CN114485710A

CN114485710A - Map navigation method, system, device, electronic equipment and storage medium

Info

Publication number: CN114485710A
Application number: CN202210059614.1A
Authority: CN
Inventors: 张鑫
Original assignee: Beijing Baidu Netcom Science and Technology Co Ltd
Current assignee: Beijing Baidu Netcom Science and Technology Co Ltd
Priority date: 2022-01-19
Filing date: 2022-01-19
Publication date: 2022-05-13

Abstract

The disclosure provides a map navigation method, a map navigation system, a map navigation device, an electronic device and a storage medium, and relates to the technical field of data processing, in particular to the technical field of navigation. The specific implementation scheme is as follows: and after a navigation request of a target client is acquired, acquiring each recommended route based on the request, current road condition information and pre-stored intelligent driving road section information, displaying each recommended route to a target user, and performing map navigation on the target user according to the target route selected by the target user based on each recommended route. By applying the embodiment of the disclosure, the intelligent driving road section information is acquired in advance based on the stored intelligent driving behavior data of the user, the recommended route is acquired based on the intelligent driving road section information, and the intelligent driving behavior data of each user is acted on the map navigation, so that the user can know the road section suitable for intelligent driving, the utilization rate of intelligent driving functions such as automatic driving and auxiliary intelligent driving is improved, and the use experience of the user on the map navigation is improved.

Description

Map navigation method, system, device, electronic equipment and storage medium

Technical Field

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

Background

At present, in order to better assist the driver in driving, a map navigation mode is generally used for planning a route, driving reminding and the like for the driver.

Disclosure of Invention

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

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

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

acquiring each recommended route based on the navigation request, the current road condition information and pre-stored intelligent driving road section information; wherein the recommended route includes: an intelligent driving road section and/or a non-intelligent driving road section; the intelligent driving road section information is acquired in advance based on stored user intelligent driving behavior data;

displaying the recommended routes to the target user;

and carrying out 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 a map navigation system including: the map navigation client and the map cloud server:

the map navigation client is used for acquiring a navigation request sent by a target user; the navigation request includes: the starting position and the end position of navigation; displaying each recommended route sent by the map cloud server to the target user; according to a target route selected by the target user based on the displayed recommended routes, carrying out map navigation on the user;

the map cloud server is used for acquiring each recommended route and sending the recommended route to the map navigation client based on the navigation request, the current road condition information and pre-stored intelligent driving road section information; wherein the recommended route includes: an intelligent driving road section and/or a non-intelligent driving road section; the intelligent driving road section information is acquired in advance based on the stored user intelligent driving behavior data.

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

the navigation request acquisition module is used for acquiring a navigation request sent by a target user; the navigation request includes: the starting position and the end position of navigation;

the recommended route obtaining module is used for obtaining each recommended route based on the navigation request, the current road condition information and the pre-stored intelligent driving road section information; wherein the recommended route includes: an intelligent driving road section and/or a non-intelligent driving road section; the intelligent driving road section information is acquired in advance based on stored user intelligent driving behavior data;

the recommended route display module is used for displaying each recommended route to the target user;

and the map navigation module is used for carrying out map navigation on 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 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 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 having stored thereon computer instructions for causing the computer to perform any of the above-described methods of map navigation.

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

It should be understood that the statements in this section do not necessarily identify key or critical features of the embodiments of the present disclosure, nor do they limit the scope of the present disclosure. Other features of the present disclosure will become apparent from the following description.

Drawings

The drawings are included to provide a better understanding of the present solution and are not to be construed as limiting the present disclosure. Wherein:

FIG. 1 is a schematic diagram of a first embodiment of a method of map navigation provided in accordance with the present disclosure;

FIG. 2 is a schematic illustration of a specific example of a method of map navigation provided in accordance with the present disclosure;

FIG. 3 is a schematic flow chart illustrating the process of obtaining information of an intelligent driving route according to an embodiment of the present disclosure;

FIG. 4 is a schematic diagram of a second flowchart for obtaining information of an intelligent driving road segment according to an embodiment of the disclosure;

FIG. 5 is a schematic diagram of a second embodiment of a method of map navigation provided in accordance with the present disclosure;

FIG. 6a is a diagram illustrating an example of preset weights for road segments according to an embodiment of the disclosure;

FIG. 6b is a schematic diagram of the weights of the segments after the weight adjustment is performed on the basis of FIG. 6 a;

FIG. 7 is a schematic diagram of another specific example of a method of map navigation provided in accordance with the present disclosure;

FIG. 8 is a schematic diagram of a recommendation user to turn smart driving on or off via voice prompt in an embodiment of the present disclosure;

FIG. 9 is a schematic diagram of a first embodiment of a map navigation system provided in accordance with the present disclosure;

FIG. 10 is a schematic diagram of a second embodiment of a map navigation system provided in accordance with the present disclosure;

FIG. 11 is an interaction diagram of a map navigation system provided in accordance with the present disclosure;

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

fig. 13 is a block diagram of an electronic device for implementing a method of map navigation of an embodiment of the present disclosure.

Detailed Description

Exemplary embodiments of the present disclosure are described below with reference to the accompanying drawings, in which various details of the embodiments of the disclosure are included to assist understanding, and which are to be considered as merely exemplary. 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 in the following description for clarity and conciseness.

In the existing map navigation method, map data is usually collected and processed by a map product side, so that map data with a specification capable of being rendered and navigated is produced and applied to the map product. The driving behavior data of the driver are collected by the automobile factory to improve the product service experience of the automobile factory, and the driving behavior data of the driver and the map product are split, namely the driving behavior data of the driver cannot be applied to the map product and cannot bring the upgrading of navigation experience to the driver. .

Meanwhile, although the mounting rate of smart driving such as autonomous driving and assisted smart driving is already high, the usage rate is still low. Some users do not know at what time the driver can be suitable for automatic driving and auxiliary intelligent driving, driving safety can be guaranteed, and certain recommendation, guidance and education are needed, so that the utilization rate of automatic driving and auxiliary intelligent driving is improved.

Accordingly, to solve the above problems, the present disclosure provides a method, an apparatus, a system, an electronic device, and a storage medium for map navigation. First, a method of map navigation provided by the present disclosure will be described below.

Referring to fig. 1, fig. 1 is a schematic diagram of a first embodiment of a method of map navigation provided in accordance with the present disclosure. As shown in fig. 1, the method may include the steps of:

step S110, a navigation request sent by a target user is obtained.

In the embodiment of the disclosure, the target user can send out the navigation request based on the page displayed by the car-machine map navigation client. The navigation request sent by the target user may include: the starting position and the end position of the navigation. In this embodiment, the starting position of the navigation may be the current position of the vehicle, or other positions may be designated by the user as the starting position. For example, the user may send a navigation request from the current location (my location) of the vehicle to the destination location, which is the location a.

In an embodiment of the disclosure, the navigation request may further include information such as a vehicle type, and the vehicle type may include a general vehicle, a new energy vehicle, and the like.

And step S120, acquiring each recommended route based on the navigation request, the current road condition information and the pre-stored intelligent driving road section information.

In the embodiment of the present disclosure, it may be determined in real time whether to recommend the target user to turn on or off the intelligent driving functions (Adaptive Cruise Control, ACC, Adaptive Cruise Control) such as the automatic driving and the assisted intelligent driving based on the current traffic information. In an embodiment of the disclosure, in a process of map navigation for a vehicle, if an intelligent driving function is currently turned on for the vehicle, a recommended route meeting the navigation request may be acquired in real time based on a current position of the vehicle, the destination position, and pre-stored intelligent driving section information.

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

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

And step S130, displaying each recommended route to the target user.

In the embodiment of the disclosure, each recommended route may be displayed on a corresponding page of the car-in-vehicle map client. As a specific embodiment, when displaying each recommended route, the expected time consumption and the number of traffic lights passed by each recommended route, etc. may be displayed.

And step S140, performing 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 disclosure, after the target user selects the target route, the target route can be displayed on the map, the navigation starting position and the navigation end position can be identified on the map, information such as an ACC (intelligent driving) road section, an ACC mileage, a target route congestion condition and the like can be labeled, and if the vehicle type is a new energy vehicle, the charging pile information can be labeled 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 fig. 2, the navigation start position selected by the target user is the current position (my position) of the vehicle, and the end position is a point a. In this embodiment, the user may input the navigation start position and the key position by a keyboard or by voice by clicking a microphone icon shown in fig. 2. The user may also add a waypoint by clicking on the "+" icon, and the user may also swap the start and end positions by clicking on the reverse icon. The user may also select options such as taxi taking, new energy, driving, public transportation, etc.

As shown in fig. 2, the target route is marked on the map with a thick solid line, and is distinguished from other roads on the map. In this embodiment, since the target route includes the smart driving section, an ACC (smart driving) section and ACC mileage information ("ACC mileage 10 km" in fig. 2) may be also marked on the target route displayed in the map.

Meanwhile, as shown in fig. 2, the predicted arrival time may be displayed on the map, the current position of the vehicle may be displayed by an icon, the road congestion may be displayed by different colors, and the like. And because the user has selected "new forms of energy", consequently still can show for the user "fill electric pile" icon, the target user can select whether to show on the map and fill electric pile position through the control to this icon. In addition, a "restricted travel" icon, a "prompt" icon, and the like may also be displayed, so that the user can view vehicle restriction information, prompt information ("the unknown route is not available for passing, has been avoided for you"), and the like.

Therefore, according to the map navigation method provided by the embodiment of the disclosure, after the navigation request of the target client is obtained, each recommended route is obtained based on the navigation request, the current road condition information and the pre-stored intelligent driving road section information, each recommended route is displayed to the target client, and the map navigation is performed for the target client according to the target route selected by the target client based on each recommended route. By applying the embodiment of the disclosure, the intelligent driving road section information is acquired in advance based on the stored intelligent driving behavior data of the user, the recommended route is acquired based on the intelligent driving road section information, and the intelligent driving behavior data of each user is acted on the map navigation, so that the user can know the road section suitable for intelligent driving, the utilization rate of intelligent driving functions such as automatic driving and auxiliary intelligent driving is improved, and the use experience of the user on the map navigation is improved.

In an embodiment of the present disclosure, as shown in fig. 3, the intelligent driving section information may be obtained in advance by the following steps:

step S310, obtaining driving behavior data of each user stored in the cloud.

In the embodiment of the present disclosure, each of the users may be a user who has used an intelligent driving function. The driving behavior data of each user can be stored to the cloud in advance by adopting the following steps:

step 1, obtaining driving behavior data uploaded by each user.

In the embodiment of the present disclosure, each Vehicle equipped with an intelligent driving function such as automatic driving and assisted intelligent driving can transmit the driving behavior technology of the user back to the cloud end through a V2N (Vehicle to Net) technology. The driving behavior data may include information such as a driving track and driving time of the user, and the data such as the driving track information and the driving time information may have different data formats. The running track information can be acquired through GPS equipment carried on the vehicle, and meanwhile, each track information can also comprise corresponding timestamp information.

And 2, carrying out privacy data desensitization and dirty data filtering on each driving behavior data, and storing each driving behavior data after filtering to a cloud.

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

Meanwhile, in the embodiment of the disclosure, dirty data filtering can be performed on each driving behavior data. Dirty data is data that is meaningless, or illogical, to the actual business. For example, data such as 100KM for two seconds of travel of a vehicle is dirty data that is not logical and needs to be filtered. In the embodiment of the disclosure, after the driving behavior data are acquired, the dirty data are filtered, and the filtered data are stored, so that the subsequent data processing amount can be reduced, and the data processing speed is increased.

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

As described above, in the embodiment of the present disclosure, the intelligent driving behavior data may include: the time information of the intelligent driving of the vehicle is started and stopped, and the running track information of the vehicle in the period of time (namely the running track information of the vehicle in the intelligent driving state). As a specific implementation manner, the travel track information may specifically include longitude and latitude sequence information of vehicle travel. Correspondingly, when the intelligent driving behavior data are extracted, the intelligent driving behavior data can be extracted based on the data storage format of the longitude and latitude sequence information.

And step S330, matching the running track information of each vehicle in the intelligent driving state with each road in a map, and acquiring information of each road section for starting intelligent driving.

Road segment matching relates an ordered series of 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, longitude and latitude sequence information of each road and information of each road segment included in each road, and each piece of road segment information may include a road segment identifier (for example, a road segment ID, etc.) preset for each road segment and longitude and latitude sequence information of each road segment.

Therefore, as a specific implementation manner, when the road section matching is performed, the longitude and latitude sequence information may be matched with the longitude and latitude sequence information of each road in the map, so as to obtain the road section information corresponding to the driving track information of each vehicle in the intelligent driving state.

As a specific implementation manner of the embodiment of the present disclosure, the step S330 may be implemented by a pre-trained road matching model. In the embodiment of the present disclosure, the road matching Model may be a conventional HMM (Hidden Markov Model) or an HMM optimized based on reinforcement learning. The following is a brief description of a process of performing road segment matching in the embodiment of the present disclosure, taking a conventional HMM model as an example.

In the embodiment of the present disclosure, the HMM model may be trained based on position information (e.g., longitude and latitude sequences) obtained from GPS devices of each vehicle and an actual position of each vehicle. As a specific embodiment, the position information obtained from each vehicle GPS device and the map data may be input into an HMM model to be trained, a matching result output by the HMM model is obtained, the matching result is compared with each vehicle actual position (e.g., a value of a preset loss function is calculated), parameters in the HMM model are adjusted based on the comparison result, and the training may be stopped when an error between the output result of the HMM model and each vehicle actual position is less than a preset error threshold.

In the embodiment of the present disclosure, when road section matching is performed, the map data and the longitude and latitude sequence included in each intelligent driving behavior data may be input to the trained HMM model. For each longitude and latitude sequence, the HMM model may output a plurality of matching results (i.e., link positions) and a 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 road section position corresponding to the longitude and latitude sequence.

The above examples are merely exemplary to illustrate the process of matching the road segments in the disclosure, and do not specifically limit the disclosure.

And step S340, generating information of each intelligent driving road section based on the information of each road section for starting intelligent driving.

In the embodiment of the present disclosure, the information of each intelligent driving road section may be generated based on the road condition information and the state information of each road section in the road and the information of each road section for starting the intelligent driving. The intelligent driving road section is a road section which suggests automatic driving and assists intelligent driving.

In the embodiment of the disclosure, the intelligent driving behavior data of each user is matched with each road in the map, so as to generate each intelligent road section information, the experience data of each user for intelligent driving can be applied to the map data, and the experience data of the user is organically combined with the real geographic environment data, so that reference can be provided for the intelligent driving behavior of the target user, and the utilization rate and the safety of intelligent driving are further improved.

In an embodiment of the present disclosure, the intelligent driving behavior data may further include: and time period information corresponding to the running track information of the vehicle in the intelligent driving state. The time period information may be a time when the vehicle starts the smart driving function and a time when the smart driving function is ended.

As a specific implementation manner, when the driving behavior data is stored in the cloud, the driving track information may be stored in correspondence with the time period information of the section of track where the vehicle drives. Therefore, when the intelligent driving behavior data is extracted, the driving track information of the vehicle in the intelligent driving state and the corresponding time slot information can be extracted. The time period information may also be extracted based on a preset time period information storage format, which is not specifically limited in this disclosure.

That is, in the embodiment of the present disclosure, each piece of intelligent driving behavior data may include: the time of starting the intelligent driving function, the time of finishing the intelligent driving function and the running track information of the vehicle in the period of time. The unit of the above-described period information may be ms (millisecond).

Therefore, as shown in fig. 4, step S330 in fig. 3 can be further detailed as follows:

and step S331, matching the driving track information of each vehicle in the intelligent driving state with each road in a map, and acquiring each road section for starting intelligent driving.

As described above, the longitude and latitude sequence information in the driving track information may be matched with the longitude and latitude sequence information of each road to obtain the road section on which the intelligent driving is started, specifically, the position information of the road section on which the intelligent driving is started, the road section ID, and the like.

Step S332, acquiring traffic information of each road section for which intelligent driving is started in a corresponding time period from map data based on time period information corresponding to the driving track information of the vehicle in the intelligent driving state.

In the embodiment of the present disclosure, the map data may include information such as road condition information, traffic restriction information, and control policy of each road in each time period. Therefore, in the embodiment of the present disclosure, after the road segment for starting the intelligent driving is obtained, the road condition information of the road segment for starting the intelligent driving in the corresponding time period may be obtained from the map data based on the time period information corresponding to the road segment.

In the embodiment of the present disclosure, after the tracks (including the longitude and latitude sequences of the timestamps) for the automatic driving and the assisted intelligent driving are matched with the map data through the map matching algorithms such as the HMM, the track points can be bound to the corresponding roads. Therefore, the basic automatic driving and the auxiliary intelligent driving are obtained, namely the road section for starting the intelligent driving, and the road section information for starting the intelligent driving is obtained. The road section information for starting intelligent driving can comprise 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, inputting the road sections for starting intelligent driving and the road condition information of the road sections in the corresponding time period into an intelligent driving road section generation model; and generating a model of the intelligent driving road section, and generating information of each intelligent driving road section at different time periods based on the road condition information and the state information of each road section in each time period, each road section for starting intelligent driving and the road condition information of each road section in the corresponding time period.

In the embodiment of the present disclosure, the intelligent driving section generation model may be a simple threshold matching model. As a specific implementation manner, when the road segment with the smart driving enabled is driven for more than a preset number threshold and the state of the road segment is passable, the road segment with the smart driving enabled may be marked as a smart driving road segment. The road section state can be obtained through information such as restriction and control of the road. The link status may include passable, road has been deleted, no passage, and the like.

The time period in which the road section for which intelligent driving is started can be used as the intelligent driving road section can be obtained based on the road condition information of each road section in the road in each time period and the road condition information of each road section for which intelligent driving is started in the corresponding time period. As a specific implementation manner, a time slot that is the same as the road condition of the road segment in the corresponding time slot may be obtained based on the road condition information of the road segment in each time slot for which the intelligent driving is started. In these time periods, if the road segment with the intelligent driving started is driven for more than a preset time threshold value and the state of the road segment is passable, the road segment can be used as the intelligent driving road segment.

In the embodiment of the disclosure, when the intelligent driving road section information is generated, the condition that the intelligent driving function cannot be used can be set. For example, intelligent road segments may be filtered based on technical constraints (e.g., night vision capabilities of vehicle sensors and areas where intelligent driving is prohibited) to arrive at a final intelligent driving road segment.

Therefore, in the embodiment of the present disclosure, the intelligent driving road segment generation model may comprehensively generate road segments that can be automatically driven and assist intelligent driving in different time periods according to the road condition prediction ability of the map in different time periods in the future, the restriction of each road in the map, the control strategy, and the technical constraints (the recognition ability of the vehicle sensor at night is limited, and the limitation of the automatically-driven area)

The intelligent driving road section information is generated through the model based on historical experience data of users who have certain understanding on intelligent driving, so that the generated intelligent driving road section information is more referential, and intelligent driving of target users can be better assisted.

In an embodiment of the present disclosure, each piece of segment information of the map data may further include a preset weight of each segment in each road, and therefore, as shown in fig. 4, the method may further include:

and step S450, improving the static weight of each intelligent driving road section.

In the embodiment of the present disclosure, weights may be set for each link in advance. The preset weights of the road segments may be the same or different, and are not specifically limited in this disclosure.

After the information of each intelligent driving road section is obtained, the weight of each intelligent driving road section can be increased and stored as the road section information of the corresponding intelligent driving road section. Thus, the probability that each intelligent driving section is recommended can be improved.

Specifically, as shown in fig. 5, the step S120 in fig. 1 may be further detailed as follows:

and step S121, acquiring a recommended route based on the navigation request, the current road condition information, the pre-stored intelligent driving road section information and the weight of each road section.

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

As shown in fig. 6a, the route from the location v to the location w includes two routes, respectively: v- > x- > y- > w and v- > u- > w. The preset initial weight of each road section is as follows: the initial weight of the section v- > x is 1, the initial weight of the section x- > y is 1, the initial weight of the section y- > w is 1, the initial weight of the section v- > u is 3, and the initial weight of the section u- > w is 2. Therefore, the probability that the route v- > u- > w is recommended is large if calculated according to the initial weight.

If the road sections v- > x and x- > y are the road sections that can be driven automatically or assisted with intelligent driving from 10:00 to 18: 00. Then during 10:00-18:00, as shown in fig. 6b, the weights for segments v- > x and x- > y can all be promoted from 1 to 3, and then the route calculation from location v to location w would change from the selection of v- > u- > w to: v- > x- > y- > w for user decision. Therefore, the weight of the intelligent driving road section is improved, and the probability of recommendation of the automatic driving and auxiliary intelligent driving road section is improved.

In an embodiment of the present disclosure, after the recommended routes are obtained, the recommended routes may be sorted based on the weight of the road segments included in the routes. As a specific implementation manner, on the basis of fig. 1, as shown in fig. 5, before the step S130, the following steps may be further included:

step S530, ranking the recommended routes based on the preference setting of the target user for the route and the weight of the road segment included in each recommended route.

Based on fig. 1, as shown in fig. 5, the step S130 can be further detailed as follows:

and S131, displaying each sequenced recommended route to the target user.

In the disclosed embodiments, the user may select a route preference. For example, the user may select one or more of route preferences such as high speed priority, automated driving segment priority, assisted intelligent driving segment priority, time priority, and so forth. After the recommended routes are obtained, the routes can be sorted based on the preference of the user, and meanwhile, the weights of the road sections contained in the routes can be referred to during sorting. Therefore, the navigation route can be recommended to the user more accurately, and the user experience is improved. For example, if the user selects a preferred route for intelligent driving (including autonomous driving and assisted intelligent driving) road segments, and there are multiple recommended routes that include intelligent driving road segments. Then, when ranking the recommended routes, the intelligent driving route including the intelligent driving section may be ranked ahead, and further, the intelligent driving route including the section with higher weight may be ranked ahead.

Referring to fig. 7, fig. 7 is a specific example of a method of map navigation provided according to the present disclosure. As shown in fig. 7, the user may select the route preference through a page displayed by the in-vehicle map client. The route preferences may include automated driving segment prioritization, assisted intelligent driving segment prioritization, time prioritization, toll free, no speed, high speed prioritization, and the like. The user may also control the "remember options" button to reduce the number of selections of the same preference.

In the figure, the route preference selected by the user is an autonomous driving section priority and an assisted intelligent driving section priority. Based on the preference selection and the weight of each road segment, three recommended routes from the current position of the vehicle (my position) to the place a are presented to the user. In this embodiment, basic route information such as mileage, estimated time consumption, and the number of traffic lights that need to be passed can also be displayed to the user.

As shown in fig. 7, the recommended route displayed to the user includes: in the first scheme, the distance is 23 kilometers, the time is predicted to be 41 minutes, the time is predicted to be 17:04, the terminal is reached, 15 traffic lights are required to pass, and in the three routes, the time is less. And in the second scheme, the distance is 23 kilometers, the predicted time is 43 minutes, 5 traffic lights are required to pass, and in the three routes, the conventional time is spent, but the congestion is high. And in the third scheme, the distance is 25 kilometers, the predicted time is 46 minutes, and 18 traffic lights are required to pass. The user can select the above schemes, and the car navigation client can perform map navigation on 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 step S140, the method may further include:

and step S550, acquiring the current road condition and state information in real time.

In an embodiment of the disclosure, the car-on-board map client may refresh road conditions and states of each road every minute. As a specific implementation, the map navigation server may send a request to the map navigation server, and the map navigation server refreshes road conditions and states. The road state may be obtained based on information such as restriction and regulation of the road, which may specifically include passable information, deleted information, forbidden information, and the like.

Step S560, based on the current road condition and the condition that the status information matches the preset intelligent driving restriction condition, determining whether to recommend the target user to turn on or off intelligent driving.

In the embodiment of the present disclosure, intelligent driving constraints may be preset, and the constraints may include intelligent driving road conditions, road states, and technical constraints. The intelligent driving road condition can be set based on the road condition of each intelligent driving road section, the road state can be that the road can pass, and the technical constraint can be that the recognition capability of the vehicle sensor at night is limited, the region which can be intelligently driven is limited, and the like.

If the current road condition and state accord with the preset intelligent driving constraint condition, the user can be advised to start the intelligent driving functions of automatic driving, auxiliary intelligent driving and the like, and if the current road condition and state do not accord with the preset intelligent driving constraint condition, the user can be advised to turn off the intelligent driving functions.

In an embodiment of the disclosure, if it is required to suggest that the target user turn on or turn off the smart driving, a voice prompt for turning on or off the smart driving may be issued.

In the embodiment of the disclosure, if the user needs to be advised to start the intelligent driving, but the user already starts the intelligent driving, the voice prompt may not be sent out, and if the user does not start the intelligent driving, the voice prompt may be sent out when the user approaches the intelligent driving road section. If the user needs to be advised to close the intelligent driving, the user can send out corresponding voice prompt after passing through the intelligent driving section end position, and if the user closes the intelligent driving, the prompt can not be sent out. Therefore, the convenience of interaction between the map and the user is utilized, so that the user can know when to use intelligent driving, the intelligent driving utilization rate is improved, and the driving experience of the user is improved.

As a specific example, as shown in fig. 8, after approaching N meters of a road segment where ACC (adaptive cruise, smart driving according to the present disclosure) can be turned on, the following may be broadcasted: and the content is broadcasted, such as 'a plurality of users opening the ACC at the current road section are advised to open the ACC and the driving is easier'. After being close to ACC highway section ending position N meters, report: and broadcasting contents such as 'suggestion for quitting ACC and paying attention to driving safety'.

Therefore, according to the map navigation method provided by the embodiment of the disclosure, the road section information of the automatic driving and the auxiliary intelligent driving which are frequently started by the user who has certain understanding and application on the automatic driving and the auxiliary intelligent driving is mined by desensitizing the driver behavior data and matching the desensitized driver behavior data with the road of the map. And intelligent driving road section information for suggesting to start automatic driving and assisting intelligent driving is generated based on the information, the weight of the roads is improved, and the prior route planning capability of automatic driving and assisting intelligent driving priority is provided for the user by combining other road conditions, traffic restrictions and other factors. The user can select the route preference of the automatic driving road section priority and the auxiliary intelligent driving road section priority through the interface of the vehicle-mounted map client.

Besides the calculation before driving, in the embodiment of the present disclosure, the data such as the intelligent driving section information for starting the automatic driving and assisting the intelligent driving, other road condition information, and the traffic restriction information are applied to the navigation process, so as to provide a road state refreshing service. The method is characterized in that the method is combined with the changes of original road dynamic restriction, road conditions, control and the like, and whether the road sections related to the road are recommended to be opened or closed for auxiliary intelligent driving and automatic driving is updated in real time. The road condition is refreshed periodically on the vehicle-mounted map, road sections for recommending opening and closing of auxiliary intelligent driving and automatic driving in real time and corresponding mileage are refreshed, and result rendering is carried out through the vehicle-mounted map. Meanwhile, when the position of the user is close to a road section which can be opened or closed for assisting intelligent driving and automatic driving, the user can acquire the information in a voice prompt mode, so that the utilization rate of the automatic driving and the auxiliary intelligent driving is improved, and the effects of the automatic driving and the auxiliary intelligent driving are further improved.

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

the map navigation client 910 may be configured to obtain a navigation request sent by a target user; the navigation request includes: the starting position and the end position of navigation; displaying each recommended route sent by the map cloud server to the target user; according to a target route selected by the target user based on the displayed recommended routes, carrying out map navigation on the user;

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

According to the map navigation system, after the navigation request of the target client is obtained, each recommended route is obtained based on the navigation request, the current road condition information and the pre-stored intelligent driving road section information, each recommended route is displayed to the target user, and map navigation is performed on the target user according to the target route selected by the target user based on each recommended route. By applying the embodiment of the disclosure, the intelligent driving road section information is obtained in advance based on the stored intelligent driving behavior data of the user, the recommended route is obtained based on the intelligent driving road section information, and the intelligent driving behavior data of each user is acted on the map navigation, so that the user can know the road section suitable for intelligent driving, the utilization rate of intelligent driving functions such as automatic driving and intelligent driving assistance is improved, and the use experience of the user on the map navigation is improved.

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

the car factory cloud server 1030 can be used for acquiring driving behavior data of each user and storing the driving behavior data in a cloud end; extracting intelligent driving behavior data of each user based on the driving behavior data of each user, and sending the intelligent driving behavior data to the map cloud server; wherein the intelligent driving behavior data comprises: running track information of the vehicle in an intelligent driving state;

the map cloud server 920 may be further configured to perform road segment matching on the driving track information of each vehicle in the intelligent driving state with each road in the map, and acquire information of each road segment for starting intelligent driving; and generating information of each intelligent driving road section based on the information of each road section for starting intelligent driving.

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

the matching of the driving track information of each vehicle in the intelligent driving state with each road in the map to obtain each road section information for starting the intelligent driving may include:

matching the running track information of each vehicle in the intelligent driving state with each road in a map to obtain each road section for starting intelligent driving;

acquiring road condition information of each road section for starting intelligent driving in a corresponding time period from map historical data based on time period information corresponding to the running track information of the vehicle in the intelligent driving state;

generating each intelligent driving section information based on each section information for starting intelligent driving, comprising:

inputting the road sections for starting intelligent driving and road condition information of the road sections in the corresponding time period into an intelligent driving road section generation model; and generating a model of the intelligent driving road section, and generating information of each intelligent driving road section at different time periods based on the road condition information and the state information of each road section in each time period, each road section for starting intelligent driving and the road condition information of each road section in the corresponding time period.

In an embodiment of the present disclosure, the map cloud server 920 may further be configured to:

improving the static weight of each intelligent driving road section;

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

and acquiring a recommended route based on the navigation request, the current road condition information, the pre-stored intelligent driving road section information and the weight of each road section.

In an embodiment of the present disclosure, the map navigation client 910 may further be configured to:

sorting the recommended routes based on the preference setting of the target user for the routes and the weight of the road sections contained in the recommended routes;

the displaying the recommended routes to the target user includes:

and displaying each sequenced recommended route to the target user.

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

acquiring driving behavior data uploaded by each user;

and carrying out privacy data desensitization and dirty data filtering on the driving behavior data, and storing the filtered driving behavior data to a cloud.

In an embodiment of the present disclosure, the map navigation client 910 may be further configured to:

acquiring current road condition and state information in real time;

and judging whether the target user is recommended to start or stop intelligent driving or not based on the current road condition and the condition that the state information meets the preset intelligent driving constraint condition.

In an embodiment of the present disclosure, if it is required to suggest that the target user turn on or turn off the smart driving, a voice prompt for turning on or turning off the smart driving is issued.

As shown in fig. 11, fig. 11 is an interaction diagram of a map navigation system provided by an embodiment of the present disclosure in a case where a user selects an intelligent driving road section priority. The interaction process may specifically include two phases: an intelligent driving section obtaining stage and a map navigation stage. The intelligent driving section acquiring stage may include the following steps:

and (4) uploading user driving behavior data to a car factory cloud through V2N by each car.

And secondly, storing the driving behavior data of the user after the driving behavior data of the user is subjected to privacy data desensitization and dirty data cleaning by the cloud of the automobile factory.

And step three, extracting the intelligent driving behavior data from the driving behavior data of each user by the car factory cloud, and sending the intelligent driving behavior data to the map cloud.

The car factory cloud is a car factory cloud server in the embodiment of the disclosure.

The intelligent driving behavior data may include time period information for starting and ending the intelligent driving and travel track information of the vehicle in the time period. The travel track information may be longitude and latitude sequence information acquired by a vehicle GPS, and the time period information may be obtained according to timestamp information included in each longitude and latitude sequence information.

And fourthly, the map cloud carries out road section matching on the intelligent driving behavior data and the map data to obtain road section information of the intelligent driving road section opened by the vehicle and corresponding time section information, wherein the road section information comprises road condition information of the intelligent driving road section opened. And then, inputting the road section information of the vehicle starting intelligent driving road section and the corresponding time period information into an intelligent driving road section generation model, and acquiring the information of each intelligent driving road section output by the model.

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

The map navigation stage may include the following steps:

step 1, a user sends a navigation request based on a vehicle-mounted map client, and the vehicle-mounted map client initiates road calculation.

The car map client is the map navigation client in the embodiment of the present disclosure.

And 2, the map navigation server recalls the route based on the navigation request, the current road condition, the information of each intelligent driving road section and the weight of each road section, acquires each recommended route and sorts the routes of each recommended route. The recommended route may include a smart driving section and/or a non-smart driving section.

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

And 3, returning the intelligent driving road section priority route to the vehicle-mounted machine map client by the map navigation server for decision selection of a user.

In this embodiment, routes with priority for each intelligent driving road section can be returned according to the page shown in fig. 7 for the user to make a decision.

And 4, refreshing the road state and the road condition in real time in the map navigation process, and performing in-process induced recommendation according to the current road state and the road condition which are in accordance with the preset intelligent driving constraint condition, namely sending voice prompt to a user to suggest to start or close the intelligent driving function.

In this embodiment, the mileage of the intelligent driving road section that is preferred may be dynamically updated based on the road state and road condition that are refreshed in real time. Specifically, the position of the intelligent driving road section in the route and the mileage of the intelligent driving road section can be displayed to the user by using the page shown in fig. 2.

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

a navigation request obtaining module 1210, configured to obtain a navigation request sent by a target user; the navigation request includes: the starting position and the end position of navigation;

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

a recommended route display module 1230, which may be configured 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 a target route selected by the target user based on the displayed recommended routes.

According to the map navigation device, after the navigation request of the target client is obtained, each recommended route is obtained based on the navigation request, the current road condition information and the pre-stored intelligent driving road section information, each recommended route is displayed to the target user, and map navigation is performed on the target user according to the target route selected by the target user based on each recommended route. By applying the embodiment of the disclosure, the intelligent driving road section information is acquired in advance based on the stored intelligent driving behavior data of the user, the recommended route is acquired based on the intelligent driving road section information, and the intelligent driving behavior data of each user is acted on the map navigation, so that the user can know the road section suitable for intelligent driving, the utilization rate of intelligent driving functions such as automatic driving and auxiliary intelligent driving is improved, and the use experience of the user on the map navigation is improved.

In the technical scheme of the disclosure, the collection, storage, use, processing, transmission, provision, disclosure and other processing of the personal information of the related user are all in accordance with the regulations of related laws and regulations and do not violate the good customs of the public order.

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

Fig. 13 illustrates a schematic block diagram of an example electronic device 1300 that can 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, 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 disclosure described and/or claimed herein.

As shown in fig. 13, the apparatus 1300 includes a computing unit 1301 that can perform various appropriate actions and processes according to a computer program stored in a Read Only Memory (ROM)1302 or a computer program loaded from a storage unit 1308 into a Random Access Memory (RAM) 1303. In the RAM 1303, various programs and data necessary for the operation of the device 1300 can also be stored. The calculation unit 1301, the ROM 1302, and the RAM 1303 are connected to each other via a bus 1304. An input/output (I/O) interface 1305 is also connected to bus 1304.

A number of components in the device 1300 connect to the I/O interface 1305, including: an input unit 1306 such as a keyboard, a mouse, or the like; an output unit 1307 such as various types of displays, speakers, and the like; storage unit 1308, such as a magnetic disk, optical disk, or the like; and a communication unit 1309 such as a network card, modem, wireless communication transceiver, etc. 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 a variety of general and/or special purpose processing components with processing and computing capabilities. Some examples of computing unit 1301 include, but are not limited to, a Central Processing Unit (CPU), a Graphics Processing Unit (GPU), various specialized Artificial Intelligence (AI) computing chips, various computing units running machine learning model algorithms, a Digital Signal Processor (DSP), and any suitable processor, controller, microcontroller, and so forth. The calculation unit 1301 performs the respective methods and processes described above, such as a method of map navigation. For example, in some embodiments, the method of map navigation may be implemented as a computer software program tangibly embodied in a machine-readable medium, such as storage unit 1308. In some embodiments, some or all of the computer program may be loaded onto and/or installed onto device 1300 via ROM 1302 and/or communications unit 1309. When the computer program is loaded into the RAM 1303 and executed by the 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 in any other suitable manner (e.g., by means of firmware) to perform the method of map navigation.

Various implementations of the systems and techniques described here above may be implemented in digital electronic circuitry, integrated circuitry, Field Programmable Gate Arrays (FPGAs), Application Specific Integrated Circuits (ASICs), Application Specific Standard Products (ASSPs), system on a chip (SOCs), Complex Programmable Logic Devices (CPLDs), 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.

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 codes, when executed by the processor or controller, cause the functions/operations specified in the flowchart and/or block diagram to be performed. The program code may execute entirely on the machine, partly on the machine, as a stand-alone software package partly on the machine and partly on a remote machine or entirely on the remote machine or server.

In the context of this disclosure, a machine-readable medium may be a tangible medium that can contain, or store a program for use by or in connection with an instruction execution system, apparatus, or device. The machine-readable medium may be a machine-readable signal medium or a machine-readable storage medium. A machine-readable medium may include, but is not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any suitable combination of the foregoing. More specific examples of a machine-readable storage medium would include an electrical connection based on one or more wires, a portable computer diskette, a hard disk, a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.

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. The server may be a cloud server, a server of a distributed system, or a server with a combined blockchain.

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 disclosure may be executed in parallel or sequentially or in different orders, and are not limited herein as long as the desired results of the technical solutions disclosed in the present disclosure can be achieved.

The above detailed description should not be construed as limiting the scope of the disclosure. 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 disclosure should be included in the scope of protection of the present disclosure.

Claims

1. A method of map navigation, comprising:

displaying the recommended routes to the target user;

2. The method according to claim 1, wherein the intelligent driving section information is acquired in advance by adopting the following steps:

the driving behavior data of each user stored in the cloud is acquired;

extracting intelligent driving behavior data of each user based on the driving behavior data of each user; wherein the intelligent driving behavior data comprises: running track information of the vehicle in an intelligent driving state;

matching the running track information of each vehicle in the intelligent driving state with each road in a map to obtain information of each road section for starting intelligent driving;

and generating information of each intelligent driving road section based on the information of each road section for starting intelligent driving.

3. The method of claim 2, wherein,

the intelligent driving behavior data further comprises: time period information corresponding to the running track information of the vehicle in the intelligent driving state;

the step of matching the running track information of each vehicle in the intelligent driving state with each road in a map to acquire information of each road section for starting intelligent driving comprises the following steps:

the step of generating information of each intelligent driving road section based on the information of each road section for starting intelligent driving comprises the following steps:

4. The method of claim 2, further comprising:

improving the static weight of each intelligent driving road section;

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

5. The method of claim 4, wherein,

before the step of displaying the recommended routes to the target user, the method further includes:

the step of displaying the recommended routes to the target user includes:

and displaying each sequenced recommended route to the target user.

6. The method according to claim 2, wherein the driving behavior data of each user is stored in advance by:

acquiring driving behavior data uploaded by each user;

7. The method of claim 1, further comprising:

acquiring current road condition and state information in real time;

8. The method of claim 7, further comprising:

and if the target user needs to be advised to start or close the intelligent driving, sending out a voice prompt for starting or closing the intelligent driving.

9. A map navigation system, comprising: the map navigation client and the map cloud server:

10. The system of claim 9, further comprising, a car factory cloud server;

the car factory cloud server is used for acquiring driving behavior data of each user and storing the driving behavior data in a cloud end; extracting intelligent driving behavior data of each user based on the driving behavior data of each user, and sending the intelligent driving behavior data to the map cloud server; wherein the intelligent driving behavior data comprises: running track information of the vehicle in an intelligent driving state;

the map cloud server is also used for matching the running track information of each vehicle in the intelligent driving state with each road in the map to acquire each road section information for starting intelligent driving; and generating information of each intelligent driving road section based on the information of each road section for starting intelligent driving.

11. The system of claim 10, further comprising in the intelligent driving behavior data: time period information corresponding to the running track information of the vehicle in the intelligent driving state;

the step of matching the running track information of each vehicle in the intelligent driving state with each road in a map to acquire each road section information for starting intelligent driving comprises the following steps:

12. The system of claim 10, the map cloud server further to:

improving the static weight of each intelligent driving road section;

the acquiring of each recommended route based on the navigation request, the current road condition information and the pre-stored intelligent driving road section information comprises:

13. The system of claim 12, wherein,

the map navigation client is further configured to:

the displaying the recommended routes to the target user includes:

and displaying each sequenced recommended route to the target user.

14. The system of claim 10, wherein the driving behavior data of each user is pre-stored by:

acquiring driving behavior data uploaded by each user;

15. The system of claim 9, the map navigation client further to:

acquiring current road condition and state information in real time;

16. The system of claim 15, the map navigation client, further to:

17. An apparatus for map navigation, comprising:

18. An electronic device, comprising:

at least one processor; and

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 1-8.

19. 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 1-8.

20. A computer program product comprising a computer program which, when executed by a processor, implements the method according to any one of claims 1-8.