WO2018168961A1

WO2018168961A1 - Own-position estimating device

Info

Publication number: WO2018168961A1
Application number: PCT/JP2018/010068
Authority: WO
Inventors: 雄一南口; 健司三宅; 竜巳杉山; 和美伊佐治; 稔岡田; 謙太高橋
Original assignee: 株式会社デンソー
Priority date: 2017-03-16
Filing date: 2018-03-14
Publication date: 2018-09-20
Also published as: WO2018168956A1

Abstract

A host vehicle position estimating unit (204) of an own-position estimating device (20) assesses the presence or absence of a lane correspondence candidate indicating whether a within-lane position corresponds to any lane specified by lane information, on the basis of mutual relationships between the within-lane position, an absolute position, and errors therein, and estimates the position of a host vehicle corresponding to map information on the basis of the assessment result.

Description

Self-position estimation device

Cross-reference of related applications

This application consists of Japanese Patent Application No. 2017-248745 filed on December 26, 2017, Japanese Patent Application No. 2017-248744 filed on December 26, 2017, and March 16, 2017. Patent application No. 2017-051066 filed in Japan and claims the benefit of its priority, the entire contents of which are incorporated herein by reference .

This disclosure relates to a self-position estimation apparatus.

2. Description of the Related Art A self-localization device described in Patent Document 1 below is known as an apparatus for estimating a self-position of a vehicle. The self-localization device described in Patent Document 1 below uses existing road infrastructure such as white lines and road signs on the road for position calculation using GPS (Global Positioning System), inertial devices, and vehicle speed pulses. This will increase the orientation accuracy.

Specifically, the azimuth angle of the white line reflected in the image captured using the camera is calculated, and the difference between the azimuth angle of the white line stored in the azimuth database by the Kalman filter and the azimuth angle of the white line calculated from the image is calculated. Based on this, error estimation is performed.

JP 2008-249639 A

In Patent Document 1, since an image captured using a camera is used, an error cannot be estimated correctly if the image cannot be clearly obtained, such as when the weather is bad. In particular, if position estimation at the lane level is required, the technique described in Patent Document 1 cannot cope with it. In advanced driving support and automatic driving, it is necessary to specify the lane and the driving position within the lane, so that more accurate self-position estimation is required.

This disclosure aims to provide a self-position estimation device capable of highly accurate position estimation at the lane level.

The present disclosure relates to a self-position estimation device, a map information acquisition unit (201) that acquires map information including lane information that identifies a lane in which a vehicle can travel, and a position in a lane in which the host vehicle is traveling. An in-lane position detecting unit (202) for detecting in-lane position information for specifying the in-lane position, and an absolute position estimating unit (203) for estimating absolute position information for specifying the absolute position of the host vehicle and its error, A vehicle position estimation unit (204) that estimates the position of the vehicle corresponding to the lane information included in the map information based on the map information, the position information in the lane, and the absolute position information. . Based on the correlation between the in-lane position, the absolute position, and its error, the host vehicle position estimation unit determines whether there is a lane corresponding candidate for which of the lanes specified by the lane information. Based on the determination result, the position of the host vehicle corresponding to the map information is estimated.

Since the presence / absence of a lane candidate corresponding to whether the position in the lane corresponds to one of the lanes is determined, the position of the host vehicle can be estimated in consideration of the position in the lane and the absolute position in the lane candidate.

The reference numerals in parentheses described in the “Summary of the Invention” and “Claims” indicate the correspondence with the “Mode for Carrying Out the Invention” to be described later. It does not indicate that the “claims” are limited to the “modes for carrying out the invention” described below.

FIG. 1 is a block configuration diagram illustrating a functional configuration of the self-position estimation apparatus according to the embodiment. FIG. 2 is a diagram for describing self-position estimation according to the present embodiment. FIG. 3 is a block configuration diagram illustrating a functional configuration of the self-position estimation apparatus according to the embodiment. FIG. 4 is a diagram for describing self-position estimation according to the present embodiment. FIG. 5 is a diagram for describing self-position estimation according to the present embodiment. FIG. 6 is a diagram for describing self-position estimation according to the present embodiment. FIG. 7 is a diagram for explaining self-position estimation according to the present embodiment. FIG. 8 is a diagram for describing self-position estimation according to the present embodiment. FIG. 9 is a diagram for describing self-position estimation according to the present embodiment. FIG. 10 is a diagram for describing self-position estimation according to the present embodiment. FIG. 11 is a diagram for describing self-position estimation according to the present embodiment. FIG. 12 is a diagram for describing self-position estimation according to the present embodiment. FIG. 13 is a diagram for describing self-position estimation according to the present embodiment. FIG. 14 is a diagram for describing self-position estimation according to the present embodiment.

Hereinafter, the present embodiment will be described with reference to the accompanying drawings. In order to facilitate the understanding of the description, the same constituent elements in the drawings will be denoted by the same reference numerals as much as possible, and redundant description will be omitted.

The self-position estimation apparatus 10 according to the present embodiment will be described with reference to FIG. The self-position estimation apparatus 10 is configured as a computer including a calculation unit such as a CPU, a storage unit such as a RAM and a ROM, and an interface unit for exchanging data with various sensors as hardware components. Subsequently, functional components of the self-position estimation apparatus 10 will be described.

The self-position estimation apparatus 10 includes a self-position measurement unit 101, a vehicle momentum measurement unit 102, a white line recognition unit 103, a surrounding environment measurement unit 104, a route information acquisition unit 105, a dead reckoning 106, and a position estimation unit 108. A map information acquisition unit 109, a travel lane estimation unit 110, and a map information storage unit 120.

The self-position measuring unit 101 is a part for measuring the position of the own vehicle by GNSS (Global Navigation Satellite System). The self-position measuring unit 101 calculates a vehicle measurement position that is a navigation measurement position of the vehicle based on navigation signals received from a plurality of navigation satellites. The self-position measuring unit 101 outputs the calculated own vehicle measurement position to the dead reckoning 106 and the position estimating unit 108.

The vehicle momentum measuring unit 102 is a part that receives signals from sensors such as an acceleration sensor, a vehicle speed sensor, and a gyro sensor and measures the amount of movement of the vehicle. The vehicle momentum measuring unit 102 outputs information on the momentum such as the vehicle speed, the azimuth angle, the yaw rate, and the acceleration to the dead reckoning 106 and the position estimating unit 108.

The white line recognition unit 103 is a part for recognizing a white line that divides a lane using image data captured by the camera. The white line recognition unit 103 outputs information on the presence or absence of a white line and information on the type of white line to the position estimation unit 108.

The surrounding environment measurement unit 104 is a part that measures the weather and satellite arrangement information. The surrounding environment measurement unit 104 outputs weather and satellite arrangement information to the position estimation unit 108.

The route information acquisition unit 105 is a part that acquires the destination of the vehicle and the route to the destination from the navigation system. The route information acquisition unit 105 outputs information indicating the destination and the route to the travel lane estimation unit 110.

The dead reckoning 106 automatically determines the position of the host vehicle in a place where positioning is difficult with GNSS alone, based on the host vehicle measurement position output from the host position measurement unit 101 and the momentum information output from the vehicle momentum measurement unit 102. It is a part which calculates as a car gyro position. The dead reckoning 106 outputs the calculated own vehicle gyro position of the own vehicle to the position estimation unit 108.

The map information acquisition unit 109 is a part that acquires map information including lane information on which the vehicle can travel. The map information acquisition unit 109 reads the map information stored in the map information storage unit 120 and outputs the read map information to the position estimation unit 108 and the travel lane estimation unit 110.

The position estimation unit 108 is a part that estimates a corrected vehicle position that is a corrected position of the vehicle based on the map information and the vehicle measurement position and / or the vehicle gyro position. The position estimation unit 108 estimates the corrected vehicle position by superimposing the accuracy of the map information and the vehicle measurement position and / or the vehicle gyro position. As the probability of the map information and the accuracy of the absolute position of the vehicle, a probability distribution representing the probability may be used, or a numerical value representing the certainty may be used.

An example of a method for estimating the corrected vehicle position of the position estimation unit 108 will be described with reference to FIG. In FIG. 2, three lanes L1, L2, and L3 are set. A solid white line SLa is provided on the left side in the traveling direction of the lane L1. A broken line white line BLa is provided between the lane L1 and the lane L2. A broken line white line BLb is provided between the lane L2 and the lane L3. A solid white line SLb is provided on the right side in the traveling direction of the lane L3. The lane center line L1c is a line indicating the center of the lane L1. The lane center line L2c is a line indicating the center of the lane L2. The lane center line L3c is a line indicating the center of the lane L3.

In the example shown in FIG. 2, the map probability distribution PDm of the lane center line L1c, the lane center line L2c, and the lane center line L3c is regarded as the likelihood of the map information. First, it is assumed that the host vehicle is at the host vehicle measurement position Pa. For convenience of explanation, it is assumed that the host vehicle is traveling along the lane L1 from the host vehicle measurement position Pa. The own vehicle gyro position may be used instead of the own vehicle measurement position.

The position estimation unit 108 estimates the corrected vehicle position Pb by superimposing the vehicle position probability distribution PDca and the map information probability distribution PDm at the vehicle measurement position Pa at the first estimation timing. The corrected host vehicle position Pb is corrected to the lane center line L1c side by the distance d1 as compared with the case where the correction is not performed from the host vehicle measurement position Pa.

The position estimation unit 108 estimates the corrected vehicle position Pc by superimposing the vehicle position probability distribution PDcb at the corrected vehicle position Pb and the map information probability distribution PDm at the next estimation timing. The corrected vehicle position Pc is corrected to the lane center line L1c side by the distance d2 as compared to the case where no correction is made from the corrected vehicle position Pb.

The map probability distribution is not limited to the probability distribution of the lane center line, but a probability distribution indicating the certainty of the map information is used. Further, a map probability distribution offset by the driver's bag or road shape may be used. The road shape includes information such as the road width and the presence or absence of an adjacent lane.

Subsequently, a self-position estimation apparatus 20 that is a modification of the self-position estimation apparatus 10 according to the present embodiment will be described with reference to FIG. The self-position estimation apparatus 20 is configured as a computer including a calculation unit such as a CPU, a storage unit such as a RAM and a ROM, and an interface unit for exchanging data with various sensors as hardware components. Subsequently, functional components of the self-position estimation apparatus 20 will be described.

The self-position estimation apparatus 20 includes a map information acquisition unit 201, a lane position detection unit 202, an absolute position estimation unit 203, a vehicle position estimation unit 204, a comparison target detection unit 205, and a map information storage unit 211. It is equipped with.

The map information acquisition unit 201 is a part that acquires map information including lane information in which the vehicle can travel. The map information acquisition unit 201 reads the map information stored in the map information storage unit 211 and outputs the read map information to the own vehicle position estimation unit 204.

The in-lane position detection unit 202 is a part that detects in-lane position information that specifies an in-lane position that is a position in the lane in which the host vehicle is traveling. The in-lane position detection unit 202 detects in-lane position information based on the surrounding environment and lane conditions captured by the camera. The in-lane position detection unit 202 outputs in-lane position information to the own vehicle position estimation unit 204.

As shown in FIG. 5, the in-lane position detection unit 202 identifies the in-lane position from the lateral deviation and turning angle of the host vehicle, and generates in-lane position information.

The absolute position estimation unit 203 shown in FIG. 3 is a part that estimates absolute position information that specifies the absolute position of the host vehicle and its error. The absolute position estimation unit 203 outputs the estimated absolute position information to the own vehicle position estimation unit 204. The absolute position estimation unit 203 can estimate the absolute position information by various methods.

The absolute position estimation unit 203 can estimate absolute position information that specifies the absolute position of the host vehicle and its error by GNSS. The absolute position estimation unit 203 calculates a host vehicle measurement position that is a navigation measurement position of the host vehicle based on navigation signals received from a plurality of navigation satellites, and estimates absolute position information based on the host vehicle measurement position. Can do.

The absolute position estimation unit 203 can also receive signals from sensors such as an acceleration sensor, a vehicle speed sensor, and a gyro sensor, and measure the amount of movement of the host vehicle. The absolute position estimation unit 203 calculates, based on the information indicating the own vehicle measurement position and the amount of movement of the own vehicle, the position of the own vehicle at a location where positioning is difficult by GNSS alone, as the own vehicle gyro position. To estimate absolute position information.

FIG. 4 shows an example of a mode estimated by the absolute position estimation unit 203. As shown in FIG. 4, the absolute position estimation unit 203 estimates “candidate 1” and “candidate 2” as absolute position information. Candidate 1 estimates current position X ⁱ _t based on the amount of movement of the vehicle from the previous position X ⁱ _t-1 . The current position X ⁱ _t is position information including an estimation error. Candidate 2 estimates current position X ^j _t in consideration of the amount of movement of the host vehicle from previous position X ^j _t−1 . The current position X ^j _t is position information including an estimation error.

The own vehicle position estimation unit 204 shown in FIG. 3 is a part that estimates the position of the own vehicle corresponding to the lane information included in the map information based on the map information, the in-lane position information, and the absolute position information. is there. The own vehicle position estimation unit 204 determines whether or not there is a lane candidate corresponding to which of the lanes in which the lane position is specified by the lane information based on the correlation between the lane position, the absolute position, and its error. Then, based on the determination result, the position of the host vehicle corresponding to the map information is estimated. If there is no lane candidate, it may be determined that the sensor is abnormal.

Referring to FIGS. 6 and 7, an example of a method in which the own vehicle position estimation unit 204 estimates the position of the own vehicle will be described. “Gyro estimation candidate 1” and “Gyro estimation candidate 2” shown in FIG. 6 correspond to “candidate 1” and “candidate 2” shown in FIG. 4, and the estimation error of “candidate 1” in FIG. And the estimation error of “candidate 2” is superimposed on the map information. The “camera observation lateral deviation” shown in FIG. 6 is obtained by superimposing the “lateral deviation” shown in FIG. 5 on the map information for each lane.

The calculation of the estimated position and error by the gyro sensor as absolute position information will be described with reference to FIG. In FIG. 8, P _t-1 is the covariance matrix at the previous time. y _t-1 is the horizontal position of the previous time. φ _t-1 is the attitude angle at the previous time. The lateral position y _t and the posture angle φ _t are calculated by the following equation. ε is system noise.

The estimated covariance matrix Pt by the gyro sensor is calculated by the following equation using the covariance matrix P _t-1 at the previous time. M is a covariance matrix of the system noise ε, and is set based on the error specification of the gyro sensor. T represents a transposed matrix.

The information shown in FIG. 6 is sensor-fused by a Kalman filter. The Kalman gain K is calculated by the following equation. Q is an observation error matrix, and is set from the error characteristics of the observed values at the positions in the lane. H is a 2 × 2 unit matrix.

Using the Kalman gain K, an estimation result and a covariance matrix are calculated by the following equations. Z _t is a vector in which the horizontal position and the posture angle detected by the gyro sensor are vertically arranged. ΔZ is the difference between the observation value by the camera of the in-lane position and the attitude angle and the estimation result Z _t by the gyro sensor.

Associate the estimation result by the gyro sensor calculated in this way with the in-lane position observation result by the camera. An example of association is shown in FIG. In the example shown in FIG. 9A, since the error of the estimation result by the gyro sensor is small, it is estimated that the host vehicle is present in lane 2, and the lane 2 is associated with the in-lane position observation result by the camera. can do. In this case, the lane corresponding candidate is lane 2.

On the other hand, in the example shown in FIG. 9B, since the error of the estimation result by the gyro sensor is large, the possibility that the own vehicle exists in both lane 1 and lane 2 cannot be discarded. Therefore, there is a candidate that the own vehicle exists in lane 1 and a candidate that the own vehicle exists in lane 2. In this case, the lane corresponding candidates are lane 1 and lane 2.

In the present embodiment, when there are a plurality of lane matching candidates, the comparison target detection unit 205 is provided to reject any lane matching candidate. The comparison object detection unit 205 is a part that detects comparison object information that is different from information used for detection of in-lane position information and estimation of absolute position information. As comparison target information, line type information for determining lanes, road shape information in map information, position information by GPS, and the like are used. The own vehicle position estimating unit 204 determines whether or not the selection condition is satisfied based on the lane candidate and the comparison target information, and executes the selection of the lane candidate.

In the example shown in FIG. 10, when both lanes are lane candidates, line type information is used as comparison target information. The comparison target detection unit 205 detects a solid line on the left side and a broken line on the right side as line type information. Based on the line type recognition result, the host vehicle position estimation unit 204 estimates that the host vehicle is present in the lane on the left side in the traveling direction, rejects the lane on the right side in the traveling direction as a candidate, and selects lane candidates. Execute. In this case, the lane corresponding candidate is the lane on the left side in the traveling direction.

In the example shown in FIG. 11, GPS information is used as comparison target information when both lanes are lane candidates. The comparison target detection unit 205 acquires position information including an error indicated by a broken-line circle in the figure as GPS information. Based on the GPS information, the host vehicle position estimation unit 204 estimates that the host vehicle is present in the lane on the left side in the traveling direction, rejects the lane on the right side in the traveling direction as a candidate, and executes selection of lane candidates. . In this case, the lane corresponding candidate is the lane on the left side in the traveling direction.

In the example shown in FIG. 12, map information is used as comparison target information when candidates exist in both lanes. The comparison target detection unit 205 detects the road shape as the comparison target information as the map information. The vehicle position estimation unit 204 rejects the lane on the left side of the traveling direction as a candidate because the candidate on the left side in the traveling direction does not overlap with the map information as time elapses, and executes selection of the lane candidate. In this case, the candidate corresponding to the lane is the lane on the right side in the traveling direction, and the lane on the left side in the traveling direction after the lane change.

The own vehicle position estimation unit 204 can notify that the reliability of the estimation result is low when there are a plurality of lane candidates or when the absolute position error is greater than or equal to a predetermined value. As shown in FIG. 13, when there is one candidate, if the error variance is small, it is determined that the reliability is high. Even if there is only one candidate, if the error variance is large, it is determined that the reliability is low. When there are a plurality of candidates, it is determined that the reliability is low because the lane cannot be specified even when the variance of the error is small. If there are a plurality of candidates and the error variance is large, it is determined that the reliability is low.

The operation of the self-position estimation apparatus 20 will be described with reference to FIG. FIG. 14A shows the position recognition result in the lane. FIG. 14B shows the recognition result of the vehicle position. FIG. 14C shows the number of lane candidates. FIG. 14D shows the reliability.

At time t1, the position in the lane is recognized. The position of the host vehicle is recognized on the right lane. At time t2, the position in the lane is not recognized. For this reason, the error in recognizing the position of the host vehicle is large, and the reliability is low. At time t3, the in-lane position recognition is performed again, so that the error in the position recognition of the host vehicle is reduced and the reliability is also increased. At time t4, the position in the lane is not recognized, and the reliability is low again.

At time t5, the gyro estimation error is large, and it is not possible to determine in which lane the vehicle is present. At time t6, both lanes are lane candidates. From time t6 to time 8, estimation is continued with two lane candidates.

At time 9, since the right lane candidate deviates from the map information, the right lane candidate is rejected. After time t10, since the lane is changed from the left lane to the right lane, estimation is continued with the right lane as the lane corresponding candidate.

The self-position estimation device 20 according to the present embodiment is a map information acquisition unit 201 that acquires map information including lane information that identifies a lane in which the vehicle can travel, and a position in the lane in which the host vehicle is traveling. In-lane position detection unit 202 that detects in-lane position information that specifies the in-lane position, an absolute position estimation unit 203 that estimates absolute position information that specifies the absolute position of the host vehicle and its error, map information, in-lane A host vehicle position estimation unit 204 that estimates the position of the host vehicle corresponding to the lane information included in the map information based on the position information and the absolute position information. The own vehicle position estimation unit 204 determines whether or not there is a lane candidate corresponding to which of the lanes in which the lane position is specified by the lane information based on the correlation between the lane position, the absolute position, and its error. Then, based on the determination result, the position of the host vehicle corresponding to the map information is estimated.

In the present embodiment, the vehicle position estimation unit 204 determines a lane candidate based on the degree of overlap between the position in the lane including the error distribution and the absolute position including the error distribution. By superimposing the error distribution in both the in-lane position and the absolute position, the position of the host vehicle can be estimated by reflecting the error.

In the present embodiment, the host vehicle position estimation unit 204 continues the position estimation of the host vehicle corresponding to the plurality of lane candidates until the selection condition is satisfied, when a plurality of lane candidates are determined, and satisfies the selection condition. And selecting a plurality of lane candidates. When the selection condition is satisfied, a plurality of discriminated lane candidates can be selected and rejected, so that the position estimation load of the host vehicle corresponding to the plurality of lane candidates can be reduced.

The self-position estimation apparatus 20 according to the present embodiment further includes a comparison target detection unit 205 that detects comparison target information different from information used for detection of in-lane position information and absolute position information. The own vehicle position estimating unit 204 determines whether or not the selection condition is satisfied based on the lane candidate and the comparison target information, and executes the selection of the lane candidate.

Since the lane candidate candidates are selected based on the comparison target information, the lane candidate candidates can be selected from a different point of view. By increasing the reliability of the comparison target information, unnecessary lane candidates can be rejected.

In the present embodiment, the comparison target detection unit 205 is line type information detected from the image captured by the imaging device provided in the host vehicle as the comparison target information, and classifies the lane in which the host vehicle is traveling. The running line type information that identifies the line type of at least one lane boundary line is detected, and the vehicle position estimation unit 204 is traveling with the map line type information that identifies the line type of the lane boundary line included in the lane information. If the line type information does not match, it is determined that the selection condition is satisfied, and the lane candidate that does not match is rejected.

When the map line type information specifies that there are broken lines on the right and left sides as viewed from the host vehicle, for example, the line type specified by the running line type information is a continuous line on the right side and a broken line on the left side Since the map line type information and the running line type information are inconsistent, it is determined that the selection condition is satisfied, and the corresponding lane candidate is rejected. Since it is considered that the reliability of the traveling line type information detected from the image captured by the imaging device is high, unnecessary lane candidates can be rejected.

In the present embodiment, the absolute position estimation unit 203 estimates absolute position information based on the detection result of the gyro sensor, and the comparison target detection unit 205 calculates the comparison target information based on navigation signals received from a plurality of navigation satellites. The vehicle position estimation unit 204 detects and determines that the selection condition is satisfied when the lane candidate and the position of the host vehicle specified by the comparison target information do not overlap, and the lane candidate that does not overlap. Reject.

Since the comparison target information is generated based on navigation signals received from a plurality of navigation satellites, if the reception status of the navigation signals is good, the reliability can be improved more than the absolute position information based on the detection result of the gyro sensor. Yes, you can reject unnecessary lane candidates.

In the present embodiment, the own vehicle position estimation unit 204 determines that the selection condition is satisfied when there are a plurality of lane candidates and at least one of the plurality of lane candidates does not overlap with the lane information, and the overlap does not occur. Dismiss the corresponding lane candidate.

If there are multiple lane candidates and some of the lane candidates do not overlap with the lane information included in the map information, the overlapping lane candidates are selected and the lanes that do not overlap By rejecting the candidates, unnecessary lane candidates can be rejected.

In this embodiment, the own vehicle position estimation unit 204 notifies that the reliability of the estimation result is low when there are a plurality of lane candidates or when the absolute position error is greater than or equal to a predetermined value. By notifying that the reliability of the estimation result is low, the user can recognize the decrease in the reliability.

The embodiment has been described above with reference to specific examples. However, the present disclosure is not limited to these specific examples. Those in which those skilled in the art appropriately modify the design of these specific examples are also included in the scope of the present disclosure as long as they have the features of the present disclosure. Each element included in each of the specific examples described above and their arrangement, conditions, shape, and the like are not limited to those illustrated, and can be changed as appropriate. Each element included in each of the specific examples described above can be appropriately combined as long as no technical contradiction occurs.

Claims

A self-position estimation device,
A map information acquisition unit (201) for acquiring map information including lane information for specifying a lane in which the vehicle can travel;
An in-lane position detection unit (202) for detecting in-lane position information for specifying an in-lane position that is a position in the lane in which the host vehicle is traveling;
An absolute position estimator (203) for estimating absolute position information identifying the absolute position of the host vehicle and its error;
An own vehicle position estimating unit (204) for estimating the position of the own vehicle corresponding to the lane information included in the map information based on the map information, the in-lane position information, and the absolute position information; With
The own vehicle position estimation unit, based on a correlation between the position in the lane, the absolute position, and an error thereof, a lane corresponding candidate as to which of the lanes the lane position is specified by the lane information A self-position estimation device that determines the presence or absence of the vehicle and estimates the position of the host vehicle corresponding to the map information based on the determination result.
The self-position estimation apparatus according to claim 1,
The self-vehicle position estimating unit determines the lane corresponding candidate based on a degree of overlap between the in-lane position including an error distribution and the absolute position including the error distribution.
The self-position estimation apparatus according to claim 1 or 2,
The vehicle position estimation unit continues the position estimation of the vehicle corresponding to the plurality of lane candidates until a selection condition is satisfied when a plurality of lane candidates are determined, A self-position estimation device that performs a selection of a plurality of lane candidates.
The self-position estimation apparatus according to claim 3,
Furthermore, a comparison object detection unit (205) for detecting comparison object information different from information used for detection of the position information in the lane and estimation of the absolute position information,
The own vehicle position estimation unit determines whether or not the selection condition is satisfied based on the lane corresponding candidate and the comparison target information, and executes the selection of the lane corresponding candidate.
The self-position estimation apparatus according to claim 4,
The comparison target detection unit is line type information detected from an image captured by an imaging device provided in the host vehicle as the comparison target information, and at least one lane that divides a lane in which the host vehicle is traveling Detects the running line type information that identifies the line type of the boundary line,
The vehicle position estimation unit determines that the selection condition is satisfied when the map line type information for specifying the line type of the lane boundary included in the lane information and the running line type information do not match. The self-position estimation apparatus which rejects the said lane applicable candidate which is inconsistent.
The self-position estimation apparatus according to claim 4,
The absolute position estimation unit estimates the absolute position information based on a detection result of a gyro sensor,
The comparison target detection unit detects the comparison target information based on navigation signals received from a plurality of navigation satellites,
The own vehicle position estimation unit determines that the selection condition is satisfied when the lane corresponding candidate and the position of the own vehicle specified by the comparison target information are not overlapped, and the lane corresponding is not overlapped. Self-position estimation device that rejects candidates.
The self-position estimation apparatus according to claim 3,
The own vehicle position estimation unit satisfies the selection condition when there are a plurality of lane candidates and at least one of the lane candidates does not overlap with the lane information included in the map information. A self-position estimation device that judges that the lane candidate that is no longer overlapping is rejected.
The self-position estimation apparatus according to any one of claims 1 to 7,
The said own vehicle position estimation part is a self-position estimation apparatus which notifies that the reliability of an estimation result is low, when there exists two or more said lane applicable candidates, or when the error of the said absolute position is more than predetermined.