JP2023134153A - electronic control unit - Google Patents

Abstract

An electronic control device capable of detecting a decrease in positioning accuracy due to a deceptive signal is provided.
An electronic control device (2) includes a feature matching section (21), a virtual recognition section (22), and a deception determining section (23). The feature comparison unit 21 obtains the absolute position of the feature by comparing the external world recognition result based on the detection result of the feature by the external world sensor 5 with the map information. The virtual recognition unit 22 generates a virtual recognition result corresponding to the external world recognition result based on the positioning result based on the output of the receiver 3 that receives the signal from the positioning satellite and reinforcement information and the map information 4, and identifies the feature. Get the relative position of. The deception determination unit 23 detects a decrease in positioning accuracy due to the deception signal when the deception determination value, which increases as the magnitude of the error between the absolute position and the relative position of the feature and the error duration increases, exceeds a threshold value. judge.
[Selection diagram] Figure 1

Description

The present disclosure relates to an electronic control device.

2. Description of the Related Art Inventions related to techniques for measuring the position of a mobile object have been known. Patent Document 1 listed below discloses an own machine position measuring device that is mounted on a moving body and measures the own position of the moving body. This conventional aircraft position measuring device is characterized by comprising an inertial navigation device, a database, a reference point generation means, an information acquisition means, an information generation means, a position extraction means, and a navigation selection means. (Patent Document 1, claim 1, paragraph 0007, etc.).

The inertial navigation device measures the position of the moving body. The database is at least one of geographic information and astronomical information. The reference point generation means estimates the existence range of the moving object based on the inertial navigation position measured by the inertial navigation device, and divides the existence range to generate a plurality of reference points. The information acquisition means acquires at least one of ground and heaven information corresponding to the database at the inertial navigation position measured by the inertial navigation device.

The information generating means generates, based on the database, estimated information that would be obtained if at least one of ground and heavenly information corresponding to the database is acquired at each of the plurality of reference points. The position extraction means compares the information acquired by the information acquisition means with the estimated information at each of the plurality of reference points generated by the information generation means, and selects the one corresponding to the estimated information with the highest degree of matching. Extract the position of the reference point.

The navigation selection means includes navigation using the inertial navigation device and navigation for determining the position of the one reference point based on the position of the one reference point extracted by the position extraction means. The navigation method with the smallest navigation error is selected as the navigation method for measuring the own aircraft position.

This conventional aircraft position measuring device further includes a GPS receiver that receives a GPS signal including position information from a GPS satellite (Patent Document 1, claim 2, paragraph 0008, etc.). In this case, the inertial navigation device corrects the inertial navigation position based on position information included in the GPS signal. The navigation selection means includes a GPS availability determining means, a GPS unavailable time measuring means, and a composite navigation means.

The GPS usability determination means determines the reliability of the GPS signal and determines its usability based on the position information included in the GPS signal, the inertial navigation position, and the position of the one reference point. When the GPS signal is determined to be unusable, the GPS unusable time measuring means measures the GPS unusable time that has elapsed since the GPS signal was determined to be unusable. The composite navigation means selects a navigation method for measuring the position of the aircraft based on the GPS unavailable time.

Japanese Patent Application Publication No. 2019-035670

Self-driving cars achieve highly accurate positioning by, for example, receiving signals and reinforcement information from Global Navigation Satellite System (GNSS) satellites using a receiver mounted on the vehicle. Therefore, for the safety of self-driving cars, it is important to detect a decline in positioning accuracy due to intentional interference, such as a deceptive signal that deceives reinforcement information. However, as mentioned above, the conventional aircraft position measuring device described in Patent Document 1 can determine whether or not the GPS signal can be used based on the reliability of the GPS signal, but the positioning accuracy due to the deceptive signal is It is not possible to detect a decrease in

The present disclosure provides an electronic control device capable of detecting a decrease in positioning accuracy due to a deceptive signal.

One aspect of the present disclosure includes a feature matching unit that obtains the absolute position of the feature by comparing an external world recognition result based on a detection result of the feature by an external sensor with map information, and a signal and reinforcement from a positioning satellite. a virtual recognition unit that generates a virtual recognition result corresponding to the external world recognition result based on the positioning result based on the output of a receiver that receives the information and the map information to obtain the relative position of the feature; Deception determination that determines a decrease in positioning accuracy due to a deception signal when a deception determination value that increases as the magnitude of the error between the absolute position and the relative position of the feature and the error duration increases exceeds a threshold value. An electronic control device comprising:

According to the above aspect of the present disclosure, it is possible to provide an electronic control device that can detect a decrease in positioning accuracy due to a deceptive signal.

FIG. 1 is a block diagram showing Embodiment 1 of an electronic control device according to the present disclosure. 2 is a flow diagram showing an example of the flow of processing by the electronic control device of FIG. 1. FIG. FIG. 2 is an image diagram showing an example of map information in FIG. 1. FIG. A table showing an example of map information in FIG. 1. FIG. 3 is an image diagram illustrating the process of acquiring the relative position of the feature in FIG. 2; FIG. 3 is an image diagram illustrating the process of acquiring the relative position of the feature in FIG. 2; FIG. 3 is an image diagram illustrating the process of acquiring the relative position of the feature in FIG. 2; 3 is a table explaining the process of acquiring the relative position of the features in FIG. 2. FIG. 3 is a conceptual diagram of a process for calculating a deception determination value and a deception determination process in FIG. 2; FIG. 2 is a block diagram showing a second embodiment of an electronic control device according to the present disclosure. FIG. 3 is a block diagram showing a third embodiment of an electronic control device according to the present disclosure. 12 is a table showing an example of operational constraints by the constraint level output section of FIG. 11. FIG. 3 is a block diagram showing a fourth embodiment of an electronic control device according to the present disclosure. 14 is a flow diagram illustrating the operation of the recognition result verification unit of FIG. 13. FIG. FIG. 14 is a flowchart illustrating the operation of the matching determination section of FIG. 13. FIG. 14 is a flow diagram illustrating the operation of the sudden change determination section of FIG. 13. FIG. 3 is a block diagram showing a fifth embodiment of an electronic control device according to the present disclosure.

Hereinafter, embodiments of an electronic control device according to the present disclosure will be described with reference to the drawings.

[Embodiment 1]
FIG. 1 is a block diagram showing Embodiment 1 of an electronic control device according to the present disclosure. The electronic control device 2 of this embodiment is mounted on a vehicle 1 such as a self-driving car, for example, and determines a decrease in positioning accuracy due to a deception signal that deceives reinforcement information of a global positioning satellite system (GNSS). A vehicle 1 equipped with an electronic control device 2 includes, for example, a receiver 3 that receives signals and reinforcement information from GNSS satellites, map information 4 such as high-precision three-dimensional map data (HD map), and a camera. It includes an external sensor 5 such as a laser radar, and a vehicle control device 6 that controls automatic driving of the vehicle 1.

The electronic control device 2 can be configured by, for example, one or more microcontrollers including a central processing unit (CPU), a memory such as a ROM or a RAM, a timer, and an input/output section. As shown in FIG. 1, the electronic control device 2 includes, for example, a feature matching section 21, a virtual recognition section 22, and a deception determining section 23. Further, the electronic control device 2 may further include, for example, a positioning calculation section 24 and an external world recognition section 25.

Each part of the electronic control device 2 shown in FIG. 1 represents each function of the electronic control device 2, which is realized by, for example, executing a program stored in a memory by a CPU. Note that when the receiver 3 has the function of a positioning calculation unit 24 described later, the electronic control device 2 does not need to have the positioning calculation unit 24. Moreover, when the external world sensor 5 has the function of the external world recognition section 25 described later, the electronic control device 2 does not need to have the external world recognition section 25. The operation of the electronic control device 2 of this embodiment will be described below with reference to FIG.

FIG. 2 is a flow diagram showing an example of the flow of processing by the electronic control device 2 of FIG. When the electronic control device 2 starts the processing flow shown in FIG. 2, it first executes a process S1 of acquiring the absolute position of a feature. In this process S1, the external world recognition unit 25 generates an external world recognition result based on the detection result of the external world sensor 5 that detects features around the vehicle 1 including the area in front of the vehicle 1, for example. Furthermore, the feature matching unit 21 matches the map information 4 with the external world recognition result generated by the external world recognizing unit 25 to obtain the absolute position of the feature detected by the external world sensor 5.

FIG. 3 is an image diagram showing an example of the map information 4 in FIG. 1. FIG. 4 is a table showing an example of the map information 4 in FIG. 1. The map information 4 includes, for example, information on features such as buildings B1, B2, . . . , telephone poles P1, P2, . . . , signs SS1, SS2, . Further, the information on each feature in the map information 4 includes information such as identification information (ID), type, plane coordinates, height, width, and individual attributes. Note that the map information 4 may have three-dimensional coordinates instead of plane coordinates.

The plane coordinates of the buildings B1 and B2 are, for example, the coordinates of the vertices of the buildings B1 and B2 on the ground plane. Similarly, the plane coordinates of the utility poles P1 and P2 are, for example, the coordinates of the center of the utility poles P1 and P2 on the ground plane. The coordinate system of the plane coordinates may be, for example, an orthogonal coordinate system, a longitude and latitude coordinate system, or any other coordinate system.

The external world sensor 5 detects actual features around the vehicle 1 included in the map information 4 as shown in FIG. 3, for example, and outputs the detection results to the external world recognition unit 25. The external world recognition unit 25 generates an external world recognition result including the recognition result of the actual feature around the vehicle 1 based on the detection result of the external world sensor 5, and outputs it to the feature matching unit 21.

The feature matching unit 21 matches the external world recognition results including the recognition results of actual features around the vehicle 1 inputted from the external world recognition unit 25 with the information on the features of the vehicle 1 included in the map information 4. do. As a result, the feature matching unit 21 associates the feature recognition results included in the external world recognition results with the feature information included in the map information 4, and associates the feature recognition results with the planar coordinates and three-dimensional Absolute positions such as coordinates can be obtained.

In addition, if necessary, past verification results, absolute positions, recognition results of the external world recognition unit 25, etc. are retained, and the past information is used at the time of verification to narrow down the search range of the map to the vicinity, or to By comparing features in the surrounding area with the map, processing time when comparing the recognition result of the external world recognition unit 25 with the map information 4 can be shortened, and locations with similar feature placement in the map can be reduced. It is possible to prevent incorrect matching.

Furthermore, by comparing a plurality of terrestrial objects together with a map, the positional error of the terrestrial objects in the external world recognition unit 25 can be estimated. For example, while there are five features at 1m intervals on a map, the results of feature recognition using an external sensor indicate that the closest features are 1.0m, 1.1m, 0.9m, and 1.0m apart. If so, it can be estimated that the third feature has shifted 0.1m away. Alternatively, the residual of the least squares method may be treated as the position error of the feature.

In order to group the aforementioned multiple features, the features included in the field of view of the external sensor at a certain time may be treated as one group, or the relative relationships of the features may be determined using vehicle speed or wheel speed pulses, etc. You can decide and group them.

Next, as shown in FIG. 2, the electronic control device 2 executes a process S2 of acquiring the relative position of the feature. In this process S2, the positioning calculation unit 24 of the electronic control device 2 calculates the positioning result of the vehicle 1 based on the output of the receiver 3 that receives signals and reinforcement information from GNSS satellites, for example. Further, the positioning calculation unit 24 may determine, for example, whether the reception state of signals from GNSS satellites has deteriorated or whether there is an abnormality in the positioning state based on the reception state of signals from the satellites.

Furthermore, in this process S2, the virtual recognition unit 22 generates a virtual recognition result corresponding to the external world recognition result of the external world recognition unit 25, based on the positioning result of the vehicle 1 input from the positioning calculation unit 24 and the map information 4. Then, the relative positions of features around the vehicle 1 with respect to the vehicle 1 are obtained. Furthermore, based on the map information 4, the virtual recognition unit 22 reads or calculates recognition stability and recognition ease from the map information.

5 to 7 are image diagrams illustrating the process S2 of acquiring the relative position of a feature in FIG. 2, and FIG. 8 is a table illustrating the process S2. In this process S2, the electronic control device 2, for example, uses the positioning calculation unit 24 to generate a positioning result based on the output of the receiver 3 that receives the signal and reinforcement information from the positioning satellite. Further, the virtual recognition unit 22 generates a virtual recognition result VRR as shown in FIG. 6 or 7 and 8 based on the positioning result input from the positioning calculation unit 24 and the map information 4.

The virtual recognition results VRR shown in FIGS. 6 to 8 can be obtained, for example, as follows. First, as shown in FIG. 5, the vehicle 1 is placed on the map of the map information 4 based on the positioning result PR calculated by the positioning calculation unit 24. Next, based on the specifications including the imaging area IA of the external sensor 5 mounted on the vehicle 1, the detection results of the features in the map of the map information 4 are calculated, so that the image as shown in FIG. 6 or 7 is obtained. A virtual recognition result VRR is obtained.

In FIG. 6, the virtual recognition result VRR corresponding to the external world recognition result based on the detection result of the actual feature by the external sensor 5 is shown as It is represented as a three-dimensional image based on various information such as. 7 and 8 show the positions of terrestrial objects in the virtual recognition result VRR corresponding to the external world recognition result of the external world sensor 5. In FIGS. 7 and 8, the information on the feature of the map information 4 included in the image area corresponding to the imaging area IA of the external sensor 5 is expressed as a combination of the type of the feature and the coordinates.

As described above, in the process S2, the virtual recognition unit 22 responds to the external world recognition result based on the detection result of the external sensor 5 based on the positioning result of the vehicle 1 input from the positioning calculation unit 24 and the map information 4. A virtual recognition result VRR is generated to obtain the relative positions of features around the vehicle 1 with respect to the vehicle 1. Next, the electronic control device 2 executes a process S3 for calculating a deception determination value and a deception determination process S4 shown in FIG. 2.

FIG. 9 is a conceptual diagram of the process S3 for calculating the deception determination value and the deception determination process S4 in FIG. The vehicle 1 equipped with the electronic control device 2 of this embodiment is, for example, a self-driving vehicle that autonomously travels from a departure point to a destination by controlling various actuators of the vehicle 1 with the vehicle control device 6. be. For example, by receiving signals from GNSS satellites and reinforcement information using the receiver 3, the vehicle 1 can perform centimeter-level high-precision positioning and safely perform autonomous driving.

However, if the reinforcement information is deceived by an intentional deceptive signal, there is a possibility that the positioning accuracy of the vehicle 1 will decrease. Therefore, the electronic control device 2 of the present embodiment executes the process S3 of calculating the deception determination value and the deception determination process S4, and when the deception determination value exceeds the threshold value in the deception determination process S4, the electronic control device 2 of the vehicle 1 Decrease in positioning accuracy is determined (processing S5). Below, these processes S3, P4, and P5 will be explained in detail.

When the electronic control unit 2 starts processing S3 for calculating a deception determination value, the deception determination unit 23 acquires the absolute position of the feature based on the external world recognition result of the external world recognition unit 25 from the feature matching unit 21, and performs virtual recognition. The relative position of the feature with respect to the vehicle 1 is acquired from the unit 22 based on the GNSS positioning result. Further, the deception determination unit 23 calculates, for example, an error Err between the absolute position and the relative position of the feature.

As shown in FIG. 9, the features for which the error Err between the absolute position and the relative position is calculated by the deception determining unit 23 are, for example, signs SS1, SS2, SS3 in front of the vehicle 1, crosswalks C1, C2, A case is assumed in which the lane markings LM and lane markings LM are included. In this case, the lane marking line LM may be frayed due to wear or temporarily lost due to road construction, and the stability of detection by the external sensor 5 is lower than that of other features, resulting in positional errors. Err is likely to occur.

On the other hand, the signs SS1, SS2, and SS3 are, for example, less likely to be blocked by other vehicles V or other obstacles than other features, and the stability of detection by the external sensor 5 is higher than that of other features. It is high, and position error Err is less likely to occur. Therefore, in the process S3 of calculating the deception determination value, the deception determination unit 23 may use, for example, the recognition stability and recognition ease as shown in Table 1 below.

The recognition stability is, for example, an index indicating the ease with which a feature can be detected by the external sensor 5. In the example shown in FIG. 9 and Table 1, the signs SS1, SS2, and SS3 are set to 1.0, which has the highest recognition stability, because the possibility of being blocked by an obstacle is extremely low. Similarly, since the lane marking LM has a relatively low possibility of being blocked by an obstacle, the recognition stability is set to 0.9, which is relatively high. On the other hand, the recognition stability of the crosswalks C1 and C2 is set to 0.4, which is relatively low, because the crosswalks C1 and C2 may be blocked by a preceding vehicle, for example.

The recognition ease is, for example, an index indicating the ease of recognition of the own vehicle's position by the terrestrial objects detected by the external sensor 5. In the example shown in Figure 9 and Table 1, the signs SS1, SS2, and SS3 are small in number compared to other features, and if successful detection, it will be easy for the vehicle to distinguish them from other features in the vicinity. Since the position can be easily recognized, the recognition ease is set to 0.9, which is relatively high. In addition, crosswalks C1 and C2 are relatively easy to distinguish from other features because they only exist in some places, but there are cases where they are installed both before entering and after passing an intersection. If only one of the pre-approach and post-passing detections is successful, the recognition ease level is set to 0.5, which is relatively low, because if they are mixed up, the vehicle's position may be misjudged. On the other hand, since lane markings LM exist everywhere and it is difficult to distinguish a specific lane marking LM, the recognition ease is set to 0.1, which is the lowest.

Although not included in Table 1, the recognition ease is divided into a component along the direction of travel of the own vehicle (vertical component) and a component perpendicular to the direction of travel of the own vehicle (lateral component). It can also be set. In other words, for example, lane markings painted with solid lines can be detected in the vertical direction but do not provide a clue for recognizing the vehicle's position, so the recognition ease is set to a low value such as 0.0, while the horizontal Even if the direction can be detected, it may be mistaken for an adjacent lane, but if the driving lane can be identified, the lateral position within the lane can be recognized, so recognition is relatively easy. Set it to a low value of 0.5. As a result, the ease of recognizing the vehicle's position based on detected features can be treated separately in the vertical and horizontal directions, making it possible to recognize the vehicle's position flexibly. Alternatively, it becomes possible to improve accuracy in one of the lateral directions.

That is, in the example shown in Table 1, the recognition stability increases closer to 1.0 as the feature is less likely to be occluded by obstacles, and the recognition ease increases to 1.0 as the feature is easier to recognize. .0 is increasing. Note that the recognition stability shown in Table 1 is an example, and the size of the feature (the larger the size, the higher the recognition ease), the number of lanes on the road (the smaller the number, the higher the recognition stability), and the The setting may be based on the height of the object (the higher the height, the higher the recognition stability). In addition, the recognition ease is the probability of confusing the recognition target with another recognition target (the higher the possibility, the lower the recognition ease), so for example, the confusion matrix used in machine learning, It may be calculated or set based on distance or the like.

Further, the deception determination unit 23 uses the error Err between the absolute position and the relative position of the feature and the error duration time to calculate a deception determination value for determining a decrease in GNSS positioning accuracy due to the deception signal. In the example shown in FIG. 9, at time t1, an error Err between the absolute position and relative position of the lane marking LM detected by the external sensor 5 occurs, and this error Err varies from before time t1 to time t2. It continues to occur.

In this case, the deception determination unit 23 calculates the deception determination value DV in accordance with the magnitude of the error Err and the error duration time in process S3. Here, the deception determination unit 23 may calculate the deception determination value DV using the above-mentioned recognition stability and recognition ease. The deception determination unit 23 calculates the deception determination value DV, for example, by multiplying the error Err between the absolute position and the relative position of the lane marking LM by the recognition stability and recognition ease of the lane marking LM. As described above, when the recognition ease of the lane marking LM is set low, even if the error Err is relatively large, the value of the deception determination value DV is relatively small.

Thereafter, as shown in FIG. 2, the deception determination unit 23 executes a process S4 to determine whether the calculated deception determination value DV exceeds a predetermined threshold Th. As shown in FIG. 9, from time t1 to time t2, the deception determination value DV is significantly lower than the threshold Th. Therefore, the deception determination unit 23 determines in process S4 that the deception determination value DV does not exceed the predetermined threshold Th (NO). After that, the electronic control device 2 ends the processing flow shown in FIG. 2 and starts it again.

As a result, as shown in FIG. 9, at time t2, the sign SS1 is detected by the external sensor 5, and after going through processes S1 and S2, the error between the absolute position and relative position of the sign SS1 is determined by the deception determination unit 23 in process S3. Assume that Err is calculated to be approximately zero. For example, as described above, the sign SS1 has high recognition stability and recognition ease, and the reliability of the absolute position calculated in the process S1 is high. That is, at time t2, since it is unlikely that the GNSS reinforcement information is deceived, the deception determination unit 23 reduces the deception determination value DV or sets it to zero.

As a result, at time t2, the deception determination unit 23 determines in process S4 that the deception determination value DV does not exceed the predetermined threshold Th (NO), and the electronic control device 2 performs the process shown in FIG. End the flow and start it again. After that, at time t4, error Err between the absolute position and relative position of the crosswalk C1 occurs, and the deception judgment value DV increases, and from time t5 to time t9, error Err between the absolute position and relative position of the lane marking LM. is occurring continuously. In this way, the deception determination unit 23 increases the deception determination value DV as the magnitude of the error Err and the error duration time during which the error Err continues to occur increase.

In the example shown in FIG. 9, at time t6, the rear edge of the crosswalk C1 is blocked by another vehicle V and is not detected by the external sensor 5. However, as described above, the recognition stability of the crosswalk C1 is set to be relatively low, so even if it is not detected by the external sensor 5, the influence on the deception determination value DV is small.

Furthermore, at time t11, the error Err between the absolute position and the relative position of the lane marking LM is large, but the recognition ease of the lane marking LM is set to be relatively low. On the other hand, at the same time, the error Err between the absolute position and the relative position of the crosswalk C2, whose recognition ease is set relatively high, becomes almost zero. In this case, the deception determination value DV calculated by the deception determination unit 23 is less influenced by the error Err of the lane marking LM, which has a low degree of recognition, and is largely influenced by the crosswalk C2, which has a high degree of recognition.

After that, as shown in FIG. 9, at time t13, the error Err between the absolute position and relative position of sign SS2 increases, and at time t15, the error Err between the absolute position and relative position of sign SS3 is calculated in the same way. do. The recognition stability and recognition ease of the signs SS2 and SS3 are set relatively high compared to other terrestrial features, for example, as described above. Further, as described above, the deception determination unit 23 increases the deception determination value DV as the magnitude of the error Err between the absolute position and the relative position of the feature and the error duration increase.

As a result, it is assumed that the deception determination value DV exceeds the threshold Th at time t15. Then, in process S4 shown in FIG. 2, the deception determination unit 23 determines that the deception determination value DV exceeds the threshold Th (YES), and in the subsequent process S5, determines whether the positioning accuracy has decreased due to the deception signal. do. In this process S5, the deception determination unit 23 determines that, for example, there is no abnormality in the positioning state based on the output of the receiver 3 that receives signals from GNSS satellites, and the deception determination value DV exceeds the threshold Th. In this case, it may be determined whether the positioning accuracy has decreased due to the deceptive signal.

Thereafter, the electronic control device 2 terminates the processing flow shown in FIG. 2, and outputs the positioning result by GNSS and the determination result of the decrease in positioning accuracy due to the deception signal from the deception determination unit 23 to the vehicle control device 6. Note that when the deception determination unit 23 determines that the positioning accuracy has decreased due to the deception signal, the deception determination unit 23 may output the determination result to the positioning calculation unit 24. When the positioning calculation unit 24 receives a determination result indicating a decrease in positioning accuracy due to the deception signal from the deception determination unit 23, the positioning calculation unit 24 changes the method of acquiring reinforcement information, for example. Specifically, for example, when the reinforcement information has been acquired via the Internet, the positioning calculation unit 24 switches the acquisition method of the reinforcement information to via a GNSS satellite.

Further, the positioning calculation unit 24 may operate as follows when determining whether the reception status of signals from GNSS positioning satellites has deteriorated as described above. For example, when the deception determining unit 23 determines that the positioning accuracy has decreased due to the deceptive signal and the reception status of the signal from the positioning satellite has deteriorated, the positioning calculation unit 24 excludes the reinforcement information and calculates the positioning result. may be calculated.

Hereinafter, the operation of the electronic control device 2 of this embodiment will be explained.

As described above, the electronic control device 2 of this embodiment is configured to operate a vehicle 1 such as a self-driving car that performs high-precision positioning based on the output of the receiver 3 that receives signals and reinforcement information from GNSS satellites. will be installed on. The vehicle control device 6 mounted on the vehicle 1 causes the vehicle 1 to travel autonomously by controlling the actuators of each part of the vehicle 1 using, for example, the map information 4 and the positioning results obtained by the positioning calculation unit 24. Therefore, in order to ensure the safety of the vehicle 1, it is important to detect a decrease in positioning accuracy due to a deceptive signal that deceives reinforcement information. However, although the conventional aircraft position measuring device can determine whether or not a GPS signal can be used based on the reliability of the GPS signal, it cannot detect a decrease in positioning accuracy due to a deceptive signal.

On the other hand, as described above, the electronic control device 2 of this embodiment compares the external world recognition result based on the detection result of the feature by the external sensor 5 with the map information 4 to obtain the absolute position of the feature. A feature matching section 21 is provided. Further, the electronic control device 2 performs virtual recognition corresponding to the external world recognition result of the external world recognition unit 25 based on the positioning result based on the output of the receiver 3 that receives signals and reinforcement information from the positioning satellite and the map information 4. It includes a virtual recognition unit 22 that generates a result VRR and obtains the relative position of a feature. Further, the electronic control device 2 controls the electronic control unit 2 to determine whether the deception signal is used when the deception determination value DV, which increases as the error Err between the absolute position and the relative position of the feature and the error duration increases, exceeds the threshold Th. It includes a deception determining section 23 that determines a decrease in positioning accuracy.

With such a configuration, the electronic control device 2 of the present embodiment uses the feature matching unit 21 to match the recognition result of the feature detected by the external sensor 5 with the map information 4, and determines the absolute position of the feature. can be obtained. Furthermore, the electronic control device 2 of the present embodiment uses the GNSS positioning results and the map information 4 to generate a virtual recognition result VRR corresponding to the external world recognition result based on the detection result of the external sensor 5, using the virtual recognition unit 22. By doing so, it is possible to obtain the relative position of the terrestrial object based on the GNSS positioning result. Then, the electronic control device 2 causes the deception determination unit 23 to determine that the deception determination value DV, which increases as the magnitude of the error Err between the absolute position and the relative position of the feature and the error duration increases, exceeds the threshold Th. In this case, it is possible to determine whether the positioning accuracy has deteriorated due to the deceptive signal.

Therefore, according to the electronic control device 2 of this embodiment, it is possible to detect the error in the GNSS positioning result that gradually increases due to the deception of the reinforcement information, and to determine the decrease in the GNSS positioning accuracy due to the deceptive signal. . Further, according to the electronic control device 2 of the present embodiment, by using the deception determination value DV, for example, a sudden failure in detecting a target occurs in the external sensor 5, or a signal from a GNSS satellite Even if a short-term abnormality occurs in the reception status of the reinforcement signal, it is possible to prevent erroneous determination of a decrease in positioning accuracy due to the deceptive signal.

Further, in the electronic control device 2 of the present embodiment, the deception determination unit 23 multiplies the error Err by the recognition stability indicating the ease of detecting the feature by the external sensor 5 and the recognition ease indicating the ease of identifying the feature. Then, the deception judgment value DV is calculated.

With such a configuration, the electronic control device 2 of the present embodiment can reduce the influence of the error Err between the absolute position and the relative position of a feature with low recognition stability or a feature with low recognition ease on the deception determination value DV. can do. Furthermore, the electronic control device 2 of the present embodiment can increase the influence of the error Err between the absolute position and the relative position of a feature with high recognition stability or a feature with high recognition ease on the deception determination value DV. . Therefore, according to the electronic control device 2 of this embodiment, it becomes possible to more accurately detect a decrease in positioning accuracy due to a deceptive signal.

Moreover, the electronic control device 2 of this embodiment further includes a positioning calculation unit 24 that calculates a positioning result based on the output of the receiver 3 and determines whether or not there is an abnormality in the positioning state. Then, the deception determination unit 23 determines that the positioning accuracy has decreased due to the deception signal when there is no abnormality in the positioning state and the deception determination value DV exceeds the threshold Th.

With such a configuration, the electronic control device 2 of the present embodiment can interrupt determination of a decrease in positioning accuracy due to a deceptive signal when it is detected that an abnormality has occurred in the positioning state of the GNSS. Therefore, according to the electronic control device 2 of the present embodiment, when an abnormality occurs in the positioning state of GNSS, it is possible to prevent the erroneous determination that the positioning accuracy has decreased due to the deceptive signal, and it is possible to prevent the decrease in the positioning accuracy due to the deceptive signal. It becomes possible to make more accurate judgments.

Furthermore, in the electronic control device 2 of this embodiment, the positioning calculation unit 24 changes the method for acquiring reinforcement information when the deception determining unit 23 determines that the positioning accuracy has decreased due to the deceptive signal. More specifically, for example, when the positioning calculation unit 24 has been acquiring reinforcement information via the Internet, by switching to acquiring reinforcement information via a satellite, it is possible to use reinforcement information that is not deceived. Thus, the safety of automatic driving of the vehicle 1 can be improved.

Furthermore, in the electronic control device 2 of this embodiment, the positioning calculation unit 24 determines whether the reception state of signals from GNSS positioning satellites has deteriorated. In addition, when the deception determining unit 23 determines that the positioning accuracy has decreased due to the deceptive signal and the reception state of the signal from the positioning satellite has deteriorated, the positioning calculation unit 24 excludes the reinforcement information and generates the positioning result. Calculate.

With such a configuration, the electronic control device 2 of the present embodiment is not affected by the deception signal even when the reception condition of the signal from the positioning satellite deteriorates and the reinforcement information is deceived by the deception signal. It becomes possible to perform the minimum positioning. Therefore, according to the electronic control device 2 of this embodiment, the safety of automatic driving of the vehicle 1 can be improved.

As described above, according to the present embodiment, it is possible to provide the electronic control device 2 that can detect a decrease in positioning accuracy due to a deceptive signal.

[Embodiment 2]
Embodiment 2 of the electronic control device according to the present disclosure will be described below with reference to FIG. 10. FIG. 10 is a block diagram showing Embodiment 2 of an electronic control device according to the present disclosure. The electronic control device 2 of this embodiment differs from the electronic control device 2 of the above-described first embodiment in that it further includes an external environment determination section 26 and the content of the deception determination process S4 by the deception determination section 23. Other points of the electronic control device 2 of this embodiment are the same as those of the electronic control device 2 of the above-described first embodiment, so similar parts are given the same reference numerals and description thereof will be omitted.

As shown in FIG. 10, the electronic control device 2 of this embodiment further includes an external environment determination section 26. The external environment determining unit 26 determines whether the detection accuracy of the external sensor 5 has decreased due to the external environment. More specifically, the external environment determination unit 26 determines, for example, the brightness, sunny weather, rain, and snow around the vehicle 1 based on the external world recognition result recognized by the external world recognition unit 25 based on the detection result of the external world sensor 5. , a decrease in the detection accuracy of the external sensor 5 due to weather conditions such as fog or the external environment including dirt on the external sensor 5 is determined. The deception determination unit 23 increases the threshold Th of the deception determination value DV when the external environment determination unit 26 determines that the detection accuracy of the external sensor 5 has decreased.

According to the electronic control device 2 of this embodiment, it is possible to not only achieve the same effects as the electronic control device 2 of the above-described first embodiment, but also to determine whether the detection accuracy of the external sensor 5 is reduced due to the external environment. Misjudgment in process S4 can be prevented. Therefore, according to the electronic control device 2 of the present embodiment, it is possible to more accurately determine a decrease in positioning accuracy due to a deceptive signal.

[Embodiment 3]
Embodiment 3 of the electronic control device according to the present disclosure will be described below with reference to FIGS. 11 and 12. FIG. 11 is a block diagram showing a third embodiment of an electronic control device according to the present disclosure. FIG. 12 is a table showing an example of operational constraints by the constraint level output unit 27 included in the electronic control device 2 of this embodiment shown in FIG. 11.

The electronic control device 2 of the present embodiment is different from the electronic control device 2 of the first embodiment described above in that it further includes a constraint level output section 27 and that the output of the constraint level output section 27 is input to the vehicle control device 6. It is different from Other points of the electronic control device 2 of this embodiment are the same as those of the electronic control device 2 of the above-described first embodiment, so similar parts are given the same reference numerals and description thereof will be omitted.

The electronic control device 2 of the present embodiment further includes a constraint level output unit 27 that outputs a constraint level of the operation related to automatic driving of the vehicle 1 based on the reinforcement information acquisition method and whether or not the reinforcement information is excluded. The constraint level output unit 27 outputs, for example, the time series of the error Err between the absolute position and the relative position of the feature inputted from the deception determination unit 23 and the reinforcement information acquisition route or the exclusion of the reinforcement route by the positioning calculation unit 24. Based on the presence or absence, operation constraints to be output to the vehicle control device 6 are determined.

More specifically, the constraint level output unit 27, for example, as shown in FIG. Based on the exclusion status, the level Lv of the operation restriction of the vehicle control device 6 is determined. More specifically, when it is input that the error Err has been less than or equal to the predetermined error threshold Err1 for the past 60 seconds and that the reinforcement information acquisition method is via the Internet, the constraint level output unit 27 outputs the constraint level Lv Set to 1. As a result, all operational restrictions on the vehicle control device 6 are released, and all operations of the vehicle control device 6 are permitted.

In addition, the error Err for the past 60 seconds is less than or equal to a predetermined error threshold Err1, and the error Err for the most recent 10 seconds is greater than the error threshold Err1 and smaller than the error threshold Err2, and the reinforcement information acquisition method When it is input that is via the Internet, the constraint level output unit 27 sets the constraint level Lv to 2. As a result, the operation of the vehicle control device 6 is subject to operational restrictions such as being inhibited from automatically approaching a loading platform in a limited area such as a logistics truck accumulation area. Furthermore, on general roads, operational restrictions are imposed, such as preventing right or left turns at intersections with a certain road width or less.

Further, if it is input that the error Err has been equal to or greater than the predetermined error threshold Err2 for the last 10 seconds or more and that the reinforcement information acquisition method is via satellite, the constraint level output unit 27 outputs the constraint level Lv Set to 3. As a result, the operation of the vehicle control device 6 is such that, for example, automatic parking within a parking space is inhibited in a limited area such as a logistics truck accumulation area, and a route that passes through a route that is less than a certain road width on a general road is set. Operational restrictions are imposed, such as changing the route to a wider road when

Further, when it is input that the error Err is equal to or higher than the predetermined error threshold Err3 for 10 seconds or more and that reinforcement information is excluded, the constraint level output unit 27 sets the constraint level Lv to 4. do. As a result, the operation of the vehicle control device 6 is, for example, restricted from automatic driving and switched to driving support. Further, when it is input that the error Err is greater than or equal to the predetermined error threshold Err4 and that reinforcement information is excluded, the constraint level output unit 27 sets the constraint level Lv to 5. As a result, the vehicle control device 6 cancels automatic driving and driving support, for example, and accepts manual driving by the driver.

As described above, the electronic control device 2 of the present embodiment further includes the constraint level output unit 27 that outputs the constraint level of the operation related to automatic driving of the vehicle based on the reinforcement information acquisition method and whether or not the reinforcement information is excluded. We are prepared. With such a configuration, the electronic control device 2 of this embodiment can improve the safety of automatic driving of the vehicle 1 by the vehicle control device 6.

[Embodiment 4]
Embodiment 4 of the electronic control device according to the present disclosure will be described below with reference to FIGS. 13 to 16. A block diagram showing Embodiment 4 of the electronic control device according to the present disclosure is the same as any one of FIG. 1, FIG. 10, and FIG. 11 similarly to Embodiments 1 to 3. However, in the present embodiment, the external world recognition unit 25 generates external world recognition results including recognition results of actual features around the vehicle 1 based on the detection results of the external world sensor 5; This embodiment differs from Embodiments 1 to 3 described above in that the recognition confidence of actual features around 1 is generated and output to the feature matching unit 21. Other points of the electronic control device 2 of this embodiment are the same as those of the electronic control device 2 of the above-described embodiments 1 to 3, so similar parts are given the same reference numerals and description thereof will be omitted.

FIG. 13 is a block diagram of the deception determination unit 23 shown in FIG. 1, FIG. 10, or FIG. 11. The deception determining section 23 includes a feature position estimation result receiving section 30 that receives the output from the feature matching section 21 and a recognition confidence receiving section 31 . The deception determination unit 23 also includes a virtual recognition result reception unit 32 that receives the output from the virtual recognition unit 22, a recognition result verification unit 33, a consistency determination unit 34, a sudden change determination unit 35, and a deception determination result transmission unit. It has a section 36.

FIG. 14 is a flow diagram illustrating the operation of the recognition result verification unit 33 of FIG. 13. In step S<b>11 , the feature position estimation result receiving unit 30 acquires the absolute position of the feature based on the external world recognition result of the external world recognition unit 25 and the map information 4 from the feature matching unit 21 . In addition, the recognition confidence receiving unit 31 receives the recognition certainty of the feature based on the certainty of the external world recognition in the external world recognition unit 25 and the certainty when comparing the absolute position of the external world recognition unit 25 with the map information 4. get.

In step S12, the virtual recognition result receiving unit 32 determines the relative position of the feature with respect to the vehicle 1 based on the GNSS positioning result output from the virtual recognition unit 22, the recognition stability, and the recognition ease. get.

Next, in steps S13 to S18, the recognition result verification unit 33 receives the position estimation results and positions of features in the external world from the feature position estimation result receiving unit 30, recognition confidence receiving unit 31, and virtual recognition result receiving unit 32. Based on the error estimation result, the recognition confidence, the relative position of the feature based on the map, and the recognition stability/ease of recognition, a virtual recognition result at either the feature recognition time of the external sensor 5 or the positioning time of the receiver 3. It is determined whether each feature included in the image was recognized or not.

In step S13, the following procedures from step S14 to step S17 are executed for all the features in the virtual recognition result. In step S14, the terrestrial features of the external world recognition results that correspond to the terrestrial features that are the targets of the virtual recognition results are estimated. This estimation of the corresponding feature can be performed using, for example, the ICP (Iterative Closest Point) method.

In step S15, for the associated feature, the difference between the feature position of the virtual recognition result and the feature position of the external world recognition result, that is, the position error is smaller than a predetermined threshold, and the feature position of the external world recognition result is If the recognition certainty is greater than a predetermined threshold, the recognition result verification is determined to be a success (Yes in S15), and if not, the recognition result verification is determined to be a failure (No in S15). Note that, for example, like the similarity in the template matching method, the confidence is an index that increases and approaches 1 when the certainty of matching is high, and decreases and approaches 0 when the certainty is low.

In step S16, the recognition result of the current execution of the recognition result verification unit 33 is updated. Specifically, the number of recognition targets is added by 1, the number of successful recognition verifications is added by 1, and the position errors are accumulated to update the error amount. Note that the recognition target here refers to a feature, and in this paper, the two will be described without making any particular distinction.

Similarly, in step S17, the recognition result in the current execution of the recognition result verification unit 33 is updated. Specifically, the number of recognition targets is added by 1, position errors are accumulated, and the error amount is updated. When the execution is completed for all the features in the virtual recognition results, the process advances to step S18, where the number of recognition targets, the number of successful recognition verifications, the cumulative error amount, and the error amount of each feature are recorded in a recording device (not shown), and the matching is performed. It is output to the determination section 34.

FIG. 15 is a flow diagram illustrating the operation of the matching determination unit 34 of FIG. 13. The matching determination unit 34 determines that there is a difference between the external world recognition result based on the external world sensor 5 and the positioning calculation result based on the GNSS receiver 3 in either the feature recognition time of the external world sensor 5 or the positioning time of the receiver 3. Determine whether or not.

In step S21, the matching determination unit 34 receives the number of recognition targets, the number of successful recognition verifications, the cumulative error amount, and the error amount of each feature output from the recognition result verification unit 33. In step S22, the matching determination unit 34 obtains the recognition stability and recognition ease of each feature output from the virtual recognition result receiving unit 32. In step S23, the following steps S24 and S25 are executed for all the features of the virtual recognition result.

In step S24, if the amount of error for each recognition target (=the amount of error for each feature) is greater than or equal to a predetermined threshold (Yes in S24), there is a high possibility that there is a difference between the external world recognition result and the positioning calculation result. , the process proceeds to step S25 where the mismatch certainty factor is added. If the error amount of each recognition target is not greater than or equal to the predetermined threshold (No in S24), the mismatch certainty is not changed.

In step S25, mismatch certainty is added based on the recognition ease and recognition stability. The recognition ease and recognition stability are expressed as values from 0 to 1 as shown in Table 1, and based on this and the amount of error for each recognition target, the amount of addition of the inconsistency certainty is calculated, for example, as follows: Calculated using formula (1).

(Amount of addition) = (Amount of error) × (Ease of recognition) × (Recognition stability) ... (1)

When the processing is completed for all the virtual recognition result features, in step S26, if the calculated inconsistency certainty is equal to or higher than a predetermined threshold, there is a difference between the external world recognition result and the positioning calculation result at the relevant time. It is determined that (step S27). If the calculated mismatch certainty is not equal to or greater than the predetermined threshold, it is determined that the external world recognition result and the positioning calculation result match at the time (step S28). Here, the determination result is, for example, 1 if there is a difference, and 0 if there is no difference. In step S29, the determination result and the mismatch reliability are recorded in a recording device (not shown) and are output to the sudden change determining section 35.

FIG. 16 is a flow diagram illustrating the operation of the sudden change determination section 35 of FIG. 13. The sudden change determination unit 35 determines whether the position error suddenly increases when a recognition error occurs in external world recognition, or when the position error gradually increases when the positioning radio waves or reinforcement information of satellite positioning is gradually deceived. Separate the events that occur.

In step S31, the sudden change determination unit 35 obtains the accumulated results of the recognition result verification unit 33, such as the number of recognition targets, the number of successful recognition verifications, the cumulative error amount, and the error amount of each feature. In step S32, the sudden change determination unit 35 obtains the consistency determination result and the accumulation result of the mismatch certainty factor, which are the outputs of the consistency determination unit 34.

In step S33, a moving average of a predetermined time interval is calculated for the matching determination result, and if the value is greater than or equal to a predetermined threshold value, it is determined that there is a mismatch between the external sensor result and the positioning result (Yes in S33. Step S34). Conversely, if it is not greater than the predetermined threshold, it is determined that there is no mismatch between the external sensor result and the positioning result (No in S33. Step S35).

In step S36, the variance value of the error amount of the recognition result verification unit 33 included in the predetermined time interval is less than or equal to a predetermined threshold value, and the maximum value of the error amount of the recognition result verification unit 33 included in the predetermined time interval is a predetermined value. If the position error is greater than or equal to the threshold value, it is determined that the position error is a sudden change (Yes in S36. S37). If not, it is determined that the position error is a continuous change (No in S36, S38).

In step S39, if there is a mismatch between the external sensor result and the positioning result (step S34), the position error is a continuous change (step S38), and the mismatch confidence is greater than or equal to a predetermined threshold, the GNSS positioning It determines that the signal or reinforcement information is deceptive, and outputs the determination result.

The deception determination result transmission section 36 smoothes the output of the sudden change determination section 35 over time in order to suppress fluctuating output of the determination result, and then outputs the smoothed output to the vehicle control device 6 . The processing after this output is the same as in Embodiments 1 to 3, so the details will be omitted.

According to the electronic control device 2 of this embodiment, not only can the same effects as the electronic control device 2 of the above-described embodiments 1 to 3 be achieved, but also mutually similar Even in situations where trade name processing tends to become unstable, such as when the recognition target is nearby, it is possible to prevent erroneous determination in the deception determination process S4. Therefore, according to the electronic control device 2 of the present embodiment, it is possible to more accurately determine a decrease in positioning accuracy due to a deceptive signal.

[Embodiment 5]
Embodiment 5 of the electronic control device according to the present disclosure will be described below with reference to FIG. 17. FIG. 17 is a block diagram showing Embodiment 5 of an electronic control device according to the present disclosure. The block diagram of FIG. 17 is the same as that of FIG. 10 as in the second embodiment, except that this embodiment includes an external environment determination result receiving section 37 that receives the results of the external environment determining section 26. Other points of the electronic control device 2 of this embodiment are the same as those of the electronic control device 2 of the above-described embodiments 1 to 3, so similar parts are given the same reference numerals and description thereof will be omitted.

The deception determining section 23 includes a feature position estimation result receiving section 30 that receives the output from the feature matching section 21 and a recognition confidence receiving section 31 . The deception determination unit 23 also includes a virtual recognition result reception unit 32 that receives the output from the virtual recognition unit 22, a recognition result verification unit 33, a consistency determination unit 34, a sudden change determination unit 35, and a deception determination result transmission unit 36. , and an external environment determination result receiving section 37.

The external environment determination result receiving unit 37 determines an external environment index value that increases and approaches 1 when the external environment provides good performance for the external sensor, and decreases and approaches 0 when the external environment degrades the performance. Received from the external environment determination unit 26. For example, in the case of an image sensor, this external environment index value becomes 1 during cloudy weather during the day, and approaches 0 during nighttime or rainy weather.

In step S15 shown in FIG. 14, the recognition result verification unit 33 determines whether the recognition result verification is successful or unsuccessful. In the loop of step S13, the external environment determination result receiving unit sets a threshold value for calculating the position error between the virtual recognition result feature position and the external world recognition result feature position for the associated feature. The difference is that it is varied depending on the result. Specifically, the threshold value A(t) for determining the position error of a feature at a certain time t is set such that A(t+1)=A( t)+α×{Amax−A(t)}. Here, 0<α<1. Furthermore, when the external environment index value is less than or equal to a predetermined threshold, the threshold B(t) for determining the recognition certainty of a feature is set to B(t+1)=B(t)−β×{B (t)-Bmin}. Here, 0<β<1.

As a result, in a situation where recognition performance deteriorates due to the external environment, the threshold value A increases so as to gradually approach the maximum value Amax at a speed α. Further, the threshold value B decreases so as to gradually approach the minimum value Bmin at a speed β. As a result, the determination in step S15 is likely to be Yes.

In step S24 shown in FIG. 15, the matching determination unit 34 sets a threshold value for calculating the position error between the feature position of the virtual recognition result and the feature position of the external world recognition result for the associated feature. , is varied according to the result of the external environment determination result receiving section. Specifically, the threshold value C(t) for determining the position error of a feature at a certain time t is set such that when the external environment index value is less than or equal to a predetermined threshold value, at the next time t+1, C(t+1)=C( t)+γ×{Cmax−C(t)}. Here, 0<γ<1.

As a result, in a situation where the recognition performance deteriorates due to the external environment, the threshold value C increases so as to gradually approach the maximum value Cmax at a speed γ. As a result, the determination in step S25 is likely to be Yes. Since the subsequent processing is the same as in the fourth embodiment, the subsequent processing will be omitted.

According to the electronic control device 2 of this embodiment, it is possible to not only achieve the same effects as the electronic control device 2 of the above-described embodiments 1 to 4, but also to use the diagnosis result of the external environment. Even in situations where recognition performance tends to become unstable, such as during stormy weather, it is possible to prevent erroneous determination in the deception determination process S4. Therefore, according to the electronic control device 2 of the present embodiment, it is possible to more accurately determine a decrease in positioning accuracy due to a deceptive signal.

Although the embodiment of the electronic control device according to the present disclosure has been described above in detail using the drawings, the specific configuration is not limited to this embodiment, and design changes may be made within the scope of the gist of the present disclosure. etc., they are included in the present disclosure.

2: Electronic control device, 21: Feature matching unit, 22: Virtual recognition unit, 23: Deception determination unit, 24: Positioning calculation unit, 26: External environment determination unit, 27: Constraint level output unit, 3: Receiver, 4: Map information, 5: External sensor, B1-B4: Building (feature), DV: Deception judgment value, Err: Error, LM: Lane marking (feature), P1-P4: Telephone pole (feature), R1-R2: Road (feature), RS1: Road marking (feature), SS1-SS3: Sign (feature), Th: Threshold, VRR: Virtual recognition result.

Claims (7)

a feature matching unit that obtains the absolute position of the feature by comparing the external world recognition result based on the detection result of the feature by the external sensor with map information;
Generate a virtual recognition result corresponding to the external world recognition result based on the map information and a positioning result based on the output of a receiver that receives signals and reinforcement information from a positioning satellite, and obtain the relative position of the feature. a virtual recognition unit,
Decrease in positioning accuracy due to a deception signal is determined when a deception determination value, which increases as the magnitude of the error between the absolute position and the relative position of the feature and the error duration increases, exceeds a threshold value. A determination section;
An electronic control device comprising: The deception determination unit calculates the deception determination value by multiplying the error by a recognition stability indicating ease of detection of the feature by the external sensor and a recognition ease indicating ease of identification of the feature. Item 1. The electronic control device according to item 1. further comprising a positioning calculation unit that calculates the positioning result based on the output of the receiver and determines whether there is an abnormality in the positioning state,
The electronic control device according to claim 1, wherein the deception determination unit determines a decrease in positioning accuracy due to a deception signal when there is no abnormality in the positioning state and the deception determination value exceeds the threshold. The electronic control device according to claim 3, wherein the positioning calculation unit changes the method for acquiring the reinforcement information when the deception determination unit determines that the positioning accuracy has decreased due to the deception signal. The positioning calculation unit determines whether the reception state of the signal from the positioning satellite has deteriorated, and the deception determination unit determines whether the positioning accuracy has deteriorated due to the deception signal, and the positioning calculation unit determines whether the reception state of the signal from the positioning satellite has deteriorated. The electronic control device according to claim 3, wherein the positioning result is calculated by excluding the reinforcement information when deterioration is determined. further comprising an external environment determination unit that determines a decrease in detection accuracy of the external sensor due to the external environment,
The electronic control device according to claim 1, wherein the deception determination unit increases the threshold value of the deception determination value when the external environment determination unit determines that the detection accuracy of the external sensor has decreased. The electronic control device according to claim 5, further comprising a constraint level output unit that outputs a constraint level of operations related to automatic driving of a vehicle based on the method of acquiring the reinforcement information and whether or not the reinforcement information is excluded.

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