CN113990093A - System and method for dynamically sharing and scheduling unmanned electric taxi - Google Patents
System and method for dynamically sharing and scheduling unmanned electric taxi Download PDFInfo
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Abstract
The invention relates to the technical field of unmanned driving, in particular to a system and a method for dynamically sharing and dispatching an unmanned electric taxi, wherein S1: the user side sends the order information to the communication module; s2: the communication module uploads the information to the control module; s3: the information storage module uploads the data to the control module; s4: the control module generates a user service plan and a vehicle scheduling scheme; s5: the control module respectively uploads a user service plan and a vehicle scheduling scheme to the communication module and the information storage module, the communication module respectively sends the adjusted user receiving and sending places and the estimated receiving and sending time to each client side, the vehicle scheduling scheme is sent to each unmanned vehicle-mounted communication terminal, and the information storage module updates corresponding user and vehicle information according to the received user service plan and the received vehicle scheduling scheme; the vehicle scheduling strategy can be quickly generated with high user co-riding matching efficiency according to the real-time road network traffic state.
Description
Technical Field
The invention relates to the technical field of unmanned driving, in particular to a system and a method for dynamically sharing and dispatching an unmanned electric taxi.
Background
With the gradual maturity of the unmanned electric vehicle technology, the relevant mature products are gradually accepted by people, and meanwhile, the travel mode of people is also changed. In the foreseeable future, the combination of unmanned electric vehicle technology with existing internet taxi platforms will subvert the entire industry's mode of operation. On one hand, the unmanned electric automobile can save labor cost, can thoroughly solve the multi-party contradiction between a driver and a platform enterprise and between the driver and a user from a technical level, and fully guarantees the user rights and interests and personal safety. On the other hand, with the support of technologies such as internet of things, electronic maps, satellite navigation and cloud computing, the internet taxi platform can collect information such as road network traffic, user demands, vehicle positions and basic states in real time. And then the centralized dispatching of the unmanned electric taxi is realized according to the information, and the operation efficiency of the system is improved by fully utilizing the vehicles. The unmanned electric taxi system is characterized in that a control center senses all information such as user requirements, vehicle states, traffic states and the like in real time through an informatization and intelligent technology, global optimization scheduling is carried out on the basis of the information and intelligent technology from the system level, decision making is more macroscopic and comprehensive, and limitation of single user and vehicle information acquisition and decision making is avoided. Therefore, the utilization rate of vehicles can be improved, traffic jam is reduced, the service efficiency of the system is improved, users are further encouraged to ride together for travel, the travel cost of the users can be reduced, waiting queuing time is shortened in the peak period, and the system operation efficiency and profit of platform enterprises can be improved. However, in the scheduling method for the unmanned electric taxi in the prior art, a scheduling strategy for how to quickly generate a vehicle with high user co-riding matching efficiency according to a real-time road network traffic state does not exist.
Disclosure of Invention
The invention aims to provide a system and a method for dynamically co-taking and scheduling an unmanned electric taxi, which can quickly generate a scheduling strategy of a vehicle with higher user co-taking matching efficiency according to a real-time road network traffic state.
The purpose of the invention is realized by the following technical scheme:
a dynamic co-riding scheduling system for an unmanned electric taxi comprises a communication module, an information storage module and a control module which are in mutual information communication, and further comprises a user side and an unmanned vehicle-mounted communication terminal which are in mutual information communication with the communication module;
the communication module is used for information communication between the information storage module and the control module and between the user side and the unmanned vehicle-mounted communication terminal, and the information transmitted by the communication module comprises real-time road network traffic information, user trip demand information, vehicle positions, electric quantity states, passenger carrying states and the like;
road network topology data, road network rasterization data, grid number data of the optimal path between any two places in the road network under different scenes, user basic information data, electric vehicle basic information data and the like are stored in the information storage module; the basic information data of the user comprise gender, age, preference and the like, and the basic information data of the electric automobile comprise a vehicle type, maximum cruising and maximum passenger carrying and the like;
the control module is used for generating a user service plan and a vehicle scheduling scheme according to the data information received from the communication module and the information storage module;
the user side can also display part of system information for the user, such as part of real-time positions of electric vehicles, part of information of passengers co-riding, travel routes and the like;
a dynamic co-riding scheduling method for an unmanned electric taxi comprises the following steps:
s1: the user side sends information such as starting and ending points, the number of users and the like to a communication module of the dispatching center through the terminal in an order form;
s2: the communication module uploads order information, real-time road network traffic information acquired in real time, electric automobile position information, electric quantity information and passenger carrying information to the control module;
s3: the information storage module uploads road network topology data, road network rasterization data, grid number data of an optimal path between any two places in the road network under different scenes, user basic information data and electric vehicle basic information data to the control module;
s4: the control module receives the information data of the communication module and the information storage module, executes a dynamic scheduling method of the unmanned electric taxi, and generates a user service plan and a vehicle scheduling scheme;
s5: the control module respectively uploads a user service plan and a vehicle scheduling scheme to the communication module and the information storage module, the communication module respectively sends the adjusted user receiving and sending places and the estimated receiving and sending time to each client side, the vehicle scheduling scheme is sent to each unmanned vehicle-mounted communication terminal, and the information storage module updates corresponding user and vehicle information according to the received user service plan and the received vehicle scheduling scheme;
the user service plan comprises user receiving and sending places, estimated user receiving and sending time and service vehicle information of each user, and the vehicle scheduling scheme comprises a task place, a driving path and a charging scheme, which are required to be reached by the vehicle;
the road network rasterization data is that the road network is rasterized according to a square grid with the side length as a preset value I to generate grid numbers, road network node numbers and adjacent grid numbers contained in the grids, wherein the adjacent grids are defined as that at least one pair of nodes which can be directly connected with each other by road sections without other nodes exist between the road network and the grids;
the method for generating the grid number data of the optimal path between any two places in the road network under different scenes comprises the following steps:
s31: forming a plurality of groups of representative road network traffic state scenes by a clustering method according to the collected road network traffic information data and road network rasterization data at fixed time intervals and the average driving speed of vehicles in each grid in the road network at each time point;
s32: aiming at each group of scenes, according to all commonly used paths between any two nodes in the road network, if the length difference between the length of the least time-used path and the shortest distance path is not more than a preset value II, setting the least time-used path as an optimal path, jumping to the next step, and if not, rejecting the path and repeating the step;
s33: recording the optimal path distance between two points in the road network of each group of scenes, the number of all nodes passing through and the number of grids where the nodes are located;
the method for dynamically scheduling the unmanned electric taxi comprises the following steps:
s41: adding order information of all clients to a task summary table;
s42: matching user service tasks in a user order list pairwise according to a service task matching rule, and adding a successfully matched ride-sharing task to the top end of a task summary table;
s43: for all ride sharing tasks, searching vehicles capable of executing the tasks within the range of the radius smaller than the threshold value IV according to the matching conditions of the vehicles and the tasks, if the vehicles exist, distributing the ride sharing tasks to the vehicles, meanwhile, sending task information to relevant users for confirmation, if all the users agree, the matching is successful, S46 is executed, otherwise, the matching is unsuccessful, and the ride sharing tasks are deleted;
s44: for all non-ride-sharing tasks, searching for empty vehicles or vehicles in which the tasks are to be completed within the time threshold V within the range of the radius smaller than the threshold IV, if the empty vehicles or the vehicles exist, executing S46, and otherwise, jumping to S45;
s45: searching a task path which will pass through the area in the range that the peripheral radius of the receiving point is smaller than the threshold value IV, judging whether the order can be inserted into the task path according to an order insertion rule, if so, executing S46, otherwise, deleting the task from the task summary table;
s46: deleting all service tasks of related users in the task summary table, updating user order distribution and co-passenger information and a vehicle task schedule table, and adding a vehicle charging schedule behind the vehicle task schedule table if the electric quantity of the vehicle is lower than a threshold value VI after the vehicle completes the tasks; wherein the threshold VI is the lowest safe electric quantity of the battery;
the service task matching rule in the S42 comprises the following steps:
s421: if the grid where the receiving or sending place of one user is located is overlapped or adjacent to the grid where the optimal path between the receiving or sending place of the other passenger passes, adding 1 to the matching degree;
s422: users with a matching degree of not less than 2 consider that the users can be multiplied. Sequentially connecting two user receiving and sending points by an optimal path to form a combined path;
s423: and if the difference between the adjusted path length and the respective original optimal path lengths of the two users does not exceed a threshold value III, the matching is successful.
The vehicle and task matching condition in the S43 comprises the following elements:
s431: empty or vehicles in which the task is to be completed within a time threshold v;
s432: the remaining power after the task is performed is above the threshold vi.
The order insertion rule in S45 includes the following:
s451: if the grid where the receiving or sending point of the order is located is overlapped or adjacent to the grid where the task path passes through, adding 1 to the matching degree;
s452: if the grid where the task point is located is overlapped or adjacent to the optimal path between the order and the passenger through the grid, the matching degree is increased by 1;
s453: if the matching degree is not less than 2, sequentially connecting the customer receiving and sending points and the task points of the order by using the optimal path to form a new task path;
s454: if the residual electric quantity of the vehicle after the vehicle executes the new mission path is higher than the threshold value VI, the order can be inserted into the mission path.
The vehicle charging plan in S46, including the steps of:
s461: evaluating the residual electric quantity of the vehicle after the vehicle completes the task;
s462: and searching the idle charging parking spaces within the reachable range according to the residual electric quantity. If the parking space exists, the parking space is locked, and an optimal path from the task point to the parking space is set and added into a vehicle task schedule; otherwise, the control center reports the charging requirement, including the charging range and the estimated time.
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The invention is described in further detail below with reference to the accompanying drawings and specific embodiments.
FIG. 1 is a schematic diagram of a dynamic co-riding scheduling system for an unmanned electric taxi according to the present invention;
fig. 2 is a flow chart of the dynamic co-riding scheduling method for the unmanned electric taxi.
Detailed Description
The present invention will be described in further detail with reference to the accompanying drawings.
As shown in fig. 1, a schematic diagram of a dynamic co-taking scheduling system for an unmanned electric taxi is shown, and in order to solve the technical problem of how to quickly generate a scheduling policy of a vehicle with high user co-taking matching efficiency according to a real-time road network traffic state, functions and components of the dynamic co-taking scheduling system for the unmanned electric taxi are described in detail below;
a dynamic co-riding scheduling system for an unmanned electric taxi comprises a communication module, an information storage module and a control module which are in mutual information communication, and further comprises a user side and an unmanned vehicle-mounted communication terminal which are in mutual information communication with the communication module;
the communication module is used for information communication between the information storage module and the control module and between the user side and the unmanned vehicle-mounted communication terminal, and the information transmitted by the communication module comprises real-time road network traffic information, user trip demand information, vehicle positions, electric quantity states, passenger carrying states and the like;
road network topology data, road network rasterization data, grid number data of the optimal path between any two places in the road network under different scenes, user basic information data, electric vehicle basic information data and the like are stored in the information storage module; the basic information data of the user comprise gender, age, preference and the like, and the basic information data of the electric automobile comprise a vehicle type, maximum cruising and maximum passenger carrying and the like;
the control module is used for generating a user service plan and a vehicle scheduling scheme according to the data information received from the communication module and the information storage module;
the user side can also display part of system information for the user, such as part of real-time positions of electric vehicles, part of information of passengers co-riding, travel routes and the like;
compared with the prior invention patents, the method provides various more flexible and diversified user co-multiplication schemes such as order combination and order insertion based on road network rasterization, and improves the co-multiplication probability. In particular, in consideration of the technical characteristics of the electric unmanned vehicle, a multi-scenario optimal path and a charging scheme based on a threshold trigger mechanism are provided. The method is more practical and reliable;
considering that the unmanned electric taxi is mainly applied to urban areas, and considering the complex structure and the variable traffic states of an urban traffic network, the invention improves the efficiency and the success rate of ride-sharing matching by providing a multi-scene and rasterized offline data pre-storage technology and multi-mode user ride-sharing matching strategies such as optimal path fusion and insertion and the like, thereby realizing the rapid generation of a comprehensive scheduling scheme;
in consideration of the electric quantity limitation of the unmanned electric taxi, the invention provides an integrated electric vehicle scheduling strategy for unmanned electric taxi path planning, which is capable of feeding back the electric quantity state in real time and considering the electric quantity limitation, and ensures the completion of the pick-up and delivery tasks of the user.
As shown in fig. 2, the following describes the steps and functions of a dynamic co-riding scheduling method for an unmanned electric taxi in detail;
a dynamic co-riding scheduling method for an unmanned electric taxi comprises the following steps:
s1: the user side sends information such as starting and ending points, the number of users and the like to a communication module of the dispatching center through the terminal in an order form;
s2: the communication module uploads order information, real-time road network traffic information acquired in real time, electric automobile position information, electric quantity information and passenger carrying information to the control module;
s3: the information storage module uploads road network topology data, road network rasterization data, grid number data of an optimal path between any two places in the road network under different scenes, user basic information data and electric vehicle basic information data to the control module;
s4: the control module receives the information data of the communication module and the information storage module, executes a dynamic scheduling method of the unmanned electric taxi, and generates a user service plan and a vehicle scheduling scheme;
s5: the control module respectively uploads a user service plan and a vehicle scheduling scheme to the communication module and the information storage module, the communication module respectively sends the adjusted user receiving and sending places and the estimated receiving and sending time to each client side, the vehicle scheduling scheme is sent to each unmanned vehicle-mounted communication terminal, and the information storage module updates corresponding user and vehicle information according to the received user service plan and the received vehicle scheduling scheme;
the user service plan comprises user receiving and sending places, estimated user receiving and sending time and service vehicle information of each user, and the vehicle scheduling scheme comprises a task place, a driving path and a charging scheme, which are required to be reached by the vehicle;
the road network rasterization data is that the road network is rasterized according to a square grid with the side length as a preset value I to generate grid numbers, road network node numbers and adjacent grid numbers contained in the grids, wherein the adjacent grids are defined as that at least one pair of nodes which can be directly connected with each other by road sections without other nodes exist between the road network and the grids;
the method for generating the grid number data of the optimal path between any two places in the road network under different scenes comprises the following steps:
s31: forming a plurality of groups of representative road network traffic state scenes by a clustering method according to the collected road network traffic information data and road network rasterization data at fixed time intervals and the average driving speed of vehicles in each grid in the road network at each time point; the data is described below by way of example for a certain urban summer peak-time scenario, as shown in table 1:
| grid numbering | Nodes of the grid | Adjacent grids | Average vehicle speed |
| 1 | 35，6，37，32 | 2，1101，1102，1202 | 24.1 |
| 2 | 24，745，8643，2452 | 1，3，1101，1102 | 38.3 |
| …… | …… | …… | 35.2 |
TABLE 1 road network raster data table under summer peak scene of a certain city
S32: aiming at each group of scenes, according to all commonly used paths between any two nodes in the road network, if the length difference between the length of the least time-used path and the shortest distance path is not more than a preset value II, setting the least time-used path as an optimal path, jumping to the next step, and if not, rejecting the path and repeating the step; the data is described below by way of example for a certain urban summer peak-time scenario, as shown in table 2:
| path numbering | Starting point | Terminal point | Passing point | Passing through a grid | Travel time (min) | Electric power consumption (kW.h) |
| 1 | 1 | 2 | 3，4 | 2，3，7 | 15 | 3 |
| 2 | 1 | 3 | 4，8 | 2，3，9 | 11 | 2 |
| …… | …… | …… | …… | …… | …… | …… |
TABLE 2 example of optimal path data of two nodes of road network under summer peak time scene of a certain city
S33: recording the optimal path distance between two points in the road network of each group of scenes, the number of all nodes passing through and the number of grids where the nodes are located;
the method for dynamically scheduling the unmanned electric taxi comprises the following steps:
s41: adding order information of all clients to a task summary table; as exemplified in table 3 below;
table 3 user order list example
S42: matching user service tasks in a user order list pairwise according to a service task matching rule, and adding a successfully matched ride-sharing task to the top end of a task summary table;
s43: for all ride sharing tasks, searching vehicles capable of executing the tasks within the range of the radius smaller than the threshold value IV according to the matching conditions of the vehicles and the tasks, if the vehicles exist, distributing the ride sharing tasks to the vehicles, meanwhile, sending task information to relevant users for confirmation, if all the users agree, the matching is successful, S46 is executed, otherwise, the matching is unsuccessful, and the ride sharing tasks are deleted;
s44: for all non-ride-sharing tasks, searching for empty vehicles or vehicles in which the tasks are to be completed within the time threshold V within the range of the radius smaller than the threshold IV, if the empty vehicles or the vehicles exist, executing S46, and otherwise, jumping to S45;
s45: searching a task path which will pass through the area in the range that the peripheral radius of the receiving point is smaller than the threshold value IV, judging whether the order can be inserted into the task path according to an order insertion rule, if so, executing S46, otherwise, deleting the task from the task summary table;
s46: deleting all service tasks of related users in the task summary table, updating user order distribution and co-passenger information and a vehicle task schedule table, and adding a vehicle charging schedule behind the vehicle task schedule table if the electric quantity of the vehicle is lower than a threshold value VI after the vehicle completes the tasks; the threshold VI is the lowest safe electric quantity of the battery, the electric quantity consumed by all nodes in the road network to reach the respective nearest charging stations is measured according to the distribution of the charging stations in the road network, and the maximum value of the electric quantities is taken as the lowest safe electric quantity;
the service task matching rule in the S42 comprises the following steps:
s421: if the grid where the receiving or sending place of one user is located is overlapped or adjacent to the grid where the optimal path between the receiving or sending place of the other passenger passes, adding 1 to the matching degree;
for example, the customer reception point of the user 1 is 6, the starting and ending points of the user 2 are 1 and 3 respectively, and the optimal path 2 corresponding to the customer reception point can be known from the table 2 and passes through the grids 2, 3 and 9; since grid 2 is adjacent to grid 6, the matching degree of users 1 and 2 is increased by 1;
s422: users with a matching degree of not less than 2 consider that the users can be multiplied. Sequentially connecting two user receiving and sending points by an optimal path to form a combined path;
s423: and if the difference between the adjusted path length and the respective original optimal path lengths of the two users does not exceed a threshold value III, the matching is successful.
The vehicle and task matching condition in the S43 comprises the following elements:
s431: empty or vehicles in which the task is to be completed within a time threshold v;
s432: the remaining power after the task is performed is above the threshold vi.
The order insertion rule in S45 includes the following:
s451: if the grid where the receiving or sending point of the order is located is overlapped or adjacent to the grid where the task path passes through, adding 1 to the matching degree;
s452: if the grid where the task point is located is overlapped or adjacent to the optimal path between the order and the passenger through the grid, the matching degree is increased by 1;
s453: if the matching degree is not less than 2, sequentially connecting the customer receiving and sending points and the task points of the order by using the optimal path to form a new task path;
s454: if the residual electric quantity of the vehicle after the vehicle executes the new mission path is higher than the threshold value VI, the order can be inserted into the mission path.
The vehicle charging plan of S46, comprising the steps of:
s461: taking the position of the vehicle as a center, searching the positions of the charging stations within the range of the radius smaller than the threshold VII, and sequencing the charging stations from near to far according to the distance between the vehicle and each charging station to form a list;
s462: if the charging station at the topmost end of the list has an idle charging parking space or an idle charging parking space within the time threshold VIII, jumping to S464, otherwise, removing the charging station from the list, and executing the step again;
s463: if all charging stations are traversed and no available charging parking spaces still exist, the position information and the charging parking space requirement application are sent to a communication module of the control center;
s464: and sending the position of the charging station and the shortest path from the vehicle to the charging station to the unmanned vehicle to guide the unmanned vehicle to go to the charging station for charging.
Claims (10)
1. The utility model provides an unmanned electronic taxi developments scheduling system that cooperates, includes communication module, information storage module and the control module of mutual information exchange, its characterized in that: the unmanned vehicle-mounted communication terminal is characterized by further comprising a user side and an unmanned vehicle-mounted communication terminal, wherein the user side and the communication module are in mutual information communication.
2. The system according to claim 1, wherein the system comprises: the communication module is used for information communication between the information storage module and the control module and between the user side and the unmanned vehicle-mounted communication terminal.
3. The system according to claim 2, wherein the system comprises: the information transmitted by the communication module comprises real-time road network traffic information, user travel demand information, vehicle positions, electric quantity states and passenger carrying states.
4. The system according to claim 1, wherein the system comprises: the information storage module is stored with road network topology data, road network rasterization data, raster number data of the optimal path between any two places in the road network under different scenes, user basic information data and electric vehicle basic information data.
5. The system according to claim 1, wherein the system comprises: and the control module is used for generating a user service plan and a vehicle scheduling scheme according to the data information received from the communication module and the information storage module.
6. A dynamic co-riding scheduling method for an unmanned electric taxi is characterized by comprising the following steps: the method comprises the following steps:
s1: the user side sends the order information to the communication module;
s2: the communication module uploads order information, real-time road network traffic information acquired in real time, electric automobile position information, electric quantity information and passenger carrying information to the control module;
s3: the information storage module uploads road network topology data, road network rasterization data, grid number data of an optimal path between any two places in the road network under different scenes, user basic information data and electric vehicle basic information data to the control module;
s4: the control module receives the information data of the communication module and the information storage module, executes a dynamic scheduling method of the unmanned electric taxi, and generates a user service plan and a vehicle scheduling scheme;
s5: the control module uploads the user service plan and the vehicle scheduling scheme to the communication module and the information storage module respectively, the communication module sends the adjusted user receiving and sending places and the estimated receiving and sending time to each client respectively, the vehicle scheduling scheme is sent to each unmanned vehicle-mounted communication terminal, and the information storage module updates corresponding user and vehicle information according to the received user service plan and the received vehicle scheduling scheme.
7. The method for dynamically sharing and dispatching the unmanned electric taxi according to claim 6, wherein the method comprises the following steps: the user service plan comprises user receiving and sending places, estimated user receiving and sending time and service vehicle information of each user, and the vehicle scheduling scheme comprises a task place, a driving path and a charging scheme, wherein the task place, the driving path and the charging scheme are required to be reached by the vehicle.
8. The method for dynamically sharing and dispatching the unmanned electric taxi according to claim 6, wherein the method comprises the following steps: the road network rasterization data is that the road network is rasterized according to a square grid with the side length as a preset value I to generate grid numbers, road network node numbers contained in grids and adjacent grid numbers thereof, wherein the adjacent grids are defined as that at least one pair of nodes which can be directly connected with each other by road sections without other nodes exist between the adjacent grids.
9. The method for dynamically sharing and dispatching the unmanned electric taxi according to claim 6, wherein the method comprises the following steps: the method for generating the grid number data of the optimal path between any two places in the road network under different scenes comprises the following steps:
s31: forming a plurality of groups of representative road network traffic state scenes by a clustering method according to the collected road network traffic information data and road network rasterization data at fixed time intervals and the average driving speed of vehicles in each grid in the road network at each time point;
s32: aiming at each group of scenes, according to all commonly used paths between any two nodes in the road network, if the length difference between the length of the least time-used path and the shortest distance path is not more than a preset value II, setting the least time-used path as an optimal path, jumping to the next step, and if not, rejecting the path and repeating the step;
s33: and recording the optimal path distance between two points in the road network of each group of scenes, the number of all nodes passing through and the number of grids where the nodes are located.
10. The method for dynamically sharing and dispatching the unmanned electric taxi according to claim 6, wherein the method comprises the following steps: the method for dynamically scheduling the unmanned electric taxi comprises the following steps:
s41: adding order information of all clients to a task summary table;
s42: matching user service tasks in a user order list pairwise according to a service task matching rule, and adding a successfully matched ride-sharing task to the top end of a task summary table;
s43: for all ride sharing tasks, searching vehicles capable of executing the tasks within the range of the radius smaller than the threshold value IV according to the matching conditions of the vehicles and the tasks, if the vehicles exist, distributing the ride sharing tasks to the vehicles, meanwhile, sending task information to relevant users for confirmation, if all the users agree, the matching is successful, S46 is executed, otherwise, the matching is unsuccessful, and the ride sharing tasks are deleted;
s44: for all non-ride-sharing tasks, searching for empty vehicles or vehicles in which the tasks are to be completed within the time threshold V within the range of the radius smaller than the threshold IV, if the empty vehicles or the vehicles exist, executing S46, and otherwise, jumping to S45;
s45: searching a task path which will pass through the area in the range that the peripheral radius of the receiving point is smaller than the threshold value IV, judging whether the order can be inserted into the task path according to an order insertion rule, if so, executing S46, otherwise, deleting the task from the task summary table;
s46: and deleting all service tasks of related users in the task summary table, updating user order distribution and co-multiplication information and a vehicle task schedule table, and adding a vehicle charging schedule behind the vehicle task schedule table if the electric quantity of the vehicle is lower than a threshold value VI after the vehicle completes the tasks.
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