Crystal Display (LCD), a Light Emitting Diode (LED) display, and a plasma display. In some implementations, the display device can be a touch screen.
Various implementations of the systems and techniques described here can be realized in digital electronic circuitry, integrated circuitry, application specific ASICs (application specific integrated circuits), computer hardware, firmware, software, and/or combinations thereof. These various embodiments may include: implemented in one or more computer programs that are executable and/or interpretable on a programmable system including at least one programmable processor, which may be special or general purpose, receiving data and instructions from, and transmitting data and instructions to, a storage system, at least one input device, and at least one output device.
These computer programs (also known as programs, software applications, or code) include machine instructions for a programmable processor, and may be implemented using high-level procedural and/or object-oriented programming languages, and/or assembly/machine languages. As used herein, the terms "machine-readable medium" and "computer-readable medium" refer to any computer program product, apparatus, and/or device (e.g., magnetic discs, optical disks, memory, Programmable Logic Devices (PLDs)) used to provide machine instructions and/or data to a programmable processor, including a machine-readable medium that receives machine instructions as a machine-readable signal. The term "machine-readable signal" refers to any signal used to provide machine instructions and/or data to a programmable processor.
To provide for interaction with a user, the systems and techniques described here can be implemented on a computer having: a display device (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to a user; and a keyboard and a pointing device (e.g., a mouse or a trackball) by which a user can provide input to the computer. Other kinds of devices may also be used to provide for interaction with a user; for example, feedback provided to the user can be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user may be received in any form, including acoustic, speech, or tactile input.
The systems and techniques described here can be implemented in a computing system that includes a back-end component (e.g., as a data server), or that includes a middleware component (e.g., an application server), or that includes a front-end component (e.g., a user computer having a graphical user interface or a web browser through which a user can interact with an implementation of the systems and techniques described here), or any combination of such back-end, middleware, or front-end components. The components of the system can be interconnected by any form or medium of digital data communication (e.g., a communication network). Examples of communication networks include: local Area Networks (LANs), Wide Area Networks (WANs), and the Internet.
The computer system may include clients and servers. A client and server are generally remote from each other and typically interact through a communication network. The relationship of client and server arises by virtue of computer programs running on the respective computers and having a client-server relationship to each other.
It should be understood that various forms of the flows shown above may be used, with steps reordered, added, or deleted. For example, the steps described in the present application may be executed in parallel, sequentially, or in different orders, and the present invention is not limited thereto as long as the desired results of the technical solutions disclosed in the present application can be achieved.
The above-described embodiments should not be construed as limiting the scope of the present application. It should be understood by those skilled in the art that various modifications, combinations, sub-combinations and substitutions may be made in accordance with design requirements and other factors. Any modification, equivalent replacement, and improvement made within the spirit and principle of the present application shall be included in the protection scope of the present application.
Claims (22)
1. A method of route planning for navigation of a vehicle, comprising:
in response to a received route planning request, determining a set of paths to be recommended, wherein the set of paths to be recommended comprises at least one path to be recommended;
determining automatic driving operation information for driving each path to be recommended in the set of paths to be recommended based on an automatic driving function of the vehicle;
and selecting and outputting at least one navigation route from each path to be recommended according to the automatic driving operation information.
2. The method of claim 1, wherein the route planning request includes a start point and a destination; in response to the received route planning request, determining a set of paths to be recommended, including:
acquiring attribute information of each road section from the departure place to the destination;
determining operation information of the automatic driving function when the automatic driving function drives on each road section according to the attribute information;
and combining the road sections according to the operation information to generate each path to be recommended in the path set to be recommended.
3. The method of claim 2, wherein the attribute information comprises: at least one of historical track data, real-time dynamic data, user-generated content data, and high-precision map data.
4. The method of claim 3, wherein if the attribute information includes the historical track data, the real-time dynamic data, the user-generated content data, and high-precision map data, the operational information includes operational information corresponding to each of the historical track data, the real-time dynamic data, the user-generated content data, and the high-precision map data.
5. The method according to claim 4, wherein the combining the road segments according to the operation information to generate each path to be recommended in the set of paths to be recommended comprises:
determining respective corresponding application weights of the automatic driving functions of the vehicle when the vehicle runs on each road section according to the respective corresponding running information;
and combining the road sections according to the application weight to generate each path to be recommended in the path set to be recommended.
6. The method of any of claims 2 to 5, further comprising:
collecting sample information, the sample information comprising: sample attribute information of each road section, and calibration operation information of an automatic driving function applied when the vehicle runs on each road section on the basis of the sample attribute information;
training a preset basic network model according to the sample information to generate a prediction model;
and determining operation information of the automatic driving function when the automatic driving function is driven on each road section according to the attribute information, wherein the operation information of the automatic driving function comprises: and inputting the attribute information into the prediction model to generate the operation information.
7. The method of any of claims 2-5, wherein the autopilot maneuver information includes: the service time of the automatic driving function and/or the service distance of the automatic driving function; determining that the vehicle is based on an automatic driving function, and the automatic driving operation information for driving each recommended route in the set of routes to be recommended comprises:
and determining the use duration and/or the use distance according to the operation information.
8. The method of any of claims 2 to 5, further comprising:
determining manual driving operation information according to the operation information;
determining a success rate of the vehicle when the vehicle runs on each road section based on an automatic driving function according to the attribute information;
and the step of selecting and outputting at least one navigation route from the paths to be recommended according to the automatic driving operation information comprises the following steps: and selecting and outputting the navigation route from each path to be recommended according to the automatic driving operation information, the manual driving operation information and the success rate.
9. The method of any of claims 3 to 5, wherein the real-time dynamic data comprises weather data and sensor data.
10. A route planning apparatus for vehicle navigation, comprising:
the generating module is used for responding to the received route planning request and determining a path set to be recommended, wherein the path set to be recommended comprises at least one path to be recommended;
the first determination module is used for determining the automatic driving operation information of each path to be recommended in the set of paths to be recommended based on the automatic driving function of the vehicle;
the selection module is used for selecting at least one navigation route from each path to be recommended according to the automatic driving operation information;
and the output module is used for outputting the navigation route.
11. The apparatus of claim 10, wherein the route planning request includes a start location and a destination; the generation module is used for acquiring attribute information of each road section from the departure place to the destination, determining running information of the automatic driving function of the vehicle when the vehicle runs on each road section based on the automatic driving function according to the attribute information, and combining each road section according to the running information to generate each path to be recommended in the path set to be recommended.
12. The apparatus of claim 11, wherein the attribute information comprises: at least one of historical track data, real-time dynamic data, user-generated content data, and high-precision map data.
13. The apparatus of claim 12, wherein if the attribute information includes the historical trajectory data, the real-time dynamic data, the user-generated content data, and high-precision map data, the operational information includes operational information corresponding to each of the historical trajectory data, the real-time dynamic data, the user-generated content data, and the high-precision map data.
14. The device according to claim 13, wherein the generating module is configured to determine, according to the respective corresponding operation information, respective corresponding application weights of an automatic driving function of the vehicle during driving on the respective road segments, and combine the respective road segments according to the application weights to generate each to-be-recommended route in the to-be-recommended route set.
15. The apparatus of any of claims 11 to 14, further comprising:
an acquisition module configured to acquire sample information, the sample information comprising: sample attribute information of each road section, and calibration operation information of an automatic driving function applied when the vehicle runs on each road section on the basis of the sample attribute information;
the training module is used for training a preset basic network model according to the sample information to generate a prediction model;
and the generating module is used for inputting the attribute information into the prediction model and generating the operation information.
16. The apparatus according to any one of claims 11 to 14, wherein the automatic driving operation information includes: the service time of the automatic driving function and/or the service distance of the automatic driving function; the first determining module is used for determining the use duration and/or the use distance according to the operation information.
17. The apparatus of any of claims 11 to 14, further comprising:
the second determination module is used for determining manual driving operation information according to the operation information and determining the success rate of the vehicle when the vehicle runs on each road section based on the automatic driving function according to the attribute information;
and the selection module is used for selecting the navigation route from each path to be recommended according to the automatic driving operation information, the manual driving operation information and the success rate.
18. The apparatus of any one of claims 12 to 14, wherein the real-time dynamic data comprises weather data and sensor data.
19. A navigation system, comprising: a cloud platform and a vehicle, wherein,
the cloud platform comprises a route planning device applied to vehicle navigation according to any one of claims 10 to 18;
the vehicle is used for sending the acquired route planning request to the route planning device applied to vehicle navigation and outputting the navigation route fed back by the route planning device applied to vehicle navigation.
20. The system of claim 19, wherein the vehicle is further configured to implement a driving strategy corresponding to a target navigation route selected by a user from the navigation routes.
21. An electronic device, comprising:
at least one processor; and
a memory communicatively coupled to the at least one processor; wherein the content of the first and second substances,
the memory stores instructions executable by the at least one processor to enable the at least one processor to perform the method of any one of claims 1 to 9.
22. A non-transitory computer readable storage medium having stored thereon computer instructions for causing the computer to perform the method of any one of claims 1 to 9.
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Cited By (12)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN112710323A (en) * | 2020-12-23 | 2021-04-27 | 广州橙行智动汽车科技有限公司 | Vehicle-mounted navigation method and device |
| CN113128766A (en) * | 2021-04-21 | 2021-07-16 | 科大讯飞股份有限公司 | Destination prejudging method and device, electronic equipment and storage medium |
| CN113401147A (en) * | 2021-07-30 | 2021-09-17 | 国汽智控(北京)科技有限公司 | Automatic driving method and device and electronic equipment |
| CN113405561A (en) * | 2021-07-06 | 2021-09-17 | 文诚恒远(天津)供应链管理服务有限公司 | Driving route recommendation method, device, equipment and storage medium |
| CN114013452A (en) * | 2021-09-29 | 2022-02-08 | 江铃汽车股份有限公司 | Automatic driving control method, system, readable storage medium and vehicle |
| CN114255607A (en) * | 2021-12-20 | 2022-03-29 | 浙江吉利控股集团有限公司 | Driving path recommendation method, system, medium, device and program product |
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| CN114295144A (en) * | 2021-12-30 | 2022-04-08 | 海南大学 | DIKW-based vehicle path planning method |
| CN114485710A (en) * | 2022-01-19 | 2022-05-13 | 北京百度网讯科技有限公司 | Map navigation method, system, device, electronic equipment and storage medium |
| CN114898584A (en) * | 2022-03-31 | 2022-08-12 | 广州汽车集团股份有限公司 | Recommendation method, device and equipment for vehicle driving modes and storage medium |
| WO2023044794A1 (en) * | 2021-09-24 | 2023-03-30 | 华为技术有限公司 | Navigation method and related apparatus |
| CN116182879A (en) * | 2023-04-25 | 2023-05-30 | 中通服建设有限公司 | Intelligent automatic driving system |
Citations (8)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| JP2017151041A (en) * | 2016-02-26 | 2017-08-31 | 株式会社デンソー | Driving support device and center |
| CN107430007A (en) * | 2014-12-30 | 2017-12-01 | 罗伯特·博世有限公司 | The route selection of preference ratio is driven based on auto-manual |
| WO2018109519A1 (en) * | 2016-12-16 | 2018-06-21 | 日産自動車株式会社 | Route-discovering method and device for automatic driving assistance |
| CN108932862A (en) * | 2018-07-27 | 2018-12-04 | 北京小马智行科技有限公司 | A kind of automatic driving vehicle dispatching method and device |
| US20190011918A1 (en) * | 2017-07-06 | 2019-01-10 | Lg Electronics Inc. | Driving system for vehicle and vehicle thereof |
| CN110657818A (en) * | 2019-10-17 | 2020-01-07 | 北京百度网讯科技有限公司 | Path planning method, device and equipment for automatic driving vehicle and storage medium |
| CN111465824A (en) * | 2017-12-18 | 2020-07-28 | 智加科技公司 | Method and system for personalized self-aware path planning in autonomous vehicles |
| CN111480052A (en) * | 2017-12-15 | 2020-07-31 | 伟摩有限责任公司 | Resolving scene difficulties in vehicle routes using predictive models |
-
2020
- 2020-09-17 CN CN202010981824.7A patent/CN112050824B/en active Active
Patent Citations (8)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN107430007A (en) * | 2014-12-30 | 2017-12-01 | 罗伯特·博世有限公司 | The route selection of preference ratio is driven based on auto-manual |
| JP2017151041A (en) * | 2016-02-26 | 2017-08-31 | 株式会社デンソー | Driving support device and center |
| WO2018109519A1 (en) * | 2016-12-16 | 2018-06-21 | 日産自動車株式会社 | Route-discovering method and device for automatic driving assistance |
| US20190011918A1 (en) * | 2017-07-06 | 2019-01-10 | Lg Electronics Inc. | Driving system for vehicle and vehicle thereof |
| CN111480052A (en) * | 2017-12-15 | 2020-07-31 | 伟摩有限责任公司 | Resolving scene difficulties in vehicle routes using predictive models |
| CN111465824A (en) * | 2017-12-18 | 2020-07-28 | 智加科技公司 | Method and system for personalized self-aware path planning in autonomous vehicles |
| CN108932862A (en) * | 2018-07-27 | 2018-12-04 | 北京小马智行科技有限公司 | A kind of automatic driving vehicle dispatching method and device |
| CN110657818A (en) * | 2019-10-17 | 2020-01-07 | 北京百度网讯科技有限公司 | Path planning method, device and equipment for automatic driving vehicle and storage medium |
Cited By (18)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN112710323A (en) * | 2020-12-23 | 2021-04-27 | 广州橙行智动汽车科技有限公司 | Vehicle-mounted navigation method and device |
| CN112710323B (en) * | 2020-12-23 | 2023-11-03 | 广州橙行智动汽车科技有限公司 | Vehicle navigation method and device |
| CN113128766A (en) * | 2021-04-21 | 2021-07-16 | 科大讯飞股份有限公司 | Destination prejudging method and device, electronic equipment and storage medium |
| CN113405561B (en) * | 2021-07-06 | 2022-11-25 | 文诚恒远(天津)供应链管理服务有限公司 | Driving route recommendation method, device, equipment and storage medium |
| CN113405561A (en) * | 2021-07-06 | 2021-09-17 | 文诚恒远(天津)供应链管理服务有限公司 | Driving route recommendation method, device, equipment and storage medium |
| CN113401147A (en) * | 2021-07-30 | 2021-09-17 | 国汽智控(北京)科技有限公司 | Automatic driving method and device and electronic equipment |
| CN113401147B (en) * | 2021-07-30 | 2022-02-25 | 国汽智控(北京)科技有限公司 | Automatic driving method and device and electronic equipment |
| WO2023044794A1 (en) * | 2021-09-24 | 2023-03-30 | 华为技术有限公司 | Navigation method and related apparatus |
| CN114013452A (en) * | 2021-09-29 | 2022-02-08 | 江铃汽车股份有限公司 | Automatic driving control method, system, readable storage medium and vehicle |
| CN114013452B (en) * | 2021-09-29 | 2024-02-06 | 江铃汽车股份有限公司 | Automatic driving control method, system, readable storage medium and vehicle |
| CN114255607B (en) * | 2021-12-20 | 2023-04-07 | 浙江吉利控股集团有限公司 | Driving path recommendation method, system, medium and equipment |
| CN114255607A (en) * | 2021-12-20 | 2022-03-29 | 浙江吉利控股集团有限公司 | Driving path recommendation method, system, medium, device and program product |
| CN114279459A (en) * | 2021-12-28 | 2022-04-05 | 清华大学苏州汽车研究院（吴江） | Route navigation system with separated front end and rear end for automatic driving |
| CN114295144A (en) * | 2021-12-30 | 2022-04-08 | 海南大学 | DIKW-based vehicle path planning method |
| CN114295144B (en) * | 2021-12-30 | 2023-05-26 | 海南大学 | DIKW-based vehicle path planning method |
| CN114485710A (en) * | 2022-01-19 | 2022-05-13 | 北京百度网讯科技有限公司 | Map navigation method, system, device, electronic equipment and storage medium |
| CN114898584A (en) * | 2022-03-31 | 2022-08-12 | 广州汽车集团股份有限公司 | Recommendation method, device and equipment for vehicle driving modes and storage medium |
| CN116182879A (en) * | 2023-04-25 | 2023-05-30 | 中通服建设有限公司 | Intelligent automatic driving system |
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