KR20180055433A - Autonomous driving system fail-safe utility and method thereof - Google Patents

Abstract

Disclosed is a fail-safe method of an autonomous driving system. The method includes a step of performing a fail-safe diagnosis for a single sensor by using a first distribution including estimated location measurement results obtained by projecting location measurement results of a past time point input from the single sensor and a location measurement result at a current time point currently input from the single sensor; and performing a fail-safe diagnosis of complex sensors including the single sensor by analyzing a second distribution including estimated location measurement results obtained by projecting location measurement results measured most recently with respect to the current time from the complex sensors to a current time point when it is determined that the fail-safe diagnosis of the single sensor is successful or can be safe-processed. According to the present invention, the performance and reliability of the autonomous driving system can be improved by providing an autonomous driving system having a fail-safe diagnosis function for estimating location measurement information to secure the reliability of the location measurement information.

Description

BACKGROUND OF THE INVENTION 1. Field of the Invention [0001] The present invention relates to an autonomous driving system having a fail-

The present invention relates to an autonomous running system having a fail-safe function and a method thereof.

The autonomous navigation system estimates positioning information that estimates the current position of the moving vehicle based on road map information constructed using GPS and various sensors (Radar, LiDAR, Camera, etc.) AVM, and uses the estimated positioning information Thereby controlling the autonomous running of the vehicle.

In such an autonomous navigation system, positioning techniques for estimating the current position of a vehicle include satellite navigation, map matching, and the like. In this positioning technique, the reliability of the estimated positioning information varies greatly depending on the surrounding environment of the vehicle.

The fluid reliability of such positioning information results in degrading the overall performance of the autonomous navigation system.

In this way, the existing positioning technique estimates relatively accurate positioning information, but does not guarantee the reliability of the positioning information, so that there is a possibility that the wrong positioning information is utilized in the autonomous traveling system.

Therefore, a Fail-Safe function for robust positioning information estimation is indispensably required to ensure the reliability of the positioning information, but development of an autonomous travel system having a fail-safe function is insufficient.

SUMMARY OF THE INVENTION Accordingly, it is an object of the present invention to provide an autonomous navigation system and a method thereof having a fail-safe diagnosis function for positioning information estimation to ensure reliability of positioning information.

The problems to be solved by the present invention are not limited to those mentioned above, and other solutions not mentioned can be clearly understood by those skilled in the art from the following description.

According to an aspect of the present invention, there is provided a failure-safe method for an autonomous navigation system, the method comprising the steps of: estimating positioning results of past time points input from a single sensor, Performing a fail-safe diagnosis on the single sensor using a first distribution map including a current positional measurement result; And when the fail-safe diagnosis for the single sensor is judged to be successful or safe processing is possible, the most recently measured positioning results from the multiple sensors including the single sensor, And performing a fail-safe diagnosis on the hybrid sensors by analyzing a second distribution diagram made up of the estimated positioning results projected on the hybrid sensors.

In accordance with another aspect of the present invention, there is provided an autonomous traveling system having a fail-safe diagnosis function, wherein a failure occurrence of the single sensor is determined by a single diagnosis based on the frequency and distribution of positioning results input from a single sensor An initial diagnosis unit for performing an initial diagnosis; Wherein when the initial diagnosis is determined to be successful, the estimated positioning results of the past time points input from the single sensor after the initial diagnosis are projected at the current time point and the positioning results of the current time point currently input from the single sensor A single sensor diagnosis unit for diagnosing a fail-safe of the single sensor using the first distribution map; And when the fail-safe diagnosis for the single sensor is judged to be successful or safe processing is possible, the most recently measured positioning results from the multiple sensors including the single sensor, And performs a fail-safe diagnosis on the complex sensors by analyzing a second distribution diagram made up of the estimated positioning results projected on the complex sensors.

According to the present invention, it is possible to improve the performance and reliability of an autonomous traveling system by providing an autonomous traveling system having a fail-safe diagnosis function for positioning information estimation to ensure the reliability of positioning information.

The effects of the present invention are not limited to those mentioned above, and other effects not mentioned may be clearly understood by those skilled in the art from the following description.

1 is a block diagram of an autonomous navigation system according to an embodiment of the present invention.
FIG. 2 is a diagram schematically showing an initial diagnostic process performed based on a distribution diagram of positioning results in the initial diagnosis unit shown in FIG. 1. FIG.
3 is a diagram schematically illustrating a process of performing fail-safe diagnosis for a single sensor in the single sensor fail-safe diagnosis unit shown in FIG. 1. FIG.
FIG. 4 is a diagram schematically illustrating a fail-safe diagnosis process for the complex sensor in the complex sensor fail-safe diagnosis unit shown in FIG. 1. FIG.
5 is a flowchart illustrating a fail-safe diagnosis method for a positioning result in an autonomous navigation system according to an embodiment of the present invention.

It is noted that the technical terms used herein are used only to describe specific embodiments and are not intended to limit the invention. It is also to be understood that the technical terms used herein are to be interpreted in a sense generally understood by a person skilled in the art to which the present invention belongs, Should not be construed to mean, or be interpreted in an excessively reduced sense. Further, when a technical term used herein is an erroneous technical term that does not accurately express the spirit of the present invention, it should be understood that technical terms that can be understood by a person skilled in the art are replaced. In addition, the general terms used in the present invention should be interpreted according to a predefined or prior context, and should not be construed as being excessively reduced.

Also, the singular forms "as used herein include plural referents unless the context clearly dictates otherwise. In the present application, the term "comprising" or "comprising" or the like should not be construed as necessarily including the various elements or steps described in the specification, Or may be further comprised of additional components or steps. Also, suffixes "module", "unit" and "part" for the components used in the present specification are given or mixed in consideration of ease of specification, and each component having its own distinct meaning or role no.

The autonomous navigation system of the present invention can perform sensor fusion positioning based on map matching.

In addition, the autonomous navigation system of the present invention can perform fail-safe diagnosis that improves the reliability of the system in the course of performing sensor fusion positioning and enables calculation (calculation or estimation) of robust and stable positioning information.

Further, the fail-safe diagnosis of the present invention performs analytic redundancy-based fault diagnosis based on analytic redundancy, so that no additional hardware is required.

The fail-safe diagnosis of the present invention includes an initialization diagnosis process, a fail-safe diagnosis process based on a single map matching, and a fail-safe diagnosis process based on a complex map matching. The fail-safe diagnosis includes detection of a fail sensor, The sensor is safe.

Accordingly, the reliability of a system utilizing positioning information such as autonomous navigation can be greatly improved.

Hereinafter, preferred embodiments of the present invention will be described in detail with reference to the accompanying drawings.

1 is a block diagram of an autonomous navigation system according to an embodiment of the present invention.

Referring to FIG. 1, an autonomous navigation system according to an exemplary embodiment of the present invention may include a fused positioning module, a fail-safe diagnosis module 130, and an output unit 150.

The sensor fusion positioning module 110 may include a positioning sensor unit 112 and a positioning calculation unit 114.

The positioning sensor unit 112 measures the current position of an unmanned autonomous vehicle (hereinafter, referred to as a " vehicle "). The positioning sensor unit 112 includes a plurality of positioning sensors of the same type, ≪ / RTI > The positioning sensor may be, for example, a GPS sensor, a Radar sensor, a LiDAR sensor, a camera sensor, or the like, but the present invention is not limited thereto.

The positioning calculation unit 114 fuses position values measured by the plurality of sensors, and calculates a positioning result matched with the previously prepared road map. The positioning result may include location information of the vehicle on the road map, traveling route information on the road map, and lane information based on the traveling route information.

The fail-safe diagnosis module 130 performs the fail-safe diagnosis on the positioning result input from the sensor fusion positioning module 110 and, when failures or abnormal symptoms are diagnosed on the positioning result, A warning message is output, and when the safety is diagnosed on the positioning result, the positioning result can be corrected. The description will be described in detail below.

The output unit 150 processes the warning message into visual, audible, or combined information and outputs the processed information. In addition, the output unit 150 outputs a normalized positioning result obtained according to the fail-safe diagnosis result performed by the fail-safe diagnostic module 130 or a map obtained by matching the positioning result, which is recovered in accordance with the safe processing operation, And outputs the matching result as visual, auditory, or combined information. The output unit 150 may be a video output module, an audio output module, or a combination thereof. The video output module includes an image processing unit for converting the warning message or the corrected positioning result into image data such as text data and graphic data that can be output to an image screen, and a display unit (LCD, etc.), and the audio output module may include an audio processing restoration module for converting the warning message or the corrected positioning result into audio data such as voice data, And a speaker unit for outputting data.

Hereinafter, the fail-safe diagnostic module 130 described above will be described in detail with reference to FIGS. 2 to 4. FIG.

The fail-safe diagnosis module 130 includes an initial diagnosis unit 132, a fail-safe diagnosis unit 134, and a fail-safe diagnosis unit 132 to perform fail-safe diagnosis of positioning results input from the sensor fusion positioning module 110. [ And a diagnosis result output unit 136. [

The initial diagnosis unit 132

The initial diagnosis unit 132 analyzes the frequency and distribution of positioning results input from the sensors of the sensor fusion positioning module 110 to diagnose an initial diagnosis, that is, an initial fail-safe, of the positioning result.

The frequency can be defined as the number of times of input of the positioning result (measurement frequency) input from the position sensor for a predetermined time. Here, the predetermined time may be variously set according to the design, and may be, for example, 500 ms. That is, the initial diagnosis unit 132 counts the number of times of inputting the positioning result for the predetermined time, and based on the result of the comparison between the counted number of times of input and the preset number of times (for example, five times) , And diagnoses whether or not an initial failure has occurred with respect to the positioning result. For example, if the number of inputs is five or more, the initial diagnosis for the positioning result is determined to be successful, and if the number of inputs is less than five, the initial diagnosis for the positioning result can be determined as fail.

When the initial diagnosis of the frequency of the positioning result is completed, the initial diagnosis unit 132 may perform initial diagnosis of the positioning result input from the sensor fusion positioning module 110 based on the distribution map of the positioning result have.

Hereinafter, a process of performing initial diagnosis based on the distribution diagram of the positioning result will be described with reference to FIG. 2A.

,

,

)을 수집하고, 상기 수집된 과거 시점의 측위 결과들(

,

,

)이 현재 시점에서 투영(prediction)하여, 현재의 측위 결과들(이하, '추정 측위 결과들'이라 함.)을 추정한다. 여기서, 과거의 측위 결과들로부터 현재의 추정 측위 결과들을 추정하는 방법으로, 추측항법(Dead Reckoning: DR)이 이용될 수 있다.First, the initial diagnosis unit 132 obtains the positioning results

,

,

Collects the collected past positioning results (

,

,

Estimates the current positioning results (hereinafter, referred to as 'estimated positioning results'). Here, dead reckoning (DR) can be used as a method of estimating current estimated positioning results from past positioning results.

,

,

)이 추정되면, 상기 초기 진단 유닛(132)은 현재의 추정 측위 결과들(

,

,

)과 상기 센서 융합 측위 모듈에서 실제 입력되는 현재의 측위 결과(

)가 나타내는 위치들이 서로 떨어진 정도를 나타내는 분포(분포도 또는 분포 영역)(22 또는 24)의 표준편차(deviation)를 계산한다. Current estimated positioning results (

,

,

), The initial diagnosis unit 132 determines the current estimated positioning results (

,

,

And a current positioning result actually input in the sensor fusion positioning module

(Distribution or distribution area) (22 or 24) indicating the degree to which the positions indicated by the reference points are separated from each other.

,

,

)과 현재의 측위 결과(

) 간들의 분포 범위가 좁을수록 초기 진단을 성공으로 판단할 확률이 높음을 알 수 있다.When the standard deviation is calculated, the initial diagnosis unit 132 compares the calculated standard deviation with a specific threshold value. If the deviation is below a specific threshold value, the initial diagnosis unit 132 determines that the initial diagnosis is successful. , The initial diagnosis is judged as fail. 2 (A) illustrates the case where the initial diagnosis is determined as fail, and FIG. 2 (B) illustrates the case where the initial diagnosis is determined to be successful. That is, the current estimated positioning results (

,

,

) And the current positioning result (

) The narrower the distribution range of the livers, the higher the probability of judging the initial diagnosis to be successful.

If the initial diagnosis of the frequency and the distribution of the positioning results is determined to be successful, the initial diagnosis unit 132 requests the fail-safe diagnosis unit 134 to start fail-safe diagnosis. If failing at least one of the initial diagnosis of the frequency and distribution of the positioning result, fail-safe diagnosis performed by the fail-safe diagnostic module 130 is not performed. Meanwhile, the initial diagnosis process by the initial diagnosis unit 132 may be performed only once.

1, the fail-safe diagnosis unit 134 generates a fail-safe diagnosis message in response to a start request message (or a start request command) for the fail-safe diagnosis from the initial diagnosis unit 132, Diagnosis can be performed.

To perform the fail-safe diagnosis, the fail-safe diagnosis unit 134 may include a single sensor fail-safe diagnosis unit 134A and a complex sensor fail-safe diagnosis unit 134B.

Single sensor Fail - Safe Diagnosis department (134A)

The single sensor fail-safe diagnosis unit 134A can perform the fail-safe diagnosis of each of the single sensors. FIG. 3 schematically shows a process of performing fail-safe diagnosis for a single sensor in the single sensor fail-safe diagnosis unit shown in FIG. 1, wherein FIG. 3A shows fail-safe diagnosis for a single sensor (B) shows a case in which it is judged as fail.

To perform a fail-safe diagnosis of each of the single sensors, an identifier may be assigned to each of the single sensors.

,

,

)을 수집하고, 수집된 과거의 측위 결과들(

,

,

)을 현재에서의 측위 결과들(

,

,

)(이하, 추정 측위 결과들이라 함)을 추정하고, 추정된 현재의 추정 측위 결과들(

,

,

)과 현재에서 실제 측정된 현재의 측위 결과(

)를 비교하여 페일 발생 여부를 진단할 수 있다. 이때, 페일을 진단하기 위한 임계값이 설정될 수 있으며, 상기 임계값은 현재의 측위 결과(

)를 기준으로 상기 대상 단일 센서의 스펙에 정의된 허용 오차 범위(30)(이하, 페일 허용 오차 범위 또는 제1 페일 허용 오차 범위)일 수 있다. The single sensor fail-safe diagnosis unit 1354A uses past dead reckoning (DR) to detect past positioning results measured by the target single sensor

,

,

), And the collected past positioning results (

,

,

) To the current positioning results (

,

,

) (Hereinafter referred to as estimated positioning results), and estimates the current estimated positioning results (

,

,

) And the actual measured current positioning result at present

), It is possible to diagnose whether a failure has occurred. At this time, a threshold value for diagnosing a fail may be set, and the threshold value may be a current positioning result

(Hereinafter, referred to as a " fail tolerance range " or a " first fail tolerance range ") defined in the specification of the target single sensor.

,

,

)의 분포 영역(위치 분포도, 분포도 또는 제1 분포도)(

)의 전체가 상기 페일 허용 오차 범위(30) 내에 존재하면, 페일-세이프 진단을 성공으로 판단한다. 성공으로 판단되면, 상기 현재의 측위 결과(

)는 정상 측위 결과로 결정할 수 있다. As a method for diagnosing whether a failure has occurred for the target single sensor, for example, as shown in (A) of FIG. 3, the current estimated positioning results

,

,

(Position distribution chart, distribution chart or first distribution chart) (

Is within the fail tolerance range 30, the fail-safe diagnosis is judged to be successful. If it is determined to be successful, the current positioning result (

) Can be determined as the normal positioning result.

,

,

)의 분포 영역의 전체가 상기 페일 허용 오차 범위(30)를 밖에 존재하면, 페일-세이프 진단을 페일로 진단한다. 만일 페일로 진단되는 경우, 세이프 처리동작을 수행할 수 있다. 여기서, 상기 세이프 처리동작은 페일로 진단된 측위 결과를 복구하는 처리 동작으로, 예를 들면, 현재의 측위 결과(

)는 폐기하고, 현재의 추정 측위 결과들(

,

,

) 중에서 어느 하나의 측위 결과를 선택하고, 선택된 측위결과를 상기 폐기된 현재의 측위 결과(

)를 복구한 측위 결과로 결정할 수 있다. 또는 현재의 추정 측위 결과들(

,

,

)이 각각 나타내는 위치들을 연결하는 도형을 생성하고, 생성된 도형의 중심 위치를 상기 폐기된 현재의 측위 결과(

)를 복구한 측위 결과로 결정할 수 있다.Conversely, as shown in FIG. 3 (B), the current estimated positioning results (

,

,

Fail diagnosis area is diagnosed as fail when the whole of the distribution area of the fail-safe area 30 is outside the fail tolerance range 30. [ If diagnosed as fail, a safing operation can be performed. Here, the safe processing operation is a processing operation for restoring the positioning result diagnosed by fail, for example, the current positioning result (

) And discard the current estimated positioning results (

,

,

, And outputs the selected positioning result to the discarded current positioning result (

) Can be determined as the result of the restoration. Or current estimated positioning results (

,

,

), And the center position of the generated figure is determined as the discarded current positioning result (

) Can be determined as the result of the restoration.

,

,

)로부터 상기 폐기된 현재의 측위 결과(

)를 복구한 측위 결과로 결정하는 것은 앞서 진행한 초기 진단 과정을 통과했기 때문이다. Thus, the current estimated positioning results (

,

,

) From the discarded current positioning result (

) Is determined by the result of the restored positioning because it has passed the earlier diagnosis process.

,

,

)은 정상적인 측위 결과로 보아도 무방하므로, 이러한 추정 측위 결과들을 이용하여 복구 데이터를 결정하는 세이프 처리 동작을 수행할 있는 것이다.At least the current estimated positioning results (

,

,

) May be regarded as a normal positioning result, so that a safe processing operation for determining recovery data using the estimated positioning results is performed.

,

,

)의 분포 영역(

, 분포도)의 일부가 상기 페일 허용 오차 범위(30) 내에 존재하는 경우는, 완전한 페일로 진단할 수 없는 상황이다. 따라서, 이 경우도 현재의 측위 결과(

)를 정상적인 측위 결과로 판단할 수 있으며, 전술한 바와 같은 측위 결과를 복구하는 세이프 처리동작은 수행하지 않는다. 다만, 이상 징후 발생을 의심할 수 있는 경고 상황으로 진단할 수 있다.Meanwhile, the current estimated positioning results (

,

,

) Distribution region (

, Distribution diagram) exist in the fail tolerance range 30, it is impossible to diagnose the failure completely. Therefore, in this case as well,

) Can be determined as a normal positioning result, and a safe processing operation for restoring the positioning result as described above is not performed. However, it is possible to diagnose a warning situation in which an abnormal symptom may be suspected.

As described above, when fail-safe diagnosis is completed by the single-sensor fail-safe diagnosis unit 134A, the single-sensor fail-safe diagnosis unit 134A determines whether or not the single sensor fail- Safe diagnosis unit 134B, and request the start of the fail-safe diagnosis for the complex sensor.

The composite sensor Fail - Safe Diagnosis department (134B)

In response to the request of the single sensor fail-safe diagnosis unit 134A, the multi-sensor fail-safe diagnosis unit 134B can perform fail-safe diagnosis for the multi-sensor including single sensors of different kinds have. FIG. 4 is a diagram illustrating a fail-safe diagnosis process for the complex sensor in the complex sensor fail-safe diagnosis unit shown in FIG. 1. FIG. 4 (A) (B) indicates that only a single sensor among all the single sensors included in the composite sensor is determined to be successful, and the remaining single sensors are determined to fail.

The fail-safe diagnosis process for the complex sensor may be similar to the initial diagnosis process based on the above-described distribution diagram.

,

,

)을 수집하고, 수집된 가장 최근에 측정한 과거의 측위 결과들(

,

,

)을 추측 항법(DR)에 따라 현재에 투영하여, 현재의 추정 측정 결과들(

,

,

)을 추정한다.Assuming that the composite sensor includes first to third sensors (sensor # 1, sensor # 2 and sensor # 3), it is assumed that each sensor (sensor # 1, sensor # 2 and sensor # 3) The most recently measured past positioning results (

,

,

), And collects the most recently measured past positioning results (

,

,

) To the present in accordance with the speculative navigation (DR), and outputs the current estimated measurement results (

,

,

).

,

,

)의 분포도(

)(분포 영역, 위치 분포 또는 제2 분포도)의 표준 편차를 계산한 후, 표준 편차와 페일 허용 오차 범위(제2 페일 허용 오차 범위)를 나타내는 임계값을 비교하여, 표준 편차가 상기 임계값 이하인 경우(도 4의 (A)), 즉, 현재의 추정 측정 결과들(

,

,

) 모두가 페일 허용 오차 범위(40) 내에 존재하는 경우, 상기 현재의 추정 측정 결과들(

,

,

) 모두를 성공으로 판단하고, 표준 편차가 상기 임계값을 초과하는 경우, 상기 현재의 추정 측정 결과들(

,

,

) 모두를 페일로 진단한다.Thereafter, the estimated current estimated measurement results (

,

,

) Distribution of

(Standard deviation) and a threshold value indicating a fail tolerance range (second fail tolerance range) are compared with each other, and the standard deviation is equal to or less than the threshold value (Fig. 4 (A)), i.e., the current estimated measurement results (

,

,

) Are all within the fail tolerance range 40, the current estimated measurement results (

,

,

), And if the standard deviation exceeds the threshold value, the current estimated measurement results (

,

,

) Diagnose all failures.

)가 페일 허용 오차 범위(40)에 존재하는 경우, 페일로 진단하지 않고, 세이프 처리 동작을 수행하여, 상기 추정 측위 결과(

)를 페일로 판단되는 나머지 추정 측위 결과들(

,

)을 복구한 추정 측위 결과(

)로 결정한다. 이때, 페일 허용 오차 범위(40)에 존재하는 추정 측위 결과가 복수인 경우, 페일 허용 오차 범위(40)에 존재하는 복수의 추정 측위 결과를 생성한 각각 센서들 중 신뢰도가 가장 높은 센서로부터 추정된 추정 측위 결과를 복구된 측위 결과로 결정하는 세이프 처리 동작을 수행할 수 있다. 여기서, 신뢰도가 가장 높은 센서는 각 센서의 스펙에 존재하는 허용 오차 범위가 가장 작은 센서로 정의할 수 있다. 한편, 상기 페일 허용 오차 범위에 존재하는 센서들을 식별하기 위해 다양한 기하학적 방법이 활용될 수 있다. On the other hand, as shown in (B) of FIG. 4, the position measurement measured by some of the sensors (sensor # 1, sensor # 2 and sensor # 3) The estimated positioning result that projected the result to the present (

Is present in the fail tolerance range 40, the safety processing operation is performed without diagnosing the failure, and the estimated positioning result (

) To the remaining estimated positioning results (

,

) And the estimated positioning result (

). In this case, when there are a plurality of estimated positioning results in the fail tolerance range 40, it is assumed that a plurality of estimated positioning results in the fail tolerance range 40 are estimated from the sensors having the highest reliability among the sensors A safe processing operation for determining the estimated positioning result as the restored positioning result can be performed. Here, the sensor with the highest reliability can be defined as a sensor having the smallest tolerance range in the specification of each sensor. On the other hand, various geometric methods may be utilized to identify the sensors that are in the fail tolerance range.

The diagnosis result output unit 136 outputs,

1, when the fail-safe diagnosis unit 134 completes all the fail-safe diagnoses on the single sensor and the complex sensor, the diagnosis result output unit 136 outputs the diagnosis result to a plurality of failures The user can provide the user with a message corresponding to the diagnostic result by referring to the table below.

Fail  level
(Fail Level) Condition Countermeasure Availability
(Availability) Level0 Steady state Normal output Available Level1 Anomalous symptoms Alert Available Level2 Some sensor failures Safe processing Available Level3 Full sensor fail generation Fail handling can not be used

Table 1 above is a table defining a fail level according to a fail-safe diagnosis result according to an embodiment of the present invention.

)과 페일 오차 범위(30)가 일부 겹치는 경우이다. Level2은 페일-세이프 진단에서 세이프 처리가 가능한 페일이 발생한 경우이다. Level2은 세이프 처리가 불가능한 Fail이 발생한 경우이다. Level 0, 1, 2는 Fail이 발생한 경우에는 측위 결과의 사용이 가능하지만 Level 3은 측위결과의 사용이 불가능하며 시스템은 다시 초기화 진단을 진행하게 된다. In one example, the fail level may be configured in four stages. Level0 is a case where the positioning result is normal. In other words, initial diagnosis, fail-safe diagnosis for single sensor and complex sensor are all considered successful. Level 1 indicates a warning signal. In the fail-safe diagnosis for a single sensor,

) And the fail error range 30 partially overlap. Level 2 is when fail-safe fail-safe occurs in fail-safe diagnosis. Level 2 is a case where a fail that can not be safely processed occurs. Level 0, 1 and 2 can use positioning results when fail occurs, but level 3 can not use positioning results and the system will reinitialize diagnosis.

5 is a flowchart illustrating a fail-safe diagnosis method for a positioning result in an autonomous navigation system according to an embodiment of the present invention. In each of the following steps, Will be briefly described or omitted.

Referring to FIG. 5, first, in step S510, an initial diagnosis of the positioning result is performed by analyzing frequency and distribution of positioning results input from a single sensor included in the sensor fusion positioning module 110. FIG. This initial diagnosis can be performed once for each sensor.

As described above, the frequency may be the number of times of input of the positioning result input from the positioning sensor (measurement frequency) for a predetermined time. In this case, for example, if the number of times of input is five or more, the initial diagnosis for the positioning result is determined to be successful, and if the number of times of input is less than five, the initial diagnosis for the positioning result can be determined as fail .

,

,

)을 현재 시점에서 투영(prediction)한 추정 측위 결과들과 상기 센서에서 실제 입력되는 현재의 측위 결과(

)가 나타내는 위치들이 서로 떨어진 정도를 나타내는 분포(분포도 또는 분포 영역)(22 또는 24)의 표준편차(deviation)를 계산하고, 계산된 표준편차와 특정 임계값을 비교한다.Once the initial diagnosis of the frequency is completed, an initial diagnosis can be performed based on the distribution map of the positioning result. Specifically, the positioning results of the past time points (

,

,

) Estimated from the current position and the current positioning result actually input from the sensor

(Distribution or distribution area) (22 or 24) indicating the degree to which the positions indicated by the reference points are separated from each other, and compares the calculated standard deviation with a specific threshold value.

If the deviation is below a certain threshold value, the initial diagnosis is determined to be successful, and if the deviation exceeds a certain threshold value, the initial diagnosis is judged to fail.

If all of the initial diagnoses based on the frequency and the distribution of the positioning results are successful, in step S520, the estimated positioning results obtained by projecting the past positioning results input from the single sensor after the initial diagnosis at the present time, Fail-safe diagnosis of the single sensor is performed using the first distribution map which is the positioning result of the current time point input from the sensor.

Specifically, based on the comparison result of the standard deviation of the first distribution diagram and the threshold value representing the first fail tolerance range defined in the single sensor on the basis of the current positioning result, - Perform Safe Diagnostics. For example, if the standard deviation exceeds the threshold value, fail-safe diagnosis for the single sensor is determined as fail, and if the standard deviation is below the threshold value, fail-safe diagnosis for the single sensor is performed It is determined that success or safe processing is possible.

If it is determined that the fail-safe diagnosis for the single sensor is successful or safe, it is determined in step S530 whether or not the most recently measured position Safe diagnosis of the complex sensors by analyzing the second distribution diagram made up of the estimated positioning results projecting the results at the present point in time.

Specifically, fail-safe diagnosis of the complex sensor is performed based on a result of a comparison between a standard deviation of the second distribution diagram and a threshold value representing a second fail tolerance range defined in advance. For example, if the standard deviation exceeds the threshold value, fail-safe diagnosis for all the composite sensors is determined as fail, and if the standard deviation is below the threshold value, - Safe diagnosis is judged to be successful.

After completion of the fail-safe diagnosis for all the hybrid sensors, the diagnosis result is output in step S540.

The diagnosis result may be output in the form of a message composed of, for example, four levels of fail levels (Level 0 to Level 3).

)과 페일 오차 범위(30)가 일부 겹치는 경우이다. Level2은 페일-세이프 진단에서 세이프 처리가 가능한 페일이 발생한 경우이다. Level2은 세이프 처리가 불가능한 Fail이 발생한 경우이다. Level 0, 1, 2는 Fail이 발생한 경우에는 측위 결과의 사용이 가능하지만 Level 3은 측위결과의 사용이 불가능하며 시스템은 다시 초기화 진단을 진행하게 된다. Level0 is a case where the positioning result is normal. In other words, initial diagnosis, fail-safe diagnosis for single sensor and complex sensor are all considered successful. Level 1 indicates a warning signal. In the fail-safe diagnosis for a single sensor,

) And the fail error range 30 partially overlap. Level 2 is when fail-safe fail-safe occurs in fail-safe diagnosis. Level 2 is a case where a fail that can not be safely processed occurs. Level 0, 1 and 2 can use positioning results when fail occurs, but level 3 can not use positioning results and the system will reinitialize diagnosis.

It should be understood, on the other hand, that the block diagram of FIG. 1, which represents an autonomous drive system having a fail-safe function, embodies the principles of the invention from a functional viewpoint. Similarly, the flowchart of FIG. 5 should be understood to represent various processes that may be substantially represented on a computer-readable medium and executed by a computer or processor, whether or not the computer or processor is explicitly shown.

The blocks of FIG. 1, shown in a processor or similar concept, may be provided with dedicated hardware as well as the use of hardware capable of executing software.

When the blocks of FIG. 1 are implemented by a processor, the functionality of the blocks shown in FIG. 1 may be provided by a single dedicated processor, a single shared processor, or a plurality of individual processors, some of which may be shared.

Also, the explicit use of terms that are presented in terms of processor, control, or similar concepts should not be interpreted exclusively as hardware capable of executing the software, but may include without limitation, digital signal processor (DSP) hardware, (ROM), random access memory (RAM), and non-volatile memory. Other hardware, of course, may also be included.

It will be apparent to those skilled in the art that various modifications, substitutions and substitutions are possible, without departing from the scope and spirit of the invention as disclosed in the accompanying claims. will be. Therefore, the embodiments disclosed in the present invention and the accompanying drawings are intended to illustrate and not to limit the technical spirit of the present invention, and the scope of the technical idea of the present invention is not limited by these embodiments and the accompanying drawings . The scope of protection of the present invention should be construed according to the following claims, and all technical ideas within the scope of equivalents should be construed as falling within the scope of the present invention.

Claims (12)

A first sensor for detecting a current position of a single sensor and a second sensor for detecting a current position of the sensor, fail-safe diagnosis; And
When the fail-safe diagnosis for the single sensor is judged to be successful or safe processing is possible, the positioning results most recently measured on the basis of the current are read from the composite sensors including the single sensor at the present time Performing a fail-safe diagnosis on the complex sensors by analyzing a second distribution diagram made up of the projected positioning results,
Fail-safe method of an autonomous navigation system.
2. The method of claim 1, wherein performing fail-safe diagnostics on the single sensor comprises:
Based on a result of comparison and analysis of a threshold indicating a first fail tolerance range defined in the single sensor based on the standard deviation of the first distribution map and the current positioning result, fail-safe diagnosis The fail-safe method of the autonomous traveling system.
3. The method of claim 2, wherein performing fail-safe diagnostics for the single sensor comprises:
Fail diagnosis of the single sensor is judged as fail if the standard deviation exceeds the threshold value and fail-safe diagnosis of the single sensor is judged as successful if the standard deviation is below the threshold value Wherein the fail-safe method comprises the steps of:
2. The method of claim 1, wherein performing fail-safe diagnostics on the single sensor comprises:
The method of claim 1, wherein, when fail-safe diagnosis of the single sensor is determined to fail, a graphical object for discarding the positioning result of the current point of time, connecting the positions indicated by the estimated positioning results, And performing a safing process for determining a result of the discarded current position as a recovered positioning result.
2. The method of claim 1, wherein performing fail-safe diagnostics on the composite sensors comprises:
Safely diagnosing the hybrid sensor based on a result of a comparison between a standard deviation of the second distribution diagram and a threshold value representing a second fail tolerance range defined in advance, Fail-safe method of the system.
6. The method of claim 5, wherein performing fail-safe diagnostics on the hybrid sensors comprises:
Fail diagnosis for all of the multiple sensors is determined to fail if the standard deviation exceeds the threshold value and fail-safe diagnosis is performed for all of the multiple sensors if the standard deviation is below the threshold value. And judging that the vehicle is a success vehicle.
6. The method of claim 5, wherein performing fail-safe diagnostics on the hybrid sensors comprises:
The estimated positioning results estimated from some sensors of the complex sensors are within the second fail tolerance range and are determined to be successful and the estimated positioning results estimated from the remaining sensors are located outside the second fail tolerance range And performing a safe processing to determine, as a result of the restoration, the estimated positioning results estimated by the sensor with the highest reliability as the failed positioning results, Fail-safe method.
The method of claim 1, wherein prior to performing the fail-safe diagnosis for the single sensor,
Further comprising the step of performing an initial diagnosis to diagnose whether or not a failure has occurred to the single sensor based on the frequency and distribution of the positioning results input from the single sensor. .
The method of claim 8, wherein, in performing the initial diagnosis,
Wherein fail-safe diagnosis of the single sensor and fail-safe diagnosis of the multiple sensors are not performed.
9. The method of claim 8, wherein the frequency of positioning results input from the single sensor
And the single sensor measures the number of times the positioning results are measured for a preset time.
9. The method of claim 8, wherein the distribution of the positioning results input from the single sensor comprises:
Wherein the distribution is a distribution consisting of estimated positioning results obtained by projecting past positioning results input from a single sensor at the current time and positioning results currently being input from the single sensor.
An initial diagnosis unit for performing an initial diagnosis to diagnose a failure of the single sensor based on frequency and distribution of positioning results input from a single sensor;
Wherein when the initial diagnosis is determined to be successful, the estimated positioning results of the past time points input from the single sensor after the initial diagnosis are projected at the current time point and the positioning results of the current time point currently input from the single sensor A single sensor diagnosis unit for diagnosing a fail-safe of the single sensor using the first distribution map; And
When the fail-safe diagnosis for the single sensor is judged to be successful or safe processing is possible, the positioning results most recently measured on the basis of the current are read from the composite sensors including the single sensor at the present time A complex sensor diagnosis unit for analyzing a second distribution diagram made up of the projected positioning results and performing fail-safe diagnosis for the complex sensors,
And an autonomous running system having a fail-safe function.

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