CN115862391B - Airport road car following safety judging method oriented to intelligent networking environment - Google Patents

Abstract

The invention discloses a car-following safety evaluation method for airport roads facing an intelligent network connection environment, specifically: Step 1: Acquiring the real-time speed of the aircraft, the real-time speed of the guiding vehicle, and the real-time distance between the aircraft and the guiding vehicle ; Step 2: Carry out vehicle-machine information interaction based on the airport surface communication system and network environment; Step 3: Calculate the force F _VA of the safety potential field of the aircraft receiving the guidance vehicle; Step 4: According to the calculated F _VA security status at the time. The present invention proposes a method for judging the car-machine safety status by using the force of the safety potential field of the pilot vehicle, which not only takes into account the influence of the potential field caused by the pilot vehicle on the aircraft, but also measures the properties of different types of aircraft impact on safety assessments.

Description

A car-following safety evaluation method for airport roads oriented to intelligent network environment

technical field

The invention belongs to the technical field of airport intelligent management and control.

Background technique

With the continuous development of the civil aviation industry, the number of aircraft at the airport has increased rapidly, and more and more vehicles are serving the aircraft. The apron operation has become increasingly busy, and the complexity of the traffic operation on the scene has increased, resulting in high pressure on operational safety and road traffic. low efficiency. Therefore, the vehicle safety evaluation method developed for the airport scene is of great significance.

In the current vehicle-vehicle following traffic scene, vehicle-machine and vehicle-vehicle information interaction mainly relies on acousto-optic information, and acousto-optic information is easily affected by weather conditions and driver uncertainty. The method of visually judging whether the operation is safe by the operator is not accurate enough, which will lead to a series of problems such as misleading, missing quoting, and lost tracking. A large number of unsafe incidents at the airport show that only relying on the current surface monitoring equipment and safety evaluation methods is no longer sufficient to ensure the safe operation of the airport under complex conditions and large flow environments.

At this stage, although the relevant technologies of intelligent network connection have been gradually applied to airport traffic, their main application is still limited to the planning of passage paths for aircraft and ground vehicles on the airport roads, and the safe operation of the scene is guaranteed by planning conflict-free passage routes in advance . The application depth of this technology in real-time safety assessment and early warning is still insufficient, and there are still problems such as weak active coordination of active targets in the airport scene, information islands among operating entities, and poor self-adjustment capabilities.

Contents of the invention

Purpose of the invention: In order to solve the problems existing in the above-mentioned prior art, the present invention proposes a car-following safety evaluation method for airports and roads oriented to an intelligent network environment.

Technical solution: The present invention proposes a car-following safety evaluation method for airport roads and roads oriented to an intelligent network environment, which specifically includes the following steps:

Step 1: Obtain the real-time taxiing speed of the aircraft, the real-time speed of the guiding vehicle and the real-time distance between the aircraft and the guiding vehicle;

Step 2: Carry out vehicle-machine information interaction based on the airport surface communication system and the air-ground integrated network environment;

Step 3: Calculate the equivalent momentum M _A of the aircraft and the potential field |E _total | of the aircraft according to the real-time speed of the aircraft, the real-time speed of the guide vehicle and the real-time distance between the aircraft and the guide vehicle; according to MA _and |E _total |Calculate the force F _VA of the safety potential field of the pilot vehicle;

Step 4: According to the calculated F _VA , determine the safe state of the aircraft at this time, specifically: if F _VA <W ₁ , determine that the aircraft is in a safe state at this time, and if W ₁ <F _VA <W ₂ , then determine The aircraft is in a dangerous state at this time, and if F _VA >W ₂ , it is considered that the aircraft is in an extremely dangerous state at this time; both W ₁ and W ₂ are preset thresholds, and 0<W ₁ <W ₂ .

Further, in the step 3, the safe potential field force F _VA of the aircraft subjected to the guiding vehicle is calculated according to the following formula:

F _VA ＝|E _total |·M _A ·(1+DR _P )

Among them, _DRP represents the pilot risk coefficient of flying the aircraft.

Further, the equivalent momentum M _A of the aircraft is calculated based on the following assumptions:

Hypothesis 1: The equivalent momentum is related to the mass and taxiing speed of the aircraft

Hypothesis 2: When the mass of the aircraft is less than or equal to the preset mass threshold, the loss caused by the accident of the aircraft is proportional to the mass of the aircraft; when the mass of the aircraft is greater than the preset mass threshold, as the mass of the aircraft increases, the aircraft accident The rate of increase in losses from accidents has stabilized;

Hypothesis 3: The greater the speed of the aircraft, the greater the accident loss and the greater the rate of change of the accident loss;

The expression to calculate the equivalent momentum M _A of the aircraft is as follows:

Among them, m _A represents the mass of the aircraft, v _A represents the real-time speed of the aircraft, k ₁ and k ₂ are undetermined parameters.

Further, the expression of the potential field |E _total | subjected to by the aircraft is:

|E _total |＝(ω _V +ω _D ·DR _D )·|E _V |

Among them, ω _V is the vehicle potential field weight of the leading vehicle, ω _D represents the weight of the pilot potential field, DR _D represents the risk coefficient of the driver driving the leading vehicle, and E _V is the vehicle potential field generated by the leading vehicle; the expression of E _V for:

Among them, Δx is the real-time distance between the guiding vehicle and the aircraft; k ₃ , k ₄ , and k ₅ are undetermined parameters; M _V is the virtual mass of the guiding vehicle, and the expression of M _V is:

Among them, m _V represents the quality of the leading vehicle, v _V represents the real-time speed of the leading vehicle, and k ₆ , k ₇ , and k ₈ are parameters to be determined.

Further, the method also includes calculating the speed adjustment of the aircraft when the aircraft is in a relatively dangerous state or an extremely dangerous state for reference by the pilot, specifically:

Calculate the acceleration a _VA of the aircraft under the force of the safety potential field of the leading vehicle:

Among them, m _A represents the mass of the aircraft;

Computer car-following model:

Among them, a _max is the maximum acceleration of the aircraft taxiing process allowed by the airport scene, v _expect is the expected taxiing speed of the aircraft on the airport scene, is the actual acceleration of the aircraft, δ is the coefficient value of the difference between the current aircraft taxiing speed and the desired speed, v _A is the real-time speed of the aircraft;

according to Calculate the adjustment to the aircraft speed.

Beneficial effect:

(1) The present invention proposes a car-following safety evaluation method for airport roads and roads oriented to an intelligent network environment, which can effectively use the existing monitoring equipment on the airport scene, and use the intelligent network environment to improve the speed between operating subjects. information interaction and the information perception ability of the operating subject to the surrounding environment.

(2) The present invention proposes a method for judging the safety status of vehicle-machine following by using the force of the safety potential field of the pilot vehicle, which not only considers the influence of the potential field caused by the pilot vehicle on the aircraft, but also can measure different types of aircraft The influence of its own nature on safety evaluation.

(3) The present invention proposes the concept of equivalent momentum to describe the motion state of the aircraft, and the vehicle-machine car-following model based on the safety potential field can be derived through the calculated potential field force of the aircraft, so as to describe it more realistically The car-following behavior of a traffic individual with such a large mass and low taxiing speed is prevented.

Description of drawings

Fig. 1 is a flow chart of the method of the present invention.

Fig. 2 is a flow chart of the vehicle-machine car-following model based on the safety potential field.

Detailed ways

The drawings constituting a part of the present invention are used to provide a further understanding of the present invention, and the schematic embodiments and descriptions of the present invention are used to explain the present invention, and do not constitute an improper limitation of the present invention.

As shown in Figure 1, the present invention is based on the safety potential field theory, comprehensively considers the influence of multiple factors of the airport scene guidance vehicle, guidance vehicle driver, and aircraft driver on the car-following process, and combines the intervention of the network environment to reflect The safety status of two traffic individuals with huge momentum differences in the car-following process is revealed, and the interactive relationship of "human-vehicle-machine-network" can be predicted, and the change trend of safety risks can be predicted, and by performing the following steps S1-S5, the Make a safety judgment based on the safety status of the aircraft at a certain moment:

Step S1: Based on the vehicle/vehicle monitoring and identification equipment, obtain the basic information of the vehicle and machine, real-time motion status and location information of the aircraft and the guiding vehicle;

The direction of the basic information of the vehicle and machine described in step S1 includes the guide vehicle and the aircraft. For the guide vehicle, its basic information includes mass (unit: kg) and type (in this method, it is regarded as a standard vehicle), and for the aircraft , its basic information includes mass (unit: kg) and model type (manufacturer, model, engine model, etc.); the real-time information includes the real-time speed of the leading vehicle (unit: m/s) and the real-time Speed (unit: m/s); position information guides the distance between the leading vehicle and the aircraft (unit: m).

The machine/vehicle detection and identification equipment includes GPS positioning systems, motion detection radars, distance measuring radars or speed measuring sensors of aircrafts or guided vehicles.

Step S2: Carry out vehicle-machine information interaction based on the airport surface communication system and networked environment;

The airport scene networking environment and communication system described in step S2 includes various signal generators, signal receivers and routers at the airport scene. The information of the vehicle-machine information interaction in step S2 mainly includes the real-time speed (unit: m/s), mass (unit: kg), model type (especially the manufacturer, model, engine model, etc. ) and real-time distance (unit: m), these information are obtained by step S1.

Step S3: Based on the basic vehicle information, motion state information, location information, etc. obtained in steps S1 and S2, calculate the vehicle safety potential field force on the aircraft.

Step S31 calculates the virtual mass of the vehicle according to the mass, model type and real-time speed of the vehicle. In this embodiment, the leading car is regarded as a standard car, so the virtual mass M _V of the leading car in the car-following process is defined as:

In the formula, m _V and v _V respectively represent the mass (unit: kg) and speed (unit: m/s) of the leading vehicle during the car-following process, and k ₆ , k ₇ , and k ₈ are undetermined parameters. The virtual mass _MV of the guide vehicle can be determined by the severity of the accident or conflict between the guide vehicle and the aircraft on the airport scene and the consequences of the loss.

Step S32 calculates the vehicle potential field generated by the leading vehicle. Since the car and machine are considered to be moving on the same straight line during the car-following process, the relative position vectors of the two are always collinear with the direction of motion of the aircraft. Only its size needs to be considered, and the influence of the road boundary does not need to be considered; the road conditions of the airport scene It is generally better, and the influence factors of the road surface are not considered here, so the calculation formula of the vehicle potential field E _V generated by the guiding vehicle can be obtained:

In the formula, M _V is the virtual mass of the guide vehicle, Δx is the real-time distance between the guide vehicle and the aircraft (unit: m), v _V is the real-time speed of the guide vehicle (unit: m/s), k ₃ , k ₄ , k ₅ is an undetermined parameter.

Step S33 calculates the potential field experienced by the aircraft. During the following process of the aircraft, the influence of the leading vehicle and its pilot on the aircraft should be considered. In order to consider the influence of the pilot's psychophysiology, personal cognition, driving skills, and risk of violating regulations, the pilot risk coefficient is introduced, and the potential field E _total of the aircraft can be expressed as:

|E _total |＝ω _V ·|E _V |+ω _D ·|E _D |

＝ω _V ·|E _V |+ω _D ·DR _D ·|E _V |

＝(ω _V +ω _D ·DR _D )·|E _V |

In the formula, ω _V and ω _D represent the weights of the potential field of the guiding vehicle and the potential field of the driver, respectively, and DR _D represents the risk coefficient of the driver driving the guiding vehicle, and DR _D ∈ [0, 1], and the larger DR _D is, ω _D ·The larger the value _of DR _D , when ω _V and |E _V | , when the personal cognition is wider, the driving skills are higher, and the risk of violation is lower, the _DDR should be smaller.

Step S34 calculates the equivalent momentum of the aircraft. Different from the traditional car-following behavior, there are significant differences between the leading car and the aircraft in the process of car-following in terms of size, mass and acceleration. In order to truly describe the car-following behavior of a traffic individual with large mass and low taxiing speed such as an aircraft in the theory of road traffic flow, this embodiment introduces the concept of equivalent momentum to describe the motion state of the aircraft. The definition of equivalent momentum makes the following assumptions:

(1) The equivalent momentum is related to the mass and taxiing speed of the aircraft;

(2) When the mass of the aircraft is very small, the loss caused by the accident is small; when the mass of the object is large, the loss caused by the accident caused by the accident is huge; assuming that the mass of the aircraft reaches a certain level, as the mass of the aircraft increases, the loss caused by the accident The increase in accident losses is relatively small (that is, the increase in losses caused by accidents tends to be stable);

(3) The accident loss caused by the speed increase of the aircraft increases, and the change rate of the loss increases with the speed increase.

Based on the above three assumptions, the equivalent momentum definition formula is obtained:

In the formula, m _A and v _A are the mass (unit: kg) and speed (unit: m/s) of the aircraft during the car-following process respectively, _MA is the equivalent momentum of the aircraft, and k ₁ and k ₂ are undetermined parameter. The parameter calibration of the equivalent momentum can be calibrated according to the car-machine car-following model based on the safety potential field in the networked environment, which is derived from the force of the safety potential field of the pilot vehicle.

Step S35 calculates the force acting on the aircraft caused by the safety potential field caused by the guiding vehicle. From the electric field force formula F=Eq, wherein the potential field strength E in this formula is equivalently understood as the vehicle potential field E _V obtained by the front vehicle to the aircraft; The defined attributes in the vehicle field are defined as the equivalent momentum M _A of the aircraft and the pilot risk coefficient DR _P . The formula for calculating the safety potential field force F _VA of the pilot vehicle on the aircraft is:

F _VA ＝|E _total |·M _A ·(1+DR _P )

In the formula, |E _total | is the potential field caused by the pilot vehicle and the pilot vehicle, _MA is the equivalent momentum of the aircraft, and _DRP is the risk coefficient of the pilot who drives the aircraft, which is related to the psychological and physiological parameters of the pilot himself. , personal cognition, driving skills, etc.

Step S4: Based on the safety potential field force calculated in step S3, compare it with a pre-set threshold;

The threshold described in step S4 is calculated from historical accident data, and historical accidents can be defined by guiding the vehicle into a specific area around the aircraft. Since _FVA is a variable to measure the influence of the guiding vehicle on the aircraft, the larger the _FVA , the greater the influence of the guiding vehicle on the aircraft, and the more dangerous the aircraft's safety status at this time, and vice versa, the safer the aircraft. By setting thresholds W ₁ , W ₂ (0<W ₁ <W ₂ ) to judge the relationship between _FVA and W ₁ , W ₂ , data preparation is made for the next step of measuring the safety status of the aircraft. Different types of aircraft may have different threshold settings because of their different mass, dynamic characteristics, engine attraction or the wake effect caused by them.

Step S5: Based on the comparison result obtained in step S4, judge the safe state of the aircraft at this time, and based on the network environment, feed back the judgment result to the aircraft and the guidance vehicle.

Step S5 evaluates the safety status of the aircraft on the basis of the _FVA obtained in step S4 and the magnitude relationship between W ₁ and W ₂ . When F _VA < W ₁ , the aircraft is considered to be in a safe state; when W ₁ < F _VA < W ₂ , the aircraft is considered to be in a dangerous state; when F _VA > W ₂ , the aircraft is considered to be in an extreme state. dangerous state. Step S5 feeds back the safety status of the aircraft to the pilot and the driver of the guidance vehicle through the networked environment and communication system described in step S2, so as to provide decision-making assistance for the pilot's next action.

In the process of safety judgment, combined with the results of step S3, the car-following model based on the safety potential field in the networked environment can also be derived. The derivation process is as follows:

As shown in Figure 2, firstly, from the safety potential field force of the guide vehicle to the aircraft calculated in step S3, the acceleration a _VA of the potential field force experienced by the aircraft is calculated:

In the formula, m _A is the mass of the aircraft itself, |E _total | is the potential field caused by the pilot vehicle and the pilot of the pilot vehicle, _MA is the equivalent momentum of the aircraft, and _DRP is the risk factor of the pilot.

Car-following model based on security potential field in network environment:

In the formula Indicates the actual acceleration of the aircraft obtained from the car-following model, a _max is the maximum acceleration of the aircraft taxiing process allowed by the airport scene, δ is the coefficient value of the difference between the current aircraft taxiing speed and the expected speed, v _expect is the aircraft at the airport The expected taxiing speed of the scene, ω _V , ω _D represent the weights of the potential field of the leading vehicle and the potential field of the driver, respectively, D _D , D _P are the risk coefficient of the driver and the pilot, respectively, m _V , m _A , v _V , v _A represents the mass and speed of the leading vehicle and the aircraft respectively, Δx represents the distance between the vehicle and the aircraft, and k ₁ , k ₂ , k ₃ , k ₄ , k ₅ , k ₆ , k ₇ , and k ₈ all represent undetermined parameters. The flow chart of the derivation of the car-following model is shown in Figure 2. Adjust the speed of the plane according to the speed of the car.

Using the vehicle-machine car-following model based on the safety potential field in this networked environment, the relationship between the vehicle-machine car-following process can be described, and the adjustment amount of the aircraft speed can also be calculated when the aircraft is in a dangerous state.

Claims (1)

1. An airport road car following safety judging method facing intelligent network environment is characterized by comprising the following steps:

step 1: acquiring the real-time speed of the aircraft, the real-time speed of the guide vehicle and the real-time distance between the aircraft and the guide vehicle;

step 2: based on the airport scene communication system and the Internet environment, vehicle-machine information interaction is carried out;

step 3: calculating the equivalent momentum M of the airplane according to the real-time speed of the airplane, the real-time speed of the guided vehicle and the real-time distance between the airplane and the guided vehicle _A Potential field |E to which an aircraft is subjected _total I (I); according to M _A And |E _total Computing the safety potential field acting force F of the guided vehicle of the aircraft _VA ；

Step 4: according to the calculated F _VA The safety state of the aircraft at the moment is judged, specifically: if F _VA ＜W ₁ If the aircraft is determined to be in a safe state at the moment, if W ₁ ＜F _VA ＜W ₂ If F, determining that the aircraft is in a dangerous state _VA ＞W ₂ Judging that the aircraft is in an extremely dangerous state at the moment; w (W) ₁ And W is ₂ Are all preset threshold values, and 0 is less than W ₁ ＜W ₂ ；

In the step 3, the safety potential field acting force F of the guided vehicle of the airplane is calculated according to the following formula _VA ：

F _VA ＝|E _total |·M _A ·(1+DR _P )

Wherein DR _P Representing a pilot risk factor for piloting the aircraft;

calculating the equivalent momentum M of an aircraft based on the following assumption _A ：

Suppose 1: the equivalent momentum is related to the mass of the aircraft and the taxiing speed;

suppose 2: when the mass of the aircraft is smaller than or equal to a preset mass threshold value, the loss caused by the accident of the aircraft is in direct proportion to the mass of the aircraft; when the aircraft mass is larger than a preset mass threshold value, the increase of the loss caused by the accident of the aircraft tends to be stable along with the increase of the aircraft mass;

suppose 3: an increase in the speed of the aircraft can result in an increase in the loss due to the accident and an increase in the rate of change of the loss due to the accident;

calculating the equivalent momentum M of an aircraft _A The expression of (2) is as follows:

wherein m is _A Representing the mass of an aircraft, v _A Representing real-time speed, k of aircraft ₁ 、k ₂ Are all undetermined parameters;

potential field |E to which the aircraft is subjected _total The expression of i is:

|E _total |＝(ω _V +ω _D ·DR _D )·|E _V |

wherein omega _V To guide the vehicle potential field weight, ω _D Representing pilot potential field weights, DR _D Representing a risk factor of a driver driving the lead vehicle, E _V A vehicle potential field generated for the lead vehicle; e (E) _V The expression of (2) is:

wherein Δx is the real-time separation between the lead vehicle and the aircraft; k (k) ₃ 、k ₄ 、k ₅ Are all undetermined parameters; m is M _V To guide the virtual mass of the vehicle, M _V The expression of (2) is:

wherein m is _V Indicating the mass of the guided vehicle, v _V Representing the real-time speed, k, of the lead vehicle ₆ 、k ₇ 、k ₈ Are all undetermined parameters;

the method further comprises calculating a speed adjustment of the aircraft for reference by the pilot when the aircraft is in a dangerous or extremely dangerous state, in particular:

calculating acceleration a of an aircraft under the action force of a safety potential field of a guided vehicle _VA ：

Wherein m is _A Representing the mass of the aircraft;

computer car follows the model:

wherein a is _max Maximum acceleration, v, of the aircraft taxiing procedure allowed for airport scenes _expect For a desired taxiing speed of an aircraft on an airport scene,is the actual acceleration of the aircraft, delta is the currentCoefficient value of the difference between the taxiing speed and the desired speed, v _A Is the real-time speed of the aircraft;

according toAnd calculating to obtain the adjustment quantity of the aircraft speed.