A port unmanned container truck intersection cooperative passing method and system and medium
By optimizing the traffic sequence of the set-card system using time series prioritization and an improved whale optimization algorithm, the conflict and deadlock problems in multi-vehicle cooperative traffic at port intersections were solved, improving traffic efficiency and saving labor costs.
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Patents(China)
- Current Assignee / Owner
- 东风悦享科技有限公司
- Filing Date
- 2023-11-13
- Publication Date
- 2026-06-26
AI Technical Summary
In ports, multiple container trucks encountering collisions and deadlocks at intersections due to a lack of coordinated dispatching affects system operational efficiency. Existing collision avoidance strategies, relying only on local obstacle avoidance functions, fail to effectively solve the problem of global coordinated passage.
The time series priority algorithm and the improved whale optimization algorithm are used to optimize the truck traffic sequence, determine the traffic priority of the intersection, perform preliminary sorting through the time series priority algorithm, and further optimize through the improved whale optimization algorithm to output the optimal traffic sequence, ensuring that trucks pass through the intersection without collision.
This effectively avoids the deadlock problem caused by multiple trucks entering the intersection at the same time, improves traffic efficiency and work efficiency, and saves labor costs.
Smart Images
Figure CN117765719B_ABST
Abstract
Description
Technical Field
[0001] This invention relates to the field of unmanned container truck technology in ports, and in particular to a method, system and medium for cooperative passage of unmanned container trucks at intersections in ports. Background Technology
[0002] After planning a global driving path for each truck, it is also necessary to coordinate and adjust the passage order of multiple trucks at intersections. If each truck only travels according to its own planned path without considering the influence of other trucks, multi-vehicle conflicts or deadlocks can easily occur, thus affecting the overall operational efficiency of the system. Currently, the port's collision avoidance strategy relies solely on sensors and the local obstacle avoidance functions of the vehicles themselves, without coordinating and scheduling multiple trucks about to pass through the same intersection at the system-wide level.
[0003] The purpose of the intersection cooperative traffic algorithm is to pre-determine the traffic priority of all container trucks at the intersections they are about to pass, allowing some trucks to wait while others proceed first, thus avoiding deadlock problems caused by multiple vehicles entering the intersection simultaneously. If container trucks on the same road have different operating speeds or are engaged in loading / unloading operations, the vehicles maintain a safe distance based on sensor information and autonomously control their acceleration, deceleration, following, or temporary stopping. Therefore, avoiding collisions or deadlocks through multi-vehicle cooperation within port operation areas primarily requires coordinating and controlling the passage of multiple vehicles at intersections to prevent collisions or deadlocks. Summary of the Invention
[0004] In view of the above problems, the present invention provides a method, system and medium for unmanned container trucks to cooperate in passing through intersections in ports. It not only determines the priority of all container trucks at the intersections they are about to pass through in advance, allowing some container trucks to wait and others to go first, but also avoids the deadlock problem caused by multiple vehicles entering the intersection at the same time, thereby improving traffic efficiency, improving work efficiency and saving labor costs.
[0005] To achieve the above and other related objectives, the present invention provides the following technical solution:
[0006] A method for coordinating traffic at an unmanned truck intersection in a port, the method comprising:
[0007] L1. The unmanned truck sequence performs port operation tasks. When passing through intersections, the time sequence data of the vehicles arriving at the intersection and the vehicle number data are obtained in real time based on the road test equipment at the intersection. Based on the vehicle number data, the task trajectory data of the vehicles is obtained.
[0008] L2. Based on the vehicle arrival time series data and vehicle number data, and using the vehicle number data as a basis, a time series priority algorithm is used to perform preliminary optimization of the vehicle passage sequence to obtain preliminary optimized vehicle passage sequence data.
[0009] L3. Based on the preliminary optimized traffic sequence data and the vehicle's mission trajectory data, an improved whale optimization algorithm is used to optimize the vehicle's traffic sequence and output the optimal traffic data.
[0010] Furthermore, in step L2, the preliminary optimization of the vehicle passage sequence using a time series priority algorithm includes:
[0011] L21. Based on the vehicle arrival time series data and vehicle number data, establish a two-dimensional array (x... i c i ), where x i For the number data of the i-th vehicle, c i Given the time series data of the i-th vehicle, we obtain a two-dimensional array of vehicle data.
[0012] L22. Based on the two-dimensional array data information of the vehicle, establish a time series optimization function G.
[0013] ,
[0014] Where i=1,2,3..nj=1,2,3,...n, we obtain the sorted two-dimensional array data information of the vehicles;
[0015] L23. Based on the sorted two-dimensional array data of the vehicles, the vehicle passage sequence is optimized to obtain preliminary optimized vehicle passage sequence data.
[0016] Furthermore, based on the time series optimization function G, if c i =c j Then it is determined that the vehicles are in different lanes and arrive at the same intersection at the same time, where c i For the time series data of the i-th vehicle, c j This is the time series data information for the j-th vehicle.
[0017] Furthermore, in step L3, the optimization of the vehicle passage sequence using the improved whale optimization algorithm includes:
[0018] L31. Based on the preliminary optimized passage sequence data of the vehicle, redefine the whale and its position in the whale optimization algorithm, and set the population size, learning factor and iteration number of the whale swarm algorithm to obtain the initialized whale population data.
[0019] L32. Based on the initialized whale population data, establish the whale population fitness function H.
[0020] ,
[0021] Where n is the population size, h i For initial whale population data, λ i The fitness factor of the whale population is used to obtain updated vehicle passage sequence data.
[0022] L33. Determine whether the updated vehicle passage sequence data is a passable vehicle sequence based on the vehicle's mission trajectory data. If so, output the vehicle passage data.
[0023] L34. Repeat steps L31-L33 to iteratively optimize the vehicle traffic data information to obtain the optimal vehicle traffic data information.
[0024] Furthermore, the fitness factor λ of the whale population i The constraints are:
[0025] ,
[0026] ,
[0027] Where f is the constraint function of the fitness factor.
[0028] Furthermore, the optimal data information for vehicle passage is the vehicle numbering and sorting information of the unmanned truck sequence passing through the intersection without collision with other vehicles.
[0029] To achieve the above and other related objectives, the present invention also provides a system for implementing the cooperative passage method for unmanned trucks at intersections in ports as described in any of the claims.
[0030] The data acquisition module is used to acquire unmanned truck sequence operation task data, time series data, and number data.
[0031] A data optimization processing module, connected to the data acquisition module, is used to optimize the vehicle passage sequence using an improved whale optimization algorithm;
[0032] The data execution module, connected to the data optimization processing module, is used to receive optimized data information on vehicle traffic and to control and adjust the vehicles.
[0033] Furthermore, the system also includes intersection road testing equipment, used to acquire time series data information of vehicle arrivals at intersections and vehicle number data information.
[0034] Furthermore, the system also includes an early warning module connected to the data execution module, used to provide early warnings when vehicle malfunctions.
[0035] To achieve the above and other related objectives, the present invention also provides a computer-readable storage medium storing a computer program programmed or configured to perform any of the port unmanned truck intersection cooperative passage methods described herein.
[0036] The present invention has the following positive effects:
[0037] 1. This invention uses a time series priority algorithm to initially optimize the vehicle passage sequence. This algorithm has strong applicability and can effectively sort the vehicle passage sequence, thereby improving the efficiency of vehicle passage and facilitating further optimization of vehicles.
[0038] 2. This invention optimizes the vehicle passage sequence by using an improved whale optimization algorithm. It not only determines the passage priority of all trucks at the intersections they are about to pass in advance, allowing some trucks to wait and others to go first, but also avoids the deadlock problem caused by multiple trucks entering the intersection at the same time, thereby improving traffic efficiency, improving work efficiency, and saving labor costs. Attached Figure Description
[0039] Figure 1 This is a schematic diagram of the method flow of the present invention;
[0040] Figure 2 This is a schematic diagram of intersection traffic flow according to the present invention (I);
[0041] Figure 3 This is a schematic diagram (II) of the intersection traffic flow according to the present invention. Detailed Implementation
[0042] The exemplary embodiments of this disclosure are described below with reference to the accompanying drawings, including various details of the embodiments to aid understanding, and should be considered merely exemplary. Therefore, those skilled in the art will recognize that various changes and modifications can be made to the embodiments described herein without departing from the scope and spirit of this disclosure. Similarly, for clarity and brevity, descriptions of well-known functions and structures are omitted in the following description.
[0043] Example 1: As Figure 1 As shown, a method for coordinated passage of unmanned container trucks at a port intersection includes:
[0044] L1. The unmanned truck sequence performs port operation tasks. When passing through intersections, the time sequence data of the vehicles arriving at the intersection and the vehicle number data are obtained in real time based on the road test equipment at the intersection. Based on the vehicle number data, the task trajectory data of the vehicles is obtained.
[0045] L2. Based on the vehicle arrival time series data and vehicle number data, and using the vehicle number data as a basis, a time series priority algorithm is used to perform preliminary optimization of the vehicle passage sequence to obtain preliminary optimized vehicle passage sequence data.
[0046] L3. Based on the preliminary optimized traffic sequence data and the vehicle's mission trajectory data, an improved whale optimization algorithm is used to optimize the vehicle's traffic sequence and output the optimal traffic data.
[0047] In this embodiment, step L2, the preliminary optimization of the vehicle passage sequence using a time series priority algorithm, includes:
[0048] L21. Based on the vehicle arrival time series data and vehicle number data, establish a two-dimensional array (x... i c i ), where x i For the number data of the i-th vehicle, c i Given the time series data of the i-th vehicle, we obtain a two-dimensional array of vehicle data.
[0049] L22. Based on the two-dimensional array data information of the vehicle, establish a time series optimization function G.
[0050] ,
[0051] Where i=1,2,3..nj=1,2,3,...n, we obtain the sorted two-dimensional array data information of the vehicles;
[0052] L23. Based on the sorted two-dimensional array data of the vehicles, the vehicle passage sequence is optimized to obtain preliminary optimized vehicle passage sequence data.
[0053] In this embodiment, based on the time series optimization function G, if c i =c j Then it is determined that the vehicles are in different lanes and arrive at the same intersection at the same time, where c iFor the time series data of the i-th vehicle, c j This is the time series data information for the j-th vehicle.
[0054] In this embodiment, step L3, which involves optimizing the vehicle passage sequence using the improved whale optimization algorithm, includes:
[0055] L31. Based on the preliminary optimized passage sequence data of the vehicle, redefine the whale and its position in the whale optimization algorithm, and set the population size, learning factor and iteration number of the whale swarm algorithm to obtain the initialized whale population data.
[0056] L32. Based on the initialized whale population data, establish the whale population fitness function H.
[0057] ,
[0058] Where n is the population size, h i For initial whale population data, λ i The fitness factor of the whale population is used to obtain updated vehicle passage sequence data.
[0059] L33. Determine whether the updated vehicle passage sequence data is a passable vehicle sequence based on the vehicle's mission trajectory data. If so, output the vehicle passage data.
[0060] L34. Repeat steps L31-L33 to iteratively optimize the vehicle traffic data information to obtain the optimal vehicle traffic data information.
[0061] In this embodiment, the fitness factor λ of the whale population i The constraints are:
[0062] ,
[0063] ,
[0064] Where f is the constraint function of the fitness factor.
[0065] In this embodiment, the optimal data information for vehicle passage is the vehicle numbering and sorting information of the unmanned truck sequence passing through the intersection without collision with other vehicles.
[0066] Example 2: Based on the method for coordinated passage of unmanned container trucks at intersections in a port as described in Example 1, the present invention will be further explained and described below.
[0067] like Figure 1As shown, a method for coordinated passage of unmanned container trucks at a port intersection includes:
[0068] L1. The unmanned truck sequence performs port operation tasks. When passing through intersections, the time sequence data of the vehicles arriving at the intersection and the vehicle number data are obtained in real time based on the road test equipment at the intersection. Based on the vehicle number data, the task trajectory data of the vehicles is obtained.
[0069] L2. Based on the vehicle arrival time series data and vehicle number data, and using the vehicle number data as a basis, a time series priority algorithm is used to perform preliminary optimization of the vehicle passage sequence to obtain preliminary optimized vehicle passage sequence data.
[0070] L3. Based on the preliminary optimized traffic sequence data and the vehicle's mission trajectory data, an improved whale optimization algorithm is used to optimize the vehicle's traffic sequence and output the optimal traffic data.
[0071] In this embodiment, the real-time vehicle information is first processed using electronic fences to count the vehicles arriving at each intersection's electronic fence and record and update the arrival time of the corresponding vehicles. Then, the following logic is executed for each intersection: All trucks within the electronic fence of the current intersection are sequentially traversed. If no trucks are currently passing through the intersection, the earliest arriving truck that has not yet entered the intersection is allowed to pass through. Otherwise, it is necessary to determine whether there is a collision risk between the current truck and all trucks currently passing through the intersection. If a conflict exists, the current truck is skipped and temporarily stopped; if there is no conflict, the current truck enters the intersection normally. This method ensures that the trajectories of all vehicles currently passing through the intersection will not collide, thus largely avoiding collisions or deadlocks within the intersection. Intersections formed by one-way work roads and main roads within the yard's partitions are also treated as intersections.
[0072] In this embodiment, as Figure 2 or Figure 3 As shown, the arrival times of the trucks are in the following order: truck 1, truck 2, and truck 3. First, when truck 1 arrives, there are no vehicles passing through the intersection, so truck 1 enters the intersection. Then, it is determined whether truck 2 can enter the intersection. It is found that the driving trajectories of truck 2 and truck 1, which are passing through the intersection, do not conflict, so truck 2 also passes through the intersection normally. Finally, it is determined that truck 3 has a trajectory conflict with trucks 1 and 2, which are passing through the intersection. Therefore, truck 3 needs to wait for trucks 1 and 2 to pass before it can enter the intersection.
[0073] Based on the same judgment process described above, it can be seen that truck 1 has priority in passing through the intersection. Then, trucks 2 and 3 can enter the intersection simultaneously because their driving trajectories do not conflict. Therefore, the passage order at the intersection is truck 1 > truck 2, truck 3. This method detects and determines the vehicle passage order at all intersections in real time during the truck's journey, thereby effectively avoiding the deadlock problem that may occur at intersections.
[0074] This invention provides a system for implementing the cooperative passage method for unmanned trucks at intersections in ports as described in any of the claims.
[0075] The data acquisition module is used to acquire unmanned truck sequence operation task data, time series data, and number data.
[0076] A data optimization processing module, connected to the data acquisition module, is used to optimize the vehicle passage sequence using an improved whale optimization algorithm;
[0077] The data execution module, connected to the data optimization processing module, is used to receive optimized data information on vehicle traffic and to control and adjust the vehicles.
[0078] In this embodiment, the system also includes a roadside testing device for acquiring time-series data of vehicle arrivals at the intersection and vehicle identification number data.
[0079] In this embodiment, the system further includes an early warning module connected to the data execution module, used to provide early warning reminders when a vehicle malfunctions.
[0080] The present invention provides a computer-readable storage medium storing a computer program programmed or configured to perform any of the port unmanned truck intersection cooperative passage methods described herein.
[0081] Any references to memory, storage, database, or other media used in the embodiments provided in this application may include non-volatile and / or volatile memory. Non-volatile memory may include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM), or flash memory. Volatile memory may include random access memory (RAM) or external cache memory. By way of illustration and not limitation, RAM is available in a variety of forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), dual data rate SDRAM (DDRSDRAM), enhanced SDRAM (ESDRAM), synchronous link DRAM (SLDRAM), RAMbus direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and RAMbus dynamic RAM (RDRAM), etc.
[0082] In summary, this invention not only determines the priority of all trucks at the intersections they are about to pass in advance, allowing some trucks to wait and others to go first, but also avoids the deadlock problem caused by multiple vehicles entering the intersection at the same time, thereby improving traffic efficiency, increasing work efficiency, and saving labor costs.
[0083] The specific embodiments described above do not constitute a limitation on the scope of protection of this disclosure. Those skilled in the art should understand that various modifications, combinations, sub-combinations, and substitutions can be made according to design requirements and other factors. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of this disclosure should be included within the scope of protection of this disclosure.
Claims
1. A method for cooperative passage of unmanned container trucks at intersections in ports, characterized in that, The method includes: L1. The unmanned truck sequence performs port operation tasks. When passing through intersections, the time sequence data of the vehicles arriving at the intersection and the vehicle number data are obtained in real time based on the road test equipment at the intersection. Based on the vehicle number data, the task trajectory data of the vehicles is obtained. L2. Based on the vehicle arrival time series data and vehicle number data, and using the vehicle number data as a basis, a time series priority algorithm is used to perform preliminary optimization of the vehicle passage sequence to obtain preliminary optimized vehicle passage sequence data. L3. Based on the preliminary optimized passage sequence data information and the vehicle's task trajectory data information, the improved whale optimization algorithm is used to optimize the vehicle's passage sequence and output the optimal data information for vehicle passage. In step L2, the preliminary optimization of the vehicle passage sequence using a time series priority algorithm includes: L21. Based on the vehicle arrival time series data and vehicle number data, establish a two-dimensional array (x... i c i ), where x i For the number data of the i-th vehicle, c i Given the time series data of the i-th vehicle arriving at the intersection, we obtain a two-dimensional array of vehicle data. L22. Based on the two-dimensional array data information of the vehicle, establish a time series optimization function G. ， Where i=1,2,3,...,n, j=1,2,3,...n, we obtain the sorted two-dimensional array data information of the vehicles; L23. Based on the sorted two-dimensional array data of the vehicles, the vehicle passage sequence is optimized to obtain preliminary optimized vehicle passage sequence data. In step L3, optimizing the vehicle passage sequence using the improved whale optimization algorithm includes: L31. Based on the preliminary optimized passage sequence data of the vehicle, redefine the whale and its position in the whale optimization algorithm, and set the population size, learning factor and iteration number of the whale swarm algorithm to obtain the initialized whale population data. L32. Based on the initialized whale population data, establish the whale population fitness function H. ， Where n is the population size, h i For initial whale population data, λ i The fitness factor of the whale population is used to obtain updated vehicle passage sequence data. L33. Determine whether the updated vehicle passage sequence data is a passable vehicle sequence based on the vehicle's mission trajectory data. If so, output the vehicle passage data. L34. Repeat steps L31-L33 to iteratively optimize the vehicle traffic data information to obtain the optimal vehicle traffic data information.
2. The method for cooperative passage of unmanned trucks at intersections in ports according to claim 1, characterized in that: Based on the time series optimization function G, if c i =c j Then it is determined that the vehicles are in different lanes and arrive at the same intersection at the same time, where c i For the time series data of the i-th vehicle arriving at the intersection, c j This is the time series data of the j-th vehicle arriving at the intersection.
3. The method for cooperative passage of unmanned container trucks at intersections in ports according to claim 1, characterized in that, The fitness factor λ of the whale population i The constraints are: ， ， Where f is the constraint function of the fitness factor.
4. The method for cooperative passage of unmanned container trucks at intersections in ports according to claim 1, characterized in that: The optimal data information for vehicle passage is the vehicle numbering and sorting information of the unmanned truck sequence passing through the intersection without collision with other vehicles.
5. A system for implementing the cooperative passage method for unmanned trucks at intersections in ports as described in any one of claims 1-4, characterized in that: The data acquisition module is used to acquire unmanned truck sequence operation task data, time series data, and number data. A data optimization processing module, connected to the data acquisition module, is used to optimize the vehicle passage sequence using an improved whale optimization algorithm; The data execution module, connected to the data optimization processing module, is used to receive optimized data information on vehicle traffic and to control and adjust the vehicles.
6. The system according to claim 5, characterized in that: The system also includes roadside testing equipment at intersections, used to acquire time-series data of vehicle arrivals at intersections and vehicle identification numbers.
7. The system according to claim 5, characterized in that: The system also includes an early warning module, which is connected to the data execution module, and is used to provide early warnings when the vehicle malfunctions.
8. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores a computer program that is programmed or configured to perform the port unmanned truck intersection cooperative passage method according to any one of claims 1 to 4.