An intelligent shuttle method and system based on a modular autonomous driving vehicle

Abstract

The present invention provides an intelligent connection method and system based on a modular autonomous vehicle, which relates to the technical field of intelligent traffic management. The method includes obtaining the connection reservation information of passengers and the historical connection demand information of passengers; planning the optimal departure time interval and the service community area based on the historical connection demand information and the connection reservation information of passengers; performing vehicle scheduling and route planning based on the connection reservation information of passengers, the planned optimal departure time interval, and the service community area; controlling the vehicle decoupling for passenger connection based on the planned vehicle route scheduling information, and real-time obtaining the vehicle vacancy information and the connection reservation information of passengers, and repeating vehicle scheduling and route planning until the modular autonomous vehicle reaches a preset first designated location for formation; and the modular autonomous vehicle after formation runs to the first designated location according to a preset route. The present invention improves the resource utilization rate and the flexibility of the vehicle.

Description

Technical Field

[0001] The present invention relates to the technical field of intelligent transportation management. Specifically, it relates to an intelligent connection method and system based on modular autonomous vehicles. Background Art

[0002] The scale of urban space in China shows an expanding trend. In many large cities, suburbanization is obvious. More and more people choose to live on the urban fringe far from the city center. Residents need to take connecting buses to reach the starting stations of urban rail transit or transportation hubs such as nearby airports and stations. In low-density areas, the travel demand of residents is small and their living locations are scattered, making it difficult to concentrate on regular bus routes. Therefore, passengers need to walk to fixed bus stops, and the bus departure intervals in low-density areas are large, making it difficult to meet immediate needs. For special groups such as the elderly or disabled passengers in the suburbs, bus travel is even more inconvenient. In areas with low passenger flow density, the operating costs of regular bus lines are high and the return on investment is low. Some newly developed suburban areas have even become blind spots for bus services. As a result, the number of private cars has further increased, and the resulting environmental problems and urban congestion problems have become increasingly serious. What kind of operation method can balance the interests between operators and passengers has become an urgent problem to be solved.

[0003] Therefore, there is an urgent need for a method and system that can intelligently connect passengers to solve the above problems. Summary of the Invention

[0004] The purpose of the present invention is to provide an intelligent connection method and system based on modular autonomous vehicles to improve the above problems. To achieve the above purpose, the technical solutions adopted by the present invention are as follows:

[0005] In a first aspect, the present application provides an intelligent connection method based on modular autonomous vehicles, including:

[0006] Obtaining the connection reservation information of passengers and the historical connection demand information of passengers. The reservation connection information of passengers includes the time information and connection location information reserved by passengers, and the historical connection demand information of passengers includes the historical connection demand quantity information and historical connection demand location information of passengers;

[0007] Sending the historical connection demand information of passengers and the connection reservation information of passengers to a preset modular autonomous vehicle planning model for optimal departure time interval planning and service area planning of modular autonomous vehicles, and obtaining the optimal departure time interval and service area of the modular autonomous vehicle after planning;

[0008] Based on the passenger's connection reservation information, the optimal departure time interval of the planned modular autonomous vehicle, and the service community area, vehicle scheduling and path planning are performed to obtain the planned vehicle scheduling path information;

[0009] Based on the planned vehicle path scheduling information, the modular autonomous vehicle is controlled to uncouple for passenger connection, and the vehicle vacancy information and the passenger's connection reservation information are obtained in real time. Vehicle scheduling and path planning are repeated until the modular autonomous vehicle reaches a preset first designated location for formation;

[0010] The modular autonomous vehicle after formation runs to the first designated location according to the preset route.

[0011] In a second aspect, the present application also provides an intelligent connection system based on a modular autonomous vehicle, including:

[0012] An acquisition unit, configured to acquire the passenger's connection reservation information and the passenger's historical connection demand information. The passenger's reserved connection information includes the time information and the connection location information reserved by the passenger, and the passenger's historical connection demand information includes the historical connection demand quantity information and the historical connection demand location information of the passenger;

[0013] A first processing unit, configured to send the passenger's historical connection demand information and the passenger's connection reservation information to a preset modular autonomous vehicle planning model for planning the optimal departure time interval and the service community area of the modular autonomous vehicle, so as to obtain the optimal departure time interval and the service community area of the planned modular autonomous vehicle;

[0014] A second processing unit, configured to perform vehicle scheduling and path planning based on the passenger's connection reservation information, the optimal departure time interval of the planned modular autonomous vehicle, and the service community area, so as to obtain the planned vehicle scheduling path information;

[0015] A third processing unit, configured to control the modular autonomous vehicle to uncouple for passenger connection based on the planned vehicle path scheduling information, and to obtain the vehicle vacancy information and the passenger's connection reservation information in real time. Vehicle scheduling and path planning are repeated until the modular autonomous vehicle reaches a preset first designated location for formation;

[0016] A fourth processing unit, configured to make the modular autonomous vehicle after formation run to the first designated location according to the preset route.

[0017] The beneficial effects of the present invention are:

[0018] According to the actual travel situation of passengers, the system of the present invention utilizes demand response technology to achieve intelligent scheduling according to the reservation situation or immediate needs of passengers, optimize the vehicle operation route and departure interval, thereby improving the reliability and efficiency of the shuttle service; the system uses modular design, and the vehicle can be dynamically disassembled and assembled according to needs, improving resource utilization and vehicle flexibility. In addition, the application of autonomous driving technology enables the vehicle to autonomously sense the surrounding environment, plan the driving route, and execute driving tasks, significantly enhancing driving safety and traffic efficiency, and reducing traffic accidents and congestion problems.

[0019] Other features and advantages of the present invention will be described in the subsequent specification, and, in part, will be obvious from the specification, or can be understood by implementing the embodiments of the present invention. The objectives and other advantages of the present invention can be achieved and obtained through the structures specifically pointed out in the written specification, claims, and drawings. Brief Description of the Drawings

[0020] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings required for use in the embodiments will be briefly introduced below. It should be understood that the following drawings only show some embodiments of the present invention, and therefore should not be regarded as limiting the scope. For those of ordinary skill in the art, without creative efforts, other related drawings can also be obtained based on these drawings.

[0021] Figure 1 It is a schematic flow chart of the intelligent shuttle method based on a modular autonomous vehicle described in the embodiments of the present invention;

[0022] Figure 2 It is a schematic structural diagram of the intelligent shuttle system based on a modular autonomous vehicle described in the embodiments of the present invention.

[0023] In the figure: 701, acquisition unit; 702, first processing unit; 703, second processing unit; 704, third processing unit; 705, fourth processing unit; 7021, first calculation sub-unit; 7022, second calculation sub-unit; 7023, third calculation sub-unit; 70221, fourth calculation sub-unit; 70222, fifth calculation sub-unit; 70223, sixth calculation sub-unit; 70224, seventh calculation sub-unit; 70225, eighth calculation sub-unit; 70231, ninth calculation sub-unit; 70232, tenth calculation sub-unit; 70233, eleventh calculation sub-unit; 7031, first processing sub-unit; 7032, second processing sub-unit; 7033, third processing sub-unit; 7034, fourth processing sub-unit; 70311, fifth processing sub-unit; 70312, sixth processing sub-unit; 70313, seventh processing sub-unit. Detailed Embodiments

[0024] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are some, but not all, of the embodiments of the present invention. The components of the embodiments of the present invention described and illustrated herein generally can be arranged and designed in a variety of different configurations. Therefore, the following detailed description of the embodiments of the present invention provided in the drawings is not intended to limit the scope of the claimed invention, but merely represents selected embodiments of the present invention. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts fall within the scope of protection of the present invention.

[0025] It should be noted that like reference numerals and letters denote like items in the following drawings. Therefore, once an item is defined in one drawing, it does not need to be further defined and explained in subsequent drawings. At the same time, in the description of the present invention, terms such as "first" and "second" are only used for descriptive distinction and cannot be construed as indicating or implying relative importance.

[0026] Embodiment 1:

[0027] This embodiment provides an intelligent connection method based on a modular autonomous vehicle.

[0028] See Figure 1 , which shows that this method includes steps S1, S2, S3, S4, and S5.

[0029] Step S1: Obtain the connection reservation information of the passenger and the historical connection demand information of the passenger. The reservation connection information of the passenger includes the time information and the connection location information reserved by the passenger. The historical connection demand information of the passenger includes the historical connection demand quantity information and the historical connection demand location information of the passenger;

[0030] It can be understood that by obtaining the connection reservation information and historical demands of passengers in this step, vehicles can be better arranged, the connection efficiency can be improved, and the waiting time of passengers can be reduced. According to the historical demand data, the peak connection period and popular connection locations can be predicted, thereby optimizing the allocation of vehicles and resources. By analyzing the historical behaviors and demands of passengers, more personalized and accurate services can be provided, enhancing user satisfaction. And it can provide data support for subsequent vehicle scheduling and route planning.

[0031] Step S2: Send the historical transfer demand information of the passengers and the transfer reservation information of the passengers to a preset modular autonomous driving vehicle planning model for planning the optimal departure time interval and service area of the modular autonomous driving vehicle, and obtain the optimal departure time interval and service area of the planned modular autonomous driving vehicle;

[0032] It can be understood that in this step, by sending the historical transfer demand information and transfer reservation information of the passengers to the planning model for accurate planning of the departure time interval and service area, the utilization rate of the vehicle can be maximized and the transfer efficiency can be improved. The planning model is based on demand density and cost calculation, can reasonably allocate vehicles and resources, and avoid resource waste. The planning model optimizes the scheduling plan by comprehensively calculating the costs of passengers and operators, and reduces the overall operating cost. Through accurate time and area planning, the planning model reduces the waiting time and travel cost of passengers, and improves user satisfaction. In this step, step S2 includes step S21, step S22 and step S23.

[0033] Step S21: Calculate the demand density of the passengers based on the historical transfer demand information of the passengers to obtain the demand density information of the passengers;

[0034] It can be understood that in this step, the demand density of the passengers is calculated by dividing the demand quantity by the area of the region, and the demand density information of the passengers can be obtained. Through the demand density calculation, the regions with large transfer demand can be accurately identified, providing a basis for subsequent planning and scheduling.

[0035] Step S22: Calculate the travel cost information of the passengers and the operating cost information of the operator based on the demand density information of the passengers and the transfer reservation information of the passengers;

[0036] It can be understood that this step provides basic data for subsequent optimization. By comprehensively considering the costs of passengers and operators, the optimal scheduling plan is realized. In this step, step S22 includes

[0037] Step S221: Calculate the average waiting time cost of the passengers based on the transfer reservation information of the passengers, the preset vehicle status information and the average waiting time cost calculation formula of the passengers;

[0038] It can be understood that the average waiting time cost calculation formula in this step is as follows:

[0039]

[0040] where C w$C_w$ is the average waiting time cost of passengers, $w$ is the unit waiting time cost of passengers, $L$ is the length of the rectangular service area, $W$ is the width of the rectangular service area, $Q$ is the demand density of the service area, $h$ is the departure interval, $k$ is the coefficient in the TSP shortest expected path formula, $A$ is the area of the service cell, and $c$ is the capacity of the vehicle unit.

[0041] Step S222: Calculate the average in-vehicle time cost of passengers based on the pick-up reservation information of passengers, the preset vehicle status information, and the average in-vehicle time cost calculation formula of passengers;

[0042] It can be understood that the average in-vehicle time cost calculation formula of passengers in this step is as follows:

[0043]

[0044] Among them, $C$ V represents the average in-vehicle time cost of passengers, $J$ is the length of the pick-up distance, that is, the distance from the subway station to the pick-up area, $L$ is the length of the rectangular service area, $W$ is the width of the rectangular service area, $V$ is the driving speed of the modular unit vehicle, where $\beta$ is a constant in the TSP longest expected distance, $k$ is the coefficient in the TSP shortest expected path formula, $A$ is the area of the service cell, $c$ is the capacity of the vehicle unit, and $V$ is the driving speed of the modular unit vehicle.

[0045] Among them, assume that the TSP detour time $t$ of each vehicle unit in the cell i is independent and follows a uniform distribution, and the value range is The mean is Among them, the coefficient $\sigma$ is the degree of $t$ i deviating from the average value, and the value is $0\leq\sigma\leq1$. Introduce the random array $r$ i , which follows a uniform distribution on $[0,1]$, then follows a uniform distribution on, and the mean is Replace $t$ i with Then the expected value $T$ of the maximum time of the vehicle unit's TSP C can be expressed as Among them

[0046] Step S223: Sum the average waiting time cost and the average in-vehicle time cost of passengers to obtain the travel cost information of passengers;

[0047] It can be understood that in this step, by accurately calculating the operating cost, the system can optimize resource allocation, reduce the operating cost, and improve economic benefits.

[0048] Step S224: Calculate based on the preset historical operation information, the preset long-haul transportation segment cost calculation formula, and the feeder transportation segment cost calculation formula to obtain the operator's long-haul transportation segment operation cost and feeder transportation segment operation cost;

[0049] It can be understood that the preset long-haul transportation segment cost calculation formula in this step is as follows:

[0050]

[0051] Among them, C L represents the long-haul transportation segment cost, a is the fixed cost in the unit operation cost, b is the positive correlation coefficient between the variable cost and the number and capacity of modular unit vehicles, A is the area of the service area, Q is the demand density of the service area, h is the departure interval, J is the feeder distance length, that is, the distance length from the subway station to the area to be fed, L is the length of the rectangular service area, W is the width of the rectangular service area, and V is the driving speed of the modular unit vehicle;

[0052] Among them, the feeder transportation segment cost calculation formula is as follows:

[0053]

[0054] Among them, C c represents the feeder transportation segment cost, a is the fixed cost in the unit operation cost, b is the positive correlation coefficient between the variable cost and the number and capacity of modular unit vehicles, c is the capacity of the vehicle unit, β is the constant in the TSP longest expected distance, k is the coefficient in the TSP shortest expected path formula, A is the area of the service area, Q is the demand density of the service area, L is the length of the rectangular service area, W is the width of the rectangular service area, and V is the driving speed of the modular unit vehicle.

[0055] Step S225: Perform a summation calculation based on the operator's long-haul transportation segment operation cost and feeder transportation segment operation cost to obtain the operator's operation cost information.

[0056] It can be understood that by calculating the operator's operation cost information in this step, the total cost of the operator in the entire feeder service process can be comprehensively evaluated, providing data support for cost management and optimization.

[0057] Step S23: Calculate the average total cost of passenger feeding based on the passenger's travel cost information and the operator's operation cost information, and calculate based on the average total cost of passenger feeding, the preset optimal departure interval calculation formula, and the service area area calculation formula to obtain the optimal departure time interval and service area area of the modular autonomous vehicle.

[0058] It can be understood that through scientific calculation of the optimal departure time interval in this step, the system can effectively reduce the waiting time of passengers and improve passenger satisfaction. By calculating the optimal service cell area, the system can cover more high-demand areas and improve service efficiency. Considering the costs of passengers and operators comprehensively, the system can provide high-quality feeder services while reducing operating costs.

[0059] In this step, step S33 includes step S331, step S332, and step S333.

[0060] Step S331: Sum and calculate the average total cost of passenger feeder by adding the travel cost information of the passengers and the operating cost information of the operator.

[0061] It can be understood that the calculation formula for the average total cost of passenger feeder in this step is as follows:

[0062]

[0063] Where, C a is the average total cost of passenger feeder, a is the fixed cost in the unit operating cost, b is the positive correlation coefficient between the variable cost and the number and capacity of modular unit vehicles, c is the capacity of the vehicle unit, w is the waiting cost per unit time of passengers, β is the constant in the longest expected distance of TSP, k is the coefficient in the shortest expected path formula of TSP, A is the area of the service cell, Q is the demand density of the service area, L is the length of the rectangular service area, W is the width of the rectangular service area, J is the length of the feeder distance, that is, the distance from the subway station to the area to be fed, V is the driving speed of the modular unit vehicle, w is the waiting cost per unit time of passengers, h is the departure interval, and v is the cost per unit time of passengers in the vehicle.

[0064] Step S332: Take the first-order partial derivatives of the departure interval and the service cell area in the calculation formula of the average total cost of passenger feeder, and set them equal to zero to obtain the first-order partial derivative formula for calculating the departure interval and the service cell area.

[0065] It can be understood that in this step, with the minimum average system cost per trip as the final objective function, the calculation formula of the average total cost of passenger feeder is processed, and by taking the first-order partial derivative formula and setting it equal to 0, where the final objective function is as follows:

[0066]

[0067] Constraint conditions:

[0068]

[0069] h≥hmin , A ≥ 0

[0070] Among them, C a is the average total cost of passenger connection, a is the fixed cost in the unit operation cost, b is the positive correlation coefficient between the variable cost and the number and capacity of modular unit vehicles, c is the capacity of the vehicle unit, w is the waiting cost per unit time of passengers, β is the constant in the longest expected distance of TSP, k is the coefficient in the shortest expected path formula of TSP, A is the area of the service community, Q is the demand density of the service area, L is the length of the rectangular service area, W is the width of the rectangular service area, J is the connection distance length, that is, the distance length from the subway station to the area to be connected, V is the driving speed of the modular unit vehicle, h is the departure interval, h min is the minimum departure interval, and w is the waiting cost per unit time of passengers represents a positive integer, and v is the cost per unit time of passengers in the vehicle;

[0071] Among them, I is the number of vehicle units, which must be a positive integer, and due to road condition restrictions, the maximum number of vehicle units is 11; the second constraint is the non - negative constraint of the decision variable, h min is the minimum departure interval, which is determined by objective factors such as actual safety requirements and road traffic capacity

[0072] Among them, the results of the first - order partial derivative formula are as follows:

[0073]

[0074] Among them, C a is the average total cost of passenger connection, a is the fixed cost in the unit operation cost, b is the positive correlation coefficient between the variable cost and the number and capacity of modular unit vehicles, c is the capacity of the vehicle unit, w is the waiting cost per unit time of passengers, β is the constant in the longest expected distance of TSP, k is the coefficient in the shortest expected path formula of TSP, A is the area of the service community, Q is the demand density of the service area, L is the length of the rectangular service area, W is the width of the rectangular service area, J is the connection distance length, that is, the distance length from the subway station to the area to be connected, V is the driving speed of the modular unit vehicle, h is the departure interval, w is the waiting cost per unit time of passengers, and v is the cost per unit time of passengers in the vehicle

[0075] Step S333: Perform the second - order partial derivative calculation of the departure interval and the service community area based on the first - order partial derivative formula of the departure interval and the service community area, and obtain the optimal departure interval and the optimal service community area

[0076] It can be understood that the formula obtained by the second - order partial derivative calculation in this step is as follows:

[0077]

[0078] Among them, A * represents the optimal service cell area, h * represents the optimal departure headway, a is the fixed cost in the unit operation cost, b is the positive correlation coefficient of the variable cost with the number and capacity of modular unit vehicles, c is the capacity of the vehicle unit, w is the waiting cost per unit time of passengers, β is the constant in the longest expected distance of TSP, k is the coefficient in the shortest expected path formula of TSP, Q is the demand density of the service area, L is the length of the rectangular service area, W is the width of the rectangular service area, J is the length of the connection distance, that is, the distance from the subway station to the area to be connected, V is the driving speed of the modular unit vehicle, w is the waiting time cost per unit of passengers, and v is the in-vehicle time cost per unit of passengers.

[0079] Step S3: Based on the passenger connection reservation information, the optimal departure time interval and service cell area of the planned modular autonomous vehicle, perform vehicle scheduling and path planning to obtain the planned vehicle scheduling path information;

[0080] It can be understood that in this step, through a reasonable scheduling model and optimal path planning, it is ensured that the vehicle can arrive at the connection location on time, improving the connection efficiency. Among them, by using an optimization algorithm for path planning, it is ensured that the vehicle selects the optimal path, reducing the driving time and distance, and lowering the operation cost. By optimizing vehicle scheduling and path planning, the system can effectively control the operation cost and improve the economic benefit. In this step, step S3 includes step S31, step S32, step S33, and step S34.

[0081] Step S31: Construct a connection network model according to the passenger connection reservation information, and establish a weight matrix for passengers to take the vehicle based on the passenger reservation time information and connection location information of the passengers;

[0082] It can be understood that in this step, the passenger connection reservation information is transformed into a graph structure, which is convenient for subsequent path planning and scheduling decisions. Among them, the establishment of the weight matrix is based on the actual passenger reservation information, ensuring the accuracy and effectiveness of path planning. Through the weight matrix, the subsequent steps can calculate the optimal path, reducing the vehicle driving time and distance, and improving the connection efficiency. In this step, step S31 includes step S311, step S312, and step S313.

[0083] Step S311: Perform area judgment on the passenger connection reservation information, the departure time interval of the modular autonomous vehicle, and the service cell area to obtain the location information of the passengers to be connected and the corresponding cell map data information;

[0084] It can be understood that through area judgment in this step, the specific location information of each passenger to be connected can be determined, ensuring that the vehicle can accurately reach the connection location. By judging whether the passenger is within the service area, the system can optimize the service scope of the vehicle and improve the connection efficiency. By obtaining the community map data, the system can obtain detailed geographical information, providing data support for subsequent path planning and scheduling. Through accurate area judgment and efficient path planning, the system can reduce the driving time and distance of the vehicle and lower the operating cost.

[0085] Step S312: Extract features from the corresponding community map data information. Among them, feature extraction is carried out by extracting all paths available for vehicle operation in the community to obtain the location information of each passenger to be connected and the path data between each pair of passengers to be connected.

[0086] It can be understood that through feature extraction in this step, the system can identify all paths available for vehicle operation in the community, ensuring the accuracy of path data. By extracting and calculating the path data between each pair of passengers to be connected, the system can perform efficient path planning, reducing the driving time and distance. Through accurate path data, the system can optimize the connection efficiency of the vehicle and improve the satisfaction of passengers. Through efficient path planning and optimized connection, the system can reduce the operating cost of the vehicle and improve the overall service quality.

[0087] Step S313: Construct a model with the location information of each passenger to be connected and the path data between each pair of passengers to be connected. Among them, the location information of each passenger to be connected is used as a node, the path data between each pair of passengers to be connected is used as an edge, and each node and each edge are connected to obtain a connection network model.

[0088] It can be understood that by structuring the scattered location information and path data into a complete connection network model in this step, it helps with data organization and management.

[0089] Step S32: Based on the weight matrix of passengers taking the vehicle, sort the location nodes of passengers in the connection network model according to importance.

[0090] It can be understood that through sorting the nodes in this step, the system can arrange vehicle connection more reasonably, reducing the waiting time and driving distance of passengers. Priority is given to handling passengers with high demand to ensure that their connection needs are met in a timely manner, enhancing the satisfaction and experience of passengers.

[0091] Step S33: Take the point closest to the preset decoupling position point of the modular autonomous vehicle in the connection network model as the starting point, and sequentially visit other nodes in descending order of importance to obtain at least one connection path corresponding to the decoupled modular autonomous vehicle.

[0092] It can be understood that by sorting according to importance, it is ensured that nodes with high weights are accessed first, meeting the connection needs of passengers with high demands and improving passenger satisfaction.

[0093] Step S34: Respectively associate the connection paths corresponding to all the decoded modular autonomous vehicles with the optimal departure time interval and the service community area, and obtain the connection path information corresponding to the modular autonomous vehicles for each departure time interval.

[0094] It can be understood that in this step, through correlation analysis, it is ensured that each path operates within the most suitable time period, improving the overall efficiency of the connection service. By optimizing the service community area and the departure time interval, the passenger demand coverage is maximized, and the passenger satisfaction is improved. Furthermore, dynamic adjustment is made according to real-time data to adapt to different passenger demands and traffic conditions.

[0095] Step S4: Based on the planned vehicle path scheduling information, control the decoding of the modular autonomous vehicle to pick up and drop off passengers, and obtain the vehicle vacancy information and the passenger connection reservation information in real time. Repeat vehicle scheduling and path planning until the modular autonomous vehicle reaches the preset first designated location for formation.

[0096] It can be understood that the control of the modular autonomous vehicle in this step is mainly composed of an information collection system, an information transmission system, an information processing system (dispatching command center), and an information release system. The demand response system center mainly operates through modern technologies such as mobile 5G traffic, Geographic Information System (GIS), Global Positioning System (GPS), Passenger Information System (PIS), and Automatic Vehicle Location (AV).

[0097] Through GIS and GPS, the operating status of the modular vehicle unit can be grasped at any time, and information on the road traffic network can be obtained, such as road congestion conditions and intersection locations, to ensure the smooth operation of the vehicle; accurately locate the passenger position within the community, plan the TSP route, and provide the fastest connection service.

[0098] PIS is an important operation service production system, which can provide passengers with dynamic vehicle operation information, such as the elapsed time and the estimated arrival time.

[0099] AVL can upload the positions of modular vehicle units and the positions of train vehicles formed by the formation to the operation dispatching center for control at any time. It accurately tracks the operating status of the vehicles and each vehicle unit, and this technology plays a key role when a vehicle breaks down.

[0100] It can be understood that in this step, the modular autonomous driving vehicle is controlled to uncouple and couple, and divided into multiple shuttle vehicles, and then quickly and effectively shuttle according to the planned route.

[0101] Step S5: The modular autonomous driving vehicle after formation runs to the first designated location according to the preset route.

[0102] Embodiment 2:

[0103] As Figure 2 shown, this embodiment provides an intelligent shuttle system based on modular autonomous driving vehicles. Refer to Figure 2 The system includes an acquisition unit 701, a first processing unit 702, a second processing unit 703, a third processing unit 704, and a fourth processing unit 705.

[0104] The acquisition unit 701 is used to acquire the shuttle reservation information of passengers and the historical shuttle demand information of passengers. The reservation shuttle information of passengers includes the time information and shuttle location information reserved by passengers. The historical shuttle demand information of passengers includes the historical shuttle demand quantity information and the historical shuttle demand location information of passengers;

[0105] The first processing unit 702 is used to send the historical shuttle demand information of passengers and the shuttle reservation information of passengers to a preset modular autonomous driving vehicle planning model for planning the optimal departure time interval and service area of the modular autonomous driving vehicle, and obtain the optimal departure time interval and service area of the modular autonomous driving vehicle after planning;

[0106] Among them, the first processing unit 702 includes a first calculation subunit 7021, a second calculation subunit 7022, and a third calculation subunit 7023.

[0107] The first calculation subunit 7021 is used to calculate the demand density of passengers based on the historical shuttle demand information of passengers, and obtain the demand density information of passengers;

[0108] The second calculation subunit 7022 is used to calculate the travel cost information of passengers and the operation cost information of the operator based on the demand density information of passengers and the shuttle reservation information of passengers;

[0109] Among them, the second calculation subunit 7022 includes a fourth calculation subunit 70221, a fifth calculation subunit 70222, a sixth calculation subunit 70223, a seventh calculation subunit 70224, and an eighth calculation subunit 70225.

[0110] The fourth calculation subunit 70221 is configured to calculate the average waiting time cost of passengers based on the transfer reservation information of passengers, the preset vehicle status information, and the average waiting time cost calculation formula of passengers.

[0111] The fifth calculation subunit 70222 is configured to calculate the average in-vehicle time cost of passengers based on the transfer reservation information of passengers, the preset vehicle status information, and the average in-vehicle time cost calculation formula of passengers.

[0112] The sixth calculation subunit 70223 is configured to perform a summation calculation on the average waiting time cost and the average in-vehicle time cost of passengers to obtain the travel cost information of passengers.

[0113] The seventh calculation subunit 70224 is configured to calculate based on the preset historical operation information, the preset long-distance transportation section cost calculation formula, and the transfer transportation section cost calculation formula to obtain the long-distance transportation section operation cost and the transfer transportation section operation cost of the operator.

[0114] The eighth calculation subunit 70225 is configured to perform a summation calculation based on the long-distance transportation section operation cost and the transfer transportation section operation cost of the operator to obtain the operation cost information of the operator.

[0115] The third calculation subunit 7023 is configured to calculate the average total cost of passenger transfer based on the travel cost information of passengers and the operation cost information of the operator, and calculate based on the average total cost of passenger transfer, the preset optimal departure interval calculation formula, and the service area calculation formula of the service area to obtain the optimal departure interval and the service area of the modular autonomous vehicle.

[0116] Among them, the third calculation subunit 7023 includes a ninth calculation subunit 70231, a tenth calculation subunit 70232, and an eleventh calculation subunit 70233.

[0117] The ninth calculation subunit 70231 is configured to perform a summation calculation and an average calculation on the travel cost information of passengers and the operation cost information of the operator to obtain the average total cost of passenger transfer.

[0118] The tenth calculation subunit 70232 is configured to take the first-order partial derivatives of the departure interval and the service area in the calculation formula of the average total cost of passenger transfer and set them equal to zero to obtain the first-order partial derivative formula for calculating the departure interval and the service area.

[0119] The eleventh calculation subunit 70233 is configured to perform second-order partial derivative calculations on the departure interval and the service cell area based on the first-order partial derivative formula of the departure interval and the service cell area, so as to obtain the optimal departure interval and the optimal service cell area.

[0120] The second processing unit 703 is configured to perform vehicle scheduling and path planning based on the passenger's connection reservation information, the optimal departure time interval of the planned modular autonomous vehicle, and the service cell area, so as to obtain the planned vehicle scheduling path information;

[0121] Wherein, the second processing unit 703 includes a first processing subunit 7031, a second processing subunit 7032, a third processing subunit 7033, and a fourth processing subunit 7034.

[0122] The first processing subunit 7031 is configured to construct a connection network model according to the passenger's connection reservation information, and establish a weight matrix for the passenger to take the vehicle based on the time information and connection location information reserved by the passenger.

[0123] The second processing subunit 7032 is configured to sort the position nodes of the passengers in the connection network model according to importance based on the weight matrix for the passenger to take the vehicle.

[0124] The third processing subunit 7033 is configured to use the position point closest to the preset uncoupling position of the modular autonomous vehicle in the connection network model as the starting point, and sequentially visit other nodes in descending order of importance to obtain at least one connection path corresponding to the uncoupled modular autonomous vehicle.

[0125] The fourth processing subunit 7034 is configured to perform correlation analysis on the connection paths corresponding to all the uncoupled modular autonomous vehicles respectively with the optimal departure time interval and the service cell area, so as to obtain the connection path information corresponding to the modular autonomous vehicle for each departure time interval.

[0126] Wherein, the first processing subunit 7031 includes a fifth processing subunit 70311, a sixth processing subunit 70312, and a seventh processing subunit 70313.

[0127] The fifth processing subunit 70311 is configured to perform area judgment on the passenger's connection reservation information, the departure time interval of the modular autonomous vehicle, and the service cell area, so as to obtain the position information of the passengers to be connected and the corresponding cell map data information.

[0128] The sixth processing subunit 70312 is configured to perform feature extraction on the corresponding cell map data information thereof. Specifically, feature extraction is performed by extracting all paths available for vehicle operation within the cell, and the position information of each passenger to be connected and the path data between each pair of passengers to be connected are obtained.

[0129] The seventh processing subunit 70313 is configured to construct a model using the position information of each passenger to be connected and the path data between each pair of passengers to be connected. Specifically, the position information of each passenger to be connected is used as a node, the path data between each pair of passengers to be connected is used as an edge, and each node and each edge are connected to obtain a connection network model.

[0130] The third processing unit 704 is configured to control the decoupling of modular autonomous vehicles for passenger connection based on the planned vehicle path scheduling information, and to obtain the vehicle vacancy information and the passenger connection reservation information in real time, and to repeat vehicle scheduling and path planning until the modular autonomous vehicle reaches a preset first designated location for formation.

[0131] The fourth processing unit 705 is configured to make the modular autonomous vehicle after formation run to the first designated location according to a preset route.

[0132] It should be noted that regarding the system in the above embodiments, the specific manners in which each module performs operations have been described in detail in the embodiments related to the method, and will not be elaborated herein.

[0133] The above are only the preferred embodiments of the present invention, and are not intended to limit the present invention. For those skilled in the art, the present invention may have various modifications and changes. Any modifications, equivalent replacements, improvements, etc. made within the spirit and principle of the present invention shall be included within the protection scope of the present invention.

[0134] The above is only the specific implementation manner of the present invention, but the protection scope of the present invention is not limited thereto. Any person skilled in the art can easily think of changes or replacements within the technical scope disclosed by the present invention, and all should be covered by the protection scope of the present invention. Therefore, the protection scope of the present invention shall be subject to the protection scope of the claims.

Claims

1. An intelligent docking method based on modular autonomous driving vehicles, characterized in that: include: Acquire the passenger's transfer reservation information and the passenger's historical transfer demand information, wherein the passenger's transfer reservation information includes the passenger's reservation time information and transfer location information, and the passenger's historical transfer demand information includes the passenger's historical transfer demand quantity information and the passenger's historical transfer demand location information; Send the historical connection demand information and the connection reservation information of the passengers to the preset modular autonomous driving vehicle planning model to perform optimal departure time interval planning and service community area planning of the modular autonomous driving vehicles, and obtain the optimal departure time interval and service community area of ​​the planned modular autonomous driving vehicles; Performing vehicle dispatching and path planning based on the passenger's pick-up reservation information, the optimal departure time interval of the planned modular autonomous driving vehicle, and the service area, to obtain the planned vehicle dispatch path information; Based on the planned vehicle path scheduling information, the modular autonomous driving vehicle is controlled to disassemble and group passengers, and the vehicle vacancy information and the passenger's grouping reservation information are obtained in real time, and the vehicle scheduling and path planning are repeated until the modular autonomous driving vehicle arrives at the preset first designated location for grouping; The assembled modular autonomous driving vehicles run to the first designated location according to the preset route; The sending of the historical connection demand information and the connection reservation information of the passengers to the preset modular autonomous driving vehicle planning model to perform optimal departure time interval planning and service area planning of the modular autonomous driving vehicle includes: Calculate the passenger demand density based on the historical connection demand information of the passengers to obtain the passenger demand density information; Calculate the passenger's travel cost information and the operator's operating cost information based on the passenger's demand density information and the passenger's connection reservation information; The average total cost of passenger connection is calculated based on the passenger travel cost information and the operator's operating cost information, and the optimal departure time interval and service cell area of ​​the modular autonomous driving vehicle are calculated based on the average total cost of passenger connection and the preset optimal departure interval calculation formula and service cell area calculation formula; The vehicle dispatching and route planning are performed based on the passenger's pick-up reservation information, the optimal departure time interval of the planned modular autonomous driving vehicle, and the service area, including: A connection network model is constructed based on the connection reservation information of the passenger, and a weight matrix of the passenger's ride is established based on the time information of the passenger reservation and the connection location information of the passenger; Based on the passenger riding weight matrix, the location nodes of the passengers in the connecting network model are sorted according to their importance; Taking the preset unpacking position point closest to the modular autonomous driving vehicle in the docking network model as the starting point, and visiting other nodes in descending order of importance to obtain at least one docking path corresponding to the unpacked modular autonomous driving vehicle; The connecting paths corresponding to all the disassembled modular autonomous driving vehicles are respectively correlated with the optimal departure time interval and the service cell area to obtain the connecting path information corresponding to the modular autonomous driving vehicles at each departure time interval.

2. The intelligent docking method based on modular autonomous driving vehicles according to claim 1, characterized in that ,Calculate the passenger's travel cost information and the operator's operating cost information based on the passenger's demand density information and the passenger's connection reservation information, including: The average waiting time cost of passengers is calculated based on the passenger's connection reservation information, the preset vehicle status information and the average waiting time cost calculation formula of passengers; The average time cost of passengers in the vehicle is calculated based on the passenger's connection reservation information, the preset vehicle status information and the average time cost calculation formula of passengers in the vehicle; The average waiting time cost of the passengers and the average on-board time cost of the passengers are summed up to obtain the travel cost information of the passengers; Based on the preset historical operation information, the preset long-distance transport segment cost calculation formula and the preset connecting transport segment cost calculation formula, the operator's long-distance transport segment operation cost and connecting transport segment operation cost are calculated; The operator's operating cost information is obtained by summing up the operator's long-distance transport segment operating cost and the connecting transport segment operating cost.

3. The intelligent docking method based on modular autonomous driving vehicles according to claim 1, characterized in that , based on the passenger's travel cost information and the operator's operating cost information, the average total cost of passenger connection is calculated, and the average total cost of passenger connection and the preset optimal departure interval calculation formula and service community area calculation formula are calculated, including: The passenger's travel cost information and the operator's operating cost information are summed and averaged to obtain an average total cost of passenger transfers; Taking the first-order partial derivative of the departure interval and the service cell area in the calculation formula of the average total cost of the passenger connection and setting them equal to zero, a first-order partial derivative formula for calculating the departure interval and the service cell area is obtained; Based on the first-order partial derivative formula of the departure interval and the service cell area, the second-order partial derivative of the departure interval and the service cell area is calculated to obtain the optimal departure interval and the optimal service cell area.

4. An intelligent docking system based on modular autonomous driving vehicles, characterized in that: include: an acquisition unit, configured to acquire the passenger's connection reservation information and the passenger's historical connection demand information, wherein the passenger's connection reservation information includes the passenger's reservation time information and connection location information, and the passenger's historical connection demand information includes the passenger's historical connection demand quantity information and the passenger's historical connection demand location information; The first processing unit is used to send the historical connection demand information and the connection reservation information of the passenger to a preset modular autonomous driving vehicle planning model to perform optimal departure time interval planning and service cell area planning of the modular autonomous driving vehicle, and obtain the optimal departure time interval and service cell area of ​​the planned modular autonomous driving vehicle; A second processing unit is used to perform vehicle scheduling and path planning based on the passenger's pick-up reservation information, the planned optimal departure time interval of the modular autonomous driving vehicle, and the service cell area, to obtain the planned vehicle scheduling path information; A third processing unit is used to control the modular autonomous driving vehicle to disassemble and perform passenger docking based on the planned vehicle path scheduling information, and to obtain vehicle vacancy information and passenger docking reservation information in real time, and to repeat vehicle scheduling and path planning until the modular autonomous driving vehicle arrives at a preset first designated location for marshaling; A fourth processing unit is used to drive the assembled modular autonomous driving vehicles to the first designated location according to a preset route; Wherein, the first processing unit includes: A first calculation subunit is used to calculate the passenger demand density based on the historical connection demand information of the passenger to obtain the passenger demand density information; A second calculation subunit, for calculating the passenger's travel cost information and the operator's operating cost information based on the passenger's demand density information and the passenger's connection reservation information; A third calculation subunit is used to calculate the average total cost of passenger connection based on the travel cost information of the passenger and the operating cost information of the operator, and to calculate based on the average total cost of passenger connection and a preset optimal departure interval calculation formula and a service cell area calculation formula to obtain the optimal departure time interval and service cell area of ​​the modular autonomous driving vehicle; Wherein, the second processing unit includes: A first processing subunit is used to construct a connection network model according to the connection reservation information of the passenger, and to establish a weight matrix for the passenger to take the bus based on the time information of the passenger reservation and the connection location information of the passenger; A second processing subunit is used to sort the location nodes of passengers in the connection network model according to importance based on the weight matrix of passengers riding the bus; A third processing subunit is configured to use the preset unpacking position point closest to the modular autonomous driving vehicle in the docking network model as a starting point, and sequentially visit other nodes in descending order of importance to obtain at least one docking path corresponding to the unpacked modular autonomous driving vehicle; The fourth processing sub-unit is used to perform correlation analysis on the connecting paths corresponding to all the disassembled modular autonomous driving vehicles with the optimal departure time interval and the service cell area, so as to obtain the connecting path information corresponding to the modular autonomous driving vehicles at each departure time interval.

5. The intelligent docking system based on modular autonomous driving vehicles according to claim 4, characterized in that: The second computing subunit comprises: A fourth calculation subunit, configured to calculate the average waiting time cost of the passenger based on the passenger's connection reservation information, the preset vehicle status information and the average waiting time cost calculation formula of the passenger; A fifth calculation subunit, configured to calculate the average on-board time cost of the passenger based on the passenger's transfer reservation information, the preset vehicle status information and the average on-board time cost calculation formula of the passenger; a sixth calculation subunit, configured to calculate the sum of the average waiting time cost of the passengers and the average on-board time cost of the passengers to obtain the travel cost information of the passengers; The seventh calculation subunit is used to calculate the historical operation information based on the preset 、 The preset long-distance transport segment cost calculation formula and the connecting transport segment cost calculation formula are used to calculate and obtain the operator's long-distance transport segment operating cost and the connecting transport segment operating cost; The eighth calculation subunit is used to perform a summation calculation based on the operator's long-distance transport segment operating cost and the connecting transport segment operating cost to obtain the operator's operating cost information.

6. The intelligent docking system based on modular autonomous driving vehicles according to claim 4, characterized in that: The third computing subunit comprises: A ninth calculation subunit, configured to perform sum calculation and average calculation on the passenger's travel cost information and the operator's operating cost information to obtain an average total cost of passenger transfer; a tenth calculation subunit, configured to obtain a first-order partial derivative of the departure interval and the service cell area in the calculation formula of the average total cost of the passenger connection, and set them equal to zero, so as to obtain a first-order partial derivative formula for calculating the departure interval and the service cell area; The eleventh calculation subunit is used to calculate the second-order partial derivative of the departure interval and the service cell area based on the first-order partial derivative formula of the departure interval and the service cell area, so as to obtain the optimal departure interval and the optimal service cell area.

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