Path planning method and related equipment

Abstract

The invention relates to the technical field of automatic driving, in particular to a path planning method and related equipment. The method comprises the following steps: determining a path set according to a starting point and an ending point; the path set comprises various potential paths with different paths; determining a path score of each type of potential path in the path set according to the traffic information corresponding to each type of potential path; copying the potential paths of which the path scores are greater than the extinction threshold according to a preset copying probability; and according to a preset extinction probability, eliminating the potential paths of which the path scores are smaller than or equal to the extinction threshold value, so as to iteratively update the potential paths contained in the path set to obtain an updated path set, and when it is detected that the updated path set meets a preset condition, determining the most potential paths in the updated path set as the optimal path from the starting point to the ending point.

Description

Technical Field

[0001] This application relates to the field of autonomous driving technology, and in particular to a path planning method and related equipment. Background Technology

[0002] In autonomous driving technology, path planning is one of the core problems. It is crucial for vehicle safety and driving efficiency. Graph search-based algorithms and heuristic algorithms are widely used in path planning. However, in complex and ever-changing traffic environments, these two algorithms suffer from problems such as finding local optima and low computational efficiency. Furthermore, graph search-based and heuristic algorithms are not sufficiently adaptable to dynamic environments. Therefore, obtaining an effective, fast, and dynamic path planning algorithm is a pressing issue that needs to be addressed. Summary of the Invention

[0003] In view of this, this application provides a path planning method and related equipment, which uses a microbial community optimization algorithm to perform path planning and applies it to autonomous driving technology, thereby overcoming the problem of poor dynamic environment adaptation in the prior art.

[0004] In a first aspect, embodiments of the present invention provide a path planning method, including:

[0005] A set of paths is determined based on the starting point and the ending point; the set of paths contains potential paths of different types.

[0006] The path score for each type of potential path in the path set is determined based on the traffic information corresponding to each type of potential path;

[0007] Based on a preset replication probability, potential paths with path scores greater than the extinction threshold are replicated; and based on a preset extinction probability, potential paths with path scores less than or equal to the extinction threshold are excluded, so as to iteratively update the potential paths contained in the path set and obtain an updated path set.

[0008] When the updated path set is detected to meet the preset conditions, the type of potential path with the largest number in the updated path set is determined as the best path from the starting point to the destination.

[0009] In one possible implementation, determining the path score for each class of potential paths in the path set based on traffic information corresponding to each class of potential paths includes:

[0010] Based on the traffic information corresponding to each type of potential path, determine one or more of the following factors for each type of potential path: path length, travel time, congestion level, safety, and energy consumption.

[0011] The path score for each type of potential path is determined based on one or more of the following: path length, travel time, congestion level, safety, and energy consumption.

[0012] In one possible implementation, the path score for each type of potential path is determined based on one or more combinations of path length, travel time, congestion level, safety, and energy consumption, including:

[0013] Based on the path length, travel time, congestion level, safety, and energy consumption, a path length score, travel time score, congestion level score, safety score, and energy consumption score are determined for each type of potential path.

[0014] The weighted calculation is performed on one or more of the route length score, travel time score, congestion score, safety score, and energy consumption score.

[0015] The path score for each type of potential path is determined based on the results of the weighted calculation.

[0016] In one possible implementation, the preset conditions include at least one of the following: the number of iterations is greater than a first threshold, there is a potential path with a first ratio greater than a second threshold, and the update rate of the potential paths contained in the path set is less than or equal to a third threshold; wherein, the first ratio is the ratio of the number of potential paths of the same type to the total number of potential paths contained in the path set.

[0017] In one possible implementation, the method further includes:

[0018] Determine whether the updated path set satisfies the path expansion condition;

[0019] If the updated path set satisfies the path expansion condition, an expanded path is generated; the expanded path is different from all potential paths included in the updated path set.

[0020] The expanded path is added as a potential path to the updated path set for iterative updates.

[0021] In one possible implementation, generating the extended path includes:

[0022] The potential paths contained in the updated path set are locally adjusted to obtain the extended paths.

[0023] In one possible implementation, generating the extended path includes:

[0024] The potential paths contained in the updated path set are adjusted globally to obtain the expanded paths.

[0025] In a second aspect, embodiments of the present invention provide a path planning device, comprising:

[0026] The path set determination module is used to determine a path set based on the starting point and the ending point; the path set contains multiple potential paths with different types of paths.

[0027] The scoring module is used to determine the path score for each type of potential path in the path set based on the traffic information corresponding to each type of potential path.

[0028] An iterative module is used to copy potential paths with path scores greater than a disappearance threshold according to a preset copy probability; and to exclude potential paths with path scores less than or equal to the disappearance threshold according to a preset disappearance probability, so as to iteratively update the potential paths contained in the path set and obtain an updated path set.

[0029] The optimal path determination module is used to determine the most numerous potential path in the updated path set as the optimal path from the starting point to the ending point when the updated path set is detected to meet the preset conditions.

[0030] Thirdly, embodiments of the present invention provide an electronic device, comprising:

[0031] At least one processor; and

[0032] At least one memory communicatively connected to the processor, wherein:

[0033] The memory stores program instructions that can be executed by the processor, and the processor can execute the method described in the first aspect by calling the program instructions.

[0034] Fourthly, embodiments of the present invention provide a computer-readable storage medium storing computer instructions that cause the computer to perform the method described in the first aspect.

[0035] In this embodiment of the invention, the shortcomings of dynamic optimization are overcome by simulating the process of bacteria searching for food, such as replication, death, chemotaxis, and migration, in the microbial community optimization algorithm, so that the path planning algorithm has excellent search capabilities in multimodal and dynamic optimization problems. Attached Figure Description

[0036] To more clearly illustrate the technical solutions of the embodiments of this application, the drawings used in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0037] Figure 1 A flowchart of a path planning method provided in an embodiment of the present invention;

[0038] Figure 2 This is a schematic diagram of the structure of a path planning device provided in an embodiment of the present invention;

[0039] Figure 3 This is a schematic diagram of the structure of an electronic device provided in an embodiment of the present invention. Detailed Implementation

[0040] To better understand the technical solution of this application, the embodiments of this application will be described in detail below with reference to the accompanying drawings.

[0041] It should be understood that the described embodiments are merely some, not all, of the embodiments in this application. All other embodiments obtained by those skilled in the art based on the embodiments in this application without inventive effort are within the scope of protection of this application.

[0042] The terminology used in the embodiments of this application is for the purpose of describing particular embodiments only and is not intended to be limiting of this application. The singular forms “a,” “the,” and “the” used in the embodiments of this application and the appended claims are also intended to include the plural forms unless the context clearly indicates otherwise.

[0043] It should be understood that the term "and / or" used in this article is merely a description of the relationship between related objects, indicating that three relationships can exist. For example, A and / or B can represent: A existing alone, A and B existing simultaneously, or B existing alone. Additionally, the character " / " in this article generally indicates that the preceding and following related objects have an "or" relationship.

[0044] Bioinspired algorithms, due to their unique global search capabilities and excellent parallel processing characteristics, have been proposed for solving complex optimization problems. Among them, the microbial community optimization algorithm, inspired by the foraging behavior of bacteria in nature, has been proven to have excellent search capabilities in multimodal and dynamic optimization problems by simulating processes such as bacterial chemotaxis, replication, death, and migration. Based on this, this invention provides a path planning method that applies the microbial community optimization algorithm to the field of path planning to address the problem of poor adaptability to dynamic environments in existing technologies. Figure 1 A flowchart illustrating a path planning algorithm provided in an embodiment of the present invention. Figure 1 As shown, the method includes:

[0045] Step 101: Determine the path set based on the starting point and the ending point. The path set contains potential paths of different types.

[0046] This process involves randomly generating multiple potential paths from the start point to the end point to obtain a path set. Potential paths with the same path form a class of potential paths, and all classes of potential paths are distinct. Alternatively, traditional heuristic algorithms, DiJkstra's algorithm, or AI algorithms can be used to generate multiple different potential paths from the start point to the end point. This step is crucial in the bacterial community optimization algorithm for generating an initial bacterial population; the path set corresponds to the bacterial population. Each class of potential paths corresponds to each bacterium within the bacterial population.

[0047] Step 102: Determine the path score for each type of potential path in the path set based on the traffic information corresponding to each type of potential path.

[0048] This step corresponds to the step of evaluating nutritional value in the microbial community optimization algorithm. It can calculate the nutritional value (path score) of each bacterium (potential path) based on traffic information to evaluate the quality of each bacterium (potential path).

[0049] Based on the traffic information corresponding to each type of potential path, one or more of the following parameters can be determined: path length, travel time, congestion level, safety, and energy consumption. Traffic information can be real-time traffic data. Path length is the length of the potential path. Travel time is the time required for the vehicle to travel the entire potential path. Congestion level is the real-time congestion situation of the potential path. Safety is the real-time safety of the potential path. Energy consumption is the electricity or fuel consumption of the vehicle to complete the entire potential path.

[0050] Then, the path score for each type of potential path can be determined based on one or more of the following factors: path length, travel time, congestion level, safety, and energy consumption.

[0051] Specifically, each type of potential route can be assigned a route length score, travel time score, congestion score, safety score, and energy consumption score based on route length, travel time, congestion level, safety score, and energy consumption. A shorter potential route receives a higher route length score. A shorter travel time receives a higher travel time score. A lower congestion level receives a higher congestion score. A safer potential route receives a higher safety score. A lower energy consumption potential route receives a higher energy consumption score.

[0052] In some embodiments, the path length score can be calculated using the formula v(r) = -k_r * L, where k_r is the path weight coefficient and L is the path length. The travel time score can be calculated using the formula v(t) = -k_t * T, where k_t is the travel time weight coefficient and T is the travel time. The congestion level score can be calculated using the formula v(c) = -k_c * C, where k_c is the congestion level weight coefficient and C is the congestion level. The safety performance score can be calculated using the formula v(s) = k_s * S, where k_s is the safety weight coefficient and S is the safety rating. The energy consumption score can be calculated using the formula v(e) = -k_e * E, where k_e is the energy consumption weight coefficient and E is the energy consumption rating.

[0053] Then, a weighted calculation is performed on one or more of the following: route length score, travel time score, congestion score, safety score, and energy consumption score. The route score for each type of potential route is determined based on the weighted calculation result. Optionally, the route score of a potential route can be calculated using the formula v(all) = In itValue + v(r) + v(t) + v(c) + v(s) + v(e). Here, in itValue is an offset introduced for ease of calculation, ensuring that the final route score is a positive number.

[0054] Step 103: Copy potential paths with path scores greater than the extinction threshold according to a preset replication probability. Also, exclude potential paths with path scores less than or equal to the extinction threshold according to a preset extinction probability, to iteratively update the potential paths in the path set, resulting in an updated path set.

[0055] This step corresponds to the replication and extinction processes in bacterial community optimization algorithms. Bacteria are sorted according to their nutritional value; high-nutritional-value bacteria are retained and probabilistically replicated based on their replication rate, thereby increasing the proportion of high-quality pathways in the community. Conversely, bacteria with lower nutritional value are probabilistically eliminated based on their extinction rate, thus reducing the proportion of harmful bacteria in the community.

[0056] In some embodiments, when generating multiple types of potential paths based on the start and end points in the initial stage, each type of potential path contains 1 potential path. After copying and elimination, the number of potential paths in each type of potential path will change. Furthermore, the paths of multiple potential paths corresponding to each type of potential path are identical. For example, in the initial stage, two types of potential paths are determined based on the start and end points, namely type A and type B. Type A potential paths contain potential path 'a', and type B potential paths contain potential path 'b'. After one copying and elimination process, type A is successfully copied, while type B is not. At this point, only type A potential paths remain in the path set. Type A potential paths now include potential path 'a' and potential path 'a' obtained by copying potential path 'a'. Since potential path 'a' is obtained by copying potential path 'a', potential path 'a' is completely identical to potential path 'a'.

[0057] For example, the preset replication probability is 0.8, the preset extinction probability is 0.9, and the extinction threshold is 0.6. That is, potential paths with a path score higher than the extinction threshold of 0.6 are replicated and retained. Potential paths with a path score equal to or lower than the extinction threshold of 0.6 are eliminated. Assume there are four different types of potential paths: a, b, c, and d. Their path scores are 0.9, 0.7, 0.5, and 0.3, respectively. Paths a and b have path scores higher than the extinction threshold and are retained and probabilistically replicated. The replication probability of a is: preset replication probability × path score = 0.8 × 0.9 = 0.72. The replication probability of b is: preset replication probability × path score = 0.8 × 0.7 = 0.56. Paths c and d are probabilistically eliminated because their path scores are lower than the extinction threshold. The extinction probability of c is: preset extinction probability × (1 - path score) = 0.9 × (1 - 0.5) = 0.45. The probability of path d disappearing is: preset disappearance probability × (1 - path score) = 0.9 × (1 - 0.3) = 0.63. After the first copy and disappearance (i.e., the first iteration update), a is successfully copied, b is copied but fails to copy, c fails to disappear, and d is successfully disappeared. Therefore, after copying a and deleting d, the potential paths contained in the path set after the first iteration update are: a, a_copy, b, and c. Here, a_copy is a copy of a.

[0058] Step 104: When the updated path set is detected to meet the preset conditions, the type of potential path with the largest number in the updated path set is determined as the best path from the start point to the end point.

[0059] The preset conditions can be implemented as follows: the number of iterations exceeds a first threshold, there exists a potential path with a first ratio greater than a second threshold, and the update rate of the potential paths included in the path set is less than or equal to a third threshold. The first ratio is the ratio of the number of potential paths of the same type to the total number of potential paths included in the path set. The iteration termination condition is met when the proportion of a certain bacterial species in the total bacterial population after iterative updates exceeds the first ratio, which can be set to 80%–90%. Alternatively, the iteration termination condition is met when the number of iterations reaches the maximum first threshold. Or, the iteration termination condition is met when the quality of the solution no longer significantly improves, i.e., the update rate of the potential paths included in the path set is less than or equal to the third threshold. If the preset conditions are not met, iterative updates continue.

[0060] In some embodiments, random perturbations can be introduced during the iterative update process to avoid getting trapped in local optima. Specifically, it can be determined whether the updated path set satisfies the path expansion condition. If the updated path set satisfies the path expansion condition, an expanded path is generated. The expanded path is different from all potential paths included in the updated path set. The expanded path is then added as a potential path to the updated path set for continued iterative updates. The path expansion condition can be specifically implemented as an iteration count threshold. That is, path expansion is performed once after every certain number of iterations.

[0061] Path expansion can be divided into two types: partial updates of existing potential paths and full updates of existing potential paths. Partial updates involve adjusting the routes of potential paths within the updated path set to obtain the expanded paths.

[0062] The local update method corresponds to the chemotaxis process in bacterial community optimization algorithms. Chemotaxis is a local search process that simulates the behavior of bacteria swimming to find nutrient-rich areas. In this embodiment of the invention, chemotaxis involves attempting small changes to existing potential paths. Each bacterium attempts a local search, fine-tuning the path it represents to explore new potential paths that may be more advantageous nearby. For example, in bacterium 1 (i.e., potential path 1), because AX, XY, YD, the potential path is AXYD. Then AZ and ZD are discovered, so the new extended path found is AZD.

[0063] The global update method involves globally adjusting the routes of potential paths within the updated path set to obtain expanded paths. This global update method corresponds to the migration process in bacterial community optimization algorithms. Migration is a global search process and can be viewed as a diversity introduction mechanism. It simulates a situation in nature where certain bacterial populations are wiped out due to sudden events, while other bacteria randomly spread to new locations. In path planning, this can be analogized to completely abandoning some paths and randomly generating some entirely new paths, or making significant modifications to existing paths.

[0064] In some embodiments, the types of potential paths contained in the path set can be updated simultaneously through migration and chemotaxis. The frequency of migration can be less than the frequency of chemotaxis, that is, the number of iterations required to trigger migration is greater than the number of iterations required to trigger chemotaxis.

[0065] In some embodiments, the chemotaxis process is implemented based on a chemotaxis step size. The chemotaxis step size refers to the size of the movement steps bacteria take when searching for a better path in a microbial community optimization algorithm. The chemotaxis step size determines the distance the bacteria move when searching for a new path. It also determines the distance the bacteria move during each local search. A larger chemotaxis step size helps explore farther regions, enhancing global search capabilities. A smaller step size helps perform a more refined search within the current region, improving the quality of the solution. The chemotaxis step size can be dynamically adjusted based on the degree of change in the best solution over previous generations. For example, if the best solution has not changed significantly in several consecutive iterations, the chemotaxis step size can be increased to encourage a broader search. For example, in bacteria 1, suppose that because the distance between AX, XY, and YD is 2, AXYD is found. The distance between AZ and ZD is 3, and the distance between ZD and ZD is 2. When the chemotaxis step size is changed to 3, a new path AZD can be found.

[0066] In some embodiments, the preset replication probability and preset extinction probability can be dynamically adjusted during iterative updates. The preset replication probability can affect the number of excellent paths retained. Generally, bacteria with high nutritional value (i.e., potential paths with high path scores) will be replicated to preserve their "genes." If the optimization process has stabilized, the preset replication probability can be increased to allow better solutions to occupy a larger proportion, further refining the search. If in the early exploration stage, the preset replication probability needs to be reduced to maintain the diversity of potential path types.

[0067] The preset extinction probability determines the proportion of bacteria (potential paths) that will be eliminated. A high preset extinction probability can quickly remove poorly performing bacteria (i.e., potential paths with poor scores), but may lose potential non-mainstream excellent solutions. A low preset extinction probability, on the other hand, can maintain population diversity. Adjusting the preset extinction probability usually corresponds to the preset replication probability, depending on whether the search process needs to introduce new "blood" (i.e., expand paths).

[0068] Specifically, when adjusting the preset replication probability and preset extinction probability during the iteration process, a performance baseline can be set, and the average improvement of the best solution in the most recent iterations can be determined, or a target threshold can be set. If the performance improvement exceeds the baseline or reaches the target threshold, it indicates good results, and the chemiluminescence step size can be reduced, the preset replication probability increased, and the preset extinction probability decreased. If the performance improvement does not exceed the baseline or reach the target threshold, or even decreases, it indicates that more exploration is needed, and the chemiluminescence step size can be increased, the preset replication probability decreased, and the preset extinction probability increased. Through the above adjustments, the path planning method provided by the embodiments of the present invention can self-adjust during the iterative update process, enhance the global search or local search in a timely manner, and thus more effectively cope with complex and changing optimization problems.

[0069] Corresponding to the path planning method described above, this embodiment of the invention provides a path planning device. Figure 2 This is a schematic diagram of a path planning device provided in an embodiment of the present invention. Figure 2 As shown, the path planning device includes: a path set determination module 201, a scoring module 202, an iteration module 203, and an optimal path determination module 204.

[0070] The path set determination module 201 is used to determine the path set based on the starting point and the ending point. The path set contains potential paths of different types.

[0071] The scoring module 202 is used to determine the path score of each potential path in the path set based on the traffic information corresponding to each type of potential path.

[0072] The iteration module 203 is used to replicate potential paths with path scores greater than a disappearance threshold according to a preset replication probability, and to exclude potential paths with path scores less than or equal to the disappearance threshold according to a preset disappearance probability, so as to iteratively update the potential paths contained in the path set and obtain an updated path set.

[0073] The optimal path determination module 204 is used to determine the most numerous potential path in the updated path set as the optimal path from the start point to the end point when the updated path set is detected to meet the preset conditions.

[0074] Figure 2 The path planning device provided in the illustrated embodiment can be used to execute this specification. Figure 1 The implementation principle and technical effects of the method embodiment shown can be further referred to the relevant description in the method embodiment.

[0075] Figure 3 This is a schematic diagram of the structure of an electronic device provided in an embodiment of the present invention, such as... Figure 3 As shown, the aforementioned electronic device may include at least one processor and at least one memory communicatively connected to the processor, wherein the memory stores program instructions executable by the processor, and the processor can execute this specification by calling the program instructions. Figure 1 The path planning method provided in the illustrated embodiment.

[0076] like Figure 3 As shown, the electronic device is represented in the form of a general-purpose computing device. The components of the electronic device may include, but are not limited to: one or more processors 310, communication interface 320 and memory 330, and a communication bus 340 connecting different system components (including memory 330, communication interface 320 and processor 310).

[0077] Communication bus 340 represents one or more of several bus architectures, including a memory bus or memory controller, a peripheral bus, a graphics acceleration port, a processor, or a local bus using any of the various bus architectures. For example, these architectures include, but are not limited to, the Industry Standard Architecture (ISA) bus, the Micro Channel Architecture (MAC) bus, the Enhanced ISA bus, the Video Electronics Standards Association (VESA) local bus, and the Peripheral Component Interconnect (PCI) bus.

[0078] Electronic devices typically include a variety of computer-readable media. These media can be any available media that can be accessed by the electronic device, including volatile and non-volatile media, and removable and non-removable media.

[0079] Memory 330 may include computer system readable media in the form of volatile memory, such as random access memory (RAM) and / or cache memory. The electronic device may further include other removable / non-removable, volatile / non-volatile computer system storage media. Memory 330 may include at least one program product having a set (e.g., at least one) of program modules configured to perform the functions of the embodiments described herein.

[0080] A program / utility having a set (at least one) of program modules can be stored in memory 330. Such program modules include—but are not limited to—an operating system, one or more application programs, other program modules, and program data. Each or some combination of these examples may include an implementation of a network environment. The program modules typically perform the functions and / or methods described in the embodiments of this specification.

[0081] Processor 310 executes various functional applications and data processing by running programs stored in memory 330, such as implementing the functions described in this specification. Figure 1 The path planning method provided in the illustrated embodiment.

[0082] This specification provides a computer-readable storage medium storing computer instructions that cause a computer to execute this specification. Figure 1 The path planning method provided in the illustrated embodiment.

[0083] The aforementioned computer-readable storage medium may be any combination of one or more computer-readable media. A computer-readable medium may be a computer-readable signal medium or a computer-readable storage medium. A computer-readable storage medium may be, for example—but not limited to—an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination thereof. More specific examples (a non-exhaustive list) of computer-readable storage media include: an electrical connection having one or more wires, a portable computer disk, a hard disk, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM), or flash memory, optical fiber, portable compact disk read-only memory (CD-ROM), optical storage device, magnetic storage device, or any suitable combination thereof. In this document, a computer-readable storage medium may be any tangible medium that contains or stores a program that may be used by or in connection with an instruction execution system, apparatus, or device.

[0084] The foregoing has described specific embodiments of this specification. Other embodiments are within the scope of the appended claims. In some cases, the actions or steps recited in the claims may be performed in a different order than that shown in the embodiments and may still achieve the desired result. Furthermore, the processes depicted in the drawings do not necessarily require the specific or sequential order shown to achieve the desired result. In some embodiments, multitasking and parallel processing are possible or may be advantageous.

[0085] In the description of this specification, the references to terms such as "one embodiment," "some embodiments," "example," "specific example," or "some examples," etc., indicate that a specific feature, structure, material, or characteristic described in connection with that embodiment or example is included in at least one embodiment or example of this specification. In this specification, the illustrative expressions of the above terms do not necessarily refer to the same embodiment or example. Furthermore, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in one or more embodiments or examples. Moreover, without contradiction, those skilled in the art can combine and integrate the different embodiments or examples described in this specification, as well as the features of different embodiments or examples.

[0086] Furthermore, the terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of technical features indicated. Thus, a feature defined as "first" or "second" may explicitly or implicitly include at least one of that feature. In the description of this specification, "a plurality of" means at least two, such as two, three, etc., unless otherwise explicitly specified.

[0087] Any process or method described in the flowchart or otherwise herein can be understood as representing a module, segment, or portion of code comprising one or more executable instructions for implementing custom logic functions or processes, and the scope of the preferred embodiments of this specification includes additional implementations in which functions may be performed not in the order shown or discussed, including substantially simultaneously or in reverse order depending on the functions involved, as will be understood by those skilled in the art to which the embodiments of this specification pertain.

[0088] Depending on the context, the word "if" as used here can be interpreted as "when," "when," "in response to determination," or "in response to detection." Similarly, depending on the context, the phrase "if determination" or "if detection (of the stated condition or event)" can be interpreted as "when determination," "in response to determination," "when detection (of the stated condition or event)," or "in response to detection (of the stated condition or event)."

[0089] It should be noted that the devices involved in the embodiments of this specification may include, but are not limited to, personal computers (PCs), personal digital assistants (PDAs), wireless handheld devices, tablet computers, mobile phones, MP3 displays, MP4 displays, etc.

[0090] In the several embodiments provided in this specification, it should be understood that the disclosed systems, apparatuses, and methods can be implemented in other ways. For example, the apparatus embodiments described above are merely illustrative; for instance, the division of units is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be through some interfaces; the indirect coupling or communication connection between apparatuses or units may be electrical, mechanical, or other forms.

[0091] Furthermore, the functional units in the various embodiments of this specification can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or in a combination of hardware and software functional units.

[0092] The integrated units implemented as software functional units described above can be stored in a computer-readable storage medium. These software functional units, stored in a storage medium, include several instructions to cause a computer device (which may be a personal computer, a connector, or a network device, etc.) or processor to execute some steps of the methods described in the various embodiments of this specification. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0093] The above description is merely a preferred embodiment of this specification and is not intended to limit this specification. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of this specification should be included within the scope of protection of this specification.

[0094] The same or similar parts between the various embodiments in this specification can be referred to mutually. In particular, the device embodiments and terminal embodiments are basically similar to the method embodiments, so the description is relatively simple, and the relevant parts can be referred to the description in the method embodiments.

Claims

1. A path planning method characterized by, The method comprises the following steps: determining a path set according to a starting point and an ending point; the path set comprises a plurality of potential paths of different types; determining a path score of each type of potential path in the path set according to traffic information corresponding to each type of potential path; iteratively updating the potential paths contained in the path set according to a preset replication probability to replicate potential paths with a path score greater than a threshold value and according to a preset elimination probability to eliminate potential paths with a path score less than or equal to the threshold value, to obtain an updated path set; when it is detected that the updated path set meets a preset condition, determining a type of potential path with the largest quantity in the updated path set as the best path from the starting point to the ending point.

2. The method of claim 1, wherein, The step of determining a path score of each type of potential path in the path set according to traffic information corresponding to each type of potential path comprises the following steps: determining one or more combinations of path length, travel time, congestion degree, safety and energy consumption of each type of potential path according to the traffic information corresponding to each type of potential path; determining the path score of each type of potential path according to one or more combinations of path length, travel time, congestion degree, safety and energy consumption.

3. The method of claim 2, wherein, The step of determining the path score of each type of potential path according to one or more combinations of path length, travel time, congestion degree, safety and energy consumption comprises the following steps: determining path length score, travel time score, congestion degree score, safety score and energy consumption score of each type of potential path according to the path length, travel time, congestion degree, safety and energy consumption; performing weighted calculation on one or more combinations of the path length score, travel time score, congestion degree score, safety score and energy consumption score; determining the path score of each type of potential path according to the result of the weighted calculation.

4. The method of claim 1, wherein, The preset condition at least includes any one of the following: the number of iterations is greater than a first threshold value, there is a type of potential path with a first ratio greater than a second threshold value, and the update rate of the potential paths contained in the path set is less than or equal to a third threshold value; wherein the first ratio is the ratio of the number of the same type of potential path to the number of all potential paths contained in the path set.

5. The method of claim 1, wherein, The method further comprises the following steps: determining whether the updated path set meets a path expansion condition; generating an expansion path if the updated path set meets the path expansion condition; the expansion path is different from each type of potential path contained in the updated path set; adding the expansion path as a potential path to the updated path set to continue the iterative updating.

6. The method of claim 5, wherein, The step of generating an expansion path comprises the following steps: performing local route adjustment on the potential paths contained in the updated path set to obtain the expansion path.

7. The method of claim 5, wherein, The step of generating an expansion path comprises the following steps: performing global route adjustment on the potential paths contained in the updated path set to obtain the expansion path.

8. A route planning apparatus characterized by comprising: The method comprises the following steps: a path set determination module configured to determine a path set according to a starting point and an ending point; the path set comprises a plurality of potential paths of different types; The scoring module is configured to determine a path score of each type of the potential paths in the path set according to traffic information corresponding to each type of the potential paths; The iteration module is configured to replicate the potential paths with the path score greater than a death threshold according to a preset replication probability, and to exclude the potential paths with the path score less than or equal to the death threshold according to a preset death probability, so as to iteratively update the potential paths contained in the path set and obtain an updated path set; The optimal path determination module is configured to determine a type of the potential paths with the largest quantity in the updated path set as the optimal path from the starting point to the ending point when it is detected that the updated path set satisfies a preset condition.

9. An electronic device, comprising: comprise: at least one processor; and at least one memory connected to the processor in communication, wherein: the memory stores program instructions executable by the processor, and the processor invoking the program instructions can execute the method of any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that, The computer readable storage medium stores computer instructions, and the computer instructions cause the computer to execute the method of any one of claims 1 to 7.

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