CN111882907A

CN111882907A - Navigation early warning method, device, equipment and storage medium for vehicle

Info

Publication number: CN111882907A
Application number: CN202010559914.7A
Authority: CN
Inventors: 金亮
Original assignee: Beijing Qisheng Technology Co Ltd
Current assignee: Beijing Qisheng Technology Co Ltd
Priority date: 2020-06-18
Filing date: 2020-06-18
Publication date: 2020-11-03
Anticipated expiration: 2040-06-18
Also published as: CN111882907B

Abstract

The application relates to a navigation early warning method and device for a vehicle, computer equipment and a storage medium. The method comprises the steps that after a navigation early warning analysis instruction which is sent by a user side and carries a navigation path is received, the server obtains driving information of vehicles driving on the navigation path within a preset time period, determines abnormal road section information including abnormal road section types and abnormal road section positions on the navigation path by analyzing the driving information of the vehicles, and then carries the abnormal road section information in early warning prompt information to be sent to the user side. The navigation early warning method realizes the function of early warning while navigation is carried out on the user side, namely, the user can acquire related early warning prompt information while starting the navigation function on the user side, so that the user can predict the abnormal condition on the front path, and the safety of driving a vehicle by the user is improved.

Description

Navigation early warning method, device, equipment and storage medium for vehicle

Technical Field

The present application relates to the field of travel technologies, and in particular, to a navigation early warning method, apparatus, device, and storage medium for a vehicle.

Background

With the emerging development of the shared vehicle industry, people have an increasing demand for shared vehicles, so that how to bring convenience to the life of people and better ensure the safety of people driving the shared vehicles in the process of providing the shared vehicles and related services for people by a shared vehicle platform becomes a problem of concern in the current shared vehicle market.

Nowadays, when a user drives a shared vehicle, some shared vehicles provide a driving path for the user during driving so that the user can quickly reach a destination. For example, a user may search for a travel path using an app on a user terminal during travel and then view the searched travel path on a display screen of the user terminal, or the shared vehicle may have a navigation function, and the user directly starts the navigation function on the shared vehicle and inputs destination information on the shared vehicle, thereby learning the travel path.

However, the above-described method of determining a travel path has a problem of low safety.

Disclosure of Invention

In view of the above, it is necessary to provide a navigation early warning method, device, apparatus and storage medium for a vehicle, which can effectively improve the driving safety of the vehicle.

In a first aspect, an embodiment of the present disclosure provides a navigation early warning method for a vehicle, where the method includes:

receiving a navigation early warning analysis instruction sent by a user side; the navigation early warning analysis instruction comprises a navigation path to be analyzed;

acquiring the driving information of the vehicles driving on the navigation path within a preset time period;

determining abnormal road section information on the navigation path by analyzing the driving information of each vehicle; the abnormal road section information comprises an abnormal road section type and an abnormal road section position;

and carrying the abnormal road section information in early warning prompt information and sending the early warning prompt information to the user side.

In a second aspect, an embodiment of the present disclosure provides a navigation early warning method for a vehicle, where the method includes:

sending a navigation early warning analysis instruction to a server; the navigation early warning analysis instruction comprises a navigation path to be analyzed;

receiving early warning prompt information sent by the server; the early warning prompt information carries abnormal road section information, and the abnormal road section information is determined by the server according to the driving information of the vehicles driving on the navigation path within a preset time period; the abnormal road section information comprises an abnormal road section type and an abnormal road section position;

and outputting the early warning prompt information.

In a third aspect, an embodiment of the present disclosure provides a navigation early warning apparatus for a vehicle, where the apparatus includes:

the receiving module is used for receiving a navigation early warning analysis instruction sent by a user side; the navigation early warning analysis instruction comprises a navigation path to be analyzed;

the acquisition module is used for acquiring the driving information of the vehicles driving on the navigation path within a preset time period;

the determining module is used for determining abnormal road section information on the navigation path by analyzing the driving information of each vehicle; the abnormal road section information comprises an abnormal road section type and an abnormal road section position; and the sending module is used for carrying the abnormal road section information in early warning prompt information and sending the early warning prompt information to the user side.

In a fourth aspect, an embodiment of the present disclosure provides a navigation early warning apparatus for a vehicle, where the apparatus includes:

the sending module is used for sending a navigation early warning analysis instruction to the server; the navigation early warning analysis instruction comprises a navigation path to be analyzed;

the receiving module is used for receiving the early warning prompt information sent by the server; the early warning prompt information carries abnormal road section information, and the abnormal road section information is determined by the server according to the driving information of the vehicles driving on the navigation path within a preset time period; the abnormal road section information comprises an abnormal road section type and an abnormal road section position;

and the output module is used for outputting the early warning prompt information.

In a fifth aspect, an embodiment of the present disclosure provides a user end, including: a transmitter, a receiver, a processor, an output device, a memory, and a computer program stored on the memory and executable on the processor; the processor, when executing the computer program, is configured to control the operation of the transmitter, the receiver, the output device;

the transmitter is used for transmitting a navigation early warning analysis instruction to a server under the control of the processor, wherein the navigation early warning analysis instruction comprises a navigation path to be analyzed;

the receiver is used for receiving early warning prompt information which is sent by the server and carries abnormal road section information under the control of the processor; the abnormal road section information comprises an abnormal road section type and an abnormal road section position;

and the processor is used for controlling the output equipment to output the early warning prompt information.

In a sixth aspect, an embodiment of the present disclosure provides a server, including: a receiver, a transmitter, a processor, a memory, and a computer program stored on the memory and executable on the processor; the processor, when executing the computer program, is configured to control the operation of the transmitter, the receiver, the processor;

the receiver is used for receiving a navigation early warning analysis instruction sent by a user side under the control of the processor, wherein the navigation early warning analysis instruction comprises a navigation path to be analyzed;

the transmitter is used for transmitting early warning prompt information carrying abnormal road section information to the user side under the control of the processor; the abnormal road section information is determined according to the driving information of the vehicles driving on the navigation path within a preset time period; the abnormal road section information comprises an abnormal road section type and an abnormal road section position;

the processor is used for acquiring the driving information of the vehicles driving on the navigation path within a preset time period, and determining the abnormal road section information on the navigation path by analyzing the driving information of each vehicle.

In a seventh aspect, the disclosed embodiments provide a computer-readable storage medium, on which a computer program is stored, where the computer program, when executed by a processor, implements the method of the first or second aspect.

According to the navigation early warning method, the navigation early warning device, the computer equipment and the storage medium of the transportation tool, after the server receives the navigation early warning analysis instruction which is sent by the user side and carries the navigation path, the server obtains the driving information of the transportation tool which drives on the navigation path in the preset time period, determines the abnormal road section information which comprises the type and the position of the abnormal road section on the navigation path by analyzing the driving information of each transportation tool, and then carries the abnormal road section information in the early warning prompt information to be sent to the user side. The navigation early warning method realizes the function of early warning while navigation is carried out on the user side, namely, the user can acquire related early warning prompt information while starting the navigation function on the user side, so that the user can predict the abnormal condition on the front path, and the safety of driving a vehicle by the user is improved. Moreover, the early warning prompt information comprises the type and the position of the abnormal road section, so that the early warning prompt information not only plays a role in reminding a user of moving ahead carefully, but also specifically informs the user of the type and the position of the abnormal road section, so that the user can know the abnormal condition more visually, and further can select whether to move ahead continuously or not according to the abnormal condition, and the use requirements of the user are greatly met.

Drawings

FIG. 1 is a diagram of an embodiment of an application environment of a navigation warning method for a vehicle;

FIG. 2 is a flow diagram illustrating a method for navigation early warning of a vehicle according to one embodiment;

FIG. 3 is a flowchart illustrating an implementation manner of S103 in the embodiment of FIG. 2;

FIG. 4 is a flow diagram illustrating a method for navigation early warning of a vehicle according to one embodiment;

FIG. 5 is a schematic flow chart diagram of a training method in one embodiment;

FIG. 6 is a diagram illustrating a structure of an abnormal road segment analysis network according to an embodiment;

FIG. 7 is a schematic flow chart diagram of a training method in one embodiment;

FIG. 8 is a flow diagram of a method for navigation early warning of a vehicle in one embodiment;

FIG. 9 is a flow diagram of a method for navigation early warning of a vehicle in one embodiment;

FIG. 10 is a flow diagram illustrating a method for navigation early warning of a vehicle, according to one embodiment;

FIG. 11 is a flow diagram illustrating a method for navigation early warning of a vehicle, according to one embodiment;

FIG. 12 is a block diagram showing a navigation warning device of a vehicle according to an embodiment;

FIG. 13 is a block diagram showing a navigation warning device of a vehicle according to an embodiment;

FIG. 14 is a block diagram showing a navigation warning device of a vehicle according to an embodiment;

FIG. 15 is a block diagram showing a navigation warning device of a vehicle according to an embodiment;

FIG. 16 is a block diagram showing a navigation warning device of a vehicle according to an embodiment;

FIG. 17 is a block diagram showing a navigation warning device of a vehicle according to an embodiment;

FIG. 18 is a block diagram showing a navigation warning device of a vehicle according to an embodiment;

FIG. 19 is a block diagram showing the structure of a navigation warning device of a vehicle according to an embodiment;

FIG. 20 is a block diagram showing a navigation warning device of a vehicle according to an embodiment;

FIG. 21 is a block diagram showing a navigation warning device of a vehicle according to an embodiment;

fig. 22 is an internal structural diagram of a user terminal in one embodiment;

fig. 23 is an internal configuration diagram of a server in one embodiment.

Detailed Description

In order to make the objects, technical solutions and advantages of the present application more apparent, the present application is described in further detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative of the present application and are not intended to limit the present application.

First, before specifically describing the technical solution of the embodiment of the present disclosure, a technical background or a technical evolution context on which the embodiment of the present disclosure is based is described. In general, in the field of shared vehicle traveling, especially shared vehicle traveling, the current technical background (illustrated by a shared vehicle) is: when a user rides a shared bicycle on a riding road, the riding user often encounters various abnormal scenes, such as road surface damage, road surface potholes, road surface construction, and the like. Under the condition, generally, a riding user cannot predict various abnormal scenes on a riding road, great potential safety hazards are brought to the riding user, and the riding comfort level of the riding user is also greatly reduced due to the influence of the abnormal scenes on the riding road, for example, the riding user feels very uncomfortable when encountering a pothole road surface to cause vehicle jolt. Based on the technical problems, in the current shared vehicle platform market, how to ensure the convenience of using the shared vehicle by the user and simultaneously improve the safety and comfort of driving the shared vehicle by the user become a difficult problem to be solved urgently at present. In addition, it should be noted that, in the process of obtaining the warning prompt information and the technical scheme introduced in the following embodiment, the applicant pays a lot of creative labor.

The following describes technical solutions related to the embodiments of the present disclosure with reference to a scenario in which the embodiments of the present disclosure are applied.

The navigation early warning method of the vehicle provided by the embodiment of the disclosure can be applied to the application environment shown in fig. 1. The user terminal 102 communicates with the server 104 through a network. The user terminal 102 may be a user terminal for controlling a usage flow of a vehicle, may also be a vehicle, and may also include both the user terminal and the vehicle. The user terminal may be, but is not limited to, various personal computers, notebook computers, smart phones, tablet computers, and portable wearable devices, and the vehicle may be, but is not limited to, various types of bicycles, electric vehicles, automobiles, and the like (only illustrated as a bicycle in fig. 1). The user terminal can be a stand-alone device or an electronic device integrated on a vehicle; a communication component may be disposed on the vehicle that may communicate with the user terminal 102 and/or the server 104 in a wireless manner; the server 104 may be implemented as a stand-alone server or as a server cluster comprised of multiple servers. The communication method between the user terminal 102 and the server 104 is not limited in the embodiments of the present disclosure.

In one embodiment, as shown in fig. 2, a navigation early warning method for a vehicle is provided, which is illustrated by being applied to the server in fig. 1, and includes the following steps:

s101, receiving a navigation early warning analysis instruction sent by a user side; the navigation early warning analysis instruction comprises a navigation path to be analyzed.

The navigation path to be analyzed is a path which needs to be traveled when the user drives the vehicle. The navigation early warning analysis instruction is used for indicating the server to acquire abnormal conditions which are possibly met when the vehicle runs on the navigation path, generating early warning prompt information according to the abnormal conditions and sending the early warning prompt information to the user side for early warning. The navigation path may be a path that is constructed by the user terminal in real time according to the user navigation requirement and in combination with the geographic map, or may also be a planned path that is acquired by the user terminal from the database according to the user navigation requirement, which is not limited in the embodiment of the present disclosure.

Specifically, in practical application, when a user drives a vehicle to travel on a road, the user can start a navigation function at a user side, and the user side acquires a navigation path of the vehicle after the user starts the navigation function, generates a navigation early warning analysis instruction according to the navigation path, and sends the navigation early warning analysis instruction to a server. For example, the user may open a navigation Application (App) on the user side, input a travel destination, and click a navigation button. When a user clicks a navigation key, the user side can acquire a navigation path matched with a driving destination, immediately generate a navigation early warning analysis instruction and send the navigation early warning analysis instruction to the server; for another example, the user can also input a driving destination on the user terminal in a voice password mode, and then the user terminal automatically acquires a navigation path matched with the driving destination, and immediately generates a navigation early warning analysis instruction and sends the navigation early warning analysis instruction to the server.

And S102, acquiring the running information of the vehicles running on the navigation path within a preset time period.

The preset time period may be a time period predetermined by the server according to timeliness or accuracy of obtaining the driving information, for example, the preset time period may be different time periods of the same day, the last two days, the last week, and the like. The travel information may indicate a travel state of the vehicle, for example, whether the vehicle is in high-speed travel; may also represent a travel route of the vehicle; environmental information around the vehicle, such as the distance of the vehicle from the vehicle ahead of the ride, may also be represented. The driving information includes driving information of a vehicle currently driving on the navigation path, and may also include historical driving information of a vehicle that has once driven on the navigation path.

Specifically, when the server receives a navigation early warning analysis instruction sent by the user side, the navigation path to be analyzed can be further extracted from the navigation early warning analysis instruction, and then the running information reported by all vehicles or a preset number of vehicles running on the navigation path within a preset time period is acquired from the database. Of course, when the server obtains the driving information of the preset number of vehicles, the preset number of vehicles can be randomly selected, and the preset number of vehicles can also be selected according to certain rules, so that the accuracy of the server for predicting the abnormal road section is improved. For example, the server may select a vehicle whose model matches the current vehicle model based on the vehicle model, may select a vehicle whose volume is the same as the current vehicle volume based on the vehicle volume, and so on.

S103, determining abnormal road information on the navigation path by analyzing the driving information of each vehicle; the abnormal link information includes an abnormal link type and an abnormal link position.

The abnormal section information is used for indicating whether a section which can not be driven or a section which is difficult to drive exists on a navigation path which is pre-driven by a user, and the type and the position of the abnormal section. The abnormal section type may include sections of various abnormal scenes such as a hollow type, a construction type, a road surface breakage type, an accident type, a jam type, and the like. The abnormal road section position may be a coordinate (longitude and latitude) position of the abnormal road section on a map, or may also be a geographic position of the abnormal road section on the map, for example, a road intersection of a country building, which is not limited herein.

Specifically, when the server obtains the driving information of the vehicle driving on the navigation path, the server may further analyze the driving information of the vehicle to obtain the driving state of the vehicle, and then determine whether the vehicle is in an abnormal condition when driving on the navigation path according to the driving state of the vehicle, if the vehicle is in an abnormal condition, the road section in which the abnormal condition occurs is an abnormal road section, that is, the position of the abnormal road section may be determined, and then the server further determines the type of the abnormal road section according to the specific state of the vehicle. For example, the server may determine whether the vehicle has a sudden braking state on the navigation path from the driving speed of the vehicle, and if the sudden braking state occurs, it indicates that the road section ahead of the vehicle braking is abnormal, that is, the corresponding road section ahead of the vehicle braking is an abnormal road section, and the type of the road section ahead of the vehicle braking is most likely to be a construction road section or an accident road section. Alternatively, the server may also obtain the travel track of the vehicle from the travel information of the vehicle, determine whether the vehicle travels along the normal track when traveling along the navigation path according to the travel track of the vehicle, if the vehicle does not travel along the normal track, indicate that the vehicle encounters an abnormal condition, and similarly, if the vehicle encounters an abnormal condition, indicate that the road section where the abnormal condition occurs is an abnormal road section, and then the server further determines the type and the position of the abnormal road section according to the travel track of the vehicle. For example, if the navigation path is a straight path, the server may determine whether the vehicle is traveling straight from the travel path of the vehicle, and if the travel path of the vehicle is not straight or is not a route matching the navigation path, the server may indicate that the vehicle has an abnormal situation.

And S104, carrying the abnormal road section information in the early warning prompt information and sending the early warning prompt information to a user side.

The early warning prompt information is used for reminding a user whether a road section which cannot be driven or a road section which is difficult to drive exists on a navigation path which is pre-driven, and the type and the position of the road section which cannot be driven or the road section which is difficult to drive, so that the user can automatically judge whether to continue driving or not according to the early warning prompt information, or the user needs to pay attention to which road sections to drive carefully when continuing driving. The above-mentioned sections that cannot be traveled or sections that are difficult to travel are abnormal sections.

Specifically, when the server determines abnormal road information on the navigation path by analyzing the driving information of each vehicle, the server can directly carry the abnormal road information in the early warning prompt information and send the early warning prompt information to the user side for early warning; alternatively, the server may also obtain other information related to the abnormal road segment from the database (for example, image information of the abnormal road segment, etc.), and then the server carries the information together with the abnormal road segment information in the warning information and sends the warning information to the user terminal.

In the navigation early warning method for the transportation means, after receiving a navigation early warning analysis instruction which is sent by a user side and carries a navigation path, a server acquires the driving information of the transportation means which drive on the navigation path within a preset time period, determines the abnormal road section information which comprises the type and the position of the abnormal road section on the navigation path by analyzing the driving information of each transportation means, and then carries the abnormal road section information in the early warning prompt information to send to the user side. The navigation early warning method realizes the function of early warning while navigation is carried out on the user side, namely, the user can acquire related early warning prompt information while starting the navigation function on the user side, so that the user can predict the abnormal condition on the front path, and the safety of driving a vehicle by the user is improved. Moreover, the early warning prompt information comprises the type and the position of the abnormal road section, so that the early warning prompt information not only plays a role in reminding a user of moving ahead carefully, but also specifically informs the user of the type and the position of the abnormal road section, so that the user can know the abnormal condition more visually, and further can select whether to move ahead continuously or not according to the abnormal condition, and the use requirements of the user are greatly met.

Optionally, the travel information in the foregoing embodiment specifically includes travel route and/or travel state information of the vehicle.

The driving route represents an actual driving track of the vehicle when the vehicle drives on the navigation route, and may be represented by a straight line or a curved line. The travel state information includes various information capable of representing the state of the vehicle during travel, and for example, the travel state information may include the speed, acceleration, and the like of the travel speed of the vehicle; the running state information may include the degree of balance of running balance of the vehicle, the degree of yaw, and the like; a course curvature, a yaw angle, etc., representing a traveling direction of the vehicle; the running state information may include the vibration frequency, the vibration intensity, and the like of the running wave of the vehicle; the travel state information may include a position, a displacement, and the like of a travel trajectory of the vehicle.

Optionally, the travel path of the vehicle in the foregoing embodiment is a travel path determined according to Global Positioning System (GPS) Positioning information reported by the vehicle and/or Positioning data output by Positioning sensors installed on each vehicle.

The GPS positioning information includes a mobile position or mobile coordinates of the vehicle. The positioning sensor is used for detecting the moving position of the vehicle and outputting positioning data containing the moving position or moving coordinates of the vehicle.

Specifically, in the process that a user drives a vehicle to travel, a positioning device on the vehicle reports positioning information of the vehicle to a server in real time, and the server can obtain a travel path of the vehicle in the travel process according to the moving position or the moving coordinate of the vehicle in the positioning information. When the vehicle is provided with a positioning sensor for detecting the moving position or the moving coordinate of the vehicle, the vehicle can report the positioning data output by the positioning sensor to the server in real time in the driving process, and the server can obtain the driving path of the vehicle in the driving process according to the moving position or the moving coordinate of the vehicle in the positioning data. After the server determines the driving path of each vehicle, the driving path of each vehicle can be stored in a corresponding database as the driving information of each vehicle, so that the server can be used when abnormal road section information on the navigation path needs to be analyzed later. The method for determining the driving path according to the positioning information and/or the positioning data can realize accurate positioning of the vehicle, further improve the accuracy of determining the driving path, provide a reliable data source for a mode of determining the abnormal road section information according to the driving path, and further improve the accuracy of determining the abnormal road section information.

Alternatively, the driving state information in the foregoing embodiment is information obtained from data collected by sensors mounted on each vehicle.

Among them, the sensors mounted on the vehicle may be various types of sensors, for example, a positioning sensor, a speed sensor, an acceleration sensor, an angle sensor, a curvature sensor, a balance sensor, a vibration sensor, and the like. Moreover, the sensors on different vehicles may or may not be identical.

Specifically, the vehicle reports data output by various types of sensors installed to the server in real time during the driving process, and the server can obtain the driving state information of the vehicle according to the data. After the server determines the driving state information of each vehicle, the driving state information of each vehicle can be stored in a corresponding database as the driving information of each vehicle, so that the server can be used when the server needs to analyze the abnormal road section information on the navigation path. The driving state information comprises data output by various sensors, so that the driving state of the vehicle can be reflected from multiple angles, the server can judge the driving state of the vehicle more accurately, a reliable data source is provided for a mode of determining abnormal road section information according to the driving state information, and the accuracy of determining the abnormal road section information can be improved.

It can be understood that the server can simultaneously acquire the GPS positioning information reported by the transportation means during the driving process and the data output by each type of sensor, determine the corresponding navigation path and driving state information, and then analyze the abnormal road section information on the navigation path according to the navigation path and the driving state information, thereby improving the accuracy of determining the abnormal road section information.

Optionally, the embodiment of fig. 3 provides a specific implementation manner of the foregoing S103, and as shown in fig. 3, the implementation manner includes:

s201, determining abnormal vehicles by analyzing the running information of each vehicle; the abnormal vehicle has abnormal running path and/or abnormal running state.

The abnormal vehicle may include a vehicle with an abnormal travel track on the navigation path, for example, if the navigation path is a straight path, and the actual travel track of the vehicle when traveling on the navigation path is a curve, it indicates that the travel path of the vehicle is abnormal, and the vehicle is the abnormal vehicle. The abnormal transportation means may also include transportation means with abnormal driving state on the navigation path, for example, the speed of the transportation means when the transportation means normally drives on the navigation path should be 60km/h, and actually, the speed of the transportation means when the transportation means drives on the navigation path is changed, and the speed is reduced from 60km/h until the vehicle stops, which indicates that the driving state of the transportation means is abnormal, and then the transportation means is the abnormal transportation means.

Specifically, when the server obtains the travel information of each vehicle based on the step of S102, the travel route in the travel information of each vehicle may be further analyzed to determine whether the travel route of each vehicle is abnormal, for example, the vehicle travels a half route on the navigation route, the travel route of the vehicle has a small inflection point compared to the navigation route, and the like. Alternatively, the driving state information in the driving information of each vehicle may be further analyzed to determine whether the driving state of each vehicle is abnormal, for example, a deceleration section occurs during the driving of the vehicle on the navigation path, the direction of the vehicle is shifted during the driving of the vehicle on the navigation path, and the like. And finally, screening out the vehicles with abnormal driving paths and/or abnormal driving states, and determining the vehicles as abnormal vehicles.

S202, determining abnormal road information on the navigation path according to the number of the abnormal vehicles and a preset number threshold.

Wherein the preset quantity threshold is determined by the server in advance according to the quantity of the collected vehicles and the accuracy of prediction. For example, if the server collects the traveling information of 10 vehicles on the navigation path, the preset number threshold may be set to a value greater than 7, which is only an example and is not limited herein.

Specifically, when the server determines abnormal vehicles running on the navigation path, the number of the abnormal vehicles can be compared with a preset number threshold, and if the number of the abnormal vehicles is greater than the preset number threshold, the probability that an abnormal condition occurs on the navigation path is extremely high, and an abnormal road section exists on the navigation path; if the number of the abnormal vehicles is smaller than or equal to the preset number threshold, the probability of abnormal conditions on the navigation path is low, and the navigation path does not have abnormal road sections. When the server determines that the abnormal road section exists on the navigation path, the server can determine the abnormal road section information on the navigation path by analyzing the driving path and/or the driving state of the abnormal vehicle.

The method for determining the abnormal road information on the navigation path is a probability statistical method, namely, whether the navigation path is abnormal or not is predicted by counting the number of abnormal vehicles. The method has high accuracy by collecting a large amount of data for analysis and judgment.

Specifically, when the server executes the step S102 and the acquired travel information of the transportation includes different contents, the server determines that the abnormal transportation mode is different, and the corresponding server determines that the abnormal link information is different. The following examples are illustrative.

In an example, when the driving information of the transportation means acquired by the server includes the driving route of each transportation means, the server determines an abnormal transportation means manner (a specific implementation manner of the foregoing S201), which specifically includes: and comparing the running route of each vehicle with the navigation route, and determining the vehicle with the running route inconsistent with the navigation route as an abnormal vehicle.

The inconsistency between the driving route and the navigation route may indicate that the shapes of the driving route and the navigation route are inconsistent, the direction of the driving route and the direction of the navigation route are inconsistent, the lengths of the driving route and the navigation route are inconsistent, and the like. For example, the navigation path is a straight line, and the travel path is a curved line; the navigation path is from south to north, and the driving path is from north to south; the navigation path is long, and the driving path is short.

Specifically, when the server obtains the travel route of each vehicle, it may further determine whether the travel route of each vehicle is abnormal by comparing the characteristics of the travel route and the navigation route, such as shape, length, or direction, and if so, it may be determined that the vehicle is an abnormal vehicle. The method for determining the abnormal transportation means can be realized only by comparing the navigation path with the driving path, the process is simple, the speed of identifying the abnormal transportation means by the server is improved, and the speed of identifying the abnormal road section on the navigation road section by the server can be further improved.

Accordingly, the method for determining the abnormal section information by the server (a specific implementation manner of the above S202) specifically includes: and if the number of the abnormal vehicles is larger than the preset number threshold, determining that the position of the abnormal road section is the position of the road section with the driving path of the abnormal vehicles inconsistent with the navigation path, and determining that the type of the abnormal road section is a construction type or an accident type.

The construction type road section represents a road section which cannot pass through, or a road section which cannot pass through in a straight line and needs to be bypassed.

Specifically, when the server determines that the driving path of the abnormal vehicle is inconsistent with the navigation path, the server may further determine the position of the abnormal road segment on the navigation path by determining the position of the inconsistent road segment on the navigation path, for example, the driving path is inconsistent with the shape of the navigation path, and the driving path has an inflection point compared with the navigation path, so that the road segment corresponding to the inflection point is the position of the inconsistent road segment, that is, the position of the abnormal road segment. When the driving path is inconsistent with the navigation path, the abnormal condition is that the navigation path has a construction road section or a road section with an accident, and the vehicle needs to detour when driving to the construction road section or needs to change the road, so that the driving path of the vehicle is inconsistent with the navigation path, and in this case, the type of the road section with the inconsistent driving path and the navigation path is directly determined as the construction type or the accident type. Obviously, the method for determining the abnormal road section information can be realized by comparing the driving path with the navigation path, and can quickly and accurately identify the abnormal road section with the construction type or the accident type and the position of each abnormal road section.

In a second example, when the driving information of the transportation means acquired by the server includes the driving state information of each transportation means, the server determines an abnormal transportation means manner (a specific implementation manner of the foregoing S201), which specifically includes: and comparing the value of the state information of each vehicle with a preset state threshold value to determine the abnormal vehicle.

The value of the state information is quantized data representing the state of the vehicle, and for example, the value of the state information may be a value of a velocity, a value of an acceleration, a vibration frequency, or the like. The preset state threshold is a value of state information that the vehicle has when in a normal driving state on the navigation path, which can be used to evaluate whether the driving state of the vehicle on the navigation path is abnormal.

Specifically, when the server obtains the state information of each vehicle, it may further determine whether the driving state of each vehicle is abnormal by comparing the value of the state information with a preset state threshold, and if the driving state of each vehicle is abnormal, it indicates that the vehicle is abnormally driven when the vehicle is driven on the navigation path, and the vehicle is the abnormal vehicle. In the method for determining the abnormal transportation means, the driving state information can directly and accurately reflect the state of the transportation means, so that the method for determining the abnormal transportation means by analyzing the driving state information of the transportation means can improve the identification accuracy of the server for identifying the abnormal transportation means, and further improve the accuracy of identifying the abnormal road section information later.

Accordingly, the method for determining the abnormal section information by the server (a specific implementation manner of the above S202) specifically includes: and if the number of the abnormal vehicles is larger than the preset number threshold, determining the abnormal road information on the navigation path according to the driving state information of the abnormal vehicles.

In detail, if the driving state information of the abnormal transportation includes different information, the manner of determining the abnormal transportation (one specific implementation manner of S201) and the manner of determining the abnormal link information (one specific implementation manner of S202) are different, and the following embodiment describes different manners of determining the abnormal transportation and determining the abnormal link information according to different driving state information.

In a third example, when the driving state information of each vehicle acquired by the server includes a vibration frequency and/or a balance degree, the server determines an abnormal vehicle mode (a specific implementation manner of the foregoing S201), which specifically includes: and if the vibration frequency is greater than a preset vibration threshold value and/or the balance degree is less than a preset balance degree threshold value, determining that the vehicle is an abnormal vehicle.

The preset vibration threshold is the vibration frequency of the vehicle in the normal driving state on the navigation path, and the preset balance degree threshold is the balance degree of the vehicle in the normal driving state on the navigation path, and can be used for evaluating whether the driving state of the vehicle on the navigation path is abnormal or not.

Specifically, when the server obtains the vibration frequency of each vehicle, the vibration frequency is compared with a preset vibration threshold, and if the vibration frequency is greater than the preset vibration threshold, it is determined that a road section is abnormal (for example, bumpy) when the vehicle travels on the navigation path, so that the vibration frequency of the vehicle is increased, and thus it is determined that the traveling state of the vehicle is abnormal, and the vehicle is determined to be an abnormal vehicle. When the server obtains the balance degree of each vehicle, the balance degree is compared with a preset balance degree threshold value, if the balance degree is larger than the preset balance degree threshold value, the fact that a road section is abnormal when the vehicle runs on the navigation path is indicated, the balance degree of the vehicle is reduced, therefore, the fact that the running state of the vehicle is abnormal is judged, and the vehicle is determined to be an abnormal vehicle. In the method for determining the abnormal transportation means, the existing sensor with high precision can be used for data acquisition for acquiring the vibration frequency and the balance degree, so that the method for determining the abnormal transportation means by analyzing the vibration frequency and the balance degree of the transportation means can improve the identification accuracy of the server for identifying the abnormal transportation means, and further improve the accuracy of identifying the information of the abnormal road section.

Accordingly, the method for determining the abnormal section information by the server (a specific implementation manner of the above S202) specifically includes: if the number of the abnormal vehicles is larger than a preset number threshold, determining that the position of the abnormal road section is the position of a target road section in the driving path of the abnormal vehicles, and determining that the type of the abnormal road section is a hollow type; the target road section position is a road section position of which the vibration frequency is greater than a preset vibration threshold value in a driving path of the abnormal vehicle, and/or a road section position of which the balance degree is less than a preset balance degree threshold value.

The pothole type road section indicates a road section which is difficult to pass through, and when a vehicle travels through the road section, the vibration frequency of the vehicle is increased, and the degree of balance is lowered.

Specifically, when the server determines the abnormal transportation means, the position of the road section of the abnormal transportation means in the driving path with the vibration frequency greater than the preset vibration threshold value may be further determined, and the position of the road section may be directly determined as the position of the abnormal road section, or the position of the road section of the abnormal transportation means in the driving path with the balance degree less than the preset balance degree threshold value may be determined, and the position of the road section may be directly determined as the position of the abnormal road section. In practical applications, when a vehicle travels on a rough or dimpled road surface, the vibration frequency of the vehicle is generally increased and the degree of balance of the vehicle is generally decreased, and in this case, the type of an abnormal section on which the vehicle travels with the increased vibration frequency or the decreased degree of balance is directly determined as the dimple type. Obviously, since the vibration frequency or the balance degree can accurately reflect the degree of the vehicle jolt, the above method for determining the abnormal road section information by analyzing the vibration frequency or the balance degree can accurately identify the abnormal road section of the pothole type and the position of each abnormal road section.

In a fourth example, when the driving state information of each vehicle acquired by the server includes a curvature and/or an angle, the server determines an abnormal vehicle mode (a specific implementation manner of the foregoing S201), which specifically includes: and if the curvature is larger than a preset curvature threshold value and/or the angle is larger than a preset angle threshold value, determining that the vehicle is an abnormal vehicle.

The preset curvature threshold is the curvature of the running track of the vehicle in the normal running state on the navigation path, and the preset angle threshold is the angle of the running direction of the vehicle in the normal running state on the navigation path, and can be used for evaluating whether the running state of the vehicle on the navigation path is abnormal or not.

Specifically, when the server obtains the curvature of the travel track of each vehicle, the curvature is compared with a preset curvature threshold value, if the curvature is larger than the preset curvature threshold value, it is indicated that a road section is abnormal when the vehicle travels on the navigation path, and the curvature of the travel track of the vehicle is increased, so that the abnormal travel state of the vehicle is determined, and the vehicle is determined to be an abnormal vehicle. When the server obtains the angle of the driving direction of each vehicle, the angle is compared with a preset angle threshold value, if the angle is larger than the preset angle threshold value, the fact that the road section is abnormal when the vehicle drives on the navigation path is indicated, the angle of the driving direction of the vehicle is increased, therefore, the fact that the driving state of the vehicle is abnormal is judged, and the vehicle is determined to be an abnormal vehicle. In the method for determining the abnormal transportation means, the curvature and the angle can be acquired by adopting the existing sensor with high precision for data acquisition, so that the method for determining the abnormal transportation means by analyzing the curvature or the angle of the transportation means can improve the identification accuracy of the server for identifying the abnormal transportation means, and further improve the accuracy of identifying the information of the abnormal road section.

Accordingly, the method for determining the abnormal section information by the server (a specific implementation manner of the above S202) specifically includes: if the number of the abnormal vehicles is larger than a preset number threshold, determining that the position of the abnormal road section is the position of a target road section in the driving path of the abnormal vehicles, and determining that the type of the abnormal road section is a construction type; the target road section position is a road section position of which the curvature is larger than a preset curvature threshold value in the driving path of the abnormal vehicle, and/or a road section position of which the angle is larger than a preset angle threshold value.

Specifically, when the server determines the abnormal transportation means, the position of the road section where the curvature of the travel track of the abnormal transportation means in the travel path is greater than the preset curvature threshold value may be further determined, and the position of the road section may be directly determined as the position of the abnormal road section, or the position of the road section where the angle of the travel direction of the abnormal transportation means in the travel path is greater than the preset angle threshold value may be determined, and the position of the road section may be directly determined as the position of the abnormal road section. In practical applications, when the curvature of the travel track of the vehicle on the navigation path increases or the angle of the travel direction increases, an abnormal situation that usually occurs is a road segment on which construction is performed or a road segment in which an accident occurs on the navigation path, thereby causing the curvature of the travel track of the vehicle to increase or the angle of the travel direction to increase, and in this case, the type of the abnormal road segment on which the vehicle with the increased curvature or the increased angle travels is directly determined as the construction type. Obviously, since the curvature or the angle can accurately reflect the state of the vehicle in the detour or abrupt direction, the method for determining the abnormal road section information by analyzing the curvature or the angle can accurately identify the abnormal road section with the construction type or the accident type and the position of each abnormal road section.

In an example five, when the driving state information of each vehicle acquired by the server includes a speed and/or an acceleration, the server determines an abnormal vehicle mode (a specific implementation manner of the foregoing S201), which specifically includes: and if the speed is less than a preset speed threshold value and/or the acceleration is less than a preset acceleration threshold value, determining that the vehicle is an abnormal vehicle.

The preset speed threshold is the lowest running speed of the vehicle in a normal running state on the navigation path, and the preset acceleration threshold is the lowest acceleration of the vehicle in the normal running state on the navigation path, and can be used for evaluating whether the running state of the vehicle on the navigation path is abnormal or not.

Specifically, when the server obtains the speed of each vehicle traveling on the navigation path, the speed is compared with a preset speed threshold, and if the speed is smaller than the preset speed threshold, it is indicated that a road section is abnormal when the vehicle travels on the navigation path, so that the traveling speed of the vehicle is reduced, and thus the abnormal traveling state of the vehicle is determined, and the vehicle is determined to be an abnormal vehicle. When the server obtains the acceleration of each vehicle running on the navigation path, the acceleration is compared with the preset acceleration threshold, if the acceleration is smaller than the preset acceleration threshold, the fact that the road section is abnormal when the vehicle runs on the navigation path is indicated, the acceleration of the vehicle running is reduced, therefore, the running state of the vehicle is judged to be abnormal, and the vehicle is determined to be an abnormal vehicle. In the method for determining the abnormal transportation means, the existing sensor with high precision can be used for acquiring data for acquiring the speed and the acceleration, so that the method for determining the abnormal transportation means by analyzing the speed or the acceleration of the transportation means can improve the identification accuracy of the server for identifying the abnormal transportation means, and further improve the accuracy of identifying the information of the abnormal road section.

Accordingly, the method for determining the abnormal section information by the server (a specific implementation manner of the above S202) specifically includes: if the number of the abnormal vehicles is larger than a preset number threshold, determining that the position of the abnormal road section is the position of a target road section in the driving path of the abnormal vehicles, and determining that the type of the abnormal road section is a narrow type; the target road section position is a road section position of which the speed is smaller than a preset speed threshold value in a driving path of the abnormal vehicle, and/or a road section position of which the acceleration is smaller than a preset acceleration threshold value.

Here, the narrow type link means a link where congestion is likely to occur, and the speed or acceleration of the vehicle is reduced when the vehicle travels through the link.

Specifically, when the server determines the abnormal transportation means, the position of the road segment where the driving speed of the abnormal transportation means in the driving path is less than the preset speed threshold may be further determined, and the position of the road segment is directly determined as the abnormal road segment position, or the position of the road segment where the speed of the abnormal transportation means in the driving path is less than the preset speed threshold may be determined, and the position of the road segment is directly determined as the abnormal road segment position. In practical applications, when a vehicle travels on a narrow section of road, the speed and acceleration of the vehicle travel are generally reduced, and in this case, the type of the abnormal section of road on which the vehicle travels with the reduced speed and acceleration is directly determined as the narrow type. Obviously, since the speed and the acceleration can accurately reflect the state of the vehicle in the detour or abrupt direction, the above method for determining the abnormal road section information by analyzing the speed or the acceleration can accurately identify the abnormal road section in which the stenosis occurs and the position of each abnormal road section.

In one embodiment, the embodiment of the present disclosure further provides a specific implementation manner of S103, where the implementation manner includes: and inputting the running state information of each vehicle into a preset abnormal road section analysis network for analysis to obtain abnormal road section information on the navigation path.

The abnormal road section analysis network is a pre-trained network and is used for identifying road sections with abnormal conditions possibly occurring on the navigation path according to the driving state information of the vehicle and identifying the positions and the types of the road sections with the abnormal conditions. The abnormal section analysis network may specifically adopt various types of neural networks or machine learning networks, and is not limited herein.

Specifically, various sensors are arranged on the vehicle, various driving state information of the vehicle running on the navigation path is collected in real time, the driving state information is reported to the server periodically, and the server stores the driving state information according to time and longitude and latitude. When the server receives the navigation early warning analysis sent by the user side, the server immediately acquires the stored running state information of the vehicles in the preset number in the preset time period, inputs the running state information of the vehicles into the trained abnormal road section analysis network for analysis, and obtains an analysis result, wherein the analysis result can indicate whether an abnormal road section exists on a navigation path and indicate the position and the type of the abnormal road section.

Compared with a method for determining the abnormal road section by comparing a large number of running paths of vehicles with the navigation path, the method not only simplifies the process of identifying the abnormal road section by the server, but also has high identification accuracy because the abnormal road section analysis network is a pre-trained network based on a large number of data, thereby improving the accuracy of later-stage early warning.

Optionally, based on the application context, the "inputting the driving state information of each vehicle into a preset abnormal road segment analysis network for analysis to obtain the abnormal road segment information on the navigation path" as shown in fig. 4 specifically includes:

s401, dividing the navigation path into a plurality of unit road sections.

The length of the unit road section can be determined by the server according to the actual identification precision. When the server needs to analyze the abnormal condition on the navigation path by using the preset abnormal road segment analysis network, the server may divide the navigation path into a plurality of unit road segments, so as to obtain the driving state information on each unit road segment, and analyze the abnormal condition on each unit road segment according to the driving state information on each unit road segment.

S402, acquiring the driving state information of each vehicle on each unit road section.

Specifically, after the navigation path is divided into a plurality of unit road segments, the server may screen the driving state information of all vehicles or a preset number of vehicles within a preset time period from the stored driving state information of each vehicle on the navigation path. Alternatively, the server may also acquire the travel state information of all the vehicles traveling on the navigation path at the present time in real time. For example, the server may filter the travel state information (e.g., travel speed) of 10 vehicles on the navigation route in the last week from the stored travel state information of the vehicles.

S403, inputting the driving state information corresponding to each unit road section into an abnormal road section analysis network for analysis to obtain an analysis result; the analysis result indicates whether the unit link is an abnormal link, and the type and location of the abnormal link.

When the server obtains the driving state information of each vehicle on each unit road section based on the steps, the abnormal conditions on each unit road section can be analyzed, namely the driving state information is directly input into the abnormal road section analysis network for analysis, and an analysis result is obtained, and the server can obtain whether each unit road section is an abnormal road section or not, and the position of the abnormal road section on the navigation path and the type of the abnormal road section from the analysis result. For example, the navigation path includes 5 unit links, and after the driving state information on the 5 unit links is input into the abnormal link analysis network for analysis, it can be obtained that the 3 rd unit link of the 5 unit links is an abnormal link, the position of the unit link is 1000 meters on the navigation path, and the type of the unit link is the construction type.

And S404, obtaining abnormal road section information on the navigation path according to the analysis result.

After the server obtains the analysis results of each unit road section through the steps, whether the navigation path has the abnormal road section or not and the position and the type of the abnormal road section can be determined according to the analysis results. Reference is made in particular to the above illustrations.

In an embodiment, the embodiment of the present disclosure further provides a method for training the abnormal road segment analysis network, as shown in fig. 5, the training method includes:

s501, obtaining sample state information and a corresponding sample state label; the sample state label is used for identifying whether the road section corresponding to the sample state information is an abnormal road section or not, and the type and the position of the abnormal road section.

The type of the sample state information is the same as the type of the driving state information in the foregoing embodiment, and for a detailed description, refer to the foregoing contents, which are not repeated herein. Specifically, when the server needs to train the abnormal section analysis network, the server can acquire data output by sensors of a large number of existing vehicles to obtain sample state information. The server can identify the data output by the sensor to obtain whether the road section corresponding to the data is an abnormal road section and determine the position of the abnormal road section, and identify the type of the data output by the sensor to determine the type of the abnormal road section, so that the sample state label is obtained; optionally, the server may also obtain the sample state label by a line down stepping mode, that is, when the user arrives at the field road section and finds an abnormal road section, the abnormal road section is classified.

And S502, inputting the sample state information into an initial abnormal road section analysis network to obtain an output result.

The initial abnormal road section analysis network is an abnormal road section analysis network to be trained, and the output result comprises abnormal road section information. When the server acquires the sample state information, the sample state information can be input to the abnormal road section analysis network to be trained for analysis, and an output result is obtained.

And S503, calculating a loss value according to the output result and the sample state label.

And the server performs loss operation on the output result and the sample state label to obtain a loss value. The specific loss operation may adopt the conventional cross entropy loss operation, or may adopt other loss operations, which is not limited herein.

S504, adjusting parameters of the initial abnormal road section analysis network according to the loss value until the loss value meets the preset training condition, and obtaining the abnormal road section analysis network.

The preset training condition may be determined by the server in advance according to an actual training requirement, for example, when the loss value reaches a preset standard value, or reaches a maximum number of iterations. After the server obtains the loss value, parameters of the initial abnormal road section analysis network can be adjusted according to the loss value, then the sample state information is input into the abnormal road section analysis network after the parameters are adjusted, an output result is obtained, the loss value is calculated, then the parameters of the abnormal road section analysis network are adjusted according to the loss value, the process is circulated until the loss value meets the preset training condition, and at the moment, the parameters of the final abnormal road section analysis network are obtained, so that the abnormal road section analysis network used in the embodiment is obtained.

In an embodiment, the present disclosure further provides a structure of an abnormal road segment analysis network, as shown in fig. 6, where the structure includes: at least two parallel branch networks; different branch networks are used to analyze different types of driving state information.

Based on the structure of the abnormal road segment analysis network described in the embodiment of fig. 6, the embodiment of the present disclosure further provides a specific implementation manner of S103 corresponding to the abnormal road segment analysis network, where the implementation manner includes: and inputting the running state information into the corresponding branch network for analysis according to the type of the running state information to obtain the abnormal road information on the navigation path.

Specifically, when the server obtains various driving state information reported by the transportation means on the navigation path, the driving state information may be respectively input into the corresponding branch networks according to the type of the driving state information for analysis, so as to obtain analysis results output by the branch networks, and then the analysis results are integrated, so as to obtain abnormal road information on the navigation path. In the method, each branch network corresponds to each type of running state information, namely each branch network only needs to identify the running state information of the corresponding type, and does not need to classify the running state information first and then identify the running state information of each type, so that the analysis process of the abnormal road section analysis network is greatly simplified, and the identification speed and the identification accuracy of the abnormal road section analysis network are greatly improved.

In an embodiment, the present disclosure further provides a method for training the abnormal road segment analysis network, as shown in fig. 7, the training method includes:

s601, obtaining sample state information and a corresponding sample state label; the sample state label is used for identifying whether the road section corresponding to the sample state information is an abnormal road section or not, and the type and the position of the abnormal road section.

This step is the same as the method described in step S501, and please refer to the foregoing description for details, which is not repeated herein.

And S602, respectively inputting the sample state information into the corresponding initial branch networks according to the types of the sample state information, and obtaining the output results of the branch networks.

The initial branch network is a branch network to be trained, and the output result of each branch network comprises abnormal road section information corresponding to the type of the driving state information. For example, if the traveling state information includes two types of data, i.e., speed and vibration frequency, the output result of the branch network corresponding to the speed is the construction-type abnormal link and location, and the output result of the branch network corresponding to the vibration frequency is the pothole-type abnormal link and location.

Specifically, when the server collects a large amount of sample state information, the sample state information may be classified first to obtain various types of sample state information; the server may also directly gather different types of sample state information. And then the server inputs the collected sample state information into the corresponding initial branch networks respectively according to the types of the sample state information to obtain the output results of each branch network. In this process, the server may sequentially input different types of driving state information into the corresponding branch networks, or may simultaneously input different types of driving state information into the respective corresponding branch networks, so as to obtain an output result of each branch network, so as to be used when analyzing each type of abnormal path later.

And S603, calculating the loss value of each branch network according to the output result of each branch network and the corresponding sample state label.

The method for calculating the loss value of each branch network in this step is similar to the method described in step S503, and please refer to the foregoing description for details, which is not repeated herein.

And S604, adjusting parameters of each branch network according to the loss value of each branch network to obtain an abnormal road section analysis network.

The method for adjusting the parameters of each branch network in this step is similar to the method described in step S503, and please refer to the foregoing description for details, which is not described herein again.

In one embodiment, the vehicle may be a two-wheeled vehicle, i.e., a bicycle, an electric vehicle, etc. The existing bicycles or electric vehicles, particularly shared bicycles or shared electric vehicles, are exemplified by the shared bicycle, and because the shared bicycle does not have a navigation function and an early warning function, a user often searches a navigation path through a user terminal and then rides the bicycle on the navigation path to reach a destination. Meanwhile, the riding safety of the user is reduced. Based on the technical problem, the navigation early warning method of the vehicle provided by the embodiment of the disclosure enables the shared bicycle to have an early warning function when riding on the navigation path, enables a user to advance an abnormal road section on the front path, and automatically select to ride around or push the vehicle to ride after passing through the abnormal road section. The safety of riding of the user is improved, and the service life of the vehicle is prolonged.

It is understood that, when the vehicle driven by the user is a two-wheeled vehicle, the navigation path sent by the user terminal to the server is a path on a non-motor lane. Abnormal conditions such as road surface potholes, road surface damage, road surface construction and the like often occur on the non-motor vehicle lane, and more pedestrians often exist on the non-motor vehicle lane. The user can often encounter the above situations when riding the vehicle on the non-motor lane, if the early warning of the abnormal situation can be obtained in time, the user can avoid riding on the lane with the abnormal situation or riding carefully when riding the vehicle, and the safety of the user riding on pedestrians on the non-motor lane is improved.

In some embodiments, the user terminal is a vehicle or a user terminal that initiates a usage flow for a vehicle. That is, the user end may be a vehicle, e.g., a bicycle, an electric vehicle, an automobile, etc.; when the user side is a vehicle, the vehicle can send a navigation path selected by a user to the server, when the user drives the vehicle to run on the navigation path, the server sends early warning prompt information to the vehicle, and the vehicle outputs the early warning prompt information to prompt the user; the user terminal may also be a user terminal that initiates a usage flow on the vehicle, that is, the user terminal may perform data interaction with the vehicle to instruct the vehicle to perform a corresponding operation. When the user side is a user terminal initiating a use process on a vehicle, a user can determine a navigation path on the user terminal and send the navigation path to a server, and the corresponding server sends early warning prompt information on the navigation path to the user terminal to prompt the user; alternatively, when the user performs an operation of determining a navigation path on the user terminal, the server may also transmit warning prompt information on the navigation path to the vehicle. It is noted that the user terminal may be an electronic device independent from the vehicle, for example, the user terminal may be a mobile phone or an IPAD, and the user terminal may also be an electronic device or an apparatus integrated with the vehicle.

The user side can be a vehicle or a user terminal which initiates a use process for the vehicle, that is, the user can trigger a navigation early warning analysis instruction through the vehicle or trigger the navigation early warning analysis instruction through the user terminal, and the user can output early warning prompt information through the vehicle or output the early warning prompt information through the user terminal. The user can flexibly select the mode of receiving the early warning prompt information according to the specific condition of driving the vehicle, and the driving safety and convenience of the user driving the vehicle are improved. For example, when the user side is a bicycle, the user can ride the bicycle and know the early warning prompt information output by the bicycle at the same time, and does not need to stop the bicycle and obtain the early warning prompt information by using other equipment.

The above embodiment describes the navigation early warning method of a vehicle applied to the server in fig. 1 as an example, and the following embodiment describes the navigation early warning method of a vehicle applied to the user side in fig. 1 as an example.

In an embodiment, as shown in fig. 8, a navigation early warning method for a vehicle is provided, which is described by taking the method as an example of being applied to a user end in fig. 1, and includes the following steps:

s701, sending a navigation early warning analysis instruction to a server; the navigation early warning analysis instruction comprises a navigation path.

The steps in this embodiment correspond to the contents included in the step S101 in the embodiment of fig. 2, and please refer to the foregoing description for detailed description, which is not repeated herein.

S702, receiving early warning prompt information sent by a server; the early warning prompt information carries abnormal road section information, and the abnormal road section information is determined by the server according to the driving information of the vehicles driving on the navigation path within a preset time period; the abnormal link information includes an abnormal link type and an abnormal link position.

The steps in this embodiment correspond to the contents included in the steps S102-S104 in the embodiment of fig. 2, and please refer to the foregoing description for detailed description, which is not repeated herein.

And S703, outputting early warning prompt information.

When the user side receives the early warning prompt information sent by the server, the early warning prompt information can be further output, so that the user can know which abnormal road sections exist on the navigation path to be driven in advance and the positions and types of the abnormal road sections through the early warning prompt information, and therefore whether the user drives according to the navigation path or carefully drives in the driving process is selected. It should be noted that the user end may output the warning prompt information in a variety of ways, for example, the user end may display the position and the type of the abnormal road segment on the navigation path in an image or icon display manner; the user side can dynamically or statically display the abnormal road sections on the navigation path, and the method for outputting the early warning prompt information is not limited in the embodiment.

In the navigation early warning method for the vehicle, the user side sends a navigation early warning analysis instruction carrying a navigation path to the server, and correspondingly receives early warning prompt information containing abnormal road section information sent by the server so as to remind the user of abnormal conditions on the navigation path. The navigation early warning method realizes the function of early warning while navigation is carried out on the user side, namely, the user can acquire related early warning prompt information while starting the navigation function on the user side, so that the user can predict the abnormal condition on the front path, and the safety of driving a vehicle by the user is improved. Moreover, the early warning prompt information comprises the type and the position of the abnormal road section, so that the early warning prompt information not only plays a role in reminding a user of moving ahead carefully, but also specifically informs the user of the type and the position of the abnormal road section, so that the user can know the abnormal condition more visually, and further can select whether to move ahead continuously or not according to the abnormal condition, and the use requirements of the user are greatly met.

In an embodiment, based on the above-mentioned embodiment of fig. 8, before the user side performs step S701, the method for warning navigation of a vehicle, as shown in fig. 9, further includes:

s801, detecting a path selection operation of a user on a path selection interface of a user terminal.

The path selection interface can be an interface of the user terminal when an application program is opened, and the application program has a navigation function. The path selection operation may include various selection operations such as a pull-down menu, inputting text, inputting a password, and the like, but is not limited thereto.

Specifically, when the user needs to perform a navigation operation, the user may open a corresponding navigation application on the user side and perform a path selection operation on a selection interface of the application, for example, the user inputs an address of a navigation destination, or the user directly searches a path from a departure location to a destination location, or the like. When the user finishes the path selection operation, the user terminal can immediately detect the path selection operation of the user, so that the user terminal responds according to the path selection operation later.

S802, responding to the path selection operation, and determining the selected navigation path as the navigation path to be analyzed.

When the user side detects the path selection operation of the user, the navigation path which is pre-traveled by the user can be further obtained according to the path selection operation of the user, and the navigation path is taken as the navigation path to be analyzed and carried in the navigation early warning analysis instruction so as to instruct the server to analyze the abnormal condition on the navigation path and feed back the abnormal condition to the user side.

The navigation early warning analysis instruction determined by the method is determined by the selection of the user, so that the navigation early warning method of the vehicle provided by the embodiment of the disclosure can early warn abnormal conditions on any navigation path by combining the method of the embodiment, so that the user side using the navigation early warning method has stronger applicability.

In one embodiment, when the user terminal executes the step S103 of outputting the warning prompt message in the embodiment of fig. 8, the user terminal outputs the warning prompt message during the process that the vehicle travels on the navigation path to be analyzed. That is to say, the user can obtain the early warning prompt information output by the user end while driving the vehicle to travel on the navigation path, learn which road sections on the navigation path being traveled are abnormal road sections, and know the type of the abnormal road sections (for example, pothole road sections), so as to drive carefully or drive around the road, thereby improving the safety of the travel.

Optionally, the travel information in the foregoing embodiment specifically includes travel route and/or travel state information of the vehicle. The content of the driving information in this embodiment is the same as the content of the driving information in the server side, and for a detailed description, reference is made to the foregoing description, which is not repeated herein.

In one embodiment, the vehicle referred to above may be a two-wheeled vehicle, and the corresponding navigation path is a path on a non-motorized lane. The transportation means related to this embodiment is the same as the transportation means described at the server side, and the navigation path is also the same, so for a detailed description, refer to the foregoing description, which is not repeated herein.

In one embodiment, the above mentioned vehicle is provided with various types of sensors, which specifically include: at least one of a positioning sensor, a velocity sensor, an acceleration sensor, an angle sensor, a curvature sensor, a balance sensor, and a vibration sensor; the corresponding user side obtains the running information of the vehicle according to the sensors, wherein the running information comprises at least one of running path, speed, acceleration, angle, curvature, balance degree, vibration frequency and vibration amplitude. It should be noted that, the above various types of sensors may adopt an existing sensing device, as long as the existing sensing device can collect corresponding types of data, and the model and specification of each sensor are not limited herein. One type of sensor or a plurality of types of sensors can be installed on the vehicles, and the same type of sensor or different types of sensors can be installed on different vehicles.

The navigation early warning method for the vehicle provided by the embodiment of the disclosure can detect various types of abnormal road sections, and specifically, the types of the detected abnormal road sections include: at least one of a pothole road section, a construction road section, an accident road section, and a narrow road section. That is to say, the user side may perform the warning prompt on one type of abnormal path, or may perform the warning prompt on multiple types of abnormal road segments at the same time. The early warning prompt information is more specific, and provides more sufficient data for judging whether the user selects to continue driving the vehicle to run on the navigation path.

In one embodiment, when the user terminal finishes executing the step S702, the navigation route may be further displayed, based on which, the embodiment of the present disclosure provides three specific implementation manners for the user terminal to output the warning prompt information, the first is to display the warning prompt information on the display screen of the user terminal in the form of a picture; secondly, broadcasting early warning prompt information in a voice broadcasting mode; the third type is that the early warning prompt information is displayed on the display screen of the user side in a picture mode, and meanwhile, the early warning prompt information is broadcasted in a voice broadcasting mode.

In the first mode, when the user receives the warning prompt information sent by the server, the warning prompt information can be displayed on the display screen in a static or dynamic picture mode, and an abnormal road section, an abnormal road section position and an abnormal road section type on the navigation path can be displayed on the display screen, wherein the abnormal road section can be represented by an icon; the position of the abnormal road section can be represented by a coordinate value, a longitude and latitude value, or a distance value from the current position of the vehicle (for example, an abnormal road section exists at a position 100 meters away from the current position); the abnormal link type may be represented by an icon, a character, or the like. The display mode of the early warning prompt information on the display comment is not limited. The method for outputting the early warning display information can enable a user to visually see the abnormal road section, the position of the abnormal road section and the type of the abnormal road section on the display screen, so that the user can be familiar with the abnormal condition on the navigation path conveniently, and the user can be promoted to quickly take countermeasures.

Under above-mentioned second mode, when the user received the early warning reminder message that the server sent, broadcast this early warning reminder message with voice broadcast's form, for example, the user can report: the construction road section is arranged at the position 100 meters ahead. It should be noted that, when the user end broadcasts the warning prompt message in a voice broadcast manner, the user end may be equipped with a corresponding voice playing device, such as a speaker, a voice player, etc. Obviously, the second display method can enable the user to listen to the early warning prompt information broadcasted by the user side while driving the vehicle to run on the navigation path, and can also acquire the abnormal condition on the navigation path in advance without influencing the driving operation of the user, thereby improving the safety of the user in driving the vehicle.

In one embodiment, the user terminal involved in the above includes a vehicle or a user terminal that initiates a usage flow for a vehicle. The user side related to the present embodiment is the same as the user side of the server side, and for a specific description, reference is made to the foregoing description, which is not repeated herein.

In an embodiment, based on the above-mentioned embodiment of fig. 8, when the user performs any step therein, the method for warning navigation of a vehicle, as shown in fig. 10, further includes:

and S901, acquiring sensing data output by at least one type of sensor installed on a vehicle.

When the vehicle runs, no matter the vehicle runs on a navigation path or other paths, the user side can acquire sensing data output by at least one type of sensor installed on the vehicle in real time so as to detect the running state of the vehicle in an all-around manner.

And S902, generating running information according to the sensing data and sending the running information to a server.

When the user side acquires the sensing data of the vehicles, the sensing data can be carried in the driving information and reported to the server, so that the server can store the driving information of the vehicles, and the abnormal road section can be analyzed conveniently.

According to the method, the client reports various types of sensing data to the server anytime and anywhere, so that full data storage is provided for the server, the accuracy of determining the early warning prompt information by the server in the later period is improved, and the accuracy of early warning of the client is further improved.

In an embodiment, the present disclosure further provides a navigation early warning method for a vehicle, where the method is applied to a client and a server in fig. 1, and as shown in fig. 11, the method includes:

s1001, a user side sends a navigation early warning analysis instruction to a server; the navigation early warning analysis instruction comprises a navigation path.

S1002, the server receives a navigation early warning analysis instruction sent by the user side.

S1003, the server acquires the running information of the vehicles running on the navigation path within a preset time period.

S1004, the server determines abnormal road information on the navigation path by analyzing the driving information of each vehicle; the abnormal link information includes an abnormal link type and an abnormal link position.

And S1005, the server carries the abnormal road section information in the early warning prompt information and sends the early warning prompt information to the user side.

S1006, the user receives the early warning prompt information sent by the server.

And S1007, the user side outputs early warning prompt information.

The methods described in S1001 to S1007 are the same as those described in the server-side embodiment and the user-side embodiment, and for a detailed description, refer to the foregoing description, which is not repeated herein.

The navigation early warning method for the vehicle in the embodiment realizes the function that the user obtains the early warning prompt information on the navigation path while navigating through the data interaction between the user side and the server. Therefore, the navigation early warning method of the vehicle provided by the embodiment of the disclosure can improve the driving safety of the user when providing driving convenience for the user to drive the vehicle.

It should be understood that although the various steps in the flow charts of fig. 2-11 are shown in order as indicated by the arrows, the steps are not necessarily performed in order as indicated by the arrows. The steps are not performed in the exact order shown and described, and may be performed in other orders, unless explicitly stated otherwise. Moreover, at least some of the steps in fig. 2-11 may include multiple steps or multiple stages, which are not necessarily performed at the same time, but may be performed at different times, which are not necessarily performed in sequence, but may be performed in turn or alternately with other steps or at least some of the other steps.

In one embodiment, as shown in fig. 12, there is provided a navigation early warning apparatus for a vehicle, including: a receiving module 11, an obtaining module 12, a determining module 13 and a sending module 14, wherein:

the receiving module 11 is configured to receive a navigation early warning analysis instruction sent by a user side; the navigation early warning analysis instruction comprises a navigation path to be analyzed;

the obtaining module 12 is configured to obtain driving information of a vehicle driving on the navigation path within a preset time period;

a determining module 13, configured to determine abnormal road segment information on the navigation path by analyzing driving information of each vehicle; the abnormal road section information comprises an abnormal road section type and an abnormal road section position;

and the sending module 14 is configured to carry the abnormal road section information in the early warning prompt information and send the early warning prompt information to the user side.

In one embodiment, the travel information includes travel path and/or travel state information of the vehicle.

In one embodiment, the driving path of the vehicle is a driving path determined according to GPS positioning information reported by the vehicle and/or positioning data output by a positioning sensor mounted on each vehicle;

the driving state information is information obtained according to data collected by sensors installed on the vehicles.

In an embodiment, the determining module 13, as shown in fig. 13, includes:

a first determination unit 131 for determining an abnormal vehicle by analyzing the travel information of each of the vehicles; the abnormal vehicle has abnormal running path and/or abnormal running state;

a second determining unit 132, configured to determine abnormal road segment information on the navigation path according to the number of the abnormal vehicles and a preset number threshold.

In one embodiment, the first determining unit 131 is further configured to compare the travel path of each vehicle with the navigation path, and determine a vehicle with a travel path inconsistent with the navigation path as the abnormal vehicle;

the second determining unit 132 is further configured to determine that the abnormal road segment position is a road segment position where a driving path of the abnormal vehicle is inconsistent with the navigation path, and determine that the abnormal road segment type is a construction type or an accident type, when the number of the abnormal vehicles is greater than a preset number threshold.

In one embodiment, the first determining unit 131 is further configured to compare the value of the driving state information of each vehicle with a preset state threshold value to determine the abnormal vehicle;

the second determining unit 132 is further configured to determine abnormal road section information on the navigation path according to the driving state information of the abnormal transportation means when the number of the abnormal transportation means is greater than a preset number threshold.

In an embodiment, if the driving status information includes a vibration frequency and/or a balance degree, the first determining unit 131 is specifically configured to determine that the vehicle is an abnormal vehicle when the vibration frequency is greater than a preset vibration threshold and/or the balance degree is less than a preset balance degree threshold.

In one embodiment, the second determining unit 132 is specifically configured to determine that the abnormal road segment position is a target road segment position in a driving path of the abnormal vehicle when the number of the abnormal vehicles is greater than a preset number threshold, and determine that the abnormal road segment type is a pothole type; the target road section position is a road section position of which the vibration frequency is greater than a preset vibration threshold value in the running path of the abnormal vehicle, and/or a road section position of which the balance degree is less than a preset balance degree threshold value.

In an embodiment, if the state information includes a curvature and/or an angle, the first determining unit 131 is specifically configured to determine that the vehicle is an abnormal vehicle when the curvature is greater than a preset curvature threshold and/or the angle is greater than a preset angle threshold.

In one embodiment, the second determining unit 132 is specifically configured to determine that the abnormal road segment location is a target road segment location in a driving path of the abnormal vehicle when the number of the abnormal vehicles is greater than a preset number threshold, and determine that the type of the abnormal road segment is a construction type; the target road section position is a road section position of which the curvature is larger than a preset curvature threshold value in the driving path of the abnormal vehicle, and/or a road section position of which the angle is larger than a preset angle threshold value.

In an embodiment, if the status information includes a speed and/or an acceleration, the first determining unit 131 is specifically configured to determine that the vehicle is an abnormal vehicle when the speed is less than a preset speed threshold and/or the acceleration is less than a preset acceleration threshold.

In one embodiment, the second determining unit 132 is specifically configured to determine that the abnormal road segment location is a target road segment location in a driving path of the abnormal vehicle when the number of the abnormal vehicles is greater than a preset number threshold, and determine that the type of the abnormal road segment is a narrow type; the target road section position is a road section position of which the speed is smaller than a preset speed threshold value in the running path of the abnormal vehicle, and/or a road section position of which the acceleration is smaller than a preset acceleration threshold value.

In one embodiment, as shown in fig. 14, the determining module 13 includes:

the network analysis unit 133 is configured to input the driving state information of each vehicle into a preset abnormal road segment analysis network for analysis, so as to obtain abnormal road segment information on the navigation path.

In one embodiment, as shown in fig. 15, the network analyzing unit 133 includes:

a splitting subunit 1331, configured to split the navigation path into a plurality of unit road segments;

an acquisition subunit 1332 configured to acquire driving state information of each of the vehicles on each of the unit links;

an analysis subunit 1333, configured to input the driving state information corresponding to each unit road segment into the abnormal road segment analysis network for analysis, so as to obtain an analysis result; the analysis result represents whether the unit road section is an abnormal road section or not, and the type and the position of the abnormal road section;

and an output subunit 1334, configured to obtain information of the abnormal road segment on the navigation path according to the analysis result.

In one embodiment, the navigation early warning apparatus further includes: a first training module 10, said training module, as shown in fig. 16, comprising:

a first sample obtaining unit 101, configured to obtain sample state information and a corresponding sample state label; the sample state label is used for identifying whether the road section corresponding to the sample state information is an abnormal road section, and the type and the position of the abnormal road section;

the first network analysis unit 102 is configured to input the sample state information to an initial abnormal road segment analysis network to obtain an output result;

a first calculating unit 103, configured to calculate a loss value according to the output result and the sample state label;

the first training unit 104 is configured to adjust parameters of the initial abnormal road section analysis network according to the loss value until the loss value meets a preset training condition, so as to obtain the abnormal road section analysis network.

In one embodiment, the abnormal section analysis network includes: at least two parallel branch networks; different branch networks are used for analyzing different types of driving state information;

the network analysis unit 133 is configured to input the driving state information into a corresponding branch network for analysis according to the type of the driving state information, so as to obtain information of an abnormal road segment on the navigation path.

In an embodiment, the navigation early warning apparatus further includes a second training module 20, and the second training module 20, as shown in fig. 17, includes:

a second sample obtaining unit 201, configured to obtain sample state information and a corresponding sample state label; the sample state label is used for identifying whether the road section corresponding to the sample state information is an abnormal road section, and the type and the position of the abnormal road section;

the second network analysis unit 202 is configured to input the sample state information into corresponding initial branch networks respectively according to the type of the sample state information, so as to obtain output results of the branch networks;

a second calculating unit 203, configured to calculate a loss value of each branch network according to an output result of each branch network and a corresponding sample state label;

the second training unit 204 is configured to adjust parameters of each branch network according to the loss value of each branch network, so as to obtain the abnormal road segment analysis network.

In one embodiment, the vehicle is a two-wheeled vehicle.

In one embodiment, the navigation path is a path on a non-motorized lane.

In one embodiment, the user terminal comprises the vehicle or a user terminal initiating a usage flow for the vehicle.

In one embodiment, as shown in fig. 18, there is provided a navigation early warning apparatus for a vehicle, including: a sending module 21, a receiving module 22 and an output module 23, wherein:

the sending module 21 is configured to send a navigation early warning analysis instruction to the server; the navigation early warning analysis instruction comprises a navigation path to be analyzed;

a receiving module 22, configured to receive the warning prompt information sent by the server; the early warning prompt information carries abnormal road section information, and the abnormal road section information is determined by the server according to the driving information of the vehicles driving on the navigation path within a preset time period; the abnormal road section information comprises an abnormal road section type and an abnormal road section position;

and the output module 23 is configured to output the early warning prompt information.

In an embodiment, before the sending module 21 in the navigation warning apparatus, as shown in fig. 19, the navigation warning apparatus further includes:

the detection module 24 is configured to detect a path selection operation of a user on a path selection interface of a user side;

a determining module 25, configured to determine, in response to the path selection operation, the selected navigation path as the navigation path to be analyzed.

In one embodiment, the output module 23 is further configured to output the warning prompt information during the process that the vehicle travels on the navigation path to be analyzed.

In one embodiment, the vehicle is a two-wheeled vehicle.

In one embodiment, the navigation path is a path on a non-motorized lane.

In one embodiment, the navigation warning apparatus, as shown in fig. 20, further includes:

an acquisition module 26 for acquiring sensing data output by at least one type of sensor mounted on the vehicle;

and a generating module 27, configured to generate the driving information according to the sensing data and send the driving information to the server.

In one embodiment, the vehicle is provided with at least one of a positioning sensor, a speed sensor, an acceleration sensor, an angle sensor, a curvature sensor, a balance sensor and a vibration sensor; the travel information includes at least one of a travel path, a speed, an acceleration, an angle, a curvature, a balance, a vibration frequency, and a vibration amplitude.

In one embodiment, the abnormal section type includes: at least one of a pothole road section, a construction road section, an accident road section, and a narrow road section.

In one embodiment, the output module 23, as shown in fig. 21, includes:

the display unit 231 is used for displaying the early warning prompt information on a display screen of the user side in a picture mode;

and/or the broadcasting unit 232 is used for broadcasting the early warning prompt information in a voice broadcasting mode.

For specific limitations of the navigation early warning apparatus for a vehicle, reference may be made to the above limitations of the navigation early warning method for a vehicle, which are not described herein again. All or part of the modules in the navigation early warning device of the vehicle can be realized by software, hardware and a combination thereof. The modules can be embedded in a processor in the user side or independent of the processor in the user side in a hardware form, and can also be stored in a memory in the user side in a software form, so that the processor can call and execute the corresponding operations of the modules.

Fig. 22 is a block diagram illustrating a user terminal 1300 according to an example embodiment. When the user terminal is a user terminal, the user terminal may be a mobile phone, a computer, a digital broadcast terminal, a messaging device, a tablet device, a personal digital assistant, or the like.

Referring to fig. 22, the user terminal 1300 may include one or more of the following components: a processing component 1302, a memory 1304, a power component 1306, a multimedia component 1308, an audio component 1310, an input/output (I/O) interface 1312, a sensor component 1314, and a communication component 1316. Wherein the memory has stored thereon a computer program or instructions for execution on the processor.

The processing component 1302 generally controls the overall operations of the user terminal 1300, such as operations associated with display, telephone calls, data communications, camera operations, and recording operations. The processing component 1302 may include one or more processors 1320 to execute instructions to perform all or part of the steps of the method described above. Further, the processing component 1302 can include one or more modules that facilitate interaction between the processing component 1302 and other components. For example, the processing component 1302 may include a multimedia module to facilitate interaction between the multimedia component 1308 and the processing component 1302.

The memory 1304 is configured to store various types of data to support operations at the user terminal 1300. Examples of such data include instructions for any application or method operating on the user terminal 1300, contact data, phonebook data, messages, pictures, videos, and so forth. The memory 1304 may be implemented by any type or combination of volatile or non-volatile memory devices such as Static Random Access Memory (SRAM), electrically erasable programmable read-only memory (EEPROM), erasable programmable read-only memory (EPROM), programmable read-only memory (PROM), read-only memory (ROM), magnetic memory, flash memory, magnetic or optical disks.

The power supply component 1306 provides power to the various components of the user terminal 1300. The power components 1306 may include a power management system, one or more power sources, and other components associated with generating, managing, and distributing power for the user terminal 1300.

The multimedia component 1308 includes a touch-sensitive display screen between the user terminal 1300 and the user that provides an output interface. In some embodiments, the touch display screen may include a Liquid Crystal Display (LCD) and a Touch Panel (TP). The touch panel includes one or more touch sensors to sense touch, slide, and gestures on the touch panel. The touch sensor may not only sense the boundary of a touch or slide action, but also detect the duration and pressure associated with the touch or slide operation. In some embodiments, the multimedia component 1308 includes a front facing camera and/or a rear facing camera. When the user terminal 1300 is in an operation mode, such as a shooting mode or a video mode, the front camera and/or the rear camera can receive external multimedia data. Each front camera and rear camera may be a fixed optical lens system or have a focal length and optical zoom capability.

The audio component 1310 is configured to output and/or input audio signals. For example, the audio component 1310 includes a Microphone (MIC) configured to receive an external audio signal when the user terminal 1300 is in an operation mode, such as a call mode, a recording mode, and a voice recognition mode. The received audio signals may further be stored in the memory 1304 or transmitted via the communication component 1316. In some embodiments, the audio component 1310 also includes a speaker for outputting audio signals.

The I/O interface 1312 provides an interface between the processing component 1302 and peripheral interface modules, which may be keyboards, click wheels, buttons, etc. These buttons may include, but are not limited to: a home button, a volume button, a start button, and a lock button.

The sensor assembly 1314 includes one or more sensors for providing various aspects of status assessment to the user terminal 1300. For example, the sensor assembly 1314 can detect the open/closed state of the user terminal 1300, the relative positioning of the components, such as the display and keypad of the user terminal 1300, the change in position of the user terminal 1300 or a component of the user terminal 1300, the presence or absence of user contact with the user terminal 1300, the orientation or acceleration/deceleration of the user terminal 1300, and the change in temperature of the user terminal 1300. The sensor assembly 1314 may include a proximity sensor configured to detect the presence of a nearby object without any physical contact. The sensor assembly 1314 may also include a light sensor, such as a CMOS or CCD image sensor, for use in imaging applications. In some embodiments, the sensor assembly 1314 may also include an acceleration sensor, a gyroscope sensor, a magnetic sensor, a pressure sensor, or a temperature sensor.

The communication component 1316 is configured to facilitate communications between the user terminal 1300 and other devices in a wired or wireless manner. The user terminal 1300 may access a wireless network based on a communication standard, such as WiFi, 2G or 3G, or a combination thereof. In an exemplary embodiment, the communication component 1316 receives broadcast signals or broadcast related information from an external broadcast management system via a broadcast channel. In an exemplary embodiment, the communications component 1316 also includes a Near Field Communications (NFC) module to facilitate short-range communications. For example, the NFC module may be implemented based on Radio Frequency Identification (RFID) technology, infrared data association (IrDA) technology, Ultra Wideband (UWB) technology, Bluetooth (BT) technology, and other technologies.

In an exemplary embodiment, the user terminal may be implemented by one or more Application Specific Integrated Circuits (ASICs), Digital Signal Processors (DSPs), Digital Signal Processing Devices (DSPDs), Programmable Logic Devices (PLDs), Field Programmable Gate Arrays (FPGAs), controllers, microcontrollers, microprocessors or other electronic components for performing the navigation warning method of the vehicle.

In an exemplary embodiment, the user terminal may be a user terminal or a vehicle. When the user side is a user terminal, the user terminal can be provided with controls for triggering the navigation instruction by one key, such as a mechanical control, a virtual control, a voice device and the like, and when the user side is a vehicle, the vehicle can also be provided with controls for triggering the navigation instruction by one key, such as a mechanical button, a virtual control, a voice device and the like.

Fig. 23 is a block diagram illustrating a server 1400 in accordance with an example embodiment. Referring to fig. 23, server 1400 includes a processing component 1420, which further includes one or more processors, and memory resources, represented by memory 1422, for storing instructions or computer programs, e.g., applications, that are executable by processing component 1420. The application programs stored in memory 1422 may include one or more modules that each correspond to a set of instructions. Further, the processing component 1420 is configured to execute instructions to perform the navigation warning method of the vehicle described above.

The server 1400 may also include a power component 1424 configured to perform power management of the device 1400, a wired or wireless network interface 1426 configured to connect the device 1400 to a network, and an input/output (I/O) interface 1428. The server 1400 may operate based on an operating system stored in memory 1422, such as Window 1414 over, Mac O14XTM, UnixTM, Linux, FreeB14DTM, or the like.

In an exemplary embodiment, a storage medium comprising instructions, such as the memory 1422 comprising instructions, executable by the processor of the server 1400 to perform the above-described method is also provided. The storage medium may be a non-transitory computer readable storage medium, which may be, for example, a ROM, a Random Access Memory (RAM), a CD-ROM, a magnetic tape, a floppy disk, an optical data storage device, and the like.

It will be understood by those skilled in the art that all or part of the processes of the methods of the embodiments described above can be implemented by hardware instructions of a computer program, which can be stored in a non-volatile computer-readable storage medium, and when executed, can include the processes of the embodiments of the methods described above. Any reference to memory, storage, database or other medium used in the embodiments provided by the embodiments of the disclosure may include at least one of non-volatile and volatile memory. Non-volatile Memory may include Read-Only Memory (ROM), magnetic tape, floppy disk, flash Memory, optical storage, or the like. Volatile Memory can include Random Access Memory (RAM) or external cache Memory. By way of illustration and not limitation, RAM can take many forms, such as Static Random Access Memory (SRAM) or Dynamic Random Access Memory (DRAM), among others.

The technical features of the above embodiments can be arbitrarily combined, and for the sake of brevity, all possible combinations of the technical features in the above embodiments are not described, but should be considered as the scope of the present specification as long as there is no contradiction between the combinations of the technical features.

The above-mentioned embodiments only express a few implementation modes of the embodiments of the present disclosure, and the description thereof is specific and detailed, but not construed as limiting the scope of the invention. It should be noted that, for those skilled in the art, variations and modifications can be made without departing from the concept of the embodiments of the present disclosure, and these are all within the scope of the embodiments of the present disclosure. Therefore, the protection scope of the patent of the embodiment of the disclosure should be subject to the appended claims.

Claims

1. A navigation early warning method for a vehicle, the method comprising:

2. The method according to claim 1, characterized in that the driving information comprises driving path and/or driving state information of the vehicle.

3. The method according to claim 2, wherein the driving route of the vehicle is a driving route determined according to GPS positioning information reported by the vehicle and/or positioning data output by a positioning sensor installed on each vehicle;

4. The method according to claim 2, wherein the determining abnormal section information on the navigation path by analyzing the travel information of each of the vehicles comprises:

determining abnormal vehicles by analyzing the driving information of each vehicle; the abnormal vehicle has abnormal running path and/or abnormal running state;

and determining abnormal road section information on the navigation path according to the number of the abnormal vehicles and a preset number threshold.

5. The method of claim 4, wherein the travel information includes a travel path of each of the vehicles, and the determining abnormal vehicles by analyzing the travel information of each of the vehicles comprises:

comparing the running path of each vehicle with the navigation path, and determining the vehicle with the running path inconsistent with the navigation path as the abnormal vehicle;

determining abnormal road section information on the navigation path according to the number of the abnormal vehicles and a preset number threshold, wherein the determining of the abnormal road section information on the navigation path comprises the following steps:

if the number of the abnormal vehicles is larger than a preset number threshold, determining that the position of the abnormal road section is a road section position where a driving path of the abnormal vehicles is inconsistent with the navigation path, and determining that the type of the abnormal road section is a construction type or an accident type.

6. The method of claim 4, wherein the driving information includes the driving state information, and wherein determining abnormal vehicles by analyzing the driving information of each of the vehicles comprises:

comparing the value of the running state information of each vehicle with a preset state threshold value to determine the abnormal vehicle;

and if the number of the abnormal vehicles is larger than a preset number threshold, determining abnormal road section information on the navigation path according to the driving state information of the abnormal vehicles.

7. The method according to claim 6, wherein if the driving state information includes a vibration frequency and/or a balance degree, the comparing the value of the state information of each vehicle with a preset state threshold value to determine the abnormal vehicle includes:

and if the vibration frequency is greater than a preset vibration threshold value and/or the balance degree is less than a preset balance degree threshold value, determining that the vehicle is an abnormal vehicle.

8. The method according to claim 7, wherein if the number of the abnormal vehicles is greater than a preset number threshold, determining abnormal section information on the navigation path according to the driving state information of the abnormal vehicles comprises:

if the number of the abnormal vehicles is larger than a preset number threshold, determining that the position of the abnormal road section is a target road section position in a driving path of the abnormal vehicles, and determining that the type of the abnormal road section is a hollow type; the target road section position is a road section position of which the vibration frequency is greater than a preset vibration threshold value in the running path of the abnormal vehicle, and/or a road section position of which the balance degree is less than a preset balance degree threshold value.

9. The method according to claim 6, wherein if the state information includes a curvature and/or an angle, the comparing the value of the driving state information of each vehicle with a preset state threshold value to determine the abnormal vehicle includes:

and if the curvature is larger than a preset curvature threshold value and/or the angle is larger than a preset angle threshold value, determining that the vehicle is an abnormal vehicle.

10. The method according to claim 9, wherein if the number of the abnormal vehicles is greater than a preset number threshold, determining abnormal road segment information on the navigation path according to the state information of the abnormal vehicles comprises:

if the number of the abnormal vehicles is larger than a preset number threshold, determining that the position of the abnormal road section is a target road section position in a driving path of the abnormal vehicles, and determining that the type of the abnormal road section is a construction type; the target road section position is a road section position of which the curvature is larger than a preset curvature threshold value in the driving path of the abnormal vehicle, and/or a road section position of which the angle is larger than a preset angle threshold value.

11. The method according to claim 6, wherein if the status information includes speed and/or acceleration, the comparing the value of the status information of each vehicle with a preset status threshold to determine the abnormal vehicle comprises:

and if the speed is less than a preset speed threshold value and/or the acceleration is less than a preset acceleration threshold value, determining that the vehicle is an abnormal vehicle.

12. The method according to claim 11, wherein if the number of the abnormal vehicles is greater than a preset number threshold, determining abnormal road segment information on the navigation path according to the state information of the abnormal vehicles comprises:

if the number of the abnormal vehicles is larger than a preset number threshold, determining that the position of the abnormal road section is a target road section position in a driving path of the abnormal vehicles, and determining that the type of the abnormal road section is a narrow type; the target road section position is a road section position of which the speed is smaller than a preset speed threshold value in the running path of the abnormal vehicle, and/or a road section position of which the acceleration is smaller than a preset acceleration threshold value.

13. The method according to claim 2, wherein the travel information includes the state information, and the determining of the abnormal section information on the navigation path by analyzing the travel information of each of the vehicles includes:

and inputting the running state information of each vehicle into a preset abnormal road section analysis network for analysis to obtain abnormal road section information on the navigation path.

14. The method of claim 13, wherein the inputting the driving state information of each vehicle into a preset abnormal road section analysis network for analysis to obtain the abnormal road section information on the navigation path comprises:

dividing the navigation path into a plurality of unit road segments;

acquiring driving state information of each vehicle on each unit road section;

inputting the driving state information corresponding to each unit road section into the abnormal road section analysis network for analysis to obtain an analysis result; the analysis result represents whether the unit road section is an abnormal road section or not, and the type and the position of the abnormal road section;

and obtaining abnormal road section information on the navigation path according to the analysis result.

15. The method of claim 14, wherein the method of training the abnormal road segment analysis network comprises:

acquiring sample state information and a corresponding sample state label; the sample state label is used for identifying whether the road section corresponding to the sample state information is an abnormal road section, and the type and the position of the abnormal road section;

inputting the sample state information into an initial abnormal road section analysis network to obtain an output result;

calculating a loss value according to the output result and the sample state label;

and adjusting parameters of the initial abnormal road section analysis network according to the loss value until the loss value meets a preset training condition to obtain the abnormal road section analysis network.

16. The method of claim 13, wherein the abnormal segment analysis network comprises: at least two parallel branch networks; different branch networks are used for analyzing different types of driving state information;

the step of inputting the running state information of each vehicle into a preset abnormal road section analysis network for analysis to obtain the abnormal road section information on the navigation path comprises the following steps:

and inputting the running state information into a corresponding branch network for analysis according to the type of the running state information to obtain abnormal road section information on the navigation path.

17. The method of claim 16, wherein the method of training the abnormal road segment analysis network comprises:

respectively inputting the sample state information into corresponding initial branch networks according to the types of the sample state information to obtain the output results of the branch networks;

calculating the loss value of each branch network according to the output result of each branch network and the corresponding sample state label;

and adjusting the parameters of each branch network according to the loss value of each branch network to obtain the abnormal road section analysis network.

18. The method of claim 1, wherein the vehicle is a two-wheeled vehicle.

19. The method of claim 18, wherein the navigation path is a path on a non-motorized lane.

20. The method of claim 1, wherein the user terminal comprises the vehicle or a user terminal that initiates a usage flow for the vehicle.

21. A navigation early warning method for a vehicle, the method comprising:

and outputting the early warning prompt information.

22. The method of claim 21, wherein the travel information comprises travel path and/or travel state information of the vehicle.

23. The method of claim 21, wherein prior to sending the navigational alert analysis instructions to the server, the method further comprises:

detecting a path selection operation of a user on a path selection interface of a user side;

and determining the selected navigation path as the navigation path to be analyzed in response to the path selection operation.

24. The method of claim 21, wherein outputting the warning alert message comprises:

and outputting the early warning prompt information when the vehicle runs on the navigation path to be analyzed.

25. The method of claim 21, wherein the vehicle is a two-wheeled vehicle.

26. The method of claim 25, wherein the navigation path is a path on a non-motorized lane.

27. The method of claim 21, further comprising:

acquiring sensing data output by at least one type of sensor installed on the vehicle;

and generating the driving information according to the sensing data and sending the driving information to the server.

28. The method of claim 21, wherein at least one of a position sensor, a speed sensor, an acceleration sensor, an angle sensor, a curvature sensor, a balance sensor, and a vibration sensor is provided on the vehicle; the travel information includes at least one of a travel path, a speed, an acceleration, an angle, a curvature, a balance, a vibration frequency, and a vibration amplitude.

29. The method according to any one of claims 21 to 28, wherein the abnormal road segment type includes: at least one of a pothole road section, a construction road section, an accident road section, and a narrow road section.

30. The method of claim 21, wherein outputting the warning alert message comprises:

displaying the early warning prompt information on a display screen of the user side in a picture mode;

and/or broadcasting the early warning prompt information in a voice broadcasting mode.

31. The method of claim 21, wherein the user terminal comprises the vehicle or a user terminal that initiates a usage flow for the vehicle.

32. A navigation early warning device for a vehicle, the navigation early warning device comprising:

the determining module is used for determining abnormal road section information on the navigation path by analyzing the driving information of each vehicle; the abnormal road section information comprises an abnormal road section type and an abnormal road section position;

and the sending module is used for carrying the abnormal road section information in early warning prompt information and sending the early warning prompt information to the user side.

33. The navigational alert device of claim 32, wherein the travel information includes a travel path and/or travel status information of the vehicle.

34. The navigation early warning device according to claim 33, wherein the driving route of the vehicle is determined according to GPS positioning information reported by the vehicle and/or positioning data output by a positioning sensor installed on each vehicle;

35. The navigational alert device of claim 33, wherein the determination module comprises:

a first determination unit configured to determine an abnormal vehicle by analyzing travel information of each of the vehicles; the abnormal vehicle has abnormal running path and/or abnormal running state;

and the second determining unit is used for determining the abnormal road section information on the navigation path according to the number of the abnormal vehicles and a preset number threshold.

36. The navigational alert device of claim 35,

the first determining unit is further used for comparing the running path of each vehicle with the navigation path, and determining the vehicle with the running path inconsistent with the navigation path as the abnormal vehicle;

the second determining unit is further configured to determine that the abnormal road segment position is a road segment position where a driving path of the abnormal vehicle is inconsistent with the navigation path and determine that the abnormal road segment type is a construction type or an accident type when the number of the abnormal vehicles is greater than a preset number threshold.

37. The navigational alert device of claim 35,

the first determining unit is further used for comparing the value of the running state information of each vehicle with a preset state threshold value to determine the abnormal vehicle;

the second determining unit is further configured to determine abnormal road section information on the navigation path according to the driving state information of the abnormal transportation means when the number of the abnormal transportation means is greater than a preset number threshold.

38. The navigation early warning device according to claim 37, wherein if the driving status information includes a vibration frequency and/or a balance degree, the first determining unit is specifically configured to determine that the vehicle is an abnormal vehicle when the vibration frequency is greater than a preset vibration threshold value and/or the balance degree is less than a preset balance degree threshold value.

39. The navigation early warning device according to claim 38, wherein the second determining unit is specifically configured to determine that the abnormal road section position is a target road section position in a driving path of the abnormal vehicle when the number of the abnormal vehicles is greater than a preset number threshold, and determine that the abnormal road section type is a pothole type; the target road section position is a road section position of which the vibration frequency is greater than a preset vibration threshold value in the running path of the abnormal vehicle, and/or a road section position of which the balance degree is less than a preset balance degree threshold value.

40. The navigation warning apparatus of claim 37, wherein if the status information includes a curvature and/or an angle, the first determining unit is specifically configured to determine that the vehicle is an abnormal vehicle when the curvature is greater than a preset curvature threshold and/or the angle is greater than a preset angle threshold.

41. The navigation early warning device according to claim 40, wherein the second determining unit is specifically configured to determine that the abnormal road section position is a target road section position in a driving path of the abnormal vehicle when the number of the abnormal vehicles is greater than a preset number threshold, and determine that the abnormal road section type is a construction type; the target road section position is a road section position of which the curvature is larger than a preset curvature threshold value in the driving path of the abnormal vehicle, and/or a road section position of which the angle is larger than a preset angle threshold value.

42. The navigation warning device of claim 37, wherein if the status information includes a speed and/or an acceleration, the first determining unit is specifically configured to determine that the vehicle is an abnormal vehicle when the speed is less than a preset speed threshold and/or the acceleration is less than a preset acceleration threshold.

43. The navigation early warning device according to claim 42, wherein the second determining unit is specifically configured to determine that the abnormal road section position is a target road section position in a driving path of the abnormal vehicle when the number of the abnormal vehicles is greater than a preset number threshold, and determine that the abnormal road section type is a narrow type; the target road section position is a road section position of which the speed is smaller than a preset speed threshold value in the running path of the abnormal vehicle, and/or a road section position of which the acceleration is smaller than a preset acceleration threshold value.

44. The navigational alert device of claim 33, wherein the determination module comprises:

and the network analysis unit is used for inputting the running state information of each vehicle into a preset abnormal road section analysis network for analysis to obtain the abnormal road section information on the navigation path.

45. The navigational alert device of claim 44, wherein the network analysis unit comprises:

the splitting subunit is used for splitting the navigation path into a plurality of unit road sections;

an acquisition subunit configured to acquire driving state information of each of the vehicles on each of the unit links;

the analysis subunit is used for inputting the driving state information corresponding to each unit road section into the abnormal road section analysis network for analysis to obtain an analysis result; the analysis result represents whether the unit road section is an abnormal road section or not, and the type and the position of the abnormal road section;

and the output subunit is used for obtaining the abnormal road section information on the navigation path according to the analysis result.

46. The navigational alert device of claim 45, wherein the navigational alert device further comprises: a first training module, the training module comprising:

the first sample obtaining unit is used for obtaining sample state information and a corresponding sample state label; the sample state label is used for identifying whether the road section corresponding to the sample state information is an abnormal road section, and the type and the position of the abnormal road section;

the first network analysis unit is used for inputting the sample state information into an initial abnormal road section analysis network to obtain an output result;

the first calculation unit is used for calculating a loss value according to the output result and the sample state label;

and the first training unit is used for adjusting the parameters of the initial abnormal road section analysis network according to the loss value until the loss value meets a preset training condition, so as to obtain the abnormal road section analysis network.

47. The navigation early warning device of claim 44, wherein the abnormal section analysis network comprises: at least two parallel branch networks; different branch networks are used for analyzing different types of driving state information;

and the network analysis unit is used for inputting the running state information into a corresponding branch network for analysis according to the type of the running state information to obtain abnormal road section information on the navigation path.

48. The navigational warning device of claim 47, further comprising a second training module, the second training module comprising:

the second sample obtaining unit is used for obtaining sample state information and a corresponding sample state label; the sample state label is used for identifying whether the road section corresponding to the sample state information is an abnormal road section, and the type and the position of the abnormal road section;

the second network analysis unit is used for respectively inputting the sample state information into corresponding initial branch networks according to the types of the sample state information to obtain the output results of all the branch networks;

the second calculation unit is used for calculating the loss value of each branch network according to the output result of each branch network and the corresponding sample state label;

and the second training unit is used for adjusting the parameters of each branch network according to the loss value of each branch network to obtain the abnormal road section analysis network.

49. The navigational alert device of claim 32, wherein the vehicle is a two-wheeled vehicle.

50. The navigational alert device of claim 48, wherein the navigational path is a path on a non-motorized lane.

51. The navigation warning apparatus of claim 32, wherein the user terminal comprises the vehicle or a user terminal that initiates a usage procedure for the vehicle.

52. A navigation early warning device for a vehicle, the navigation early warning device comprising:

53. The navigational alert device of claim 52, wherein the travel information includes a travel path and/or travel status information of the vehicle.

54. The navigational warning device of claim 52, wherein the transmitting module of the navigational warning device further comprises:

the detection module is used for detecting the path selection operation of a user on a path selection interface of a user side;

and the determining module is used for responding to the path selection operation and determining the selected navigation path as the navigation path to be analyzed.

55. The navigation warning device of claim 52, wherein the output module is further configured to output the warning prompt message during the vehicle traveling on the navigation path to be analyzed.

56. The navigational alert device of claim 52, wherein the vehicle is a two-wheeled vehicle.

57. The navigational alert device of claim 52, wherein the navigational path is a path on a non-motorized lane.

58. The navigational alert device of claim 52, further comprising:

the acquisition module is used for acquiring sensing data output by at least one type of sensor installed on the vehicle;

and the generating module is used for generating the driving information according to the sensing data and sending the driving information to the server.

59. The navigational warning device of claim 52, wherein the vehicle is provided with at least one of a positioning sensor, a speed sensor, an acceleration sensor, an angle sensor, a curvature sensor, a balance sensor and a vibration sensor; the travel information includes at least one of a travel path, a speed, an acceleration, an angle, a curvature, a balance, a vibration frequency, and a vibration amplitude.

60. The navigational alert device of any one of claims 52 to 59, wherein the abnormal section type comprises: at least one of a pothole road section, a construction road section, an accident road section, and a narrow road section.

61. The navigational alert device of claim 52, wherein the output module comprises:

the display unit is used for displaying the early warning prompt information on a display screen of the user side in a picture mode;

and/or the broadcasting unit is used for broadcasting the early warning prompt information in a voice broadcasting mode.

62. The navigational alert device of claim 52, wherein the user terminal comprises the vehicle or a user terminal that initiates a usage procedure for the vehicle.

63. A user terminal, comprising: a transmitter, a receiver, a processor, an output device, a memory, and a computer program stored on the memory and executable on the processor; the processor, when executing the computer program, is configured to control the operation of the transmitter, the receiver, the output device;

64. The user terminal according to claim 63, wherein the processor, when executing the computer program, implements the steps of the method according to any of claims 2 to 20.

65. A server, comprising: a receiver, a transmitter, a processor, a memory, and a computer program stored on the memory and executable on the processor; the processor, when executing the computer program, is configured to control the operation of the transmitter, the receiver, the processor;

66. A server as claimed in claim 63, wherein the processor, when executing the computer program, performs the steps of the method of any one of claims 22 to 31.

67. A storage medium having a computer program stored thereon, the computer program, when being executed by a processor, realizing the steps of the method of any one of claims 1 to 31.