CN114093191A - Unmanned intelligent scheduling system and automatic driving method - Google Patents
Unmanned intelligent scheduling system and automatic driving method Download PDFInfo
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- CN114093191A CN114093191A CN202111414179.1A CN202111414179A CN114093191A CN 114093191 A CN114093191 A CN 114093191A CN 202111414179 A CN202111414179 A CN 202111414179A CN 114093191 A CN114093191 A CN 114093191A
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- 238000000034 method Methods 0.000 title claims abstract description 30
- 238000013024 troubleshooting Methods 0.000 claims abstract description 6
- 238000013475 authorization Methods 0.000 claims description 3
- 230000007774 longterm Effects 0.000 claims description 3
- 238000012546 transfer Methods 0.000 abstract description 3
- 238000005516 engineering process Methods 0.000 description 4
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- G—PHYSICS
- G08—SIGNALLING
- G08G—TRAFFIC CONTROL SYSTEMS
- G08G1/00—Traffic control systems for road vehicles
- G08G1/123—Traffic control systems for road vehicles indicating the position of vehicles, e.g. scheduled vehicles; Managing passenger vehicles circulating according to a fixed timetable, e.g. buses, trains, trams
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- G—PHYSICS
- G08—SIGNALLING
- G08G—TRAFFIC CONTROL SYSTEMS
- G08G1/00—Traffic control systems for road vehicles
- G08G1/123—Traffic control systems for road vehicles indicating the position of vehicles, e.g. scheduled vehicles; Managing passenger vehicles circulating according to a fixed timetable, e.g. buses, trains, trams
- G08G1/127—Traffic control systems for road vehicles indicating the position of vehicles, e.g. scheduled vehicles; Managing passenger vehicles circulating according to a fixed timetable, e.g. buses, trains, trams to a central station ; Indicators in a central station
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- G—PHYSICS
- G08—SIGNALLING
- G08G—TRAFFIC CONTROL SYSTEMS
- G08G1/00—Traffic control systems for road vehicles
- G08G1/123—Traffic control systems for road vehicles indicating the position of vehicles, e.g. scheduled vehicles; Managing passenger vehicles circulating according to a fixed timetable, e.g. buses, trains, trams
- G08G1/133—Traffic control systems for road vehicles indicating the position of vehicles, e.g. scheduled vehicles; Managing passenger vehicles circulating according to a fixed timetable, e.g. buses, trains, trams within the vehicle ; Indicators inside the vehicles or at stops
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Abstract
The invention provides an unmanned intelligent scheduling system and an automatic driving method, and relates to the field of vehicle scheduling. The unmanned intelligent scheduling system comprises task receiving and assignment, namely tasks are distributed and assigned to vehicles, a relay module is used for improving scheduling efficiency in a transfer mode, a fault removing module is used for solving the problem that how the scheduling system performs scheduling after the vehicles meet emergency, a probability analysis module is used for improving corresponding speed by analyzing and calculating the number of vehicles reserved in an area, and an automatic driving method comprises a task analysis module and a driving module which are distributed and used for task processing and automatic driving. The efficiency of vehicle scheduling can be improved through the relay module and the troubleshooting module.
Description
Technical Field
The invention relates to the technical field of vehicle scheduling, in particular to an unmanned intelligent scheduling system and an automatic driving method.
Background
For passengers or for transporting goods, which travel on the road on vehicles. With the development of technology, unmanned vehicles have emerged. The unmanned technology is a complex of multi-leading-edge subjects such as sensors, computers, artificial intelligence, communication, navigation positioning, mode recognition, machine vision, intelligent control and the like. And the unmanned intelligent scheduling refers to the way of assigning the vehicles to the vehicle, so that the overall running efficiency of a plurality of vehicles is improved.
In a city scenario, the user is traveling in a vehicle to another destination, or the user needs to transport an item. And the location of the user and the location of the destination are random. Therefore, it is necessary to provide an intelligent unmanned dispatching system to meet the requirements of users and improve the efficiency of automatic driving operation. Or in other scenarios, such as the field of logistics, an unmanned delivery vehicle is required to deliver the courier.
Disclosure of Invention
Technical problem to be solved
Aiming at the defects of the prior art, the invention provides an unmanned intelligent scheduling system and an automatic driving method, and solves the problem of low overall operation efficiency of automatic vehicles in the existing scheduling.
(II) technical scheme
In order to achieve the purpose, the invention is realized by the following technical scheme: the unmanned intelligent scheduling system comprises the following contents:
task reception and assignment: the scheduling system receives tasks, the tasks comprise task starting points and task end points, then the scheduling system distributes the tasks to the vehicles, and after the feedback of the vehicles, task assignment is carried out;
a relay module: a relay point is arranged between the task starting point and the task end point, and the other vehicle finishes a subsequent task after the conveying vehicle reaches the relay point;
the obstacle removing module: the method comprises the steps that obstacles going to a task place and obstacles going to a task end point are included, in the process of going to the task place, when traffic jam and vehicle faults occur, the excluded time is estimated, if the time exceeds the specified time, the task is cancelled to a scheduling system, the scheduling system assigns the task to other vehicles again, and if the task is not cancelled by the scheduling system and the vehicles can run, the current vehicle continues to finish the process; in the process of going to a task terminal, under the authorization of a principal, a relay module is executed, the current location is taken as a relay point, and if the principal is not authorized and the vehicle can run, the current vehicle continues to carry out the task;
a probability analysis module: judging the number of initiated tasks in a certain time period of the area through long-term data recording, and providing the number of vehicles in the area by a dispatching system in combination with the judgment; and comparing the success rate of completing the tasks on time once in the area with the success rate of completing the tasks on time after the relay is added, and the scheduling system preferably selects a task assignment mode.
Preferably, in the task receiving and assigning, the scheduling system sends the task to the vehicle within a specified distance from the task start point, determines the expected time for completing the task according to the task start point and the task end point in the task, feeds the expected time back to the scheduling system, and then the scheduling system assigns the task to the vehicle with the shortest completion time.
Preferably, the starting mode of the relay module is that in the process of being called and task assignment in the troubleshooting module, the currently assigned vehicles complete the tasks across the regions, and after the tasks are completed, the number of reserved vehicles in the current region does not meet the requirement, and then the vehicles are called.
Preferably, another vehicle continues to complete the task in the troubleshooting module due to a problem with the vehicle itself.
The automatic driving method comprises the following steps:
a task analysis module: after the vehicle receives the task, planning a route according to the position and the traffic condition of the vehicle, and judging the time for predicting the completion of the task;
a driving module: the system comprises a navigation module, an image recognition module and a real-time scene modeling module, wherein the navigation module can be communicated with a satellite, determines the position of the navigation module and obtains the traffic jam condition through a network, the image recognition module comprises an image sensor, the image sensor is used for recognizing a road scene, the real-time scene modeling module comprises a laser radar, and the laser radar is used for measuring the distance between an entity and a vehicle and is combined with the image recognition module to obtain the real-time modeling of the road scene.
Preferably, the driving module comprises at least one image sensor.
Preferably, the driving module comprises at least one lidar.
(III) advantageous effects
The invention provides an unmanned intelligent scheduling system and an automatic driving method. The method has the following beneficial effects:
1. the invention provides a relay module, which improves the transportation efficiency of vehicles by using a transfer mode.
2. The invention is provided with the obstacle removing module to solve the emergency situation in the task executing process.
3. The invention is provided with a probability analysis module for improving the response speed of dispatching by controlling the number of vehicles in the area.
Drawings
FIG. 1 is a schematic diagram of a dispatch system of the present invention;
FIG. 2 is a schematic diagram of an autopilot system of the present invention.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
Example (b):
as shown in fig. 1, an embodiment of the present invention provides an unmanned intelligent scheduling system, which includes the following:
task reception and assignment: the dispatching system receives tasks which comprise a task starting point, a task ending point and a conveyed type, distributes the tasks to vehicles, determines the types of the tasks before distributing the tasks, distributes the tasks to matched vehicles according to the types, and carries out task assignment after the tasks are fed back by the vehicles, namely the time required to complete the tasks.
A relay module: the relay points are arranged between the task starting point and the task ending point, the subsequent tasks are completed by another vehicle after the delivery vehicle reaches the relay points, similarly to transfer, the delivery efficiency is improved, reasonable distribution of the vehicles in the dispatching system area is guaranteed, the user experience is guaranteed, and only one relay point is required to be arranged.
The obstacle removing module: the method comprises the steps that obstacles going to a task place and obstacles going to a task end point are included, in the process of going to the task place, when traffic jam and vehicle faults occur, the excluded time is estimated, if the time exceeds the specified time, the task is cancelled to a scheduling system, the scheduling system assigns the task to other vehicles again, and if the task is not cancelled by the scheduling system and the vehicles can run, the current vehicle continues to finish the process; and in the process of going to the task terminal, under the authorization of the principal, executing the relay module, taking the current place as the relay point, if the principal is not authorized and the vehicle can run, continuing the task by the current vehicle, and continuing to finish the task by another vehicle caused by the problems of the vehicle, such as vehicle anchorage, abnormal tire pressure and the like, in the obstacle removing module.
The obstacle clearance module is also provided with a recording module for recording the starting scene and the solution of the obstacle clearance module, and meanwhile, judging which mode should be adopted immediately when the similar scene is encountered next time, so as to improve the efficiency of obstacle clearance.
A probability analysis module: judging the number of initiated tasks in a certain time period in an area through long-term data recording, and providing the number of vehicles in the area by a scheduling system in combination with the judgment to ensure that the scheduling system can respond at the first time and the vehicles arrive after the tasks are sent out; and comparing the success rate of completing the task on time once in the area with the success rate of completing the task on time after the relay is added, and optimizing a task assignment mode by a scheduling system to improve the experience of a user.
In the task receiving and assigning process, the scheduling system sends the task to the vehicle within the appointed distance from the task starting point, judges the predicted time for completing the task according to the task starting point and the task end point in the task, feeds the predicted time back to the scheduling system, and then the scheduling system assigns the task to the vehicle with the shortest completion time.
The starting mode of the relay module is that in the process of being called and task assignment in the troubleshooting module, the currently assigned vehicles complete tasks across areas, and after the tasks are completed, the number of reserved vehicles in the current area does not meet the requirement, and then the vehicles are called.
As shown in fig. 2, the automatic driving method includes the following steps:
a task analysis module: after the vehicle receives the task, planning a route according to the position and the traffic condition of the vehicle, and judging the time of the predicted completion of the task, wherein the planned route can be updated regularly after the vehicle is assigned with the task because the traffic condition changes constantly along with the time, and the route can be changed according to the combination of the original route and the new planned route if the time for the new planned route is shorter;
a driving module: the system comprises a navigation module, an image recognition module and a real-time scene modeling module, wherein the navigation module can be communicated with a satellite, the position of the navigation module is determined, the traffic jam condition is obtained through a network, the image recognition module comprises an image sensor and at least one image sensor, the image sensor is used for recognizing a road scene, the real-time scene modeling module comprises a laser radar and at least one laser radar, and the laser radar is used for measuring the distance between an entity and a vehicle and is combined with the image recognition module to obtain real-time modeling of the road scene.
The automatic driving of the vehicle is realized by the navigation module, the image recognition module and the real-time scene modeling module and by adopting the existing automatic driving technology.
The dispatching system is arranged on the terminal platform, the terminal platforms can be arranged in a plurality of numbers, the automatic driving system is arranged on the vehicle, and the terminal platform and the vehicle transmit data with the existing communication technology.
Although embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that changes, modifications, substitutions and alterations can be made in these embodiments without departing from the principles and spirit of the invention, the scope of which is defined in the appended claims and their equivalents.
Claims (7)
1. The unmanned intelligent scheduling system is characterized by comprising the following contents:
task reception and assignment: the scheduling system receives tasks, the tasks comprise task starting points and task end points, then the scheduling system distributes the tasks to the vehicles, and after the feedback of the vehicles, task assignment is carried out;
a relay module: a relay point is arranged between the task starting point and the task end point, and the other vehicle finishes a subsequent task after the conveying vehicle reaches the relay point;
the obstacle removing module: the method comprises the steps that obstacles for going to a task place and obstacles for going to a task end point are included, in the process of going to the task place, when traffic jam and vehicle faults occur, the excluded time is estimated, if the time exceeds the specified time, the task is cancelled to a scheduling system, the scheduling system assigns the task to other vehicles again, and if the task is not cancelled by the scheduling system, the vehicle can run; in the process of going to a task terminal, under the authorization of a principal, a relay module is executed, the current location is taken as a relay point, and if the principal is not authorized and the vehicle can run, the current vehicle continues to carry out the task;
a probability analysis module: judging the number of initiated tasks in a certain time period of the area through long-term data recording, and providing the number of vehicles in the area by a dispatching system in combination with the judgment; and comparing the success rate of completing the tasks on time once in the area with the success rate of completing the tasks on time after the relay is added, and the scheduling system preferably selects a task assignment mode.
2. The unmanned intelligent scheduling system of claim 1, wherein: in the task receiving and assigning process, the scheduling system sends the task to the vehicle within the appointed distance from the task starting point, judges the predicted time for completing the task according to the task starting point and the task end point in the task, feeds the predicted time back to the scheduling system, and then the scheduling system assigns the task to the vehicle with the shortest completed time.
3. The unmanned intelligent scheduling system of claim 1, wherein: the starting mode of the relay module is that in the process of being called and task assignment in the troubleshooting module, the current assigned vehicle completes the task across the area, and after the task is completed, the number of reserved vehicles in the current area does not meet the requirement, and then the relay module is called.
4. The unmanned intelligent scheduling system of claim 1, wherein: another vehicle continues to complete the task in the troubleshooting module due to the problem with the vehicle itself.
5. An automatic driving method, characterized by comprising the following:
a task analysis module: after the vehicle receives the task, planning a route according to the position and the traffic condition of the vehicle, and judging the time for predicting the completion of the task;
a driving module: the system comprises a navigation module, an image recognition module and a real-time scene modeling module, wherein the navigation module can be communicated with a satellite, determines the position of the navigation module and obtains the traffic jam condition through a network, the image recognition module comprises an image sensor, the image sensor is used for recognizing a road scene, the real-time scene modeling module comprises a laser radar, and the laser radar is used for measuring the distance between an entity and a vehicle and is combined with the image recognition module to obtain the real-time modeling of the road scene.
6. The automated driving method of claim 5, wherein: the driving module at least comprises one image sensor.
7. The automated driving method of claim 5, wherein: the driving module at least comprises a laser radar.
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| CN202111414179.1A CN114093191A (en) | 2021-11-25 | 2021-11-25 | Unmanned intelligent scheduling system and automatic driving method |
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Cited By (1)
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