Vehicle path decision-making method, system and device, chip and storage medium

Abstract

The invention provides a vehicle path decision-making method, system and device, a chip and a storage medium, and the method comprises the steps: obtaining the current vehicle information, vehicle environment information and vehicle navigation task information of a port container truck, and determining each vehicle path based on a preset behavior tree algorithm model, the first sub-tree sends a driving instruction to the vehicle according to the updated vehicle information, the vehicle environment information and the vehicle navigation task information, the second sub-tree determines that the vehicle performs long-path driving according to the updated information, and the third sub-tree determines that the vehicle performs short-path driving according to the updated information. According to the technical scheme, under the condition that interference with other vehicles is avoided, optimal path decision making is carried out as far as possible, multiple judgment conditions are added while the system architecture is not changed, the system architecture is simplified to a great extent, and the operation and maintenance difficulty of the whole system is reduced.

Description

Technical Field

[0001] This invention relates to the field of autonomous driving, and in particular to a vehicle path decision-making method; furthermore, this invention also relates to a vehicle path decision-making system. Background Technology

[0002] In the field of autonomous driving, especially in closed environments, research on multi-vehicle cooperation problems is gradually becoming a focal point. The core of multi-vehicle cooperation lies in how to plan a path that achieves the optimal solution while avoiding interference with other vehicles. Currently, the main method used in the industry is the CBS (collision-based search) algorithm. This algorithm is decentralized; each node considers all vehicle paths while calculating whether its path interferes with other nodes, then cuts and replans based on the interference points.

[0003] To avoid issues such as excessive demands on computing nodes that arise when using the CBS algorithm, a novel centralized algorithm—a long-short path combination method—has proven feasible in engineering practice. In the long path phase, overlapping paths do not affect safety; vehicles only execute the shortest path. Short path generation is based on long paths but also considers the short paths of other vehicles. If a node's short path intersects with other nodes, that short path is invalid. Only valid short paths are assigned to vehicles for execution. This ensures the safety of vehicle execution.

[0004] However, in conventional computer program architecture design, conditional judgment problems typically use a selection branch architecture, i.e., a combination of if-else statements to implement the combined logic of long and short paths for a request. But when there are many and complex conditions to consider, the selection branch architecture becomes very bloated and out of control in terms of scale, resulting in a large amount of duplicate code, which greatly increases the maintenance difficulty of the entire system. Summary of the Invention

[0005] To address the problems existing in the prior art, at least one embodiment of the present invention provides a vehicle routing decision method. The vehicle travel path obtained by this method solves the multi-vehicle cooperation problem, achieving the optimal solution as much as possible while avoiding interference with other vehicles. This method uses behavior trees to make decisions on long and short paths, adding multiple judgment conditions without changing the system architecture, greatly simplifying the entire program design architecture and reducing the overall system maintenance difficulty. Therefore, at least one embodiment of the present invention also provides a vehicle routing decision system.

[0006] In a first aspect, embodiments of the present invention propose a vehicle routing decision method, the method comprising: Obtain current vehicle information, vehicle environment information, and vehicle navigation task information for container trucks at the port. Based on a pre-defined behavior tree algorithm model, the path for each vehicle is determined according to vehicle information, vehicle environment information, and vehicle navigation task information. The behavior tree includes multiple subtrees. The first subtree is used to issue driving instructions to the vehicle based on the updated vehicle information, vehicle environment information, and vehicle navigation task information. The second subtree is used to determine whether the vehicle should travel a long route based on the updated vehicle information, vehicle environment information, and vehicle navigation task information. The third subtree is used to determine whether the vehicle should travel a short route based on the updated vehicle information, vehicle environment information, and vehicle navigation task information.

[0007] In some embodiments, the vehicle routing decision method provided by the present invention includes a first subtree comprising a parallel node, a first execution node, a first selection node, a first condition node, and a control node. Parallel nodes are root nodes and are used to update vehicle information, vehicle environment information, and vehicle navigation task information. The first execution node, connected to the parallel node, is used to update vehicle information, vehicle environment information, and vehicle navigation task information. The first selection node, connected to the parallel node, is used to issue driving preparation commands to the vehicle. The first condition node, connected to the first selection node, is the left child node of the first selection node and is used to determine whether the vehicle has reached its destination. The control node, connected to the first selection node, is the right child node of the first selection node and is used to issue a definite driving command to the vehicle.

[0008] In some embodiments, the vehicle routing decision method provided by the present invention has a control node as a sequential node, a second subtree as the left subtree of the control node, and a third subtree as the right subtree of the control node.

[0009] In some embodiments, the vehicle routing decision method provided by the present invention includes a second subtree comprising a second selection node, at least one second condition node, a third selection node, at least one third condition node, and a second execution node. The second selection node is connected to the control node and is the left child node of the control node; The second condition node, connected to the second selection node, is used to determine the conditions for long-path travel instructions. The third selection node, connected to the second selection node and located to the right of the second condition node, is used to issue long-path travel instructions. The third condition node, connected to the third selection node, is used to determine whether the conditions for issuing a long-path travel instruction are met. The second execution node, connected to the third selection node and located to the right of the third condition node, is used to issue the requested long-path travel instruction.

[0010] In some embodiments, the vehicle routing decision method provided by the present invention includes a third subtree comprising a fourth selection node, at least one fourth condition node, a fifth selection node, a fifth condition node, and a third execution node. The fourth selection node is connected to the control node and is the right child node of the control node; The fourth condition node, connected to the fourth selection node, is used to determine the conditions for the shortest path travel instruction. The fifth selection node, connected to the fourth selection node and located to the right of the fourth condition node, is used to issue long-path travel instructions. The fifth condition node, connected to the fifth selection node, is used to determine whether the conditions for issuing the shortest path travel instruction are met. The third execution node, connected to the fifth selection node and located to the right of the fifth condition node, is used to issue the requested short-path travel instruction.

[0011] In some embodiments, the vehicle routing decision method provided by the present invention further includes: A finite state machine is used to manage individual vehicle navigation tasks.

[0012] In some embodiments, the vehicle routing decision method provided by the present invention further includes: The vehicle's route is determined by sending navigation commands directly to the vehicle.

[0013] Secondly, embodiments of the present invention also provide a vehicle routing decision system, comprising: The acquisition module is used to acquire current vehicle information, vehicle environment information, and vehicle navigation task information of container trucks at the port. The path determination module is used to determine the path of each vehicle based on a preset behavior tree algorithm model, vehicle information, vehicle environment information, and vehicle navigation task information. The behavior tree module includes multiple subtree modules. The first subtree module is used to issue driving instructions to the vehicle based on the updated vehicle information, vehicle environment information, and vehicle navigation task information. The second subtree module is used to determine whether the vehicle should travel a long route based on the updated vehicle information, vehicle environment information, and vehicle navigation task information. The third subtree module is used to determine whether the vehicle should travel a short route based on the updated vehicle information, vehicle environment information, and vehicle navigation task information.

[0014] In some embodiments, the vehicle routing decision system provided by the present invention includes a first subtree module comprising a parallel node, a first execution node, a first selection node, a first condition node, and a control node. Parallel nodes are root nodes and are used to update vehicle information, vehicle environment information, and vehicle navigation task information. The first execution node, connected to the parallel node, is used to update vehicle information, vehicle environment information, and vehicle navigation task information. The first selection node, connected to the parallel node, is used to issue driving preparation commands to the vehicle. The first condition node, connected to the first selection node, is the left child node of the first selection node and is used to determine whether the vehicle has reached its destination. The control node, connected to the first selection node, is the right child node of the first selection node and is used to issue a definite driving command to the vehicle.

[0015] In some embodiments, the vehicle routing decision system provided by the present invention has a control node as a sequential node, a second subtree module as the left subtree of the control node, and a third subtree module as the right subtree of the control node.

[0016] In some embodiments, the vehicle routing decision system provided by the present invention includes a second subtree module comprising a second selection node, at least one second condition node, a third selection node, at least one third condition node, and a second execution node. The second selection node is connected to the control node and is the left child node of the control node; The second condition node, connected to the second selection node, is used to determine the conditions for long-path travel instructions. The third selection node, connected to the second selection node and located to the right of the second condition node, is used to issue long-path travel instructions. The third condition node, connected to the third selection node, is used to determine whether the conditions for issuing a long-path travel instruction are met. The second execution node, connected to the third selection node and located to the right of the third condition node, is used to issue the requested long-path travel instruction.

[0017] In some embodiments, the vehicle routing decision system provided by the present invention includes a third subtree module comprising a fourth selection node, at least one fourth condition node, a fifth selection node, a fifth condition node, and a third execution node. The fourth selection node is connected to the control node and is the right child node of the control node; The fourth condition node, connected to the fourth selection node, is used to determine the conditions for the shortest path travel instruction. The fifth selection node, connected to the fourth selection node and located to the right of the fourth condition node, is used to issue long-path travel instructions. The fifth condition node, connected to the fifth selection node, is used to determine whether the conditions for issuing the shortest path travel instruction are met. The third execution node, connected to the fifth selection node and located to the right of the fifth condition node, is used to issue the requested short-path travel instruction.

[0018] In some embodiments, the vehicle routing decision system provided by the present invention further includes: The finite state machine module is used to manage a single vehicle navigation task through a finite state machine.

[0019] In some embodiments, the vehicle routing decision system provided by the present invention further includes: The navigation command sending module is used to determine the vehicle's route by sending navigation commands to the vehicle.

[0020] Thirdly, embodiments of the present invention also provide a vehicle routing decision device, including at least one processor; a memory coupled to the at least one processor, the memory storing executable instructions, which, when executed by the at least one processor, cause the steps of any of the methods described in the first aspect above to be implemented.

[0021] Fourthly, embodiments of the present invention also provide a chip for performing the steps of the method in the first aspect described above. Specifically, the chip includes a processor for retrieving and running a computer program from a memory, such that a device having the chip installed performs the steps of the method in the first aspect described above.

[0022] Fifthly, embodiments of the present invention also provide a computer-readable storage medium storing a computer program, which, when executed by a processor, implements the steps of any of the methods described in the first aspect above.

[0023] As can be seen, the vehicle path decision-making method, system, device, chip and storage medium of the present invention adopts the decision logic of behavior tree, and makes decisions on the length of the path by judging the results of vehicle information, environmental state information and task execution state information. While avoiding interference with other vehicles, it can obtain the optimal solution as much as possible. Moreover, it adds multiple judgment conditions without changing the system architecture, which greatly simplifies the entire program design architecture and reduces the operation and maintenance difficulty of the entire system. Attached Figure Description

[0024] To more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0025] Figure 1The flowchart shown is a vehicle routing decision method according to an embodiment of the present invention. Figure 2 The diagram shown is a structural diagram of the behavior tree in an embodiment of the present invention; Figure 3 This is a schematic diagram showing the behavioral transition relationships of each state in an embodiment of the present invention; Figure 4 The diagram shown is a framework schematic of a vehicle routing decision system according to an embodiment of the present invention. Detailed Implementation Plan To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0026] It should be noted that, in this document, relational terms such as "first" and "second" are used merely to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. In this document, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Without further limitations, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes that element.

[0027] The inventors of this solution discovered that in existing conventional computer program architecture design, conditional judgment problems typically use a selection branch architecture, i.e., a combination of if-else statements to implement the combined logic of application long and short paths. However, when there are many and complex judgment conditions to consider, the selection branch architecture becomes very bloated and its scale becomes uncontrollable, resulting in a large amount of duplicate code, which greatly increases the operational and maintenance difficulty of the entire system. The embodiments of this invention provide the following solution: like Figure 1 As shown, in a first aspect, embodiments of the present invention provide a vehicle route decision-making method, the method comprising: Obtain current vehicle information, vehicle environment information, and vehicle navigation task information for container trucks at the port. Based on a pre-defined behavior tree algorithm model, the path for each vehicle is determined according to vehicle information, vehicle environment information, and vehicle navigation task information. The behavior tree includes multiple subtrees. The first subtree is used to issue driving instructions to the vehicle based on the updated vehicle information, vehicle environment information, and vehicle navigation task information. The second subtree is used to determine whether the vehicle should travel a long route based on the updated vehicle information, vehicle environment information, and vehicle navigation task information. The third subtree is used to determine whether the vehicle should travel a short route based on the updated vehicle information, vehicle environment information, and vehicle navigation task information.

[0028] It should be noted that vehicle information includes the vehicle's license plate number, vehicle model, and location information. Environmental status information includes the vehicle's position within its lane, the lane's shape, and its color. Lane position information includes, for example, the lane's coordinates in a road photograph; lane shape information includes, for example, herringbone patterns, cones, curbs, dashed lines, and solid lines; and lane color information includes, for example, white or yellow. Task execution status information includes, for example, whether the vehicle is on a long-distance or short-distance journey. The behavior tree is essentially a continuously looping architecture; therefore, each loop requires updating the behavior tree blackboard with the latest vehicle information, environmental status information, and task execution status information. The behavior tree blackboard refers to the key / value store shared by all nodes in the behavior tree.

[0029] like Figure 2 As shown, the first subtree includes a parallel node, a first execution node, a first selection node, a first condition node, and a control node.

[0030] Parallel nodes are root nodes and are used to update vehicle information, vehicle environment information, and vehicle navigation task information.

[0031] The first execution node, connected to the parallel node, is used to update vehicle information, vehicle environment information, and vehicle navigation task information.

[0032] The first selection node, connected to the parallel node, is used to issue driving preparation commands to the vehicle.

[0033] It is understandable that when a driving task is executed, the first execution node and the first selection node are executed simultaneously. Specifically, a driving preparation command is issued to the vehicle while updating the vehicle's environmental information.

[0034] The first condition node, connected to the first selection node, is the left child node of the first selection node and is used to determine whether the vehicle has reached its destination.

[0035] The control node, connected to the first selection node, is the right child node of the first selection node and is used to issue a definite driving command to the vehicle.

[0036] Understandably, when a driving task is executed, the first condition node is executed first. If the first condition node returns failure, the second control node is executed. If the first condition node returns success, the entire driving task ends. Specifically, if it is determined that the vehicle has not reached its destination, a driving command is issued to the vehicle; if it is determined that the vehicle has reached its destination, the entire driving task ends.

[0037] The control node is a sequential node, the second subtree is the left subtree of the control node, and the third subtree is the right subtree of the control node.

[0038] The second subtree includes a second selection node, at least one second condition node, a third selection node, at least one third condition node, and a second execution node.

[0039] The second selection node is connected to the control node and is the left child node of the control node.

[0040] The second condition node, connected to the second selection node, is used to determine the conditions for long-path travel instructions.

[0041] It should be noted that multiple second condition nodes can be designed according to actual needs. This means that multiple conditions for judging long-path driving instructions can be added without changing the overall system architecture, which greatly reduces the difficulty of operation and maintenance.

[0042] The third selection node, connected to the second selection node and located to the right of the second condition node, is used to issue long-path travel instructions.

[0043] Understandably, when a driving task is executed, the second condition node is executed first. If the second condition node returns failure, the third selection node is executed. If the second condition node returns success, the current driving task is exited. Specifically, when it is determined that the current condition is not met, a long-path driving command is issued to the vehicle; when it is determined that the current condition is met, the current driving task is exited, and the cycle of the next driving task begins.

[0044] The third condition node, connected to the third selection node, is used to determine whether the conditions for issuing a long-path travel instruction are met.

[0045] It should be noted that multiple fourth condition nodes can be designed according to actual needs. That is, multiple judgment conditions that satisfy the issuance of long-path travel instructions can be added without changing the entire system architecture, which greatly reduces the difficulty of operation and maintenance.

[0046] The second execution node, connected to the third selection node and located to the right of the third condition node, is used to issue the requested long-path travel instruction.

[0047] Understandably, when a driving task is executed, the third condition node is executed first. If the third condition node returns success, the second execution node is executed. If the third condition node returns failure, the current driving task is terminated. Specifically, if the conditions for issuing a long-path driving instruction are met, the requested long-path driving instruction is issued to the vehicle; if the conditions for issuing a long-path driving instruction are not met, the current driving task is terminated, and the cycle of the next driving task begins.

[0048] The third subtree includes a fourth selection node, at least one fourth condition node, a fifth selection node, a fifth condition node, and a third execution node.

[0049] The fourth selected node is connected to the control node and is the right child node of the control node.

[0050] The fourth condition node, connected to the fourth selection node, is used to determine the conditions for the shortest path travel instruction.

[0051] It should be noted that multiple fourth condition nodes can be designed according to actual needs. That is, multiple conditions for judging short-path driving instructions can be added without changing the overall system architecture, which greatly reduces the difficulty of operation and maintenance.

[0052] The fifth selection node, connected to the fourth selection node and located to the right of the fourth condition node, is used to issue long-path travel instructions.

[0053] Understandably, when a driving task is executed, the fourth condition node is executed first. If the fourth condition node returns failure, the fifth selection node is executed. If the fourth condition node returns success, the current driving task is exited. Specifically, when it is determined that the current condition is not met, a short-path driving command is issued to the vehicle; when it is determined that the current condition is met, the current driving task is exited, and the cycle of the next driving task begins.

[0054] The fifth condition node, connected to the fifth selection node, is used to determine whether the conditions for issuing a short-path travel instruction are met.

[0055] It should be noted that multiple fifth condition nodes can be designed according to actual needs. That is, multiple judgment conditions that satisfy the issuance of short-path travel instructions can be added without changing the overall system architecture, which greatly reduces the difficulty of operation and maintenance.

[0056] The third execution node, connected to the fifth selection node and located to the right of the fifth condition node, is used to issue the requested short-path travel instruction.

[0057] Understandably, when a driving task is executed, the fifth condition node is executed first. If the fifth condition node returns success, the third execution node is executed. If the fifth condition node returns failure, the current driving task is terminated. Specifically, if the conditions for issuing a short-path driving instruction are met, the requested short-path driving instruction is issued to the vehicle; if the conditions for issuing a short-path driving instruction are not met, the current driving task is terminated, and the cycle of the next driving task begins.

[0058] like Figure 3 As shown, the method also includes managing individual vehicle navigation tasks using a finite state machine. It should be noted that since the navigation task of a vehicle traveling from its starting point to its destination is a continuous process, and the vehicle's movement relies on the continuous updating of shortest paths, the management of vehicle navigation tasks inevitably involves a large number of preemptive tasks. A state machine is used to manage individual navigation tasks, including task creation, execution, cancellation, and preemption.

[0059] In addition to controlling vehicle routes through behavior trees, navigation commands can also be sent directly to the vehicle to determine its path. Understandably, in the event of a sudden emergency, sending navigation commands directly to the vehicle to determine its route can effectively prevent some dangerous situations from occurring.

[0060] like Figure 4 As shown, in a second aspect, embodiments of the present invention also provide a vehicle route decision-making system, including an acquisition module, a route determination module, a first subtree module, a second subtree module, and a third subtree module.

[0061] The acquisition module is used to acquire current vehicle information, vehicle environment information, and vehicle navigation task information of container trucks at the port.

[0062] The path determination module is used to determine the path of each vehicle based on a preset behavior tree algorithm model, vehicle information, vehicle environment information, and vehicle navigation task information.

[0063] The behavior tree module includes multiple subtree modules. The first subtree module is used to issue driving instructions to the vehicle based on the updated vehicle information, vehicle environment information, and vehicle navigation task information. The second subtree module is used to determine whether the vehicle should travel a long route based on the updated vehicle information, vehicle environment information, and vehicle navigation task information. The third subtree module is used to determine whether the vehicle should travel a short route based on the updated vehicle information, vehicle environment information, and vehicle navigation task information.

[0064] The first subtree module includes parallel nodes, first execution nodes, first selection nodes, first condition nodes, and control nodes.

[0065] Parallel nodes are root nodes and are used to update vehicle information, vehicle environment information, and vehicle navigation task information.

[0066] The first execution node, connected to the parallel node, is used to update vehicle information, vehicle environment information, and vehicle navigation task information.

[0067] The first selection node, connected to the parallel node, is used to issue driving preparation commands to the vehicle.

[0068] The first condition node, connected to the first selection node, is the left child node of the first selection node and is used to determine whether the vehicle has reached its destination.

[0069] The control node, connected to the first selection node, is the right child node of the first selection node and is used to issue a definite driving command to the vehicle.

[0070] The control node is a sequential node, the second subtree module is the left subtree of the control node, and the third subtree module is the right subtree of the control node.

[0071] The second subtree module includes a second selection node, at least one second condition node, a third selection node, at least one third condition node, and a second execution node.

[0072] The second selection node is connected to the control node and is the left child node of the control node.

[0073] The second condition node, connected to the second selection node, is used to determine the conditions for long-path travel instructions.

[0074] The third selection node, connected to the second selection node and located to the right of the second condition node, is used to issue long-path travel instructions.

[0075] The third condition node, connected to the third selection node, is used to determine whether the conditions for issuing a long-path travel instruction are met.

[0076] The second execution node, connected to the third selection node and located to the right of the third condition node, is used to issue the requested long-path travel instruction.

[0077] The third subtree module includes a fourth selection node, at least one fourth condition node, a fifth selection node, a fifth condition node, and a third execution node.

[0078] The fourth selected node is connected to the control node and is the right child node of the control node.

[0079] The fourth condition node, connected to the fourth selection node, is used to determine the conditions for the shortest path travel instruction.

[0080] The fifth selection node, connected to the fourth selection node and located to the right of the fourth condition node, is used to issue long-path travel instructions.

[0081] The fifth condition node, connected to the fifth selection node, is used to determine whether the conditions for issuing a short-path travel instruction are met.

[0082] The third execution node, connected to the fifth selection node and located to the right of the fifth condition node, is used to issue the requested short-path travel instruction.

[0083] This system also includes a finite state machine module and a navigation command sending module. The finite state machine module is used to manage individual vehicle navigation tasks using a finite state machine. The navigation command sending module is used to determine the vehicle's path by sending navigation commands to the vehicle.

[0084] Thirdly, embodiments of the present invention also provide a vehicle routing decision-making device, comprising: At least one processor; a memory coupled to the at least one processor, the memory storing executable instructions, wherein the executable instructions, when executed by the at least one processor, cause the method steps of the first aspect of the invention to be implemented.

[0085] The vehicle routing decision device provided in this embodiment of the invention can have its processor and memory set up separately or integrated together.

[0086] For example, memory may include random access memory, flash memory, read-only memory, programmable read-only memory, non-volatile memory, or registers. The processor may be a central processing unit (CPU), or a graphics processing unit (GPU). Memory can store executable instructions. The processor can execute the executable instructions stored in memory to implement the various processes described herein.

[0087] It is understood that the memory in this embodiment can be volatile memory or non-volatile memory, or may include both. The non-volatile memory can be ROM (Read-Only Memory), PROM (Programmable ROM), EPROM (Erasable PROM), EEPROM (Electrically Erasable EPROM), or flash memory. The volatile memory can be RAM (Random Access Memory), which is used as an external cache. By way of example, but not limitation, many forms of RAM are available, such as SRAM (Static RAM), DRAM (Dynamic RAM), SDRAM (Synchronous DRAM), DDR SDRAM (Double Data Rate SDRAM), ESDRAM (Enhanced SDRAM), SLDRAM (Synchlink DRAM), and DRRAM (Direct Rambus RAM). The memories described herein are intended to include, but are not limited to, these and any other suitable types of memory.

[0088] In some implementations, the memory stores elements such as upgrade packages, executable units, or data structures, or subsets thereof, or extended sets thereof: operating systems and applications.

[0089] The operating system includes various system programs, such as the framework layer, core library layer, and driver layer, used to implement various basic business functions and handle hardware-based tasks. The application programs include various applications used to implement various application functions. Programs implementing the methods of this invention can be included within these application programs.

[0090] In this embodiment of the invention, the processor executes the method steps provided in the first aspect by calling a program or instruction stored in the memory, specifically, a program or instruction stored in an application program.

[0091] Fourthly, embodiments of the present invention also provide a chip for performing the method in the first aspect described above. Specifically, the chip includes a processor for calling and running a computer program from a memory, such that a device having the chip installed performs the method in the first aspect described above.

[0092] Fifthly, embodiments of the present invention also provide a computer-readable storage medium storing a computer program, which, when executed by a processor, implements the steps of the method of the first aspect of the present invention.

[0093] For example, machine-readable storage media can include, but are not limited to, various known and unknown types of non-volatile memory.

[0094] In summary, the vehicle path decision-making method, system, device, chip, and storage medium of this invention adopts behavior tree decision logic. It makes decisions on the length of the path based on the judgment results of vehicle information, environmental state information, and task execution state information. It obtains the optimal solution as much as possible while avoiding interference with other vehicles. Moreover, it adds multiple judgment conditions without changing the system architecture, which greatly simplifies the entire program design architecture and reduces the operation and maintenance difficulty of the entire system.

[0095] Those skilled in the art will understand that the units and algorithm steps of the various examples described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of software and electronic hardware. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can implement the described functions in different ways for each specific application, but such implementation should not be considered beyond the scope of this application.

[0096] In the embodiments of this application, the disclosed systems, apparatuses, and methods can be implemented in other ways. For example, the division of units is merely a logical functional division, and other division methods may exist in actual implementation. For example, multiple units or components may be combined or integrated into another system. Furthermore, the coupling between the various units can be direct coupling or indirect coupling. Additionally, the functional units in the embodiments of this application can be integrated into a processing unit, or they can exist as separate physical entities, etc.

[0097] It should be understood that in the various embodiments of this application, the sequence number of each process does not imply the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of this application.

[0098] If the aforementioned functions are implemented as software functional units and sold or used as independent products, they can be stored in a machine-readable storage medium. Therefore, the technical solution of this application can be embodied in the form of a software product, which can be stored in a machine-readable storage medium. This software product may include several instructions to cause an electronic device to execute all or part of the processes of the technical solution described in the embodiments of this application. The aforementioned storage medium may include various media capable of storing program code, such as ROM, RAM, removable disk, hard disk, magnetic disk, or optical disk.

[0099] The above description is merely a specific embodiment of this application, and the scope of protection of this application is not limited thereto. Those skilled in the art can make changes or substitutions within the technical scope disclosed in this application, and all such changes or substitutions should be within the scope of protection of this application.

Claims

1. A vehicle routing decision method, characterized in that, The method includes: Obtain current vehicle information, vehicle environment information, and vehicle navigation task information for container trucks at the port. Based on a preset behavior tree algorithm model, the path of each vehicle is determined according to the vehicle information, vehicle environment information and vehicle navigation task information. The behavior tree includes multiple subtrees. The first subtree is used to issue driving instructions to the vehicle based on updated vehicle information, vehicle environment information, and vehicle navigation task information. The second subtree is used to determine that the vehicle will travel a long path based on updated vehicle information, vehicle environment information, and vehicle navigation task information. The third subtree is used to determine that the vehicle will travel a short path based on updated vehicle information, vehicle environment information, and vehicle navigation task information.

2. The vehicle routing decision method according to claim 1, characterized in that, The first subtree includes a parallel node, a first execution node, a first selection node, a first condition node, and a control node; The parallel node is the root node, used to update vehicle information, vehicle environment information, and vehicle navigation task information; The first execution node is connected to the parallel node and is used to update vehicle information, vehicle environment information, and vehicle navigation task information. The first selection node, connected to the parallel node, is used to issue a driving preparation command to the vehicle. The first condition node is connected to the first selection node and is the left child node of the first selection node. It is used to determine whether the vehicle has reached its destination. The control node is connected to the first selection node and is the right child node of the first selection node. It is used to issue a driving command to the vehicle.

3. The vehicle routing decision method according to claim 2, characterized in that, The control node is a sequential node, the second subtree is the left subtree of the control node, and the third subtree is the right subtree of the control node.

4. The vehicle routing decision method according to claim 3, characterized in that, The second subtree includes a second selection node, at least one second condition node, a third selection node, at least one third condition node, and a second execution node; The second selection node is connected to the control node and is the left child node of the control node; The second condition node, connected to the second selection node, is used to determine the conditions for a long-path travel instruction. The third selection node is connected to the second selection node and is located to the right of the second condition node, and is used to issue long-path travel instructions; The third condition node is connected to the third selection node and is used to determine whether the conditions for issuing a long-path travel instruction are met. The second execution node, connected to the third selection node and located to the right of the third condition node, is used to issue a long-path travel instruction.

5. The vehicle routing decision method according to claim 3, characterized in that, The third subtree includes a fourth selection node, at least one fourth condition node, a fifth selection node, a fifth condition node, and a third execution node; The fourth selection node is connected to the control node and is the right child node of the control node; The fourth condition node is connected to the fourth selection node and is used to determine the short-path driving instruction conditions; The fifth selection node is connected to the fourth selection node and is located to the right of the fourth condition node. It is used to issue long-path travel instructions. The fifth condition node is connected to the fifth selection node and is used to determine whether the conditions for issuing a short-path travel instruction are met. The third execution node, connected to the fifth selection node and located to the right of the fifth condition node, is used to issue the requested short-path travel instruction.

6. The vehicle routing decision method according to claim 1, characterized in that, The method further includes: A finite state machine is used to manage individual vehicle navigation tasks.

7. The vehicle routing decision method according to claim 1, characterized in that, The method further includes: The vehicle's route is determined by sending navigation commands directly to the vehicle.

8. A vehicle routing decision system, characterized in that, include: The acquisition module is used to acquire current vehicle information, vehicle environment information, and vehicle navigation task information of container trucks at the port. The path determination module is used to determine the path of each vehicle based on a preset behavior tree algorithm model, according to the vehicle information, vehicle environment information and vehicle navigation task information. The behavior tree module includes multiple subtree modules. The first subtree module is used to issue driving instructions to the vehicle based on the updated vehicle information, vehicle environment information, and vehicle navigation task information. The second subtree module is used to determine whether the vehicle should travel a long route based on the updated vehicle information, vehicle environment information, and vehicle navigation task information. The third subtree module is used to determine whether the vehicle should travel a short route based on the updated vehicle information, vehicle environment information, and vehicle navigation task information.

9. The vehicle routing decision system according to claim 8, characterized in that, The first subtree module includes a parallel node, a first execution node, a first selection node, a first condition node, and a control node; The parallel node is the root node, used to update vehicle information, vehicle environment information, and vehicle navigation task information; The first execution node is connected to the parallel node and is used to update vehicle information, vehicle environment information, and vehicle navigation task information. The first selection node, connected to the parallel node, is used to issue a driving preparation command to the vehicle. The first condition node is connected to the first selection node and is the left child node of the first selection node. It is used to determine whether the vehicle has reached its destination. The control node is connected to the first selection node and is the right child node of the first selection node. It is used to issue a driving command to the vehicle.

10. The vehicle routing decision system according to claim 9, characterized in that, The control node is a sequential node, the second subtree module is the left subtree of the control node, and the third subtree module is the right subtree of the control node.

11. The vehicle routing decision system according to claim 10, characterized in that, The second subtree module includes a second selection node, at least one second condition node, a third selection node, at least one third condition node, and a second execution node; The second selection node is connected to the control node and is the left child node of the control node; The second condition node, connected to the second selection node, is used to determine the conditions for a long-path travel instruction. The third selection node is connected to the second selection node and is located to the right of the second condition node, and is used to issue long-path travel instructions; The third condition node is connected to the third selection node and is used to determine whether the conditions for issuing a long-path travel instruction are met. The second execution node, connected to the third selection node and located to the right of the third condition node, is used to issue a long-path travel instruction.

12. The vehicle routing decision system according to claim 10, characterized in that, The third subtree module includes a fourth selection node, at least one fourth condition node, a fifth selection node, a fifth condition node, and a third execution node; The fourth selection node is connected to the control node and is the right child node of the control node; The fourth condition node is connected to the fourth selection node and is used to determine the short-path driving instruction conditions; The fifth selection node is connected to the fourth selection node and is located to the right of the fourth condition node. It is used to issue long-path travel instructions. The fifth condition node is connected to the fifth selection node and is used to determine whether the conditions for issuing a short-path travel instruction are met. The third execution node, connected to the fifth selection node and located to the right of the fifth condition node, is used to issue the requested short-path travel instruction.

13. The vehicle routing decision system according to claim 8, characterized in that, Also includes: The finite state machine module is used to manage a single vehicle navigation task through a finite state machine.

14. The vehicle routing decision system according to claim 8, characterized in that, Also includes: The navigation command sending module is used to determine the vehicle's route by sending navigation commands to the vehicle.

15. A vehicle routing decision device, comprising at least one processor; and a memory coupled to said at least one processor, said memory storing executable instructions, characterized in that: When executed by the at least one processor, the executable instructions cause the implementation of the steps of the method according to any one of claims 1 to 7.

16. A chip, characterized in that: Includes a processor for retrieving and running a computer program from memory, causing a device on which the chip is mounted to perform the steps of the method as described in any one of claims 1 to 7.

17. A computer-readable storage medium storing a computer program thereon, characterized in that: When the computer program is executed by a processor, it implements the steps of the method as described in any one of claims 1 to 7 above.

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