CN113759934A - Method and system for configuring and scheduling unmanned campus bus - Google Patents
Method and system for configuring and scheduling unmanned campus bus Download PDFInfo
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Abstract
The invention discloses a method and a system for configuring and scheduling a campus unmanned bus, wherein the method comprises the following steps: step 1, receiving vehicle related information and scheduled riding information of a passenger target, sending campus map information to a minibus, and sending a vehicle scheduling task to the minibus closest to the passenger target; step 2, judging whether the bus is in an emergency state; step 3, executing a vehicle scheduling task, and judging whether the current passenger target number in the vehicle is less than a set threshold value; step 4, docking the passenger targets according to the priority order, and judging whether the priority is reduced to a set threshold value; and 5, conveying the passenger target to a destination in the scheduled riding information according to the global planned path in the vehicle scheduling task. The invention can flexibly configure and dispatch the campus unmanned bus according to the actual requirements of passengers, and can solve the problems of unreasonable route, long waiting time and the like in the operation process of the traditional campus bus.
Description
Technical Field
The invention relates to the technical field of automatic driving, in particular to a campus unmanned bus configuration and scheduling method and system.
Background
The autonomous vehicle can be used for transporting people and goods in various environments such as roads, ports, mines and campuses. There have been cases of automated driving at the level of L3 that have been successfully achieved in simpler campus environments, as opposed to complex road situations, as represented by campuses. The traditional campus bus connects passengers in the campus in various modes such as manual driving on a set route, telephone reservation and the like, and has the defects of small bus number, less and unreasonable station setting, overlong waiting time and the like. By configuring the unmanned bus in the campus, more real-time and intelligent transportation of people and goods can be provided for passengers through better planning and scheduling algorithms.
Disclosure of Invention
It is an object of the present invention to provide a campus drone chin configuration and scheduling method and system that overcomes or at least alleviates at least one of the above-mentioned deficiencies of the prior art.
In order to achieve the above object, the present invention provides a campus unmanned bus configuration and scheduling method, including:
the priority obtaining method comprises the following steps:
wherein F (priority) represents the priority, and the priority values are set to [0,1 ] in the order from small to large]In the range of (1), TemergencyI represents that the demand situation information is a first emergency degree, TemergencyII indicates that the demand situation information is a second urgency level, f (T)waiting) A waiting time function representing the passenger's goal, and t represents the waiting time actually since the issuance of the reservation information until the present moment.
Further, in step 1, the campus map information includes ID information of sub-areas included in each of the cells and divided into a plurality of cells, the vehicle-related information includes information on a position of the minibus and a type and a number of passenger targets in the area where the minibus is located, and the scheduled riding information includes information on a current position and a demand situation of the passenger targets;
the method for sending the vehicle scheduling task to the minibus closest to the passenger target specifically comprises the following steps:
step 11, calculating the Manhattan distance between the sub-area ID corresponding to the current position of the passenger target and each small bar in the cell where the sub-area ID is located;
step 12, if the Manhattan distance calculated in the step 11 is smaller than the radius of the cell corresponding to the current position of the passenger target, selecting the minibus with the minimum Manhattan distance to send a vehicle scheduling task;
and step 13, if the Manhattan distance calculated in the step 11 is larger than the radius of the cell corresponding to the current position of the passenger target, continuously calculating the Manhattan distances between all the directly adjacent cell interior bars around the cell corresponding to the current position of the passenger target and the passenger target, and selecting the cell with the smallest Manhattan distance to send out a vehicle scheduling task.
Further, the demand situation information is divided into two types, namely a common state and an emergency state;
the method for judging whether the chin is in the emergency state in the step 2 specifically comprises the following steps:
according to the sensing result of the surrounding environment of the kid, which is acquired by the kid in real time, evaluating by utilizing an evaluation function, wherein the evaluation result is divided into a common state and an emergency state;
and if any one of the demand situation information and the evaluation result is an emergency state, setting the mini-bus state as an emergency state, otherwise, setting the mini-bus state as a common state.
Further, in step 4, the method for obtaining the global planned path specifically includes:
the cloud control center is combined with the campus map information and the scheduled riding information to model the whole campus environment into a graph model, and the cloud control center specifically comprises the following steps:
abstracting all campus roads in a campus environment into an edge V, abstracting all entrances of all buildings into a node E, and establishing a directed graph G (V, E), wherein the total weight W of space-time edges of G (V, E)outputDetermined by the following formula (1):
Woutput＝Wdistance+Wgeometry+Wexperience (1)
wherein, WdistanceManhattan distance weight, W, representing pathgeometryWeight of geometric characteristic, W, representing pathexperienceRepresentative roadEmpirical congestion weight of the path.
The invention also provides a campus unmanned bus configuration and scheduling system, which comprises:
the cloud control center is used for receiving vehicle related information and scheduled riding information of the passenger targets, sending campus map information to all the minibuses in the campus, and sending vehicle scheduling tasks to the minibuses closest to the passenger targets;
the vehicle-mounted decision control unit is used for judging whether the bus is in an emergency state or not, if the bus is judged to be in the emergency state, the passenger targets are docked according to the sequence of the priority, a planned route generated by a dynamic planning algorithm is adopted, the function of immediate stop after hand calling is closed in the operation process, and whether the priority is reduced to a set threshold value or not is judged; if the bus is judged to be in a common state, executing the vehicle scheduling task, and judging whether the current passenger target number in the vehicle is smaller than a set threshold value or not; starting the immediate stop calling function under the condition that the current passenger target number in the vehicle is less than a set threshold value, closing the immediate stop calling function under the condition that the current passenger target number in the vehicle is greater than or equal to the set threshold value, planning a local path of the vehicle according to a global planning path in the vehicle scheduling task, taking an environment sensing result as input, and carrying a passenger target to a destination in the preset riding information through calculation;
the priority obtaining method comprises the following steps:
wherein F (priority) represents the priority, and the priority values are set to [0,1 ] in the order from small to large]In the range of (1), TemergencyI represents that the demand situation information is a first emergency degree, TemergencyII indicates that the demand situation information isTwo degrees of urgency, f (T)waiting) A waiting time function representing the passenger's goal, and t represents the waiting time actually since the issuance of the reservation information until the present moment.
Further, the campus map information comprises ID information of sub-areas which are divided into a plurality of cells and each cell contains, the vehicle-related information comprises the position information of the minibus and the category and the number of passenger targets in the area of the minibus, and the scheduled riding information comprises the current position and the demand situation information of the passenger targets;
the method for sending the vehicle scheduling task to the minibus closest to the passenger target by the cloud control center specifically comprises the following steps:
calculating the Manhattan distance between the sub-area ID corresponding to the current position of the passenger target and each small bar in the cell where the sub-area ID is located;
if the Manhattan distance is smaller than the radius of the cell corresponding to the current position of the passenger target, selecting to send a vehicle scheduling task to the small bar with the minimum Manhattan distance;
and if the Manhattan distance is larger than the radius of the cell corresponding to the current position of the passenger target, continuously calculating the Manhattan distances between all the directly adjacent intra-cell minibars around the cell corresponding to the current position of the passenger target and the passenger target, and selecting the minibars of the cell with the smallest Manhattan distance to send out a vehicle scheduling task.
Further, the demand situation information is divided into two types, namely a common state and an emergency state;
the method for judging whether the bus is in the emergency state by the vehicle-mounted decision control unit specifically comprises the following steps:
according to the sensing result of the surrounding environment of the kid, which is acquired by the kid in real time, evaluating by utilizing an evaluation function, wherein the evaluation result is divided into a common state and an emergency state;
and if any one of the demand situation information and the evaluation result is an emergency state, setting the mini-bus state as an emergency state, otherwise, setting the mini-bus state as a common state.
Further, the method for acquiring the global planned path specifically includes:
the cloud control center is combined with the campus map information and the scheduled riding information to model the whole campus environment into a graph model, and the cloud control center specifically comprises the following steps:
abstracting all campus roads in a campus environment into an edge V, abstracting all entrances of all buildings into a node E, and establishing a directed graph G (V, E), wherein the total weight W of space-time edges of G (V, E)outputDetermined by the following formula (1):
Woutput＝Wdistance+Wgeometry+Wexperience (1)
wherein, WdistanceManhattan distance weight, W, representing pathgeometryWeight of geometric characteristic, W, representing pathexperienceAn empirical congestion weight representing the path.
The invention can flexibly configure and dispatch the campus unmanned bus according to the actual requirements of passengers, and can solve the problems of unreasonable route, long waiting time and the like in the operation process of the traditional campus bus.
Drawings
Fig. 1 is a schematic diagram of a campus unmanned bus configuration and scheduling system according to an embodiment of the present invention.
Fig. 2 is a schematic flow chart of a campus unmanned chin configuration and scheduling method according to an embodiment of the present invention.
Fig. 3 is a schematic view of an application scenario for issuing a vehicle scheduling task to a minibus closest to a passenger target according to an embodiment of the present invention.
Detailed Description
The invention is described in detail below with reference to the figures and examples.
The invention aims at different requirements of passengers and allocates different dispatching schemes to each unmanned bus so as to meet the target requirements of the passengers more quickly.
As shown in fig. 1 and fig. 2, a method for configuring and scheduling a campus unmanned minibus according to an embodiment of the present invention includes:
The cloud control center is characterized by comprising the following functions:
on one hand, the storage function: the cloud control center stores a vehicle management list and campus information. The vehicle management list updates the relevant information of the vehicle of each small bus in the campus in real time, such as: the ID of the small bar, the location information, and the category and number of passenger targets in the area of the small bar and their corresponding history information. The campus information includes ID information of a campus map, a plurality of cells divided into the campus map, and a sub-area included in each of the cells.
On the other hand, the information receiving function: the cloud control center receives ambient state information from the treated mini-bus, reservation requests from passenger targets, and real-time weather information from an external campus.
Wherein, the ambient environment state information after the mini-bar processing is obtained by the mini-bar being equipped with an environment perception module. After the environment sensing module acquires the surrounding environment state information (local), the information such as the passenger target category, the number and the local position of the cell in which the environment sensing module is located is uploaded to the cloud control center after the information is processed by a machine learning algorithm.
The scheduled riding information can be sent in the modes of instant messaging software and the like, and comprises the current position, the destination and the demand situation information.
The demand situation information is divided into two types, namely a common state and an emergency state. Wherein, the requirement situation of the common status type comprises the requirement that non-emergency general study, transportation character and the like can endure waiting in a certain time. The demand situation of the emergency type includes the demand that the class time is close, the passenger target is sick or the passenger target waiting time is too long, and the like.
In yet another aspect, the information sending function: the cloud control center continuously sends campus high-definition maps to all unmanned minibuses according to set fixed time, and simultaneously sends real-time weather conditions and vehicle scheduling tasks to the minibuses closest to the passenger targets. The cloud control center can adopt a broadcasting mode, adopts a communication mode without limitation, and sends the campus map and the real-time weather condition to each mini bus in real time.
And 2, analyzing and estimating the current passenger target demand situation and the external environment state by the minibus according to the vehicle scheduling task, and judging whether the vehicle running state of the minibus is an emergency state or not.
If it is determined that the vehicle running state of the small bus is the emergency state, the process proceeds to step 4.
If the vehicle running state of the small bus is determined to be the normal state, the process proceeds to step 3.
And 3, if the vehicle running state of the minibus is a common state, the situation that all passenger target demands are non-emergency demands is indicated, the minibus can run stably, namely the minibus executes the vehicle scheduling task, and whether the current passenger target number in the vehicle is smaller than a set threshold value or not is judged.
And if the current passenger target number in the vehicle is less than the set threshold value, starting the function of immediate stop by calling and returning to the step 1. If the function of stopping immediately after starting the waving is started, the passenger targets with requirements around can be accessed into the small bus in real time, and the no-load rate of the bus can be reduced.
And if the number of the current passenger targets in the vehicle is larger than or equal to the set threshold value, closing the waving and immediate stopping function, and entering the step 5.
The 'number of current passenger targets in the vehicle' can be obtained through a forward-looking vehicle-mounted camera in the environment sensing module, and the computing unit respectively performs human body detection and gesture recognition through a pre-trained human body detection and gesture recognition model which can be in real time. Firstly, human body detection is carried out, after the human body is detected, secondary recognition is carried out through gesture recognition, and if the human body is recognized as a hand-waving, the vehicle decelerates and slowly moves until the vehicle reaches a passenger target. The data of human body detection and gesture recognition are obtained by calculating the number of people in the vehicle in real time through a counting module in the foresight vehicle-mounted camera. Of course, they can be obtained by other methods, which are not listed here.
The "set threshold" is determined according to the maximum passenger capacity of the small bus, for example, the set threshold may be empirically determined according to the full seats of the small bus and the actual situation of the campus, and is set to 80% of the maximum passenger number.
And 4, the running state of the vehicle of the small bus is an emergency state, and the connection sequence of the passenger targets has priority. The cloud control center divides the passenger target states into different priorities and uniformly sends the priorities to the kids, and the consideration factors comprise passenger scheduled riding information and actual experience. The kids will dock passenger targets in sequence according to the order of priority.
In the conveying process, a planning route generated by a dynamic planning algorithm is adopted, the 'immediate-holding' function is turned off in the process, whether the priority level is reduced to a set threshold value or not is judged, if the priority level is reduced to be lower than the set threshold value, the step 3 is carried out, and therefore the no-load rate of the bus can be reduced. If the priority level falls above the set threshold, the process proceeds to step 5.
And 5, conveying the passenger target to the destination according to the global planned path in the vehicle scheduling task. The method for acquiring the global planned path specifically comprises the following steps:
and modeling the whole campus environment into a graph model by combining the campus map information and the scheduled riding information through the cloud control center, establishing a global path plan without priority among all passenger targets, and sequentially connecting according to the Manhattan distance from near to far. The global planning method may include, but is not limited to, algorithms such as a × a, RRT, and the like, which specifically include:
the method for constructing the school garden as the graph model is to take the actual conditions of the campus and the requirements of passengers under various actual complex conditions into consideration and carry out gridding processing on the campus environment. Specifically, all campus roads are abstracted into an edge V, all entrances of all buildings are abstracted into a node E, and a directed graph G is created as (V, E). The use of directed graphs can embody that the same passenger target is transported between two sites at different time periods in campus buses with different spatiotemporal edge weights. The weight of an edge is a function of various factors actually affecting the transportation, as shown in the following formula (1):
Woutput＝Wdistance+Wgeometry+Wexperience (1)
wherein, WdistanceThe manhattan distance weight of the representative path is determined by the manhattan distance of the path, and the weight is smaller when the distance is larger. WgeometryThe geometric characteristic weight of the representative path is determined by road geometric topology and is divided into two factors of road width and gradient, and the geometric characteristic weight is divided according to anthropomorphic experience, wherein the weight is smaller when the road section is narrower and the gradient is larger. WexperienceThe empirical congestion weight of the representative route is obtained empirically through the campus practice, and the more congested sections are empirically, the smaller the weight is. Wdistance、Wgeometry、WexperienceThe specific values of (A) can be obtained through experience, and in this embodiment, each value can be defined as 1/3, and finally the total weight W is outputoutputHas a value range of [0,1 ]]The global path corresponding to the larger total weight value is usually selected.
In one embodiment, in step 5, according to the global planned path in the vehicle scheduling task, the ciba is always started to sense the environment in the running process, and the vehicle local path planning is performed through calculation by using the result of the environment sensing as input. The local path refers to a long path plan in a shorter road section, such as within 200 meters. The local path planning method includes, but is not limited to, algorithms such as DWA. The environment sensing result is obtained by the environment sensing unit, and the environment sensing unit is provided with various sensors such as a laser, a camera, a millimeter wave radar and an ultrasonic radar and can acquire surrounding environment information and road surface information in real time.
Through the steps, the traffic demand of the passenger target in the campus under the characteristics of safety, stability and humanization can be met in a short time.
In one embodiment, the method for issuing a vehicle dispatching task to the minibus closest to the passenger target specifically comprises:
and 11, calculating the Manhattan distance between the sub-area ID corresponding to the current position of the passenger target and each small bar in the cell in which the sub-area ID is positioned.
Step 12, if the Manhattan distance calculated in the step 11 is smaller than the radius of the cell corresponding to the current position of the passenger target, selecting the minibus with the minimum Manhattan distance to send a vehicle scheduling task;
and step 13, if the Manhattan distance calculated in the step 11 is larger than the radius of the cell corresponding to the current position of the passenger target, continuously calculating the Manhattan distances between all the directly adjacent cell interior bars around the cell corresponding to the current position of the passenger target and the passenger target, and selecting the cell with the smallest Manhattan distance to send out a vehicle scheduling task.
The calculation is shown in fig. 3, for example. Two adjacent cells 1 and 2 in a campus are divided into 9 sub-regions in each cell, and the distance between every two sub-regions is 1. Assuming that person 1 is located in the 9 sub-areas of cell 1 and needs to ride, if there are both small bars in sub-areas 1 and 5, the manhattan distance from small bar 1 to person 1 is 3, and the manhattan distance from small bar 2 to person 1 is 5, then the dispatch task is sent to small bar 1 at this time. If there is no small bar 1 but only small bar 2, then the vehicle dispatch task is sent to small bar 3, since there is a small bar 3 in the neighboring cell 2 and the distance of small bar 3 from person 1 is 1.
Of course, the vehicle dispatching task can be issued to the minibus closest to the passenger target by other methods besides the above-mentioned embodiments, and the core is to find the minibus closest to the passenger target, which will not be described herein.
In one embodiment, the method of "determining whether the chin is in the emergency state" in step 2 specifically includes:
and evaluating by utilizing an evaluation function of an external environment state according to a sensing result of the surrounding environment of the kid, which is acquired by the kid in real time, wherein the evaluation result is divided into a common state and an emergency state. The input of the evaluation function is an environment perception result, and the evaluation function needs to be calibrated into a common state or an emergency state manually. And training a classification algorithm by using the data after manual calibration. The classification method used may be any classification algorithm in machine learning, including but not limited to logistic regression, support vector machines, neural networks, and the like.
And if any one of the demand situation information and the evaluation result is an emergency state, setting the mini-bus state as an emergency state, otherwise, setting the mini-bus state as a common state.
In one embodiment, in step 4, the priority is calculated by considering 2 factors: passenger target situation urgency level TemergencyWaiting time T with passenger's targetwaiting. The method for acquiring the priority comprises the following steps:
wherein F (priority) represents the priority, and the priority values are set to [0,1 ] in the order from small to large]When 1, the priority is the largest, which corresponds to the target that is the first to be docked at minibar. T isemergencyI indicates that the demand situation information is of a first, highest, urgency, such as a threat level to the life of the passenger target, which may occur only rarely and should be handled as soon as it occurs. T isemergencyII indicates that the demand situation information is of a second urgency, such as a time-critical demand such as a passenger target urgent need for class, and is typically presented as a discrete value, such as one of 0.1, 0.5, 0.8. f (T)waiting) A waiting time function representing the passenger's goal, and t represents the waiting time actually since the issuance of the reservation information until the present moment.
The scheduling planning method used after determining the priority is the same as the method for acquiring the global planned path in the above embodiment, except that planning calculation is performed only for a single passenger target each time, or planning calculation may be performed by using a dynamic planning algorithm, so that the minibus directly reaches the passenger target with the highest priority at present.
Minibus riding in emergencyAnd if the overall priority of the passenger target is lower than the overall priority of the set passenger target, adjusting the bus from the emergency state to the common state. The method for determining the set passenger target overall priority is characterized in that the priority of the passenger target is not TemergencyClass I tasks, followed by F (priority) < H, H is a set threshold, which may be 0.8, for example.
The embodiment of the invention also provides a campus unmanned minibus configuration and scheduling system, which comprises a cloud control center and a vehicle-mounted decision control unit:
the cloud control center is used for receiving vehicle related information and scheduled riding information of the passenger targets, sending campus map information to all the minibuses in the campus, and sending vehicle scheduling tasks to the minibuses closest to the passenger targets;
the vehicle-mounted decision control unit is used for judging whether the bus is in an emergency state or not, if the bus is judged to be in the emergency state, the passenger targets are docked according to the sequence of the priority, a planned route generated by a dynamic planning algorithm is adopted, the function of immediate stop after hand calling is closed in the operation process, and whether the priority is reduced to a set threshold value or not is judged; if the bus is judged to be in a common state, executing the vehicle scheduling task, and judging whether the current passenger target number in the vehicle is smaller than a set threshold value or not; starting the immediate stop calling function under the condition that the current passenger target number in the vehicle is less than a set threshold value, closing the immediate stop calling function under the condition that the current passenger target number in the vehicle is greater than or equal to the set threshold value, and conveying the passenger target to a destination in the preset riding information according to a global planned path in the vehicle scheduling task;
the priority obtaining method comprises the following steps:
in the formula (I), the compound is shown in the specification,f (priority) represents the priority, and the priority values are set to be 0,1 in the order from small to large]In the range of (1), TemergencyI represents that the demand situation information is a first emergency degree, TemergencyII indicates that the demand situation information is a second urgency level, f (T)waiting) A waiting time function representing the passenger's goal, and t represents the waiting time actually since the issuance of the reservation information until the present moment.
Information interaction is carried out between the cloud control center and the vehicle-mounted decision control unit through the communication unit, and combination of cloud control and local perception is achieved to achieve better perception of the road section.
In one embodiment, the campus map information includes ID information divided into a plurality of cells and sub-regions included in each of the cells, the vehicle-related information includes information on the position of the bus and the type and number of passenger targets in the region of the bus, and the scheduled riding information includes information on the current position and demand situation of the passenger targets;
the method for sending the vehicle scheduling task to the minibus closest to the passenger target by the cloud control center specifically comprises the following steps:
calculating the Manhattan distance between the sub-area ID corresponding to the current position of the passenger target and each small bar in the cell where the sub-area ID is located;
if the Manhattan distance is smaller than the radius of the cell corresponding to the current position of the passenger target, selecting to send a vehicle scheduling task to the small bar with the minimum Manhattan distance;
and if the Manhattan distance is larger than the radius of the cell corresponding to the current position of the passenger target, continuously calculating the Manhattan distances between all the directly adjacent intra-cell minibars around the cell corresponding to the current position of the passenger target and the passenger target, and selecting the minibars of the cell with the smallest Manhattan distance to send out a vehicle scheduling task.
In one embodiment, the demand situation information is divided into two types, namely a normal state and an emergency state;
the method for judging whether the bus is in the emergency state by the vehicle-mounted decision control unit specifically comprises the following steps:
according to the sensing result of the surrounding environment of the kid, which is acquired by the kid in real time, evaluating by utilizing an evaluation function, wherein the evaluation result is divided into a common state and an emergency state;
and if any one of the demand situation information and the evaluation result is an emergency state, setting the mini-bus state as an emergency state, otherwise, setting the mini-bus state as a common state.
In one embodiment, the method for obtaining the global planned path specifically includes:
the cloud control center is combined with the campus map information and the scheduled riding information to model the whole campus environment into a graph model, and the cloud control center specifically comprises the following steps:
abstracting all campus roads in a campus environment into an edge V, abstracting all entrances of all buildings into a node E, and establishing a directed graph G (V, E), wherein the total weight W of space-time edges of G (V, E)outputDetermined by the following formula (1):
Woutput＝Wdistance+Wgeometry+Wexperience (1)
wherein, WdistanceManhattan distance weight, W, representing pathgeometryWeight of geometric characteristic, W, representing pathexperienceAn empirical congestion weight representing the path.
The campus unmanned bus integrates an environment sensing unit, a decision control unit, a communication unit and the like. The environment sensing unit part is provided with various sensors such as laser, a camera, a millimeter wave radar and an ultrasonic radar, and can acquire surrounding environment information and road surface information in real time. The decision control unit controls the local and global path planning of the unmanned bus in the campus. The communication unit can receive information of the cloud control center in real time, and the cloud control and local sensing are combined to sense the road sections better.
By integrating the modules, the safe, stable and humanized configuration scheduling of the campus bus can be realized. The existing manual driving scheduling method generally runs on the basis of a preset route, and does not consider the periodic travel distribution, the mixed flow of people and vehicles, different weather conditions and real-time special requirements of passenger targets in a campus environment. Moreover, the campus buses are generally distributed to various places of the campus, and different scheduling schemes should be distributed to each vehicle according to different requirements of passengers so as to meet the target requirements of the passengers more quickly, and the overall process schematic diagram is shown in fig. 1.
Finally, it should be pointed out that: the above examples are only for illustrating the technical solutions of the present invention, and are not limited thereto. Those of ordinary skill in the art will understand that: modifications can be made to the technical solutions described in the foregoing embodiments, or some technical features may be equivalently replaced; such modifications or substitutions do not depart from the spirit and scope of the corresponding technical solutions of the embodiments of the present invention.
Claims (8)
1. A campus unmanned minibus configuration and scheduling method is characterized by comprising the following steps:
step 1, receiving vehicle related information and scheduled riding information of a passenger target through a cloud control center, sending campus map information to all minibuses in a campus, and sending a vehicle scheduling task to the minibus closest to the passenger target;
step 2, judging whether the bus is in an emergency state, if so, entering a step 4, otherwise, judging to be in a common state, and entering a step 3;
step 3, executing the vehicle scheduling task, judging whether the current passenger target number in the vehicle is smaller than a set threshold value, if so, starting the immediate stop function of the hand-calling, returning to the step 1, if not, closing the immediate stop function of the hand-calling, and entering the step 5;
step 4, docking the passenger targets according to the priority order, adopting a planning route generated by a dynamic planning algorithm, closing the hand-calling immediate stop function in the operation process, judging whether the priority is reduced to a set threshold value, if so, entering step 3, and if not, entering step 5;
step 5, according to the global planning path in the vehicle scheduling task, and by using an environment sensing result as input, performing local path planning on the vehicle through calculation, and conveying a passenger target to a destination in the scheduled riding information;
the priority obtaining method comprises the following steps:
wherein F (priority) represents the priority, and the priority values are set to [0,1 ] in the order from small to large]In the range of (1), TemergencyI represents that the demand situation information is a first emergency degree, TemergencyII indicates that the demand situation information is a second urgency level, f (T)waiting) A waiting time function representing the passenger's goal, and t represents the waiting time actually since the issuance of the reservation information until the present moment.
2. The campus unmanned chin configuration and scheduling method of claim 1, wherein in step 1, the campus map information includes ID information divided into a plurality of cells and sub-regions included in each of the cells, the vehicle-related information includes chin location information and categories and numbers of passenger targets in the region where the chin is located, and the predetermined riding information includes current locations and demand situation information of the passenger targets;
the method for sending the vehicle scheduling task to the minibus closest to the passenger target specifically comprises the following steps:
step 11, calculating the Manhattan distance between the sub-area ID corresponding to the current position of the passenger target and each small bar in the cell where the sub-area ID is located;
step 12, if the Manhattan distance calculated in the step 11 is smaller than the radius of the cell corresponding to the current position of the passenger target, selecting the minibus with the minimum Manhattan distance to send a vehicle scheduling task;
and step 13, if the Manhattan distance calculated in the step 11 is larger than the radius of the cell corresponding to the current position of the passenger target, continuously calculating the Manhattan distances between all the directly adjacent cell interior bars around the cell corresponding to the current position of the passenger target and the passenger target, and selecting the cell with the smallest Manhattan distance to send out a vehicle scheduling task.
3. The campus drone chin configuration and scheduling method of claim 2 wherein the demand situation information is classified into two types, normal and emergency;
the method for judging whether the chin is in the emergency state in the step 2 specifically comprises the following steps:
according to the sensing result of the surrounding environment of the kid, which is acquired by the kid in real time, evaluating by utilizing an evaluation function, wherein the evaluation result is divided into a common state and an emergency state;
and if any one of the demand situation information and the evaluation result is an emergency state, setting the mini-bus state as an emergency state, otherwise, setting the mini-bus state as a common state.
4. The campus unmanned chin configuration and scheduling method of any one of claims 1 to 3, wherein in step 4, the method for obtaining the global planned path specifically comprises:
the cloud control center is combined with the campus map information and the scheduled riding information to model the whole campus environment into a graph model, and the cloud control center specifically comprises the following steps:
abstracting all campus roads in a campus environment into an edge V, abstracting all entrances of all buildings into a node E, and establishing a directed graph G (V, E), wherein the total weight W of space-time edges of G (V, E)outputDetermined by the following formula (1):
Woutput＝Wdistance+Wgeometry+Wexperience (1)
wherein, WdistanceThe manhattan distance weight representing the path,Wgeometryweight of geometric characteristic, W, representing pathexperienceAn empirical congestion weight representing the path.
5. A campus unmanned chin configuration and dispatch system, comprising:
the cloud control center is used for receiving vehicle related information and scheduled riding information of the passenger targets, sending campus map information to all the minibuses in the campus, and sending vehicle scheduling tasks to the minibuses closest to the passenger targets;
the vehicle-mounted decision control unit is used for judging whether the bus is in an emergency state or not, if the bus is judged to be in the emergency state, the passenger targets are docked according to the sequence of the priority, a planned route generated by a dynamic planning algorithm is adopted, the function of immediate stop after hand calling is closed in the operation process, and whether the priority is reduced to a set threshold value or not is judged; if the bus is judged to be in a common state, executing the vehicle scheduling task, and judging whether the current passenger target number in the vehicle is smaller than a set threshold value or not; starting the immediate stop calling function under the condition that the current passenger target number in the vehicle is less than a set threshold value, closing the immediate stop calling function under the condition that the current passenger target number in the vehicle is greater than or equal to the set threshold value, planning a local path of the vehicle according to a global planning path in the vehicle scheduling task, taking an environment sensing result as input, and carrying a passenger target to a destination in the preset riding information through calculation;
the priority obtaining method comprises the following steps:
wherein, F (priority) represents the priority, and the priority value is set in the order from small to large[0,1]In the range of (1), TemergencyI represents that the demand situation information is a first emergency degree, TemergencyII indicates that the demand situation information is a second urgency level, f (T)waiting) A waiting time function representing the passenger's goal, and t represents the waiting time actually since the issuance of the reservation information until the present moment.
6. The campus unmanned chin configuration and dispatch system of claim 5, wherein the campus map information comprises ID information divided into a plurality of cells and sub-regions included in each of the cells, the vehicle-related information comprises chin position information and categories and numbers of passenger targets in the region of the chin, and the scheduled ride information comprises current positions and demand situation information of the passenger targets;
the method for sending the vehicle scheduling task to the minibus closest to the passenger target by the cloud control center specifically comprises the following steps:
calculating the Manhattan distance between the sub-area ID corresponding to the current position of the passenger target and each small bar in the cell where the sub-area ID is located;
if the Manhattan distance is smaller than the radius of the cell corresponding to the current position of the passenger target, selecting to send a vehicle scheduling task to the small bar with the minimum Manhattan distance;
and if the Manhattan distance is larger than the radius of the cell corresponding to the current position of the passenger target, continuously calculating the Manhattan distances between all the directly adjacent intra-cell minibars around the cell corresponding to the current position of the passenger target and the passenger target, and selecting the minibars of the cell with the smallest Manhattan distance to send out a vehicle scheduling task.
7. The campus unmanned chin configuration and dispatch system of claim 6, wherein the demand situation information is classified into two types, normal status and emergency status;
the method for judging whether the bus is in the emergency state by the vehicle-mounted decision control unit specifically comprises the following steps:
according to the sensing result of the surrounding environment of the kid, which is acquired by the kid in real time, evaluating by utilizing an evaluation function, wherein the evaluation result is divided into a common state and an emergency state;
and if any one of the demand situation information and the evaluation result is an emergency state, setting the mini-bus state as an emergency state, otherwise, setting the mini-bus state as a common state.
8. The campus unmanned chin configuration and scheduling system of any one of claims 5 to 7, wherein the method of obtaining the global planned path specifically comprises:
the cloud control center is combined with the campus map information and the scheduled riding information to model the whole campus environment into a graph model, and the cloud control center specifically comprises the following steps:
abstracting all campus roads in a campus environment into an edge V, abstracting all entrances of all buildings into a node E, and establishing a directed graph G (V, E), wherein the total weight W of space-time edges of G (V, E)outputDetermined by the following formula (1):
Woutput＝Wdistance+Wgeometry+Wexperience (1)
wherein, WdistanceManhattan distance weight, W, representing pathgeometryWeight of geometric characteristic, W, representing pathexperienceAn empirical congestion weight representing the path.
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