JP2018169207A - Vehicle position detector - Google Patents

Abstract

To provide a vehicle position detector with which it is possible to accurately specify the position of the own vehicle on a map even when positioning by a navigation satellite is difficult.SOLUTION: A curvature information selection unit 303 of a vehicle position detector 1 selects, as a curvature for specifying a vehicle position, one of a first curvature after correction that is derived by correcting a first curvature calculated by a first curvature information calculation unit 301 on the basis of a vehicle position change amount of the own vehicle relative to a traffic lane and a second curvature calculated by a second curvature information calculation unit 302. A vehicle position specifying unit 304 compares a first approximate straight line extracted from a first curvature data group after correction or a second approximate straight line extracted from a second curvature data group with an approximate straight line per section extracted from a curvature-on-map data group to examine the degree of match of both approximate straight lines, and searches for a section where the degree of match is largest. Then, the point at the end of a section where the degree of match of both approximate straight lines is largest is specified as the vehicle position of the own vehicle.SELECTED DRAWING: Figure 1

Description

  The present invention relates to a vehicle position detection device that detects a vehicle position of a host vehicle on a map using lane curvature information.

  In vehicles such as automobiles, as a method of positioning the position of the own vehicle, satellite navigation that receives signals from navigation satellites such as GPS satellites, and azimuth and relative movement detected by sensors mounted on the vehicle are used. Autonomous navigation is known based on autonomous navigation, and is usually based on satellite navigation with higher accuracy.When the driving environment cannot receive signals from navigation satellites such as in tunnels, positioning by autonomous navigation is recommended. It is common to switch to.

  For example, in Patent Document 1, regarding a portable navigation device that does not use a vehicle speed signal from a vehicle, when the GPS signal cannot be received, the second vehicle speed is set based on the time change of the image captured by the in-vehicle camera. A technique for calculating and calculating the position of the vehicle by autonomous navigation using the second vehicle speed is disclosed.

JP 2009-168614 A

  However, as described in Patent Document 1, when positioning by the navigation satellite is difficult, it is inevitable that an integration error will occur in the relative movement amount of the vehicle calculated from the vehicle speed simply by switching to positioning by autonomous navigation. However, the required positioning accuracy is not always satisfied.

  The present invention has been made in view of the above circumstances, and provides a vehicle position detection device capable of accurately identifying the position of the host vehicle on a map even when positioning by a navigation satellite is difficult. It is aimed.

  A vehicle position detection device according to an aspect of the present invention provides a map database holding map data including information related to a lane on which a vehicle travels, and curvature information of a travel locus of the host vehicle based on a travel state of the host vehicle. A first curvature information calculation unit that calculates the first curvature information, and a first curvature information correction unit that corrects the first curvature information based on a change amount of the vehicle position of the host vehicle with respect to a lane recognized from an image obtained by imaging the front of the host vehicle. 1 curvature information correction unit, a second curvature information calculation unit that calculates, as second curvature information, curvature information of a lane recognized from an image captured in front of the host vehicle, and a curvature of the lane obtained from the map database Vehicle position that identifies the current position of the host vehicle on the map data of the map database based on the correlation between the information and the corrected first curvature information or the second curvature information And a tough.

  According to the present invention, even if positioning by a navigation satellite is difficult, the position of the host vehicle can be accurately identified on a map.

Configuration diagram of vehicle position detection device Illustration of travel route by image recognition Explanatory drawing which shows the amount of angle change by the change of the lateral position to a lane Explanatory drawing which shows 1st curvature data Explanatory drawing which shows 2nd curvature data Explanatory drawing showing the curvature data of the section near the current position on the map Explanatory drawing which shows the curvature data which shifted the area start point of FIG. Explanatory drawing which shows the curvature data which shifted the section start point of FIG. 6 further from FIG. Flowchart of vehicle position detection processing

  Embodiments of the present invention will be described below with reference to the drawings. In FIG. 1, reference numeral 1 denotes a vehicle position detection device that is mounted on a vehicle such as an automobile and detects the vehicle position of the host vehicle, and provides vehicle position data used for various driving support controls including route guidance and automatic driving for the driver. To do. In the present embodiment, the vehicle position detection device 1 includes an external environment recognition device 10, a map information processing device 20, and a position detection processing device 30, and these devices are connected to a network via a communication bus 150. ing.

  The external environment recognition device 10 is based on detection information of an object around the host vehicle by various devices such as an in-vehicle camera unit 11, a radar device 12 such as a millimeter wave radar or a laser radar, and infrastructure communication such as road-to-vehicle communication or vehicle-to-vehicle communication. The external environment around the host vehicle is recognized based on the acquired traffic information, the map information from the map information processing device 20, the position information of the host vehicle from the position detection processing device 30, and the like.

  Hereinafter, as external environment recognition processing in the external environment recognition apparatus 10, processing that recognizes the external environment by processing an image captured by the camera unit 11 will be mainly described. The camera unit 11 is, for example, a stereo camera composed of two cameras that capture the same object from different viewpoints, and is composed of a shutter-synchronized camera having an image sensor such as a CCD or CMOS. The camera unit 11 is configured, for example, such that two cameras are disposed on the left and right sides in the vehicle width direction with a predetermined baseline length in the vicinity of a room mirror inside the front window at the upper part of the vehicle interior.

  For a pair of left and right images captured by the camera unit 11 as a stereo camera, for example, a pixel shift amount (parallax) at a corresponding position of the left and right images is obtained by stereo matching processing, and the pixel shift amount is converted into luminance data or the like. A distance image is generated. The point on the distance image is a point in the real space based on the principle of triangulation, where the vehicle width direction of the own vehicle, that is, the left-right direction is the X axis, the vehicle height direction is the Y axis, and the vehicle length direction, that is, the distance direction is the Z axis. The coordinates are converted into a white line (lane) on the road on which the host vehicle travels, an obstacle, a vehicle traveling in front of the host vehicle, and the like are three-dimensionally recognized.

  The map information processing apparatus 20 includes a map database DB, and identifies the vehicle position of the host vehicle on the map data of the map database DB from the position data measured by the position detection processing device 30. In the map database DB, for example, navigation map data that is mainly referred to when displaying vehicle route guidance and the current position of the vehicle, and driving support control including automatic driving, which is more detailed than the map data, are provided. Map data for travel control which is referred to when performing is stored.

  In the map data for navigation, the previous node and the next node are linked to the current node via links, and information about traffic lights, road signs, buildings, etc. is stored in each link. ing. On the other hand, high-definition map data for traveling control has a plurality of data points between a node and the next node. These data points include road shape data such as curvature, lane width, and shoulder width for each road lane, and data for driving control such as road azimuth, road white line type, and number of lanes. Is stored together with attribute data such as date.

  The map information processing apparatus 20 collates the positioning result of the own vehicle position with the map data, and presents driving route guidance and traffic information based on the collation result to the driver via a display device (not shown). Further, the map information processing apparatus 20 is a road control map such as road shape data such as curvature, lane width, and shoulder width for each lane of the road on which the host vehicle travels, road azimuth, road white line type, and number of lanes. Information is transmitted to another control device via the communication bus 150.

  Further, the map information processing apparatus 20 performs maintenance management of the map database DB, and verifies the nodes, links, and data points of the map database DB so that they are always kept up-to-date, and for areas where no data exists on the database. Create and add new data to build a more detailed database. Data update in the map database DB and addition of new data are performed by collating the position data measured by the position detection processing device 30 with the data stored in the map database DB.

  The position detection processing device 30 detects the vehicle position of the host vehicle by using positioning based on signals from a plurality of navigation satellites 200 such as GPS satellites and positioning using in-vehicle sensors such as the gyro sensor 32 and the vehicle speed sensor 33. . In positioning by a plurality of navigation satellites 200, a signal including information on the trajectory and time transmitted from the navigation satellite 200 is received via the receiver 31, and based on the received signal, the vehicle's own position is calculated as longitude, Position as an absolute position including latitude, altitude, and time information.

  On the other hand, places where positioning by navigation satellites is difficult, such as places where radio waves from navigation satellites such as inside tunnels cannot be received, or places where reception accuracy is reduced due to multipath due to reflections of radio waves by many high-rise buildings, etc. Then, the position detection processing device 30 calculates curvature information of the lane in which the host vehicle is traveling from the traveling state of the host vehicle or an image ahead of the host vehicle. Then, a correlation between the calculated curvature information of the lane and the curvature information of the lane obtained from the high-definition map data is examined to identify the current position of the host vehicle.

  In the present embodiment, the lane curvature information includes the curvature for each lane stored in the map database DB, the curvature calculated from the traveling state of the host vehicle, and the curvature of the traveling lane recognized from the image. . The curvature data based on the running state or the image is acquired in a time series over a predetermined section, and the correlation with the curvature of the corresponding section on the map is examined. The curvature of the lane on the map is stored in the map database DB as the curvature of the route set at the center of the lane.

  For this reason, the position detection processing device 30 has a first curvature information calculation unit 301 and a first curvature information correction as a function unit for accurately detecting the vehicle position of the host vehicle when positioning by the navigation satellite is difficult. 301A, the 2nd curvature information calculation part 302, the curvature information selection part 303, and the vehicle position specific | specification part 304 are provided. These functional units make it possible to reliably detect the vehicle position of the host vehicle and always grasp the accurate vehicle position even when the reception state of the signal from the navigation satellite deteriorates.

The first curvature information calculation unit 301 calculates the curvature of the travel locus calculated from the traveling state of the host vehicle as the first curvature K1. Specifically, the first curvature K1 is obtained by dividing the travel distance ΔS in the traveling direction of the host vehicle per predetermined time by the angle change amount Δθ of the traveling azimuth of the host vehicle, as shown in the following equation (1). It can be approximated by the value obtained. The movement distance ΔS is calculated from the vehicle speed sensor 33. Further, the angle change amount Δθ of the traveling azimuth can be obtained from the yaw rate of the vehicle detected by the gyro sensor 32.
K1 = ΔS / Δθ (1)

  The first curvature K1 based on the traveling locus of the host vehicle of the formula (1) is corrected based on the traveling state of the host vehicle based on image information from the camera unit 11 by the first curvature information correcting unit 301A. The correction of the first curvature K1 by this image information will be described later.

  The second curvature information calculation unit 302 recognizes the traveling lane in which the host vehicle is traveling from the image ahead of the host vehicle imaged by the camera unit 11, and calculates the recognized curvature of the traveling lane as the second curvature K2. To do. The second curvature K2 can be obtained by recognizing the road white line as a lane from the image and identifying the recognized lane with an approximate expression.

  A road white line as a lane can be recognized by extracting a point group that is a candidate for a white line from an image and calculating a straight line or a curve connecting the candidate points. For example, in a white line detection region set on an image, an edge whose luminance changes more than a predetermined value is detected on a plurality of search lines set in the horizontal direction (vehicle width direction), and one set of white lines is set for each search line. A start point and a white line end point are detected, and an intermediate region between the white line start point and the white line end point is extracted as a white line candidate point.

Then, the time series data of the spatial coordinate positions of the white line candidate points based on the vehicle movement amount per unit time is processed to calculate a model that approximates the left and right white lines, and the white line is recognized by this approximate model. As an approximate model of the white line, for example, a model approximated by a curve such as a quadratic expression can be used. This curve can be a route along which the host vehicle follows.
X = A · Z ² + B · Z + C (2)

  As shown in FIG. 2, coefficients A, B, and C in the equation (2) represent components that constitute a path (the path indicated by the two-dot chain line at the center position of the left and right white lines) along which the host vehicle follows. The coefficient A represents the curvature component of the path, and the second curvature information calculation unit 302 obtains the curvature based on the coefficient A as the second curvature K2. The coefficient B in the equation (2) is the yaw angle deviation of the route with respect to the own vehicle (angle deviation between the longitudinal axis of the own vehicle and the route (tangent)) θy, and the coefficient C is the lateral direction of the route with respect to the own vehicle. This represents the lateral position deviation δ in the (X-axis direction).

In the case where the steering control is performed using the route in the center of the lane in FIG. 2 as a target route on which the host vehicle travels, for example, a target steering angle αref as shown in the following equation (3) is set, and this target steering angle αref The actual steering angle is controlled so as to match.
αref = Gff · K2 + Gp · δf + Gi · ∫δdt + Gy · θy + Gd · dθy / dt (3)
Gff: Feed forward gain with respect to curvature K2 of the target route Gp: Proportional gain with respect to lateral position deviation δf of a predetermined distance when traveling at the current steering angle Gi: Integral gain with respect to current lateral position deviation δ Gy: Target route and own vehicle Feedback gain for relative yaw angle θy with respect to vehicle Gd: differential gain for relative yaw angle θy between target path and host vehicle

  The second curvature K2 by the second curvature information calculation unit 302 described above is calculated from the route shape at the center of the lane, similarly to the curvature of the lane on the map data. On the other hand, the first curvature K1 by the first curvature information calculation unit 301 described above depends on the traveling route of the host vehicle and may not indicate the curvature of the route in the center of the lane. That is, if the vehicle position cannot be accurately detected, the lateral position of the vehicle may deviate from the center position of the lane. If the first curvature K1 is used as it is, the correlation with the curvature on the map data can be properly grasped. There is a possibility that it cannot be done.

  Therefore, the first curvature information correction unit 301A uses the first curvature K1 calculated by the first curvature calculation unit 301 based on the change amount of the position of the host vehicle with respect to the lane calculated from the image as the lane center position. It is corrected to a value that represents the curvature of. Specifically, as shown in FIG. 3, the lateral position XT in the lane width direction of the host vehicle with respect to the lane at time T and the lateral position XT + 1 at time T + 1 are obtained from images, and the vehicle speed sensor 33 The travel distance ΔS in the traveling direction of the host vehicle at the time T to T + 1 is calculated based on the signal from.

Next, an angle change amount Δθ ′ with respect to the lane of the host vehicle is calculated from the difference ΔX between the lateral positions XT to XT + 1 at the time T to T + 1 and the movement distance ΔS, and from the angle change amount Δθ ′ with respect to the lane, The curvature K1 ′ shown in the following equation (1 ′) is obtained. The curvature K1 ′ indicates the curvature of the movement of the host vehicle with respect to the lane, and the difference between the first curvature K1 and the curvature K1 ′, that is, the angle change amount Δθ of the traveling azimuth of the host vehicle and the angle of the host vehicle with respect to the lane. In consideration of the difference from the change amount Δθ ′, the first curvature K1 can be corrected as the traveling locus of the host vehicle coincides with the lane center position.
K1 ′ = ΔS / Δθ ′ (1 ′)

  In the present embodiment, a value obtained by subtracting the curvature K1 ′ from the first curvature K1 is used as the corrected first curvature K1H (K1H ← K1−K1 ′). In the following curvature information selection unit 303 and vehicle position specifying unit 304, processing is performed for the first curvature K1H after correction.

  The curvature information selection unit 303 selects one of the first curvature K1H and the second curvature K2 as the vehicle position specifying curvature Kref and transmits it to the vehicle position specifying unit 304. The first curvature K1H and the second curvature K2 are calculated in a time-series manner for a predetermined distance in curvature data at a point where the curvature changes such as the entrance and exit of the curve, and are used for vehicle position specification. Curvature data selected as the curvature Kref is transmitted to the vehicle position specifying unit 304.

  In general, since the accuracy of the traveling lane recognized from the image decreases when the lane curve becomes steep, the curvature information selection unit 303 sets the first curvature as the curvature Kref for specifying the vehicle position when the curve is relatively steep. When K1H is selected and the curve is relatively gentle, the second curvature K2 is selected as the curvature Kref for specifying the vehicle position.

  Specifically, the curvature information selection unit 303 acquires the curvature Kmap on the map data at the vehicle position immediately before positioning by the navigation satellite becomes difficult, and the curvature Kmap on the map data is a threshold value Kc (for example, Kc = 1/400)) Check whether the following curve is relatively gentle. When the curvature Kmap is less than or equal to the threshold value Kc, the second curvature K2 is selected as the curvature Kref for specifying the vehicle position. On the other hand, when the curvature Kmap exceeds the threshold value Kc, the first curvature K1H is selected as the curvature Kref for specifying the vehicle position.

  The vehicle position specifying unit 304 checks the correlation between the curvature Kmap near the own vehicle position estimated on the map data and the curvature Kref for specifying the vehicle position from the map database DB, and specifies the vehicle position of the own vehicle. Specifically, the vehicle position of the host vehicle is specified by searching for a position on the map that has the curvature Kmap closest to the curvature Kref in the vicinity of the vehicle position last measured by the navigation satellite.

  For example, when the first curvature K1H is selected as the curvature Kref for specifying the vehicle position, the vehicle position specifying unit 304 stores the data of the first curvature K1H in the past every predetermined time as shown in FIG. The first approximate straight line L1 is extracted by applying the least square method to the data group obtained for a predetermined distance (for example, the past 50 m).

  When the second curvature K2 is selected as the curvature Kref for specifying the vehicle position, as shown in FIG. 5, the vehicle position specifying unit 304 sets the data of the second curvature K2 in increments of a predetermined time. The second approximate straight line L2 is extracted by applying the least square method to the data group obtained for the past predetermined distance (for example, the past 50 m).

  Further, as shown in FIG. 6, the vehicle position specifying unit 304 collects a data group of curvature Kmap in a section corresponding to a predetermined distance of the curvature Kref in the vicinity of the own vehicle position estimated on the map data. Generate an interval graph for. The section graph of the curvature data is shown in FIG. 6 as data groups Kmap2 and Kmap3 in FIGS. 7 and 8 in which the data group in FIG. 6 is Kmap1 and the section start point of the data group Kmap1 is shifted by a predetermined distance. A plurality of interval graphs with shifted start points are created, and approximate straight lines Lm1, Lm2, Lm3,.

  The vehicle position specifying unit 304 includes the first approximate straight line L1 extracted from the data group of the first curvature K1H or the second approximate straight line L2 extracted from the data group of the second curvature K2, and the curvature Kmap on the map. Are compared with the approximate straight lines Lm1, Lm2, Lm3,... For each section extracted from the data group, and the degree of coincidence between the two approximate lines is examined, and the section with the highest degree of coincidence is searched. Then, the end point of the section having the highest degree of coincidence between the two approximate lines is specified as the vehicle position of the host vehicle.

  Next, the vehicle position detection program process in the position detection processing device 30 will be described with reference to the flowchart shown in FIG.

  In this vehicle position detection process, first, in step S1, it is determined whether or not positioning by a navigation satellite is possible. For example, when the number of captured navigation satellites is greater than the number that can be measured and the accuracy of positioning by the captured navigation satellite is greater than or equal to a predetermined accuracy, it is determined that positioning by the satellite can be performed normally, and the process proceeds from step S1 to step S2. The current position on the map of the host vehicle is specified by comparing the position data based on the information from the satellite and the data stored in the map database DB.

  On the other hand, when the necessary number of satellites cannot be supplemented, such as in tunnels or under elevated buildings, or the required positioning accuracy cannot be secured due to the effects of multipath, and positioning by satellite is judged to be difficult Advances from step S1 to step S3, reads the curvature Kmap of the travel lane of the last point measured by the satellite from the map database DB, and compares it with the threshold value Kc (for example, Kc = 1/400), near the corresponding point Check whether the curve of the section is steep.

  In step 3, if Kmap> Kc and the curve is steep, the process proceeds to step S4, and the first curvature K1 based on the traveling locus of the host vehicle based on the signals from the gyro sensor 32 and the vehicle speed sensor 33 is calculated. Then, the process proceeds from step S4 to step S5, and as described with reference to FIG. 3, the first curvature K1 ′ calculated from the difference ΔX between the lateral positions XT to XT + 1 in the lane width direction of the host vehicle and the movement distance ΔS. After correcting the curvature K1, the corrected first curvature K1H is selected as a position specifying curvature, and a data group of the first curvature K1H in a predetermined section ahead of the current position is calculated in time series. .

  On the other hand, if Kmap ≦ Kc in step S3 and the curve is gentle, the process proceeds from step S3 to step S6 and the second curvature K2 of the travel lane recognized from the image ahead of the host vehicle captured by the camera unit 11 is obtained. Is selected as a curvature for specifying the position, and a data group of the second curvature K2 in a predetermined section ahead from the vicinity of the current position is calculated in time series.

  Thereafter, the process proceeds from step S5 or step S6 to step S7, and a first-order approximation line (first approximation line L1 or second N approximation) from a data group of curvature (first curvature K1H or second curvature K2) in a predetermined section. A straight line L2) is extracted, and the process proceeds to step S8. In step S8, a plurality of section graphs are generated by shifting the section start point by a predetermined distance in the vicinity of the estimated position of the host vehicle with respect to the curvature Kmap obtained from the map data, and approximate straight lines Lm1, Lm2 for each data group in each section. , Lm3,.

  Next, in step S9, the approximate straight line (approximate straight line L1 or approximate straight line L2) of the curvature data for specifying the position is compared with the approximate straight lines Lm1, Lm2, Lm3,. Search for a section with a high degree of coincidence. In step S10, the end point of the section searched as the section with the highest degree of coincidence between the two approximate lines is specified as the current position of the host vehicle on the map.

  As described above, in the present embodiment, the curvature information of the lane obtained from the map and the curvature information obtained by correcting the curvature information based on the traveling locus of the own vehicle based on the change amount of the vehicle position of the own vehicle with respect to the lane, or the image The position of the host vehicle on the map is specified by examining the correlation with the recognized curvature information of the lane. Thereby, even if positioning by the navigation satellite is difficult, the position of the own vehicle can be accurately detected.

DESCRIPTION OF SYMBOLS 1 Vehicle position detection apparatus 10 External environment recognition apparatus 11 Camera unit 12 Radar apparatus 20 Map information processing apparatus 30 Position detection processing apparatus 32 Gyro sensor 33 Vehicle speed sensor 200 Navigation satellite 301 1st curvature information calculation part 301A 1st curvature information correction Section 302 Second curvature information calculation section 303 Curvature information selection section 304 Vehicle position specifying section DB Map database L1 First approximate line L2 Second approximate line Lm1, Lm2, Lm3 Approximate line K1 First curvature K1H After correction 1st curvature K2 2nd curvature Kmap Curvature on map data

Claims (4)

A map database holding map data including information relating to the lane in which the vehicle is traveling;
A first curvature information calculating unit that calculates curvature information of a traveling locus of the own vehicle as the first curvature information based on a traveling state of the own vehicle;
A first curvature information correction unit that corrects the first curvature information based on a change amount of the vehicle position of the host vehicle with respect to a lane recognized from an image obtained by imaging the front of the host vehicle;
A second curvature information calculation unit that calculates curvature information of the lane recognized from an image obtained by imaging the front of the host vehicle as second curvature information;
Based on the correlation between the curvature information of the lane obtained from the map database and the corrected first curvature information or the second curvature information, the current position of the vehicle on the map data of the map database A vehicle position detection device comprising: a vehicle position specifying unit that specifies   The first curvature information correction unit corrects the first curvature information with curvature information calculated from a positional change amount of the host vehicle in the lane width direction and a travel distance in the traveling direction with respect to the lane. The vehicle position detection device according to claim 1.   Based on the curvature information of the lane near the current position estimated from the map data of the map database, either the first curvature information after the correction or the second curvature information is corrected on the map data. The vehicle position detection device according to claim 1, further comprising a curvature information selection unit that selects as curvature information for specifying the current position of the host vehicle.   The vehicle position specifying unit is an approximate straight line for each section extracted from a data group of lane curvature information obtained from the map database for a plurality of sections near the current position estimated from the map data of the map database. And the degree of coincidence between the corrected first approximate line extracted from the first curvature information data group or the second approximate line extracted from the second curvature information data group, The vehicle position detection device according to any one of claims 1 to 3, wherein the current position of the host vehicle is specified in a section having the highest degree of coincidence.

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