KR20190123470A - Apparatus for determining an optimal velocity for an unmanned ground vehicle, method therefor, and unmanned ground vehicle - Google Patents

Abstract

According to an embodiment of the present invention, in real-time driving performance analysis based on a driving stability index of an unmanned vehicle, a plurality of constant velocity candidate driving speeds for analyzing an initial equilibrium state of the unmanned vehicle and parallel processing based on a regional route and altitude map data are calculated, and after a driving analysis and driving stability index using parallel processing technique for each candidate driving speed is generated, optimal velocity for each route point is calculated on the basis of the driving stability index and vehicle performance to enable the vehicle to be stably driven in an open field environment.

Description

TECHNICAL DEVICE, METHOD AND METHOD FOR DETERMINING OPTIMATE SPEED OF A UNAVINED VEHICLE

The present invention relates to an unmanned ground vehicle (UGV), and more particularly, to an apparatus and method for determining an optimum speed of an unmanned vehicle in consideration of dynamic driving stability in a field environment.

In a field environment, when an unmanned vehicle travels at a high speed, driving at an arbitrarily given operating speed from an operator that does not consider dynamic driving stability may cause a very dangerous situation such as overturning. Therefore, in general passenger car research, Unified Chassis Control (Unified Chassis Control), which integrates suspension control, steering control, and driving / braking control, has been developed to secure vehicle dynamic stability in rapid steering. And Electric Power Steering System (EPS) have already been commercialized.

However, the above techniques are control technologies based on the current state of the vehicle on a flat road surface, and an unmanned vehicle operating on a field while following a given route requires prediction of dynamic stability according to a road state to be driven.

In addition, the unmanned vehicle should take into account the vertical vehicle dynamics stability according to the field driving as well as the consideration of the dynamic stability by turning as in a general passenger car.

Therefore, in the field driving of unmanned vehicles, there is a need for a driving technology in consideration of such vehicle dynamic stability. For this purpose, in addition to the current state information of the unmanned vehicle driving a given route, LiDAR (Light Detection And Ranging) It is necessary to predict the dynamic stability of the unmanned vehicle using the road surface information obtained through the vehicle and to determine the maximum allowable speed accordingly.

(Patent literature)

Republic of Korea Patent Publication No. 10-2016-0116582 (published October 10, 2016)

Accordingly, in an embodiment of the present invention, the initial equilibrium state of the unmanned vehicle is calculated based on the regional route and the altitude map data, the plurality of constant speed candidate driving speeds are calculated for parallel processing analysis, and the parallel processing technique for each candidate driving speed is performed. Apparatus and method for determining the optimum speed of an unmanned vehicle for stable driving even in a field environment by calculating the optimum speed for each route point based on the driving stability index and the performance of the vehicle after generating driving analysis and driving stability index using To provide.

An apparatus for determining an optimum speed of an unmanned vehicle according to an embodiment of the present invention described above, and determining a plurality of candidate driving speeds by analyzing an initial equilibrium state of the unmanned vehicle based on information on a regional route and altitude map data. And performing a constant speed driving simulation at the plurality of candidate driving speeds based on the preprocessor and the information on the area route and the altitude map data, respectively, for each path point on the area path on which the simulation is performed. And a real-time driving performance analysis unit for generating a driving stability index of the unmanned vehicle, and a post-processing unit for determining an optimum speed of the unmanned vehicle at each of the route points based on the generated driving stability index.

The preprocessing unit may determine the plurality of candidate driving speeds based on the initial speed of the unmanned vehicle at the start route point of the local route and the inclination at the start route point.

The driving stability index may be at least one of a roll stability index (RSM), a pitch stability index (PSM), a lateral stability index (LSM), and a vertical acceleration stability index (VSM).

In addition, the real-time driving performance analysis unit, the magnitude of the vertical tire force of the left wheel and or right wheel of the unmanned vehicle and the center of gravity of the unmanned vehicle when the unmanned vehicle is flat, the left The roll stability index (RSM) is generated based on the magnitude of the real-time vertical tire force of the right wheel or the left wheel.

In addition, the real-time driving performance analysis unit, the magnitude of the vertical tire force of the front wheel or the rear wheel of the unmanned vehicle when the unmanned vehicle is flat, and the rear when the center of gravity of the unmanned vehicle moves forward or rearward And generate the pitch stability index (PSM) based on the magnitude of the real-time vertical tire force of the wheel or front wheel.

In addition, the real-time driving performance analysis unit, the greater the magnitude of the real-time vertical tire force of the right wheel or the left wheel, or the real-time vertical tire force of the rear wheel or front wheel closer to 0, the risk of overturning the unmanned vehicle It is characterized by determining that this is higher.

The real-time driving performance analysis unit may generate the lateral stability index LMS based on a preset maximum lateral position error and a real-time lateral position error of the unmanned vehicle.

The real-time driving performance analysis unit may generate the vertical acceleration stability index VSM based on a preset maximum vertical acceleration and a real-time vertical acceleration acting on the center of gravity of the unmanned vehicle.

The post-processing unit may determine the maximum traveling speed of the unmanned vehicle at each of the route points based on the generated driving stability index, and may determine the optimum speed based on the maximum traveling speed and the performance of the unmanned vehicle. It is characterized by determining.

The post-processing unit may store the optimum speed at each of the route points as an optimum speed profile for the unmanned vehicle.

The driving simulation may be performed at predetermined time intervals.

In addition, as a method for determining an optimum speed of an unmanned vehicle according to an embodiment of the present invention, a plurality of candidate driving speeds are determined by analyzing an initial equilibrium state of the unmanned vehicle based on information on a regional route and altitude map data. And performing constant velocity driving simulations at the plurality of candidate driving speeds, respectively, based on the information on the regional route and the altitude map data, and performing the unmanned operation for each of the route points on the regional route in which the simulation is performed. Generating a driving stability index of the vehicle and determining an optimum speed of the unmanned vehicle at each of the route points based on the generated driving stability index.

The determining of the plurality of candidate driving speeds may include: determining the plurality of candidate driving speeds based on the initial speed of the unmanned vehicle and the inclination at the starting route point at the starting route point of the regional route. Characterized in that it comprises a.

The driving stability index may be at least one of a roll stability index (RSM), a pitch stability index (PSM), a lateral stability index (LSM), and a vertical acceleration stability index (VSM).

The generating of the driving stability index may include the magnitude of the vertical tire force of the left wheel and the right wheel of the unmanned vehicle and the center of gravity of the unmanned vehicle when the driverless vehicle is on a flat surface. Generating the roll stability index (RSM) based on the magnitude of the real-time vertical tire force of the right wheel or the left wheel when moving.

The generating of the driving stability index may include the magnitude of the vertical tire force of the front wheel or the rear wheel of the unmanned vehicle and the center of gravity of the unmanned vehicle when the driverless vehicle is on a flat surface. And generating the pitch stability index (PSM) based on the magnitude of the real-time vertical tire force of the rear wheel or the front wheel.

The generating of the driving stability index may include generating the transverse stability index (LSM) based on a preset maximum transverse position error and a real-time transverse position error of the unmanned vehicle. It is done.

The generating of the driving stability index may include generating the vertical acceleration stability index VSM based on a preset maximum vertical acceleration and a real time vertical acceleration acting on the center of gravity of the unmanned vehicle. It features.

The determining of the optimum speed may include determining a maximum traveling speed of the unmanned vehicle at each of the route points based on the generated driving stability index, and determining the maximum traveling speed and the performance of the unmanned vehicle. Determining the optimum speed based on the determination.

In addition, an unmanned vehicle according to an embodiment of the present invention includes a storage device in which an optimum speed profile provided from an optimum speed determination device is stored, and the unmanned vehicle runs using the optimum speed profile. .

According to an embodiment of the present invention, in the real-time driving performance analysis based on the driving stability index of the unmanned vehicle, a plurality of constant velocity candidate driving for analyzing the initial equilibrium state of the unmanned vehicle and parallel processing analysis based on the regional route and altitude map data It calculates the speed, generates driving analysis and driving stability index using parallel processing technique for each candidate driving speed, and calculates the optimum speed for each route point based on driving stability index and vehicle performance to ensure stable driving even in field environment. Do it.

1 is a detailed block diagram of a device for determining an optimum speed of an unmanned vehicle according to an embodiment of the present invention.
2 is a conceptual view of operations performed in each component of the apparatus for determining an optimum speed of an unmanned vehicle according to an embodiment of the present invention.
3 is a conceptual diagram of a driving route and a route point of an unmanned vehicle according to an embodiment of the present invention.
4 is a conceptual diagram for calculating a roll stability index of an unmanned vehicle according to an embodiment of the present invention.
5 is a conceptual diagram for calculating the pitch stability index of the unmanned vehicle according to an embodiment of the present invention.
6 is a flowchart illustrating operation of the apparatus for determining an optimum speed of an unmanned vehicle according to an embodiment of the present invention.
7 to 9 are diagrams illustrating an optimum speed graph for each path point for a severely curved section of a road according to an embodiment of the present invention.
10 to 12 are diagrams illustrating an optimum speed graph for each path point for an environment in which a continuous asymmetric bump exists on a road surface according to an embodiment of the present invention.

Hereinafter, with reference to the accompanying drawings will be described in detail the operating principle of the present invention. In the following description of the present invention, when it is determined that a detailed description of a known function or configuration may unnecessarily obscure the subject matter of the present invention, the detailed description thereof will be omitted. Terms to be described later are terms defined in consideration of functions in the present invention, and may be changed according to intentions or customs of users or operators. Therefore, the definition should be made based on the contents throughout the specification.

1 is a block diagram illustrating a detailed speed determining apparatus for an unmanned vehicle according to an embodiment of the present invention.

2 illustrates an operation concept performed on each component of an apparatus for determining an optimum speed of an unmanned vehicle according to an embodiment of the present invention.

1 and 2 will be described in detail the operation of each component of the apparatus 100 for determining the optimum speed of an unmanned vehicle according to an embodiment of the present invention.

First, the preprocessing unit 110 analyzes the initial equilibrium state of the unmanned vehicle 200 based on the regional route and the altitude map data of the location where the unmanned vehicle is located.

That is, the preprocessor 110 uses the GPS information on the basis of financial information related to the model and vehicle performance of the pre-stored unmanned vehicle 200 and altitude map data linked to the local route and the regional route. After assuming that is located at the initial position in the local route or located, and analyzes the initial equilibrium state of the wheel of the unmanned vehicle 200 is seated on the ground on the altitude map data based on the altitude map data.

In this case, the local route may mean a path to be driven by the unmanned vehicle, a road of an area where the unmanned vehicle is located, and traffic-related information, and the altitude map data may mean information about an altitude, a slope, an uneven state of the ground, It is not limited to this.

In addition, in the analysis of the initial equilibrium state of the unmanned vehicle 200, the preprocessor 110 may designate the initial position of the unmanned vehicle 200 to be the same position as the start of a given local route, and the unmanned vehicle 200 The initial posture of can be set to the posture calculated through the initial equilibrium analysis. In addition, the preprocessor 110 may calculate the initial speed of the unmanned vehicle 200 based on the angular velocity of the wheel of the unmanned vehicle 200.

In addition, the preprocessor 110 includes a plurality of preset numbers for each route point based on information such as initial speed and acceleration capability of the unmanned vehicle 200, deceleration capability, and inclination of a point where the unmanned vehicle 200 is located on the local route. Calculate the constant speed candidate running speed.

This route point 302 is located above the local route 300 on which the unmanned vehicle 200 will autonomously travel from the initial position where the unmanned vehicle 200 is located in the regional route 300 as shown in FIG. 3. It can mean a specific point. In this case, a plurality of candidate driving speeds may be generated for each route point, and the generated candidate driving speeds may be input as an initial value of a parallel thread of the real-time driving performance analyzer 120 as shown in FIG. 2. . Meanwhile, in one embodiment of the present invention, for example, the preprocessing unit 110 generates seven candidate driving speeds for each route point, for example, but the number of candidate driving speeds is not limited thereto.

The real-time driving performance analysis unit 120 performs the constant speed driving simulation at a plurality of candidate driving speeds at predetermined time intervals based on the information on the regional route and the altitude map data, and the route on the regional route 300 on which the simulation is performed. A driving stability index of the unmanned vehicle 200 is generated for each of the points 302.

That is, the real-time driving performance analysis unit 120 receives the seven candidate driving speeds provided from the preprocessor 110 and assigns them to each parallel thread, and performs constant velocity prediction simulation at a predetermined speed for each parallel thread. Perform driving analysis. In this case, the simulation may be performed at predetermined time intervals, but is not limited thereto.

In addition, the real-time driving performance analysis unit 120 calculates the driving stability index of the unmanned vehicle 200 for each parallel thread in the driving analysis through the constant speed traveling prediction simulation, the post-processing unit 130 ) To be used to calculate the optimal speed for each route point of the unmanned vehicle 200.

At this time, the real-time runningability analysis unit 120 is a road stability indicator as described above, for example, roll stability metric (RSM), pitch stability metric (PSM), transverse stability metric (Lateral Stability Metric; LSM) and the Vertical Stability Metric (VSM) can be calculated, but are not limited thereto.

Hereinafter, an operation of calculating each driving stability index in the real-time driving performance analysis unit 120 will be described in more detail.

First, the roll stability index (RSM) is an indicator indicating the risk of the vehicle overturning to the left or right side of the vehicle, where the magnitude of the roll energy of the unmanned vehicle 200 causes one wheel of the vehicle to float from the ground. It can be defined as the critical state of vehicle rollover risk when the level of.

In order to measure the risk of vehicle rollover, it is necessary to calculate the roll energy. However, since calculating the tire force in the vertical direction from the vehicle dynamics model may determine whether the vehicle is rolled over, in one embodiment of the present invention, the roll energy is not calculated. Instead, by directly obtaining tire force in the vertical direction, it is determined whether the vehicle is overturned or not. At this time, the kinetic energy portion of the roll energy is applicable by calculating the angular velocity at the center of gravity of the vehicle body and calculating the magnitude of the vertical tire force along the angular velocity direction.

Therefore, the real-time runningability analysis unit 120 may calculate the roll stability index based on the size of the tire force in the vertical direction of the wheel corresponding to the angular velocity direction in which the roll occurred in the vehicle as described above.

That is, assuming that an unmanned vehicle has three wheels on the left side and three wheels on the right side, as shown in FIG. 4, the tire force of the left wheel is observed when the vehicle is currently inclined in the right wheel direction. If the left three vertical tire forces are all zeros, it can be determined that the vehicle is in a critical state.

At this time, for example, the roll stability index is set to "0" when the vehicle is in the highest risk of overturning and "1" when the vehicle is in the most stable state without any risk of overturning. Assuming that the roll stability indicators are matched by tire force, the roll stability indicator may be matched to "0" if the vertical tire force reaches zero and the vehicle is at risk of overturning, and the vehicle is at risk of overturning. It can be determined that the state.

)와 각속도(

)의 합을 임계 각도(

)로 나누고, 이 값이 0.5를 넘을 경우에만 수직 방향 타이어력을 계산하도록 하였으나, 이에 한정되는 것은 아니다.However, when only the vertical tire force is considered when calculating the roll stability index (RSM), the value of the roll stability index may be close to 1 despite the fact that the actual roll angle or angular velocity is small when driving or passing the bump. In order to prevent this problem, in one embodiment of the present invention, as shown in [Equation 1] below the roll angle (

) And angular velocity (

) Is the sum of the critical angles (

By dividing by) and only calculating the vertical tire force when this value exceeds 0.5, but the present invention is not limited thereto.

는 튜닝 파라미터이고,

는 롤 각속도,

와

는 각각 차량이 평지에 놓여 있을 때 각각 좌, 우측 휠에 작용하는 수직 방향 타이어력의 합을 나타내고,

와

는 각각 실시간 좌, 우측 수직 방향 타이어력의 합을 나타낸다. here

Is the tuning parameter,

Roll angular velocity,

Wow

Represents the sum of the vertical tire forces acting on the left and right wheels respectively when the vehicle is lying on the ground,

Wow

Denotes the sum of the real-time left and right vertical tire forces, respectively.

Next, the pitch stability index (PSM) is an index indicating the risk that the vehicle overturns to the front or rear of the vehicle, while the roll stability index is an index indicating the risk of overturning according to the left and right tilt of the vehicle, while the pitch stability index is the index of the vehicle. It is an index indicating the risk of overturning according to the forward and backward slope.

That is, the pitch stability index uses the energy concept in the same manner as the roll stability index, and directly calculates the pitch stability index by using the vertical tire force obtained from the vehicle dynamics model to prevent the rollover from occurring in the pitch direction. .

When the real-time driving performance analysis unit 120 generates the pitch stability index, when the center of gravity of the unmanned vehicle 200 moves toward the rear, for example, the pitch motion is applied to the unmanned vehicle 200 in front of the unmanned vehicle 200. The pitch stability index can be calculated based on the magnitude of the vertical tire force of the wheel located.

Referring to FIG. 5, when the pitch motion is applied to the vehicle and the center of gravity of the vehicle body is moved to the rear side, the vertical tire force of the front wheel is relatively decreased. The pitch stability index calculates the sum of the vertical tire forces of the front and middle or rear and middle wheels according to the sign (direction) of the pitch rotation angle acting on the vehicle, and when the sum of the vertical tire forces is zero, the vehicle It is determined that this is in danger of overturning. At this time, the pitch stability index, like the roll stability index, adds a condition for calculating the tire force by dividing the sum of the pitch angle and the pitch angular velocity by the critical angle. Equation 2 below shows the definition of the pitch stability index as an expression.

는 롤 각속도,

과

은 각각 차량이 평지에 놓여 있을 때 각각 전방(Front)과 중간(Middle) 휠에 작용하는 수직 방향 타이어력의 합과 후방(Rear)과 중간(Middle) 휠에 작용하는 수직 방향 타이어력의 합을 나타내고,

과

은 각각 해당 위치의 수직 방향 타이어력의 실시간 합을 나타낸다.here

Roll angular velocity,

and

Is the sum of the vertical tire forces acting on the front and middle wheels and the vertical tire forces acting on the rear and middle wheels, respectively, when the vehicle is lying flat. Indicate,

and

Respectively represent the real-time sum of the vertical tire force at the corresponding position.

Next, the lateral stability index (LSM) is an index indicating whether the unmanned vehicle 200 follows the given area route 300 well.

The real-time driving capability analysis unit 120 generates a lateral stability index, and performs a route driving simulation so that the unmanned vehicle 200 includes a plurality of route points 302 as shown in FIG. 3. In the horizontal path 300 when driving along the direction is generated a value corresponding to the transverse position error that deviates from the path in the section bent in the transverse direction as a transverse stability index.

That is, the real-time runningability analysis unit 120 is a lateral stability index based on the maximum transverse position error preset for each of the route points 302 and the real-time transverse position errors of the unmanned vehicle at each of the route points 302. (LSM) can be created.

At this time, in an embodiment of the present invention, when performing a real-time path driving simulation for the unmanned vehicle 200, the horizontal position error for a given regional path 300 is set to 0.3 m, which is a level within the width of the tire. One example is illustrated but is not limited thereto. That is, the lateral stability index may be calculated as shown in Equation 3 below as a ratio of the lateral position error of the actual vehicle from the allowable value of the lateral position error.

은 무인 차량의 실시간 횡 방향 위치 오차이고,

는 허용 가능 횡 방향 위치 오차이다.here

Is the real-time lateral position error of the driverless vehicle,

Is the allowable transverse position error.

Next, the vertical acceleration stability index (VSM) is an index indicating the impact acting on the center of gravity of the unmanned vehicle 200.

This vertical acceleration stability indicator is used to prevent damage to the mechanical or electronic components by the impact that the unmanned vehicle 200 acts on the vehicle during driving or rough terrain. The real-time driving performance analysis unit 120 calculates the vertical acceleration stability index as shown in Equation 4 below by using the vertical acceleration acting on the center of gravity of the vehicle body in the vehicle dynamics model.

는 최대 수직 가속도 값으로 예시적으로 1.5 G로 설정한다.

는 차량에 작용하는 실시간 수직 가속도이다.here

Is set to 1.5 G as an example of the maximum vertical acceleration value.

Is the real time vertical acceleration acting on the vehicle.

That is, the real-time driving performance analysis unit 120 may generate a vertical acceleration stability index (VSM), for example, based on a preset maximum vertical acceleration and a real-time vertical acceleration acting on the center of gravity of the unmanned vehicle.

On the other hand, the post-processing unit 130 calculates the optimal speed for each route point at which the unmanned vehicle 200 runs on the local route 300 based on the driving stability index calculated for each candidate driving speed.

That is, for example, when the unmanned vehicle runs autonomously, as shown in FIG. 3, the autonomous driving is performed while passing through a plurality of route points 302 formed on the local route 300 at an initial position. In this case, the unmanned vehicle 200 is based on the road surface information for each route point generated based on the altitude map data from the real-time driving capability analysis unit 120 for the seven candidate driving speeds generated by the preprocessor 110 at each route point. The driving stability index is generated.

Then, the post-processing unit 130 analyzes the driving stability index calculated for each candidate driving speed generated at the path point 302 on the regional path 300, and then the driving stability index of the first candidate driving speed among the seven candidate driving speeds. When is highest, the maximum speed of the unmanned vehicle 200 from the route point 302 to the next route point 303 may be set as the first candidate travel speed.

Subsequently, when the maximum speed is set as described above, the post-processing unit 130 may set an optimum speed suitable for the unmanned vehicle 200 based on performance information such as the maximum driving force and the maximum braking force of the unmanned vehicle 200.

In addition, the post-processing unit 130 may store the optimum speed at each path point as the optimum speed profile for the unmanned vehicle. In addition, an unmanned vehicle can also drive safely using this optimum speed profile.

6 illustrates an operation control flow in the apparatus 100 for determining an optimum speed of an unmanned vehicle according to an embodiment of the present invention.

Hereinafter, an operation according to an embodiment of the present invention will be described in detail with reference to FIGS. 1 to 6.

First, the apparatus 100 for determining an optimal speed of an unmanned vehicle uses GPS information based on model information on a previously stored driverless vehicle 200, financial information related to vehicle performance, and altitude map data linked to a regional route and a regional route. After the unmanned vehicle 200 is positioned at an initial position on the local route, the initial equilibrium state of the wheel of the unmanned vehicle 200 seated on the ground on the elevation map data is analyzed based on the elevation map data (S600).

At this time, in the analysis of the initial equilibrium state of the unmanned vehicle 200, the optimum speed determination device 100 of the unmanned vehicle 200 may designate the initial position of the unmanned vehicle 200 to the same position as the start of the given area route, The initial posture of the unmanned vehicle 200 may use information as a posture calculated through an initial equilibrium analysis. In addition, the optimum speed determining apparatus 100 of the unmanned vehicle may calculate the initial speed of the unmanned vehicle based on the angular velocity of the wheel of the unmanned vehicle 200.

Subsequently, the apparatus 100 for determining the optimum speed of the unmanned vehicle 100 may include performance information such as initial speed of the unmanned vehicle 200, acceleration capability of the unmanned vehicle 200, deceleration capability, and information such as inclination of a point where the unmanned vehicle is located on a local route. A plurality of constant speed candidate traveling speeds of a predetermined number for each route point are calculated based on the step S602.

Subsequently, the apparatus 100 for determining the optimum speed of the unmanned vehicle analyzes the road surface state of each route point on the area route where the unmanned vehicle 200 is located based on the area route and the altitude map data, and based on the information on the road surface state. The driving stability index of the unmanned vehicle is calculated at each route point through the simulation of the constant velocity driving prediction for each candidate driving speed (S604).

In this case, the optimum speed determining apparatus 100 of the unmanned vehicle may calculate, for example, four indicators such as a roll stability index, a pitch stability index, a lateral stability index, and a vertical stability index as driving stability indexes. no.

Subsequently, the apparatus 100 for determining an optimum speed of the unmanned vehicle calculates an optimal speed for each path point on the local route 300 on which the unmanned vehicle 200 is traveling based on the driving stability index calculated for each candidate driving speed (S606). ).

That is, for example, when the unmanned vehicle 200 autonomously travels along the local path 300, the autonomous vehicle passes autonomously while passing through a plurality of path points formed on the local path 300 at an initial position as shown in FIG. 3. You will drive. At this time, the optimum speed determination apparatus 100 of the unmanned vehicle generates the driving stability index based on the road surface state information for the seven candidate driving speeds generated for each route point.

Subsequently, the apparatus 100 for determining the optimum speed of the unmanned vehicle analyzes the calculated driving stability indicators for the candidate driving speeds generated from the initial position on the regional route 300 to the route point 302, and the seven candidate driving speeds as a result of the analysis. If the driving stability index of the first candidate driving speed is the highest, the maximum speed of the unmanned vehicle 200 from the route point 302 to the next route point 303 may be set as the first candidate traveling speed.

In addition, when the maximum speed is set as described above, the apparatus 100 for determining the optimum speed of the unmanned vehicle may set the optimum speed suitable for the unmanned vehicle 200 based on performance information such as the maximum driving force and the maximum braking force of the unmanned vehicle 200. do.

7 to 9 are based on the driving stability indicators in the optimum speed determination device 100 of the unmanned vehicle for a section of severe road curvature (for example, symmetrical bumps continuously on the road) according to an embodiment of the present invention The graph is an example of calculating the optimum speed for each unmanned vehicle route point.

First, as shown in FIG. 7, when the unmanned vehicle 200 travels in the direction of an arrow on a local path, it is assumed that a curved road surface as shown in FIG. 8 exists up to a path point 700 on a forward path of the unmanned vehicle 200. Shall be.

Then, the optimum speed determining apparatus 100 of the unmanned vehicle calculates the driving stability index according to the road surface state in front of the unmanned vehicle 200, and calculates the optimum speed for each route point based on the driving stability index.

FIG. 9 illustrates a graph in which an optimum speed for each route point is calculated based on a driving stability index by the apparatus 100 for determining an optimum speed of an unmanned vehicle according to an embodiment of the present invention.

As shown in FIG. 9, the driving stability index is calculated as a value between "0" and "1" according to the state of the road surface, and according to the change of the value of the driving stability index of the unmanned vehicle 200 in each route point section. It can be seen that the optimum speed 900 changes dynamically.

In this case, it can be seen that the optimum speed 900 of the unmanned vehicle 200 is adjusted relatively low at the point where the road surface is severely curved, so that the speed of the unmanned vehicle 200 is adjusted to the road surface. By automatically adjusting based on the conditions, the road can be stably driven even in uneven road conditions.

10 to 12 are diagrams illustrating an optimum speed for each point of an unmanned vehicle based on a driving stability index in an apparatus 100 for determining an optimum speed of an unmanned vehicle in an environment in which continuous asymmetric bumps exist on a road surface according to an embodiment of the present invention. It is a graph example figure computed.

First, when an unmanned vehicle travels in the direction of an arrow on a local path as shown in FIG. 10, it is assumed that there is a road surface of continuous asymmetric bumps as shown in FIG. 11 up to a path point 1000 on a forward path of the unmanned vehicle 200. do.

Then, the optimum speed determining apparatus 100 of the unmanned vehicle calculates the driving stability index according to the road surface state in front of the unmanned vehicle 200, and calculates the optimum speed for each route point based on the driving stability index.

12 illustrates a graph in which the optimum speed for each route point is calculated based on the driving stability index by the apparatus 100 for determining an optimum speed of the unmanned vehicle according to an embodiment of the present invention.

As shown in FIG. 12, the driving stability index is calculated as a value between "0" and "1" according to the state of the road surface, and the unmanned vehicle 200 in each route point section according to the change of the value of the driving stability index. It can be seen that the optimum speed 1200 is changed.

In this case, it can be seen that the optimum speed 1200 of the unmanned vehicle 200 is adjusted relatively low based on the driving stability index value at the point where the continuous asymmetric bump is present on the road surface, and accordingly, the speed of the unmanned vehicle 200 is adjusted. By automatically adjusting based on the condition of road surface, it is possible to stably run even in uneven road environment.

As described above, according to an embodiment of the present invention, in the real-time runningability analysis based on the driving stability index of the unmanned vehicle, the initial equilibrium state of the unmanned vehicle is analyzed and parallel processing analysis based on the regional route and altitude map data. It calculates a plurality of constant speed candidate driving speeds, generates driving analysis and driving stability indexes using the parallel processing technique for each candidate driving speed, and calculates the optimum speed for each route point based on the driving stability index and the performance of the vehicle. To ensure stable running even in.

Combinations of the steps of each flowchart attached to the present invention may be performed by computer program instructions. These computer program instructions may be mounted on a processor of a general purpose computer, special purpose computer, or other programmable data processing equipment such that the instructions performed through the processor of the computer or other programmable data processing equipment are described in each step of the flowchart. It will create a means to perform them. These computer program instructions may be stored in a computer usable or computer readable memory that can be directed to a computer or other programmable data processing equipment to implement functionality in a particular manner, and thus the computer usable or computer readable memory. It is also possible for the instructions stored therein to produce an article of manufacture containing instruction means for performing the functions described in each step of the flowchart. Computer program instructions may also be mounted on a computer or other programmable data processing equipment, such that a series of operating steps may be performed on the computer or other programmable data processing equipment to create a computer-implemented process to create a computer or other programmable data. Instructions for performing the processing equipment may also provide steps for executing the functions described in each step of the flowchart.

In addition, each step may represent a module, segment or portion of code that includes one or more executable instructions for executing a specified logical function (s). It should also be noted that in some alternative embodiments, the functions noted in the steps may occur out of order. For example, the two steps shown in succession may in fact be performed substantially simultaneously or the steps may sometimes be performed in the reverse order, depending on the function in question.

Meanwhile, in the above description of the present invention, specific embodiments have been described, but various modifications may be made without departing from the scope of the present invention. Therefore, the scope of the invention should be determined by the claims rather than by the described embodiments.

100: optimum speed determination device of the unmanned vehicle 110: pre-processing unit
120: real-time driving analysis unit 130: post-processing unit

Claims (20)

A device for determining the optimum speed of an unmanned vehicle,
A pre-processing unit for determining a plurality of candidate driving speeds by analyzing an initial equilibrium state of the unmanned vehicle based on information on a regional route and altitude map data;
Driving stability of the unmanned vehicle with respect to each of the route points on the regional route on which the simulation is performed at each of the constant speed driving simulations based on the information on the regional route and the altitude map data. A real-time driveability analysis unit for generating an index,
And a post-processing unit for determining an optimum speed of the unmanned vehicle at each of the route points based on the generated driving stability index.
Determination of the optimum speed of unmanned vehicles. The method of claim 1,
The preprocessing unit,
Determining the plurality of candidate traveling speeds based on an initial speed of the unmanned vehicle at a starting route point of the regional route and an inclination at the starting route point.
Determination of the optimum speed of unmanned vehicles. The method of claim 1,
The driving stability index is at least one of a roll stability index (RSM), a pitch stability index (PSM), a transverse stability index (LSM), a vertical acceleration stability index (VSM).
Determination of the optimum speed of unmanned vehicles. The method of claim 3, wherein
The real-time runability analysis unit,
The magnitude of the vertical tire force of the left wheel and / or the right wheel of the unmanned vehicle when the driverless vehicle is on a flat surface,
The magnitude of the real-time vertical tire force of the right wheel or the left wheel when the center of gravity of the unmanned vehicle moves left or right
To generate the roll stability index (RSM) based on
Determination of the optimum speed of unmanned vehicles. The method of claim 3, wherein
The real-time runability analysis unit,
The magnitude of the vertical tire force of the front wheel or the rear wheel of the unmanned vehicle when the driverless vehicle is on a flat surface,
The magnitude of the real-time vertical tire force of the rear wheel or the front wheel when the center of gravity of the unmanned vehicle moves forward or backward
To generate the pitch stability index (PSM) based on
Determination of the optimum speed of unmanned vehicles. The method according to claim 4 or 5,
The real-time runability analysis unit,
The magnitude of the real-time vertical tire force of the right wheel or the left wheel, or
The magnitude of the real-time vertical tire force of the rear wheel or front wheel
The closer to 0, the higher the risk of overturning the unmanned vehicle.
Determination of the optimum speed of unmanned vehicles. The method of claim 3, wherein
The real-time runability analysis unit,
Preset maximum transverse position error,
Real time transverse position error of the unmanned vehicle
To generate the transverse stability index (LSM) based on
Determination of the optimum speed of unmanned vehicles. The method of claim 3, wherein
The real-time runability analysis unit,
Preset maximum vertical acceleration,
Generating the vertical acceleration stability index (VSM) based on real time vertical acceleration acting on the center of gravity of the unmanned vehicle
Determination of the optimum speed of unmanned vehicles. The method of claim 1,
The post-processing unit,
Determine the maximum traveling speed of the unmanned vehicle at each of the route points based on the generated driving stability index,
Determining the optimum speed based on the maximum traveling speed and the performance of the driverless vehicle
Determination of the optimum speed of unmanned vehicles. The method of claim 9,
The post-processing unit,
Storing the optimum speed at each of the route points as an optimum speed profile for the unmanned vehicle.
Determination of the optimum speed of unmanned vehicles. The method of claim 1,
The driving simulation is performed at predetermined time intervals
Determination of the optimum speed of unmanned vehicles. As a method of determining the optimum speed of an unmanned vehicle,
Determining a plurality of candidate driving speeds by analyzing an initial equilibrium state of the unmanned vehicle based on information on a regional route and altitude map data;
Driving stability of the unmanned vehicle with respect to each of the route points on the regional route on which the simulation is performed at each of the constant speed driving simulations based on the information on the regional route and the altitude map data. Creating an indicator,
Determining an optimum speed of the unmanned vehicle at each of the route points based on the generated driving stability index.
How to determine the optimum speed for unmanned vehicles. The method of claim 12,
Determining the plurality of candidate driving speeds,
Determining the plurality of candidate travel speeds based on the initial speed of the unmanned vehicle at the start route point of the regional route and the inclination at the start route point.
How to determine the optimum speed for unmanned vehicles. The method of claim 12,
The driving stability index is at least one of a roll stability index (RSM), a pitch stability index (PSM), a transverse stability index (LSM), a vertical acceleration stability index (VSM).
How to determine the optimum speed for unmanned vehicles. The method of claim 14,
Generating the driving stability index,
The magnitude of the vertical tire force of the left wheel and / or the right wheel of the unmanned vehicle when the driverless vehicle is on a flat surface,
The magnitude of the real-time vertical tire force of the right wheel or the left wheel when the center of gravity of the unmanned vehicle moves left or right
Generating the roll stability index (RSM) based on the
How to determine the optimum speed for unmanned vehicles. The method of claim 14,
Generating the driving stability index,
The magnitude of the vertical tire force of the front wheel or the rear wheel of the unmanned vehicle when the driverless vehicle is on a flat surface,
The magnitude of the real-time vertical tire force of the rear wheel or the front wheel when the center of gravity of the unmanned vehicle moves forward or backward
Generating the pitch stability index (PSM) based on the
How to determine the optimum speed for unmanned vehicles. The method of claim 14,
Generating the driving stability index,
Preset maximum transverse position error,
Real time transverse position error of the unmanned vehicle
Generating the lateral stability index (LSM) based on the
How to determine the optimum speed for unmanned vehicles. The method of claim 14,
Generating the driving stability index,
Preset maximum vertical acceleration,
Generating the vertical acceleration stability index (VSM) based on real time vertical acceleration acting on the center of gravity of the unmanned vehicle;
How to determine the optimum speed for unmanned vehicles. The method of claim 11,
Determining the optimum speed,
Determining a maximum traveling speed of the unmanned vehicle at each of the route points based on the generated driving stability indicators;
Determining the optimum speed based on the maximum traveling speed and the performance of the driverless vehicle.
How to determine the optimum speed for unmanned vehicles. As an unmanned vehicle,
A storage device in which an optimum speed profile provided from the optimum speed determining device of claim 10 is stored;
The unmanned vehicle travels using the optimum speed profile.
Driverless vehicles.

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