CN113592335A - Unmanned connection vehicle passenger demand matching and vehicle scheduling method - Google Patents
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Abstract
The invention discloses a method for demand matching and vehicle scheduling of unmanned connected vehicle passengers, which comprises the following steps: step 1, passengers determine a departure point and a destination in advance; step 2, the passenger sends a riding command through the equipment; step 3, the unmanned vehicle dispatching center receives a riding command; step 4, establishing a model by the unmanned vehicle dispatching center, dispatching a plurality of vehicles by adopting a genetic algorithm, and planning an optimal route and an optimal vehicle; and 5, when the vehicle reaches the destination, the passenger is picked up and picked up, the passenger needs to confirm getting on after getting on, and the passenger needs to confirm getting off. The method has the advantages that the mobile reservation or the fixed device reservation and the unmanned vehicle dispatching center are used for modeling and constraining, an operator algorithm is used for processing the model, no fixed station is set, the reservation is convenient for passengers, the technical effect of saving time is achieved, meanwhile, the unmanned vehicle passenger receiving system is optimized, and the optimal passenger receiving and sending scheme is obtained.
Description
Technical Field
The invention relates to the field of unmanned vehicle passenger receiving, in particular to a method for demand matching and vehicle dispatching of unmanned vehicle passengers.
Background
An autonomous vehicle, also known as a robotic vehicle, an autonomous vehicle, or an unmanned vehicle, is a vehicle that is capable of sensing its environment and driving with little or no manual input. Autonomous vehicles incorporate a variety of sensors to sense the surrounding environment, such as radar, lidar, sonar, global positioning systems, odometers, and inertial measurement units. Advanced control systems interpret the sensed information to identify appropriate navigation paths, obstacles, and associated landmarks. However, at present, unmanned automobile passengers are still in a development stage, although the unmanned automobile passengers are often used in partial areas, the use range is small, and the use method and the use mode are all randomly fixed and lack of flexibility.
Chinese patent CN106218639B this application discloses an unmanned vehicle, a method and an apparatus for controlling an unmanned vehicle. One embodiment of the method comprises: when an unmanned vehicle is in an automatic driving mode, acquiring running environment information of the unmanned vehicle; judging whether the unmanned vehicle is in a dangerous state or not according to the running environment information; if so, exiting the autonomous driving mode in response to detecting an operation to change a travel speed or a travel direction of the unmanned vehicle. According to the embodiment, when danger is possibly caused due to the problem of the automatic driving mode, emergency intervention can be performed according to a human safe driving method, and therefore the safety of the unmanned vehicle is improved. But only discloses a mechanical mechanism that allows it to switch back and forth between automatic and manual driving. However, the use of the system for receiving the customers is not mentioned, and a complete system is required for supporting the system for receiving the customers.
Chinese patent CN111009114A discloses a vehicle rental intelligent scheduling management system and its vehicle-mounted terminal and vehicle communication protocol. The system comprises a passenger end, a vehicle-mounted end, a dispatching management center and a vehicle communication protocol. Passengers can order cars by using telephones, computers, intelligent equipment and special equipment and can also be raised at the roadside; the vehicle can be used immediately or ordered. The dispatching management center combines the traffic and meteorological information, and adopts artificial intelligence and big data technology to dispatch vehicles and dispatch vehicle-renting orders; the empty vehicle dispatching is optimized, the empty driving distance and time are reduced, the operation cost is reduced, the turnover is increased, the waiting time of passengers is shortened, and the satisfaction is improved. The vehicle-mounted terminal intelligently identifies the passengers raised and recruited by the roadside; interacting with passengers and drivers; collecting and receiving vehicles and travel information thereof; transmitting the trip information to the vehicle; the safety of passengers, drivers and vehicles is guaranteed. Vehicles conforming to the vehicle communication protocol are all suitable for the system; not only is convenient for producing the applicable vehicle, but also is convenient for purchasing the applicable vehicle. The system is suitable for the unmanned taxi and is compatible with the manual driving taxi; it is suitable for hiring passenger and freight vehicles. However, the structure is only simply published, and how to process data is not published, so that the whole system is hollow and weak, and the invention has no theoretical support.
Most of unmanned vehicles put into use in the market at present can only be used in a specific stage and in a specific time, but on the aspect that the unmanned vehicles are used for receiving passengers or planning routes, no new technology is updated, the unmanned vehicles are simply ordered by customers, the unmanned vehicles arrive and are delivered, and one vehicle can only deliver one person, so that the resource waste is caused. Therefore, it is important to model the passenger appointment and the process again and solve the model by some method to obtain the best choice.
Disclosure of Invention
In order to ensure the safety, scientificity and optimization of the unmanned vehicle, the algorithm of the dispatching center is optimized, a model is established, the unmanned vehicle dispatching center is used for processing, the algorithm is normalized, the established model is processed by the algorithm, the dispatching is more perfect, safe and efficient, and the selection optimization is obtained.
In order to achieve the effect, the invention designs a method for demand matching and vehicle dispatching of the unmanned connecting vehicle passengers.
A passenger demand matching and vehicle scheduling method for an unmanned docked vehicle comprises the following steps:
step 1: the passenger determines a departure point and a destination in advance;
step 2: the passenger sends a riding command through the equipment;
and step 3: the unmanned vehicle dispatching center receives a riding command;
and 4, step 4: the unmanned vehicle dispatching center dispatches a plurality of vehicles and plans an optimal route and optimal vehicles;
and 5: when the vehicle reaches the destination, the passengers are picked up and get on the vehicle, the passengers need to confirm getting on the vehicle after getting on the vehicle, and the passengers also need to confirm getting off the vehicle.
Preferably, in step 1, the passenger determines a departure point and a destination in advance, and may select the departure point and the destination by himself/herself, and the departure point and the destination are not limited by a fixed station.
Preferably, in step 2, the passenger may issue a riding command through a device, where the device includes a mobile device and a fixed device, and is not limited to a mobile device such as a mobile phone, a computer, and a tablet computer, and a fixedly-set reservation device.
Preferably, in the step 4, after receiving the riding command, the unmanned vehicle dispatching center processes the reservation information through the dispatching center, performs path planning and vehicle dispatching according to the regional demand condition, and the vehicle directly provides a door-to-door service for the reserved passenger without setting a fixed departure station.
Preferably, the basic scheduling principle after the unmanned vehicle scheduling center receives the instruction is as follows: one vehicle can serve a plurality of passengers, but each passenger can be served by only one vehicle, and the transfer condition does not exist.
Preferably, if the time when the vehicle arrives at a certain departure point is earlier than the earliest departure time of the departure point of the service requirement, the vehicle waits for the earliest departure time and departs at the earliest departure time, if the vehicle arrival time is within the time window range, the passenger can get on at the vehicle arrival time, and the vehicle immediately drives to the next node, and the vehicle arrival time is the vehicle departure time.
Preferably, all vehicles smoothly run on the road at the same uniform speed, so that the traffic jam condition is avoided, and the trouble-free running of the vehicles is guaranteed.
Preferably, step 5, the vehicle reaches the destination, the passenger is picked up, and timely confirmation is needed after the passenger gets on and off the vehicle, and the confirmation device includes but is not limited to a mobile phone and other mobile devices and a vehicle-mounted fixed confirmation device.
Preferably, after receiving the passenger getting-off instruction, the unmanned vehicle dispatching center replans the vehicle and delivers a new batch of passengers.
Preferably, if the passenger does not arrive within the preset time range, the vehicle waits for 5 minutes additionally, and if the passenger does not arrive, the unmanned vehicle sends out a passenger non-arrival instruction and receives the command of the unmanned vehicle dispatching center to go to a new place.
Compared with the prior art, the technical scheme of the invention has the following beneficial effects:
1) modeling is carried out in the field of passenger receiving and delivering of the unmanned vehicle, modeling and constraint conditions are carried out through an unmanned vehicle dispatching center, so that the passenger receiving system of the unmanned vehicle is optimized, and an optimal passenger receiving and delivering scheme is obtained;
2) the genetic algorithm is adopted for model solving, so that the model solving is quicker and more efficient, and the operation result is more accurate;
3) the mobile reservation and the fixed device are used for reservation, a fixed station is not set, so that the reservation can be conveniently carried out by a passenger, the time is saved, the passenger does not need to go to the station, and convenience is provided for the passenger;
4) one vehicle can serve a plurality of passengers, but each passenger can only be served by one vehicle, so that the vehicle management and control are facilitated, and the resources are saved.
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In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings needed to be used in the description of the embodiments or the prior art will be briefly introduced below, and it is obvious that the drawings in the following description are some embodiments of the present application, and it is obvious for those skilled in the art to obtain other drawings based on these drawings without creative efforts. Throughout the drawings, like elements or portions are generally identified by like reference numerals. In the drawings, elements or portions are not necessarily drawn to scale.
FIG. 1 is a diagram of specific steps of a method for matching the demand of an unmanned docked vehicle passenger and scheduling the vehicle;
FIG. 2 is a flow chart of a calculation of an unmanned vehicle dispatch center in an unmanned docked vehicle passenger demand matching and vehicle dispatching method;
fig. 3 is a flow of passenger reservation, getting-on, and getting-off in a method of unmanned docked vehicle passenger demand matching and vehicle scheduling.
Detailed Description
In order to make the objects, technical solutions and advantages of the embodiments of the present application clearer, the technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the drawings in the embodiments of the present application, and it is obvious that the described embodiments are some embodiments of the present application, but not all embodiments. In the following description, specific details such as specific configurations and components are provided only to help the embodiments of the present application be fully understood. Accordingly, it will be apparent to those skilled in the art that various changes and modifications may be made to the embodiments described herein without departing from the scope and spirit of the present application. In addition, descriptions of well-known functions and constructions are omitted in the embodiments for clarity and conciseness.
It should be appreciated that reference throughout this specification to "one embodiment" or "the embodiment" means that a particular feature, structure or characteristic described in connection with the embodiment is included in at least one embodiment of the present application. Thus, the appearances of the phrase "one embodiment" or "the present embodiment" in various places throughout this specification are not necessarily all referring to the same embodiment. Furthermore, the particular features, structures, or characteristics may be combined in any suitable manner in one or more embodiments.
Further, the present application may repeat reference numerals and/or letters in the various examples. This repetition is for the purpose of simplicity and clarity and does not in itself dictate a relationship between the various embodiments and/or configurations discussed.
The term "and/or" herein is merely an association describing an associated object, meaning that three relationships may exist, e.g., a and/or B, may mean: a exists alone, B exists alone, and A and B exist at the same time, and the term "/and" is used herein to describe another association object relationship, which means that two relationships may exist, for example, A/and B, may mean: a alone, and both a and B alone, and further, the character "/" in this document generally means that the former and latter associated objects are in an "or" relationship.
The term "at least one" herein is merely an association relationship describing an associated object, and means that there may be three relationships, for example, at least one of a and B, may mean: a exists alone, A and B exist simultaneously, and B exists alone.
It is further noted that, herein, relational terms such as first and second, and the like may be used solely to distinguish one entity or action from another entity or action without necessarily requiring or implying any actual such relationship or order between such entities or actions. Also, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion.
Example 1
The present embodiment mainly describes the specific steps and details of the method for matching the passenger demand of the unmanned docked vehicle and scheduling the vehicle according to the present invention. Refer to fig. 1.
A passenger demand matching and vehicle scheduling method for an unmanned docked vehicle comprises the following steps:
step 1: the passenger determines a departure point and a destination in advance;
step 2: the passenger sends a riding command through the equipment;
and step 3: the unmanned vehicle dispatching center receives a riding command;
and 4, step 4: the unmanned vehicle dispatching center dispatches a plurality of vehicles and plans an optimal route and optimal vehicles;
and 5: when the vehicle reaches the destination, the passengers are picked up and get on the vehicle, the passengers need to confirm getting on the vehicle after getting on the vehicle, and the passengers also need to confirm getting off the vehicle.
Further, in the step 1, the passenger determines the departure point and the destination in advance, and can select the departure point and the destination by himself/herself, and the departure point and the destination are not limited by the fixed station.
Further, in step 2, the passenger may send a riding command through a device, where the device includes a mobile device and a fixed device, and is not limited to a mobile device such as a mobile phone, a computer, and a tablet computer, and a fixedly set reservation device.
Further, in the step 4, after receiving the riding command, the unmanned vehicle dispatching center processes the reservation information through the dispatching center, performs path planning and vehicle dispatching according to the regional demand condition, and directly provides the 'door-to-door' service for the reserved passengers without setting a fixed departure station.
Further, the basic scheduling principle after the unmanned vehicle scheduling center receives the instruction is as follows: one vehicle can serve a plurality of passengers, but each passenger can be served by only one vehicle, and the transfer condition does not exist.
Further, if the time when the vehicle arrives at a certain departure point is earlier than the earliest departure time of the departure point of the service requirement, the vehicle needs to wait until the earliest departure time and leave at the earliest departure time, if the vehicle arrival time is within the time window range, the passenger can get on at the vehicle arrival time, and the vehicle immediately drives to the next node, wherein the vehicle arrival time is the vehicle leaving time.
Furthermore, all vehicles smoothly run on the road at the same uniform speed, so that the traffic jam condition is avoided, and the vehicles can run without faults.
Further, in step 5, the vehicle reaches the destination, the passenger is picked up, and the passenger needs to be confirmed in time after getting on and off the vehicle, and the confirmation device includes but is not limited to a mobile phone, other mobile devices and a vehicle-mounted fixed confirmation device.
Furthermore, after the unmanned vehicle dispatching center receives the passenger getting-off instruction, the unmanned vehicle dispatching center plans the vehicle again and delivers a new batch of passengers.
Further, if the passenger does not arrive within the preset time range, the vehicle waits for 5 minutes additionally, and if the passenger does not arrive, the unmanned vehicle sends out a passenger non-arrival instruction and receives the command of the unmanned vehicle dispatching center to go to a new place.
The unmanned vehicle dispatching center carries out modeling and constraint conditions, so that the unmanned vehicle can receive passengers to carry out standardization, and operation in real life is facilitated.
Through using removal reservation and fixing device reservation, do not set up fixed website, when the passenger of being convenient for carries out the reservation, save time also need not the passenger and goes to the website, provides convenient for the passenger.
The system can serve a plurality of passengers by arranging one vehicle, but each passenger can only be served by one vehicle, so that the vehicle management and control are facilitated, and resources are saved.
Example 2
Based on embodiment 1, this embodiment focuses on the modeling of the present invention.
(1) Model parameters
(2) Model preparation
Firstly, a matrix is constructed, the set of all points in the region is H, and the distance between any two points in H is calculated by a Dijkstra algorithm.
Dijkstra algorithm: the basic idea is to record the distance from the node directly connected with the starting point with the edge to the starting point as the weight value of the corresponding edge, and record the distance from the node directly connected with the starting point without the edge to the starting point as infinity. And then expanding outwards layer by taking the starting point as a center, and calculating the shortest distance from all nodes to the starting point. And updating the shortest distance from the node directly adjacent to the edge to the starting point after newly extending to a point with the shortest distance each time. When all points are expanded, the shortest distance from all nodes to the starting point can not be changed any more, thereby ensuring the correctness of the algorithm. The method comprises the following basic steps:
(a) and setting up two sets of Y and N, wherein Y is used for storing all nodes waiting for access, and N records all the accessed nodes.
(b) And accessing a node which is closest to the starting node and is not accessed in the network nodes, and putting the node into Y for waiting for access.
(c) And finding out the node closest to the starting point from the Y, putting the node into the N, updating the shortest distance from the adjacent node directly connected with the node with edges to the starting node, and adding the adjacent nodes into the Y.
(d) And (4) repeating the steps (2) and (3) until the Y set is empty and the N set is all nodes in the network.
(E) For the set of edges in a region, E { (x, y) | x, y ∈ H }, and the distance matrix a for each edge { (a) | ax,yI x, y belongs to H, and the running time matrix T between two points is T ═ Tx,yL x, y ∈ H }, and
v represents the average running speed of the vehicle.
(3) Objective function
On the premise of ensuring that the number of the served passengers at the demand points is the maximum, the total cost of all vehicles is the minimum, and the travel time of the passengers is the shortest.
The objective function of the model is set to M ═ min (M)1+M2-M3) Where M represents the overall objective function, M1Representing the total cost of the vehicle docked, M1＝∑i∈ZUi(fc+c*ai,j) Wherein Z represents a vehicle set, Z ═ {1,2, …, m } UiRepresenting a variable of 0, 1, fc representing a fixed cost of the vehicle, c representing an operating cost per kilometer of the vehicle, ai,jRepresenting the kilometers that the vehicle travels from station i to station j; m2Represents the total travel time of passengers
In the formula, QpRepresenting a set of boarding points required by the passenger,
representing the time, e, at which vehicle i reaches demand point m + n-xxReserving the earliest departure time for the passenger; m3Which represents the total number of service passengers,
in the formula, QPRepresenting a set of boarding points.
Is a 0, 1 variable, indicating that vehicle i is traveling from demand point i to demand point y. q. q.sxIndicating when the vehicle leaves the demand point x
The number of passengers to be loaded.
(4) Constraint conditions
1) Vehicle arrival time constraint: the time of the vehicle arriving at the demand point is within the time window range required by the passenger, the departure time of the vehicle cannot be earlier than the earliest departure time required by the passenger, cannot be later than the latest departure time required by the passenger, and the arrival time of the vehicle at the destination point cannot be later than the latest arrival time required by the passenger. Otherwise the path is not feasible.
In the formula (I), the compound is shown in the specification,
indicating the time at which the vehicle reaches the requested point, exIndicating the earliest departure time requested by the passenger,/xIndicating that the passenger requires the latest arrival time.
2) And (3) vehicle reasonable path constraint: the vehicle can not pass through a certain getting-on/off point in the running process. In addition, a vehicle is not allowed to arrive at a location twice in one service:
3) constraint of a vehicle starting point and a vehicle finishing point: the vehicle must end up from a specified departure point to a specified destination point
4) The getting-on point x and the getting-off point m + n + x of the passenger service demand are both served and are served by the same vehicle, or the getting-on and getting-off points are not served
5) Access order constraints: the vehicle must first pass the departure point of the passenger travel demand and then reach the destination:
6) the vehicle carrying capacity is restricted, and the number of passengers carried in the vehicle at any time can not exceed the maximum free capacity of the vehicle i
And a model establishing mode is adopted, so that unified regulation and control of a dispatching center are facilitated.
Example 3
Based on embodiment 2, this embodiment mainly introduces the method used in the present invention to perform model solution; the method comprises the following specific steps:
step 1: encoding
Genetic algorithms are suitable for solving genetic space problems and are not appropriate for the space of the actual solution. Therefore, the solution space must be converted into the genetic space. The choice of coding strategy directly affects the quality and speed of problem solving. Common coding modes include binary coding, gray code coding, real number coding, natural number coding and permutation coding, and the natural number coding mode is relatively more intuitive aiming at the problem of completely flexible road vehicle scheduling. Each gene represents a vehicle number carried by a passenger demand point. Let the vehicle number be [0, k ], where 0 indicates that the passenger demand point does not match to a suitable vehicle. Such as: there are three operating vehicles and 5 passengers initiate reservation requests. Individual Y is 0,3,1,2,2 indicates that passenger 1 is not served, passenger demand point 2 carries vehicle No. 3, passenger demand point 3 carries vehicle No. 1, passenger demand point 4 carries vehicle No. 2, and passenger demand point 5 carries vehicle No. 2. The gene length depends on the number of demand points of the passenger.
Step 2: generating an initial population
In the design of genetic algorithms, random methods are generally used to generate several individuals as an initial population. Aiming at the scheduling problem, an algorithm for generating an initial solution is improved, and a plurality of individuals meeting model constraints are randomly generated to serve as an initial population, namely the generation of the initial population is a series of codes meeting the constraints of vehicle capacity, travel time and the like.
And step 3: fitness function
In the genetic algorithm, the fitness value is used as a standard to measure the degree that each individual in the population can reach or approach to the optimal solution in the optimization calculation, and the individual with high fitness value is inherited to the next generation with higher probability. The magnitude of the fitness value determines the quality of the individuals in the population. The fitness function is typically designed as an objective function or by appropriate transformation of an objective function. In consideration of both the maximum value and the minimum value in the model, a specific fitness function is designed. Each individual has 3 indices, which are the number of passengers served (Num), total Cost of the vehicle (Cost) and total Time taken by the passengers (Time). The method is characterized in that N individuals are arranged in an initial population, the number of passengers of the N individuals is arranged according to an ascending order, the total Cost of vehicles is arranged according to a descending order, the total riding Time of the passengers is arranged according to a descending order, the arranged serial numbers are used as new Num1, Cost1 and Time1, and the maximum number of the demand points of the served car sharing passengers is preferentially ensured in 3 objective functions of the model, so that the demand points of the car sharing passengers are endowed with a large weight value M. The fitness function for this model is: F-MNum 1+ Cost1+ Time 1.
And 4, step 4: selection operator
The operator algorithm comprises a crossover operator and a mutation operator, and data processing is carried out.
And 5: termination rule
The algorithm progressively retains and combines good model processing data through successive iterations to produce better individuals. However, the above steps are repeated, and a termination rule needs to be established to end the iteration. The main methods include target value change control, frequency control and iteration steps. By adopting the criterion of stopping after iterating a certain number of steps, the solving precision and the operation time of the algorithm can be effectively controlled, and the algorithm is simple and easy to operate, so that the algorithm stopping rule determines the well-determined number of iteration steps for evolution.
And a genetic algorithm is adopted to solve the model, so that the model is solved more quickly and efficiently.
The above description is only a preferred embodiment of the present invention, and it is not intended to limit the scope of the present invention, and various modifications and changes may be made by those skilled in the art. Variations, modifications, substitutions, integrations and parameter changes of the embodiments may be made without departing from the principle and spirit of the invention, which may be within the spirit and principle of the invention, by conventional substitution or may realize the same function.
Claims (10)
1. A passenger demand matching and vehicle dispatching method for an unmanned connected vehicle is characterized by comprising the following steps:
step 1: the passenger determines a departure point and a destination in advance;
step 2: the passenger sends a riding command through the equipment;
and step 3: the unmanned vehicle dispatching center receives a riding command;
and 4, step 4: the unmanned vehicle dispatching center establishes a model, adopts a genetic algorithm to dispatch a plurality of vehicles, and plans an optimal route and optimal vehicles;
and 5: when the vehicle reaches the destination, the passengers are picked up and get on the vehicle, the passengers need to confirm getting on the vehicle after getting on the vehicle, and the passengers also need to confirm getting off the vehicle.
2. The method for passenger demand matching and vehicle dispatching for unmanned docked vehicle of claim 1, wherein the step 1 passenger determines the departure point and the destination in advance, and can select the departure point and the destination by oneself, and the departure point and the destination are not limited by fixed stations.
3. The method for passenger demand matching and vehicle dispatching in unmanned connected vehicle according to claim 2, wherein in step 2, the passenger can issue a riding command through devices, including a mobile reservation device and a fixed reservation device, and the mobile reservation device includes but is not limited to a mobile phone, a computer, and a tablet computer.
4. The method for demand matching and vehicle dispatching of unmanned docked vehicle passengers as claimed in claim 3, wherein in step 4, after receiving the riding command, the unmanned vehicle dispatching center processes the reservation information, and performs path planning and vehicle dispatching according to the area demand condition, without setting a fixed departure station, the vehicle directly provides the 'door-to-door' service for the reserved passengers.
5. The method of claim 4, wherein the unmanned vehicle dispatch center, upon receiving the command, models and processes the models using a genetic operator algorithm; the basic scheduling principle is as follows: one vehicle can serve a plurality of passengers, but each passenger can be served by only one vehicle, and the transfer condition does not exist.
6. The method for matching passenger demand and scheduling of a vehicle as recited in claims 4-5, wherein if the time of arrival of the vehicle at a certain pick-up point is earlier than the earliest departure time of the pick-up point for the service demand, the vehicle waits for the earliest departure time and departs at the earliest departure time, and if the time of arrival of the vehicle is within the time window, the passenger can get on at the time of arrival of the vehicle, and the vehicle drives to the next node, and the time of arrival of the vehicle is the time of departure of the vehicle.
7. The method of claim 6, wherein all vehicles smoothly travel on the road at the same uniform speed, thereby avoiding traffic congestion and ensuring that the vehicles travel without failure.
8. The method of claim 1, wherein step 5 is performed to confirm timely arrival of the vehicle at the destination, arrival and departure of the vehicle at the destination, and the confirmation means includes, but is not limited to, cell phones, other mobile devices, and vehicle-mounted fixed confirmation means.
9. The method of claim 8, wherein the unmanned vehicle dispatch center receives a passenger disembarking command and then re-programs the vehicle to pick up a new batch of passengers.
10. The method for matching passenger requirements and scheduling of an unmanned docked vehicle according to any one of claims 1-9, wherein if the passenger does not arrive within the predetermined time frame, the vehicle waits for an additional 5 minutes, and if the passenger does not arrive, the unmanned vehicle issues a passenger miss command and accepts the command from the unmanned vehicle scheduling center to travel to a new location.
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