CN112053433A - Construction method of civil aviation airport dynamic scene and airport special vehicle training method - Google Patents
Construction method of civil aviation airport dynamic scene and airport special vehicle training method Download PDFInfo
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Abstract
The invention relates to a construction method of a civil aviation airport dynamic scene and an airport special vehicle training method. The construction method comprises the following steps: (a) setting a vehicle type and dynamic scene parameters; (b) initializing a dynamic scene: setting a vehicle role, a vehicle initial position and vehicle initial state parameters; (c) setting events and actions: when the vehicle meets the preset event occurrence condition, the vehicle completes dynamic action; (d) and (c) storing the data in the steps (a) to (c) into a modifiable format, exporting the data before training, and editing the exported data by a student according to needs to realize the construction of the dynamic scene. The method can construct various dynamic scenes of civil aviation airports, students export data before training, edit the exported data according to needs, and realize the construction of a plurality of dynamic scenes with different parameters, so that the students can train under different dynamic scenes, the processing capacity of the students on emergency events is enhanced, and the occurrence of special vehicle operation accidents is prevented.
Description
Technical Field
The invention relates to the field of virtual environment airports, in particular to a construction method of a dynamic scene of a civil aviation airport and a training method of special vehicles in the airport.
Background
With the vigorous development of the aviation industry in China, by the end of 2019, the number of the registered transportation airplanes at the end of the period of China's civil aviation and the whole industry is 3818, in 2019, the passenger throughput of the national civil aviation transportation airport is 13.52 hundred million people, which is 6.9% higher than the last year, and the freight mail throughput of the national civil aviation transportation airport is 1710.01 ten thousand tons in 2019, which is 2.1% higher than the last year. China has become a big civil aviation nation, and the preliminary construction of China is predicted to become a strong civil aviation nation by the end of 2020. The increase in the number of airplanes in an airport inevitably leads to an increase in the demand for ground resources, i.e. equipment for performing a specific operation in the airport, one type of which is a specific vehicle required for ground support in the flight area. Airport special vehicles are of a wide variety and each category has its specific functions. For example, an airport ferry vehicle is a special vehicle which runs between a terminal building and an aircraft park on the airport apron and is responsible for receiving and delivering passengers taking an airplane, and the working principle of the airport ferry vehicle is similar to that of a bus; the luggage conveying vehicle is ground support equipment which can support passengers, luggage and goods to smoothly enter and exit from the bottom cabin of the airplane through a vehicle-mounted motorized conveying device, and the equipment can convey articles among different height points.
The continuing increase in the number of aircraft and ground vehicles necessarily increases the complexity of traffic and the difficulty of managing moving objects within an airport environment. During the daily operation of an airport, collision may occur between vehicles, between vehicles and airplanes, or even between airplanes, which may not only affect the operation capability of the airport, but also cause significant safety accidents. For example, in the hong kong airport at 7/8 th of 2008, when a boeing passenger plane in national aviation slides in the rain, the wings of the plane and the roof of a engineering vehicle collide, so that passengers and crew on the plane are emergently evacuated. In addition, a boeing 737 aircraft in the new york buffalo international airport collides with a ground refueller; a microbus performing the work of the aircraft in the Gaoshaki International airport carelessly scrapes an aircraft parked on the apron. The above events indicate an increasing demand for airport utility vehicles and equipment operators as airports expand in size and airport utility vehicles increase. If the operation of special vehicles in an airport is lack of a vivid training environment, the operation of drivers is unskilled, so that the working efficiency is low and potential safety hazards exist.
Most of the current research and results are focused on the aspects of operation optimization, path planning, comprehensive scheduling management and the like of airport special vehicles, and although the prior patents relate to driving training methods of airport special vehicles, most of the prior patents are directed at a single static scene. The driving environment of the special vehicle in the airport presents different characteristics along with weather, seasons and different operation tasks, and the traffic complexity and the management difficulty of the moving target are increased due to the increase of the number of the special vehicles, so that the site cognition and training method for the dynamic user-configurable customized working condition is of great significance for preventing the operation accidents of the special vehicle and improving the handling capacity of the emergency.
In view of the above, the present invention is particularly proposed.
Disclosure of Invention
The invention aims to provide a method for constructing dynamic scenes of civil aviation airports, which can construct various dynamic scenes of the civil aviation airports, wherein a student can derive data before training and edit the derived data according to needs, so that the construction of the dynamic scenes with a plurality of different parameters is realized, the student can train in different dynamic scenes, the processing capability of the student on emergency is enhanced, and the occurrence of special vehicle operation accidents is prevented.
A second object of the present invention is to provide a construction system of dynamic scenes at civil aviation airports, which is used for implementing the above construction method and therefore has at least the same advantages as the above method.
The third purpose of the invention is to provide a training method for airport special vehicles, which adopts the method to construct a dynamic scene and then is used for training, thereby having at least the same advantages as the construction method.
The invention also aims to provide an airport special vehicle training system.
In order to achieve the purpose, the invention adopts the following technical scheme:
in a first aspect, the invention provides a method for constructing a dynamic scene of a civil aviation airport, which comprises the following steps:
(a) setting a vehicle type and dynamic scene parameters;
(b) initializing a dynamic scene: setting a vehicle role, a vehicle initial position and vehicle initial state parameters;
(c) setting events and actions: when the vehicle meets the preset event occurrence condition, the vehicle completes dynamic action;
(d) and (c) storing the data in the steps (a) to (c) into a modifiable format, exporting the data before training, and editing the exported data by a student according to needs to realize the construction of the dynamic scene.
As a further preferable technical scheme, the vehicle types include a luggage tractor, a luggage conveying vehicle, a dining vehicle, a snow sweeper, an ice sweeper, a guide vehicle, an airplane tractor, a passenger ladder vehicle and a ferry vehicle;
preferably, the dynamic scene parameters include vehicle parameters, driver parameters, pedestrian parameter environment parameters and route parameters;
preferably, the vehicle parameters include size, mass, center of mass, maximum speed, maximum acceleration, and vehicle dynamics parameters;
preferably, the driver parameters include height, weight, stadia, age and gender;
preferably, the pedestrian parameters include height, weight, age, gender, speed, and initial position;
preferably, the environmental parameter comprises sunny, cloudy, wind, frost, rain, snow, fog or haze.
As a further preferred technical solution, the vehicle role includes a training vehicle or a participating vehicle;
preferably, the initial position of the vehicle comprises vehicle coordinates and a lane where the vehicle is located;
preferably, the vehicle initial-state parameters include at least one of initial speed, roll, pitch, and yaw angle.
As a further preferred technical scheme, the dynamic scene comprises pedestrian avoidance, car following, lane changing, traffic jam, overtaking, sudden braking or traffic accidents;
preferably, the dynamic scenario is a passing, and the setting of the passing event and the action includes: setting the longitudinal speed of the vehicle in a given time period, when the longitudinal distance between the overtaking vehicle and the overtaken training vehicle meets the condition set by the trainee, the overtaking vehicle makes a lane change action and accelerates to overtake, and returns to the initial lane after the overtaking action is finished;
preferably, the dynamic scene is pedestrian avoidance, and the setting of the pedestrian avoidance event and the action includes: setting the longitudinal speed of the vehicle in a given time period, and when the longitudinal distance between the training vehicle and the appearance position of the pedestrian is equal to the set distance, the pedestrian passes through the front of the vehicle at a preset speed and route;
preferably, the dynamic scene is car following, and setting car following events and actions includes: setting the longitudinal speed of the vehicle in a given time period, and when the longitudinal distance between the training vehicle and the tracked vehicle meets a preset numerical value, changing the speed and the driving lane of the tracked vehicle according to the setting of a user;
preferably, in the step (d), the data is stored in an openscene format.
In a second aspect, the invention provides a construction system of a dynamic scene of a civil aviation airport, which is used for executing the construction method of the dynamic scene of the civil aviation airport.
As a further preferable technical solution, the construction system includes a vehicle type setting module, a dynamic scene parameter setting module, a dynamic scene initialization module, an event setting module, an action setting module, a data storage module, and a data export module;
the vehicle type setting module is used for setting the type of the vehicle;
the dynamic scene parameter setting module is used for setting dynamic scene parameters;
the dynamic scene initialization module is used for initializing a dynamic scene;
the event setting module is used for setting triggering conditions of vehicle and/or pedestrian actions;
the action setting module is used for setting the action of the vehicle and/or the pedestrian;
the data storage module is used for storing data of vehicle types, dynamic scene parameters, dynamic scene initialization, events and actions;
the data export module is used for exporting the data in the data storage module.
In a third aspect, the invention provides an airport special vehicle training method, wherein a dynamic scene is constructed by adopting the construction method of the civil aviation airport dynamic scene and then used for training.
As a further preferred technical solution, the training method comprises the steps of:
s1, creating a three-dimensional model: creating a three-dimensional model with the same size as the real object according to the actual airport information;
s2, importing the three-dimensional model into a virtual engine, and then constructing a dynamic scene according to the construction method of the civil aviation airport dynamic scene;
s3, creating a roaming sand table;
s4, driving training or assessment;
preferably, the airport actual information comprises a flight place, an airplane, a special vehicle, a person, a building and an indication information signboard;
preferably, in step S4, the driver logs in the examination training system, selects a special vehicle, selects a default scene or a customized driving condition, wears the head sight device, and operates the external intelligent driving simulator to complete the training and examination of the driving scene.
In a fourth aspect, the invention provides an airport special vehicle training system which is used for executing the airport special vehicle training method.
As a further preferred technical scheme, the training system comprises a virtual engine vehicle training scene building module, a roaming-capable sand table creating module and a driving simulator;
the virtual engine vehicle training scene building module is used for importing a three-dimensional model created by three-dimensional modeling software, and a building system of the civil aviation airport dynamic scene is arranged in the virtual engine;
the roaming sandbox creating module is used for creating a roaming sandbox;
the driving simulator is used for training or assessment.
Compared with the prior art, the invention has the beneficial effects that:
the construction method of the civil aviation airport dynamic scene provided by the invention adds the setting of dynamic scene parameters, the initialization setting of the dynamic scene and the setting of events and actions, thereby being capable of constructing various basic dynamic scenes, and the existing construction method can only construct fixed static scenes (the environment and the conditions of each training are the same). According to the construction method, after various basic dynamic scenes are constructed, data of the basic dynamic scenes are stored into a modifiable format, then the data are exported before training, and a student edits the exported data as required, so that the construction of the dynamic scenes with a plurality of different parameters is realized, the student can train under different dynamic scenes conveniently, the processing capability of the student on emergency events is enhanced, and the occurrence of special vehicle operation accidents is prevented.
The airport special vehicle training method provided by the invention adopts the method to construct the dynamic scene, and then is used for training, thereby having at least the same advantages as the construction method.
Furthermore, the training method comprises the steps of firstly establishing a three-dimensional model, then introducing the model into a virtual engine, and then constructing a dynamic scene by adopting the method, wherein trainees can configure training conditions (such as typical accident conditions) in a self-defined mode, and can start training or examination after being familiar with a training field by wearing a virtual reality device roaming sand table. The method is scientific and reasonable, and can effectively utilize the method to construct the required dynamic scene, thereby facilitating trainees to train or assess under different dynamic scenes, enhancing the capability of the trainees to process emergencies and preventing the occurrence of special vehicle operation accidents.
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, and it is obvious that the drawings in the following description are some embodiments of the present invention, and other drawings can be obtained by those skilled in the art without creative efforts.
FIG. 1 is a schematic flow chart of a method for constructing a dynamic scene of a civil aviation airport according to an embodiment of the present invention;
fig. 2 is a schematic flow chart of an airport special vehicle training method according to an embodiment of the invention.
Detailed Description
In order to make the objects, technical solutions and advantages of the present invention more apparent, the technical solutions of the present invention will be clearly and completely described below. It is to be understood that the described embodiments are merely exemplary of the invention, and not restrictive of the full scope of the 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.
It should be noted that: the numbering of each step in the method of the invention is only an exemplary representation, does not imply a sequence, and does not particularly limit the sequence of each step, as long as the final purpose (such as dynamic scene construction or special vehicle training) can be achieved.
According to an aspect of the present invention, as shown in fig. 1, there is provided a method for constructing a dynamic scene of a civil aviation airport, comprising the following steps:
(a) setting a vehicle type and dynamic scene parameters;
(b) initializing a dynamic scene: setting a vehicle role, a vehicle initial position and vehicle initial state parameters;
(c) setting events and actions: when the vehicle meets the preset event occurrence condition, the vehicle completes dynamic action;
(d) and (c) storing the data in the steps (a) to (c) into a modifiable format, exporting the data before training, and editing the exported data by a student according to needs to realize the construction of the dynamic scene.
The construction method of the civil aviation airport dynamic scene comprises the steps of firstly constructing a basic dynamic scene by setting a series of parameters including vehicle types, dynamic scene parameters, dynamic scene initialization parameters, event and action parameters and the like, then storing data into a modifiable format, exporting the data before training, and enabling students to edit the exported data according to the training requirements, thereby realizing the real-time construction of various dynamic scenes.
The construction method adds the setting of dynamic scene parameters, the initialization setting of the dynamic scene and the setting of events and actions, so that various basic dynamic scenes can be constructed, and the existing construction method can only construct fixed static scenes (the environment and the conditions of each training are the same). According to the construction method, after various basic dynamic scenes are constructed, data of the basic dynamic scenes are stored into a modifiable format, then the data are exported before training, and a student edits the exported data as required, so that the construction of the dynamic scenes with a plurality of different parameters is realized, the student can train under different dynamic scenes conveniently, the processing capability of the student on emergency events is enhanced, and the occurrence of special vehicle operation accidents is prevented.
It should be noted that, the trainee can also directly operate the steps (a) to (c) in the interface setting of the simulation system and then directly perform the training, which has the disadvantage that all parameters need to be set once before the training each time, and the convenience is relatively poor.
In a preferred embodiment, the vehicle types include a luggage tractor, a luggage transfer cart, a dining cart, a snow sweeper, an ice sweeper, a lead cart, an airplane tractor, a passenger ladder cart, and a ferry cart.
Preferably, the dynamic scene parameters include vehicle parameters, driver parameters, pedestrian parameters, environmental parameters and route parameters.
Preferably, the vehicle parameters include size, mass, center of mass, maximum speed, maximum acceleration, and vehicle dynamics parameters. The "vehicle dynamics parameters" refer to the shaft curve, engine-related parameters, differential-related parameters, transmission-related parameters, steering curve, wheel-related parameters, and the like.
Preferably, the driver parameters include height, weight, line of sight, age and gender.
Preferably, the pedestrian parameters include height, weight, age, gender, speed and initial position.
Preferably, the environmental parameter comprises sunny, cloudy, wind, frost, rain, snow, fog or haze.
It should be noted that:
if the vehicle is a participating vehicle, the driver parameters are not set. The "participating vehicle" refers to a vehicle corresponding to the training vehicle at the time of training, i.e., a vehicle that completes an event and an action in cooperation with the training vehicle.
The route parameter refers to a route on which the vehicle travels.
In a preferred embodiment, the vehicle character comprises a training vehicle or a participating vehicle.
Preferably, the vehicle initial position includes vehicle coordinates and a lane in which the vehicle is located.
Preferably, the vehicle initial-state parameters include at least one of initial speed, roll, pitch, and yaw angle. The vehicle initial-state parameters include, but are not limited to, initial speed, yaw, pitch, yaw angle, a combination of initial speed and yaw, a combination of pitch and yaw angle, a combination of initial speed, yaw, and pitch, or a combination of initial speed, yaw, pitch, and yaw angle.
In a preferred embodiment, the dynamic scene includes pedestrian avoidance, car following, lane change, traffic jam, overtaking, sudden braking or traffic accident.
Preferably, the dynamic scenario is a passing, and the setting of the passing event and the action includes: and setting the longitudinal speed of the vehicle in a given time period, and when the longitudinal distance between the overtaking vehicle and the overtaken training vehicle meets the condition set by the trainee, the overtaking vehicle makes a lane change action and accelerates to overtake, and returns to the initial lane after the overtaking action is finished.
Preferably, the dynamic scene is pedestrian avoidance, and the setting of the pedestrian avoidance event and the action includes: the longitudinal speed of the vehicle is set for a given period of time, and when the longitudinal distance of the training vehicle from the pedestrian's presence location is equal to the set distance, the pedestrian passes in front of the vehicle at a preset speed and course. The above number of pedestrians may be set to single or plural.
Preferably, the dynamic scene is car following, and setting car following events and actions includes: the longitudinal speed of the vehicle is set for a given period of time, and when the longitudinal distance between the training vehicle and the tracked vehicle satisfies a preset value, the speed and the lane of travel of the tracked vehicle are changed according to the setting of the user. The car following scene can be used for checking the stable car following behavior or the emergency response capability of the emergency brake of the front car.
In a preferred embodiment, in step (d), the data is stored in openscene format.
According to another aspect of the invention, a construction system of a dynamic scene of a civil aviation airport is provided, and the construction system is used for executing the construction method of the dynamic scene of the civil aviation airport. The building system is used for performing the above building method and therefore has at least the same advantages as the above method.
In a preferred embodiment, the construction system comprises a vehicle type setting module, a dynamic scene parameter setting module, a dynamic scene initialization module, an event setting module, an action setting module, a data storage module and a data derivation module;
the vehicle type setting module is used for setting the type of the vehicle;
the dynamic scene parameter setting module is used for setting dynamic scene parameters;
the dynamic scene initialization module is used for initializing a dynamic scene;
the event setting module is used for setting triggering conditions of vehicle and/or pedestrian actions;
the action setting module is used for setting the action of the vehicle and/or the pedestrian;
the data storage module is used for storing data of vehicle types, dynamic scene parameters, dynamic scene initialization, events and actions;
the data export module is used for exporting the data in the data storage module.
During construction of an actual scene, after the data is exported by the data export module, the data can be directly used for training, or the exported data can be further modified and then used for training, and during modification, the data is still edited in the vehicle type setting module, the dynamic scene parameter setting module, the dynamic scene initialization module, the event setting module and the action setting module.
According to another aspect of the invention, an airport special vehicle training method is provided, wherein the construction method of the civil aviation airport dynamic scene is adopted to construct the dynamic scene, and then the dynamic scene is used for training. The training method adopts the method to construct the dynamic scene and then is used for training, thereby having at least the same advantages as the construction method.
In a preferred embodiment, as shown in fig. 2, the training method comprises the steps of:
s1, creating a three-dimensional model: creating a static model and a three-dimensional model with the same size as a real object according to the actual airport information;
s2, importing the three-dimensional model into a virtual engine, and then constructing a dynamic scene according to the construction method of the civil aviation airport dynamic scene;
s3, creating a roaming sand table;
s4, driving training or assessment.
The training method comprises the steps of firstly establishing a three-dimensional dynamic model, then introducing the model into a virtual engine, and then establishing a dynamic scene by adopting the method, wherein trainees can configure training conditions (such as typical accident conditions) in a self-defined mode, and can start training or examination after the trainees are familiar with a training field by wearing a virtual reality device roaming sand table. The method is scientific and reasonable, and can effectively utilize the method to construct the required dynamic scene, thereby facilitating trainees to train or assess under different dynamic scenes, enhancing the capability of the trainees to process emergencies and preventing the occurrence of special vehicle operation accidents.
After the created three-dimensional model is led into the virtual engine, a model classification library and a plurality of single scene maps can be formed, scene elements such as light, weather and sound are blended, the real environment is restored, and a driving student can configure training conditions (dynamic scenes) in a user-defined mode. The dynamic scene mainly refers to the real-time motion track and speed of airplanes, various special vehicles, personnel and the like. The driving trainees can even set up typical accident conditions to check the handling capacity of the emergency. The method is characterized in that path planning is carried out on the movement track of people in a scene, and program design and implementation of interactive operation are completed aiming at different operation platforms of different types of special vehicles according to actual driving conditions.
The static model comprises buildings, signboards, roads and the ground, and the dynamic model comprises pedestrians, various vehicles and airplanes.
In a preferred embodiment, the airport physical information includes a flight place, an airplane, a special vehicle, a person, a building, and an indication information signboard. The indication information signboard is a signboard provided with various indication information including speed limit, no-pass, driving route prompt, and the like.
Optionally, after the creation of the roaming sand table is completed, a trainee or a teacher needs to be connected with hardware devices such as a virtual reality helmet, a data glove and a treadmill externally, and the driver can be immersed in the specific conditions of a certain area of the airport in 360 degrees, such as information of speed limit, no passing, driving path prompt and the like, through an interactive signboard arranged in the scene, so as to ensure that the driver can fully know the conditions of the driving field before driving examination and training.
Preferably, in step S4, the driver logs in the examination training system, selects a special vehicle, selects a default scene or a customized driving condition, wears the head sight device, and operates the external intelligent driving simulator to complete the training and examination of the driving scene. After the training and examination are finished, the system generates the animation of the whole driving process, and the driving trainees can check the evaluation reports of the current and historical examinations. By comparing the evaluation report with the driving overall process animation, the qualified driving operation can be used as a typical teaching case; for unqualified driving operation, errors in the driving process can be used as key problems and difficult problems to carry out purposeful reinforced training on the driving trainees.
The training system refers to 'civil airport aircraft activity area road traffic management rules', combines practical application of special vehicles, specifies corresponding training paths, and realizes all judgment standards through blueprint logic language, so that a driver can obtain an evaluation report after each training is finished, scene restoration in the driving training process can also be checked, and safe and efficient simulation training with feedback is realized.
According to another aspect of the invention, an airport special vehicle training system is provided, which is used for executing the airport special vehicle training method. The training system is used for executing the airport special vehicle training method and therefore has at least the same advantages as the training system.
In a preferred embodiment, the training system comprises a virtual engine vehicle training scene building module, a navigable sand table creation module, and a driving simulator;
the virtual engine vehicle training scene building module is used for importing a three-dimensional model created in three-dimensional modeling software, and a building system of the civil aviation airport dynamic scene is arranged in the virtual engine;
the roaming sandbox creating module is used for creating a roaming sandbox;
the driving simulator is used for training or assessment.
The specific operation process of the training system comprises the following steps: the method comprises the steps of creating a model in three-dimensional modeling software, storing the model as a file in an obj format, importing all files in the obj format into a development environment of a training platform (namely, in a virtual engine, available virtual engine software is UnrealEngine), conducting secondary rendering on the three-dimensional model in the virtual engine, conducting skeleton binding on the three-dimensional dynamic model, and placing the three-dimensional dynamic model according to the actual situation of an airport. After the relative positions of all models of the whole airport are set, the whole airport is reduced in equal proportion to form a mini scene required by the roaming sand table. Of course, according to the specific requirements during training, the whole airport virtual scene can be stored as a plurality of small maps under a single scene, such as an airport terminal map, an airport apron map, an airport runway map and the like. And after the required training map is created, accessing the intelligent driving simulator, and realizing the binding relationship and data receiving and transmitting between the trained vehicle and the intelligent driving simulator by referring to the communication protocol in the product manual of the intelligent driving simulator.
Finally, it should be noted that: the above embodiments are only used to illustrate the technical solution of the present invention, and not to limit the same; while the invention has been described in detail and with reference to the foregoing embodiments, it will be understood by those skilled in the art that: the technical solutions described in the foregoing embodiments may still be modified, or some or all of the technical features may be equivalently replaced; and the modifications or the substitutions do not make the essence of the corresponding technical solutions deviate from the technical solutions of the embodiments of the present invention.
Claims (10)
1. A construction method of a dynamic scene of a civil aviation airport is characterized by comprising the following steps:
(a) setting a vehicle type and dynamic scene parameters;
(b) initializing a dynamic scene: setting a vehicle role, a vehicle initial position and vehicle initial state parameters;
(c) setting events and actions: when the vehicle meets the preset event occurrence condition, the vehicle completes dynamic action;
(d) and (c) storing the data in the steps (a) to (c) into a modifiable format, exporting the data before training, and editing the exported data by a student according to needs to realize the construction of the dynamic scene.
2. The construction method of the dynamic scene of the civil aviation airport according to claim 1, wherein the vehicle types include a luggage tractor, a luggage conveying vehicle, a dining vehicle, a snow sweeper, an ice sweeper, a guiding vehicle, an airplane tractor, a passenger ladder vehicle and a ferry vehicle;
preferably, the dynamic scene parameters include vehicle parameters, driver parameters, pedestrian parameter environment parameters and route parameters;
preferably, the vehicle parameters include size, mass, center of mass, maximum speed, maximum acceleration, and vehicle dynamics parameters;
preferably, the driver parameters include height, weight, stadia, age and gender;
preferably, the pedestrian parameters include height, weight, age, gender, speed, and initial position;
preferably, the environmental parameter comprises sunny, cloudy, wind, frost, rain, snow, fog or haze.
3. The method for constructing a dynamic scene of a civil aviation airport according to claim 1, wherein the vehicle roles comprise training vehicles or participating vehicles;
preferably, the initial position of the vehicle comprises vehicle coordinates and a lane where the vehicle is located;
preferably, the vehicle initial-state parameters include at least one of initial speed, roll, pitch, and yaw angle.
4. The construction method of the civil aviation airport dynamic scene of any one of claims 1 to 3, wherein the dynamic scene comprises pedestrian avoidance, car following, lane change, traffic jam, overtaking, sudden braking or traffic accident;
preferably, the dynamic scenario is a passing, and the setting of the passing event and the action includes: setting the longitudinal speed of the vehicle in a given time period, when the longitudinal distance between the overtaking vehicle and the overtaken training vehicle meets the condition set by the trainee, the overtaking vehicle makes a lane change action and accelerates to overtake, and returns to the initial lane after the overtaking action is finished;
preferably, the dynamic scene is pedestrian avoidance, and the setting of the pedestrian avoidance event and the action includes: setting the longitudinal speed of the vehicle in a given time period, and when the longitudinal distance between the training vehicle and the appearance position of the pedestrian is equal to the set distance, the pedestrian passes through the front of the vehicle at a preset speed and route;
preferably, the dynamic scene is car following, and setting car following events and actions includes: setting the longitudinal speed of the vehicle in a given time period, and when the longitudinal distance between the training vehicle and the tracked vehicle meets a preset numerical value, changing the speed and the driving lane of the tracked vehicle according to the setting of a user;
preferably, in the step (d), the data is stored in an openscene format.
5. A construction system of a dynamic scene of a civil aviation airport, which is characterized by being used for executing the construction method of the dynamic scene of the civil aviation airport according to any one of claims 1 to 4.
6. The civil aviation airport dynamic scene construction system according to claim 5, wherein the construction system comprises a vehicle type setting module, a dynamic scene parameter setting module, a dynamic scene initialization module, an event setting module, an action setting module, a data storage module and a data derivation module;
the vehicle type setting module is used for setting the type of the vehicle;
the dynamic scene parameter setting module is used for setting dynamic scene parameters;
the dynamic scene initialization module is used for initializing a dynamic scene;
the event setting module is used for setting triggering conditions of vehicle and/or pedestrian actions;
the action setting module is used for setting the action of the vehicle and/or the pedestrian;
the data storage module is used for storing data of vehicle types, dynamic scene parameters, dynamic scene initialization, events and actions;
the data export module is used for exporting the data in the data storage module.
7. A training method for special vehicles in airports is characterized in that a dynamic scene is constructed by adopting the construction method for the dynamic scene of the civil aviation airport according to any one of claims 1 to 4 and then used for training.
8. The airport special vehicle training method of claim 7, wherein said training method comprises the steps of:
s1, creating a three-dimensional model: establishing a static model and a dynamic model with the same size as the real object according to the actual airport information;
s2, importing the three-dimensional model into a virtual engine, and then constructing a dynamic scene according to the construction method of the civil aviation airport dynamic scene;
s3, creating a roaming sand table;
s4, driving training or assessment;
preferably, the airport actual information comprises a flight place, an airplane, a special vehicle, a person, a building and an indication information signboard;
preferably, in step S4, the driver logs in the examination training system, selects a special vehicle, selects a default scene or a customized driving condition, wears the head sight device, and operates the external intelligent driving simulator to complete the training and examination of the driving scene.
9. An airport special vehicle training system, wherein the training system is used for executing the airport special vehicle training method of claim 7 or 8.
10. The airport special vehicle training system of claim 9, wherein the training system comprises a virtual engine vehicle training scenario construction module, a navigable sand table creation module, and a driving simulator;
the virtual engine vehicle training scene building module is used for importing a three-dimensional model created in three-dimensional modeling software, and a building system of the civil aviation airport dynamic scene is arranged in the virtual engine;
the roaming sandbox creating module is used for creating a roaming sandbox;
the driving simulator is used for training or assessment.
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Cited By (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN113761096A (en) * | 2021-09-03 | 2021-12-07 | 深圳清航智行科技有限公司 | Map making method and device and computer readable storage medium |
| CN115796397A (en) * | 2023-02-06 | 2023-03-14 | 中国民航大学 | Scene generation method and scene generation system for airport operation guarantee process |
| CN115983732A (en) * | 2022-12-23 | 2023-04-18 | 中国民航大学 | Airport land side traffic sand table scheduling system based on vehicle-road cooperation |
Citations (8)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US5921780A (en) * | 1996-06-28 | 1999-07-13 | Myers; Nicole J. | Racecar simulator and driver training system and method |
| CN105448158A (en) * | 2015-11-30 | 2016-03-30 | 武汉华瑞密达虚拟现实技术研发有限公司 | Operation training system and method of special vehicles |
| CN107945604A (en) * | 2017-12-07 | 2018-04-20 | 中国航空工业集团公司西安航空计算技术研究所 | A kind of traffic safety immersion experiencing system based on virtual reality technology |
| CN108062875A (en) * | 2017-12-30 | 2018-05-22 | 上海通创信息技术股份有限公司 | A kind of cloud driving training system based on virtual reality and big data on-line analysis |
| CN108399820A (en) * | 2018-05-22 | 2018-08-14 | 北京超弦科技有限公司 | A kind of driving simulation system and drive simulating method |
| WO2019142949A1 (en) * | 2018-01-16 | 2019-07-25 | (주)피디케이리미티드 | Interactive virtual reality bicycle riding system and simulation method therefor |
| US10636295B1 (en) * | 2019-01-30 | 2020-04-28 | StradVision, Inc. | Method and device for creating traffic scenario with domain adaptation on virtual driving environment for testing, validating, and training autonomous vehicle |
| CN111409648A (en) * | 2019-01-08 | 2020-07-14 | 上海汽车集团股份有限公司 | Driving behavior analysis method and device |
-
2020
- 2020-09-30 CN CN202011054041.0A patent/CN112053433A/en active Pending
Patent Citations (8)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US5921780A (en) * | 1996-06-28 | 1999-07-13 | Myers; Nicole J. | Racecar simulator and driver training system and method |
| CN105448158A (en) * | 2015-11-30 | 2016-03-30 | 武汉华瑞密达虚拟现实技术研发有限公司 | Operation training system and method of special vehicles |
| CN107945604A (en) * | 2017-12-07 | 2018-04-20 | 中国航空工业集团公司西安航空计算技术研究所 | A kind of traffic safety immersion experiencing system based on virtual reality technology |
| CN108062875A (en) * | 2017-12-30 | 2018-05-22 | 上海通创信息技术股份有限公司 | A kind of cloud driving training system based on virtual reality and big data on-line analysis |
| WO2019142949A1 (en) * | 2018-01-16 | 2019-07-25 | (주)피디케이리미티드 | Interactive virtual reality bicycle riding system and simulation method therefor |
| CN108399820A (en) * | 2018-05-22 | 2018-08-14 | 北京超弦科技有限公司 | A kind of driving simulation system and drive simulating method |
| CN111409648A (en) * | 2019-01-08 | 2020-07-14 | 上海汽车集团股份有限公司 | Driving behavior analysis method and device |
| US10636295B1 (en) * | 2019-01-30 | 2020-04-28 | StradVision, Inc. | Method and device for creating traffic scenario with domain adaptation on virtual driving environment for testing, validating, and training autonomous vehicle |
Cited By (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN113761096A (en) * | 2021-09-03 | 2021-12-07 | 深圳清航智行科技有限公司 | Map making method and device and computer readable storage medium |
| CN113761096B (en) * | 2021-09-03 | 2024-03-08 | 深圳清航智行科技有限公司 | Map compiling method and device and computer readable storage medium |
| CN115983732A (en) * | 2022-12-23 | 2023-04-18 | 中国民航大学 | Airport land side traffic sand table scheduling system based on vehicle-road cooperation |
| CN115796397A (en) * | 2023-02-06 | 2023-03-14 | 中国民航大学 | Scene generation method and scene generation system for airport operation guarantee process |
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