Navigation method and device based on driving task complexity, electronic equipment and medium

Abstract

The invention provides a navigation method and device based on driving task complexity, electronic equipment, a computer readable storage medium and a computer program product, and relates to the field of computers, in particular to the technical field of automatic driving, map navigation and navigation broadcasting. Determining a to-be-driven path corresponding to the target vehicle; obtaining road information in the to-be-driven path; based on the navigation information, determining navigation instruction information corresponding to the target vehicle in the to-be-driven path; obtaining driving behavior information corresponding to the target vehicle; based on the road information, the navigation instruction information and the driving behavior information, determining the driving task complexity of the target vehicle on the to-be-driven path; and broadcasting the navigation information based on a broadcasting mode corresponding to the driving task complexity so as to guide the target vehicle to drive along the to-be-driven path.

Description

TECHNICAL FIELD

[0001] The present disclosure relates to the field of computers, in particular to the technical field of automatic driving, map navigation, navigation broadcasting, and specifically to a navigation method and device based on driving task complexity, electronic equipment, computer readable storage medium and computer program product. BACKGROUND

[0002] The field of map navigation is a comprehensive field that uses satellite positioning, network communication, geographic information systems and other technologies to provide users with map display, location positioning, path planning and navigation guidance services. It is widely used in traffic, logistics, tourism, intelligent devices and other aspects.

[0003] The methods described in this section can not necessarily be the methods previously conceived or used. Unless otherwise indicated, nothing in this section should be assumed to be prior art merely because it is included in this section. Similarly, unless otherwise indicated, nothing in this section should be assumed to have been admitted prior to the filing date of the present application. SUMMARY

[0004] The present disclosure provides a navigation method and device based on driving task complexity, electronic equipment, computer readable storage medium and computer program product.

[0005] According to an aspect of the present disclosure, a navigation method based on driving task complexity is provided, comprising: determining a to-be-traveled path corresponding to a target vehicle based on navigation information corresponding to the target vehicle; obtaining road information in the to-be-traveled path; determining navigation instruction information corresponding to the target vehicle in the to-be-traveled path based on the navigation information; obtaining driving behavior information corresponding to the target vehicle; determining driving task complexity of the target vehicle on the to-be-traveled path based on the road information, the navigation instruction information and the driving behavior information; and broadcasting the navigation information based on a broadcasting mode corresponding to the driving task complexity to guide the target vehicle to travel along the to-be-traveled path.

[0006] According to another aspect of the present disclosure, there is provided a navigation device based on driving task complexity, comprising: a path determination module configured to determine a to-be-traveled path corresponding to a target vehicle based on navigation information corresponding to the target vehicle; a first information acquisition module configured to acquire road information in the to-be-traveled path; an instruction determination module configured to determine navigation instruction information corresponding to the target vehicle in the to-be-traveled path based on the navigation information; a second information acquisition module configured to acquire driving behavior information corresponding to the target vehicle; a complexity determination module configured to determine driving task complexity of the target vehicle on the to-be-traveled path based on the road information, the navigation instruction information, and the driving behavior information; and a broadcast module configured to broadcast the navigation information based on a broadcast mode corresponding to the driving task complexity, so as to guide the target vehicle to travel along the to-be-traveled path.

[0007] According to another aspect of the present disclosure, there is provided an electronic device, comprising: at least one processor; and a memory communicatively connected with the at least one processor; the memory stores instructions executable by the at least one processor, and the instructions are executed by the at least one processor to enable the at least one processor to perform the method of the present disclosure.

[0008] According to another aspect of the present disclosure, there is provided a non-transitory computer readable storage medium storing computer instructions for causing a computer to perform the method of the present disclosure.

[0009] According to another aspect of the present disclosure, there is provided a computer program product comprising a computer program which, when executed by a processor, implements the method of the present disclosure.

[0010] It should be understood that the matters described herein are intended to be illustrative rather than restrictive. The disclosure is not limited to the embodiments described herein, but can be practiced with modification and alteration within the scope of the disclosure. BRIEF DESCRIPTION OF DRAWINGS

[0011] The accompanying drawings, which are included to provide a further understanding of the application and are incorporated in and constitute a part of this application, illustrate embodiments of the application and together with the description serve to explain the principles of the application. The illustrated embodiments are merely examples of the present application and are not intended to restrict the scope of the claims in any way.

[0012] Figure 1 shows a schematic diagram of an exemplary system in which the various methods described herein can be implemented according to embodiments of the present disclosure; Figure 2 shows a flowchart of a navigation method based on driving task complexity 200 according to embodiments of the present disclosure; Figure 3 A schematic diagram showing a driving scenario according to an embodiment of the present disclosure is shown; Figure 4 shows a structural block diagram of a navigation device based on driving task complexity according to an embodiment of the present disclosure; and Figure 5 A structural block diagram of an exemplary electronic device that can be used to implement the embodiments of the present disclosure is shown. DETAILED DESCRIPTION

[0013] The following description of exemplary embodiments of the present disclosure is made in conjunction with the accompanying drawings, including various details of the embodiments of the present disclosure to facilitate understanding, which should be considered as merely exemplary. Therefore, it should be appreciated by those skilled in the art that various changes and modifications may be made to the embodiments described herein without departing from the scope of the present disclosure. Similarly, for the sake of clarity and conciseness, descriptions of well-known functions and structures are omitted in the following description.

[0014] In this disclosure, unless otherwise specified, the use of terms such as "first" and "second" to describe various elements is not intended to limit the positional relationship, temporal relationship, or importance relationship of these elements. Such terms are only used to distinguish one element from another. In some examples, the first element and the second element may refer to the same instance of the element, while in some cases, based on the context of the description, they may also refer to different instances.

[0015] The terms used in the descriptions of the various examples described in this disclosure are for the purpose of describing specific examples only and are not intended to be limiting. Unless the context clearly indicates otherwise, if the number of elements is not specifically limited, the element may be one or more. In addition, the term "and / or" used in this disclosure encompasses any one and all possible combinations of the listed items.

[0016] The embodiments of the present disclosure will be described in detail below with reference to the accompanying drawings.

[0017] Figure 1 FIG2 is a schematic diagram of an exemplary system 100 in which the various methods and apparatuses described herein may be implemented according to an embodiment of the present disclosure. Figure 1 , the system 100 includes one or more client devices 101, 102, 103, 104, 105, and 106, a server 120, and one or more communication networks 110 coupling the one or more client devices to the server 120. The client devices 101, 102, 103, 104, 105, and 106 can be configured to execute one or more applications.

[0018] In embodiments of the present disclosure, the server 120 can run one or more services or software applications that enable the execution of the navigation methods based on driving task complexity.

[0019] In certain embodiments, the server 120 can also provide other services or software applications, which can include non-virtual and virtual environments. In certain embodiments, these services can be provided as web-based services or cloud services, for example, to users of the client devices 101, 102, 103, 104, 105, and / or 106 under a software as a service (SaaS) model.

[0020] In Figure 1 In the illustrated configuration, the server 120 can include one or more components that implement the functionality performed by the server 120. These components can include software components that are executable by one or more processors, hardware components, or combinations thereof. Users operating the client devices 101, 102, 103, 104, 105, and / or 106 can in turn utilize one or more client applications to interact with the server 120 to utilize the services provided by these components. It should be understood that a wide variety of different system configurations are possible, which can differ from system 100. Therefore, Figure 1 is one example of a system for implementing the various methods described herein and is not intended to be limiting.

[0021] The client devices 101, 102, 103, 104, 105, and / or 106 can be used by users to receive navigation information and road information, send driving behavior information. The client devices can provide interfaces that enable users of the client devices to interact with the client devices. The client devices can also output information to the users via the interfaces. Although Figure 1 Only six client devices are depicted, but one of skill in the art will appreciate that the present disclosure can support any number of client devices.

[0022] Client devices 101, 102, 103, 104, 105, and / or 106 can include various types of computer devices, such as portable handheld devices, general purpose computers (such as personal computers and laptop computers), workstation computers, wearable devices, smart screen devices, self-service kiosk devices, service robots, gaming systems, thin clients, various messaging devices, sensors or other sensing devices, and the like. These computer devices can run various types and versions of software applications and operating systems, such as MICROSOFT Windows, APPLE iOS, UNIX-like operating systems, Linux or Linux-like operating systems (such as GOOGLE Chrome OS); or including various mobile operating systems, such as MICROSOFT Windows Mobile OS, iOS, Windows Phone, Android. Portable handheld devices can include cellular telephones, smartphones, tablet computers, personal digital assistants (PDAs), and the like. Wearable devices can include head-mounted displays (such as smart glasses) and other devices. Gaming systems can include various handheld gaming devices, Internet-enabled gaming devices, and the like. Client devices are capable of executing a variety of different applications, such as various Internet-related applications, communication applications (such as email applications), short message service (SMS) applications, and can use various communication protocols.

[0023] Network 110 can be any type of network familiar to those skilled in the art that can support data communications using any of a variety of available protocols, including without limitation TCP / IP, SNA, IPX, etc. As examples, one or more of networks 110 can be a local area network (LAN), an Ethernet-based network, a token ring, a wide area network (WAN), the Internet, a virtual network, a virtual private network (VPN), an intranet, an extranet, a blockchain network, a public switched telephone network (PSTN), an infrared network, a wireless network (e.g., a Bluetooth, WIFI), and / or any combination of these and / or other networks.

[0024] Server 120 can include one or more general purpose computers, special purpose server computers (e.g., PC (personal computer) servers, UNIX servers, midrange servers), blade servers, mainframe computers, server clusters, or any other appropriate arrangement and / or combination. Server 120 can include one or more virtual machines running a virtual operating system, or other computing architectures involving virtualization (such as one or more flexible pools of logical storage devices that can be virtualized to maintain virtual storage devices for servers). In various embodiments, server 120 can run one or more services or software applications that provide the functionality described below.

[0025] The computing units in the server 120 can run one or more operating systems including any of the operating systems described above, as well as any commercially available server operating systems. Server 120 can also run any of a variety of additional server applications and / or mid-tier applications, including HTTP servers, FTP servers, CGI servers, JAVA servers, database servers, and the like.

[0026] In some embodiments, the server 120 can include one or more applications to analyze and consolidate data feeds and / or event updates from users of the client devices 101, 102, 103, 104, 105, and 106. The server 120 can also include one or more applications to display the data feeds and / or real-time events via one or more display devices of the client devices 101, 102, 103, 104, 105, and 106.

[0027] In some embodiments, the server 120 can be a server of a distributed system, or a server combined with a blockchain. The server 120 can also be a cloud server, or an intelligent cloud computing server or intelligent cloud host with artificial intelligence technology. The cloud server is a host product in the cloud computing service system, to solve the defects of large management difficulty and weak business scalability in traditional physical host and virtual private server (VPS, Virtual Private Server) services.

[0028] The system 100 can also include one or more databases 130. In certain embodiments, these databases can be used to store data and other information. For example, one or more of the databases 130 can be used to store navigation information, road information, and announcement modes. The databases 130 can reside in a variety of locations. For example, databases used by the server 120 can reside locally to the server 120, or can be remote from the server 120 and can communicate with the server 120 via a network- or application-specific connection. The databases 130 can be of different types. In certain embodiments, databases used by the server 120 can be, for example, relational databases. One or more of these databases can store, update, and retrieve data to and from the databases in response to commands.

[0029] In certain embodiments, one or more of the databases 130 can also be used by applications to store application data. Databases used by applications can be different types of databases, such as key-value stores, object stores, or regular stores backed by file systems.

[0030] Figure 1The system 100 can be configured and operated in various ways to enable the application of various methods and apparatuses described in accordance with the present disclosure.

[0031] Existing vehicle navigation systems mostly use static, preset announcement frequency and prompt rhythm, and cannot dynamically adjust according to the complexity of the driving task. For example, in complex urban road conditions, there are many branch points and dense turns, but the navigation prompts are not timely enhanced, which can easily cause the driver to miss the guidance; while in the scene of straight driving at high speed, too many prompts can cause interference and even visual fatigue.

[0032] Therefore, according to embodiments of the present disclosure, a navigation method based on driving task complexity is provided. Figure 2 A flowchart of a traffic data processing method according to an embodiment of the present disclosure is shown, as shown in Figure 2 As shown, the method 200 includes: determining a to-be-traveled path corresponding to a target vehicle based on navigation information corresponding to the target vehicle (step 210); obtaining road information in the to-be-traveled path (step 220); determining navigation instruction information corresponding to the target vehicle in the to-be-traveled path based on the navigation information (step 230); obtaining driving behavior information corresponding to the target vehicle (step 240); determining driving task complexity of the target vehicle on the to-be-traveled path based on the road information, the navigation instruction information, and the driving behavior information (step 250); and announcing the navigation information based on an announcement mode corresponding to the driving task complexity to guide the target vehicle to travel along the to-be-traveled path (step 260).

[0033] In some embodiments, the navigation information can be basic data used for path planning in a vehicle navigation system, which can not only give the to-be-traveled path of the vehicle, but also give the navigation instruction information that needs to be followed in the path. These instruction information can be operation guidance to guide the vehicle to complete path travel, so as to reduce the difficulty of driving operation. The road information can reflect the inherent characteristics of the path traveled by the target vehicle, for example, can include but not limited to road type, width, geographic location information, road level, etc. The driving behavior information of the target vehicle can reflect the operation or driving state of the target vehicle in the driving process.

[0034] In some embodiments, the to-be-traveled path of the target vehicle can be a road segment to be traveled by the vehicle in front of the current road.

[0035] According to an embodiment of the present disclosure, by fusing the features of the road information, the navigation instruction information, and the driving behavior information, the driving task complexity of the target vehicle on the to-be-traveled path can be determined, so as to quantify the difficulty of the current driving task, and adjust the navigation broadcast mode based on the difficulty. Thus, the navigation content based on the corresponding navigation broadcast mode is more suitable for the current driving task difficulty, optimizes the driving attention allocation, and reduces the cognitive load.

[0036] For example, in some embodiments, different broadcast frequencies, detail levels, and the like can be adapted according to the complexity, so that the navigation guidance can be matched with the actual difficulty of the driving task, and the vehicle is effectively guided. Therefore, by dynamically adapting the navigation information broadcast mode to the driving task complexity, the navigation guidance is more accurate and timely in complex road conditions, and more concise and moderate in simple road conditions, effectively optimizing the driver's attention allocation, and improving the adaptability of the navigation system and the driving safety.

[0037] According to an embodiment of the present disclosure, the road information includes road attribute information, and the road attribute information includes a weight value of a road type corresponding to the to-be-traveled path, wherein the weight value is related to at least one of the number of intersections and the number of branching intersections.

[0038] In some embodiments, the road attribute information can be information for representing the road structure features of the to-be-traveled path. The road attribute information can also include at least one of the number of intersections and the number of branching intersections. An intersection can refer to a place where two or more roads intersect with each other, and the most common case is a crossroad where multiple roads intersect, and vehicles and pedestrians need to intersect and turn according to traffic rules. The number of intersections can be obtained by counting the number of intersections in the map data. A branching intersection can be a point where one or a smaller number of roads are divided into two or other multiple roads in different directions (i.e., a node in the path where multiple optional directions exist), and vehicles and pedestrians can choose the appropriate road to proceed according to their own destinations. The number of branching intersections can be determined by counting the number of intersections in the map data that meet this feature, for example, the three exits of the ramp in the expressway, i.e., “to A city”, “to B city”, and “to C city”.

[0039] In some embodiments, the weight value of the road type corresponding to the to-be-traveled path can be a weight coefficient preset according to the road type to which the path belongs (such as urban road, expressway, etc.), and the weight coefficient can be related to the number of intersections and / or the number of branching intersections. For example, different types of roads have different weights due to the difference in branch road density and operation pressure, for example, the weight value of the urban road can be higher than that of the expressway due to the dense branch roads.

[0040] Therefore, by quantifying the road attribute information, the structural complexity of the path can be accurately reflected, and effective basis for the regulation of navigation information is provided, thereby improving the adaptability of navigation and driving tasks.

[0041] According to an embodiment of the present disclosure, the navigation instruction information comprises at least one of the following: a number of navigation instructions for lane changing, a continuous instruction flag, wherein the continuous instruction flag is used to identify whether a road distance corresponding to two continuous navigation instructions is less than a preset distance threshold, or to identify whether a time interval between two continuous navigation instructions is less than a preset time threshold.

[0042] In some embodiments, the number of navigation instructions for lane changing can refer to the total number of navigation instructions that need to perform lane changing operations in the path to be driven, for example, can include instructions such as "drive on the right", "main and auxiliary road switching", etc. The more the number of such instructions, the more frequent the lane changing operations that the driver needs to complete, and the greater the driving operation pressure.

[0043] In some embodiments, the continuous instruction flag can be used to identify the intensity of two continuous navigation instructions. Specifically, when the road distance between two continuous navigation instructions (such as "drive on the left" followed by "left turn ahead") is less than a preset distance threshold (such as 300 meters), or the time interval between the two is less than a preset time threshold (such as 10 seconds), the flag can be activated to reflect the continuity of the instructions. Intensive continuous instructions will require the driver to complete multiple operations in a short period of time, significantly increasing the driving load. By calculating the actual distance or time interval between the continuous instruction flags and comparing it with the preset threshold, it can be determined whether the flag is enabled.

[0044] Therefore, by quantifying the intensity of navigation instructions and operation requirements, the instruction pressure in the driving process can be accurately reflected, data support is provided for the dynamic regulation of navigation information, the adaptability of navigation and driving tasks is improved, and the cognitive load of the driver is reduced.

[0045] According to an embodiment of the present disclosure, the driving behavior information comprises at least one of the following: a speed fluctuation amplitude, an average steering wheel angular velocity, an acceleration fluctuation amplitude, an abnormal operation flag, wherein the abnormal operation flag is used to identify that the acceleration of the target vehicle exceeds a preset acceleration threshold for a continuous second time period within a first time period, and / or the angular velocity of the target vehicle exceeds a preset angular velocity threshold for a continuous fourth time period within a third time period.

[0046] In some embodiments, the speed fluctuation amplitude can refer to the degree of change of the driving speed of the target vehicle in a recent specific period. For example, the difference between the maximum speed and the minimum speed of the target vehicle in a preset time period (e.g., after normalization processing) can be calculated, and the greater the value, the more frequent the acceleration and deceleration operation, which can indirectly reflect the higher requirement of the current driving task on speed control. For example, frequent acceleration and deceleration in a congested road section can cause the value to increase significantly.

[0047] In some examples, the speed sequence of the target vehicle can be collected in a preset time window of 10s: V = [v1, v2, …, vn] (n is a positive integer greater than 0); the difference between the maximum value and the minimum value in the speed sequence is calculated, i.e., AV = max(V) - min(V); and finally, normalization processing is performed (e.g., the maximum difference value is set to 60 km / h), to obtain AV_norm = AV / 60.

[0048] In some embodiments, the average steering wheel angular velocity can refer to the average level of the steering wheel rotation angular velocity of the target vehicle in a recent specific period, for judging whether the steering or direction adjustment is frequent. For example, the steering wheel angle data can be continuously collected, the angular velocity of each frame can be calculated, and the average value (e.g., after further normalization) can be obtained, which can be used to represent the frequency of the steering operation. For example, in a continuous curved road section or a high complexity urban area, the value can increase due to frequent steering.

[0049] In some embodiments, the acceleration fluctuation amplitude can reflect the change range of the acceleration of the target vehicle in a recent specific period. For example, the acceleration synthesis vector module output by, for example, an inertial measurement unit (IMU) can be collected, the difference between the maximum value and the minimum value (e.g., after further normalization) can be calculated, and the value can represent the degree of sudden change in the driving operation, such as sudden braking or acceleration, which can increase the value.

[0050] In some embodiments, the abnormal operation flag can be used to identify whether an operation beyond the normal range appears in the driving process. Specifically, when the acceleration of the target vehicle in a recent first time period (e.g., 10 seconds) continuously exceeds a preset acceleration threshold (e.g., 2 m / s2) for a second time period (e.g., a time period corresponding to 2-3 frames), and / or when the angular velocity in a recent third time period (e.g., 10 seconds) continuously exceeds a preset angular velocity threshold (e.g., 60° / s) for a fourth time period (e.g., more than 0.5 seconds), the flag can be activated to reflect the possible emergency operation pressure in the driving process, such as the sudden steering when avoiding obstacles, which can trigger the flag.

[0051] ​Therefore, by acquiring the driving behavior information, fluctuation and abnormal operation state of the driving behavior can be quantified from the dynamic operation perspective, and the accuracy of the driving task complexity evaluation is improved.

[0052] According to an embodiment of the present disclosure, the road information comprises road environment information, wherein the road environment information comprises at least one of the following: road obstacle density, weather visibility, road traffic density.

[0053] In some embodiments, the road environment information can reflect key parameters of the surrounding environment of the target vehicle to be driven, which affect the driving operation. Among them, the road obstacle density can refer to the density of objects including low-speed vehicles, pedestrians, bicycles, etc. that can affect the normal driving of the vehicle within a unit road section. The higher the value, the more objects need to be avoided during driving, and the more difficult the operation is. For example, the number of obstacles within a preset range (e.g. 1 km) can be collected by a vehicle body radar, and then the obstacle density can be obtained by normalization processing.

[0054] In some examples, if the vehicle has no radar module, it can be degraded to be represented by road level, for example, the default density of urban trunk road is 0.5; the default density of highway is 0.1.

[0055] In some embodiments, the weather visibility can be used to represent the influence of atmospheric conditions on the driving field of view. The lower the visibility, the more difficult it is for the driver to observe the road conditions and navigation instructions, and the higher the consumption of driving attention. The value can increase as the visibility decreases to intuitively reflect the degree of limited field of view.

[0056] For example, weather data can be obtained through the API provided by the map engine or other paths to obtain the current visibility distance. After obtaining the visibility distance, it can be normalized, for example, by calculating it with a preset visibility threshold, the above-mentioned weather visibility data can be obtained, that is, Visibility Score=(800-X) / 800, where X is the current visibility distance directly obtained from the map related data, and 800 is the preset visibility threshold, that is, 800 meters.

[0057] In some embodiments, the road traffic density can be the degree of road congestion, that is, the density of vehicles driving on the road. Congestion can cause frequent changes in vehicle driving speed and shorten the following distance, significantly increasing the driving operation pressure. For example, the API provided by the map engine or the local traffic flow caching system can be used to obtain the traffic density information of the current road in real time. For example, the road section within a range of 2 kilometers in front of the current navigation path is queried, and the "congestion index" field defined by the system is obtained, the range is: congestion_index , (0 means smooth; 10 means extremely congested). Then, further normalization processing can also be performed.

[0058] Therefore, by quantifying the interference factors in the road environment, the comprehensiveness and accuracy of the driving task complexity evaluation are improved, the navigation information is more in line with the actual environmental pressure, the driver's attention allocation is effectively optimized, and the driving safety is enhanced.

[0059] According to an embodiment of the present disclosure, determining the driving task complexity of the target vehicle on the to-be-traveled path based on the road information, the navigation instruction information, and the driving behavior information includes: for each of the road information, the navigation instruction information, and the driving behavior information, performing normalization operation on multiple pieces of data in the information, and performing weighted summation on the multiple pieces of data after the normalization operation to obtain a value corresponding to the information; and performing weighted summation on the values corresponding to the road information, the navigation instruction information, and the driving behavior data respectively to obtain the driving task complexity.

[0060] In some embodiments, due to the differences in the original magnitude and unit of each piece of data, for example, the number of intersections is an integer and the fluctuation amplitude is a proportional value, normalization operation can be performed to make different data operable. Specifically, the normalization operation can be to convert multiple pieces of data in each information, such as road attribute information, speed fluctuation amplitude, average steering wheel angular velocity, etc. in the driving behavior information, into standardized values in the range of 0-1.

[0061] In addition, in some embodiments, since each piece of data has different influence on the complexity in the corresponding information (for example, the continuous instruction flag in the navigation instruction information can better reflect the operation pressure than the number of lane changes), the normalized multiple pieces of data can be weighted and summed by a preset weight, and the value obtained after weighting is the contribution value of the information to the overall complexity. On this basis, the contribution values of the road information, the navigation instruction information, and the driving behavior information can be weighted and summed, and the contributions of the three are fused by setting a total weight (for example, the road information accounts for 0.4, and the navigation instruction information and the driving behavior information each accounts for 0.3). The final comprehensive value obtained is the driving task complexity, which can intuitively reflect the overall difficulty of the current driving task.

[0062] In some examples, in the city complex intersection scenario, the road structure itself may not be complex (the road is usually straight and flat), but the navigation instructions are extremely dense and have low fault tolerance, and missing an intersection will have serious consequences. At this time, the main pressure of the driver comes from understanding and executing complex instructions. Therefore, in the city complex intersection scenario, the weight of the navigation instruction information can be appropriately increased, for example, the weight of the navigation instruction information is set to be greater than the weights corresponding to the other items.

[0063] In some examples, in the highway cruising scenario, the road line is simple and straight, and the navigation instructions are sparse. At this time, the main stress of the driver comes from keeping the lane, maintaining the distance, and responding to sudden traffic events (such as sudden braking in front), and the monotonous environment also easily leads to distraction. Therefore, in the highway cruising scenario, the weight of the road information and the driving behavior information can be appropriately increased, and the weight of the navigation instruction information can be reduced. In some examples, in the mountainous winding road scenario, the road line is the dominant factor of complexity. At this time, the weight of the road information can also be appropriately increased.

[0064] Therefore, in some embodiments, the map data, GPS, camera, radar, and other perception sources can be used to determine in real time which scenario the vehicle is in (for example, “highway cruising”, “urban complex intersection scenario”, “mountainous winding road”). And by pre-setting a weight mapping table, the identified scenario is mapped to a set of optimal weight configurations. Thus, when the scenario is switched, the jump of the complexity value is avoided. Through this dynamic weight mechanism, the evaluation of the driving task complexity will be more accurate and accurate, and can provide more reliable decision basis for autonomous driving, assisted driving, etc.

[0065] In an example embodiment, the road information, navigation instruction information, and driving behavior information each correspond to a vector obtained by the above embodiment, and each group of vectors has a respective set of characteristic values. In order to calculate, each characteristic value in these vectors needs to be converted to a value between 0 and 1 through normalization; then, for each of the road information, navigation instruction information, and driving behavior information, the above processed values can be further weighted and summed to obtain the respective value of each information. For example, for road information, the normalized characteristic values can be weighted and summed according to: 35% for the number of intersections, 35% for the number of branch intersections, 15% for the continuous instruction sign, and 15% for the road type weight. Finally, as described above, the contribution values of the road information, navigation instruction information, and driving behavior information can be weighted and summed, and the contributions of the three are fused by setting the total weight (such as 0.4 for road information, 0.3 for navigation instruction information, and 0.3 for driving behavior information). The final comprehensive value obtained is the driving task complexity, which can intuitively reflect the overall difficulty of the current driving task.

[0066] Therefore, by normalization, the data scale difference is eliminated, and by weighting, the importance of each factor is represented, making the driving task complexity evaluation more accurate.

[0067] For example, the numerical value calculated by fusing the contributions of the three embodiments described above can be used to represent the complexity of the corresponding driving task. For example, when the calculated numerical value Score < 0.35, it can be determined as a low complexity level; when 0.35 ≤ Score < 0.65, it can be determined as a medium complexity level; and when Score ≥ 0.65, it can be determined as a high complexity level.

[0068] Therefore, according to the embodiments of the present disclosure, the driving task complexity is a corresponding one of low complexity, medium complexity, and high complexity, and wherein the navigation information is announced based on the announcement mode corresponding to the driving task complexity, including: when it is determined that the driving task complexity is the low complexity, determining the position information corresponding to the to-be-announced instruction in the navigation information, so as to start repeating multiple times to announce the to-be-announced instruction at a first preset distance from the position information; when it is determined that the driving task complexity is the medium complexity, determining the position information corresponding to the to-be-announced instruction in the navigation information, so as to start repeating multiple times to announce the to-be-announced instruction at a second preset distance from the position information; and when it is determined that the driving task complexity is the high complexity, determining the position information corresponding to the to-be-announced instruction in the navigation information, so as to start repeating multiple times to announce the to-be-announced instruction at a third preset distance from the position information, wherein the third preset distance is greater than the second preset distance, and the second preset distance is greater than the first preset distance.

[0069] In some embodiments, by dividing the driving task complexity into three levels of low, medium, and high, the difficulty of the driving task can be represented, wherein the low complexity can correspond to a simple road condition and small operation pressure scenario (such as straight driving on a highway), the medium complexity can correspond to a medium operation requirement scenario (such as suburban roads), and the high complexity can correspond to an operation-intensive and high-environmental-pressure scenario (such as urban dense intersections). This division method can be based on the comprehensive evaluation result (i.e., the above-mentioned comprehensive numerical value) of road information, navigation instruction information, and driving behavior information, to provide a basis for navigation announcement mode. When announcing based on the above driving task complexity level, the corresponding advance prompt distance can be matched to ensure that the prompt timing is adapted to the decision needs of the driver.

[0070] In the above embodiments, the position information of the to-be-announced instruction can be a specific road node (such as a turning intersection or a coordinate position of a road changing section) at which the instruction is executed, and the real-time distance of the vehicle from the position can be determined by comparing the position obtained from the map engine with the real-time positioning of the vehicle. When the complexity is low, the to-be-announced instruction can be announced from a first preset distance (such as a distance of 200 m) because the driver has sufficient time to respond in a low complexity scenario and does not need to be prompted too early to avoid interference. When the complexity is medium, the to-be-announced instruction can be announced from a second preset distance (such as a distance of 500 m) to reserve sufficient decision-making time for the driver. When the complexity is high, the to-be-announced instruction can be announced from a third preset distance (such as a distance of 1000 m) because the driver needs to be prompted early to ensure that the driver is prepared in advance in a high complexity scenario. Meanwhile, the third preset distance is greater than the second preset distance, and the second preset distance is greater than the first preset distance, which is consistent with the driving logic that the higher the complexity, the earlier the prompt. Each time the to-be-announced instruction is repeatedly announced, the interval can be gradually shortened as the vehicle approaches the instruction position, forming a progressive reminder. Therefore, the strategy of corresponding different announcement manners based on the driving task complexity not only improves the effectiveness of the prompt in a complex scenario, but also reduces interference in a simple scenario, and optimizes the transmission efficiency of the navigation information.

[0071] Figure 3 A driving scenario diagram according to an embodiment of the present disclosure is shown. As shown in Figure 3 The target vehicle 301 is driving along a to-be-traveled path 302, and a branch intersection 303 in front of the target vehicle 301 is a to-be-announced instruction position at which a right branch road is to be entered. According to an embodiment of the present disclosure, announcing the navigation information based on the announcement manner corresponding to the driving task complexity includes: when it is determined that the driving task complexity is the high complexity, starting to repeatedly announce the to-be-announced instruction every fifth time period at a fourth preset distance from the position information, wherein the fourth preset distance is less than the third preset distance.

[0072] In some embodiments, in a scenario where the driving task complexity is high, the prompt density in the near distance stage of the navigation information announcement can be intensified. The fourth preset distance can be a threshold value closer than the third preset distance (the starting distance of the first announcement in the high complexity scenario), for example, within 150 meters. The fifth time period can be the time interval of repeated announcement, for example, 2 seconds. For example, when the distance is reduced to within the fourth preset distance, for example, within 150 meters, “turn right, keep right” can be repeated every 2 seconds until the instruction is completed, to ensure that the driver receives the guidance and makes up for the risk of attention dispersion in a high complexity scenario.

[0073] Therefore, by setting high-frequency repeated broadcasting in the close-range stage of a high-complexity scene, the prompting strength of the instruction in the key operation stage is strengthened, the probability of the driver missing the instruction due to high cognitive load is effectively reduced, and the reliability and driving safety of the navigation guidance are improved.

[0074] According to an embodiment of the present disclosure, the broadcasting of the navigation information based on the broadcasting mode corresponding to the driving task complexity comprises: determining a corresponding voice broadcasting style based on the driving task complexity, so as to broadcast the navigation information based on the voice broadcasting style and the broadcasting mode corresponding to the driving task complexity.

[0075] In some examples, the broadcasting style and mode under a low-complexity task can be calm, moderate speed, soft tone, and only provide the most necessary information to avoid redundancy; the broadcasting style and mode under a medium-complexity task can be more assertive, slightly faster but still clear, avoid ambiguity, and use direct and action-oriented language; and the broadcasting style and mode under a high-complexity task can be resolute, significantly faster (but each word is still clear), the tone can be raised to cause high alertness, and the shortest and most core words are used.

[0076] It can be understood that the above-mentioned broadcasting style and mode are only exemplary and are not limited herein.

[0077] According to an embodiment of the present disclosure, the instruction to be broadcasted comprises driving task necessary information and driving task non-necessary information, and wherein the broadcasting of the navigation information based on the broadcasting mode corresponding to the driving task complexity comprises: when it is determined that the driving task complexity is the low complexity, broadcasting the instruction corresponding to the driving task non-necessary information.

[0078] In some embodiments, the driving task necessary information can refer to core instructions that directly guide the safe driving of the vehicle along the to-be-traveled path, such as turning, speed limit, lane changing, and other operation instructions, the absence of which can cause driving errors; and the driving task non-necessary information can be auxiliary content that does not directly affect the driving operation, such as surrounding points of interest, weather forecast, estimated arrival time, and other supplementary information of the driving task. When it is determined that the driving task complexity is low complexity (such as a scene with small operation pressure such as straight driving on a highway), the driver has low cognitive load and sufficient attention to process additional information, and therefore the instructions corresponding to the driving task non-necessary information can be broadcasted at the same time as the driving task necessary information is broadcasted.

[0079] Therefore, by reasonably including non-necessary information broadcasting in a low-complexity scene, the navigation content is enriched without increasing the cognitive load of the driver, the integrity and practicality of information transmission are optimized, and the information acquisition experience in the driving process is improved.

[0080] In one example embodiment according to the present disclosure, in a specific broadcast, low complexity can correspond to a general priority level, at which a regular three-section broadcast can be adopted, for example, according to the position information of the to-be-executed instruction, a prompt can be given at three nodes of 500 meters, 200 meters, and 50 meters away from the position, and the voice style can adopt a verbose form, such as “Please note that a right turn is needed 500 meters ahead, please drive on the right”. That is, the broadcast content can contain all information, such as the remaining distance, road name, and various information, to balance the completeness of the prompt and the driving interference. Medium complexity can correspond to a key priority level, which is suitable for scenarios with medium operation requirements (such as national highway branch road sections), and an enhanced early warning strategy can be adopted. For example, compared with the regular three-section, the enhanced early warning strategy can advance the first prompt distance to, for example, 800 meters, and subsequent prompts can be given at 300 meters and 50 meters, and the voice style can be a compressed form, such as “500 meters right turn, drive on the right”. That is, the broadcast content can focus on key and urgent information, such as speed limit change, left driving prompt, and other information, to reserve more sufficient decision-making time for the driver. High complexity can correspond to an urgent priority level, which is suitable for scenarios with intensive operations and high environmental pressure (such as urban intensive intersections and roundabouts), and a high-frequency advance multi-section repetition strategy can be adopted, which can further advance the first prompt distance to 1000 meters, and subsequent prompts can be given at 600 meters, 200 meters, 50 meters, and 25 meters, and the voice style can be a most simple form, such as “right turn, drive on the right”. That is, the broadcast content only retains urgent information, such as turning and speed limit, and through earlier prompting and simplification of the broadcast information, the driver is ensured not to miss the key instruction in a high-load scenario.

[0081] Therefore, the broadcast style is determined based on the driving task complexity, which further improves the efficiency of navigation information broadcast and the driving experience of the driver.

[0082] According to an embodiment of the present disclosure, the broadcast of the navigation information based on the broadcast mode corresponding to the driving task complexity comprises: when it is determined that the driving task complexity is the medium complexity or the high complexity, performing an adjustment operation on a sound playing device in the target vehicle cabin to reduce the volume of sound playing devices other than the sound playing device for playing the navigation information, and to increase the volume of the sound playing device for playing the navigation information.

[0083] In some embodiments, in the scenario where the driving task complexity is medium or high, the adjustment operation on the sound playing device in the target vehicle cabin can ensure that the navigation information is clearly perceived by the driver when the cognitive load is high. The sound playing device for playing navigation information can refer to a device that specifically outputs navigation instruction voice, for example, a navigation channel of a car audio. In addition to the sound playing device, other sound playing devices can include devices that play non-navigation audio such as music and radio. In the scenario where the driving task complexity is medium or high, on the one hand, the volume of the non-navigation sound device can be reduced, for example, the music volume is reduced by 70%, to avoid distracting the driver's attention from the navigation instruction; on the other hand, the volume of the navigation sound device can be increased, for example, by 30%, to enhance the recognition of the navigation instruction.

[0084] According to an embodiment of the present disclosure, the navigation information is announced based on the announcement mode corresponding to the driving task complexity, which comprises: when it is determined that the driving task complexity returns to the low complexity, the sound parameter of the sound playing device in the target vehicle cabin before the adjustment operation is restored.

[0085] In some embodiments, the above-mentioned adjustment operation on the sound playing device in the target vehicle cabin in the scenario where the driving task complexity is medium or high can be automatically performed according to the driving task complexity level. After the vehicle exits the medium or high complexity road section, the original volume setting can be restored, which ensures the transmission efficiency of the navigation information in the complex scenario and does not require manual operation by the driver.

[0086] Therefore, by dynamically adjusting the volume ratio of the sound device in the cabin, the priority of the announcement of the navigation information in the medium or high complexity scenario is ensured, the accuracy of the driver's reception of the key instruction is improved, and the driving safety is enhanced.

[0087] According to an embodiment of the present disclosure, the navigation information is announced based on the announcement mode corresponding to the driving task complexity, which comprises: when it is determined that the driving task complexity returns to the low complexity, the sound parameter of the sound playing device in the target vehicle cabin before the adjustment operation is restored.

[0088] In some embodiments, in the scenario where the driving task complexity level changes from medium complexity to high complexity, the display interface for displaying navigation information can be adjusted. The display interface for displaying navigation information can be the screen display area of the in-vehicle navigation, and can include various information cards, pop-up windows, path schematics, and other elements. The prompt information related to the to-be-announced instruction can refer to prompt content directly assisting the current driving operation, such as a steering arrow, an intersection schematic, a speed limit sign, and the like. When the driving task complexity level changes from medium complexity to high complexity, information unrelated to the to-be-announced instruction in the display interface, such as an advertisement pop-up window, a weather card, a surrounding point of interest recommendation, and the like, can be automatically hidden, and only key operation information such as a steering guide, an exit distance, a lane prompt, and the like is reserved, so as to ensure that the information in the driver's field of view is all operation-required content. For example, when the vehicle drives from a suburban road into a complex roundabout, the display interface can automatically shield the originally displayed “3 kilometers ahead, there is a gas station” prompt, and only display, for example, a steering schematic of each exit in the roundabout, a distance countdown, and the like.

[0089] Therefore, by dynamically adjusting the navigation display interface and reserving only the key prompt content related to the to-be-executed instruction, the transmission efficiency of navigation information in a high complexity scenario is improved.

[0090] According to embodiments of the present disclosure, as Figure 4 shown in FIG. 4, a navigation device 400 based on driving task complexity is also provided, which includes a path determination module 410 configured to determine a to-be-traveled path corresponding to a target vehicle based on navigation information corresponding to the target vehicle; a first information acquisition module 420 configured to acquire road information in the to-be-traveled path; an instruction determination module 330 configured to determine navigation instruction information corresponding to the target vehicle in the to-be-traveled path based on the navigation information; a second information acquisition module 440 configured to acquire driving behavior information corresponding to the target vehicle; a complexity determination module 450 configured to determine driving task complexity of the target vehicle on the to-be-traveled path based on the road information, the navigation instruction information, and the driving behavior information; and an announcement module 460 configured to announce the navigation information based on an announcement mode corresponding to the driving task complexity, so as to guide the target vehicle to travel along the to-be-traveled path.

[0091] Here, the operations of the above-mentioned units 410-460 of the navigation device 400 based on driving task complexity are similar to the operations of the steps 210-260 described above, and will not be described here again.

[0092] In the technical solution of the present disclosure, the collection, storage, use, processing, transmission, provision, and disclosure of user personal information involved in the technical solution all comply with the provisions of relevant laws and regulations, and do not violate public order and good customs.

[0093] According to embodiments of the present disclosure, an electronic device, a readable storage medium, and a computer program product are also provided.

[0094] Reference Figure 5 A block diagram of an electronic device 500, which is an example of a hardware device that can be applied to aspects of the present disclosure, will now be described, which can be a server or a client of the present disclosure. The electronic device is intended to represent a wide variety of digital electronic computer devices such as laptops, desktops, workstations, personal digital assistants, servers, blade servers, mainframes, and other appropriate computer devices. The electronic device can also represent a wide variety of mobile devices such as personal digital assistants, cellular telephones, smart phones, wearable devices, and other like computing devices. The components shown here, their connections and relationships, and their functions, are meant to be examples only, and are not meant to limit implementations of the present disclosure described and / or claimed in this document.

[0095] As Figure 5 shown, the electronic device 500 includes a computing unit 501 that can perform various appropriate actions and processes in accordance with a computer program stored in a read-only memory (ROM) 502 or a computer program loaded into a random access memory (RAM) 503 from a storage unit 508. In the RAM 503, various programs and data required for the operation of the electronic device 500 can also be stored. The computing unit 501, the ROM 502, and the RAM 503 are connected to each other through a bus 504. An input / output (I / O) interface 505 is also connected to the bus 504.

[0096] A plurality of components in the electronic device 500 are connected to the I / O interface 505, including: an input unit 506, an output unit 507, a storage unit 508, and a communication unit 509. The input unit 506 can be any type of device that can input information to the electronic device 500, and can receive inputted digital or character information, and generate key signal inputs related to user settings and / or function controls of the electronic device, and can include, but is not limited to, a mouse, a keyboard, a touch screen, a track pad, a track ball, a joystick, a microphone, and / or a remote controller. The output unit 507 can be any type of device that can present information, and can include, but is not limited to, a display, a speaker, a video / audio output terminal, a vibrator, and / or a printer. The storage unit 508 can include, but is not limited to, a magnetic disk, an optical disk. The communication unit 509 allows the electronic device 500 to exchange information / data with other devices through a computer network such as the Internet and / or various telecommunication networks, and can include, but is not limited to, a modem, a network card, an infrared communication device, a wireless communication transceiver, and / or a chipset, such as a Bluetooth device, an 802.11 device, a WiFi device, a WiMax device, a cellular communication device, and / or the like.

[0097] The computing unit 501 can be various general and / or special purpose processing components having processing and computing capabilities. Some examples of the computing unit 501 include, but are not limited to, a central processing unit (CPU), a graphics processing unit (GPU), various special-purpose artificial intelligence (AI) computing chips, various computing units running machine learning model algorithms, a digital signal processor (DSP), and any appropriate processor, controller, microcontroller, etc. The computing unit 501 performs various methods and processes described above, such as the method 200. For example, in some embodiments, the method 200 can be implemented as a computer software program tangibly embodied in a machine-readable medium, such as the storage unit 508. In some embodiments, part or all of the computer program can be loaded and / or installed onto the electronic device 500 via the ROM 502 and / or the communication unit 509. When the computer program is loaded onto the RAM 503 and executed by the computing unit 501, one or more steps of the method 200 described above can be performed. Alternatively, in other embodiments, the computing unit 501 can be configured to perform the method 200 by any other appropriate means, such as by means of firmware.

[0098] The various embodiments of the systems and techniques described above can be implemented in digital electronic circuitry, integrated circuitry, a field programmable gate array (FPGA), an application specific integrated circuit (ASIC), a system on a chip (SOC), a complex programmable logic device (CPLD), computer hardware, firmware, software, and / or combinations thereof. These various embodiments can include implementation in one or more computer programs that are executable and / or interpretable on a programmable system including at least one programmable processor, which can be special or general purpose, coupled to receive data and instructions from, and to transmit data and instructions to, a storage system, at least one input device, and at least one output device.

[0099] Program code for carrying out methods of the present disclosure can be written in any combination of one or more programming languages. The program code can be provided to a processor or controller of a general purpose computer, special purpose computer, or other programmable data processing apparatus to produce a machine, such that the program code, when executed by the processor or controller, produces the functions / operations specified in the flowcharts and / or the block diagrams. The program code can be executed entirely on a machine, partially on a machine, partially on a machine as a stand-alone software package, or entirely on a remote machine or server.

[0100] In the context of the present disclosure, a machine-readable medium can be a tangible medium that contains or stores a program for use by or in connection with an instruction execution system, apparatus, or device. The machine-readable medium can be a machine-readable signal medium or a machine-readable storage medium. A machine-readable medium can include but is not limited to an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any suitable combination of the foregoing. More specific examples of the machine-readable storage medium will include one or more lines of electrical conductors, portable computer disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or Flash memory), optical fibers, portable compact disc read-only memories (CD-ROMs), optical storage devices, magnetic storage devices, or any suitable combination of the foregoing.

[0101] To provide interaction with a user, the systems and techniques described herein can be implemented on a computer having: a display device (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to the user; and a keyboard and pointing device (e.g., a mouse or trackball) through which the user can provide input to the computer. Other types of devices can also be used to provide interaction with the user; for example, the feedback provided to the user can be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user can be received in any form (including acoustic input, voice input, or tactile input).

[0102] The systems and techniques described herein can be implemented in a computing system that includes back-end components (e.g., as a data server), or a computing system that includes middleware components (e.g., an application server), or a computing system that includes front-end components (e.g., a user computer with a graphical user interface or web browser through which a user can interact with implementations of the systems and techniques described herein), or a computing system that includes any combination of such back-end components, middleware components, or front-end components. The components of the system can be interconnected by any form or medium of digital data communication (e.g., a communication network). Examples of communication networks include: a local area network (LAN), a wide area network (WAN), the Internet, and a blockchain network.

[0103] A computer system may include a client and a server. The client and server are generally remote from each other and typically interact through a communication network. The client-server relationship arises through computer programs running on the respective computers and having a client-server relationship with each other. The server may be a cloud server, a server in a distributed system, or a server integrated with a blockchain.

[0104] It should be understood that the various forms of the processes shown above can be used to reorder, add, or delete steps. For example, the steps described in this disclosure can be performed in parallel, sequentially, or in a different order, as long as the desired results of the technical solutions disclosed in this disclosure can be achieved. This is not limited herein.

[0105] While embodiments or examples of this disclosure have been described with reference to the figures, it will be understood that the methods, systems, and devices described above are merely exemplary embodiments or examples, and the scope of the application is not limited to these embodiments or examples. Various elements of the embodiments or examples can be omitted or substituted by equivalents thereof. Furthermore, the steps can be performed in a different order than described in the disclosure. Further, various elements of the embodiments or examples can be combined in various ways. It is important that as technology evolves, many of the elements described herein can be substituted by equivalents which serve the same function.

Claims

1. A navigation method based on driving task complexity, comprising: Determining a travel path corresponding to the target vehicle based on navigation information corresponding to the target vehicle; Acquiring road information on the route to be traveled; Determining, based on the navigation information, navigation instruction information corresponding to the target vehicle in the path to be traveled; Obtaining driving behavior information corresponding to the target vehicle; Determining the driving task complexity of the target vehicle on the to-be-traveled path based on the road information, the navigation instruction information, and the driving behavior information; as well as The navigation information is broadcasted based on a broadcasting method corresponding to the complexity of the driving task to guide the target vehicle to travel along the path to be traveled.

2. The method according to claim 1, wherein The road information includes road attribute information, and the road attribute information includes a weight value of the road type corresponding to the to-be-traveled path, wherein the weight value is related to at least one of the following items: the number of intersections and the number of forked intersections.

3. The method according to claim 1, wherein The navigation instruction information includes at least one of the following items: the number of navigation instructions for lane change, a continuous instruction flag, The continuous instruction flag is used to identify whether the road distance between two consecutive navigation instructions is less than a preset distance threshold, or to identify whether the time interval between two consecutive navigation instructions is less than a preset time threshold.

4. The method according to claim 1, wherein The driving behavior information includes at least one of the following items: speed fluctuation amplitude, steering wheel angular velocity mean, acceleration fluctuation amplitude, abnormal operation mark, The abnormal operation flag is used to indicate that the acceleration of the target vehicle in the most recent first time period exceeds the preset acceleration threshold for the second consecutive time period, and / or the angular velocity of the target vehicle in the most recent third time period exceeds the preset angular velocity threshold for the fourth consecutive time period. 5 . The method according to claim 1 , wherein the road information comprises road environment information, wherein the road environment information comprises at least one of the following items: road obstacle density, weather visibility, and road traffic density.

6. The method according to claim 1 or 5, wherein Determining the driving task complexity of the target vehicle on the to-be-traveled path based on the road information, the navigation instruction information, and the driving behavior information includes: For each of the road information, the navigation instruction information, and the driving behavior information, normalizing multiple data in the information, and performing weighted summation on the multiple data after the normalization operation to obtain a value corresponding to the information; and A weighted sum is performed on the values ​​corresponding to the road information, the navigation instruction information, and the driving behavior data to obtain the driving task complexity.

7. The method of claim 1, wherein: The driving task complexity is a corresponding one of low complexity, medium complexity, and high complexity, and wherein the reporting of the navigation information based on the reporting mode corresponding to the driving task complexity includes: When determining that the complexity of the driving task is low, determining the location information corresponding to the instruction to be broadcast in the navigation information, and repeating the broadcast of the instruction to be broadcast multiple times starting when the location information is a first preset distance away from the location information; When the complexity of the driving task is determined to be the medium complexity, determining the location information corresponding to the instruction to be broadcast in the navigation information, and starting to repeatedly broadcast the instruction to be broadcast multiple times when the location information is a second preset distance away; When the complexity of the driving task is determined to be high, determining the location information corresponding to the instruction to be broadcast in the navigation information, and starting to repeatedly broadcast the instruction to be broadcast multiple times when the location information is a third preset distance away from the instruction to be broadcast, The third preset distance is greater than the second preset distance, and the second preset distance is greater than the first preset distance.

8. The method of claim 7, wherein: The instructions to be broadcast include: necessary information for the driving task and non-essential information for the driving task, and broadcasting the navigation information based on a broadcasting method corresponding to the complexity of the driving task includes: When it is determined that the complexity of the driving task is the low complexity, an instruction corresponding to the non-essential information of the driving task is broadcast.

9. The method according to claim 7 or 8, wherein Reporting the navigation information based on the reporting method corresponding to the complexity of the driving task includes: When it is determined that the complexity of the driving task is the medium complexity or the high complexity, an adjustment operation is performed on the sound playback device in the cockpit of the target vehicle to reduce the volume of the sound playback device other than the sound playback device used to play the navigation information, and to increase the volume of the sound playback device used to play the navigation information.

10. The method of claim 9, wherein: Reporting the navigation information based on the reporting method corresponding to the complexity of the driving task includes: When it is determined that the complexity of the driving task is restored to the low complexity, the sound parameters of the sound playing device in the cockpit of the target vehicle before the adjustment operation are restored.

11. The method according to any one of claims 7 to 10, wherein: Reporting the navigation information based on the reporting method corresponding to the complexity of the driving task includes: When the complexity of the driving task is determined to be the high complexity, the to-be-broadcasted instruction is repeatedly broadcast every fifth time period starting at a fourth preset distance from the location information, wherein the fourth preset distance is less than the third preset distance.

12. The method of claim 7, wherein: Reporting the navigation information based on the reporting method corresponding to the complexity of the driving task includes: When it is determined that the complexity of the driving task changes from the medium complexity to the high complexity, the display interface for displaying the navigation information is adjusted so that it only displays prompt information related to the instructions to be broadcast.

13. The method of claim 1, wherein: Reporting the navigation information based on the reporting method corresponding to the complexity of the driving task includes: A corresponding voice broadcast style is determined based on the complexity of the driving task, and the navigation information is broadcast based on the voice broadcast style and broadcast method corresponding to the complexity of the driving task.

14. A navigation device based on driving task complexity, comprising: A path determination module is configured to determine a path to be traveled corresponding to the target vehicle based on navigation information corresponding to the target vehicle; A first information acquisition module is configured to acquire road information in the to-be-traveled route; An instruction determination module is configured to determine, based on the navigation information, navigation instruction information corresponding to the target vehicle in the path to be traveled; A second information acquisition module is configured to acquire driving behavior information corresponding to the target vehicle; a complexity determination module configured to determine the complexity of the driving task of the target vehicle on the to-be-traveled path based on the road information, the navigation instruction information, and the driving behavior information; as well as The broadcast module is configured to broadcast the navigation information based on a broadcast method corresponding to the complexity of the driving task to guide the target vehicle to travel along the path to be traveled.

15. An electronic device comprising: at least one processor; as well as a memory communicatively coupled to the at least one processor; in The memory stores instructions that can be executed by the at least one processor, and the instructions are executed by the at least one processor to enable the at least one processor to perform the method according to any one of claims 1 to 13.

16. A non-transitory computer-readable storage medium storing computer instructions, wherein: The computer instructions are used to cause the computer to execute the method according to any one of claims 1-13.

17. A computer program product comprising a computer program, wherein When the computer program is executed by a processor, the method according to any one of claims 1 to 13 is implemented.

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