JP2018169319A - Vehicle travel lane estimation device - Google Patents

Abstract

To make it possible to accurately determine whether a vehicle is travelling on a travelling path even if an error included in positional information on the vehicle changes due to a fall in reception accuracy from a positioning satellite.SOLUTION: A vehicle travel lane estimation device recognizes partition lines Ll and Lr partitioning left and right of a travel lane on the basis of pixels taken with a camera unit 12 to find a center Lw/2 of its lane width Lw (S33), moves an estimated vehicle position P detected by a GNSS receiver 15 to the lane center Lw/2 to set a virtual vehicle position P', and sets an error ellipse R of the vehicle position set with a Kalman filter centering on the virtual vehicle position P' (S34). Then, it compares a lap rate κ between the error ellipse R and the lane width Lw with a setting threshold value κo, if κ≥κo, determines that the vehicle M is on the travel lane, and lets the vehicle continue steering control by self-driving.SELECTED DRAWING: Figure 5

Description

  The present invention uses an error range in which whether or not the host vehicle is present in the traveling lane is set centered on the estimated host vehicle position by the filter process even when the accuracy of receiving from the positioning satellite is lowered. The present invention relates to a travel lane estimation device for a vehicle to be estimated.

  2. Description of the Related Art Conventionally, in a car navigation system mounted on a host vehicle, the position of the host vehicle (own vehicle position) and traveling direction are based on position information received from positioning satellites (GNSS (Global Navigation Satellite System) satellites including GPS satellites). A corner is acquired, and position information and a road azimuth of the lane center closest to the traveling direction are acquired by matching with road map information. And the radio navigation which performs steering control of automatic driving | running | working so that the own vehicle drive | works along the center of a lane is known.

  However, in the above-mentioned radio navigation, when the reception accuracy from the positioning satellite is reduced due to buildings around the road, weather conditions, etc., the error included in the vehicle position information is expanded, and steering control by stable automatic driving is performed. It may be difficult to continue.

  In order to cope with this, for example, Patent Document 1 (Japanese Patent Laid-Open No. 2011-2324) discloses a technique for estimating an error variance included in vehicle position information using a Kalman filter. That is, in this document, map matching is performed on each road data around the vehicle position using the vehicle position, error variance, and map data obtained based on information from a positioning receiver, an angular velocity sensor, a vehicle speed sensor, and the like. The candidate point with the highest likelihood is selected as the vehicle position on the map.

JP 2011-2324 A

  In the technique disclosed in the above-described document, a candidate point with the highest likelihood is estimated as the own vehicle position using a Kalman filter, and an error ellipse indicating the existence probability is set around the own vehicle position. ing.

  Therefore, for example, if the error ellipse protrudes from at least one of the lane markings that divide the left and right sides of the travel lane, there is a possibility that the own vehicle may exist at the protruding portion, and thus the steering control stops in automatic driving. Is done. This error ellipse is set larger as the error in the vehicle position increases.

  Therefore, even if the vehicle position on the map is set to a position that is biased on the lane line side and the probability that the actual vehicle is traveling in the lane is high, the error ellipse is If it protrudes, the steering control of automatic driving will be stopped. As a result, in autonomous driving, even if the vehicle position on the map is deviating from the center of the lane to one side in the vehicle width direction, the steering control is stopped due to the size of the error ellipse. The restart is intermittently repeated, and there is a disadvantage that it is difficult to continue stable steering control.

  In view of the above circumstances, the present invention accurately determines whether or not the host vehicle is traveling in the traveling path even if the accuracy of reception from the positioning satellite is reduced and the error included in the position information of the host vehicle is changed. When a vehicle is applied to steering control by automatic driving, a vehicle running lane estimation device capable of obtaining stable steering controllability by preventing the stop and restart from being repeated intermittently is provided. The purpose is to do.

  The present invention includes a position detection unit that receives a position signal from a positioning satellite and acquires position information of the host vehicle, a front recognition unit that recognizes a lane width of a traveling lane in which the host vehicle is traveling, and road map information. Storage means for storing the vehicle, traveling state detecting means for detecting the traveling state of the host vehicle, position information of the host vehicle detected by the position detecting unit, and a traveling state detected by the traveling state detecting unit are filtered. An error range calculating means for setting an error range of the position information of the host vehicle detected by the position detecting means, an error range calculated by the error range calculating means, and a lane width of the traveling lane recognized by the forward recognition means In a vehicle lane estimation device comprising presence determination means for determining whether the host vehicle is present in the travel lane by comparing the lap ratios of The virtual vehicle position is set by moving the information to the center of the lane width of the traveling lane recognized by the forward recognition means, and the error range set by the error range calculation means around the virtual vehicle position is set. A vehicle position setting unit, and the presence determination unit compares the lap ratio between the error range set by the virtual vehicle position setting unit and the lane width of the traveling lane recognized by the front recognition unit, It is determined whether or not the host vehicle is in the travel lane.

  According to the present invention, the position information detected by the position detection means is moved to the center of the lane width of the traveling lane recognized by the front recognition means to set the virtual vehicle position, and the error range is set around the virtual vehicle position. Since the error rate and the lane width of the travel lane are compared to determine whether the host vehicle is in the travel lane, the accuracy of reception from the positioning satellite is improved. Even if the error decreases and the error included in the position information of the host vehicle changes, it can be accurately determined whether or not the host vehicle is traveling in the traveling path. As a result, by applying this to automatic driving, the stop and restart of the steering control are prevented from being repeated intermittently, and stable steering controllability can be obtained.

Schematic configuration diagram of navigation device Flowchart showing own vehicle position error calculation routine Flowchart showing the Kalman filter calculation subroutine Flowchart showing own vehicle position estimation routine Flowchart showing a virtual vehicle position setting subroutine Flowchart showing lane presence determination subroutine Explanatory diagram showing the relationship between the estimated vehicle position and the error ellipse (A) is a plan view showing a state where an error ellipse protrudes from an adjacent lane, and (b) is a plan view showing a state where an error ellipse protrudes from a road shoulder. (A) is a plan view showing a state in which the estimated own vehicle position biased to the lane marking on the adjacent lane side is virtually moved to the center of the lane, and (b) is estimated to be biased to the lane marking on the road shoulder side. A plan view showing a state where the vehicle position is virtually moved to the center of the lane

  Hereinafter, an embodiment of the present invention will be described with reference to the drawings. Reference numeral 1 in FIG. 1 is a navigation device mounted on a vehicle (own vehicle, see FIG. 7) M, and has a navigation control unit (navi_ECU) 11. The navigation_ECU 11 is mainly composed of a microcomputer and has a well-known CPU, ROM, RAM, nonvolatile memory, and the like, and various programs executed by the CPU, fixed data, and the like are stored in the ROM.

  The navigation_ECU 11 includes a Kalman filter calculation unit 11a and an own vehicle position estimation calculation unit 11b as error range calculation means as a locator function. In addition to the locator function, the navigation_ECU 11 has a guidance function for guiding the host vehicle M to the destination. Since this guidance function is well known, description thereof is omitted here.

  On the input side of the navigation_ECU 11, as sensors for acquiring parameters necessary for estimating the vehicle position, a camera unit 12 as a front recognition means, a wheel speed sensor for detecting a wheel speed from the rotational speed of each wheel. 13. A three-axis acceleration sensor 14 for detecting front, rear, left and right accelerations acting on the host vehicle M, a positioning signal from a GNSS (Global Navigation Satellite System) satellite represented by a GPS satellite, and the host vehicle M A GNSS receiver 15 as position detecting means for acquiring the position information is connected. The wheel speed sensor 13 and the triaxial acceleration sensor 14 correspond to the traveling state detection means of the present invention.

  The camera unit 12 includes a stereo camera composed of a main camera 12a and a sub camera 12b, and an image processing unit (IPU) 12c, and the IPU 12c displays the traveling environment information in front of the host vehicle M captured by both the cameras 12a and 12b. Then, the image is processed in a predetermined manner and output to the navigation_EUC 11.

  A high-precision road map database 16 is connected to the navigation_ECU 11. The high-precision road map database 16 is provided in a large-capacity storage means such as an HDD, and stores high-precision road map information (dynamic map). The high-precision road map information includes a lane width database that stores lane width data, a lane center database that stores lane center position coordinate data, a lane azimuth database that stores lane azimuth data, and the like.

  Further, a steering control unit 21 is connected to the output side of the navigation_ECU 11. The steering control unit 21 is configured to cause the host vehicle M to travel along a target travel path set for guiding the host vehicle M to the destination based on the host vehicle position information from the navigation_ECU 11 in the steering control for automatic driving. The steering torque for the electric power steering (EPS) motor is controlled.

  By the way, the own vehicle position information received by the GNSS receiver 15 includes a predetermined error, but this error increases when the reception accuracy from the positioning satellite decreases due to buildings around the road, weather conditions, or the like. . The navigation_ECU 11 checks whether or not this error range is in the travel lane, and if it is within the travel lane, it estimates that the host vehicle M is traveling without protruding from the travel lane.

  Therefore, at the time of steering control of automatic driving, the Kalman filter calculation unit 11a of the navigation_ECU 11 detects the wheel speed detected by the wheel speed sensor 13, the acceleration detected by the three-axis acceleration sensor 14, and the vehicle position information received by the GNSS receiver 15. And capture. Then, the error information contained therein is integrated using a Kalman filter that is a filter process, and the average and variance of the vehicle position centered on the estimated vehicle position P (see FIG. 8) received by the GNSS receiver 15. An error ellipse R is set as an error range represented by a covariance matrix.

  The own vehicle position estimation calculation unit 11b also includes an estimated own vehicle position P received by the GNSS receiver 15 and left and right dividing lines Ll and Lr (see FIG. 8) that divide the left and right of the traveling lane acquired by the camera unit 12. A lateral position difference ΔP with respect to the center (lane center) Lc (see FIG. 9) is obtained, and the lateral position of the estimated vehicle position P is moved (offset) by the difference ΔP, so that the virtual vehicle position P ′ is moved to the lane center Lc. Is set (see FIG. 9). Then, it is estimated whether or not the own vehicle M exists in the traveling lane based on how much the virtual own vehicle position P ′ at this time wraps around the traveling lane.

  Specifically, the error ellipse centered on the estimated vehicle position P calculated by the Kalman filter calculation unit 11a is generated in the vehicle position error calculation routine shown in FIG. Moreover, the process in the own vehicle position estimation calculation part 11b is performed according to the own vehicle position estimation routine shown in FIG.

  First, the processing in the Kalman filter calculation unit 11a will be described according to the own vehicle position error calculation routine shown in FIG. In this routine, first, in step S1, the acceleration detected by the triaxial acceleration sensor 14 is read, and the process proceeds to step S2 where the wheel speed, which is the rotation speed of each wheel detected by the wheel speed sensor 13, is read. And it progresses to step S3, the own vehicle position information received with the GNSS receiver 15 is read, and a Kalman filter calculation is performed based on these in step S4, and a routine is exited.

  This Kalman filter calculation is executed according to the Kalman filter calculation subroutine shown in FIG. In this subroutine, first, in step S11, the vehicle position and its error variance are calculated from the positioning values having discrete errors received by the GNSS receiver 15, using a well-known Kalman filter. And it progresses to step S12 and the own vehicle position calculated by step S11 is set as an estimated own vehicle position (latitude, longitude, azimuth).

  Next, the process proceeds to step S13, where an error distribution ellipse (error ellipse) R centered on the estimated vehicle position P is set, and the routine is exited. The error ellipse R indicates a range where the vehicle position exists with a predetermined probability (for example, 99 [%]). As shown in FIG. 7, the error distance in the latitude direction (error diameter) A and the error distance in the longitude direction are shown. (Error diameter) B, which indicates that the positioning error is large in the major axis direction of the ellipse and the error is small in the minor axis direction of the ellipse. In the figure, the vehicle M is traveling in the east-west direction.

  The estimated vehicle position and the error ellipse R are read by the vehicle position estimation calculation unit 11b. The own vehicle position estimation calculation unit 11b performs a process of determining whether or not the own vehicle M exists in the traveling lane according to the own vehicle position estimation routine shown in FIG.

  In this routine, first, in step S21, image information in front of the host vehicle M captured by the camera unit 12 is read, and a virtual host vehicle position is calculated in step S22. The calculation of the virtual host vehicle position is performed according to a virtual host vehicle position setting subroutine shown in FIG.

  In this subroutine, first, in step S31, road information including data on the lane width Lw near the estimated vehicle position P is read from each database stored in the high-precision road map database 16, and the estimated vehicle position P and The error ellipse R is overlaid on the map.

  Next, the process proceeds to step S32, where the left and right lane markings Ll and Lr that divide the left and right of the travel lane are recognized based on the image information ahead of the host vehicle M, and the distance (lane width) Lw between the inside of both lane markings Ll and Lr. And the position (lateral position) of the vehicle M in the vehicle width direction (lane width Lw) is calculated.

  Thereafter, the process proceeds to step S33, the lane center Lw / 2 is obtained from the lane width Lw calculated in step S32, a difference ΔP from the lateral position of the estimated own vehicle position P is calculated, and the process proceeds to step S34, where the estimated own vehicle position P Is moved by a difference ΔP, a virtual vehicle position P ′ is set at the lane center Lw / 2, and an error ellipse R is set at the virtual vehicle position P ′ (see FIG. 9). Proceed to S23.

  In step S23, it is determined whether or not the host vehicle M exists in the traveling lane. This determination process is performed according to the in-lane presence determination process subroutine shown in FIG. Note that the processing in this subroutine corresponds to the presence determination means of the present invention.

  In this subroutine, first, the lap ratio κ between the error ellipse R set around the virtual vehicle position P ′ and the lane width Lw in step S41 is calculated. Next, the process proceeds to step S42, and the lap rate κ is compared with the set threshold value κo. The setting threshold value κo is a value for determining the probability of existing in the travel lane, and is set to about 80% in this embodiment.

  Then, if κ ≧ κo, it is determined that the host vehicle M is present in the travel lane, the process proceeds to step S43, and estimated host vehicle position information (latitude, longitude, azimuth) is output, and FIG. Proceed to step S24. If κ <κo, it is determined that the host vehicle M does not exist in the travel lane, in other words, the estimated host vehicle position information (latitude, longitude, azimuth) is not accurate, and the process branches to step S44. The estimated vehicle position information (latitude, longitude, azimuth) is cleared, and the process proceeds to step S24 in FIG.

  In step S24 of FIG. 4, the vehicle position estimation process is executed. In the vehicle position estimation process, when it is determined in the above-described lane presence determination process that the vehicle M is in the lane, the estimated vehicle position information (latitude, longitude, azimuth) is Set as to exit the routine. If it is determined that the estimated vehicle position information (latitude, longitude, azimuth) is not accurate, the estimated vehicle position information (latitude, longitude, azimuth) is cleared and the routine is exited.

  The own vehicle position information set in step S24 described above is read by the steering control unit 21, and when the estimated own vehicle position information (latitude, longitude, azimuth) is set as the own vehicle position, Steering control by automatic driving is continued based on the target travel path. On the other hand, if the estimated vehicle position information (latitude, longitude, azimuth) is cleared, the steering control by automatic driving is stopped, and the target travel set based on the image taken by the camera unit 12 as the travel mode. It is switched to lane keeping control (lane keeping control) or the like for running along the road.

  Thus, in the present embodiment, when examining whether or not the own vehicle M is traveling in the traveling lane based on the error ellipse R of the estimated own vehicle position P received by the GNSS receiver 15, the estimated own vehicle position P is determined. The virtual vehicle position P ′ is set by virtually moving to the lane center Lw / 2, and the error ellipse R is set around the virtual vehicle position P ′.

  Therefore, as shown in FIG. 8, when the estimated vehicle position P is displaced to one of the left and right lane markings Ll and Lr, an error ellipse R centered on the estimated vehicle position P is set large, Even if the lap rate κ with the travel lane is less than the set threshold value κo and the information of the estimated own vehicle position P is determined to be inaccurate, as shown in FIG. 9, the virtual own vehicle position P ′ is the center. When the lap rate κ between the error ellipse R and the travel lane is compared, when κ ≧ κo or more, it is determined that the host vehicle M exists in the travel lane, so the estimation received by the GNSS receiver 15 The own vehicle position P can be set as the own vehicle position when steering control is performed.

  As a result, even when the reception accuracy from the GNSS satellite is reduced and the error included in the position information of the own vehicle M is relatively large, it is accurately determined whether or not the own vehicle M is traveling in the traveling path. Therefore, the stop and restart of the steering control by automatic driving are not intermittently repeated, and stable steering controllability can be obtained.

  Note that the present invention is not limited to the above-described embodiment. For example, the forward recognition means is not limited to the camera unit 12 as long as it can recognize the lane marking that divides the left and right of the traveling lane. Other forward recognition means used may be used.

1 ... own vehicle 11 ... navigation control unit,
11a: Kalman filter calculation unit,
11b ... own vehicle position estimation calculation unit,
12 ... Camera unit
13: Wheel speed sensor,
14 ... Axial acceleration sensor,
15 ... GNSS receiver,
16 ... High-precision road map database,
21 ... Steering control unit,
Lc ... center of lane,
Ll, Lr ... left and right dividing lines,
Lw / 2 ... center of lane,
M ... own vehicle,
P '... virtual vehicle position,
P ... Estimated vehicle position,
R ... error ellipse,
ΔP ... difference,
κ ... Lap rate,
κo: Setting threshold

Claims (3)

Position detecting means for receiving a position signal from a positioning satellite and acquiring position information of the own vehicle;
Forward recognition means for recognizing a lane width of a traveling lane in which the host vehicle is traveling;
Storage means for storing road map information;
Traveling state detecting means for detecting the traveling state of the host vehicle;
An error range for setting an error range of the position information of the host vehicle detected by the position detection unit by filtering the position information of the host vehicle detected by the position detection unit and the driving state detected by the driving state detection unit Computing means;
It is determined whether or not the host vehicle is present in the travel lane by comparing the lap rate between the error range calculated by the error range calculation means and the lane width of the travel lane recognized by the forward recognition means. In the traveling lane estimation device for a vehicle comprising presence determination means,
The position information detected by the position detecting means is moved to the center of the lane width of the traveling lane recognized by the forward recognition means to set the virtual own vehicle position, and the error range calculating means with the virtual own vehicle position as the center. It further has a virtual vehicle position setting means for setting the set error range,
The presence determination means compares the lap ratio between the error range set by the virtual own vehicle position setting means and the lane width of the traveling lane recognized by the forward recognition means, and the own vehicle exists in the traveling lane. It is determined whether or not the vehicle travel lane estimation device.   The vehicle lane estimation apparatus according to claim 1, wherein the filter processing of the error range calculation means is performed by a Kalman filter.   3. The travel lane estimation apparatus for a vehicle according to claim 1, wherein when the presence determination means determines that the host vehicle is present in the travel lane, automatic driving by steering control is continued.

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