CN115035739A - Automatic driving vehicle scheduling method applied to intelligent network park - Google Patents
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Abstract
The invention relates to an automatic driving vehicle scheduling method applied to an intelligent networking park, which respectively adopts centralized decision and distributed planning, searches for an optimal path based on a Dijkstra algorithm and solves a maximum value or a minimum value based on a genetic algorithm; when the method is in normal operation, centralized scheduling optimization of the fleet of the automatic driving vehicles is carried out by taking the maximized operation income of the automatic driving vehicles and the satisfaction degree of passengers as targets; when part of vehicles are blocked, the running routes of the rest vehicles are adjusted emergently to ensure the normal operation of the motorcade of the automatic driving vehicles; when special vehicles such as ambulances and the like appear, emergency schemes need to be provided, and the situation that the traveling of the motorcade of the automatic driving vehicles is overlapped with the road required by the operation of the special vehicles is avoided in a rescheduling mode, so that the normal operation of the special vehicles is ensured.
Description
Technical Field
The invention relates to the technical field of vehicle scheduling, in particular to an automatic driving vehicle scheduling method applied to an intelligent network park.
Background
The manual vehicle dispatching system has the defects of low efficiency, easy error, difficult emergency handling and the like. The advent of networking systems has provided opportunities for the solution of this problem. The intelligent network system uploads the data of waiting personnel, road jam conditions and sudden dangerous situations occurring on an operation road to the cloud end, and the cloud end determines departure intervals according to the information; meanwhile, a new optimized route can be planned for the vehicle when the road section is blocked, so that the running efficiency of the vehicle is improved to the maximum extent; furthermore, when the special vehicle appears, the intelligent network system can quickly respond to the special vehicle and adjust the scheduling scheme on line so as to ensure the unimpeded passage and normal operation of the special vehicle.
Automatic driving has been considered as one of the future trends in intelligent traffic. According to the automatic driving grade division standard [1] proposed by SAE, automatic driving of a grade above L3 has the capability of completing all driving operations by an unmanned system, and the grade L5 is the automatic grade which does not need any operation of a human driver and completely depends on the unmanned system. In view of the current road conditions, regulations and other conditions, there is still a distance to realize automatic driving at the level of L5, so that unmanned driving at the level of L4 is a key research direction of current unmanned driving technology. All large and whole automobile factories put forward automatic driving solutions with the level L4 as the target, and the unmanned driving of the level L4 is realized in 2025 by Changan group, Guangdong automobile group and the like.
In the prior art, a public transport vehicle dispatching method (CN112309158A) based on a distribution estimation algorithm discloses a distribution Estimation (EDA) algorithm to solve the public transport vehicle dispatching problem, which is not suitable for automatically driving vehicles and can not meet the vehicle dispatching under the emergency condition.
Disclosure of Invention
In view of the defects of the prior art, the invention provides the automatic driving vehicle scheduling method applied to the intelligent network park, which not only aims at maximizing the operation income of the automatic driving vehicle and the satisfaction degree of passengers during normal operation to perform centralized scheduling optimization of the automatic driving vehicle fleet, but also emergently adjusts the driving routes of the rest vehicles under the condition that partial vehicles are blocked so as to ensure the normal operation of the automatic driving vehicle fleet, and provides the unified, high-real-time, high-reliability and high-flexibility intelligent network cloud control system architecture method.
In order to achieve the above objects and other related objects, the present invention provides the following technical solutions: an automatic driving vehicle scheduling method applied to an intelligent networking park comprises the following steps:
the method comprises the following steps: obtaining the jth time period T j Number of departure times I j Departure interval Δ t j And rate of arrival f j Information and station b k ；
Step two: based on the information, the arrival rate of the passengers is subjected to uniform distribution, and the average waiting time of the passengers in the adjacent departure intervals is as follows:
step three: based on the average waiting time T and the number R of people arriving at the station in the time period, the total waiting time in the whole dispatching cycle of all the passengers is obtained as follows:
step four: based on the arrival rate f j Station point b k And a time period T j The gross profit of the company operation obtained from the information of (2) and the information of the riding fare P is:
step five: collecting cost1 of departure loss of each minibus and departure times I j And (3) obtaining information that the company operating cost is as follows:
step six: gross profit R based on company operation 1 And the operating cost R of the company * The operation income of the company is obtained as follows: w 2 ＝R 1 -R * ；
Step seven: based on W 1 And W 2 And constructing a vehicle dispatching objective function J: maxJ ═ α W 2 -βW 1 Wherein α represents a weighting coefficient of operating income of the mini-bus company, β represents a weighting coefficient of vehicle cost of passengers and the like, namely α + β is 1;
step eight: the constraint of the objective function J can be expressed as:
step nine: and combining the vehicle scheduling objective function with a genetic algorithm to solve the optimal departure time.
Furthermore, when an emergency occurs or a certain vehicle unit of the automatic driving vehicle breaks down, the Dijkstra algorithm is adopted to carry out optimized path planning and scheduling on the single vehicle unit.
Further, the Dijkstra algorithm includes the following steps:
D1) the initialization set D is used for storing the sum of the weights from the starting point to a certain node;
D2) updating the sum of the weights of all nodes connected with the starting point in the set D;
D3) searching for an adjacent node i by taking the point with the minimum weight value as a starting point;
D4) updating the weight value of the node i in the D, and taking a smaller value;
D5) step D3), step D4) are looped until the minimum weight of all nodes is found.
Further, the optimized path planning and scheduling includes the following steps:
a) acquiring vehicle fault and accident information;
b) planning an emergency rescue path based on the garden map and the parameterized road network;
c) the method comprises the steps of reporting a blocked road section and a blocked intersection in time, and simultaneously uploading updated road condition information to a dispatching system cloud control platform;
d) and based on the new road condition information, the optimal path is re-planned, and the time for emergency dispatching and rescue in the park is shortened.
To achieve the above and other related objects, the present invention also provides a computer readable storage medium storing one or more programs, the computer readable storage medium storing one or more computer programs, which when executed by a processor, perform any of the methods described above.
The invention has the following positive effects:
1. when the automatic vehicle dispatching system is in normal operation, centralized dispatching optimization of the automatic vehicle fleet is carried out by taking the maximized operation income of the automatic vehicle and the satisfaction degree of passengers as targets; when part of vehicles are blocked, the running routes of the rest vehicles are adjusted emergently to ensure the normal operation of the motorcade of the automatic driving vehicles; when special vehicles such as ambulances and the like appear, emergency schemes need to be provided, and the situation that the traveling of the motorcade of the automatic driving vehicles is overlapped with the road required by the operation of the special vehicles is avoided in a rescheduling mode, so that the normal operation of the special vehicles is ensured.
2. The invention aims at the application requirements of massive and multi-type intelligent network connection, aims at the application in different ranges such as a park range, a road network range and the like, and is based on the space-time requirements of vehicle-road cloud fusion sensing, decision and control on interconnection and real-time calculation and the physical environment constraint of a traffic system, the invention expects to provide a unified, high-real-time, high-reliability and high-flexibility intelligent network connection cloud control system architecture method; by applying a layered configuration idea, deconstructing functional subsystems and coupling relations of the functional subsystems of the end, network and cloud three-layer architecture, and further utilizing a combined optimization method to carry out optimized configuration on edge cloud space arrangement, topological configuration and node performance under multiple constraints of communication, calculation, data and the like, the overall architecture of the cloud control system achieves the effect of supporting high-concurrency intelligent network connection driving application to carry out vehicle-road operation optimization in a network environment.
Drawings
FIG. 1 is a schematic flow diagram of the present invention;
FIG. 2 is a flow chart of Dijkstra algorithm according to the present invention;
FIG. 3 is a flow chart of the genetic algorithm of the present invention.
Detailed Description
In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly described below, and it is obvious that the drawings in the following description are only some embodiments of the present invention, and for those skilled in the art, other drawings can be obtained according to these drawings without inventive exercise.
Example (b): referring to fig. 1 and 3, an automatic driving vehicle scheduling method applied to an intelligent networking park includes the following steps:
the method comprises the following steps: obtaining the jth time period T j Number of departure times I j Departure interval Δ t j And rate of arrival f j Information and station b k ；
Step two: based on the above information, assuming that the arrival rates of passengers are uniformly distributed, the average waiting time of passengers in adjacent departure intervals is:
step three: based on the average waiting time T and the number R of people arriving at the station in the time period, the total waiting time in the whole dispatching cycle of all the passengers is obtained as follows:
step four: based on the arrival rate f j Station point b k And a time period T j The gross profit of the company operation is obtained by the information of (2) and the information of the riding fare P:
step five: collecting cost1 of departure loss of each small bus and departure times I j And (3) obtaining information that the company operating cost is as follows:
step six: gross profit R based on company operations 1 And the operating cost R of the company * And obtaining the operation income of the company as follows: w 2 ＝R 1 -R * ；
Step seven: based on W 1 And W 2 And constructing a vehicle dispatching objective function J: maxJ ═ α W 2 -βW 1 Wherein α represents a weighting coefficient of operating income of the mini-bus company, β represents a weighting coefficient of vehicle cost of passengers and the like, namely α + β is 1;
step eight: the constraint of the objective function J can be expressed as:
step nine: and combining the vehicle scheduling objective function with a genetic algorithm to solve the optimal departure time.
Further, referring to fig. 2, the Dijkstra algorithm includes the following steps:
D1) the initialization set D is used for storing the sum of the weights from the starting point to a certain node;
D2) updating the sum of the weights of all nodes connected with the starting point in the set D;
D3) searching for an adjacent node i by taking the point with the minimum weight value as a starting point;
D4) updating the weight value of the node i in the D, and taking a smaller value;
D5) step D3), step D4) are looped until the minimum weight of all nodes is found.
Further, the optimized path planning and scheduling includes the following steps:
a) acquiring vehicle fault and accident information;
b) planning an emergency rescue path based on the garden map and the parameterized road network;
c) the method comprises the steps of reporting a blocked road section and a blocked intersection in time, and simultaneously uploading updated road condition information to a dispatching system cloud control platform;
d) and based on the new road condition information, the optimal path is re-planned, and the time for emergency dispatching and rescue in the park is shortened.
Specifically, in the aspect of the minibus income, as long as the departure times are reduced and the carrying rate of each vehicle is improved, the income can be maximized; in the aspect of passenger benefits, the convenience of passenger travel is only considered, and the requirement can be met only by increasing the departure times of the bus. If the benefit of the minibus is pursued, the benefit of the passengers cannot be guaranteed, and the traveling efficiency is greatly reduced; if the passenger trip efficiency is considered unilaterally, the departure times are increased, the income of the mini-bus company is greatly reduced, obviously, the two schemes are opposite, and the benefits of the mini-bus company and the passengers need to be considered comprehensively.
When traffic accidents happen to a certain road section or other reasons cause road congestion, other buses in normal operation need to detour to ensure that passengers are normally conveyed to reach a destination.
Therefore, the benefit of the small bus is measured by the carrying rate and the departure times, and the net benefit of the small bus is recorded; the benefits of the passengers are measured by waiting for the bus time and are recorded as the satisfaction degree of the passengers; the passenger satisfaction and the minibus riding rate are contradictory to each other to a certain extent, so that a reasonable matching relationship needs to be found out between the two factors, and the passenger satisfaction and the riding rate (namely, the number of departure times is reduced) are maximized as far as possible on the premise that all passengers are carried in each time period, so that the satisfaction of both parties is well balanced; on the basis of a statistical time interval, taking the departure times as a model variable, taking the minimum loss cost of passengers waiting for the vehicle and the maximum operation income of a mini-bus company as an objective function, and simultaneously considering two constraint conditions of vehicle carrying rate and departure interval to establish a mathematical model; the objective function includes two aspects: the operating income of the small bus company is maximized even though the loss cost of passengers and the like is minimized. The passenger waiting loss cost is the waiting time of all passengers and the waiting time of each passenger in unit time; product of loss costs; the operation income of the public transport enterprise is the difference between the operation gross income and the operation cost.
In conclusion, during normal operation, the centralized dispatching optimization of the automatic driving vehicle fleet is carried out by taking the maximized operation income of the automatic driving vehicle and the satisfaction degree of passengers as targets; when part of vehicles are blocked, the running routes of the rest vehicles are adjusted emergently to ensure the normal operation of the motorcade of the automatic driving vehicles; when special vehicles such as ambulances and the like appear, emergency schemes need to be provided tightly, and the situation that the traveling of the motorcade of the automatic driving vehicles is overlapped with the road required by the operation of the special vehicles is avoided in a rescheduling mode, so that the normal operation of the special vehicles is ensured; the invention also discloses a unified, high-real-time, high-reliability and high-flexibility intelligent network interconnection cloud control system architecture method, which is expected to be provided by the invention based on space-time requirements of vehicle-road cloud fusion sensing, decision and control on interconnection and real-time calculation and physical environment constraints of a traffic system, and aims at the application of different ranges such as a park range and a road network range. By applying a layered configuration idea, deconstructing functional subsystems and coupling relations of the functional subsystems of the end, network and cloud three-layer architecture, and further utilizing a combined optimization method to carry out optimized configuration on edge cloud space arrangement, topological configuration and node performance under multiple constraints of communication, calculation, data and the like, the overall architecture of the cloud control system achieves the effect of supporting high-concurrency intelligent network connection driving application to carry out vehicle-road operation optimization in a network environment.
The above-described embodiments are merely illustrative of the principles of the present invention and are not to be construed as limiting the invention. Any person skilled in the art can modify or change the above-mentioned embodiments without departing from the spirit and scope of the present invention. Accordingly, it is intended that all equivalent modifications or changes which may be made by those skilled in the art without departing from the spirit and scope of the present invention as defined in the appended claims.
Claims (5)
1. An automatic driving vehicle scheduling method applied to an intelligent network park is characterized by comprising the following steps:
the method comprises the following steps: obtaining the jth time period T j Number of departure times I j Departure interval Δ t j And rate of arrival f j Information and station b k ；
Step two: based on the information, the arrival rates of the passengers are subjected to uniform distribution, and the average waiting time of the passengers in the adjacent departure intervals is as follows:
the number of arrival persons at adjacent departure intervals in the time period is as follows:
step three: based on the average waiting time T and the number R of people arriving at the station in the time period, the total waiting time in the whole dispatching cycle of all the passengers is obtained as follows:
step four: based on the arrival rate f j Station point b k And a time period T j The gross profit of the company operation is obtained by the information of (2) and the information of the riding fare P:
step five: collecting cost1 of departure loss of each small bus and departure times I j And (3) obtaining information that the company operating cost is as follows:
step six: gross profit R based on company operation 1 And the operating cost R of the company * The operation income of the company is obtained as follows: w is a group of 2 ＝R 1 -R * ；
Step seven: based on W 1 And W 2 And constructing a vehicle dispatching objective function J: maxJ ═ α W 2 -βW 1 Wherein α represents a weighting coefficient of operating income of the mini-bus company, β represents a weighting coefficient of vehicle cost of passengers and the like, namely α + β is 1;
step eight: the constraint of the objective function J can be expressed as:
step nine: and combining the vehicle scheduling objective function with a genetic algorithm to solve the optimal departure time.
2. The automatic driving vehicle scheduling method applied to the intelligent networked park according to claim 1, wherein the method comprises the following steps: when an emergency occurs or a certain vehicle unit of the automatic driving vehicle breaks down, the Dijkstra algorithm is adopted to carry out optimized path planning and scheduling on the single vehicle unit.
3. The automatic driving vehicle dispatching method applied to the intelligent networking park as claimed in claim 2, wherein the Dijkstra algorithm comprises the following steps:
D1) the initialization set D is used for storing the sum of the weights from the starting point to a certain node;
D2) updating the sum of the weights of all nodes connected with the starting point in the set D;
D3) searching for an adjacent node i by taking the point with the minimum weight value as a starting point;
D4) updating the weight value of the node i in the D, and taking a smaller value;
D5) step D3), step D4) are looped until the minimum weight of all nodes is found.
4. The automatic driving vehicle scheduling method applied to the intelligent networked park according to claim 2, wherein the method comprises the following steps: the optimized path planning and scheduling comprises the following steps:
a) acquiring vehicle fault and accident information;
b) planning an emergency rescue path based on the garden map and the parameterized road network;
c) the method comprises the steps of reporting a blocked road section and a blocked intersection in time, and simultaneously uploading updated road condition information to a dispatching system cloud control platform;
d) and based on the new road condition information, the optimal path is re-planned, and the time for emergency dispatching and rescue in the park is shortened.
5. A computer readable storage medium storing one or more programs, wherein the computer readable storage medium stores one or more computer programs which, when executed by a processor, perform the method of any of claims 1 to 4.
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| CN116523433A (en) * | 2023-07-03 | 2023-08-01 | 常州唯实智能物联创新中心有限公司 | Four-way vehicle scheduling method and system based on bidirectional dynamic side weight |
| CN116523433B (en) * | 2023-07-03 | 2023-09-01 | 常州唯实智能物联创新中心有限公司 | Four-way vehicle scheduling method and system based on bidirectional dynamic side weight |
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