CN111047859A - Unmanned taxi operation method - Google Patents
Unmanned taxi operation method Download PDFInfo
- Publication number
- CN111047859A CN111047859A CN201911186126.1A CN201911186126A CN111047859A CN 111047859 A CN111047859 A CN 111047859A CN 201911186126 A CN201911186126 A CN 201911186126A CN 111047859 A CN111047859 A CN 111047859A
- Authority
- CN
- China
- Prior art keywords
- task
- scheduling
- taxi
- information
- vehicle
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Pending
Links
- 238000000034 method Methods 0.000 title claims abstract description 33
- 238000001514 detection method Methods 0.000 claims abstract description 21
- 238000012545 processing Methods 0.000 claims abstract description 7
- 238000005516 engineering process Methods 0.000 claims abstract description 5
- 238000004891 communication Methods 0.000 claims abstract description 4
- 230000033001 locomotion Effects 0.000 claims description 16
- 230000008859 change Effects 0.000 claims description 10
- 238000006073 displacement reaction Methods 0.000 claims description 9
- 238000012544 monitoring process Methods 0.000 claims description 5
- 230000011218 segmentation Effects 0.000 claims description 5
- 238000000342 Monte Carlo simulation Methods 0.000 claims description 3
- 230000001133 acceleration Effects 0.000 claims description 3
- 230000002457 bidirectional effect Effects 0.000 claims description 3
- 238000004422 calculation algorithm Methods 0.000 claims description 3
- 238000004364 calculation method Methods 0.000 claims description 3
- 238000006243 chemical reaction Methods 0.000 claims description 3
- 238000012790 confirmation Methods 0.000 claims description 3
- 238000003066 decision tree Methods 0.000 claims description 3
- 230000007613 environmental effect Effects 0.000 claims description 3
- 238000003709 image segmentation Methods 0.000 claims description 3
- 238000003384 imaging method Methods 0.000 claims description 3
- 208000001491 myopia Diseases 0.000 claims description 3
- 238000005070 sampling Methods 0.000 claims description 3
- 238000012216 screening Methods 0.000 claims description 3
- 238000004088 simulation Methods 0.000 claims description 3
- 238000000605 extraction Methods 0.000 claims 1
- 238000004458 analytical method Methods 0.000 abstract description 3
- 239000000463 material Substances 0.000 abstract description 3
- 239000002699 waste material Substances 0.000 abstract description 2
- 230000004888 barrier function Effects 0.000 abstract 1
- 102100034112 Alkyldihydroxyacetonephosphate synthase, peroxisomal Human genes 0.000 description 6
- 101000799143 Homo sapiens Alkyldihydroxyacetonephosphate synthase, peroxisomal Proteins 0.000 description 6
- 238000000848 angular dependent Auger electron spectroscopy Methods 0.000 description 6
- 238000013500 data storage Methods 0.000 description 4
- 230000008569 process Effects 0.000 description 3
- 238000010586 diagram Methods 0.000 description 2
- 239000000284 extract Substances 0.000 description 2
- 230000007246 mechanism Effects 0.000 description 2
- 230000009286 beneficial effect Effects 0.000 description 1
- 238000012937 correction Methods 0.000 description 1
- 230000007547 defect Effects 0.000 description 1
- 230000007123 defense Effects 0.000 description 1
- 238000013461 design Methods 0.000 description 1
- 238000011161 development Methods 0.000 description 1
- 230000018109 developmental process Effects 0.000 description 1
- 238000012986 modification Methods 0.000 description 1
- 230000004048 modification Effects 0.000 description 1
- 230000002265 prevention Effects 0.000 description 1
- 238000006467 substitution reaction Methods 0.000 description 1
- 230000029305 taxis Effects 0.000 description 1
Images
Classifications
-
- G—PHYSICS
- G08—SIGNALLING
- G08G—TRAFFIC CONTROL SYSTEMS
- G08G1/00—Traffic control systems for road vehicles
- G08G1/01—Detecting movement of traffic to be counted or controlled
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q10/00—Administration; Management
- G06Q10/02—Reservations, e.g. for tickets, services or events
-
- G06Q50/40—
-
- G—PHYSICS
- G08—SIGNALLING
- G08G—TRAFFIC CONTROL SYSTEMS
- G08G1/00—Traffic control systems for road vehicles
- G08G1/01—Detecting movement of traffic to be counted or controlled
- G08G1/04—Detecting movement of traffic to be counted or controlled using optical or ultrasonic detectors
-
- G—PHYSICS
- G08—SIGNALLING
- G08G—TRAFFIC CONTROL SYSTEMS
- G08G1/00—Traffic control systems for road vehicles
- G08G1/09—Arrangements for giving variable traffic instructions
- G08G1/0962—Arrangements for giving variable traffic instructions having an indicator mounted inside the vehicle, e.g. giving voice messages
- G08G1/0967—Systems involving transmission of highway information, e.g. weather, speed limits
- G08G1/096708—Systems involving transmission of highway information, e.g. weather, speed limits where the received information might be used to generate an automatic action on the vehicle control
- G08G1/096725—Systems involving transmission of highway information, e.g. weather, speed limits where the received information might be used to generate an automatic action on the vehicle control where the received information generates an automatic action on the vehicle control
-
- G—PHYSICS
- G08—SIGNALLING
- G08G—TRAFFIC CONTROL SYSTEMS
- G08G1/00—Traffic control systems for road vehicles
- G08G1/09—Arrangements for giving variable traffic instructions
- G08G1/0962—Arrangements for giving variable traffic instructions having an indicator mounted inside the vehicle, e.g. giving voice messages
- G08G1/0968—Systems involving transmission of navigation instructions to the vehicle
Abstract
The invention relates to the technical field of unmanned driving, in particular to an unmanned taxi operation method, which realizes the centralized processing of taxi appointment of passengers through a mobile phone APP program and inputs the relevant information of the passengers; after the recording is finished, the recorded task is stored as a scheduling task, and the scheduler is informed that the new task reaches the scheduling program and is intelligently judged by the remote intelligent center; the unmanned taxi receives a scheduling task through a scheduling center, passengers are sent to a destination by utilizing road detection of machine vision, front vehicle identification of the machine vision and a barrier detection technology of a vehicle-mounted laser radar, operation is completed, a scheduling program, a wiring program and a server program adopt a control to be in communication connection, connection requests of wiring and a scheduling end are waited to be processed at any time, and a next scheduling task is entered, the problems that an existing taxi operation system is solved, a base operation route lacks intelligent analysis and judgment, manpower is relied on to be completed, and manpower waste and material resource redundant use are caused are solved.
Description
Technical Field
The invention relates to the technical field of unmanned driving, in particular to an unmanned taxi operation method.
Background
The advanced driving auxiliary system is a set of system which utilizes sensors such as radar, camera and ultrasonic wave to detect the surrounding environment of the vehicle so as to provide danger early warning for the driver. Through the radar sensor, the ADAS system can measure the distance between the front vehicle and the rear vehicle and the ADAS system, and monitor the speed of the surrounding vehicles and the speed of the ADAS system, so that collision prevention and blind spot detection functions are realized. Through the camera sensor, the ADAS system may warn the driver of lane departure behavior. An intelligent driving system, also known as an unmanned vehicle, is an upgraded version of the ADAS system, and at the same time must have all the functions of the ADAS system.
The intelligent driving system senses the surrounding road environment through the vehicle-mounted sensor, automatically plans a driving route, and can avoid obstacles so as to control the vehicle to go to a preset target. The generation of the intelligent driving system is a product combining rapid development of computer science, mode recognition and intelligent control technologies, and has a thousands of broad prospects in the fields of national defense and civil use.
Taxis, cars temporarily hired by a person, are charged by mileage or time, and are also called out. Taiwan is called taxi, Guangdong and Australia, China is called "taxi", and Singapore and Malaysia are called "Desy". The taxi English "taxi" is an abbreviation of "taxi", namely "odometer" or "odometer".
The existing taxi operation system basically intercepts on the spot or finishes operation by booking an APP order and accepting the order or parking a taxi driver, but the whole process needs to be finished by a full-time driver, an operation route is lack of intelligent analysis and judgment, manpower is wasted and material resources are used redundantly due to the fact that the operation route is finished by manpower, and therefore the problem is solved by the aid of the unmanned taxi operation method.
Disclosure of Invention
Aiming at the defects of the prior art, the invention discloses an unmanned taxi operation method, which is used for solving the problems that the conventional taxi operation system is basically intercepted on site, or the operation is completed by receiving an order or parking a taxi driver through APP order reservation, but the whole process needs to be completed by a full-time driver, the operation route is lack of intelligent analysis and judgment, and the operation is completed by depending on manpower, so that the manpower waste and the material resource redundant use are caused.
The invention is realized by the following technical scheme:
an unmanned taxi operation method, comprising the steps of:
s1, realizing the centralized processing of the passenger car appointment through the mobile phone APP program, and inputting the relevant information of the passenger;
s2, after the recording is finished, saving the recorded task as a scheduling task, and informing the scheduler that the new task reaches the scheduling program to be intelligently judged by the remote intelligent center;
s3, when a new scheduling task arrives, the current scheduling machine without the scheduling task tries to lock the new task, the server confirms that the scheduling machine operated firstly locks the task according to the sequence of the operation of the scheduling machine, and returns confirmation information, then the scheduling machine starts to schedule the task, and at this time, the specific information corresponding to the task is displayed on the interface of the scheduling machine;
s4 the driverless taxi receives the dispatching task through the dispatching center, and the passenger is sent to the destination by using the road detection of machine vision, the vehicle identification in front of the machine vision and the obstacle detection technology of the vehicle-mounted laser radar;
and S5, completing operation, wherein the dispatcher, the wiring program and the server program adopt the control to perform communication connection, waiting for processing connection requests of the wiring and the dispatching end at any time, and entering the next dispatching task.
Furthermore, in S3, the dispatcher receives a new dispatching task, dispatches vehicles, searches vehicles and displays tracked vehicles, and the statistical query module is configured to perform statistics on the dispatching task, statistics on the vehicle calling situations, statistics on the passenger calling situations, statistics on vehicle startup, query on alarm records, and query on timely dispatching information.
Furthermore, after the road image is divided into a road area and a non-road area by the aid of a two-dimensional Otsu method in the road detection by means of machine vision, the road edges of the road image are extracted by the aid of a Canny operator and are subjected to straight line fitting, and finally, the posterior confidence coefficients of all the fitted straight lines are evaluated on the basis of a Monte Carlo method, and the road boundary is finally determined.
Furthermore, the machine vision front vehicle identification verifies the vehicle bottom shadow candidate area on the basis of the assumption that the vehicle area histogram distribution is more regular, eliminates the interference area, extracts and judges the left and right vertical boundaries of the candidate area through symmetrical scanning, and determines the candidate area as the vehicle area if the width of the left and right boundaries exceeds the width of the vehicle pixels by a certain proportion.
Furthermore, the obstacle detection of the vehicle-mounted laser radar firstly carries out gradient segmentation on points of a three-dimensional laser radar scanning line, then false detection in the clustering segmentation section is filtered according to the attribute of the obstacle, and the ground is estimated according to reliable non-obstacle points so as to recover missed detection points.
Furthermore, the unmanned taxi adopts lane change to avoid obstacles under the condition that the unmanned taxi has enough final minimum safe distance with surrounding vehicles, the computer control unit calculates obstacle avoidance path information before lane change, the information consists of newly generated navigation points due to the fact that cosine lane change track calculation is convenient, conversion is simple and smoothness is good, and the navigation points of the newly generated path comprise coordinates and course information of a preset target position in a ground rectangular coordinate system.
Furthermore, the total displacement along the X-axis according to the lateral shift displacement H and the track-changing time can be obtained
y＝H/2·(1-cos(π·x/xM(ttat)))，0≤x≤xM(ttat)
In the formula M tatx(t)The slope at the X point can be obtained by differentiating the y for the displacement of the X axis when lateral acceleration is applied
The included angle theta between the tangent line at the point X and the X axisxIs composed of
If the course pointed by the X axis is known to be h1If the clockwise course is increased, the course corresponding to the X point is hx＝|h1-θx|。
Furthermore, when the unmanned taxi carries out route selection, the improved road image segmentation method facing the image pixel points is used for deeply excavating the contour information of the lane marking lines; extracting and screening the lane marking characteristic points by adopting a method of combining the bidirectional scanning based on the sampling line and the constraint candidate characteristic points of the imaging model; and establishing a segmented lane marking model according to the characteristics of lane markings in the far and near vision fields, and completing the solution of a road equation under a pixel coordinate system by using a least square method.
Furthermore, the decision conditions and the corresponding target quantities of the unmanned taxi motion modes under the microscopic dynamic traffic environment are designed for the two types of basic motion modes of the unmanned taxi under the microscopic dynamic traffic environment on the premise of taking a macroscopic driving path and combining the self motion state of the unmanned taxi on the basis of extracting the environmental information; establishing a motion decision model based on a decision tree on the basis; and finally, verifying the rationality of the model by constructing a microscopic dynamic traffic simulation environment.
Furthermore, after the monitoring service center obtains the position information of the taxi, the relevant information is obtained from the electronic map database, deviation information of the taxi and the like is obtained through a matching algorithm, and real-time correction is carried out on the deviation information, so that the position of the taxi in the road network is accurately displayed.
The invention has the beneficial effects that:
the unmanned taxi can sense the road environment, plan the driving path and make intelligent decision on the vehicle movement behavior, realizes self-adaptive movement control on the vehicle, accurately identifies lane markings, has higher safety and is more intelligent, and has strong market application prospect.
Drawings
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, 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 the drawings without creative efforts.
FIG. 1 is a block flow diagram of the present invention;
FIG. 2 is a diagram of a cosine-permuted track model according to an embodiment of the present invention.
Detailed Description
In order to make the objects, technical solutions and advantages of the embodiments of the present invention clearer, 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 some, but not all, embodiments of the present invention. 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 1
The embodiment discloses an unmanned taxi operation method as shown in fig. 1, which comprises the following steps:
s1, realizing the centralized processing of the passenger car appointment through the mobile phone APP program, and inputting the relevant information of the passenger;
s2, after the recording is finished, saving the recorded task as a scheduling task, and informing the scheduler that the new task reaches the scheduling program to be intelligently judged by the remote intelligent center;
s3, when a new scheduling task arrives, the current scheduling machine without the scheduling task tries to lock the new task, the server confirms that the scheduling machine operated firstly locks the task according to the sequence of the operation of the scheduling machine, and returns confirmation information, then the scheduling machine starts to schedule the task, and at this time, the specific information corresponding to the task is displayed on the interface of the scheduling machine;
s4 the driverless taxi receives the dispatching task through the dispatching center, and the passenger is sent to the destination by using the road detection of machine vision, the vehicle identification in front of the machine vision and the obstacle detection technology of the vehicle-mounted laser radar;
and S5, completing operation, wherein the dispatcher, the wiring program and the server program adopt the control to perform communication connection, waiting for processing connection requests of the wiring and the dispatching end at any time, and entering the next dispatching task.
In S3, the dispatcher receives new dispatching tasks, dispatches vehicles, searches vehicles and displays tracked vehicles, and the statistical query module is arranged to count dispatching tasks, count the calling situations of vehicles, count the calling situations of passengers by telephone, count the starting of vehicles, query alarm records and query timely dispatching information.
The database is used as a data storage part of the taxi dispatching system, the performance of the database directly influences the performance of the whole dispatching system, and when the taxi dispatching system database is designed, the data storage and reading requirements of taxi dispatching on the data are met mainly from the following aspects. The data storage requirements can be met.
The most important function of a database is to store data. Factors that affect data storage include database design, database software, the hardware platform and operating system on which the database runs, the type of data stored, and the like. The data is accessible to the end user. Availability of data, i.e., for easy access by end users. Throughout the business operations, the data should be accessible to the end user. Whether data is available or not is also related to the user's expectations and feelings of database performance.
For example, a user runs application software and requests data through the application software, which may take several minutes or seconds to complete, depending on the nature of the request, or the data is displayed immediately on the end user's screen. In reality, the user feels the fact that if the user feels that the application software or the database is executed slowly, the user may consider the problem of the database availability.
Has good database security mechanism. After the data is saved in the database, the protection of the data is also very important. A certain database security mechanism must be established to prevent illegal users from accessing the data in the database, and simultaneously, the legal users are ensured to access the data only within the authority of the legal users, thereby achieving the purpose of protecting the data.
Example 2
In the embodiment, after a road image is divided into a road region and a non-road region by a two-dimensional Otsu method in the road detection by using machine vision, the road edge of the road image is extracted by using a Canny operator and is subjected to straight line fitting, and finally, the posterior confidence coefficients of all the fitted straight lines are evaluated based on a Monte Carlo method, and the road boundary is finally determined.
The front vehicle identification of the machine vision verifies the vehicle bottom shadow candidate area on the basis of the assumption that the vehicle area histogram distribution is more regular, eliminates the interference area, extracts the left and right vertical boundaries of the candidate area through symmetrical scanning and judges the left and right vertical boundaries, and if the width of the left and right boundaries exceeds the width of the vehicle pixels by a certain proportion, the candidate area is determined to be the vehicle area.
The obstacle detection of the vehicle-mounted laser radar firstly carries out gradient segmentation on points of a three-dimensional laser radar scanning line, then false detection in a clustering segmentation section is filtered according to the attribute of the obstacle, and the ground is estimated according to reliable non-obstacle points so as to recover missed detection points.
The unmanned taxi adopts a lane change to avoid obstacles under the condition that the unmanned taxi has enough final minimum safe distance with surrounding vehicles, a computer control unit calculates obstacle avoidance path information before lane change, the cosine lane change track calculation is more convenient, the conversion is simple, the smoothness is better, the information consists of newly generated navigation points, and the navigation points of the newly generated path comprise the coordinates and the course information of a preset target position in a rectangular coordinate system of the ground as shown in figure 2.
Furthermore, the total displacement along the X-axis according to the lateral shift displacement H and the track-changing time can be obtained
y＝H/2·(1-cos(π·x/xM(ttat)))，0≤x≤xM(ttat)
In the formula M tatx(t)The displacement taken by the X-axis when lateral acceleration is applied,by taking the derivative of y, the slope at the X point can be obtained
The included angle theta between the tangent line at the point X and the X axisxIs composed of
If the course pointed by the X axis is known to be h1If the clockwise course is increased, the course corresponding to the X point is hx＝|h1-θx|。
When a taxi is not driven by a person, in the process of path selection, the improved road image segmentation method for the image pixel points is used for deeply excavating the outline information of lane marking lines; extracting and screening the lane marking characteristic points by adopting a method of combining the bidirectional scanning based on the sampling line and the constraint candidate characteristic points of the imaging model; and establishing a segmented lane marking model according to the characteristics of lane markings in the far and near vision fields, and completing the solution of a road equation under a pixel coordinate system by using a least square method.
Furthermore, the decision conditions and the corresponding target quantities of the unmanned taxi motion modes under the microscopic dynamic traffic environment are designed for the two types of basic motion modes of the unmanned taxi under the microscopic dynamic traffic environment on the premise of taking a macroscopic driving path and combining the self motion state of the unmanned taxi on the basis of extracting the environmental information; establishing a motion decision model based on a decision tree on the basis; and finally, verifying the rationality of the model by constructing a microscopic dynamic traffic simulation environment.
After the monitoring service center obtains the information of the taxi relative to the position, the monitoring service center obtains the relative information from the electronic map database, obtains the deviation information of the taxi and the like through a matching algorithm, and corrects the deviation information in real time, thereby accurately displaying the position of the taxi in a road network.
The unmanned taxi can sense the road environment, plan the driving path and make intelligent decision on the vehicle movement behavior, realizes self-adaptive movement control on the vehicle, accurately identifies lane markings, has higher safety and is more intelligent, and has strong market application prospect.
The above examples are only intended to illustrate the technical solution of the present invention, but not to limit it; although the present invention has been described in detail with reference to the foregoing embodiments, it will be understood by those of ordinary skill in the art that: the technical solutions described in the foregoing embodiments may still be modified, or some technical features may be equivalently replaced; and 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 (10)
1. An unmanned taxi operation method is characterized by comprising the following steps:
s1, realizing the centralized processing of the passenger car appointment through the mobile phone APP program, and inputting the relevant information of the passenger;
s2, after the recording is finished, saving the recorded task as a scheduling task, and informing the scheduler that the new task reaches the scheduling program to be intelligently judged by the remote intelligent center;
s3, when a new scheduling task arrives, the current scheduling machine without the scheduling task tries to lock the new task, the server confirms that the scheduling machine operated firstly locks the task according to the sequence of the operation of the scheduling machine, and returns confirmation information, then the scheduling machine starts to schedule the task, and at this time, the specific information corresponding to the task is displayed on the interface of the scheduling machine;
s4 the driverless taxi receives the dispatching task through the dispatching center, and the passenger is sent to the destination by using the road detection of machine vision, the vehicle identification in front of the machine vision and the obstacle detection technology of the vehicle-mounted laser radar;
and S5, completing operation, wherein the dispatcher, the wiring program and the server program adopt the control to perform communication connection, waiting for processing connection requests of the wiring and the dispatching end at any time, and entering the next dispatching task.
2. The method of claim 1, wherein in S3, the dispatcher receives new dispatching tasks, dispatches vehicles, searches vehicles, and displays tracked vehicles, and the statistical query module is configured to perform statistics on dispatching tasks, statistics on calling situations of vehicles, statistics on calling situations of passengers by telephone, statistics on starting up vehicles, query on alarm records, and query on timely dispatching information.
3. The unmanned taxi operation method of claim 1, wherein the road detection using machine vision is performed by dividing a road image into a road region and a non-road region by using a two-dimensional Otsu method, extracting road edges by using a Canny operator, fitting straight lines, evaluating posterior confidence of all the fitted straight lines based on a monte carlo method, and finally determining a road boundary.
4. The method of claim 1, wherein the machine vision-based front vehicle identification verifies a vehicle bottom shadow candidate region and eliminates an interference region based on an assumption that a vehicle region histogram distribution is more regular, left and right vertical boundaries of the candidate region are extracted through symmetric scanning and judged, and if the width of the left and right boundaries exceeds the width of a vehicle pixel by a certain proportion, the candidate region is determined to be a vehicle region.
5. The method of claim 1, wherein the obstacle detection of the vehicle-mounted lidar performs gradient segmentation on points of a three-dimensional lidar scan line, then filters false detections in the cluster segments according to obstacle attributes, and estimates the ground based on reliable non-obstacle points to recover missed detection points.
6. The method according to claim 1, wherein the unmanned taxi, under the condition of having a sufficient final minimum safe distance with surrounding vehicles, lane change is adopted to avoid obstacles, the computer control unit calculates obstacle avoidance path information before lane change, the information consists of newly generated navigation points due to convenient cosine track change calculation, simple conversion and good smoothness, and the navigation points of the newly generated path comprise coordinates and course information of a preset target position in a rectangular earth coordinate system.
7. The method of claim 6, wherein the total displacement along the X-axis is obtained from the lateral displacement H and the lane change time
y＝H/2·(1-cos(π·x/xM(ttat)))，０≤x≤xM(ttat)
In the formula xM(ttat) The slope at the X point can be obtained by differentiating the y for the displacement of the X axis when lateral acceleration is applied
The included angle theta between the tangent line at the point X and the X axisxIs composed of
If the course pointed by the X axis is known to be h1If the clockwise course is increased, the course corresponding to the X point is khx＝|h1-θx|。
8. The method of claim 1, wherein the driverless taxi, when performing route selection, excavates lane marking contour information in depth as an improved road image segmentation method facing image pixel points; extracting and screening the lane marking characteristic points by adopting a method of combining the bidirectional scanning based on the sampling line and the constraint candidate characteristic points of the imaging model; and establishing a segmented lane marking model according to the characteristics of lane markings in the far and near vision fields, and completing the solution of a road equation under a pixel coordinate system by using a least square method.
9. The method according to claim 1, wherein the unmanned taxi is based on macro driving route, and based on extraction of environmental information, the decision conditions and corresponding target quantities of the unmanned vehicle motion mode are designed for two types of basic motion modes of the unmanned vehicle in the micro dynamic traffic environment by combining the self motion state of the unmanned vehicle; establishing a motion decision model based on a decision tree on the basis; and finally, verifying the rationality of the model by constructing a microscopic dynamic traffic simulation environment.
10. The method according to claim 1, wherein after the monitoring service center obtains the information about the location of the taxi, the monitoring service center obtains the information from the electronic map database, obtains the deviation information of the taxi and the like through a matching algorithm, and corrects the deviation information in real time, thereby accurately displaying the location of the taxi in the road network.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201911186126.1A CN111047859A (en) | 2019-11-28 | 2019-11-28 | Unmanned taxi operation method |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201911186126.1A CN111047859A (en) | 2019-11-28 | 2019-11-28 | Unmanned taxi operation method |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN111047859A true CN111047859A (en) | 2020-04-21 |
Family
ID=70233829
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201911186126.1A Pending CN111047859A (en) | 2019-11-28 | 2019-11-28 | Unmanned taxi operation method |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN111047859A (en) |
Cited By (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN112711373A (en) * | 2020-12-28 | 2021-04-27 | 广州小马慧行科技有限公司 | Riding service processing method and device, vehicle-mounted terminal and medium |
| CN112906486A (en) * | 2021-01-26 | 2021-06-04 | 吉利汽车研究院（宁波）有限公司 | Passenger condition detection method, control method and system for unmanned taxi |
| CN113096426A (en) * | 2021-03-29 | 2021-07-09 | 紫清智行科技(北京)有限公司 | Dispatching scheduling method for shared automatic driving vehicle |
| CN113223316A (en) * | 2021-05-21 | 2021-08-06 | 深圳裹动智驾科技有限公司 | Method for quickly finding unmanned vehicle, control equipment and unmanned vehicle |
| CN116151506A (en) * | 2023-04-21 | 2023-05-23 | 成都工业职业技术学院 | Weather-based method and device for determining real-time operation route of unmanned vehicle |
Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN106096868A (en) * | 2016-07-01 | 2016-11-09 | 广州地理研究所 | A kind of multiple constraint network intensive minibus dispatching method |
| CN107341553A (en) * | 2017-05-26 | 2017-11-10 | 北京三快在线科技有限公司 | A kind of vehicle dispatching method and device, electronic equipment |
| CN108229710A (en) * | 2017-01-06 | 2018-06-29 | 胡兴旺 | Internet about vehicle |
| CN109559501A (en) * | 2018-12-03 | 2019-04-02 | 浙江智莱科技有限公司 | A kind of unmanned taxi system and its control method |
-
2019
- 2019-11-28 CN CN201911186126.1A patent/CN111047859A/en active Pending
Patent Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN106096868A (en) * | 2016-07-01 | 2016-11-09 | 广州地理研究所 | A kind of multiple constraint network intensive minibus dispatching method |
| CN108229710A (en) * | 2017-01-06 | 2018-06-29 | 胡兴旺 | Internet about vehicle |
| CN107341553A (en) * | 2017-05-26 | 2017-11-10 | 北京三快在线科技有限公司 | A kind of vehicle dispatching method and device, electronic equipment |
| CN109559501A (en) * | 2018-12-03 | 2019-04-02 | 浙江智莱科技有限公司 | A kind of unmanned taxi system and its control method |
Non-Patent Citations (1)
| Title |
|---|
| 孙振平: "自主驾驶汽车智能控制系统" * |
Cited By (9)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN112711373A (en) * | 2020-12-28 | 2021-04-27 | 广州小马慧行科技有限公司 | Riding service processing method and device, vehicle-mounted terminal and medium |
| CN112711373B (en) * | 2020-12-28 | 2022-06-14 | 广州小马慧行科技有限公司 | Riding service processing method and device, vehicle-mounted terminal and medium |
| CN112906486A (en) * | 2021-01-26 | 2021-06-04 | 吉利汽车研究院（宁波）有限公司 | Passenger condition detection method, control method and system for unmanned taxi |
| CN112906486B (en) * | 2021-01-26 | 2023-09-12 | 吉利汽车研究院（宁波）有限公司 | Passenger condition detection method, control method and system for unmanned taxi |
| CN113096426A (en) * | 2021-03-29 | 2021-07-09 | 紫清智行科技(北京)有限公司 | Dispatching scheduling method for shared automatic driving vehicle |
| CN113096426B (en) * | 2021-03-29 | 2021-11-05 | 紫清智行科技(北京)有限公司 | Dispatching scheduling method for shared automatic driving vehicle |
| CN113223316A (en) * | 2021-05-21 | 2021-08-06 | 深圳裹动智驾科技有限公司 | Method for quickly finding unmanned vehicle, control equipment and unmanned vehicle |
| CN116151506A (en) * | 2023-04-21 | 2023-05-23 | 成都工业职业技术学院 | Weather-based method and device for determining real-time operation route of unmanned vehicle |
| CN116151506B (en) * | 2023-04-21 | 2023-06-27 | 成都工业职业技术学院 | Weather-based method and device for determining real-time operation route of unmanned vehicle |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN111047859A (en) | Unmanned taxi operation method | |
| CN111551958B (en) | Mining area unmanned high-precision map manufacturing method | |
| CN110443225B (en) | Virtual and real lane line identification method and device based on feature pixel statistics | |
| WO2018068653A1 (en) | Point cloud data processing method and apparatus, and storage medium | |
| Zhao et al. | On-road vehicle trajectory collection and scene-based lane change analysis: Part i | |
| CN105620489A (en) | Driving assistance system and real-time warning and prompting method for vehicle | |
| CN107392103A (en) | The detection method and device of road surface lane line, electronic equipment | |
| US20220343758A1 (en) | Data Transmission Method and Apparatus | |
| CN108491782A (en) | A kind of vehicle identification method based on driving Image Acquisition | |
| CN106683530A (en) | Computerized judging system and method based on three-dimensional laser vision and high-precision lane model | |
| Liu et al. | Vision-based real-time lane marking detection and tracking | |
| CN104065920A (en) | Vehicle monitoring and tracking method, system and server | |
| CN104616502A (en) | License plate identification and positioning system based on combined type vehicle-road video network | |
| CN116710976A (en) | Autonomous vehicle system for intelligent on-board selection of data for training a remote machine learning model | |
| US11501538B2 (en) | Systems and methods for detecting vehicle tailgating | |
| CN113822285A (en) | Vehicle illegal parking identification method for complex application scene | |
| CN105448105A (en) | Patrol police vehicle-based monitoring system | |
| US20220012503A1 (en) | Systems and methods for deriving an agent trajectory based on multiple image sources | |
| Janda et al. | Road boundary detection for run-off road prevention based on the fusion of video and radar | |
| Zhang et al. | Tracking multi-vehicles with reference points switches at the intersection using a roadside LiDAR sensor | |
| CN114882182A (en) | Semantic map construction method based on vehicle-road cooperative sensing system | |
| Farrell et al. | Best practices for surveying and mapping roadways and intersections for connected vehicle applications | |
| CN114445798A (en) | Urban road parking space identification method and system based on deep learning | |
| US11961241B2 (en) | Systems and methods for deriving an agent trajectory based on tracking points within images | |
| KR20220073472A (en) | Cross section integrated information providing system and method based on V2X |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination |