Lane guidance method and apparatus, device, and storage medium

Abstract

The present application provides a lane guidance method and apparatus, a device, and a storage medium, applicable to fields such as maps or autonomous driving, and configured to solve the problem of low guidance accuracy during lane guidance. The method at least comprises: receiving a lane guidance instruction for a target road section, and determining a plurality of candidate lane segments between a starting lane segment and a destination lane segment of the target road section; on the basis of at least one pre-stored conditional entry probability associated with each candidate lane segment, determining a target entry probability for a target vehicle to enter the candidate lane segment, wherein each conditional entry probability represents, in view of historical driving data associated with the target road section, a conditional probability for past vehicles to enter the candidate lane segment from an adjacent lane segment neighboring the candidate lane segment under the condition that the vehicles entered the destination lane segment; and generating a lane guidance result, the lane guidance result comprising a guidance lane segment among the plurality of candidate lane segments that has a target entry probability satisfying a guidance condition.

Description

A lane guidance method, device, equipment and storage medium

[0001] Related documents

[0002] This application claims priority to Chinese Patent Application No. 202410033893.3, filed on January 10, 2024, entitled "A Lane Guidance Method, Apparatus, Device and Storage Medium", the entire contents of which are incorporated herein by reference. Technical Field

[0003] This application relates to the field of computer technology, and in particular to a lane guidance method, apparatus, device and storage medium.

[0004] Background of the Invention

[0005] With the continuous development of technology, more and more devices can provide lane-level guidance services to achieve more refined lane-level navigation functions. For example, when a vehicle needs to turn right at an intersection, it can be guided to enter the dedicated right-turn lane N meters in advance. Lane information can be obtained by collecting road network data, while various parameters used by the guidance function are set manually based on experience. These parameters include, for example, guiding the vehicle into the dedicated turning lane when it needs to turn, and guiding the vehicle to travel at least a second distance in the new lane when changing lanes, etc. Summary of the Invention

[0006] This application provides a lane guidance method, apparatus, device, and storage medium to address the problem of low guidance accuracy during lane guidance.

[0007] Each embodiment provides a lane guidance method, including:

[0008] Receive lane guidance instructions for the target road section and determine multiple candidate lane segments between the starting lane segment and the destination lane segment of the target road section;

[0009] For each of the plurality of candidate lane segments, the following operations are performed: based on at least one pre-stored conditional entry probability associated with the candidate lane segment, the target entry probability of the target vehicle entering the candidate lane segment is determined; wherein, each conditional entry probability represents: in the historical driving data associated with the target road section, the conditional probability that a passing vehicle enters the candidate lane segment from an adjacent lane segment adjacent to the candidate lane segment, with the condition of entering the target lane segment;

[0010] Generate lane guidance results, which include: among multiple candidate lane segments, the guidance lane segment whose target entry probability meets the guidance conditions.

[0011] Various embodiments also provide a lane guidance device, including:

[0012] Acquisition module: Used to receive lane guidance instructions for the target road section and determine multiple candidate lane segments between the starting lane segment and the destination lane segment of the target road section;

[0013] Processing module: For each of the plurality of candidate lane segments, perform the following operations: Determine the target entry probability of a target vehicle entering the candidate lane segment based on at least one pre-stored conditional entry probability associated with the candidate lane segment; wherein each conditional entry probability represents: in the historical driving data associated with the target road section, the conditional probability that a passing vehicle enters the candidate lane segment from an adjacent lane segment adjacent to the candidate lane segment, with the condition of entering the target lane segment;

[0014] The processing module is further configured to: generate lane guidance results, the lane guidance results including: among multiple candidate lane segments, the guidance lane segment whose target entry probability meets the guidance conditions.

[0015] Various embodiments also provide a computer program product, including a computer program that, when executed by a processor, implements the method as described in the first aspect.

[0016] Various embodiments also provide a computer device, including:

[0017] Memory, used to store program instructions;

[0018] A processor is configured to invoke program instructions stored in the memory and execute the methods of each embodiment according to the obtained program instructions.

[0019] Various embodiments also provide a computer-readable storage medium storing computer-executable instructions for causing a computer to perform the methods of the various embodiments.

[0020] In this embodiment, at least one conditional entry probability associated with each candidate lane segment is obtained in advance. The conditional entry probability represents the probability that each reference vehicle, within a historical time period, will enter a candidate lane segment from an adjacent lane segment, given the condition of entering the target lane segment. Therefore, based on the at least one conditional entry probability associated with each candidate lane segment, the target entry probability of the target vehicle entering each candidate lane segment is determined. Based on the obtained target entry probabilities, a guidance lane segment is selected to obtain lane guidance results.

[0021] Data mining was performed on the historical driving data of each reference vehicle within a historical time period to analyze the driving patterns of each reference vehicle under different road conditions. This data serves as a reference for lane guidance of the current target vehicle. As a result, targeted lane guidance can be provided for different road conditions, achieving richer lane guidance effects. There is no need to manually set multiple guidance parameters. Lane guidance is based on these set guidance parameters under any circumstances, avoiding guidance errors caused by the setting guidance parameters not matching the actual road conditions, and improving the accuracy of lane guidance.

[0022] Furthermore, the calculation process for lane guidance based on lane segments is less computationally intensive and more efficient than the calculation process based on vehicle positioning points, thus improving lane guidance efficiency to some extent.

[0023] Brief description of the attached figures

[0024] The following figures are merely examples of the technical solutions of the present invention, and the present invention is not limited to the features shown in the figures. In the following figures, similar reference numerals indicate similar elements:

[0025] Figure 1A is a schematic diagram of a scenario for lane guidance methods in related technologies;

[0026] Figure 1B is a schematic diagram of a scenario for lane guidance methods in related technologies;

[0027] Figure 1C shows an application scenario of the lane guidance method provided in the embodiment of this application;

[0028] Figure 2 is a schematic flowchart of a lane guidance method provided in an embodiment of this application;

[0029] Figure 3A is a schematic diagram of a lane guidance method provided in an embodiment of this application.

[0030] Figure 3B is a schematic diagram of a lane guidance method provided in an embodiment of this application.

[0031] Figure 4A is a schematic diagram of a lane guidance method provided in an embodiment of this application.

[0032] Figure 4B is a schematic diagram of a lane guidance method provided in an embodiment of this application.

[0033] Figure 5A is a schematic diagram of a lane guidance method provided in an embodiment of this application.

[0034] Figure 5B is a schematic diagram of a lane guidance method provided in an embodiment of this application.

[0035] Figure 6A is a schematic diagram of a lane guidance method provided in an embodiment of this application.

[0036] Figure 6B is a schematic diagram of a lane guidance method provided in an embodiment of this application.

[0037] Figure 7A is a schematic diagram of a lane guidance method provided in an embodiment of this application.

[0038] Figure 7B is a schematic flowchart of a lane guidance method provided in an embodiment of this application.

[0039] Figure 7C is a schematic diagram of a lane guidance method provided in an embodiment of this application.

[0040] Figure 7D is a schematic flowchart of a lane guidance method provided in an embodiment of this application.

[0041] Figure 7E is a schematic diagram of a lane guidance method provided in an embodiment of this application.

[0042] Figure 7F is a schematic diagram of a lane guidance method provided in an embodiment of this application.

[0043] Figure 8 is a schematic diagram of a lane guidance device provided in an embodiment of this application;

[0044] Figure 9 is a schematic diagram of a lane guidance device provided in an embodiment of this application.

[0045] Methods of implementing the present invention

[0046] To make the objectives, technical solutions, and advantages of the embodiments of this application clearer, the technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings.

[0047] The following explanations of some terms used in the embodiments of this application are provided to facilitate understanding by those skilled in the art.

[0048] (1) Global Positioning System (GPS):

[0049] The Global Positioning System (GPS) provides accurate geographic location, vehicle speed, and precise time information anywhere in the world and in near-Earth space. GPS is characterized by high precision, all-weather operation, global coverage, and convenience.

[0050] (2) High-precision map data:

[0051] The generated road lane-level map data is obtained by surveying actual road conditions using high-precision surveying technology.

[0052] (3) Lane-level guidance service:

[0053] In navigation scenarios with high-precision map data, the navigation system provides lane-level navigation guidance suggestions. This lane-level guidance service includes, but is not limited to: navigation guidance surfaces for high-precision road sections, consisting of a set of suggested lane segments; navigation guidance lines for high-precision road sections, consisting of a series of suggested continuous lane segments; navigation voice prompts and broadcasts for high-precision road sections; and signage functions for high-precision road sections, such as lane change suggestions and road marking prompts.

[0054] Intelligent Traffic Systems (ITS), also known as Intelligent Transportation Systems, effectively integrate advanced science and technology (information technology, computer technology, data communication technology, sensor technology, electronic control technology, automatic control theory, operations research, artificial intelligence, etc.) into transportation, service control, and vehicle manufacturing. This strengthens the connection between vehicles, roads, and users, thereby forming a comprehensive transportation system that ensures safety, improves efficiency, enhances the environment, and saves energy.

[0055] Intelligent Vehicle Infrastructure Cooperative Systems (IVICS) are a development direction of Intelligent Transportation Systems (ITS). IVICS utilizes advanced wireless communication and next-generation Internet technologies to implement comprehensive, real-time dynamic information exchange between vehicles and infrastructure. Based on the collection and fusion of dynamic traffic information across all times and spaces, it conducts active vehicle safety control and cooperative road management, fully realizing effective collaboration between people, vehicles, and roads, ensuring traffic safety, improving traffic efficiency, and thus forming a safe, efficient, and environmentally friendly road traffic system.

[0056] It should be noted that the embodiments of this application involve operations such as obtaining historical driving data of each reference vehicle. When the above embodiments of this application are applied to specific products or technologies, user permission or consent is required, and the collection, use and processing of related data must comply with the relevant laws, regulations and standards of the relevant countries and regions.

[0057] In this application embodiment, the terms "module" or "unit" refer to a computer program or part of a computer program that has a predetermined function and works with other related parts to achieve a predetermined goal, and can be implemented wholly or partially using software, hardware (such as processing circuitry or memory), or a combination thereof. Similarly, a processor (or multiple processors or memory) can be used to implement one or more modules or units. Furthermore, each module or unit can be part of an overall module or unit that includes the functionality of that module or unit.

[0058] The application areas of the lane guidance method provided in the embodiments of this application will be briefly introduced below.

[0059] With the continuous development of technology, more and more devices can provide lane-level guidance services to achieve more precise lane-level navigation. For example, when a vehicle needs to turn right at an intersection, it can be guided to enter the dedicated right-turn lane N meters in advance.

[0060] However, in related technologies, lane guidance methods typically involve setting multiple guidance parameters in advance based on empirical values. These parameters include guiding vehicles into a dedicated turning lane N1 meters before reaching the turning intersection for routes requiring a turn; guiding vehicles to travel at least N2 meters in the lane after the lane change for routes requiring a lane change; and not covering lanes that need to be exited within N3 meters of entering the lane during lane guidance.

[0061] After pre-setting multiple guidance parameters, the lane where the vehicle is currently located and the set of lanes that the vehicle can enter are determined by using pre-collected lane-level road network data; lane-level guidance services are provided to the vehicle according to the pre-set multiple guidance parameters.

[0062] For example, referring to Figure 1A, if a vehicle is currently traveling in the middle lane and needs to turn right at the current intersection according to the navigation route, then when the vehicle is 200 meters away from the intersection, lane guidance results will be generated for the vehicle. The lane guidance result indicates that the vehicle is being guided to enter the dedicated right-turn lane.

[0063] However, due to the wide variety of road conditions, the multiple guidance parameters set based on experience cannot meet the needs of complex road conditions, and are prone to problems such as untimely or premature lane guidance.

[0064] For example, referring to Figure 1B, a vehicle is currently traveling in the rightmost lane. According to the navigation route, the vehicle needs to turn right at the current intersection. Lane guidance is generated when the vehicle is 200 meters away from the intersection. This lane guidance indicates that the vehicle is being directed to enter the dedicated right-turn lane. However, based on the current road conditions, there is no dedicated right-turn lane 200 meters from the intersection; it only appears 50 meters before reaching the intersection. If drivers blindly follow the lane guidance without making their own judgment, it could lead to unnecessary traffic accidents.

[0065] It is evident that the lane guidance methods in related technologies provide lane-level guidance services with low accuracy.

[0066] To address the issue of low accuracy in lane guidance, this application proposes a lane guidance method. In this method, after receiving a lane guidance instruction for a target road segment, the following operations are performed for each of multiple candidate lane segments: Based on at least one pre-stored conditional entry probability associated with the candidate lane segment, a target entry probability for the target vehicle to enter the candidate lane segment is determined. The target road segment includes multiple candidate lane segments located between the starting lane segment and the destination lane segment. Each conditional entry probability represents the conditional probability of each past vehicle (also called a reference vehicle) in historical driving data associated with the target road segment entering the candidate lane segment from an adjacent lane segment, provided that entry into the destination lane segment is a condition.

[0067] After obtaining the target entry probabilities for each of the multiple candidate lane segments, the guide lane segments whose target entry probabilities satisfy the guidance conditions are determined based on the obtained target entry probabilities, thus obtaining the lane guidance result. This generates the lane guidance result, which includes: the guide lane segments among the multiple candidate lane segments whose target entry probabilities satisfy the preset guidance conditions.

[0068] This method can be performed by an electronic device. The electronic device may be, for example, an in-vehicle device, such as a navigation device in a vehicle's driving system, a stand-alone navigation device installed in the vehicle, a smart terminal running a navigation application, or a server that communicates with the navigation device to provide navigation services.

[0069] Lane guidance instructions are commands used to instruct a navigation device to provide lane-level navigation information. These instructions can be generated automatically by the navigation device or in response to a received operation command. In some embodiments, lane guidance instructions are automatically generated during a segment of the navigation route guided by the navigation device, provided that preset conditions are met. For example, preset conditions could be that the vehicle is about to enter the next segment based on a pre-defined segmentation of the navigation route. In other embodiments, lane guidance instructions are generated when the navigation device receives an operation command. For example, the operation command could be a navigation start command for a destination received via a user interface.

[0070] The target road section refers to the road segment between the starting point and the destination point corresponding to the current lane guidance instruction. In some embodiments, each lane in the road network is pre-divided into multiple lane segments along the road extension direction according to preset rules. In this case, the starting point and the destination point can be represented by the starting lane segment and the destination lane segment, respectively. The technical solutions of various embodiments determine at least two lane segments from the candidate lane segments between the starting lane segment and the destination lane segment. The selected lane segments are connected end to end to form a lane-level navigation route (also known as the lane guidance result) from the starting lane segment to the destination lane segment.

[0071] In this embodiment, at least one conditional entry probability associated with each candidate lane segment is obtained in advance based on historical driving data associated with the target road section. The conditional entry probability represents the probability that each reference vehicle, within the historical time period covered by the historical driving data, will enter the candidate lane segment from an adjacent lane segment, given the condition of entering the target lane segment. Therefore, based on the at least one conditional entry probability associated with each candidate lane segment, the target entry probability of the target vehicle entering each candidate lane segment is determined. Based on the obtained target entry probabilities, a guidance lane segment is selected to obtain lane guidance results.

[0072] Data mining was performed on the historical driving data of each reference vehicle within a historical time period to analyze the driving patterns of each reference vehicle under different road conditions. This data serves as a reference for lane guidance of the current target vehicle. As a result, targeted lane guidance can be provided for different road conditions, achieving richer lane guidance effects. There is no need to manually set multiple guidance parameters. Lane guidance is based on these set guidance parameters under any circumstances, avoiding guidance errors caused by the setting guidance parameters not matching the actual road conditions, and improving the accuracy of lane guidance.

[0073] Furthermore, the calculation process for lane guidance based on lane segments is less computationally intensive and more efficient than the calculation process based on vehicle positioning points, thus improving lane guidance efficiency to some extent.

[0074] The application scenarios of the lane guidance method provided in this application are described below.

[0075] Please refer to Figure 1C, which is a schematic diagram of an application scenario of the lane guidance method provided in this application. This application scenario includes a client 101 and a server 102. Taking a navigation client in a target vehicle as an example, client 101 and server 102 can communicate. The communication method can be wired, such as through a network cable or serial cable; or wireless, such as through Bluetooth or Wi-Fi. No specific limitation is imposed.

[0076] Client 101 generally refers to devices that can display lane guidance results, such as terminal devices, third-party applications accessible by the terminal devices, or web pages accessible by the terminal devices. Terminal devices include, but are not limited to, mobile phones, computers, smart medical devices, smart home appliances, in-vehicle terminals, or aircraft. Server 102 generally refers to devices that can generate lane guidance results, such as terminal devices or servers. Servers include, but are not limited to, cloud servers, local servers, or associated third-party servers. Both client 101 and server 102 can use cloud computing to reduce the consumption of local computing resources; similarly, they can also use cloud storage to reduce the consumption of local storage resources.

[0077] In various embodiments, the client 101 and the server 102 may be the same device or different devices, and there is no specific limitation.

[0078] The lane guidance method provided in this application embodiment will be described in detail below based on Figure 1C. Please refer to Figure 2, which is a schematic flowchart of a lane guidance method provided in this application embodiment.

[0079] S201, Receive lane guidance instructions for a target road section, and determine multiple candidate lane sections between the starting lane section and the destination lane section of the target road section.

[0080] Based on the actual road network data and the high-precision map data of the navigation map, various reference lane segments can be pre-stored. The reference lane segments can be obtained by dividing the roads in the navigation map according to the actual road conditions. For example, please refer to Figure 3A(1). According to the actual road conditions, traffic lights at the first intersection, traffic signs, the intersection leading out of the main road, and traffic lights at the second intersection are set up in sequence on a road. Then, please refer to Figure 3A(2). Based on the positions of the traffic lights at the first intersection, traffic signs, the intersection leading out of the main road, and the traffic lights at the second intersection on the road, the corresponding roads in the navigation map can be divided into four segments. Since the road has three lanes in one direction, twelve reference lane segments can be obtained.

[0081] The target road section includes: multiple candidate lane segments located between the associated starting lane segment and the associated destination lane segment in each reference lane segment.

[0082] The starting lane segment associated with the target road section can be: the destination lane segment associated with other road sections. For example, other road sections connected to the target road section but located in the opposite direction of travel of the target road section; the destination lane segment associated with these other road sections is the starting lane segment associated with the target road section. The starting lane segment associated with the target road section can also be: any lane segment that the target vehicle can enter when it is about to depart. For example, in response to a navigation operation triggered for a destination, a road navigation route from the target vehicle's origin to its destination is generated; then, the reference lane segment connected to the origin on the road navigation route is the starting lane segment.

[0083] The destination lane segment associated with the target road section can be: the starting lane segment associated with other road sections. For example, other road sections connected to the target road section and located in the same direction of travel as the target road section, the starting lane segment associated with those other road sections is the destination lane segment associated with the target road section. The destination lane segment associated with the target road section can also be: a lane segment that the target vehicle exits when it is about to arrive. For example, in response to a navigation operation triggered for a destination, a road navigation route is generated from the starting point of the target vehicle to the destination; then, the reference lane segment connected to the destination on the road navigation route is the destination lane segment.

[0084] Based on the actual road network data and the high-precision map data of the navigation map, various marked lane segments can be pre-stored, i.e., pre-marked lane segments. In this embodiment, the marked lane segments are the lane segments that vehicles can enter when entering a fork in the road. The specific settings can be made according to the actual scenario and are not limited here. The marked lane segments can be obtained by marking the reference lane segments in the navigation map according to the actual road conditions. For example, please refer to Figure 3B. The main road on the left shows twelve reference lane segments, and the auxiliary road on the right shows two reference lane segments. Since these two reference lane segments are the reference lane segments that vehicles can enter when entering the auxiliary road, these two reference lane segments are marked, and these two reference lane segments are updated to two marked lane segments.

[0085] A marked lane segment can serve as the starting or destination lane segment associated with a target road section or other road sections.

[0086] In various embodiments, lane guidance instructions for a target road section may be generated when the target vehicle is about to enter the target road section. For example, in response to a navigation operation triggered for a destination, a road navigation route from the target vehicle's origin to its destination is generated. Based on pre-stored marked lane segments and reference lane segments, multiple reference road sections included in the road navigation route are determined. When it is determined that the target vehicle is about to enter a reference road section, that reference road section is designated as the target road section, and lane guidance instructions for the target road section are generated.

[0087] Lane guidance instructions for target road sections can also be generated in response to navigation operations triggered for the destination, when generating a road navigation route from the starting point of the target vehicle to the destination, by sequentially taking each reference road section included in the road navigation route as the target road section, and generating lane guidance instructions for each target road section, without specific restrictions on the timing.

[0088] In this embodiment, the lane guidance process for one target road section in the road navigation route is described in detail. The lane guidance process for other target road sections is similar and will not be described again here.

[0089] S202, for multiple candidate lane segments, perform the following operations respectively: based on at least one conditional entry probability associated with the candidate lane segment, determine the target entry probability of the target vehicle entering the candidate lane segment.

[0090] Multiple candidate lane segments are reference lane segments that a target vehicle can choose to enter when traveling on the target road section. In this embodiment, when the target vehicle enters the target road section, a guide lane segment can be determined from multiple candidate lane segments to provide lane-level guidance services for the target vehicle to enter the marked lane segment where the intersection is located, so that the target vehicle can change lanes at a more accurate time, etc.

[0091] The process of determining the target entry probability of a target vehicle entering a candidate lane segment is described below. The process of determining the target entry probability of a target vehicle entering other candidate lane segments is similar and will not be repeated here.

[0092] Obtain at least one conditional entry probability associated with a candidate lane segment. Based on the obtained conditional entry probability, determine the target entry probability of the target vehicle entering the candidate lane segment. Each conditional entry probability represents the conditional probability of each passing vehicle in the historical driving data associated with the target road section entering the candidate lane segment from an adjacent lane segment, given that entering the target lane segment is a condition.

[0093] Please refer to Figure 4A. The lane segment shown with a diagonal striped background is a candidate lane segment, and the lane segment shown with a horizontal striped background is its three adjacent lane segments, including the first adjacent lane segment 41, the second adjacent lane segment 42, and the third adjacent lane segment 43. This candidate lane segment is associated with three conditional entry probabilities, for example, 90%, 10%, and 0%, respectively. That is, the probability of entering the candidate lane segment from the first adjacent lane segment 41 is 90%, the probability of entering from the second adjacent lane segment 42 is 10%, and the probability of entering from the third adjacent lane segment 43 is 0%.

[0094] In various embodiments, the method for determining the target entry probability of a target vehicle entering a candidate lane segment based on at least one conditional entry probability associated with a pre-stored candidate lane segment may involve obtaining at least one reference entry probability associated with each reference lane segment contained in each pre-stored reference road interval. From the obtained reference entry probabilities of each reference lane segment, at least one reference entry probability associated with each of the multiple candidate lane segments is extracted as its at least one conditional entry probability. For each of the multiple candidate lane segments, the following operations are performed: based on the at least one conditional entry probability associated with the candidate lane segment, the target entry probability of the target vehicle entering the candidate lane segment is determined.

[0095] A reference road section is defined as a road segment divided by at least two pre-defined lane marking segments. These at least two lane marking segments include a starting lane segment and a destination lane segment associated with the reference road section. Since there can be one or more starting lane segments and one or more destination lane segments associated with the reference road section, there can be at least two lane marking segments associated with the reference road section.

[0096] Each reference entry probability represents the conditional probability of a reference vehicle entering a reference lane segment from an adjacent lane segment, given that at least one marked lane segment is associated with it in the direction of travel of that reference lane segment within a historical time period. Since the at least two marked lane segments associated with the reference road segment include both the starting lane segment and the destination lane segment associated with the reference road segment, the at least one marked lane segment associated with the direction of travel of the corresponding reference lane segment is the destination lane segment associated with the reference road segment.

[0097] In various embodiments, the conditional entry probability can also characterize the conditional probability of each reference vehicle entering a candidate lane segment from an adjacent lane segment within a historical time period, given the combined conditions of entering the target lane segment and the traffic congestion level being lower than a preset level. Therefore, the conditional entry probability can also characterize the conditional probability of each reference vehicle entering a reference lane segment from an adjacent lane segment within a historical time period, given the combined conditions of entering at least one marked lane segment associated with the corresponding reference lane segment's travel direction and the traffic congestion level being lower than a preset level.

[0098] Traffic congestion levels can be represented by the time it takes for a reference vehicle to enter a candidate lane from an adjacent lane. The shorter the time, the lower the traffic congestion level; the longer the time, the higher the traffic congestion level.

[0099] Traffic congestion levels can also be represented by the traffic flow at the time and location when a reference vehicle enters the candidate lane segment from an adjacent lane segment. The lower the traffic flow, the lower the traffic congestion level; the higher the traffic flow, the higher the traffic congestion level.

[0100] The lower the traffic congestion level, the smoother the traffic flow; the higher the traffic congestion level, the more congested the traffic. By combining these conditions, richer information can be extracted from historical driving data, enabling more accurate lane-level guidance for target vehicles. For example, for congested road sections, earlier lane-changing guidance can be provided to avoid missing intersections; or, for the same road section, different lane guidance schemes can be provided at different times to avoid traffic congestion.

[0101] The composite conditions can also be expanded from other dimensions. In this embodiment of the application, entering the destination lane segment is taken as an example for introduction. The situation of composite conditions is similar and will not be described again here.

[0102] In various embodiments, each reference entry probability is obtained by data mining of the historical driving data of each reference vehicle within a historical time period, representing the driving pattern of each reference vehicle on the corresponding reference lane segment. Therefore, before obtaining at least one reference entry probability associated with each reference lane segment within each pre-stored reference road interval, the historical driving data of each reference vehicle within the historical time period can be obtained first. Based on the obtained historical driving data, and the pre-stored reference lane segments and marked lane segments, the lane segment driving sequence for each reference vehicle is determined. The lane segment driving sequence represents the historical driving data of the corresponding reference vehicle by multiple concatenated reference lane segments and multiple marked lane segments. The lane segment driving sequence for each reference vehicle within the historical time period can also be pre-determined by other devices. When lane guidance is required, the server can directly obtain the pre-stored lane segment driving sequence for each reference vehicle within the historical time period from other devices, etc., without any specific limitations.

[0103] After obtaining the lane segment driving sequences of each reference vehicle within a historical time period, at least one reference entry probability associated with each reference lane segment within each reference road interval can be determined based on the obtained lane segment driving sequences.

[0104] For example, referring to Figure 4B(1), the historical driving data of a reference vehicle can be represented by multiple Global Positioning System (GPS) positioning points, shown as dotted lines in Figure 4B(1), where each point represents GPS1, GPS2, GPS3, ..., GPS n With Lane x To indicate a reference lane segment, please refer to Figure 4B(2), which shows line segments with their two endpoints as radii. Each line segment represents a reference lane segment or marked lane segment in the lane segment driving sequence of the reference vehicle, namely Lane1, Lane2, Lane3, Lane4, Lane5, Lane6, Lane7, Lane8, Lane9, Lane1, Lane2, Lane9, Lane1, Lane2, Lane3, Lane4 ...4, Lane3, Lane4, Lane4, Lane3, Lane4, Lane4, Lane4, Lane3, Lane4, Lane4, Lane4, Lane4 标记 Lane n Lane 标记 The marked lane segment is the lane segment, while the others are reference lane segments.

[0105] In various embodiments, after obtaining the driving sequence of each lane segment, the driving sequence of each reference vehicle can be divided into at least one driving subsequence based on each reference road interval, resulting in multiple driving subsequences. Each driving subsequence contains a first lane segment and a last lane segment, which are two marked lane segments associated with the corresponding reference road interval. Therefore, based on the obtained multiple driving subsequences, at least one reference entry probability associated with each reference lane segment within each reference road interval can be determined.

[0106] For example, the lane segment driving sequence of a reference vehicle is represented as Lane1, Lane2, Lane... 标记 ,

[0107] Lane 4, Lane 5, Lane 6, Lane 标记 Lane 8, Lane 9, Lane 标记 Lane 11 Lane 标记 Therefore, the multiple driving subsequences include the first driving subsequence Lane1, Lane2, Lane... 标记 The second driving subsequence is Lane4, Lane5, Lane6, Lane... 标记 The third driving subsequence is Lane8, Lane9, Lane 标记 and the fourth driving subsequence, Lane 11 Lane 标记 .

[0108] In various embodiments, when determining at least one reference entry probability associated with each reference lane segment within each reference road interval based on the obtained lane segment driving sequence of each reference vehicle, or based on multiple obtained driving sub-sequences, the reference entry probability can be calculated by counting the number of reference vehicles.

[0109] The following describes the process of determining a reference entry probability associated with a reference lane segment within a reference road section. Other determination processes are similar and will not be repeated here.

[0110] Based on the lane segment driving sequences of each reference vehicle, the number of reference vehicles (also known as first passing vehicles) entering the reference lane segment from an adjacent lane segment adjacent to the reference lane segment is counted, assuming the marked lane segment associated with the driving direction of the corresponding reference road section. The number of other reference vehicles (also known as second passing vehicles) entering other lane segments (excluding the reference lane segment) from an adjacent lane segment is also counted.

[0111] Please refer to Figure 5A, which shows the reference lane segments contained in a reference road section, denoted as Lane1, Lane2, Lane3, Lane4, Lane5, Lane6, Lane7, Lane8, Lane9, Lane1, Lane9, Lane1, Lane9, Lane1, Lane2, Lane3, Lane4, Lane5, Lane6, Lane7, Lane8, Lane9, Lane9, Lane1, Lane9 ...9, Lane9, Lane9, 2-1 Lane 2-2 Lane 3-1 Lane 3-2 Lane 3-3 Lane 4, Lane 标记 So, regarding the reference lane segment Lane... 3-3 Statistics are based on entering the associated lane segment. 标记 As a condition, from the adjacent lane segment Lane 2-2 Enter the reference lane section 3-3 The number of reference vehicles is denoted as . In addition, statistics are based on entering lanes marked with associated signs. 标记 As a condition, from the adjacent lane segment Lane 2-2 Enter other lanes 3-1 The number of other vehicles is denoted as In addition, statistics are based on entering lanes marked with associated signs. 标记 As a condition, from the adjacent lane segment Lane 2-2 Enter other lanes 3-2 The number of other vehicles is denoted as

[0112] Based on the number of reference vehicles and other vehicles, each reference vehicle is determined, and a reference entry probability for the reference lane segment is obtained by considering the marked lane segment associated with its driving direction in the corresponding reference road section, and the conditional probability of entering the reference lane segment from an adjacent lane segment.

[0113] The ratio of the number of reference vehicles to the sum of the number of reference vehicles and the number of all other vehicles can be used as a reference entry probability for a reference lane segment. Please refer to Figure 5A further; each reference vehicle enters its associated marked lane segment. 标记 As a condition, from an adjacent lane segment 2-2 Enter the reference lane section 3-3 The reference entry probability can be expressed as formula (1).

[0114] In various embodiments, after obtaining at least one reference entry probability associated with each reference lane segment contained in each pre-stored reference road interval, the traffic signs in the navigation map can be reconfirmed based on the results of this data mining to ensure the accuracy of the traffic signs in the navigation map and avoid the process of repeatedly confirming the traffic signs in the actual road conditions, thereby improving the efficiency of confirming traffic signs.

[0115] In navigation maps, traffic signs requiring reconfirmation are marked. Therefore, it is possible to obtain at least one lane segment marked with a "to be confirmed" sign from each reference lane segment within each reference road section. A "to be confirmed" sign indicates a traffic sign to be confirmed that is present within, or between, a corresponding lane segment requiring reconfirmation.

[0116] For example, a traffic sign indicating no U-turns may be placed on a lane that is only for left turns. However, if the traffic sign is partially obscured during road data collection, it may not be accurate. Therefore, the reference lane segment where this lane is located can be considered as a lane segment to be confirmed, and a confirmation sign can be placed on this lane segment.

[0117] For example, the lane line between two adjacent lanes is a traffic sign indicating that lane changes are not allowed. However, this traffic sign may be obscured by passing vehicles during road data collection, and therefore may not be accurate. Thus, the reference lane segments where these two lanes are located can be used as two lane segments to be confirmed, and confirmation signs can be set on these two lane segments.

[0118] After obtaining at least one lane segment to be confirmed, at least one reference entry probability associated with each lane segment to be confirmed can be selected from at least one reference entry probability associated with each reference lane segment contained in each reference road interval.

[0119] Based on at least one reference entry probability associated with each of at least one lane segment to be confirmed, a traffic driving strategy corresponding to each of the at least one lane segment to be confirmed is determined. Based on the obtained traffic driving strategies, the confirmation flags of each of the at least one lane segment to be confirmed are updated.

[0120] For example, if a lane segment designated for left turns only has a traffic sign indicating that U-turns are prohibited, and at least one reference entry probability associated with this lane segment indicates that a large number of reference vehicles have made U-turns on this lane segment during a historical period, then the traffic strategy for this lane segment is that U-turns are permitted, and therefore the lane segment's sign can be updated.

[0121] For example, if the lane line between two adjacent lane segments to be confirmed is a traffic sign indicating that lane changes are not allowed, and at least one reference entry probability associated with each of these two lane segments indicates that a large number of reference vehicles changed lanes between these two lane segments during a historical period, then the traffic strategy for these two lane segments to be confirmed is that lane changes are allowed between them, and therefore the confirmation signs for these two lane segments can be updated.

[0122] In each embodiment, when updating the unconfirmed sign of at least one lane segment to be confirmed based on each obtained traffic driving strategy, the process of updating the unconfirmed sign of one lane segment to be confirmed based on one traffic driving strategy will be described as an example. Other update processes are similar and will not be described in detail here.

[0123] When a traffic driving strategy is determined and matches the traffic sign represented by the corresponding unconfirmed sign, the corresponding unconfirmed sign is removed from the map. That is, if the traffic sign set in the navigation map is accurate, then the unconfirmed sign will no longer be marked on the unconfirmed lane segment, and the unconfirmed lane segment will be used as a reference lane segment again.

[0124] When a traffic driving strategy is determined and does not match the traffic sign represented by the corresponding unconfirmed sign, the corresponding unconfirmed sign is retained. This indicates that the traffic driving strategy of each reference vehicle in the unconfirmed lane segment during the historical period does not match the traffic sign. It is possible that the traffic sign set in the navigation map is inaccurate. Therefore, the unconfirmed sign can be retained so that it can be confirmed in the actual road conditions during the next road data collection.

[0125] In various embodiments, when determining the target entry probability of a target vehicle entering a candidate lane segment based on at least one conditional entry probability associated with a pre-stored candidate lane segment, if the candidate lane segment is associated with one conditional entry probability, then the product of the conditional entry probability and its associated weight coefficient is taken as the target entry probability of the target vehicle entering the candidate lane segment. If the candidate lane segment is associated with multiple conditional entry probabilities, then the weighted sum of the multiple conditional entry probabilities can be taken as the target entry probability of the target vehicle entering the candidate lane segment; alternatively, the maximum value among the products of the multiple conditional entry probabilities and their respective associated weight coefficients can be taken as the target entry probability of the target vehicle entering the candidate lane segment, etc., without any specific limitations.

[0126] The weighting coefficient for each conditional entry probability is: the target entry probability of the adjacent lane segment determined when the corresponding adjacent lane segment is a candidate lane segment.

[0127] Please refer to Figure 5B, which includes lane segments 1, 2, 3, 4, 5, 6, and 7. The target vehicle is located in lane segment 2. Given that it is expected to enter lane segment 10, the probability of the target vehicle entering lane segment 3 from lane segment 2 is 90%; the probability of the target vehicle entering lane segment 5 from lane segment 2 is 10%.

[0128] Given that the target vehicle has a 100% probability of entering lane segment 3 from lane segment 3 and lane segment 5 from lane segment 6, the probability of entering lane segment 6 is as follows:

[0129] The probability of a target vehicle entering lane 3 from lane 2 and then lane 6 from lane 3 is included; the probability of the target vehicle entering lane 5 from lane 2 and then lane 6 from lane 5 is also included. Therefore, using 90% as the weighting coefficient for the conditional entry probability of entering lane 6 from lane 3 and 10% as the weighting coefficient for the conditional entry probability of entering lane 6 from lane 5, the target entry probability of lane 6 is calculated as 90% * 100% + 10% * 100% = 100%.

[0130] S203, Generate lane guidance results, which include: among the multiple candidate lane segments, the guidance lane segment whose target entry probability meets the preset guidance conditions.

[0131] This step determines the guiding lane segment whose target entry probability satisfies the guiding conditions among the multiple candidate lane segments based on the obtained target entry probabilities, and obtains the lane guidance result.

[0132] After obtaining the target entry probabilities corresponding to each of the multiple candidate lane segments, the guide lane segment whose target entry probability satisfies the guidance conditions can be determined based on the obtained target entry probabilities of the multiple candidate lane segments, thus obtaining the lane guidance result.

[0133] In various embodiments, satisfying the guidance condition can be either greater than a preset probability value or within a preset probability range, etc., and there is no specific limitation. This application will use the example of satisfying the guidance condition as being greater than a preset probability value for illustration.

[0134] Among multiple candidate lane segments, the lane segment with a target entry probability greater than a preset probability value is selected as the guiding lane segment. That is, based on the target entry probabilities corresponding to each of the multiple candidate lane segments, the guiding lane segment with the corresponding target entry probability greater than the preset probability value is selected. Based on the obtained guiding lane segments, the guiding driving area and guiding driving route for the target vehicle on the target road section are determined, thus obtaining the lane guidance result.

[0135] In various embodiments, the guidance driving area, or guidance surface, can characterize the direction of guided driving, such as guiding driving in the right-hand lane or the left-hand lane. The guidance driving line, or guidance line, can characterize the position where the vehicle changes lanes. For example, if a lane change occurs between two adjacent lane segments, then the two adjacent lane segments are connected to form a guidance line, thereby providing lane-level guidance services to the target vehicle.

[0136] Based on the area of ​​each guide lane segment in the preset navigation map, the combined area of ​​each guide lane segment is used as the guide driving area for the target vehicle on the target road section. That is, the guide lane segments covered by the guide surface form the guide driving area.

[0137] Please refer to Figure 6A. The area enclosed by the diagonal-patterned broken line frame is the combined area composed of all the guide lane segments. The area outside the area enclosed by the diagonal-patterned broken line frame represents the candidate lane segments other than the guide lane segments. Therefore, the area enclosed by the diagonal-patterned broken line frame can be considered as the guide driving area for the target vehicle on the target road section.

[0138] Based on the multiple guide lane segments obtained in series, a guide route for the target vehicle on the target road section is generated. If there are multiple series connection methods for each guide lane segment, and no duplicate guide lane segments exist in each series connection method, the series connection method with the highest target entry probability for each of the connected guide lane segments is selected to obtain the guide route.

[0139] When there are multiple connection methods in each guide lane segment, if one or more guide lane segments have a traffic congestion level higher than the preset level in the connection method with the highest target entry probability, then the guide driving route is obtained based on the guide lane segment with a target entry probability slightly lower than that one or more guide lane segments, i.e., the guide lane segment with the second highest target entry probability.

[0140] Therefore, based on the obtained guidance driving area and guidance driving route, lane guidance results are generated. The lane guidance results achieve a variety of guidance effects, making the driving range of the target vehicle clearer, thus providing the target vehicle with more accurate lane guidance services.

[0141] Please refer to Figure 6B, which is a schematic diagram of lane guidance results. The area enclosed by the diagonal-background broken-line box represents the guided driving area, and the black arrows represent the guided driving route. Specifically, for the target vehicle's current starting lane segment, the probability of entering the adjacent candidate lane segment to the right is the highest when changing lanes to the right; the probability of entering the adjacent candidate lane segment ahead is the second highest when moving straight ahead. Therefore, if the adjacent candidate lane segment to the right is not congested, that candidate lane segment becomes the guiding lane segment; if that candidate lane segment is congested, then the adjacent candidate lane segment ahead, which is moved straight ahead, becomes the guiding lane segment. Figure 6B shows an example where the adjacent candidate lane segment to the right is not congested.

[0142] In various embodiments, after obtaining the lane guidance results, the lane guidance results can be presented in the client, for example, by filling the guidance driving area with a striking color in the navigation map, and by drawing the guidance driving route with animated arrows.

[0143] The server can pre-store multiple multimedia presentation templates. A target presentation template matching the lane guidance result can be selected from these pre-stored templates. The multimedia presentation template is used to present content in at least one of the following formats: image, video, or audio. Therefore, the lane guidance result can be presented based on the obtained target presentation template.

[0144] For example, referring to Figure 6B, the navigation map uses a diagonal-background polygonal box to mark the guided driving area and a black arrow to draw the guided driving route, according to the target presentation template. Simultaneously, based on the target vehicle's current position and the guided driving route, the distance between the target vehicle's current position after entering the right lane and its position after entering the right-turn auxiliary lane is determined to be 200 meters; and the distance between the target vehicle's current position after entering the right-turn auxiliary lane and its position after entering the intersection is determined to be 100 meters. Therefore, according to the target presentation template, the system can announce via voice, "Currently entering the right lane, and after 200 meters, entering the right-turn auxiliary lane, and after driving 100 meters in the right-turn auxiliary lane, entering the intersection."

[0145] The lane guidance method provided in the embodiments of this application will be described in the following example.

[0146] Please refer to Figure 7A(2), which is a schematic diagram of a road section, including three lanes in one direction, an additional right-turn lane, and a fork in the road. Among them, the lane line between the left lane and the middle lane is represented by a long dashed line; the lane line between the middle lane and the right lane is represented by a line consisting of a long dashed line and a long solid line; the lane line between the right lane and the right additional lane is represented by a long dashed line; and the lane line between the right lane and the fork in the road is represented by a long dashed line. The dashed lines, consisting of five short horizontal lines, divide each lane into multiple lane segments. As shown in Figure 7A, from left to right and from bottom to top, they are lane segments 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, and 17 (the names of the lane segments are not shown in the figure), as well as the marked lane segment at the intersection, which can also be called the destination lane segment.

[0147] Therefore, the second lane segment can be used as a starting lane segment (shown as the lane segment where the vehicle is located in Figure 7A). This starting lane segment and the destination lane segment form a road section, which can be used as the target road section.

[0148] When preparing the reference entry probabilities, please refer to Figure 7B:

[0149] S701, acquires historical driving data of each reference vehicle within a historical time period.

[0150] Please refer to Figure 7C(1), which is a schematic diagram of the historical driving data of a reference vehicle in the target lane section. The dots represent the positioning points of the reference vehicle in the target lane section, and the line formed by the dots is the driving route of the reference vehicle in the target lane section.

[0151] S702, based on the obtained historical driving data, as well as the pre-stored reference lane segments and marked lane segments, determines the lane segment driving sequence for each reference vehicle.

[0152] S703, based on each reference road section, divides the driving sequence of each reference vehicle in its respective lane segment into at least one driving subsequence, thus obtaining multiple driving subsequences.

[0153] Please refer to Figure 7C(2), which is a schematic diagram of a driving subsequence of the above-mentioned reference vehicle in the target road section. The line segments with two endpoints as circles represent the driving lines of the reference vehicle in each reference lane segment included in the target lane section.

[0154] S704, based on the obtained multiple driving sub-sequences, for each reference lane segment and its adjacent one adjacent lane segment, count the number of reference vehicles and the number of other vehicles.

[0155] For details regarding the specific number of reference vehicles and other vehicle quantities, please refer to the previous introduction; these details will not be repeated here.

[0156] S705, for each reference lane segment, the ratio of the number of reference vehicles to the sum of the number of reference vehicles and the number of other vehicles is used as a reference entry probability for entering the reference lane segment from its adjacent adjacent lane segment.

[0157] Taking the fifth lane segment in Figure 7A as an example, the number of reference vehicles for the fifth lane segment and its adjacent second lane segment can be calculated using formula (2).

[0158] For the second lane segment, and the first and third lane segments adjacent to the second lane segment and excluding the fifth lane segment, the number of other vehicles can be counted by referring to formula (3).

[0159] Therefore, a reference entry probability for entering the fifth lane from the second lane can be found in formula (4).

[0160] During the lane guidance phase, please refer to Figure 7D:

[0161] S706, in response to a navigation operation triggered for a destination, generates a road navigation route from the origin of the target vehicle to the destination.

[0162] S707 determines multiple reference road sections included in the road navigation route based on pre-stored marked lane segments and reference lane segments.

[0163] S708, when it is determined that the target vehicle is about to enter a reference road section, the reference road section is used as the target road section, and lane guidance instructions are generated for the target road section.

[0164] S709, Select at least one conditional entry probability associated with each of the multiple candidate lane segments included in the target road section from the pre-stored reference entry probabilities.

[0165] S710 performs the following operations for each of the multiple candidate lane segments:

[0166] When a candidate lane segment is associated with a conditional entry probability, the product of the conditional entry probability and the associated weight coefficient is used as the target entry probability of the target vehicle entering the candidate lane segment.

[0167] When a candidate lane segment is associated with multiple conditions for entering the lane, the weighted sum of the multiple conditions for entering the lane segment is used as the target entry probability of the target vehicle entering the candidate lane segment.

[0168] Taking the target road section as shown in Figure 7A as an example, at least one conditional entry probability associated with each of the first lane segment, the second lane segment, ... and the seventeenth lane segment can be selected from the pre-stored reference entry probabilities.

[0169] The first lane segment is associated with a conditional entry probability, which represents the conditional probability that each reference vehicle within a historical time period will enter the first lane segment from the second lane segment, given that it is destined for the first lane segment. This conditional entry probability is, for example, 0%.

[0170] Since the target vehicle is currently in the second lane segment, the conditional entry probability associated with the second lane segment is not obtained.

[0171] The third lane segment is associated with a conditional entry probability, which represents the conditional probability that each reference vehicle within a historical time period will enter the third lane segment from the second lane segment, given that the destination lane segment is a given condition. This conditional entry probability is, for example, 90%.

[0172] The fourth lane segment is associated with two conditional entry probabilities. These two probabilities represent: the conditional probability of each reference vehicle entering the fourth lane segment from the first lane segment within a historical time period, given the condition of entering the destination lane segment; and the conditional probability of entering the fourth lane segment from the fifth lane segment. For example, both of these conditional entry probabilities are 0%.

[0173] The fifth lane segment is associated with three conditional entry probabilities. These three conditional entry probabilities represent: the conditional probability of each reference vehicle entering the fifth lane segment from the second lane segment within the historical time period, given the condition of entering the target lane segment; the conditional probability of entering the fifth lane segment from the fourth lane segment; and the conditional probability of entering the fifth lane segment from the sixth lane segment. The values ​​of these three conditional entry probabilities are, for example, 10%, 0%, and 0%, respectively.

[0174] The sixth lane segment is associated with two conditional entry probabilities. These two probabilities represent: the conditional probability of each reference vehicle entering the sixth lane segment from the third lane segment within a historical time period, given the condition of entering the target lane segment; and the conditional probability of entering the sixth lane segment from the fifth lane segment. For example, both conditional entry probabilities are 100%.

[0175] Lanes 7, 10, and 14 are each associated with two conditions for entering the lane, similar to the previous description, and will not be repeated here. For example, the entry probability values ​​for these six conditions are all 0%. Lanes 8, 11, 15, and 16 are each associated with three conditions for entering the lane, similar to the previous description, and will not be repeated here. For example, the entry probability values ​​for these twelve conditions are all 0%.

[0176] The ninth lane and the seventeenth lane are each associated with two conditions for entering the lane. Similar to the content introduced earlier, they will not be repeated here. For example, the values ​​of these four conditions for entering the lane are all 100%.

[0177] The twelfth lane segment is associated with three conditional entry probabilities. These three probabilities represent: the conditional probability of each reference vehicle entering the twelfth lane segment from the ninth lane segment within a historical time period, given the condition of entering the destination lane segment; the conditional probability of entering the twelfth lane segment from the eleventh lane segment; and the conditional probability of entering the twelfth lane segment from the thirteenth lane segment. The values ​​of these three conditional entry probabilities are, for example, 30%, 100%, and 0%, respectively.

[0178] The thirteenth lane segment is associated with a conditional entry probability, which represents the conditional probability that each reference vehicle within a historical time period will enter the thirteenth lane segment from the twelfth lane segment, given that the desired lane segment is being entered. This conditional entry probability is, for example, 90%.

[0179] Since the target vehicle is currently in the second lane, the probability of the target entering the second lane is considered to be 100%.

[0180] Since the target vehicle is currently located in the second lane adjacent to the first lane, the weighting coefficient of the conditional entry probability associated with the first lane is 1, so the target entry probability of the first lane is 1*0%=0%.

[0181] Since the target vehicle is currently located in the second lane, which is adjacent to the third lane, the weighting coefficient of the third lane for the probability of entering the lane is 1. Therefore, the probability of the target vehicle entering the third lane is 1 * 90% = 90%.

[0182] The probability of entering each of the following lane segments—the fourth, seventh, eighth, tenth, eleventh, fourteenth, fifteenth, and sixteenth—is 0%. Therefore, the probability of entering the target lane for each of these four lane segments is 0%.

[0183] Since the target vehicle is currently located in the second lane adjacent to the fifth lane, the target entry probability of the fourth lane is 0%, and the conditional entry probability of the fifth lane from the sixth lane is 0%, therefore, the target entry probability of the fifth lane is 1*10%+0%+0%=10%.

[0184] The probability of entering the target lane is 90%, the probability of entering the target lane is 10%, and the probability of entering the target lane is 100% for both conditions. Therefore, the probability of entering the target lane is 90%*100%+10%*100%=100%.

[0185] The probability of a target entering the eighth lane is 0%, the probability of a target entering the sixth lane is 100%, and the probability of entering the ninth lane is 100% for both conditions. Therefore, the probability of a target entering the ninth lane is 0%*100%+100%*100%=100%.

[0186] The target entry probability of the ninth lane segment is 100%, the target entry probability of the eleventh lane segment is 0%, and the conditional entry probability of the twelfth lane segment associated with the thirteenth lane segment is 0%. Therefore, the target entry probability of the twelfth lane segment is 100%*30%+0%+0%=30%.

[0187] If the target entry probability for the twelfth lane is 30%, then the target entry probability for the thirteenth lane is 30% * 90% = 27%.

[0188] The target entry probability for lane 13 is 27%, and the target entry probability for lane 16 is 0%. Therefore, the target entry probability for lane 17 is 100% * 27% = 27%.

[0189] Please refer to Figure 7E and mark the target entry probability of each lane segment at the corresponding lane segment location.

[0190] S711, based on the target entry probability corresponding to each of the multiple candidate lane segments, select the guide lane segment among the multiple candidate lane segments whose target entry probability is greater than a preset probability value, such as a preset probability value of 20%.

[0191] S712, based on the obtained guide lane segments, determine the guide driving area and guide driving route of the target vehicle on the target road section, i.e., guide surface and guide line, to obtain lane guidance results. The obtained guide lane segments form a guide surface. Among the obtained guide lane segments, when facing one or more enterable guide lane segments, select the guide lane segment with the highest probability of the target entering to form a guide line.

[0192] S713 presents the lane guidance result using the target presentation template that matches the lane guidance result from the various pre-stored multimedia presentation templates.

[0193] Please refer to Figure 7F(1), which shows the guiding driving area in the lane guidance results, i.e., one presentation effect of the guiding surface. Please refer to Figure 7F(2), which shows the guiding driving area in the lane guidance results overlaid with the guiding driving line, i.e., one presentation effect of the guiding surface and the guiding line.

[0194] In this embodiment, the process of obtaining the guided driving area and the guided driving route is optimized by data-driven methods, avoiding the problem of inaccurate guidance caused by uniform guidance parameters. By mining a large amount of historical driving data of reference vehicles, the method adapts to the actual lane change selection in different road conditions.

[0195] Based on the same inventive concept, this application provides a lane guidance device capable of realizing the functions corresponding to the aforementioned lane guidance method. Referring to Figure 8, the device includes an acquisition module 801 and a processing module 802, wherein:

[0196] Acquisition module 801: Used to receive lane guidance instructions for a target road section and determine multiple candidate lane segments between the starting lane segment and the destination lane segment of the target road section.

[0197] Processing module 802: For each of the multiple candidate lane segments, perform the following operations: Determine the target entry probability of a target vehicle entering a candidate lane segment based on at least one conditional entry probability associated with the candidate lane segment in the pre-stored candidate lane segment; wherein, each conditional entry probability represents: the conditional probability of a past vehicle entering a candidate lane segment from an adjacent lane segment adjacent to the candidate lane segment in the historical driving data associated with the target road section, with the condition of entering the target lane segment;

[0198] The processing module 802 is also used to: generate lane guidance results, the lane guidance results including: among multiple candidate lane segments, the guidance lane segment whose target entry probability meets the guidance conditions.

[0199] In various embodiments, the processing module 802 is specifically used for:

[0200] Obtain at least one reference entry probability associated with each reference lane segment contained in each pre-stored reference road interval; wherein, the reference road interval is: obtained by dividing the road by at least two associated marked lane segments; each reference entry probability represents: the conditional probability of each reference vehicle entering the reference lane segment from an adjacent lane segment within a historical time period, given that at least one marked lane segment is associated with the driving direction of the corresponding reference lane segment.

[0201] From the obtained reference entry probabilities, select at least one conditional entry probability associated with each of the multiple candidate lane segments.

[0202] For multiple candidate lane segments, perform the following operations respectively: determine the target entry probability of the target vehicle entering the candidate lane segment based on at least one conditional entry probability associated with the candidate lane segment.

[0203] In various embodiments, the processing module 802 is further configured to:

[0204] Before obtaining the at least one reference entry probability associated with each reference lane segment contained in each pre-stored reference road interval, obtain the historical driving data of each reference vehicle within the historical time period.

[0205] Based on the obtained historical driving data, as well as the pre-stored reference lane segments and marked lane segments, the lane segment driving sequence of each reference vehicle is determined; wherein, the lane segment driving sequence is represented by multiple reference lane segments and multiple marked lane segments in series, which represent the historical driving data of the corresponding reference vehicle.

[0206] Based on the lane segment driving sequences of each reference vehicle, at least one reference entry probability is determined for each reference lane segment included in each reference road interval.

[0207] In various embodiments, the processing module 802 is specifically used for:

[0208] Based on each reference road section, the driving sequence of each reference vehicle in its respective lane segment is divided into at least one driving subsequence, resulting in multiple driving subsequences; wherein, the first lane segment and the last lane segment of each driving subsequence are: the two marked lane segments associated with the corresponding reference road section;

[0209] Based on the obtained multiple driving subsequences, at least one reference entry probability is determined for each reference lane segment contained in each reference road interval.

[0210] In various embodiments, the processing module 802 is specifically used for:

[0211] For each reference lane segment contained in each reference road section, perform the following operations respectively:

[0212] Based on the lane segment driving sequence of each reference vehicle, the following statistics are calculated: the number of reference vehicles entering the reference lane segment from an adjacent lane segment adjacent to the reference lane segment, and the number of other reference vehicles entering other lane segments (excluding the reference lane segment) from an adjacent lane segment, assuming the marked lane segment associated with the driving direction of the corresponding reference road section.

[0213] Based on the number of reference vehicles and other vehicles, each reference vehicle is determined, and a reference entry probability for the reference lane segment is obtained by considering the marked lane segment associated with its driving direction in the corresponding reference road section, and the conditional probability of entering the reference lane segment from an adjacent lane segment.

[0214] In various embodiments, the processing module 802 is further configured to:

[0215] After obtaining at least one reference entry probability associated with each reference lane segment contained in each pre-stored reference road interval, at least one lane segment marked with a pending confirmation identifier is obtained in each reference lane segment contained in each reference road interval; wherein, the pending confirmation identifier represents: a traffic sign to be confirmed contained in the corresponding lane segment to be confirmed, or between the corresponding multiple lane segments to be confirmed.

[0216] From the at least one reference entry probability associated with each reference lane segment contained in each reference road interval, select at least one reference entry probability associated with each lane segment to be confirmed.

[0217] Based on at least one reference entry probability associated with each of at least one lane segment to be confirmed, determine the traffic driving strategy corresponding to each of the at least one lane segment to be confirmed.

[0218] Based on the obtained traffic driving strategies, update the unconfirmed signage for at least one lane segment to be confirmed.

[0219] In various embodiments, the processing module 802 is specifically used for:

[0220] For each traffic driving strategy obtained, perform the following operations:

[0221] When a traffic driving strategy is determined and matches the traffic sign represented by the corresponding pending confirmation sign, the corresponding pending confirmation sign is removed from the marking.

[0222] When a traffic strategy is determined that does not match the traffic sign represented by the corresponding pending confirmation sign, the corresponding pending confirmation sign is retained.

[0223] In various embodiments, the acquisition module 801 is specifically used for:

[0224] In response to a navigation action triggered for a destination, generate a road navigation route from the origin of the target vehicle to the destination;

[0225] Based on the pre-stored marked lane segments and reference lane segments, determine multiple reference road sections included in the road navigation route;

[0226] When it is determined that a target vehicle is about to enter a reference road section, the reference road section is used as the target road section, and lane guidance instructions are generated for the target road section.

[0227] In each embodiment, each conditional entry probability is associated with a weighting coefficient, which is: the target entry probability of a target vehicle entering a candidate lane segment when the adjacent lane segment represented by the corresponding conditional entry probability is used as a candidate lane segment.

[0228] Processing module 802 is specifically used for:

[0229] When a candidate lane segment is associated with a conditional entry probability, the product of the conditional entry probability and the associated weight coefficient is used as the target entry probability of the target vehicle entering the candidate lane segment.

[0230] When a candidate lane segment is associated with multiple conditions for entering the lane, the maximum value of either the weighted sum of the multiple conditions for entering the lane segment or the product of each condition for entering the lane segment with its associated weight coefficient is taken as the target entry probability of the target vehicle entering the candidate lane segment.

[0231] In various embodiments, the processing module 802 is specifically used for:

[0232] Based on the target entry probability corresponding to each of the multiple candidate lane segments, select the guide lane segment whose target entry probability is greater than the preset probability value from the multiple candidate lane segments.

[0233] Based on the obtained guide lane segments, the guide driving area and guide driving route of the target vehicle on the target road section are determined, and the lane guidance results are obtained.

[0234] In various embodiments, the processing module 802 is specifically used for:

[0235] Based on the area of ​​each guide lane segment in the preset navigation map, the combined area of ​​each guide lane segment is used as the guide driving area for the target vehicle on the target road section.

[0236] Based on the multiple guide lane segments obtained in series, a guide driving route for the target vehicle is generated on the target road section.

[0237] Based on the obtained guidance driving area and guidance driving route, lane guidance results are generated.

[0238] In various embodiments, the processing module 802 is further configured to:

[0239] After obtaining the lane guidance results, a target presentation template matching the lane guidance results is selected from the pre-stored multimedia presentation templates; wherein, the multimedia presentation template is used to present content in at least one of the following formats: image format, video format, or audio format;

[0240] Based on the obtained target presentation template, the lane guidance results are presented.

[0241] Please refer to Figure 9, which illustrates a computer device 900 provided in this embodiment of the application. This computer device 900 can be, for example, the terminal device 101 or server 102 shown in Figure 1C. The current and historical versions of the data storage program, as well as the corresponding application software, can be installed on the computer device 900. The computer device 900 includes a processor 980 and a memory 920. In some embodiments, the computer device 900 may include a display unit 940, which includes a display panel 941 for displaying a user-interactive interface, etc. In some embodiments, the computer device 900 may also include an input unit 930, which may include an image input device 931 and other input devices 932. The computer device 900 may also include a power supply 990 for powering other modules, an audio circuit 960, a near-field communication module 970, and an RF circuit 910. The audio circuit 960 specifically includes a speaker 961 and a microphone 962, etc. The computer device 900 may also include one or more sensors 950.

[0242] In various embodiments, the processor 980 is used to read a computer program and then execute the method defined by the computer program. For example, the processor 980 reads a data storage program or file, thereby running the data storage program on the computer device 900 and displaying the corresponding interface on the display unit 940. The processor 980 may include one or more general-purpose processors, and may also include one or more DSPs (Digital Signal Processors) for performing related operations to implement the technical solutions provided in the embodiments of this application.

[0243] The memory 920 generally includes main memory and secondary storage. Main memory can be random access memory (RAM), read-only memory (ROM), and cache, etc. Secondary storage can be a hard disk, optical disk, USB flash drive, floppy disk, or magnetic tape drive, etc. The memory 920 is used to store computer programs and other data. The computer programs include applications corresponding to each client, and other data may include data generated after the operating system or applications are run, including system data (e.g., operating system configuration parameters) and user data. In this embodiment, the computer program is stored in the memory 920, and the processor 980 executes the computer program in the memory 920 to implement any of the methods described in the preceding figures.

[0244] In various embodiments

[0245] In various embodiments, the number of processors 980 can be one or more, and the processors 980 and memory 920 can be coupled together or relatively independent.

[0246] In various embodiments, the processor 980 in FIG9 can be used to implement the functions of the acquisition module 801 and the processing module 802 in FIG8.

[0247] In various embodiments, the processor 980 in FIG9 can be used to implement the functions corresponding to the server or terminal device discussed above.

[0248] Those skilled in the art will understand that all or part of the steps of the above method embodiments can be implemented by a computer program. The aforementioned computer program can be stored in a computer-readable storage medium. When the computer program is executed, it performs the steps of the above method embodiments. The aforementioned storage medium includes various media capable of storing program code, such as mobile storage devices, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0249] Alternatively, if the integrated units of this invention are implemented as software functional modules and sold or used as independent products, they can also be stored in a computer-readable storage medium. Based on this understanding, the technical solutions of the embodiments of this invention, or the parts that contribute to the prior art, can be embodied in the form of software products, for example, through computer program products. These computer program products are stored in a storage medium and include computer programs used to cause a computer device to execute all or part of the methods described in the various embodiments of this invention. The aforementioned storage medium includes various media capable of storing program code, such as mobile storage devices, ROM, RAM, magnetic disks, or optical disks.

[0250] The technical features of the above embodiments can be combined in any way. For the sake of brevity, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0251] In summary, the scope of the claims should not be limited to the embodiments described in the examples above, but the specification should be taken as a whole and interpreted in the broadest possible sense.

Claims

1. A lane guidance method, characterized in that, Performed by an electronic device, including: Receive lane guidance instructions for the target road section; among which, Identify multiple candidate lane segments between the starting lane segment and the destination lane segment of the target road section; For each of the plurality of candidate lane segments, the following operations are performed: based on at least one pre-stored conditional entry probability associated with the candidate lane segment, the target entry probability of the target vehicle entering the candidate lane segment is determined; wherein, the conditional entry probability represents: in the historical driving data associated with the target road section, the conditional probability that a passing vehicle enters the candidate lane segment from an adjacent lane segment adjacent to the candidate lane segment, with the condition of entering the target lane segment; Generate lane guidance results, which include: among the multiple candidate lane segments, the guidance lane segment whose target entry probability meets the preset guidance conditions.

2. The method according to claim 1, characterized in that, Determining the target entry probability of a target vehicle entering a candidate lane segment based on at least one pre-stored conditional entry probability associated with the candidate lane segment includes: Obtain at least one reference entry probability associated with each reference lane segment contained in each pre-stored reference road interval; wherein, the reference road interval is: obtained by dividing the road with at least two preset marked lane segments; each reference entry probability represents: the conditional probability of each passing vehicle entering the reference lane segment from an adjacent lane segment of the reference lane segment within a historical time period, given that at least one marked lane segment is associated with the driving direction of the corresponding reference lane segment. From the obtained reference entry probabilities of each reference lane segment, at least one reference entry probability associated with each of the multiple candidate lane segments is extracted as the at least one conditional entry probability. For each of the multiple candidate lane segments, the following operations are performed: based on at least one conditional entry probability associated with the candidate lane segment, the target entry probability of the target vehicle entering the candidate lane segment is determined.

3. The method according to claim 2, characterized in that, Before obtaining at least one reference entry probability associated with each reference lane segment contained in each pre-stored reference road interval, the method further includes: Obtain the historical driving data of the passing vehicles; Based on the obtained historical driving data, as well as the pre-stored reference lane segments and marked lane segments, the lane segment driving sequence of each passing vehicle is determined; wherein, the lane segment driving sequence is represented by multiple reference lane segments and multiple marked lane segments connected in series, which represent the historical driving data of the corresponding passing vehicles. Based on the obtained lane segment driving sequences of each passing vehicle, at least one reference entry probability is determined for each reference lane segment included in each reference road section.

4. The method according to claim 3, characterized in that, The step of determining at least one reference entry probability associated with each reference lane segment within each reference road interval, based on the obtained lane segment driving sequences of each passing vehicle, includes: Based on each reference road section, the driving sequence of each passing vehicle in its respective lane segment is divided into at least one driving subsequence, resulting in multiple driving subsequences; wherein, the first lane segment and the last lane segment of each driving subsequence are: two marked lane segments associated with the corresponding reference road section; Based on the obtained multiple driving subsequences, at least one reference entry probability is determined for each reference lane segment contained in each reference road interval.

5. The method according to claim 3, characterized in that, The step of determining at least one reference entry probability associated with each reference lane segment within each reference road interval, based on the obtained lane segment travel sequences of each passing vehicle, includes: For each reference lane segment contained in each of the aforementioned reference road sections, the following operations shall be performed respectively: Based on the obtained lane segment driving sequence of each passing vehicle, the number of first passing vehicles entering the reference lane segment from an adjacent lane segment adjacent to the reference lane segment is counted, based on the marked lane segment associated with the driving direction of entering the corresponding reference road section, and the number of second passing vehicles entering other lane segments (excluding the reference lane segment) adjacent to the adjacent lane segment from the adjacent lane segment. Based on the obtained number of first and second passing vehicles, each passing vehicle is determined, and a reference entry probability of the reference lane segment is obtained by considering the marked lane segment associated with its driving direction in the corresponding reference road section, and the conditional probability of entering the reference lane segment from an adjacent lane segment adjacent to the reference lane segment.

6. The method according to claim 2, characterized in that, After obtaining at least one reference entry probability associated with each reference lane segment contained in each pre-stored reference road interval, the method further includes: Among the reference lane segments included in each reference road section, at least one lane segment marked with a confirmation mark is obtained; wherein, the confirmation mark indicates that a traffic sign to be confirmed is included in the corresponding lane segment to be confirmed, or between the corresponding multiple lane segments to be confirmed. From the at least one reference entry probability associated with each reference lane segment contained in each reference road interval, select at least one reference entry probability associated with each of the at least one lane segments to be confirmed. Based on at least one reference entry probability associated with each of the at least one lane segment to be confirmed, a traffic driving strategy corresponding to each of the at least one lane segment to be confirmed is determined. Based on the obtained traffic driving strategies, the unconfirmed identification of the at least one lane segment to be confirmed is updated respectively.

7. The method according to claim 6, characterized in that, The step of updating the unconfirmed identifier of the at least one lane segment to be confirmed based on the obtained traffic driving strategies includes: For each traffic driving strategy obtained, perform the following operations: When the determined traffic driving strategy matches the traffic sign represented by the corresponding unconfirmed sign, the corresponding unconfirmed sign is demarked. If the determined traffic driving strategy does not match the traffic sign represented by the corresponding unconfirmed sign, the corresponding unconfirmed sign is retained.

8. The method according to any one of claims 1 to 7, characterized in that, Receiving lane guidance instructions for a target road section includes: In response to a navigation operation triggered for a destination, a road navigation route is generated from the origin of the target vehicle to the destination; Based on the pre-stored marked lane segments and reference lane segments, determine multiple reference road sections included in the road navigation route; When it is determined that the target vehicle is about to enter a reference road section, the reference road section is taken as the target road section, and lane guidance instructions are generated for the target road section.

9. The method according to any one of claims 1 to 7, characterized in that, Each conditional entry probability is associated with a weighting coefficient, which is: the target entry probability of the target vehicle entering the candidate lane segment when the adjacent lane segment represented by the conditional entry probability of the weighting coefficient is used as a candidate lane segment. Determining the target entry probability of a target vehicle entering a candidate lane segment based on at least one pre-stored conditional entry probability associated with the candidate lane segment includes: When a candidate lane segment is associated with a conditional entry probability, the product of the conditional entry probability and the associated weight coefficient is used as the target entry probability of the target vehicle entering the candidate lane segment. When a candidate lane segment is associated with multiple conditional entry probabilities, the maximum value of the weighted sum of the multiple conditional entry probabilities, or the product of each of the multiple conditional entry probabilities and its associated weight coefficient, is taken as the target entry probability of the target vehicle entering the candidate lane segment.

10. The method according to any one of claims 1 to 7, characterized in that, Based on the obtained target entry probabilities corresponding to each of the multiple candidate lane segments, the guide lane segment whose target entry probability satisfies the guide condition is determined from among the multiple candidate lane segments, and the lane guidance result is obtained, including: Among the multiple candidate lane segments, the candidate lane segment with a target entry probability greater than a preset probability value is selected as the guide lane segment; Based on the obtained guide lane segments, the guide driving area and guide driving route of the target vehicle in the target road section are determined, and the lane guidance result is obtained.

11. The method according to claim 10, characterized in that, Based on the obtained guide lane segments, the guide driving area and guide driving route of the target vehicle on the target road section are determined to obtain lane guidance results, including: Based on the presentation area of ​​each guide lane segment in the preset navigation map, the combined area of ​​the guide lane segments is used as the guide driving area of ​​the target vehicle on the target road section. Based on the multiple guide lane segments obtained in series, a guide driving route for the target vehicle is generated on the target road section. Based on the obtained guidance driving area and guidance driving route, lane guidance results are generated.

12. The method according to any one of claims 1 to 7, characterized in that, After obtaining the lane guidance results, the following is also included: Among the pre-stored multimedia presentation templates, a target presentation template that matches the lane guidance result is selected; wherein, the multimedia presentation template is used to present content in at least one of image format, video format, or audio format; Based on the obtained target presentation template, the lane guidance results are presented.

13. A lane guidance device, characterized in that, include: Acquisition module: used to receive lane guidance instructions for a target road section and determine multiple candidate lane segments between the starting lane segment and the destination lane segment of the target road section; Processing module: For each of the plurality of candidate lane segments, perform the following operations: Determine the target entry probability of a target vehicle entering the candidate lane segment based on at least one pre-stored conditional entry probability associated with the candidate lane segment; wherein, the conditional entry probability represents: the conditional probability in historical driving data associated with the target road section, whereby a passing vehicle enters the candidate lane segment from an adjacent lane segment adjacent to the candidate lane segment, with the condition of entering the target lane segment; The processing module is further configured to: generate lane guidance results, the lane guidance results including: guidance lane segments among the plurality of candidate lane segments whose target entry probability meets preset guidance conditions.

14. A computer program product, comprising a computer program, characterized in that, When the computer program is executed by the processor, it implements the method as described in any one of claims 1 to 12.

15. A computer device, characterized in that, include: Memory, used to store program instructions; A processor is configured to invoke program instructions stored in the memory and execute the method as described in any one of claims 1 to 12 according to the obtained program instructions.

16. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores computer-executable instructions for causing a computer to perform the method as described in any one of claims 1 to 12.

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