CN114485710A - A method, system, device, electronic device and storage medium for map navigation - Google Patents

Abstract

The present disclosure provides a map navigation method, system, device, electronic device and storage medium, and relates to the technical field of data processing, in particular to the technical field of navigation. The specific implementation scheme is: after obtaining the target customer's navigation request, based on the request, current road condition information and pre-stored intelligent driving road section information, each recommended route is obtained, and each recommended route is displayed to the target user. The target route selected by the recommended route is used for map navigation. By applying the embodiments of the present disclosure, the intelligent driving section information is obtained in advance based on the stored user intelligent driving behavior data, and the recommended route is obtained based on the intelligent driving section information, and the intelligent driving behavior data of each user is applied to the map navigation, so that the user can know The road sections suitable for intelligent driving increase the utilization rate of intelligent driving functions such as automatic driving and assisted intelligent driving, and at the same time improve the user experience of map navigation.

Description

A method, system, device, electronic device and storage medium for map navigation

technical field

The present disclosure relates to the technical field of data processing, and in particular, to the technical field of navigation.

Background technique

At present, in order to better assist the driver in driving, the method of map navigation is usually used for route planning, driving reminder and so on for the driver.

SUMMARY OF THE INVENTION

The present disclosure provides a method, system, device, electronic device and storage medium for map navigation for improving the user's driving experience.

According to an aspect of the present disclosure, a method for map navigation is provided, comprising:

Obtain the navigation request sent by the target user; the navigation request includes: the starting position and the end position of the navigation;

Based on the navigation request, current road condition information and pre-stored intelligent driving section information, each recommended route is obtained; wherein, the recommended routes include: intelligent driving sections and/or non-intelligent driving sections; the intelligent driving section information is based on The stored user intelligent driving behavior data is pre-acquired;

displaying the recommended routes to the target user;

According to the target route selected by the target user based on the displayed recommended routes, map navigation is performed for the user.

According to another aspect of the present disclosure, a map navigation system is provided, including: a map navigation client and a map cloud server:

The map navigation client is used to obtain the navigation request sent by the target user; the navigation request includes: the starting position and the end position of the navigation; each recommended route sent by the map cloud server is displayed to the target user; Perform map navigation for the user according to the target route selected by the target user based on the displayed recommended routes;

The map cloud server is configured to obtain each recommended route based on the navigation request, current road condition information and pre-stored intelligent driving road section information and send it to the map navigation client; wherein the recommended route includes: an intelligent driving road section and/or non-intelligent driving sections; the intelligent driving section information is pre-acquired based on the stored user intelligent driving behavior data.

According to another aspect of the present disclosure, an apparatus for map navigation is provided, comprising:

A navigation request acquisition module, used to acquire a navigation request sent by a target user; the navigation request includes: a start position and an end position of the navigation;

A recommended route acquisition module, configured to acquire each recommended route based on the navigation request, current road condition information and pre-stored intelligent driving section information; wherein, the recommended route includes: intelligent driving section and/or non-intelligent driving section; The intelligent driving section information is pre-acquired based on the stored user intelligent driving behavior data;

a recommended route display module, configured to display the recommended routes to the target user;

The map navigation module is configured to perform map navigation for the user according to the target route selected by the target user based on the displayed recommended routes.

According to another aspect of the present disclosure, there is provided an electronic device, comprising:

at least one processor; and

a memory communicatively coupled to the at least one processor; wherein,

The memory stores instructions executable by the at least one processor, the instructions being executed by the at least one processor to enable the at least one processor to perform any of the above-described methods of map navigation.

According to another aspect of the present disclosure, there is provided a non-transitory computer-readable storage medium storing computer instructions, wherein the computer instructions are used to cause the computer to perform any of the above-described map navigation methods.

According to another aspect of the present disclosure, there is provided a computer program product, comprising a computer program that, when executed by a processor, implements any one of the map navigation methods described above.

It should be understood that what is described in this section is not intended to identify key or critical features of embodiments of the disclosure, nor is it intended to limit the scope of the disclosure. Other features of the present disclosure will become readily understood from the following description.

Description of drawings

The accompanying drawings are used for better understanding of the present solution, and do not constitute a limitation to the present disclosure. in:

1 is a schematic diagram of a first embodiment of a method for map navigation provided according to the present disclosure;

2 is a schematic diagram of a specific example of a method for map navigation provided according to the present disclosure;

FIG. 3 is a schematic flowchart of obtaining intelligent driving road segment information in an embodiment of the present disclosure;

4 is a schematic diagram of a second flow of acquiring intelligent driving road segment information in an embodiment of the present disclosure;

5 is a schematic diagram of a second embodiment of a method for map navigation provided according to the present disclosure;

FIG. 6a is a schematic diagram of a specific example of preset weights for each road section in an embodiment of the present disclosure;

Fig. 6b is a schematic diagram of the weight of each road section after the weight adjustment is performed on the basis of Fig. 6a;

7 is a schematic diagram of another specific example of the method for map navigation provided according to the present disclosure;

8 is a schematic diagram of recommending a user to turn on or off intelligent driving through a voice reminder in an embodiment of the present disclosure;

9 is a schematic diagram of a first embodiment of a map navigation system provided according to the present disclosure;

10 is a schematic diagram of a second embodiment of a map navigation system provided according to the present disclosure;

11 is a schematic diagram of an interaction of a map navigation system provided according to the present disclosure;

FIG. 12 is a schematic diagram of a first embodiment of an apparatus for map navigation provided according to the present disclosure;

13 is a block diagram of an electronic device used to implement the method of map navigation according to an embodiment of the present disclosure.

Detailed ways

Exemplary embodiments of the present disclosure are described below with reference to the accompanying drawings, which include various details of the embodiments of the present disclosure to facilitate understanding and should be considered as 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 disclosure. Also, descriptions of well-known functions and constructions are omitted from the following description for clarity and conciseness.

In the existing map navigation methods, the map product side usually collects and processes map data to produce map data of specifications that can be rendered and navigated to be applied to the map product. The driver's driving behavior data is collected by the car factory to improve the experience of the car factory's own products and services. It brings an upgrade of the navigation experience to the operator. .

At the same time, although the adoption rate of intelligent driving such as automatic driving and assisted intelligent driving is already high, the utilization rate is still low. Some users do not know when they can use automatic driving, assisted intelligent driving and can ensure driving safety, and need certain recommendation, guidance and education, so as to improve the utilization rate of automatic driving and assisted intelligent driving.

Therefore, in order to solve the above problems, the present disclosure provides a method, apparatus, system, electronic device and storage medium for map navigation. Below, the method for map navigation provided by the present disclosure is first introduced.

Referring to FIG. 1 , FIG. 1 is a schematic diagram of a first embodiment of a method for map navigation provided according to the present disclosure. As shown in Figure 1, the method may include the following steps:

Step S110, acquiring the navigation request sent by the target user.

In the embodiment of the present disclosure, the target user may send a navigation request based on the page displayed by the vehicle-machine map navigation client. The navigation request sent by the target user may include: the starting position and the ending position of the navigation. In this embodiment, the starting position of the above-mentioned navigation may be the current position of the vehicle, or another position designated by the user as the starting position. For example, the user can send a navigation request whose starting position is the current position of the vehicle (my position) to the end position is point A.

In an embodiment of the present disclosure, the above-mentioned navigation request may further include information such as vehicle types, and the above-mentioned vehicle types may include ordinary vehicles, new energy vehicles, and the like.

Step S120: Obtain each recommended route based on the navigation request, current road condition information and pre-stored intelligent driving road section information.

In the embodiment of the present disclosure, whether to recommend the target user to enable or disable intelligent driving functions (Adaptive Cruise Control, ACC, adaptive cruise control) such as automatic driving and assisted intelligent driving may be first determined in real time based on current road condition information. In an embodiment of the present disclosure, in the process of navigating the map for the vehicle, if the intelligent driving function is currently turned on in the vehicle, the vehicle can obtain information that meets the The recommended route for the above navigation request.

In the embodiment of the present disclosure, each of the above-mentioned recommended routes may include: intelligent driving sections and/or non-intelligent driving sections. The above-mentioned intelligent driving road segment information may be obtained in advance based on the stored user intelligent driving behavior data.

In the embodiment of the present disclosure, the above-mentioned intelligent driving behavior data may include the time when the vehicle turns on and off the intelligent driving, and the driving track of the vehicle during this time. Therefore, in this embodiment, the information of each intelligent driving road section can be acquired and stored based on each intelligent driving behavior data and map data in advance. In this way, the driver's experience data can be applied to the map data, so that the user can know the road section suitable for intelligent driving, and the user's intelligent driving experience can be improved.

Step S130, displaying the recommended routes to the target user.

In the embodiment of the present disclosure, each recommended route may be displayed on the corresponding page of the above-mentioned vehicle-machine map client. As a specific implementation manner, when each recommended route is displayed, the estimated time consumption of each recommended route, the number of traffic lights passed, and the like may be displayed.

Step S140: Perform map navigation for the user according to the target route selected by the target user based on the displayed recommended routes.

In the embodiment of the present disclosure, after the target user selects the target route, the target route can be displayed on the map, and the navigation start and end positions can be marked on the map. The information such as the congestion situation of the target route is marked. If the vehicle type is a new energy vehicle, the charging pile information can also be marked on the map.

Referring to FIG. 2, FIG. 2 is a schematic diagram of a specific example of a map navigation method provided according to the present disclosure.

As shown in Figure 2, the navigation start position selected by the target user is the current position of the vehicle (my position), and the end position is point A. In this embodiment, the user can input the above-mentioned navigation starting position and key position through the keyboard or by clicking the microphone icon shown in FIG. 2 through voice. The user can also add waypoints by clicking the "+" icon, and in addition, the user can exchange the starting position and the ending position by clicking the reversing icon. Users can also choose from options such as taxis, new energy sources, driving, and public transportation.

As shown in Figure 2, the target route is marked on the map with a thicker solid line to distinguish it from other roads on the map. In this embodiment, since the target route includes intelligent driving sections, ACC (intelligent driving) sections and ACC mileage information (“ACC mileage 10 km” in FIG. 2 ) can also be marked on the target route displayed on the map.

At the same time, as shown in Figure 2, the map can also display the estimated time of arrival, display the current location of the vehicle through icons, display road congestion through different colors, and so on. Since the user selects "new energy", the "charging pile" icon can also be displayed for the user, and the target user can choose whether to display the location of the charging pile on the map by controlling the icon. In addition, a "traffic restriction" icon and a "prompt" icon, etc. can also be displayed, so that the user can view the vehicle traffic restriction information and prompt information ("the unnamed road is impassable due to road closure and has been avoided for you") and so on.

It can be seen that, in the map navigation method provided by the embodiment of the present disclosure, after acquiring the navigation request of the target customer, each recommended route is acquired based on the navigation request, the current road condition information and the pre-stored intelligent driving road section information, and each recommended route is displayed. For the target user, map navigation is performed for the target user according to the target route selected by the target user based on each recommended route. By applying the embodiments of the present disclosure, the intelligent driving section information is obtained in advance based on the stored user intelligent driving behavior data, and the recommended route is obtained based on the intelligent driving section information, and the intelligent driving behavior data of each user is applied to the map navigation, so that the user can know The road sections suitable for intelligent driving increase the utilization rate of intelligent driving functions such as automatic driving and assisted intelligent driving, and at the same time improve the user experience of map navigation.

In an embodiment of the present disclosure, as shown in FIG. 3 , the above-mentioned intelligent driving road segment information can be obtained in advance by adopting the following steps:

Step S310, acquiring the driving behavior data of each user stored in the cloud.

In the embodiment of the present disclosure, each of the above-mentioned users may be users who have used the intelligent driving function. The driving behavior data of the above-mentioned users can be pre-stored to the cloud by the following steps:

Step 1: Obtain the driving behavior data uploaded by each user.

In the embodiments of the present disclosure, each vehicle equipped with intelligent driving functions such as automatic driving and assisted intelligent driving can transmit the user's driving behavior technology back to the cloud through the V2N (Vehicle to Net) technology. The above driving behavior data may include information such as the user's driving trajectory and driving time, and the data such as the driving trajectory information and time information may have different data formats. The above-mentioned driving track information may be collected by a GPS device mounted on the vehicle, and at the same time, each track information may also include corresponding time stamp information.

Step 2, performing private data desensitization and dirty data filtering on the driving behavior data, and storing the filtered driving behavior data in the cloud.

In this embodiment, after receiving the driving behavior data of each user, the cloud can desensitize the privacy data to ensure the privacy and security of the user.

Meanwhile, in the embodiment of the present disclosure, dirty data filtering can also be performed on each driving behavior data. Dirty data is data that is meaningless or illogical for the actual business. For example, data such as a vehicle traveling 100KM in two seconds is illogical dirty data and needs to be filtered. In the embodiment of the present disclosure, after each driving behavior data is acquired, the dirty data is filtered, and the filtered data is stored, which can reduce the amount of subsequent data processing and improve the data processing speed.

Step S320, extracting intelligent driving behavior data of each user based on the driving behavior data of each user.

As described above, in this embodiment of the present disclosure, the intelligent driving behavior data may include: time information when the vehicle turns on and off intelligent driving, and information on the driving track of the vehicle during this period (that is, the vehicle is in the intelligent driving state). driving track information). As a specific implementation manner, the above-mentioned travel track information may specifically include longitude and latitude sequence information of the vehicle traveling. Correspondingly, when extracting intelligent driving behavior data, each intelligent driving behavior data can be extracted based on the data storage format of the above-mentioned longitude and latitude sequence information.

Step S330: Match the driving track information of each vehicle in the intelligent driving state with each road in the map to obtain information of each road section for which intelligent driving is enabled.

Road segment matching is to associate a series of ordered trajectory location points to the actual road network. In the embodiment of the present disclosure, the map data of the map may include information of each road, specifically, the longitude and latitude sequence information of each road and the information of each road section included in each road, and each road section information may include pre-set road sections for each road section Identification (eg, road segment ID, etc.) and longitude and latitude sequence information of each road segment.

Therefore, as a specific implementation manner, when the above-mentioned road segment matching is performed, the above-mentioned longitude and latitude sequence information can be matched with the longitude and latitude sequence information of each road in the map, so as to obtain the driving track information corresponding to each of the above-mentioned vehicles in the intelligent driving state. Road segment information.

As a specific implementation of the embodiment of the present disclosure, the above step S330 may be implemented by a pre-trained road matching model. In the embodiment of the present disclosure, the above-mentioned road matching model may be a traditional HMM (Hidden Markov Model, Hidden Markov Model) or a model such as an HMM optimized based on reinforcement learning. The process of performing road segment matching in the embodiment of the present disclosure is briefly described below by taking a traditional HMM model as an example.

In the embodiment of the present disclosure, the above-mentioned HMM model can be trained based on the location information (eg, latitude and longitude sequence) obtained from GPS devices of each vehicle and the actual location of each vehicle. As a specific implementation, the location information and map data obtained from the GPS devices of each vehicle can be input into the HMM model to be trained, the matching result output by the HMM model can be obtained, and the matching result can be compared with the actual position of each vehicle. Compare (eg, calculate the value of the preset loss function), adjust the parameters in the HMM model based on the comparison result, and stop the training when the error between the result output by the HMM model and the actual position of each vehicle is less than the preset error threshold.

In the embodiment of the present disclosure, when performing road segment matching, the above-mentioned map data and the latitude and longitude sequences contained in each intelligent driving behavior data may be input into the above-mentioned trained HMM model. For each longitude and latitude sequence, the HMM model can output multiple matching results (ie, road segment positions) and the correct probability of each matching result. In this embodiment, for each longitude and latitude sequence, the matching result with the highest correct probability may be used as the position of the road section corresponding to the longitude and latitude sequence.

The above examples are merely illustrative to illustrate the process of performing road segment matching in the present disclosure, and do not specifically limit the present disclosure.

Step S340, based on the information of each road section for which intelligent driving is enabled, generate each intelligent driving road section information.

In the embodiment of the present disclosure, each intelligent driving section information may be generated based on the road condition information and status information of each section in the road and the above-mentioned information of each section in which intelligent driving is enabled. The above intelligent driving sections are sections where automatic driving and assisted intelligent driving are recommended.

In the embodiment of the present disclosure, by matching the intelligent driving behavior data of each user with each road in the map, and then generating the information of each intelligent road section, the experience data of each user on intelligent driving can be applied to the map data, and the user experience data can be matched with the map data. The organic combination of real geographic environment data can provide a reference for the intelligent driving behavior of target users, and further improve the utilization rate and safety of intelligent driving.

In an embodiment of the present disclosure, the above-mentioned intelligent driving behavior data may further include: time period information corresponding to the travel trajectory information of the vehicle in an intelligent driving state. The above-mentioned time period information may be the time when the vehicle starts the intelligent driving function and the time when the intelligent driving function ends.

As a specific implementation manner, when the above-mentioned driving behavior data is stored in the cloud, the above-mentioned driving trajectory information may be stored in correspondence with the time period information during which the vehicle travels the trajectory. Therefore, when extracting the intelligent driving behavior data, the driving track information and the corresponding time period information of the vehicle in the intelligent driving state can be extracted. The above time period information can also be extracted based on a preset time period information storage format, which is not specifically limited in the present disclosure.

That is, in the embodiment of the present disclosure, the above-mentioned intelligent driving behavior data may include: the time when the vehicle starts the intelligent driving function, the time when the intelligent driving function ends, and the driving track information of the vehicle during this period of time. The unit of the above-mentioned time period information may be ms (milliseconds).

Therefore, as shown in FIG. 4 , step S330 in FIG. 3 can be refined as follows:

Step S331: Match the driving track information of each vehicle in the intelligent driving state with each road in the map to obtain each road section for which intelligent driving is enabled.

As described above, the longitude and latitude sequence information in the driving track information can be matched with the longitude and latitude sequence information of each road to obtain the road section for enabling intelligent driving, specifically, the location information and road section ID of the road section for enabling intelligent driving can be obtained. Wait.

Step S332, based on the time period information corresponding to the driving track information of the vehicle in the intelligent driving state, obtain the road condition information in the corresponding time period of each road segment for which the intelligent driving is enabled from the map data.

In the embodiment of the present disclosure, the above-mentioned map data may include information such as road condition information, travel restriction information, and control strategy of each road in each time period. Therefore, in the embodiment of the present disclosure, after obtaining the above-mentioned road section with intelligent driving enabled, the road condition information in the corresponding time period of the above-mentioned road section with intelligent driving enabled can be obtained from the above-mentioned map data based on the time period information corresponding to the road section.

In the embodiment of the present disclosure, after the above-mentioned automatic driving and assisted intelligent driving trajectories (including the latitude and longitude sequence of time stamps) are matched with the map data through the above-mentioned map matching algorithm such as HMM, these trajectory points can be bound to the corresponding roads. . Thereby, the road sections that the basic automatic driving and the assisted intelligent driving have traveled, that is, the above-mentioned road sections with intelligent driving enabled, and then the information of the road sections with intelligent driving enabled are obtained. The road section information on which intelligent driving is enabled may include road condition information of the road section and corresponding time period information.

As shown in FIG. 4 , step S340 in FIG. 3 may include:

Step S341: Input the road sections for which intelligent driving is enabled and the road condition information of each road section in the corresponding time period into the intelligent driving section generation model; so that the intelligent driving section generation model is based on the The road condition information and state information in the time period, the road sections for which intelligent driving is enabled, and the road condition information of each road section in the corresponding time period are used to generate the information of each intelligent driving road section in different time periods.

In the embodiment of the present disclosure, the above-mentioned intelligent driving road segment generation model may be a simple threshold matching model. As a specific implementation manner, when the above-mentioned road section with intelligent driving enabled is driven for more than a preset number of times and the status of the section is passable, the section with intelligent driving enabled may be marked as an intelligent driving section. The above-mentioned road segment status can be obtained from information such as road restriction and control. The above-mentioned road segment status may include passable, road has been deleted, and prohibited, and so on.

The above-mentioned road sections with intelligent driving enabled can be used as the time period of the intelligent driving section, and can be obtained based on the road condition information of each road section in the above-mentioned road in each time period, and the above-mentioned road sections with intelligent driving enabled in the corresponding time period. As a specific implementation manner, the same time period as the road conditions of the road section in the above-mentioned corresponding time period may be obtained based on the road condition information of the above-mentioned road section for which intelligent driving is enabled in each time period. During these time periods, if the above-mentioned road section for which intelligent driving is enabled is driven for more than a preset number of times and the status of the road section is passable, the road section can be regarded as an intelligent driving road section.

In the embodiment of the present disclosure, when generating the intelligent driving road segment information, it is also possible to set the situation that the intelligent driving function cannot be used. For example, each intelligent road segment can be screened based on technical constraints (eg, night vision capability of vehicle sensors and areas where intelligent driving is prohibited) to obtain the final intelligent driving road segment.

It can be seen that, in the embodiment of the present disclosure, the above-mentioned intelligent driving road segment generation model can be based on the map's ability to predict road conditions in different time periods in the future, traffic restrictions on each road in the map, control strategies, and technical constraints (limitation of vehicle sensor recognition ability at night, automatic driving. Area restrictions) comprehensively generate road sections that can automatically drive and assist intelligent driving at different time periods

The above model generates intelligent driving section information based on the historical experience data of users who have a certain understanding of intelligent driving, so that the generated intelligent driving section information is more informative and can better assist target users in intelligent driving.

In an embodiment of the present disclosure, the information of each road section in the above-mentioned map data may further include preset weights of each road section in each road. Therefore, as shown in FIG. 4 , the above method may further include:

Step S450, increasing the static weight of each intelligent driving section.

In the embodiment of the present disclosure, weights may be set for each road segment in advance. The preset weights of the above road sections may be the same or different, which are not specifically limited in the present disclosure.

After acquiring the above-mentioned information of each intelligent driving section, the weight of each intelligent driving section may be increased, and the weight may be stored as section information corresponding to the intelligent driving section. In this way, the probability of each intelligent driving section being recommended can be increased.

Specifically, as shown in FIG. 5 , step S120 in FIG. 1 can be refined as follows:

Step S121: Obtain a recommended route based on the navigation request, current road condition information, pre-stored intelligent driving road segment information, and the weight of each road segment.

In the embodiment of the present disclosure, when the recommended route is acquired, the recommended route may be recalled based on the weight of each road segment.

As shown in Figure 6a, there are two routes from location v to location w, namely: v->x->y->w and v->u->w. The preset initial weight of each road segment is: the initial weight of road segment v->x is 1, the initial weight of road segment x->y is 1, the initial weight of road segment y->w is 1, the initial weight of road segment v->u is 3, the initial weight of road segment is 3, and the initial weight of road segment is 1. The initial weight of u->w is 2. Therefore, if calculated according to the initial weight, the route v->u->w has a high probability of being recommended.

From 10:00 to 18:00, the road segments v->x and x->y are the road segments that can be used for automatic driving or assisted intelligent driving. Then during the period of 10:00-18:00, as shown in Figure 6b, the weights of the road segments v->x and x->y can be increased from 1 to 3, then the route calculation from location v to location w is It will change from the selection of v->u->w to: v->x->y->w for user decision-making. It can be seen that increasing the weight of intelligent driving sections increases the probability of automatic driving and assisted intelligent driving sections being recommended.

In an embodiment of the present disclosure, after each of the above-mentioned recommended routes is acquired, each of the recommended routes may be sorted based on the weights of the road sections included in each of the routes. As a specific implementation manner, on the basis of FIG. 1, as shown in FIG. 5, before the above step S130, the following steps may be further included:

Step S530: Rank each recommended route based on the target user's preference setting for the route and the weight of the road sections included in each recommended route.

Based on FIG. 1 , as shown in FIG. 5 , the above step S130 can be refined as follows:

Step S131, displaying the sorted recommended routes to the target user.

In this embodiment of the present disclosure, the user can select a route preference. For example, the user may select one or more of the route preferences of high speed priority, autonomous driving segment priority, assisted intelligent driving segment priority, time priority, and the like. After the above-mentioned recommended routes are obtained, the routes can be sorted based on the user's preference, and at the same time, the weights of the road sections included in the routes can be referred to when sorting. In this way, a navigation route can be recommended for the user more accurately, and the user experience can be improved. For example, if the user selects a priority route for intelligent driving (including automatic driving and assisted intelligent driving), and there are multiple recommended routes that include intelligent driving segments. Then, when sorting the recommended routes, the intelligent driving routes including the intelligent driving sections can be ranked first, and further, the intelligent driving routes with higher weights of the included sections can be ranked first.

Referring to FIG. 7 , FIG. 7 is a specific example of a method for map navigation provided according to the present disclosure. As shown in FIG. 7 , the user can select the route preference through the page displayed by the vehicle-machine map client. The above-mentioned route preference may include priority of automatic driving section, priority of assisted intelligent driving section, time priority, less toll, no high speed, high speed priority, and the like. The user also has control over the "remember option" button to reduce the number of times the same preference is selected.

In the figure, the route preference selected by the user is the priority of the automatic driving section and the priority of the assisted intelligent driving section. Based on the above preference choices and the weights of each road segment, three recommended routes from the current position of the vehicle (my location) to location A are presented to the user. In this embodiment, basic information of the route, such as the mileage of each route, the estimated time consumption, and the number of traffic lights to be passed, can also be displayed to the user.

As shown in Figure 7, the recommended routes displayed to the user include: Option 1, the mileage is 23 kilometers, the estimated time is 41 minutes, the destination is expected to reach the end at 17:04, and 15 traffic lights are required. Among the three routes, the time spent is less . Option 2, with a mileage of 23 kilometers, is expected to take 43 minutes, and needs to pass through 5 traffic lights. Among the three routes, it takes regular time, but there is a lot of congestion. Plan 3, the mileage is 25 kilometers, it is estimated to take 46 minutes, and 18 traffic lights need to be passed. The user can select the above schemes, and the vehicle-machine navigation client can perform map navigation for the user according to the target scheme selected by the user.

In an embodiment of the present disclosure, based on FIG. 1 , as shown in FIG. 5 , after the above step S140 , it may further include:

Step S550, obtaining current road conditions and state information in real time.

In an embodiment of the present disclosure, the above-mentioned vehicle map client may refresh the road conditions and states of each road every minute. As a specific implementation manner, a request may be sent to a map navigation server, and the map navigation server refreshes the road conditions and states. The above-mentioned road status can be obtained based on information such as traffic restrictions and controls on the road, which may specifically include passable, deleted, and prohibited, and the like.

Step S560, based on the current road conditions and the compliance of the state information with the preset smart driving constraints, determine whether to recommend the target user to turn on or off smart driving.

In the embodiment of the present disclosure, intelligent driving constraints may be preset, and the above constraints may include intelligent driving road conditions, road states, and technical constraints. The above-mentioned intelligent driving road conditions can be set based on the road conditions of each intelligent driving road section. The road state can be that the road is passable, and the above technical constraints can be the limitation of vehicle sensor recognition ability at night, the area limit of intelligent driving, and so on.

If the current road conditions and states meet the preset intelligent driving constraints, the user may be advised to enable intelligent driving functions such as automatic driving and assisted intelligent driving. If not, the user may be advised to turn off the intelligent driving function.

In an embodiment of the present disclosure, if the target user needs to be advised to enable or disable intelligent driving, a voice reminder for enabling or disabling intelligent driving may be issued.

In the embodiment of the present disclosure, if the user needs to be advised to enable intelligent driving, but the user has enabled intelligent driving, a voice reminder may not be issued. If the user does not enable intelligent driving, a voice reminder may be issued when approaching an intelligent driving road section. If the user needs to be advised to turn off the smart driving, a corresponding voice reminder can be issued after passing the end position of the smart driving section. If the user has turned off the smart driving, the reminder may not be issued. In this way, by utilizing the convenience of interacting with the user on the map, the user can know when the smart driving can be used, the utilization rate of the smart driving can be improved, and the driving experience of the user can be improved.

As a specific example, as shown in FIG. 8 , after approaching N meters of a road section where ACC (adaptive cruise, intelligent driving described in this disclosure) can be turned on, broadcast: "There are many users who turn on ACC in the current road section, and it is recommended to turn on ACC. , it's easier to drive" and other broadcast content. After N meters near the end of the ACC section, broadcast: "It is recommended to exit the ACC, pay attention to driving safety" and other broadcast content.

It can be seen that the map navigation method provided by the embodiments of the present disclosure desensitizes driver behavior data and matches the road on the map, so that users who have a certain understanding and application of automatic driving and assisted intelligent driving often turn on automatic driving and auxiliary driving. Road segment information for intelligent driving. Based on this information, it generates intelligent driving section information that recommends enabling automatic driving and assisted intelligent driving, improves the weight of these roads, and provides users with automatic driving and assisted intelligent driving prioritizing route planning capabilities in combination with other road conditions, traffic restrictions and other factors. The user can choose the route preference of the automatic driving section priority and the assisted intelligent driving section priority through the interface of the car-machine map client.

In addition to the calculation of the route before the trip, the above-mentioned data such as the intelligent driving section information for enabling automatic driving and assisting intelligent driving, other road condition information, and travel restriction information are also applied to the navigation process in the embodiments of the present disclosure to provide a road state refresh service. . That is, combined with the changes of the original road dynamics, such as traffic restrictions, road conditions, and control, it is updated in real time whether it is recommended to turn on or off assisted intelligent driving and automatic driving for road sections related to road calculation. That is, while the car-machine map periodically refreshes the road conditions, the refresh provides real-time recommendations for turning on and off assisted intelligent driving and automatic driving and the corresponding mileage, and the result is rendered through the car-machine map. At the same time, when the user's location is close to the road section where assisted intelligent driving and automatic driving can be turned on or off, the user can obtain this information through voice prompts, thereby increasing the utilization rate of automatic driving and assisted intelligent driving, and further improving automatic driving. , The effect of assisted intelligent driving.

According to another aspect of the embodiments of the present disclosure, a map navigation system is also provided. As shown in FIG. 9 , the above system may include: a map navigation client 910 and a map cloud server 920;

The map navigation client 910 can be used to obtain the navigation request sent by the target user; the navigation request includes: the start position and the end position of the navigation; display each recommended route sent by the map cloud server to the target user; perform map navigation for the user according to the target route selected by the target user based on the displayed recommended routes;

The map cloud server 920 can be configured to obtain each recommended route based on the navigation request, current road condition information and pre-stored intelligent driving road section information and send it to the map navigation client; wherein the recommended route includes: intelligent Driving sections and/or non-intelligent driving sections; the intelligent driving section information is pre-acquired based on the stored user intelligent driving behavior data.

The map navigation system provided by the present disclosure, after acquiring the navigation request of the target customer, acquires each recommended route based on the navigation request, current road condition information and pre-stored intelligent driving road section information, and displays each recommended route to the target user, Map navigation is performed for the target user according to the target route selected by the target user based on each recommended route. By applying the embodiments of the present disclosure, the intelligent driving section information is obtained in advance based on the stored user intelligent driving behavior data, and the recommended route is obtained based on the intelligent driving section information, and the intelligent driving behavior data of each user is applied to the map navigation, so that the user can know The road sections suitable for intelligent driving increase the utilization rate of intelligent driving functions such as automatic driving and assisted intelligent driving, and at the same time improve the user experience of map navigation.

In an embodiment of the present disclosure, on the basis of FIG. 9 , as shown in FIG. 10 , the above system may further include a car factory cloud server 1030 ;

The car factory cloud server 1030 can be used to obtain the driving behavior data of each user and store it in the cloud; based on the driving behavior data of each user, extract the intelligent driving behavior data of each user and send it to the map cloud server; The intelligent driving behavior data includes: the driving trajectory information of the vehicle in the intelligent driving state;

The map cloud server 920 can also be used to match the driving track information of each vehicle in the intelligent driving state with each road in the map, and obtain the information of each road section for enabling intelligent driving; The information of each road section of driving is generated, and the information of each intelligent driving road section is generated.

In an embodiment of the present disclosure, the intelligent driving behavior data may further include: time period information corresponding to the driving trajectory information of the vehicle in the intelligent driving state;

Matching the driving track information of each vehicle in the intelligent driving state with each road in the map, and acquiring the information of each road section for enabling intelligent driving, may include:

Matching the driving track information of each vehicle in an intelligent driving state with each road in the map to obtain each road section for which intelligent driving is enabled;

Based on the time period information corresponding to the driving trajectory information of the vehicle in the intelligent driving state, obtain the road condition information in the corresponding time period of each road section for which intelligent driving is enabled from the map historical data;

The generating information on each road section for intelligent driving based on the information on each road section for which intelligent driving is enabled includes:

Inputting each road section for which intelligent driving is enabled and the road condition information of each road section in the corresponding time period into the intelligent driving section generation model; The road condition information and state information, the road sections for which intelligent driving is enabled, and the road conditions information of the road sections in the corresponding time period, generate the information of each intelligent driving road section in different time periods.

In an embodiment of the present disclosure, the map cloud server 920 may also be used for:

Improve the static weight of each intelligent driving section;

The obtaining of each recommended route based on the navigation request, current road condition information and pre-stored intelligent driving road segment information includes:

Based on the navigation request, current road condition information, pre-stored intelligent driving road segment information, and the weight of each road segment, a recommended route is obtained.

In an embodiment of the present disclosure, the map navigation client 910 may also be used for:

Ranking each recommended route based on the target user's preference setting for the route and the weight of the road segments included in the each recommended route;

The displaying each recommended route to the target user includes:

The sorted recommended routes are displayed to the target user.

In an embodiment of the present disclosure, the driving behavior data of each user may be pre-stored by adopting the following steps:

Obtain the driving behavior data uploaded by each user;

Perform private data desensitization and dirty data filtering on the driving behavior data, and store the filtered driving behavior data in the cloud.

In an embodiment of the present disclosure, the map navigation client 910 may also be used for:

Real-time access to current road conditions and status information;

Based on the current road conditions and the compliance of the state information with the preset smart driving constraints, it is determined whether to recommend the target user to turn on or off smart driving.

In an embodiment of the present disclosure, if the target user needs to be advised to turn on or off intelligent driving, a voice reminder for turning on or off intelligent driving is issued.

As shown in FIG. 11 , FIG. 11 is an interactive schematic diagram of a map navigation system provided by an embodiment of the present disclosure when a user selects an intelligent driving road section as a priority. Specifically, the interaction process may include two stages: a stage of acquiring intelligent driving sections and a stage of map navigation. Wherein, the above intelligent driving road segment acquisition stage may include the following steps:

Step 1. Each vehicle uploads the user's driving behavior data to the depot cloud through V2N.

Step 2: After desensitizing the private data and cleaning the dirty data of the user's driving behavior data, the depot cloud stores the user's driving behavior data.

Step 3: The Depot Cloud extracts each intelligent driving behavior data from the driving behavior data of each user, and sends it to the map cloud.

The above-mentioned car factory cloud is the car factory cloud server in the embodiment of the present disclosure.

The above-mentioned intelligent driving behavior data may include time period information for starting and ending intelligent driving, and the driving track information of the vehicle in this time period. The above-mentioned driving track information may be longitude and latitude sequence information collected by the GPS of the vehicle, and the above-mentioned time period information may be obtained according to the timestamp information contained in each longitude and latitude sequence information.

In step 4, the map cloud matches each intelligent driving behavior data with the map data, and obtains the road segment information and the corresponding time segment information of the road segment where the vehicle has enabled the intelligent driving, and the road segment information includes the road condition information of the intelligent driving segment. Then, the above-mentioned road section information of the intelligent driving section of the vehicle and the corresponding time period information are input into the intelligent driving section generation model, and the information of each intelligent driving section output by the model is obtained.

In this embodiment, after acquiring the information of each intelligent driving section, the weight of each intelligent driving section can be increased.

The above map navigation stage may include the following steps:

Step 1, the user sends a navigation request based on the car-machine map client, and the car-machine map client initiates route calculation.

The above vehicle-machine map client is the map navigation client described in the embodiments of the present disclosure.

Step 2, the map navigation server performs route recall based on the above-mentioned navigation request, current road conditions, information of each intelligent driving road section, and weight of each road section, obtains each recommended route, and sorts each recommended route. The above-mentioned recommended route may include intelligent driving sections and/or non-intelligent driving sections.

In this embodiment, the above-mentioned map cloud and the map navigation server can be implemented by using the map cloud server described in the embodiments of the present disclosure.

Step 3, the map navigation server returns the intelligent driving section priority route to the vehicle-machine map client for the user to make a decision.

In this embodiment, the priority route of each intelligent driving section can be returned according to the page shown in the above-mentioned FIG. 7 for the user to decide and select.

Step 4: During the map navigation process, the road status and road conditions are refreshed in real time, and according to the current road status and the compliance of the road conditions with the preset intelligent driving constraints, the in-line guidance recommendation is made, that is, a voice reminder is sent to the user to suggest opening. Or turn off the intelligent driving function.

In this embodiment, the above-mentioned dynamic update of the mileage priority of the intelligent driving road segment may also be performed based on the road state and road condition refreshed in real time. Specifically, the page shown in FIG. 2 can be used to display the location of the intelligent driving section in the route and the mileage of the intelligent driving section to the user.

According to another aspect of the present disclosure, an apparatus for map navigation is also provided. As shown in FIG. 12 , the apparatus may include:

The navigation request obtaining module 1210 can be used to obtain the navigation request sent by the target user; the navigation request includes: the starting position and the ending position of the navigation;

The recommended route obtaining module 1220 can be configured to obtain each recommended route based on the navigation request, current road condition information and pre-stored intelligent driving section information; wherein the recommended route includes: intelligent driving section and/or non-intelligent driving section ; The intelligent driving section information is pre-acquired based on the stored user intelligent driving behavior data;

A recommended route display module 1230, which can be used to display the recommended routes to the target user;

The map navigation module 1240 may be configured to perform map navigation for the user according to the target route selected by the target user based on the displayed recommended routes.

The device for map navigation provided by the present disclosure obtains each recommended route based on the navigation request, current road condition information and pre-stored intelligent driving section information after acquiring the navigation request of the target customer, and displays each recommended route to the target user , and perform map navigation for the target user according to the target route selected by the target user based on each recommended route. By applying the embodiments of the present disclosure, the intelligent driving section information is obtained in advance based on the stored user intelligent driving behavior data, and the recommended route is obtained based on the intelligent driving section information, and the intelligent driving behavior data of each user is applied to the map navigation, so that the user can know The road sections suitable for intelligent driving increase the utilization rate of intelligent driving functions such as automatic driving and assisted intelligent driving, and at the same time improve the user experience of map navigation.

In the technical solutions of the present disclosure, the collection, storage, use, processing, transmission, provision, and disclosure of the user's personal information involved are all in compliance with relevant laws and regulations, and do not violate public order and good customs.

According to embodiments of the present disclosure, the present disclosure also provides an electronic device, a readable storage medium, and a computer program product.

13 shows a schematic block diagram of an example electronic device 1300 that may be used to implement embodiments of the present disclosure. Electronic devices are intended to represent various forms of digital computers, such as laptops, desktops, workstations, personal digital assistants, servers, blade servers, mainframe computers, and other suitable computers. Electronic devices may also represent various forms of mobile devices, such as personal digital processors, cellular phones, smart phones, wearable devices, and other similar computing devices. The components shown herein, their connections and relationships, and their functions are by way of example only, and are not intended to limit implementations of the disclosure described and/or claimed herein.

As shown in FIG. 13 , the device 1300 includes a computing unit 1301 that can be executed according to a computer program stored in a read only memory (ROM) 1302 or a computer program loaded into a random access memory (RAM) 1303 from a storage unit 1308 Various appropriate actions and handling. In the RAM 1303, various programs and data necessary for the operation of the device 1300 can also be stored. The computing unit 1301 , the ROM 1302 , and the RAM 1303 are connected to each other through a bus 1304 . An input/output (I/O) interface 1305 is also connected to bus 1304 .

Various components in the device 1300 are connected to the I/O interface 1305, including: an input unit 1306, such as a keyboard, mouse, etc.; an output unit 1307, such as various types of displays, speakers, etc.; a storage unit 1308, such as a magnetic disk, an optical disk, etc. ; and a communication unit 1309, such as a network card, a modem, a wireless communication transceiver, and the like. The communication unit 1309 allows the device 1300 to exchange information/data with other devices through a computer network such as the Internet and/or various telecommunication networks.

Computing unit 1301 may be various general-purpose and/or special-purpose processing components with processing and computing capabilities. Some examples of computing units 1301 include, but are not limited to, central processing units (CPUs), graphics processing units (GPUs), various specialized artificial intelligence (AI) computing chips, various computing units that run machine learning model algorithms, digital signal processing processor (DSP), and any suitable processor, controller, microcontroller, etc. The computing unit 1301 executes the various methods and processes described above, such as the method of map navigation. For example, in some embodiments, the method of map navigation may be implemented as a computer software program tangibly embodied on a machine-readable medium, such as storage unit 1308 . In some embodiments, part or all of the computer program may be loaded and/or installed on device 1300 via ROM 1302 and/or communication unit 1309 . When the computer program is loaded into RAM 1303 and executed by computing unit 1301, one or more steps of the method of map navigation described above may be performed. Alternatively, in other embodiments, the computing unit 1301 may be configured by any other suitable means (eg, by means of firmware) to perform the method of map navigation.

Various implementations of the systems and techniques described herein above may be implemented in digital electronic circuitry, integrated circuit systems, field programmable gate arrays (FPGAs), application specific integrated circuits (ASICs), application specific standard products (ASSPs), systems on chips system (SOC), complex programmable logic device (CPLD), computer hardware, firmware, software, and/or combinations thereof. These various embodiments may include being implemented in one or more computer programs executable and/or interpretable on a programmable system including at least one programmable processor that The processor, which may be a special purpose or general-purpose programmable processor, may receive data and instructions from a storage system, at least one input device, and at least one output device, and transmit data and instructions to the storage system, the at least one input device, and the at least one output device an output device.

Program code for implementing the methods of the present disclosure may be written in any combination of one or more programming languages. These program codes may be provided to a processor or controller of a general purpose computer, special purpose computer or other programmable data processing apparatus, such that the program code, when executed by the processor or controller, performs the functions/functions specified in the flowcharts and/or block diagrams. Action is implemented. The program code may execute entirely on the machine, partly on the machine, partly on the machine and partly on a remote machine as a stand-alone software package or entirely on the remote machine or server.

In the context of the present disclosure, a machine-readable medium may be a tangible medium that may contain or store a program for use by or in connection with the instruction execution system, apparatus or device. The machine-readable medium may be a machine-readable signal medium or a machine-readable storage medium. Machine-readable media may include, but are not limited to, electronic, magnetic, optical, electromagnetic, infrared, or semiconductor systems, devices, or devices, or any suitable combination of the foregoing. More specific examples of machine-readable storage media would include one or more wire-based electrical connections, portable computer disks, hard disks, random access memory (RAM), read only memory (ROM), erasable programmable read only memory (EPROM or flash memory), fiber optics, compact disk read only memory (CD-ROM), optical storage, magnetic storage, or any suitable combination of the foregoing.

To provide interaction with a user, the systems and techniques described herein may be implemented on a computer having a display device (eg, a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to the user ); and a keyboard and pointing device (eg, a mouse or trackball) through which a user can provide input to the computer. Other kinds of devices can also be used to provide interaction with the user; for example, the feedback provided to the user can be any form of sensory feedback (eg, visual feedback, auditory feedback, or tactile feedback); and can be in any form (including acoustic input, voice input, or tactile input) to receive input from the user.

The systems and techniques described herein may be implemented on a computing system that includes back-end components (eg, as a data server), or a computing system that includes middleware components (eg, an application server), or a computing system that includes front-end components (eg, a user's computer having a graphical user interface or web browser through which a user may interact with implementations of the systems and techniques described herein), or including such backend components, middleware components, Or any combination of front-end components in a computing system. The components of the system may be interconnected by any form or medium of digital data communication (eg, a communication network). Examples of communication networks include: Local Area Networks (LANs), Wide Area Networks (WANs), and the Internet.

A computer system can include clients and servers. Clients and servers are generally remote from each other and usually interact through a communication network. The relationship of client and server arises by computer programs running on the respective computers and having a client-server relationship to each other. The server can be a cloud server, a distributed system server, or a server combined with blockchain.

It should be understood that steps may be reordered, added or deleted using the various forms of flow shown above. For example, the steps described in the present disclosure can be executed in parallel, sequentially, or in different orders, as long as the desired results of the technical solutions disclosed in the present disclosure can be achieved, no limitation is imposed herein.

The above-mentioned specific embodiments do not constitute a limitation on the protection scope of the present disclosure. It should be understood by those skilled in the art that various modifications, combinations, sub-combinations and substitutions may occur depending on design requirements and other factors. Any modifications, equivalent replacements, and improvements made within the spirit and principles of the present disclosure should be included within the protection scope of the present disclosure.

Claims (20)

1. A method for map navigation, comprising: Obtain the navigation request sent by the target user; the navigation request includes: the starting position and the end position of the navigation; Based on the navigation request, current road condition information and pre-stored intelligent driving section information, each recommended route is obtained; wherein, the recommended routes include: intelligent driving sections and/or non-intelligent driving sections; the intelligent driving section information is based on The stored user intelligent driving behavior data is pre-acquired; displaying the recommended routes to the target user; According to the target route selected by the target user based on the displayed recommended routes, map navigation is performed for the user. 2. The method according to claim 1, wherein the intelligent driving section information is obtained in advance by the following steps: Obtain the driving behavior data of each user stored in the cloud; Based on the driving behavior data of each user, extract the intelligent driving behavior data of each user; wherein, the intelligent driving behavior data includes: the driving track information of the vehicle in an intelligent driving state; Matching the driving track information of each vehicle in the intelligent driving state with each road in the map to obtain the information of each road section for which intelligent driving is enabled; Based on the information of each road section for which intelligent driving is enabled, each intelligent driving road section information is generated. 3. The method of claim 2, wherein, The intelligent driving behavior data further includes: time period information corresponding to the driving trajectory information of the vehicle in the intelligent driving state; The step of matching the driving track information of each vehicle in the intelligent driving state with each road in the map, and obtaining the information of each road section for enabling intelligent driving, includes: Matching the driving track information of each vehicle in an intelligent driving state with each road in the map to obtain each road section for which intelligent driving is enabled; Based on the time period information corresponding to the driving trajectory information of the vehicle in the intelligent driving state, obtain the road condition information in the corresponding time period of each road section for which intelligent driving is enabled from the map historical data; The step of generating information on each road section for intelligent driving based on the information on each road section for enabling intelligent driving includes: Inputting each road section for which intelligent driving is enabled and the road condition information of each road section in the corresponding time period into the intelligent driving section generation model; The road condition information and state information, the road sections for which intelligent driving is enabled, and the road conditions information of the road sections in the corresponding time period, generate the information of each intelligent driving road section in different time periods. 4. The method of claim 2, further comprising: Improve the static weight of each intelligent driving section; The step of obtaining each recommended route based on the navigation request, current road condition information and pre-stored intelligent driving road segment information includes: Based on the navigation request, current road condition information, pre-stored intelligent driving road segment information, and the weight of each road segment, a recommended route is obtained. 5. The method of claim 4, wherein, Before the step of displaying the recommended routes to the target user, the method further includes: Ranking each recommended route based on the target user's preference setting for the route and the weight of the road segments included in the each recommended route; The step of displaying the recommended routes to the target user includes: The sorted recommended routes are displayed to the target user. 6. The method according to claim 2, wherein the driving behavior data of each user is pre-stored by adopting the following steps: Obtain the driving behavior data uploaded by each user; Perform private data desensitization and dirty data filtering on the driving behavior data, and store the filtered driving behavior data in the cloud. 7. The method of claim 1, further comprising: Real-time access to current road conditions and status information; Based on the current road conditions and the compliance of the state information with the preset smart driving constraints, it is determined whether to recommend the target user to turn on or off smart driving. 8. The method of claim 7, further comprising: If the target user needs to be advised to turn on or off intelligent driving, a voice reminder for turning on or off intelligent driving is issued. 9. A map navigation system, comprising: a map navigation client and a map cloud server: The map navigation client is used to obtain the navigation request sent by the target user; the navigation request includes: the starting position and the end position of the navigation; and displaying each recommended route sent by the map cloud server to the target user; Perform map navigation for the user according to the target route selected by the target user based on the displayed recommended routes; The map cloud server is configured to obtain each recommended route based on the navigation request, current road condition information and pre-stored intelligent driving road section information and send it to the map navigation client; wherein the recommended route includes: an intelligent driving road section and/or non-intelligent driving sections; the intelligent driving section information is pre-acquired based on the stored user intelligent driving behavior data. 10. The system according to claim 9, further comprising: a car factory cloud server; The car factory cloud server is used to obtain the driving behavior data of each user and store it in the cloud; based on the driving behavior data of each user, extract the intelligent driving behavior data of each user and send it to the map cloud server; wherein, the intelligent driving behavior data The driving behavior data includes: the driving trajectory information of the vehicle in the intelligent driving state; The map cloud server is also used to match the driving track information of each vehicle in the intelligent driving state with each road in the map, and obtain the information of each road section for enabling intelligent driving; Each road segment information is generated to generate each intelligent driving road segment information. 11. The system according to claim 10, wherein the intelligent driving behavior data further comprises: time period information corresponding to the driving track information of the vehicle in an intelligent driving state; Matching the driving track information of each vehicle in the intelligent driving state with each road in the map to obtain the information of each road section for enabling intelligent driving, including: Matching the driving track information of each vehicle in the intelligent driving state with each road in the map to obtain each road section for which intelligent driving is enabled; Based on the time period information corresponding to the driving track information of the vehicle in the intelligent driving state, obtain the road condition information in the corresponding time period of each road segment for which intelligent driving is enabled from the map historical data; The generating information on each road section for intelligent driving based on the information on each road section for enabling intelligent driving, including: Inputting each road section for which intelligent driving is enabled and the road condition information of each road section in the corresponding time period into the intelligent driving road section generation model; The road condition information and status information, each road section for which intelligent driving is enabled, and the road condition information of each road section in the corresponding time period are generated, and each intelligent driving road section information in different time periods is generated. 12. The system according to claim 10, wherein the map cloud server is further used for: Improve the static weight of each intelligent driving section; The obtaining of each recommended route based on the navigation request, current road condition information and pre-stored intelligent driving road segment information includes: Based on the navigation request, current road condition information, pre-stored intelligent driving road segment information, and the weight of each road segment, a recommended route is obtained. 13. The system of claim 12, wherein, The map navigation client is also used for: Ranking each recommended route based on the target user's preference setting for the route and the weight of the road segments included in the each recommended route; The displaying each recommended route to the target user includes: The sorted recommended routes are displayed to the target user. 14. The system according to claim 10, wherein the driving behavior data of each user is pre-stored by the following steps: Obtain the driving behavior data uploaded by each user; Perform private data desensitization and dirty data filtering on the driving behavior data, and store the filtered driving behavior data in the cloud. 15. The system of claim 9, wherein the map navigation client is further configured to: Real-time access to current road conditions and status information; Based on the current road conditions and the compliance of the state information with the preset smart driving constraints, it is determined whether to recommend the target user to turn on or off smart driving. 16. The system of claim 15, the map navigation client, further used for: If the target user needs to be advised to turn on or off intelligent driving, a voice reminder for turning on or off intelligent driving is issued. 17. An apparatus for map navigation, comprising: A navigation request acquisition module, used to acquire a navigation request sent by a target user; the navigation request includes: a start position and an end position of the navigation; A recommended route acquisition module, configured to acquire each recommended route based on the navigation request, current road condition information and pre-stored intelligent driving section information; wherein, the recommended route includes: intelligent driving section and/or non-intelligent driving section; The intelligent driving section information is pre-acquired based on the stored user intelligent driving behavior data; a recommended route display module, configured to display the recommended routes to the target user; The map navigation module is configured to perform map navigation for the user according to the target route selected by the target user based on the displayed recommended routes. 18. An electronic device comprising: at least one processor; and a memory communicatively coupled to the at least one processor; wherein, The memory stores instructions executable by the at least one processor, the instructions being executed by the at least one processor to enable the at least one processor to perform the execution of any of claims 1-8 Methods. 19. A non-transitory computer readable storage medium storing computer instructions for causing the computer to perform the method of any of claims 1-8. 20. A computer program product comprising a computer program which, when executed by a processor, implements the method of any of claims 1-8.

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