Method for turning at intersection, and readable storage medium and intelligent device

Abstract

A method for turning at an intersection, and a readable storage medium and an intelligent device. The method comprises: acquiring an intersection redundant topology corresponding to an intersection on which planning is to be performed, wherein the intersection redundant topology is a lane link in said intersection, of which the travel probability obtained from historical manual driving trajectory data is less than a preset probability threshold, and the lane link is a topological connection relationship between different lanes that have a predecessor-successor relationship in said intersection (S101); and on the basis of the intersection redundant topology, performing path planning on said intersection, in order to realize turning of an intelligent device in an autonomous driving state at the intersection (S102). The method can avoid, when in an autonomous driving state, lane links having a low travel probability in manual driving, so that the autonomous driving state is closer to the manual driving state, thereby ensuring that in the autonomous driving state, turning at intersections is performed more safely and in greater alignment with human driving habits.

Description

Intersection turning method, readable storage medium and intelligent device This application claims priority to Chinese patent application CN202410332765.9, filed on March 18, 2024, with the invention name “Intersection turning method, readable storage medium and intelligent device”. The entire contents of the above Chinese patent application are incorporated into this application by reference. Technical Field

[0001] The present application relates to the field of autonomous driving technology, and specifically provides a method for turning at an intersection, a readable storage medium, and an intelligent device. Background Art

[0002] When vehicles and other equipment are in autonomous driving mode, there are often situations where the vehicle's behavior at intersections is not very "human," that is, it does not conform to human driving habits. For example, when there is a secondary road at the intersection, there may be a behavior of crossing multiple lanes from the leftmost lane directly to the secondary road. For another example, when turning right, there may be a situation where the vehicle makes a right-angle turn to the outermost lane and then gradually drives normally. The first situation mentioned above has the problem of not being "human," and the second situation will bring the safety risk of collision.

[0003] Accordingly, this field requires a new intersection turning solution to solve the above problems. Summary of the Invention

[0004] In order to overcome the above-mentioned defects, the present application is proposed to provide a solution or at least partially solve the problem of how to achieve safe turning at intersections in an autonomous driving mode that is more in line with human driving habits.

[0005] In a first aspect, the present application provides a method for turning at an intersection, the method being applied to a smart device, the method comprising:

[0006] Obtaining a redundant intersection topology corresponding to the intersection to be planned; wherein the redundant intersection topology is a lane link in which the driving probability of manual driving historical trajectory data at the intersection to be planned is less than a preset probability threshold; and the lane link is a topological connection relationship between different lanes having a predecessor-successor relationship at the intersection to be planned;

[0007] According to the redundant topology of the intersection, path planning is performed on the intersection to be planned, so as to enable the smart device to turn at the intersection in the automatic driving state.

[0008] In one technical solution of the above-mentioned intersection turning method, the method further includes generating the intersection redundant topology according to the following steps:

[0009] Obtaining the driving probability of each lane link at each intersection to be planned based on the manual driving history trajectory data of the intersection to be planned;

[0010] The intersection redundancy topology of each to-be-planned intersection is obtained according to the driving probability.

[0011] In one technical solution of the above-mentioned intersection turning method, obtaining the driving probability of each lane link at the intersection to be planned based on the manual driving history trajectory data of the intersection to be planned includes:

[0012] Obtain all lane links in the intersection to be planned;

[0013] For each lane link, according to the manual driving history trajectory data, obtaining a proportion of the manual driving history trajectory data traveling through the lane link to the manual driving history trajectory data traveling through all lane links;

[0014] According to the ratio, the driving probability of the lane link is obtained.

[0015] In one technical solution of the above-mentioned intersection turning method, obtaining the driving probability of the lane link according to the ratio includes:

[0016] When the ratio is less than or equal to a preset ratio threshold, determining the driving probability to be 0;

[0017] When the ratio is greater than the ratio threshold, the driving probability is determined to be 1.

[0018] In one technical solution of the above-mentioned intersection turning method, the path planning for the intersection to be planned based on the redundant topology of the intersection includes:

[0019] Obtain all lane links of the smart device at the intersection to be planned according to the lane the smart device is currently in;

[0020] The lane link corresponding to the intersection redundant topology is removed from all lane links to implement the path planning.

[0021] In one technical solution of the above-mentioned intersection turning method, obtaining the intersection redundant topology corresponding to the intersection to be planned includes:

[0022] Obtain the intersection redundancy topology through a cloud server; and send the intersection redundancy topology along with a map to the smart device to obtain the intersection redundancy topology;

[0023] Wherein, the smart device is communicatively connected to the cloud server.

[0024] In one technical solution of the above-mentioned intersection turning method, sending the intersection redundant topology along with the map to the smart device includes:

[0025] When a new version of the map is released, the redundant topology of the intersection obtained based on the manual driving history trajectory data corresponding to the previous version of the map is sent to the smart device along with the new version of the map through the cloud server.

[0026] In one technical solution of the above-mentioned intersection turning method, sending the intersection redundant topology along with the map to the smart device includes:

[0027] The redundant topology of the intersection is stored in the dynamic layer of the new version of the map through the cloud server, and the dynamic layer is sent to the smart device along with the map.

[0028] In a second aspect, a computer-readable storage medium is provided, which stores a plurality of program codes, wherein the program codes are suitable for being loaded and run by a processor to execute the intersection turning method described in any one of the technical solutions of the above-mentioned intersection turning method.

[0029] In a third aspect, a smart device is provided, comprising:

[0030] at least one processor;

[0031] and, a memory communicatively coupled to the at least one processor;

[0032] Wherein, a computer program is stored in the memory, and when the computer program is executed by the at least one processor, the intersection turning method described in any one of the technical solutions of the above-mentioned intersection turning method is implemented.

[0033] The above one or more technical solutions of this application have at least one or more of the following beneficial effects:

[0034] In implementing the technical solution of this application, this application can obtain the intersection redundancy topology corresponding to the intersection to be planned, and perform path planning for the intersection to be planned based on the intersection redundancy topology, thereby enabling the intelligent device to turn at the intersection in the autonomous driving state. Through the above configuration, since the intersection redundancy topology in this application is the lane link where the driving probability of the manual driving historical trajectory data at the intersection to be planned is less than a preset probability threshold, the path planning of the planned path based on the intersection redundancy topology can effectively avoid the lane link with a low driving probability of manual driving in the autonomous driving state, thereby achieving an autonomous driving state closer to the manual driving state, and ensuring that the autonomous driving state is more in line with human driving habits and safe turning at the intersection. BRIEF DESCRIPTION OF THE DRAWINGS

[0035] The disclosure of this application will become more easily understood with reference to the accompanying drawings. Those skilled in the art will readily appreciate that these drawings are for illustrative purposes only and are not intended to limit the scope of protection of this application. Among them:

[0036] FIG1 is a schematic flow chart of the main steps of a method for turning at an intersection according to an embodiment of the present application;

[0037] FIG2 is a schematic diagram of a lane link where a main road turns into a secondary road according to an example of an embodiment of the present application;

[0038] FIG3 is a schematic diagram of a lane link for turning right from lane 1 according to an example of an embodiment of the present application;

[0039] FIG4 is a schematic diagram of the main implementation architecture of a method for turning at an intersection according to an embodiment of the present application;

[0040] FIG5 is a schematic diagram of a connection relationship between a memory and a processor of a smart device according to an embodiment of the present application. DETAILED DESCRIPTION

[0041] Some embodiments of the present application are described below with reference to the accompanying drawings. Those skilled in the art should understand that these embodiments are only used to explain the technical principles of the present application and are not intended to limit the scope of protection of the present application.

[0042] In the description of this application, "module" and "processor" may include hardware, software, or a combination of both. A module may include hardware circuitry, various suitable sensors, communication ports, and memory. It may also include software components, such as program code, or a combination of software and hardware. A processor may be a central processing unit, a microprocessor, an image processor, a digital signal processor, or any other suitable processor. A processor has data and / or signal processing capabilities. A processor may be implemented in software, hardware, or a combination of both. Non-transitory computer-readable storage media include any suitable medium capable of storing program code, such as magnetic disks, hard disks, optical disks, flash memory, read-only memory, random access memory, etc. The term "A and / or B" refers to all possible combinations of A and B, such as only A, only B, or both A and B. The terms "at least one of A or B" or "at least one of A and B" have similar meanings to "A and / or B" and may include only A, only B, or both A and B. The singular forms "a" and "the" may also include the plural forms.

[0043] An automated driving system (ADS) is a system that continuously performs all dynamic driving tasks (DDT) within its operational domain design (ODD). Specifically, the system is only allowed to fully assume the task of autonomous vehicle control under specified appropriate driving scenarios. When the vehicle meets the ODD conditions, the system is activated, replacing the human driver as the vehicle's primary driver. The DDT refers to the continuous lateral (left and right steering) and longitudinal motion control (acceleration, deceleration, and constant speed) of the vehicle, as well as the detection and response to objects and events in the vehicle's driving environment. The ODD refers to the conditions under which the automated driving system can operate safely. These conditions can include geographic location, road type, speed range, weather, time of day, and national and local traffic laws and regulations.

[0044] Referring to FIG. 1 , FIG. 1 is a flow chart illustrating the main steps of a method for turning at an intersection according to an embodiment of the present application. As shown in FIG. 1 , the method for turning at an intersection in the embodiment of the present application is applied to a smart device and mainly includes the following steps S101 to S102 .

[0045] Step S101: Obtain a redundant intersection topology corresponding to the intersection to be planned. The redundant intersection topology is a lane link in which the driving probability of the manual driving historical trajectory data at the intersection to be planned is less than a preset probability threshold. The lane link is a topological connection relationship between different lanes in the intersection to be planned that have a predecessor-successor relationship.

[0046] In this embodiment, the intersection redundancy topology corresponding to the intersection to be planned can be obtained, that is, the lane links whose driving probability of the manual driving historical trajectory data in the intersection to be planned is less than a preset probability threshold are obtained.

[0047] In one embodiment, the smart device may be a driving device, a smart car, a robot, or the like.

[0048] In one embodiment, the intersection to be planned may be a left-turn intersection, a right-turn intersection, an intersection where multiple lanes merge into a fewer lanes, an intersection where a main road merges into a secondary road, etc.

[0049] In one embodiment, a server can obtain the intersection redundancy topology of each intersection to be planned based on the manual driving history trajectory data. The server can be a server installed on a smart device or a cloud server.

[0050] Step S102: Path planning is performed for the planned intersection based on the redundant topology of the intersection, so as to enable the smart device to turn at the intersection in the automatic driving state.

[0051] In this embodiment, the planned path can be planned based on the redundant topology of the intersection to avoid turning at the intersection through the redundant topology of the intersection in the automatic driving state.

[0052] Based on the above steps S101 and S102, the embodiment of the present application can obtain the intersection redundancy topology corresponding to the intersection to be planned, and perform path planning for the intersection to be planned based on the intersection redundancy topology, thereby enabling the smart device to turn at the intersection in the autonomous driving state. Through the above configuration, since the intersection redundancy topology in the embodiment of the present application is the lane link whose driving probability of the manual driving historical trajectory data at the intersection to be planned is less than a preset probability threshold, the path planning for the planned path based on the intersection redundancy topology can effectively avoid the lane link with a low driving probability of manual driving in the autonomous driving state, thereby achieving an autonomous driving state closer to the manual driving state, ensuring that the autonomous driving state is more in line with human driving habits and safe turning at the intersection.

[0053] The specific steps of generating the redundant topology of the intersection, step S101 and step S102 are further described below.

[0054] In one implementation of the embodiment of the present application, the present application may further include the following steps S201 and S202 to generate a redundant intersection topology:

[0055] Step S201: Obtain the driving probability of each lane link at the intersection to be planned based on the manual driving history trajectory data at each intersection to be planned.

[0056] In this embodiment, step S201 may include the following steps S2011 to S2013:

[0057] Step S2011: Obtain all lane links in the intersection to be planned.

[0058] In this implementation, all lane links in the intersection to be planned can be obtained. The lane links to be mined can be defined. For example, if a lane has three or more preceding lanes, or if a lane has three or more subsequent lanes, these can be used as scenarios for obtaining the intersection's redundant topology, thereby obtaining all lane links in the current scenario. Lanes corresponding to all preceding separation points of each lane can be obtained from the map as all lane links; alternatively, lanes corresponding to all subsequent separation points of each lane can be obtained from the map as all lane links.

[0059] Please refer to Figures 2 and 3. Figure 2 is a schematic diagram of a lane link from a main road to a secondary road according to an example embodiment of the present application; Figure 3 is a schematic diagram of a lane link for a right turn from lane 1 according to an example embodiment of the present application. As shown in Figure 2, lanes 1 to 9 represent nine lanes, and lanes 1-6, 2-7, 3-8, 4-9, 1-5, 2-5, 3-5, and 4-5 represent all lane links at the intersection. Lane links 1-6, 2-7, 3-8, and 4-9 are for straight-through traffic, while lane links 1-5, 2-5, 3-5, and 4-5 are for main-to-secondary traffic. Without any intervention, in autonomous driving mode, lane links 1-5, 2-5, 3-5, and 4-5 could all be planned paths for the intelligent device. However, lane links 1-5, 2-5, and 3-5 all carry the risk of collision with straight-moving vehicles and are inconsistent with human driver habits. Lane link 1-5, in particular, is very likely to present a collision risk as it passes through three lanes. The lane links for turning from the main road to the auxiliary road in FIG2 can be specifically referred to in Table 1 below.

[0060] Table 1 Lane link table of the main road turning into the auxiliary road in Figure 2

[0061] Among them, lane represents lane, and map version is the map version corresponding to the historical trajectory data of manual driving.

[0062] As shown in Figure 3, lanes 1 through 7 are numbered: 1 is the right-turn lane, and 2 through 7 are lanes for going straight after turning right. 1-2, 1-3, 1-4, 1-5, 1-6, and 1-7 are all lane links at the intersection. Without any intervention, in autonomous driving mode, selecting lanes 2 or 3 would result in a large turning radius and the potential for a collision with a vehicle going straight, which is not in line with human driver habits.

[0063] Step S2012: for each lane link, according to the manual driving history trajectory data, obtain the proportion of the manual driving history trajectory data traveling through the lane link in the manual driving history trajectory data traveling through all lane links.

[0064] In this embodiment, the proportion of the manual driving history trajectory data of each lane link in the manual driving history trajectory data of all lane links can be counted. The manual driving history trajectory data needs to meet the following conditions:

[0065] 1. Changing from one lane to another;

[0066] 2. The trajectory point is within the scope of the planned intersection;

[0067] 3. Non-autonomous driving state;

[0068] 4.The map version is a historical version.

[0069] Step S2013: Obtain the driving probability of the lane link according to the ratio.

[0070] In this embodiment, step S2013 may further include the following steps S20131 and S20132:

[0071] Step S20131: When the ratio is less than or equal to the preset ratio threshold, the driving probability is determined to be 0

[0072] Step S20132: When the ratio is greater than the ratio threshold, the driving probability is determined to be 1.

[0073] In this embodiment, the travel probability can be determined based on the calculated ratio. That is, if the ratio is less than or equal to a preset ratio threshold, the travel probability of the corresponding lane link can be set to 0; if the ratio is greater than the ratio threshold, the travel probability of the corresponding lane link can be set to 1. Those skilled in the art can set the ratio threshold according to actual application needs.

[0074] Continuing to refer to FIG. 2 , Table 2 shows the driving probability of lane links when the main road turns to the auxiliary road in FIG. 2 .

[0075] Table 2 Driving probability table of lane links from main road to auxiliary road in Figure 2

[0076] Step S202: Obtaining the intersection redundancy topology of each intersection to be planned based on the driving probability.

[0077] In this embodiment, lane links with a driving probability less than a preset probability threshold can be used as intersection redundancy topology for the intersection to be planned. Those skilled in the art can set the probability threshold value according to actual application needs.

[0078] In one implementation of the embodiment of the present application, step S101 may be further configured as follows:

[0079] Obtain the intersection redundancy topology through the cloud server; and send the intersection redundancy topology along with the map to the smart device to obtain the intersection redundancy topology; wherein the smart device is in communication with the cloud server.

[0080] In this embodiment, when a new version of the map is released, the intersection redundant topology obtained based on the manual driving history trajectory data corresponding to the previous version of the map can be sent to the smart device along with the new version of the map through the cloud server.

[0081] In one embodiment, the redundant topology of intersections can be stored in a dynamic layer of a new version of the map, and sent to smart devices along with the dynamic layer and the map, so that the smart devices can eliminate the redundant topology of intersections from path planning based on the redundant topology of intersections in the dynamic layer.

[0082] Continuing with Figure 2, Table 3 shows how, when a new map version is released, redundant intersection topologies obtained based on the manual driving history trajectory data corresponding to the previous map version are merged into the dynamic layer of the new map version. If the redundant intersection topology already exists in the dynamic layer of the new map version, it can be updated. If it does not exist in the dynamic layer of the new map version, the obtained redundant intersection topology can be added to the dynamic layer.

[0083] Table 3 Redundant topology information table of intersections stored in dynamic layers

[0084] Among them, the map version in Table 3 is the new version.

[0085] If the redundant topology of the intersection fails to be stored in the dynamic layer, the failed data can be saved as a file for manual confirmation or deletion.

[0086] In one implementation of the embodiment of the present application, step S102 may further include the following steps S1021 and S1022:

[0087] Step S1021: According to the lane in which the smart device is currently located, all lane links of the smart device at the intersection to be planned are obtained.

[0088] Step S1022: removing lane links corresponding to the redundant topology of the intersection from all lane links to implement path planning.

[0089] In this embodiment, when the smart device enters autonomous driving mode, it can obtain all lane links at the intersection to be planned based on the smart device's current lane and eliminate lane links corresponding to redundant intersection topology. Specifically, when the smart device performs route calculation, it can eliminate lane links with a zero driving probability from the map's dynamic layer. These zero-probability lane links are not considered during route planning, thereby achieving a safer and more human-friendly driving experience.

[0090] Please refer to Figure 4 below. Taking the intelligent device as a vehicle and the intersection redundant topology as generated by the cloud server as an example, the intersection turning method of the present application is explained. Figure 4 is a schematic diagram of the main implementation architecture of the intersection turning method according to an embodiment of the present application. As shown in Figure 4, the cloud server performs redundant scene data extraction services based on the map data of the data source in the data storage to obtain the manual driving history trajectory data that meets the conditions; the probability calculation service calculates the driving probability of each lane link based on the manual driving history trajectory data through the data development and governance platform to obtain the calculation results; the calculation results are stored in the data source, and the inheritance service is implemented through the application data platform, and the dynamic layer service in the new version of the map is updated based on the calculation results of the driving probability. The updated dynamic layer service is sent to the vehicle end, and the map module of the vehicle end receives the data and performs path planning (PNC) based on the received data to realize the vehicle control behavior in the automatic driving state.

[0091] It should be pointed out that although the various steps in the above embodiments are described in a specific order, those skilled in the art will understand that in order to achieve the effect of the present application, different steps do not have to be performed in such an order. They can be performed simultaneously (in parallel) or in other orders. These changes are within the scope of protection of the present application.

[0092] It will be understood by those skilled in the art that all or part of the processes in the method for implementing the above embodiment of the present application can also be completed by instructing the relevant hardware through a computer program. The computer program can be stored in a computer-readable storage medium. When the computer program is executed by a processor, it can implement the steps of each of the above method embodiments. The computer program includes computer program code, which can be in source code form, object code form, executable file or some intermediate form. The computer-readable storage medium can include: any entity or device, medium, USB flash drive, mobile hard disk, magnetic disk, optical disk, computer memory, read-only memory, random access memory, electric carrier signal, telecommunication signal and software distribution medium that can carry the computer program code. It should be noted that the content contained in the computer-readable storage medium can be appropriately increased or decreased according to the requirements of legislation and patent practice in the jurisdiction. For example, in some jurisdictions, according to legislation and patent practice, computer-readable storage media do not include electric carrier signals and telecommunication signals.

[0093] Furthermore, the present application also provides a computer-readable storage medium. In a computer-readable storage medium embodiment according to the present application, the computer-readable storage medium can be configured to store a program for executing the intersection turning method of the above-mentioned method embodiment, and the program can be loaded and run by the processor to implement the above-mentioned intersection turning method. For ease of explanation, only the parts related to the embodiment of the present application are shown. For specific technical details not disclosed, please refer to the method part of the embodiment of the present application. The computer-readable storage medium can be a storage device formed by various electronic devices. Optionally, the computer-readable storage medium in the embodiment of the present application is a non-transitory computer-readable storage medium.

[0094] Furthermore, the present application also provides an intelligent device. In an embodiment of an intelligent device according to the present application, the intelligent device may include at least one processor; and a memory communicatively connected to the at least one processor; wherein a computer program is stored in the memory, and when the computer program is executed by the at least one processor, the method described in any of the above embodiments is implemented. The intelligent device of the present application may include driving devices, smart cars, robots and other devices. Referring to Figure 5, Figure 5 is a schematic diagram of the connection relationship between the memory and the processor of an intelligent device according to an embodiment of the present application, and Figure 5 exemplarily shows that the memory and the processor of the intelligent device can be communicatively connected via a bus.

[0095] In some embodiments of the present application, the smart device further includes at least one sensor for sensing information. The sensor is communicatively coupled to any of the types of processors described herein. Optionally, the smart device further includes an autonomous driving system for guiding the smart device to drive autonomously or with assistance. The processor communicates with the sensor and / or autonomous driving system to perform the method described in any of the above embodiments.

[0096] Furthermore, it should be understood that since the configuration of each module is merely for the purpose of illustrating the functional units of the apparatus of the present application, the physical devices corresponding to these modules may be the processor itself, or a portion of the software in the processor, a portion of the hardware, or a combination of software and hardware. Therefore, the number of modules in the figure is merely illustrative.

[0097] Those skilled in the art will appreciate that the various modules in the device can be adaptively split or merged. Such splitting or merging of specific modules will not cause the technical solution to deviate from the principles of this application. Therefore, the technical solutions after splitting or merging will fall within the scope of protection of this application.

[0098] The relevant user personal information that may be involved in the various embodiments of this application is strictly in accordance with the requirements of laws and regulations, following the principles of legality, legitimacy and necessity, and based on the reasonable purposes of business scenarios, to process the personal information that users actively provide during the use of products / services or generated due to the use of products / services, as well as the personal information obtained with the user's authorization.

[0099] The user personal information processed in this application will vary depending on the specific product / service scenario and will be based on the specific scenario in which the user uses the product / service. This may involve the user's account information, device information, driving information, vehicle information, or other related information. The applicant will treat the user's personal information and its processing with a high degree of diligence.

[0100] This application attaches great importance to the security of user personal information and has taken reasonable and feasible security protection measures that comply with industry standards to protect user information and prevent personal information from being accessed, disclosed, used, modified, damaged or lost without authorization.

[0101] Thus far, the technical solutions of the present application have been described in conjunction with the preferred embodiments shown in the accompanying drawings. However, it is readily understood by those skilled in the art that the scope of protection of the present application is obviously not limited to these specific embodiments. Without departing from the principles of the present application, those skilled in the art may make equivalent changes or substitutions to the relevant technical features, and the technical solutions after such changes or substitutions will fall within the scope of protection of the present application.

Claims

1. A method for turning at an intersection, characterized in that: The method is applied to a smart device and includes: Obtaining a redundant intersection topology corresponding to the intersection to be planned; wherein the redundant intersection topology is a lane link in which the driving probability of manual driving historical trajectory data at the intersection to be planned is less than a preset probability threshold; and the lane link is a topological connection relationship between different lanes having a predecessor-successor relationship at the intersection to be planned; According to the redundant topology of the intersection, path planning is performed on the intersection to be planned, so as to enable the smart device to turn at the intersection in the automatic driving state.

2. The intersection turning method according to claim 1, characterized in that: The method further comprises generating the intersection redundancy topology according to the following steps: Obtaining the driving probability of each lane link at each intersection to be planned based on the manual driving history trajectory data of the intersection to be planned; The intersection redundancy topology of each to-be-planned intersection is obtained according to the driving probability.

3. The intersection turning method according to claim 2, characterized in that: The step of obtaining the driving probability of each lane link at the intersection to be planned based on the manual driving history trajectory data at the intersection to be planned includes: Obtain all lane links in the intersection to be planned; For each lane link, according to the manual driving history trajectory data, obtaining a proportion of the manual driving history trajectory data traveling through the lane link to the manual driving history trajectory data traveling through all lane links; According to the ratio, the driving probability of the lane link is obtained.

4. The intersection turning method according to claim 3, characterized in that: The obtaining, according to the ratio, the driving probability of the lane link includes: When the ratio is less than or equal to a preset ratio threshold, determining the driving probability to be 0; When the ratio is greater than the ratio threshold, the driving probability is determined to be 1.

5. The intersection turning method according to claim 1, characterized in that: The performing path planning for the intersection to be planned according to the intersection redundant topology includes: Obtain all lane links of the smart device at the intersection to be planned according to the lane the smart device is currently in; The lane link corresponding to the intersection redundant topology is removed from all lane links to implement the path planning.

6. The intersection turning method according to any one of claims 1 to 5, characterized in that: The obtaining of the intersection redundancy topology corresponding to the intersection to be planned includes: Obtain the intersection redundancy topology through a cloud server; and send the intersection redundancy topology along with a map to the smart device to obtain the intersection redundancy topology; Wherein, the smart device is communicatively connected to the cloud server.

7. The intersection turning method according to claim 6, characterized in that: The sending of the redundant topology of the intersection along with the map to the smart device includes: When a new version of the map is released, the redundant topology of the intersection obtained based on the manual driving history trajectory data corresponding to the previous version of the map is sent to the smart device along with the new version of the map through the cloud server.

8. The intersection turning method according to claim 6 or 7, characterized in that: The sending of the redundant topology of the intersection along with the map to the smart device includes: The redundant topology of the intersection is stored in the dynamic layer of the new version of the map through the cloud server, and the dynamic layer is sent to the smart device along with the map.

9. A computer-readable storage medium storing a plurality of program codes, characterized in that: The program code is suitable for being loaded and run by a processor to execute the intersection turning method according to any one of claims 1 to 8.

10. A smart device, characterized in that: The smart device includes: at least one processor; and, a memory communicatively coupled to the at least one processor; Wherein, a computer program is stored in the memory, and when the computer program is executed by the at least one processor, the intersection turning method according to any one of claims 1 to 8 is implemented.

Images