KR102610760B1 - Apparatus for controlling driving of a vehicle, a system having the same and a method thereof - Google Patents

Abstract

The present invention relates to a vehicle driving control device, a system including the same, and a method thereof. The vehicle driving control device according to an embodiment of the present invention is a vehicle driving control device based on vehicle sensor signals in an unstable driving situation where the lateral behavior of the vehicle is greater than a predetermined standard value. A processor that estimates the heel angle of the road surface and determines the condition of the road surface based on the estimated heel angle value; and a storage unit that stores the results of determining the heel angle and road surface condition estimated by the processor.

Description

Vehicle driving control device, system including the same, and method thereof {Apparatus for controlling driving of a vehicle, a system having the same and a method thereof}

The present invention relates to a vehicle driving control device, a system including the same, and a method thereof, and more specifically, to a technology for estimating a road surface slope angle and determining a banked road surface in an unstable driving situation.

In an intelligent vehicle control system, it is necessary to estimate the road surface slope angle and determine the banked road surface to control the vehicle.

Previously, in order to reduce road surface slope angle and bank judgment errors, it was possible to estimate the road surface slope angle and determine the bank surface when the vehicle was stopped or in a static driving situation.

Accordingly, in unstable driving situations where the vehicle's lateral movement is large, the error is large when estimating the lateral slope angle of the road surface or determining the banked road surface, and if these error values are used for vehicle control, the risk of an accident may occur.

Accordingly, a method has been developed to estimate the road surface inclination angle or determine the banked road surface by additionally using a separate roll rate sensor. However, roll rate sensors are used limitedly in luxury vehicles, so roll rate sensors are additionally used in general vehicles. There is a problem in that the cost burden increases when equipped.

An embodiment of the present invention provides a vehicle driving control device that accurately estimates the heel angle of the road surface using vehicle sensors in an unstable vehicle driving situation and accurately determines the state of the road surface based on the heel angle of the road surface, a system including the same, and We would like to provide that method.

The technical problems of the present invention are not limited to the technical problems mentioned above, and other technical problems not mentioned will be clearly understood by those skilled in the art from the description below.

The vehicle driving control device according to an embodiment of the present invention estimates the heel angle of the road surface based on vehicle sensor signals in an unstable driving situation where the lateral behavior of the vehicle is greater than a predetermined standard value, and determines the state of the road surface based on the estimated heel angle value. a processor that judges; and a storage unit that stores the results of determining the heel angle and road surface condition estimated by the processor.

In one embodiment, the processor may calculate a heel angle by applying it to a kinematic model based on the vehicle sensor signal.

In one embodiment, the processor may include calculating a weight according to the driving state using a centrifugal acceleration map or a switching condition of the yaw rate error.

In one embodiment, the processor calculates a weight based on a centrifugal acceleration map, calculates a weight based on a switching condition of the yaw rate error, and calculates the weight calculated by the centrifugal acceleration map and the yaw rate error. It may include selecting the minimum value among switching condition-based weights as the weight.

In one embodiment, the processor defines the difference between the steering angle yaw rate corresponding to the steering angle input and the yaw rate value measured by the vehicle sensor as the yaw rate error when the vehicle behavior is stable, and the yaw rate error is determined in advance. This may include setting the weight to 0 if a set decision condition is satisfied, and setting the weight to 1 if the decision condition is not satisfied.

In one embodiment, when the weight is 1, the processor may include correcting the heel angle using the heel angle calculated based on the kinematic model.

In one embodiment, the processor may include calculating a smaller value of the lateral tilt angle calculated based on the kinematic model and the corrected lateral tilt angle as an estimated lateral tilt angle.

In one embodiment, the processor determines whether the road surface is flat or banked based on the estimated heel angle, and if it is banked, at least one of straight bank, forward bank turn, and reverse bank turn. It may include determining whether one is aware of it or not.

In one embodiment, the processor determines that the state of the road surface is flat if the estimated lateral slope angle exceeds a first predetermined standard value, and if the estimated lateral slope angle is less than a second predetermined standard value, the state of the road surface is determined to be flat. This may include determining that it is a bank load.

In one embodiment, after determining that the state of the road surface is a bank load, the processor determines that the road surface is turning when the yaw rate sensor value of the vehicle sensor signal exceeds a predetermined third reference value, and the processor determines that the road surface is turning. If the yaw rate sensor value is less than a predetermined fourth standard value, it may include determining that the state of the road surface is bank straight.

In one embodiment, when it is determined that the road surface is turning, the processor performs a reverse bank turn if the bank direction of the road surface and the turning direction of the road surface are different, and a forward bank turn if the bank direction and the turning direction are the same. It may involve making judgments.

In one embodiment, the processor may include estimating the lateral speed change rate and steering angle yaw rate based on the steering angle among the vehicle sensor signals, and estimating the vehicle roll angle based on the lateral acceleration among the vehicle sensor signals. .

In one embodiment, the processor may include calculating the heel angle by applying lateral acceleration, yaw rate, vehicle speed, the estimated lateral speed change rate, and the vehicle roll angle among the vehicle sensor signals to the kinematic model. .

A vehicle system according to an embodiment of the present invention includes a sensing module that senses vehicle driving information; In an unstable driving situation where the lateral behavior of the vehicle is greater than a predetermined standard value, a vehicle driving control device that estimates the lateral tilt angle of the road surface based on vehicle sensor signals and determines the state of the road surface based on the estimated lateral tilt angle value; may be included. .

In one embodiment, the sensing module includes a longitudinal and lateral acceleration sensor that senses the longitudinal and lateral acceleration of the vehicle; A yaw rate sensor that senses the yaw rate of the vehicle; A steering angle sensor that senses the steering angle of the vehicle; and a wheel speed sensor that senses the vehicle speed of the vehicle.

In one embodiment, the vehicle driving control device calculates the heel angle of the road surface based on a kinematic model, and calculates the heel angle based on a weight calculated based on the centrifugal acceleration map and the switching condition of the yaw rate error. It may include correcting and calculating the smaller of the lateral tilt angle calculated based on the kinematic model and the corrected lateral tilt angle as an estimated lateral tilt angle.

A vehicle driving control method according to an embodiment of the present invention includes the steps of: estimating a lateral tilt angle of a road surface based on a vehicle sensor signal received from the sensing module in an unstable driving situation where the lateral behavior of the vehicle is greater than a predetermined standard value; And it may include determining the condition of the road surface based on the estimated heel angle estimate.

In one embodiment, the step of estimating the heel angle of the road surface includes calculating the heel angle by applying it to a kinematic model based on the vehicle sensor signal; calculating weights according to the driving state using a switching condition of the centrifugal acceleration map or yaw rate error; correcting the calculated heel angle using the weight; and calculating a smaller value of the lateral tilt angle calculated based on the kinematic model and the corrected lateral tilt angle as an estimated lateral tilt angle.

In one embodiment, the step of determining the state of the road surface includes determining whether the state of the road surface is at least one of flat, straight bank, forward bank turn, and reverse bank turn based on the heel angle estimate. can do.

In one embodiment, if the estimated heel angle value exceeds a first predetermined reference value, determining the state of the road surface as flat; determining that the state of the road surface is a bank load if the estimated lateral slope is less than a second predetermined reference value; determining that the road surface is turning when a yaw rate sensor value among the vehicle sensor signals exceeds a third predetermined reference value; If the yaw rate sensor value is less than a predetermined fourth standard value, determining that the road surface is in a straight bank state; and when it is determined that the road surface is turning, determining a reverse bank if the bank direction and the turning direction are different, and a forward bank if the bank direction and the turning direction are the same.

This technology can accurately estimate the heel angle of the road surface using vehicle sensors in unstable vehicle driving situations and accurately determine the condition of the road surface based on the heel angle of the road surface.

In addition, various effects that can be directly or indirectly identified through this document may be provided.

1 is a block diagram showing the configuration of a vehicle system including a vehicle driving control device according to an embodiment of the present invention.
Figure 2 is a flowchart showing a vehicle driving control method according to an embodiment of the present invention.
Figure 3 is an example diagram of a signal input to a vehicle driving control device according to an embodiment of the present invention.
Figure 4 is a diagram for explaining a method of calculating a lateral tilt angle estimate value of a vehicle driving control device according to an embodiment of the present invention.
Figure 5 is an exemplary diagram when estimating a lateral tilt angle based on a lateral acceleration kinematic model of a vehicle driving control device according to an embodiment of the present invention.
Figure 6 is an example diagram of a centrifugal acceleration map for calculating weights according to an embodiment of the present invention.
Figure 7 is a schematic diagram for selecting an estimated lateral acceleration value according to an embodiment of the present invention.
Figure 8 is a diagram for explaining a method for determining a bank road surface according to an embodiment of the present invention.
Figure 9 is a schematic diagram for determining a bank road surface according to an embodiment of the present invention.
Figure 10 is a graph for explaining the accuracy of slant angle estimation in an unstable driving situation according to an embodiment of the present invention.
Figure 11 is a graph for explaining the heel angle estimation performance of a reverse bank turning section with the same heel angle yaw rate sign in a stable driving situation according to an embodiment of the present invention.
Figure 12 is a graph for explaining the heel angle estimation performance of a forward bank turning section where the heel angle and yaw rate sign are the same in a stable driving situation according to an embodiment of the present invention.
Figure 13 shows a computing system according to one embodiment of the present invention.

Hereinafter, some embodiments of the present invention will be described in detail through illustrative drawings. When adding reference numerals to components in each drawing, it should be noted that identical components are given the same reference numerals as much as possible even if they are shown in different drawings. Additionally, when describing embodiments of the present invention, if detailed descriptions of related known configurations or functions are judged to impede understanding of the embodiments of the present invention, the detailed descriptions will be omitted.

In describing the components of the embodiment of the present invention, terms such as first, second, A, B, (a), and (b) may be used. These terms are only used to distinguish the component from other components, and the nature, sequence, or order of the component is not limited by the term. Additionally, unless otherwise defined, all terms used herein, including technical or scientific terms, have the same meaning as generally understood by a person of ordinary skill in the technical field to which the present invention pertains. Terms defined in commonly used dictionaries should be interpreted as having a meaning consistent with the meaning in the context of the related technology, and should not be interpreted in an ideal or excessively formal sense unless explicitly defined in the present application. No.

The present invention estimates the lateral slope angle (bank angle) of the road surface based on vehicle sensor signals in at least one of the vehicle's static driving situation, dynamic driving situation, and unstable driving situation, and determines that the road surface is flat with a bank angle of 0. A technology is disclosed that determines whether the road is a bank road, which is a road that slopes in the transverse direction, and, if the road surface is a bank road, determines whether to proceed straight on the bank road, make a forward bank turn, or make a reverse bank turn. The static driving situation of the vehicle is when the lateral acceleration is less than a predetermined first threshold, the dynamic driving situation is when the lateral acceleration is greater than the predetermined first threshold and less than the second threshold, and the unstable driving situation is when the lateral acceleration is greater than the second threshold, that is, the vehicle The lateral behavior of is very large, indicating an unstable state.

Hereinafter, embodiments of the present invention will be described in detail with reference to FIGS. 1 to 13.

1 is a block diagram showing the configuration of a vehicle system including a vehicle driving control device according to an embodiment of the present invention.

Referring to FIG. 1, a vehicle system according to an embodiment of the present invention includes a vehicle driving control device 100, a sensing module 200, a steering control device 300, a braking control device 400, and an engine control device 500. ) may include.

In an unstable driving situation where the lateral behavior of the vehicle is greater than a predetermined standard value, the vehicle driving control device 100 estimates the lateral slope angle (bank angle) of the road surface based on the vehicle sensor signal, and determines the state of the road surface based on the estimated lateral slope angle. You can judge. At this time, the lateral inclination angle of the road surface is the degree to which the road surface is inclined in the lateral direction, and the state of the road surface includes a flat surface or a bank road with an inclined road surface. At this time, when the road surface is a bank road, the vehicle driving control device 100 can determine whether the road is straight ahead, forward bank, or reverse bank.

The vehicle driving control device 100 may include a communication unit 110, a storage unit 120, and a processor 130.

The communication unit 110 is a hardware device implemented with various electronic circuits to transmit and receive signals through wireless or wired connections. In the present invention, in-vehicle communication is performed through CAN communication, LIN communication, etc. And, communication with the sensing module 200 can be performed.

The storage unit 120 may store the sensing results of the sensing module 200 and the heel angle and road surface judgment results estimated by the processor 130. The storage unit 120 has a flash memory type, a hard disk type, a micro type, and a card type (e.g., a Secure Digital Card (SD Card) or an eXtream Digital Card (XD Card). Memory such as RAM (Random Access Memory), SRAM (Static RAM), ROM (Read-Only Memory), PROM (Programmable ROM), EEPROM (Electrically Erasable PROM), and magnetic memory (MRAM) , Magnetic RAM, magnetic disk, and optical disk type memory.

The processor 130 may be electrically connected to the communication unit 110, the storage unit 120, etc., may electrically control each component, and may be an electrical circuit that executes software instructions, which will be described later. A variety of data processing and calculations can be performed.

In an unstable driving situation where the lateral behavior of the vehicle is greater than a predetermined standard, the processor 130 may estimate the lateral tilt angle of the road surface based on a vehicle sensor signal and determine the state of the road surface based on the estimated lateral tilt angle.

The processor 130 can calculate the heel angle by applying it to the kinematic model based on the vehicle sensor signal.

The processor 130 may calculate weights according to the driving state using the centrifugal acceleration map or the switching condition of the yaw rate error.

The processor 130 calculates the weight based on the centrifugal acceleration, calculates the weight based on the switching condition of the yaw rate error, and calculates the minimum value among the weight calculated by the centrifugal acceleration map and the weight based on the switching condition of the yaw rate error. You can select by weight.

The processor 130 defines the difference between the steering angle yaw rate corresponding to the steering angle input and the yaw rate value measured by the vehicle sensor as the yaw rate error when the vehicle behavior is stable, and when the yaw rate error satisfies a predetermined judgment condition, If the weight is set to 0 and the judgment condition is not satisfied, the weight can be set to 1.

When the weight is 1, the processor 130 can trust the heel angle calculated based on the kinematic model.

The processor 130 may correct the calculated lateral tilt angle using the weight.

The processor 130 may calculate the smaller of the lateral tilt angle calculated based on the kinematic model and the corrected lateral tilt angle as the estimated lateral tilt angle.

The processor 130 may determine whether the road surface is at least one of flat, straight bank, forward bank turn, and reverse bank turn based on the estimated heel angle.

The processor 130 determines that the road surface is flat when the estimated lateral slope angle exceeds a predetermined first standard value (e.g., 5 degrees), and when the estimated lateral slope angle exceeds a predetermined second standard value (e.g., 3 degrees), the processor 130 determines that the road surface is flat. If a predetermined time (e.g., 1 second) is maintained, the state of the road surface can be determined to be a bank load.

The processor 130 determines that the state of the road surface is a bank load, and then determines that the road surface is turning when the yaw rate sensor value among the vehicle sensor signals exceeds a predetermined third standard value (e.g., 3 degrees), and the yaw rate sensor If the value remains below the predetermined fourth standard value (e.g., 2 degrees) for a predetermined time (e.g., 2 seconds), it can be determined that the road surface condition is banked straight.

When it is determined that the road surface is turning, the processor 130 may determine a reverse bank turn if the bank direction and the turning direction are different, and a forward bank turn if the bank direction and the turning direction are the same. For example, if the bank direction of the road surface is lateral to the right (slanted to the right) and the turning direction of the road surface is left, it can be judged as a reverse bank turn.

The processor 130 may estimate the lateral speed change rate and steering angle yaw rate based on the steering angle among the vehicle sensor signals, and estimate the vehicle roll angle based on the lateral acceleration among the vehicle sensor signals.

The processor 130 may calculate the lateral tilt angle by applying lateral acceleration, yaw rate, vehicle speed, the estimated lateral speed change rate, and vehicle roll angle among vehicle sensor signals to the kinematic model.

The sensing module 200 includes a longitudinal/lateral acceleration sensor 210, a yaw rate sensor 220, a steering angle sensor 230, and a wheel speed sensor 230. The longitudinal/lateral acceleration sensor 210 senses the longitudinal and lateral acceleration of the vehicle. The yaw rate sensor 220 senses the yaw rate of the vehicle, and the steering angle sensor 230 senses the steering angle of the vehicle. The wheel sensor 230 senses the vehicle speed.

Hereinafter, a vehicle driving control method according to an embodiment of the present invention will be described in detail with reference to FIG. 2. Figure 2 is a flowchart for explaining a vehicle driving control method according to an embodiment of the present invention.

Hereinafter, it is assumed that the vehicle driving control device 100 of FIG. 1 performs the process of FIG. 2. Additionally, in the description of FIG. 2, operations described as being performed by the device may be understood as being controlled by the processor 130 of the vehicle driving control device 100.

Referring to FIG. 2, the vehicle driving control device 100 receives a vehicle sensor signal from the sensing module 200 (S101). Additionally, the vehicle driving control device 100 may estimate the lateral speed change rate, steering angle yaw rate, vehicle roll angle, etc. required for estimating the lateral tilt angle using vehicle sensor signals. At this time, the vehicle driving control device 100 applies the steering angle, front wheel cornering constant, rear wheel cornering constant, and vehicle specifications to the bicycle model to estimate the lateral speed change rate and steering angle yaw rate, and multiplies the lateral acceleration by the roll gain to estimate the vehicle roll angle. can do. Additionally, the vehicle driving control device 100 may receive vehicle parameters as well as vehicle sensor signals.

The vehicle driving control device 100 calculates the heel angle of the road surface using vehicle sensor signals, vehicle parameters, and estimation signals (S102).

At this time, the vehicle driving control device 100 may calculate the lateral tilt angle based on the kinematic model, and the lateral tilt angle calculated based on the kinematic model may have low reliability in unstable driving situations.

Accordingly, the vehicle driving control device 100 may calculate a weight to correct the heel angle in an unstable driving situation and correct the heel angle using the weight (S103). At this time, the vehicle driving control device 100 calculates the weight based on the centrifugal acceleration, calculates the weight based on the switching condition of the yaw rate error, and calculates the weight calculated by the centrifugal acceleration map and the weight based on the switching condition of the yaw rate error. The minimum value can be selected as the weight. Additionally, the vehicle driving control device 100 reflects the weight to the calculated lateral tilt angle and corrects the lateral tilt angle.

The vehicle driving control device 100 selects and calculates the smaller value of the lateral tilt angle calculated based on the kinematic model and the corrected lateral tilt angle as the estimated lateral tilt angle of the road surface (S104).

The vehicle driving control device 100 determines whether there is a bank load using the estimated lateral tilt angle (S106). In other words, if the estimated lateral slope angle exceeds the first predetermined standard value (e.g., 5 degrees), the state of the road surface is judged to be flat, and if the estimated lateral slope angle exceeds the predetermined second standard value (e.g., 3 degrees), the predetermined time ( If it is maintained (e.g., 1 second), the state of the road surface can be determined to be a bank load.

When the road surface is banked, the vehicle driving control device 100 can determine whether to turn (S107). The vehicle driving control device 100 determines that the state of the road surface is a bank load, and then determines that the road surface is turning when the yaw rate sensor value among the vehicle sensor signals exceeds a predetermined third reference value (e.g., 3 degrees), If the yaw rate sensor value remains below the fourth predetermined standard value (e.g., 2 degrees) for a predetermined time (e.g., 2 seconds), it can be determined that the road surface is moving straight.

If it is determined that the road surface is turning, the vehicle driving control device 100 may determine the forward/reverse bank (S108). When it is determined that the road surface is turning, the vehicle driving control device 100 may determine a reverse bank if the bank direction and the turning direction are different, and a forward bank if the bank direction and the turning direction are the same. For example, if the bank direction of the road surface is lateral to the right (direction inclined to the right) and the turning direction of the road surface is left, it can be determined to be a reverse bank turn.

Accordingly, the vehicle driving control device 100 can output the bank road surface determination result to the in-vehicle intelligent system (S109).

Figure 3 is an example diagram of a signal input to a vehicle driving control device according to an embodiment of the present invention.

, 조향각 요레이트 r_steer을 추정한다. 아래 수학식 1은 바이시클 모델의 예이다.Referring to FIG. 3, the vehicle driving control device 100 applies the steering angle SAS, front wheel cornering constant C _f , rear wheel cornering constant C _r , and vehicle specifications to the bicycle model to obtain vehicle speed Vx and lateral speed change rate.

, estimate the steering angle and yaw rate r _steer . Equation 1 below is an example of a bicycle model.

Additionally, the vehicle driving control device 100 estimates the vehicle roll angle using the lateral acceleration a _{y and snr} . At this time, the vehicle roll angle can be estimated as shown in Equation 2 below.

)과 조향각 요레이트(r_steer)는 바이시클 모델을, 차량 롤각은 횡가속도에 롤 게인을 곱한 추정값을 사용한다.That is, the rate of change in lateral speed (

) and the steering angle yaw rate (r _steer ) use a bicycle model, and the vehicle roll angle uses an estimate obtained by multiplying the lateral acceleration by the roll gain.

At this time, the vehicle driving control device 100 controls the lateral acceleration a _{y, snr} , yaw rate r _steer , steering angle SAS, front wheel cornering constant C _f , rear wheel cornering constant C _r , vehicle specifications (weight m, wheelbase L _f L _{r ,} moment of inertia I _z , steering ratio R _sg , etc.) are received.

That is, the vehicle driving control device 100 uses lateral acceleration, yaw rate, and steering angle as vehicle sensor signals, front and rear wheel cornering constants, and vehicle specifications as vehicle parameters, and vehicle speed, steering angle speed, lateral speed change rate, and steering angle yaw rate as estimation signals. Vehicle roll angle can be used.

Figure 4 is a diagram for explaining a method of calculating a lateral tilt angle estimate value of a vehicle driving control device according to an embodiment of the present invention. That is, in FIG. 4, a method of estimating the road surface heel angle in an unstable driving situation by determining the driving state from the vehicle sensor is described.

을 산출할 수 있다.Referring to FIG. 4, the vehicle driving control device 100 calculates the estimated lateral tilt angle using the lateral acceleration kinematic model as shown in Equation 3 below.

can be calculated.

Here, g is the acceleration of gravity.

However, in unstable driving situations, the lateral slope angle estimate calculated using the lateral acceleration kinematic model may have low reliability.

을 보정하여 아래 수학식 4와 같이 보정된 횡경사각 추정값

을 산출한다. Accordingly, the vehicle driving control device 100 calculates the model weight (K) based on the centrifugal acceleration map. Then, the vehicle driving control device 100 reflects the calculated model weight (K) to calculate the heel angle estimate.

The estimated value of the heel angle corrected as shown in Equation 4 below:

Calculate .

)을 신뢰하지 않으며, 가중치 값 K = 1일 경우, 횡가속도 키네마틱 모델 횡경사각(

)을 신뢰한다.When the weight value K = 0, the vehicle driving control device 100 sets the lateral acceleration kinematic model lateral tilt angle (

) is not trusted, and if the weight value K = 1, the lateral acceleration kinematic model lateral tilt angle (

) trust.

과 보정된 횡경사각 추정값

중 하나를 선택한다. After this, the vehicle driving control device 100 calculates the lateral tilt angle estimate value.

and corrected heel angle estimate

Choose one of:

Figure 5 is an exemplary diagram when estimating a lateral tilt angle based on a lateral acceleration kinematic model of a vehicle driving control device according to an embodiment of the present invention.

을 산출하는 경우, 차량 횡거동이 과다한 불안정한 주행상황에서 정확도가 매우 취약하다. 이는 수학식 3에 포함된 횡속변화율

항이 바이시클 모델 등의 방법으로 추정하기 어려운 값이기 때문이며

추정오차가 수학식 3에 반영됨으로써, 수학식 3을 단순 사용하여 노면 횡 경사각을 추정할 경우 불안정한 주행상황에서 실제보다 횡경사 각이 과대하게 추정될 수 있다.Referring to FIG. 5, the estimated lateral slope angle value is estimated using the lateral acceleration kinematic model as shown in Equation 3 below.

When calculating , accuracy is very weak in unstable driving situations where the vehicle's lateral movement is excessive. This is the lateral speed change rate included in Equation 3

This is because the term is a value that is difficult to estimate using methods such as the bicycle model.

As the estimation error is reflected in Equation 3, if the road surface lateral slope angle is estimated simply using Equation 3, the lateral slope angle may be overestimated compared to the actual one in unstable driving situations.

501 in FIG. 5 represents the difference between the true value and the estimated value of the lateral speed change rate, and 502 represents the difference between the true value and the estimated value of the lateral inclination angle.

In numbers 501 and 502 of FIG. 5, in a stable driving situation (A) where the lateral speed change rate is small, the lateral inclination angle estimate value is reliable, but when the vehicle operates in unstable behavior with a large lateral speed change rate, the lateral inclination angle estimate value is overestimated in proportion to the lateral speed change rate. You can.

에 민감하게 정확도가 변화한다. 따라서, 차량 주행 제어 장치(100)는 주행상태가 불안정한 횡거동량 과대 상황에서는 모델 가중치를 낮추어 연산된 횡경사 각(

)값을 신뢰하기 어렵다. 횡거동량(

)은 보편 양산 적용되고 있는 차량센서로 추정 또는 바이시클 모델 등으로부터 정확히 예측하기 어렵다. 따라서 차량이 스핀아웃(Spin Out) 되는 오버스티어링(Over steering)과 같은 횡거동량 과대 상황은, 필시 차량의 과대한 요 거동을 동반하기에 횡 거동과 요 거동의 비례 관계로부터, 요 거동의 불안정 함을 횡거동 과대 상황으로 간접 판단할 수 있다. 요 거동 불안정 상태는 보편 양산 적용되고 있는 요레이트 센서를 이용하여 쉽게 예측이 가능하며, ① 원심가속도(요레이트X차속) 맵과, ② 요레이트 에러(조향각 요레이트와 요레이트 센서 값 차이)의 스위칭 조건의 최소값을 사용하여 주행 상태에 따라 가중치를 연산 반영할 수 있다. In this way, the lateral acceleration kinematic model, which is the basis for lateral tilt angle estimation, is the lateral movement amount of the vehicle.

Accuracy changes sensitively. Therefore, the vehicle driving control device 100 lowers the model weight in a situation where the driving condition is unstable and the lateral movement amount is excessive, and the calculated lateral tilt angle (

) It is difficult to trust the value. Lateral movement amount (

) is a vehicle sensor that is widely used in mass production and is difficult to accurately predict from estimates or bicycle models. Therefore, situations where the amount of lateral movement is excessive, such as oversteering, which causes the vehicle to spin out, is likely to be accompanied by excessive yaw behavior of the vehicle, and from the proportional relationship between lateral and yaw behavior, the yaw behavior becomes unstable. can be indirectly judged as a situation of excessive lateral behavior. Unstable yaw behavior can be easily predicted using yaw rate sensors that are widely used in mass production, and ① centrifugal acceleration (yaw rate Using the minimum value of the switching condition, weights can be calculated and reflected according to the driving state.

That is, the vehicle driving control device 100 determines both the centrifugal acceleration map and the switching conditions of the yaw rate error, and models the smaller value of the weight calculated by the centrifugal acceleration map and the weight calculated by the switching condition of the yaw rate error. Select by weight (K).

Figure 6 is an example diagram of a centrifugal acceleration map for calculating weights according to an embodiment of the present invention.

The centrifugal acceleration range in which a vehicle can drive stably is largely dependent on road surface friction. In the case of a general road surface (high-friction road surface) with a friction coefficient μ = 0.8, vehicle behavior is stable up to a centrifugal acceleration of 0.7G, but there is a significant tendency for instability thereafter. increases. From this fact, it is possible to use a centrifugal acceleration map that has an inverse relationship with the model weight (K).

Referring to Figure 6, the ] has map data.

Meanwhile, in the case of a low-friction road surface (μ = 0.3) where oversteering (OS) or understeering (US) tendencies easily occur, the centrifugal acceleration section with stable behavior is up to 0.3G, so in this case, the centrifugal acceleration map in ① is There is no correction effect. Therefore, when the vehicle behavior is stable, the difference between the steering angle yaw rate () corresponding to the steering angle input and the sensor yaw rate value is defined as the yaw rate error, and the model weight is determined using the switching conditions below.

The conditions for determining excessive yaw rate error are as shown in Equation 5 below.

)과 같이, 횡경사 각 40deg 뱅크로드 주행시 발생하는 에러량을 에러 판단 임계치로 사용할 수 있다.The steering angle yaw rate (r _steer ) used is the yaw rate corresponding to the steering input when driving on flat ground, so on a lateral slope, an error occurs depending on the lateral slope of the road surface. Therefore, considering the actual road lateral slope and allowable values, the above formula (

), the amount of error that occurs when driving on a bank road with a lateral slope of 40deg can be used as the error judgment threshold.

The vehicle driving control device 100 determines whether the vehicle behavior is stabilized after the yaw rate error is excessive.

That is, the vehicle driving control device 100 determines that the vehicle behavior is stabilized when Equation 6 is maintained for 2 consecutive seconds.

At this time, the vehicle driving control device 100 sets K = 0 when the yaw rate error switching condition of Equation 5 is satisfied, and in other situations, K = 1 when Equation 5 is not satisfied or Equation 6 is satisfied. do.

Accordingly, the vehicle driving control device 100 selects the minimum value among the weight calculated through the centrifugal acceleration map and the weight calculated according to the yaw rate error switching condition determination as the model weight.

Figure 7 is a schematic diagram for selecting an estimated lateral acceleration value according to an embodiment of the present invention.

Referring to FIG. 7, the calculated heel angle (based on the kinematic model) and the corrected heel angle are converted to absolute values (701, 702), respectively, and the calculated heel angle (based on the kinematic model), which is an absolute value, is compared with the corrected heel angle. (703), and the smaller value is selected as the estimated heel angle (705).

)은 차량거동이 불안정한 후 안정화되면 지연없이 빠르게 추정값이 정확해진다. 이에 가중치 반영으로 인한 추정 지연을 최소화할 수 있다. 그러나, 요레이트 에러 스위칭 조건(수학식 5) 만족 후 해지(수학식 6)의 빈번한 스위칭 방지를 위한 유지 조건 때문에, 해지 조건 유지 동안 차량 거동은 이미 안정화되어 모델 횡경사각 (

)이 정확해 졌음에도 불구하고 이를 반영하지 않아 추정 지연이 발생한다. 모델 횡경사각 (

)의 경우 차량거동이 불안정하여 추정값이 부정확 할 때 추정이 과대하므로 모델 횡경사각 (

)과 보정된 횡경사각 (

) 중 작은값을 더 신뢰 할 수 있다. 따라서, 두 값 중 작은 값을 선택하여 추정 지연을 최소화 한다Heel angle estimated from the lateral acceleration kinematic model (

), when vehicle behavior stabilizes after being unstable, the estimated value becomes accurate quickly without delay. Accordingly, the estimation delay due to weight reflection can be minimized. However, because of the maintenance condition for preventing frequent switching of release (Equation 6) after the yaw rate error switching condition (Equation 5) is satisfied, the vehicle behavior is already stabilized while the release condition is maintained, so that the model heel angle (

) has become more accurate, but it is not reflected, resulting in estimation delays. Model heel angle (

), the model heel angle (

) and the corrected heel angle (

), the smaller value is more reliable. Therefore, the smaller of the two values is selected to minimize the estimation delay.

Figure 8 is a diagram for explaining a method for determining a bank road surface according to an embodiment of the present invention.

)이 인가되면, 차량 주행 제어 장치(100)는 횡경사각 추정값(

)으로부터 뱅크노면을 판단한다. 횡경사각 추정값이 5도 이상일 때, 뱅크판단 플래그를 평지노면에서 뱅크 노면 으로 천이하며 횡경사각 추정값이 3도 미만이면 판단 플래그 상태를 뱅크 노면에서 평지 노면으로 천이한다. 이때, 빈번한 플래그 천이를 방지하기 위해 횡경사각 추정값이 3도 미만으로 1초 유지시, 판단 플래그 상태를 뱅크 노면에서 평지 노면으로 천이한다.Referring to Figure 8, the estimated heel angle (

) is applied, the vehicle driving control device 100 calculates the heel angle estimate value (

) to judge the bank road surface. When the estimated heel angle is 5 degrees or more, the bank judgment flag is transitioned from a flat road surface to a banked road surface. If the estimated heel angle value is less than 3 degrees, the judgment flag state is transitioned from a banked road surface to a flat road surface. At this time, in order to prevent frequent flag transitions, when the estimated heel angle remains below 3 degrees for 1 second, the judgment flag state is transitioned from the banked road surface to the flat road surface.

Next, if the bank load determination value is false, the vehicle driving control device 100 determines the bank road surface to be flat and sets the bank road surface determination flag to “0”. If the bank load determination value is true, determines whether to turn, and determines the turning direction. Determine whether this is forward or reverse.

The vehicle driving control device 100 can determine whether to turn using the yaw rate sensor value r _steer . As an entry condition, the vehicle driving control device 100 transitions the turning judgment flag state to true when the yaw rate sensor value is 3 deg/s or more, and transitions the turning judgment flag state to false when the yaw rate sensor value is less than 2 deg/s. . At this time, in order to prevent frequent flag transitions, when the yaw rate sensor value remains below 2deg/s for 2 seconds, the turning judgment flag state is transitioned to false.

When both the bank road surface determination and the turning determination are true, the vehicle driving control device 100 determines whether the road surface condition is a forward bank turn or a reverse bank turn. According to the forward/reverse bank definition, if the bank direction and turning direction are different, it is a reverse bank, and if they are the same, it is a forward bank. Equation 7 below is used to determine the sign.

)는 아래와 같이 출력값이 -1 또는 1인 함수 이며 빈번한 출력값 변화를 막기 위해 도 9와 같이 동작한다. 도 9는 본 발명의 일 실시 예에 따른 뱅크 노면 판단을 위한 도식도이다. 도 9를 참조하면 relay(

)의 함수 출력값이 1 또는 -1일 때 선회 방향이 판단되며, -1과 1 사이의 값인 경우 재판단 하도록 한다.Here, relay(

) is a function whose output value is -1 or 1 as shown below, and operates as shown in Figure 9 to prevent frequent changes in output value. Figure 9 is a schematic diagram for determining a bank road surface according to an embodiment of the present invention. Referring to Figure 9, relay(

)'s function output value is 1 or -1, the direction of turning is determined, and if the value is between -1 and 1, it is re-determined.

Figure 10 is a graph for explaining the accuracy of slant angle estimation in an unstable driving situation according to an embodiment of the present invention. 101 in FIG. 10 represents the heel angle on flat ground and shows the estimated heel angle considering the uncorrected heel angle calculated based on the kinematic model and the heel angle corrected by reflecting the weight.

102 is an enlarged drawing showing the corrected heel angle and the true value of 101, and it can be seen that the estimated heel angle value is drawn almost similar to the true value.

103 is a bank determination flag, and indicates a flag value when the bank is determined based on the uncorrected heel angle and a flag value when the bank is determined based on the corrected heel angle. It can be seen that the bank judgment flag based on the uncorrected heel angle of 103 rises to "1" in the part where the uncompensated heel angle of 101 fluctuates.

104 shows the true and estimated values of the lateral velocity change rate and 105 shows the model weights. It can be seen that the model weight becomes “0” in the vehicle instability section where the lateral speed change rate is large.

In an unstable driving situation where the vehicle spins out, the uncorrected lateral slope angle estimate has an error of up to ±30deg, but the corrected lateral slope estimate provides a stable lateral slope angle estimate within the error range of ±3deg, and in an unstable driving situation, the lateral slope angle can be estimated with an error of up to ±30deg. It can be seen that bank judgment performance is improved by improving rectangular estimation accuracy.

Figure 11 is a graph for explaining the heel angle estimation performance of a reverse bank turning section with the same heel angle yaw rate sign in a stable driving situation according to an embodiment of the present invention.

201 in FIG. 11 indicates the true value of the lateral tilt angle, the uncorrected lateral tilt angle, and the estimated lateral tilt angle in a stable driving situation of the vehicle. In a stable vehicle driving situation, you can see a graph in which the true value of the heel angle, the uncorrected heel angle, and the estimated heel angle are almost similar.

202 represents the yaw rate sensor value, and 203 represents the bank judgment flag.

In the stable driving situation of 201, the uncorrected heel angle and the estimated heel angle are almost similar, but it can be seen that there is a difference in the bank judgment flag when judging whether the bank is turning and the bank turning direction of 203.

Figure 12 is a graph for explaining the heel angle estimation performance of a forward bank turning section where the heel angle (bank angle) and the yaw rate sign are the same in a stable driving situation according to an embodiment of the present invention.

301 in FIG. 12 indicates the true value of the lateral tilt angle, the uncorrected lateral tilt angle, and the estimated lateral tilt angle in a stable driving situation of the vehicle. In a stable vehicle driving situation, you can see a graph in which the true value of the heel angle, the uncorrected heel angle, and the estimated heel angle are almost similar.

302 represents the yaw rate sensor value, and 303 represents the bank judgment flag.

In the stable driving situation of 301, the uncorrected heel angle and the estimated heel angle are almost similar, but it can be seen that there is a difference in the bank judgment flag when determining whether the bank of 303 is turning and the bank turning direction.

In this way, the present invention uses universal vehicle sensors mounted on the vehicle without additionally providing a roll rate sensor to determine whether the vehicle is driving in at least one of the static driving situation of the vehicle, the dynamic driving situation of the vehicle, and the unstable driving situation. It accurately estimates the lateral slope angle of the road and determines the road surface condition (flat, banked, turning, forward bank, reverse bank, etc.).

Figure 13 shows a computing system according to one embodiment of the present invention.

Referring to FIG. 13, the computing system 1000 includes at least one processor 1100, a memory 1300, a user interface input device 1400, a user interface output device 1500, and storage connected through a bus 1200. It may include (1600), and a network interface (1700).

The processor 1100 may be a central processing unit (CPU) or a semiconductor device that processes instructions stored in the memory 1300 and/or storage 1600. Memory 1300 and storage 1600 may include various types of volatile or non-volatile storage media. For example, the memory 1300 may include Read Only Memory (ROM) and Random Access Memory (RAM).

Accordingly, steps of a method or algorithm described in connection with the embodiments disclosed herein may be implemented directly in hardware, software modules, or a combination of the two executed by processor 1100. Software modules reside in a storage medium (i.e., memory 1300 and/or storage 1600), such as RAM memory, flash memory, ROM memory, EPROM memory, EEPROM memory, registers, hard disk, removable disk, or CD-ROM. You may.

An exemplary storage medium is coupled to processor 1100, which can read information from and write information to the storage medium. Alternatively, the storage medium may be integrated with processor 1100. The processor and storage medium may reside within an application specific integrated circuit (ASIC). The ASIC may reside within the user terminal. Alternatively, the processor and storage medium may reside as separate components within the user terminal.

The above description is merely an illustrative explanation of the technical idea of the present invention, and various modifications and variations will be possible to those skilled in the art without departing from the essential characteristics of the present invention.

Accordingly, the embodiments disclosed in the present invention are not intended to limit the technical idea of the present invention, but are for illustrative purposes, and the scope of the technical idea of the present invention is not limited by these embodiments. The scope of protection of the present invention should be interpreted in accordance with the claims below, and all technical ideas within the equivalent scope should be construed as being included in the scope of rights of the present invention.

Claims (20)

A processor that estimates the lateral tilt angle of the road surface based on vehicle sensor signals and determines the state of the road surface based on the estimated lateral tilt angle in an unstable driving situation where the lateral behavior of the vehicle is greater than a predetermined standard value; and
A storage unit that stores the result of determining the heel angle and road surface condition estimated by the processor,
The processor,
Based on receiving the vehicle sensor signal including the lateral acceleration of the vehicle from a sensing module included in the vehicle, responding to the identification that the lateral acceleration of the vehicle is greater than a second threshold that is greater than a predetermined first threshold. Thus, identifying an unstable driving situation among the static driving situation, dynamic driving situation, and unstable driving situation of the vehicle,
While the vehicle is running in the unstable driving situation, based on at least one of the lateral speed change rate, steering angle yaw rate, vehicle roll angle, or any combination thereof included in the vehicle sensor signal transmitted from the sensing module, the road surface Estimate the heel angle, and
A vehicle driving control device that identifies whether the state of the road surface is at least one of a flat surface and a banked road, based on an estimated value of the heel angle of the road surface. In claim 1,
The processor,
A vehicle driving control device characterized in that the heel angle is calculated by applying it to a kinematic model based on the vehicle sensor signal. In claim 2,
The processor,
A vehicle driving control device that calculates weights according to driving conditions using a centrifugal acceleration map or a yaw rate error switching condition. In claim 2,
The processor,
Calculate the weight based on the centrifugal acceleration map, calculate the weight based on the switching condition of the yaw rate error, and select the minimum value among the weight calculated by the centrifugal acceleration map and the weight based on the switching condition of the yaw rate error as the weight. A vehicle driving control device characterized in that. In claim 4,
The processor,
When vehicle behavior is stable, the difference between the steering angle yaw rate corresponding to the steering angle input and the yaw rate value measured by the vehicle sensor is defined as the yaw rate error, and if the yaw rate error satisfies a predetermined judgment condition, the weight is set to 0. and setting the weight to 1 if the determination condition is not satisfied. In claim 5,
The processor,
When the weight is 1, a vehicle driving control device characterized in that the heel angle is corrected using the heel angle calculated based on the kinematic model. In claim 6,
The processor,
A vehicle driving control device characterized in that the smaller of the lateral tilt angle calculated based on the kinematic model and the corrected lateral tilt angle is calculated as the lateral tilt angle estimate. In claim 1,
The processor,
A vehicle driving control device characterized in that, when the state of the road surface is a bank load, it is determined whether the state is at least one of straight bank, forward bank turning, and reverse bank turning. In claim 1,
The processor,
If the estimated heel angle value exceeds a predetermined first standard value, the state of the road surface is determined to be flat,
A vehicle driving control device characterized in that it is determined that the state of the road surface is a bank load when the estimated lateral slope is less than a second predetermined reference value. In claim 9,
The processor,
After determining that the condition of the road surface is a bank road,
When the yaw rate sensor value among the vehicle sensor signals exceeds a predetermined third standard value, it is determined that the road surface is turning,
A vehicle driving control device characterized in that, if the yaw rate sensor value is less than a predetermined fourth standard value, the road surface condition is determined to be banked straight. In claim 10,
The processor,
If the road surface is judged to be turning,
A vehicle driving control device characterized in that it determines a reverse bank turn if the bank direction of the road surface and the turning direction of the road surface are different, and a forward bank turn if the bank direction and the turning direction are the same. In claim 1,
The processor,
A vehicle driving control device characterized in that it estimates the lateral speed change rate and steering angle yaw rate based on the steering angle among the vehicle sensor signals, and estimates the vehicle roll angle based on the lateral acceleration among the vehicle sensor signals. In claim 12,
The processor,
A vehicle driving control device, characterized in that the lateral tilt angle is calculated by applying the lateral acceleration, yaw rate, vehicle speed, the estimated lateral speed change rate, and the vehicle roll angle among the vehicle sensor signals to the kinematic model. A sensing module that senses vehicle driving information;
A vehicle driving control device that estimates the lateral tilt angle of the road surface based on the vehicle sensor signal received from the sensing module and determines the state of the road surface based on the estimated lateral tilt angle in an unstable driving situation where the lateral behavior of the vehicle is greater than a predetermined standard value. Including,
The sensing module is,
transmitting a vehicle sensor signal including at least one of vehicle lateral acceleration, lateral speed change rate, steering angle yaw rate, vehicle roll angle, or any combination thereof to the vehicle driving control device;
The vehicle driving control device,
Based on the vehicle sensor signal received from the sensing module, in response to the lateral acceleration of the vehicle being identified as being greater than a second threshold that is greater than a predetermined first threshold, the static driving situation of the vehicle, the dynamic driving situation, and Identifying unstable driving situations among unstable driving situations;
While the vehicle is running in the unstable driving situation, based on at least one of the lateral speed change rate, steering angle yaw rate, vehicle roll angle, or any combination thereof included in the vehicle sensor signal transmitted from the sensing module, the road surface Estimate the heel angle; and
A vehicle system, characterized in that it identifies whether the state of the road surface is at least one of a flat surface and a banked road, based on the estimated value of the heel angle of the road surface. In claim 14,
The sensing module is,
A longitudinal and lateral acceleration sensor that senses the longitudinal and lateral acceleration of the vehicle;
A yaw rate sensor that senses the yaw rate of the vehicle;
A steering angle sensor that senses the steering angle of the vehicle; and
A wheel speed sensor that senses the vehicle speed of the vehicle;
A vehicle system comprising: In claim 14,
The vehicle driving control device,
Calculate the heel angle of the road surface based on the kinematic model,
Correcting the heel angle based on the weight calculated based on the centrifugal acceleration map and the switching conditions of the yaw rate error,
A vehicle system characterized in that the smaller of the lateral tilt angle calculated based on the kinematic model and the corrected lateral tilt angle is calculated as the lateral tilt angle estimate. Using a sensing module included in the vehicle, in response to identifying the lateral acceleration of the vehicle as being greater than a second threshold that is greater than a predetermined first threshold, based on a vehicle sensor signal including the lateral acceleration of the vehicle, the vehicle identifying an unstable driving situation among the static driving situation, dynamic driving situation, and unstable driving situation;
In an unstable driving situation where the lateral behavior of the vehicle is more than a predetermined standard value, the sensing module is used to detect the vehicle based on at least one of the lateral speed change rate, steering angle yaw rate, vehicle roll angle, or any combination thereof included in the vehicle sensor signal. Estimating the heel angle of the road surface; and
A step of determining the condition of the road surface based on the estimated heel angle estimate,
The step of determining the condition of the road surface based on the estimated heel angle estimate is,
A vehicle driving control method comprising the step of identifying whether the state of the road surface is at least one of a flat surface and a banked road, based on an estimated value of the heel angle of the road surface. In claim 17,
The step of estimating the heel angle of the road surface is,
Calculating the heel angle by applying it to the kinematic model based on the vehicle sensor signal:
calculating weights according to the driving state using a switching condition of the centrifugal acceleration map or yaw rate error;
correcting the calculated heel angle using the weight; and
calculating a smaller value among the lateral tilt angle calculated based on the kinematic model and the corrected lateral tilt angle as an estimated lateral tilt angle;
A vehicle driving control method comprising: In claim 17,
The step of determining the condition of the road surface is,
A vehicle driving control method, characterized in that it determines whether the road surface is in at least one of the following states: flat, straight bank, forward bank turn, and reverse bank turn based on the estimated heel angle value. In claim 18,
determining that the road surface is flat when the estimated heel angle exceeds a first predetermined standard value;
determining that the state of the road surface is a bank load if the estimated lateral slope is less than a second predetermined reference value;
Step of determining that the road surface is turning when the yaw rate sensor value among the vehicle sensor signals exceeds a predetermined third standard value:
If the yaw rate sensor value is less than a predetermined fourth standard value, determining that the road surface is in a straight bank state; and
When it is determined that the road surface is turning, determining a reverse bank if the bank direction and the turning direction are different, and determining a forward bank if the bank direction and the turning direction are the same;
A vehicle driving control method comprising:

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