JP5760523B2 - Runway estimation apparatus and program - Google Patents

Description

  The present invention relates to a travel path estimation apparatus and program, and more particularly to a travel path estimation apparatus and program for estimating a travel path parameter based on an image captured by an imaging apparatus mounted on a vehicle.

  Conventionally, when a parallel line such as a white line on a road is detected from an image captured by a camera, the vanishing point position is calculated from the intersection of the parallel lines, and the depression angle of the camera is calculated based on the vanishing point position. A depression angle calculation device has been proposed (see, for example, Patent Document 1). In the depression angle calculation device described in Patent Document 1, when parallel lines cannot be detected from an image, the object positions in the image are compared between images taken at different times, and the variation amount of the depression angle is estimated from the deviation amount. The depression angle at the current time is calculated.

  A road that detects a white line position based on a plurality of past image data captured by a one-dimensional camera, and calculates a road curvature, a yaw angle, and a deviation based on the detected white line position and vehicle posture behavior. A measuring device for a shape and a vehicle posture has been proposed (see, for example, Patent Document 2).

  In addition, the front gaze position is calculated by multiplying the vehicle speed and the predetermined deviation prediction time, and the front gaze lateral displacement at the front gaze position is calculated based on the yaw angle and the front gaze position, A lane departure warning device that determines whether or not the vehicle tends to depart from the lane has been proposed (see, for example, Patent Document 3). In the lane departure warning device described in Patent Document 3, when only one lane can be detected, the predicted departure time is changed so that the influence of the yaw angle is reduced in the calculation of the forward gaze lateral displacement.

  Further, the partition line candidate line is detected from the image captured by the CCD camera, the image coordinates of the representative point set on the partition line candidate line are calculated, the inclination of the partition line candidate line at each representative point is obtained, A road partition line recognition device that estimates road parameters based on equations including road parameters, image coordinates of representative points, and inclinations of candidate partition lines has been proposed (see, for example, Patent Document 4).

JP 2007-240422 A JP-A-7-198349 JP 2004-268845 A JP 2002-236912 A

  However, as in the technique of Patent Document 1, a method for estimating a change in camera posture using images taken at different times such as monocular moving stereo vision and estimating an absolute camera posture with respect to the road surface at the current time is as follows: Since it is necessary to calculate the absolute value of the camera posture using parallel lines such as white lines on both sides at a certain time, the absolute camera posture cannot be estimated if there are no white lines on both sides. There is.

  In the technique of Patent Document 2, since the distance from the camera lens center to the intersection of the camera optical axis and the XZ plane and the camera height are known, the camera optical axis and the road surface plane (XZ plane) form. Since the depression angle, which is an angle, is a known parameter and fluctuation of the depression angle is not taken into account, there is a problem that the posture angle of the camera cannot be estimated with high accuracy.

  In addition, when only one side lane is detected, fixing the depression angle causes an error in the yaw angle, which decreases the accuracy of departure determination. In the technique of Patent Document 3, in consideration of the fact that the longer the deviation prediction time, the greater the influence of the yaw angle error on the deviation judgment, in the scene where the yaw angle decreases, the deviation prediction time is set short, which is bothersome. However, it does not improve the estimation accuracy of the depression angle and yaw angle.

  Further, in the technique of Patent Document 4, a white line candidate line in a window is detected from a white line candidate point group, and parameters are estimated using an assumption that “the candidate line may be considered to be parallel to a true white line”. However, this essentially estimates the parameters including the depression angle from the group of parallel lines in the image taken at a certain time, as in the case of detecting the white lines on both sides.

  The present invention has been made to solve the above-described problem, and even when only one side lane is detected, it is possible to estimate the road parameter including the attitude angle of the imaging unit with respect to the road without fixing the depression angle. It aims at providing a runway estimation apparatus and a program.

In order to achieve the above object, a runway estimation apparatus according to the present invention includes an imaging unit mounted on the host vehicle that images the periphery of the host vehicle, a detection unit that detects a speed of the host vehicle, and an imaging of the imaging unit. Extraction means for extracting a feature point indicating a lane from the captured image obtained by the above, and a value representing the shape of the travel path on which the host vehicle travels based on the feature point extracted by the extraction means, The value representing the shape and the yaw angle change between two different times when estimating the road parameters including the depression angle, yaw angle, and lateral position of the imaging means and determining the lane position on the captured image. under the assumption that no relationship with the track parameters between the amount of change in lateral position at a rate and the yaw angle detected by the detecting means the two time by Table Succoth between the two time, the two Each of the time In the optimization of the traffic lane position determined by the extracted feature points with the track parameters, and is configured to include a secondary time estimating means for estimating a 該走 path parameter, a.

According to the runway estimation apparatus of the present invention, the imaging means mounted on the host vehicle images the vicinity of the host vehicle, and the detection unit detects the speed of the host vehicle. And an extraction means extracts the feature point which shows a lane from the captured image obtained by the imaging of the imaging means. Then, the two-time estimation means estimates the road parameter using the different two-time feature points extracted by the extraction means. The lane parameter includes a value representing the shape of the lane on which the host vehicle is traveling, a depression angle, a yaw angle, and a lateral position of the imaging unit with respect to the lane, and determines the lane position on the captured image. This road parameter is based on the assumption that the value representing the shape of the road and the yaw angle do not change between two different times. in relation with the track parameters between the two time by Table Succoth it is estimated by optimization of the lane position determined by the two time each in the extracted feature points and track parameters.

  In this way, by using the feature points indicating the lanes extracted at two different times by associating the road parameters between two times under a predetermined condition, even if only one lane is detected, the depression angle is not fixed. The attitude angle of the imaging device mounted on the vehicle can be estimated.

  Further, the runway estimation apparatus of the present invention is configured to estimate the runway parameter by optimizing the feature points indicating the left and right lanes at one time extracted by the extraction unit and the lane position determined by the runway parameter. An estimation unit and a final estimation unit that estimates a final lane parameter using at least one of the estimation result of the one-time estimation unit and the estimation result of the two-time estimation unit may be included. Thereby, the optimal runway parameter can be estimated according to the situation.

  In that case, the two-time estimation means estimates the runway parameter using the feature points of the left lane and the right lane as the feature points, and the final estimation means The difference between the estimation result based on the feature point of the left lane and the estimation result based on the feature point of the right lane by the estimation means is within a predetermined range, and one of the estimation results by the two-time estimation means and the one When the difference from the estimation result by the time estimation means is greater than or equal to a predetermined value, the final runway parameter can be estimated using at least one of the estimation results by the two-time estimation means. As a result, even when the parallelism between the left and right lanes is lost, such as on an increased runway, the runway parameters can be estimated stably.

  The value representing the shape of the runway can be the curvature of the runway, or the curvature of the runway and the rate of change of the curvature.

  In the running path estimation program of the present invention, a computer is detected by a captured image obtained by an imaging unit mounted on the own vehicle that images the periphery of the own vehicle, and a detection unit that detects a speed of the own vehicle. An acquisition unit that acquires the speed of the host vehicle, an extraction unit that extracts a feature point indicating a lane from a captured image acquired by the acquisition unit, and the host vehicle based on the feature point extracted by the extraction unit When estimating a road parameter that includes a value representing the shape of the road on which the vehicle travels, a depression angle, a yaw angle, and a lateral position of the imaging means with respect to the road, and that determines a lane position on the captured image, Under the assumption that the value to be represented and the yaw angle do not change between two different times, the speed and yaw angle acquired by the acquisition means for the amount of change in the lateral position between the two times Represents, by optimal estimation of the lane position determined the two time feature points extracted in each and of the said runway parameters, a program for functioning as a two-time estimating means for estimating a 該走 path parameters.

  As described above, according to the track estimation device and the program of the present invention, by using the feature points indicating the lanes extracted at two different times by relating the track parameters between the two times under a predetermined condition, Even when only one side lane is detected, it is possible to estimate the road parameter including the attitude angle of the imaging means with respect to the road without fixing the depression angle.

It is a block diagram which shows the functional structure of the runway estimation apparatus which concerns on 1st Embodiment. It is a figure for demonstrating extraction of a feature point. It is a figure for demonstrating selection of a lane boundary point. It is the figure which showed typically the runway parameter to estimate. It is a figure which shows the relationship between the one-side lane boundary line of one time, and a yaw angle. It is a figure for demonstrating the change of the horizontal position between two time by the difference in a yaw angle. It is a flowchart which shows the content of the runway estimation process routine in the runway estimation apparatus which concerns on 1st Embodiment. It is a block diagram which shows the functional structure of the runway estimation apparatus which concerns on 2nd Embodiment. It is a flowchart which shows the content of the runway estimation process routine in the runway estimation apparatus which concerns on 2nd Embodiment.

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

  As shown in FIG. 1, the travel path estimation device 10 according to the first embodiment is mounted on a vehicle, and an imaging device 12 that continuously images a vehicle front area, a vehicle speed sensor 14 that detects the speed of the vehicle, And a computer 16 that executes a process for estimating a runway parameter.

  The imaging device 12 captures a target area in front of the vehicle, generates an image signal (not shown), and an A / D conversion unit (converts the image signal that is an analog signal generated by the imaging unit into a digital signal). And an image memory (not shown) for temporarily storing the A / D converted image signal.

  The computer 16 includes a CPU, a ROM for storing various programs such as a program for executing a runway estimation processing routine, which will be described later, and a ROM for storing various data, a RAM for temporarily storing data, a memory for storing various information, and the like. It includes a bus to be connected.

  If the computer 16 is described with function blocks divided for each function realizing means determined based on hardware and software, as shown in FIG. 1, a captured image captured by the imaging device 12 is acquired, and the captured image is acquired. A feature point extracting unit 20 that extracts feature points from the vehicle, a lane boundary point selecting unit 22 that selects a lane boundary point indicating a lane from the extracted feature points, and a lane boundary point selected by the lane boundary point selecting unit 22 It can be expressed by a configuration including a stored lane boundary point storage unit 24 and a lane parameter estimation unit 26 that estimates a lane parameter based on lane boundary points at two different times.

  The feature point extraction unit 20 and the lane boundary point selection unit 22 are an example of the extraction unit of the present invention, and the lane parameter estimation unit 26 is an example of the two-time estimation unit of the present invention.

  For example, the feature point extraction unit 20 scans the captured image as shown in FIG. 2A in the horizontal direction as shown in FIG. Extract points as feature points. FIG. 2C shows an example schematically showing the extracted feature points.

  The lane boundary point selection unit 22 selects the lane boundary point indicating the lane by discriminating the shape, width, color, and the like of the edge points continuously arranged from the feature points extracted by the feature point extraction unit 20. When there are a plurality of lanes, a lane boundary point indicating the innermost pair of left and right lanes is selected. FIG. 3 shows an example schematically showing the selected lane boundary point. The lane boundary point selection unit 22 stores the selected lane boundary point in the lane boundary point storage unit 24 and outputs it to the lane parameter estimation unit 26.

The lane parameter estimation unit 26 estimates, as the lane parameters, the curvature ρ of the lane on which the host vehicle travels, the depression angle φ, the yaw angle Ψ, and the lateral position e of the imaging device 12 with respect to the lane based on the lane boundary points. FIG. 4 schematically shows each of the runway parameters. More specifically, the coordinates of the lane boundary point on the captured image corresponding to the runway parameter x _t = (φ _t , ρ _t , e _t , Ψ _t ) at time _t are represented by P _t ⁱ (u _t ⁱ , v _t ⁱ ) (i = 1, 2,... N, N is the total number of selected lane boundary points), u _t ⁱ uses x _t and v _t ⁱ , and u _t ⁱ = h (x _t , it can be calculated as _v ^{t i).} _{h (x t, v t i} ) is known track parameters, and a vertical position on the image of the lane boundary point is a function for calculating the horizontal position on the image. Estimation of runway parameter is the problem of estimating the x _t from the function h and a plurality of lane boundary point group of coordinates.

Here, if both sides of the white line of the runway can be detected, the horizontal position e _{t l} of the imaging device relative to the left lane ^boundary, the lateral position relative to the right of the lane boundary line as e _t ^r, by conventional known techniques, The lane boundary point on the image at one time can be used to estimate the road parameter x ′ _t = (φ _t , ρ _t , e _t ^l , e _t ^r , Ψ _t ). However, when only one side lane can be detected, the yaw angle Ψ _t = 0 as shown in FIG. 5A and the yaw angle Ψ _t ≠ 0 as shown in FIG. 5B. Thus, even if the yaw angles are different, the position of the lane boundary line on the captured image may be the same position. In this way, when only one side lane can be detected, in the estimation of the lane parameter using the characteristic points of the lane boundary line at the time of the past, the yaw angle and the depression angle are indefinite, so the lane parameter is uniquely determined. I can't.

  Therefore, the road parameter estimation unit 26 estimates the road parameter using the lane boundary points at two times based on the following principle.

A case where only the right lane boundary line is detected will be described with reference to FIG. t = _{t 0} and _t = t _{1 (t} 1> _{t 0)} when the yaw angle Ψ is zero in, there is no change between the lateral position _{e t1} in the horizontal position _{e t0} and time _{t 1} at time _{t 0} A lane boundary appears at the same position on the captured image. On the other hand, when the yaw angle Ψ at t = t ₀ and t = t ₁ is Ψ> 0, e _t0 <e _t1 , and the lane boundary on the captured image rotates between two times (t _{0 to} t ₁ ). Appears in a counterclockwise state. Similarly, in the case of Ψ <0, e _t0 > e _t1 , and the lane boundary line on the captured image appears in a state (clockwise) rotated between two times (t _{0 to} t ₁ ). Furthermore, when a pitch variation occurs between two times, the lane boundary line appears in a state of moving in the vertical direction of the captured image.

  From this, by correlating the road parameter between two times, the road parameter can be uniquely estimated using the lane boundary point group selected at each of the two times.

More specifically, track parameters _{x t0} = at time _{t 0 (φ t0, ρ t0} , e t0, Ψ t0), and track parameters _{x t1} = _{(φ t1} at time _{t 1, ρ t1, e t1} , Ψ Assuming that the change in curvature ρ and yaw angle Ψ between two times is small for _t1 ), the relationship of the following equation (1) is approximately established. Here, V is the speed of the host vehicle detected by the vehicle speed sensor 14.

As a result, there are five parameters to be estimated: depression angle φ _t0 , φ _t1 , curvature ρ _t1 , lateral position e _t1 , and yaw angle ψ _t1 , which is determined by the lane parameter x _t0 under the condition of equation (1). The lane boundary line and the lane boundary point coordinates P _t1 ⁱ determined by the boundary line and the lane boundary point coordinates P _t0 ⁱ and the lane boundary point x _t1 are optimized by a conventionally known method such as the least square method, and the lane parameter Can be estimated.

The two-time interval (t ₁ -t ₀ ) is a time interval in which the above condition is maintained, and a time interval having a length sufficient to grasp the change in the observed value between the two times is appropriately set. Determine.

Next, with reference to FIG. 7, a runway estimation process routine executed by the computer 16 of the runway estimation apparatus 10 according to the present embodiment will be described. Here, the process at t = t ₁ will be described, and the lane boundary point storage unit 24 stores the lane boundary point selected in the process at t = t ₀ .

  In step 100, a captured image captured by the imaging device 12 is acquired, and in step 102, an edge point that is a luminance change point for each pixel of the captured image is extracted as a feature point.

Next, in step 104, the lane boundary point indicating the lane is selected by discriminating the shape, width, color, and the like of the edge points continuously arranged from the feature points extracted in step 102. The coordinates P _t1 ⁱ (u _t1 ⁱ , v _t1 ⁱ ) of the selected lane boundary point are stored in the lane boundary point storage unit 24.

Next, in step 106, the coordinates P _t0 ⁱ (u _t0 ⁱ , v _t0 ⁱ ) of the lane boundary point stored in the process of t = t ₀ are read from the lane boundary point storage unit 24. Next, in step 108, the speed V of the host vehicle detected by the vehicle speed sensor 14 is acquired.

Next, in step 110, using the relationship of the above equation (1) and the speed V acquired in step 108, the lane boundary line determined by the road parameter _xt0 and the coordinates P of the lane boundary point read out in step 106 are described. By optimizing the lane boundary determined by _t0 ⁱ and the lane parameter x _t1 and the coordinates P _t1 ⁱ of the lane boundary point selected in step 104 by a conventionally known method such as the least square method, The estimation is performed, the estimation result is output, and the process is terminated.

  As described above, according to the runway estimation apparatus of the first embodiment, based on the change between two different lane boundary points on the captured image due to the difference in lateral position and depression angle, By connecting lane parameters and using lane boundary points extracted at two different times, even if only one lane is detected, the lane parameters including the attitude angle of the imaging device with respect to the lane are fixed without fixing the depression angle. Can be estimated.

  Next, a second embodiment will be described. In the second embodiment, a case will be described in which the road parameter is estimated based on each of the left lane, the right lane, and the both side lanes. In addition, about the structure similar to the runway estimation apparatus 10 of 1st Embodiment, the code | symbol same or corresponding is attached | subjected and detailed description is abbreviate | omitted.

  As illustrated in FIG. 8, the feature point extraction unit 20 is described by using a functional block obtained by dividing the computer 216 of the lane estimation apparatus 210 according to the second embodiment for each function realization unit determined based on hardware and software. A left lane boundary point selection unit 22L that selects a lane boundary point that indicates the left lane from the extracted feature points, and a right lane boundary point selection unit 22R that selects a lane boundary point that indicates the right lane from the extracted feature points A left lane boundary point storage unit 24L that stores the lane boundary point selected by the left lane boundary point selection unit 22L, and a right lane boundary that stores the lane boundary point selected by the right lane boundary point selection unit 22R Based on the point storage unit 24R, the left lane parameter estimation unit 26L for estimating the lane parameter based on the left lane boundary point at two different times, and the lane parameter based on the right lane boundary point at two different time points. The right lane parameter estimation unit 26R for estimating the lane parameter, the left and right lane parameter estimation unit 28 for estimating the lane parameter based on the left lane boundary point and the right lane boundary point at one time, and the final output of the final estimation result And an estimation unit 30.

  Each of the left lane parameter estimation unit 26L and the right lane parameter estimation unit 26R is an example of a two-time estimation unit of the present invention, and the left and right lane parameter estimation unit 28 is an example of a one-time estimation unit of the present invention. .

  The left lane boundary point selection unit 22L selects a lane boundary point from the feature points extracted by the feature point extraction unit 20 by the same processing as the lane boundary point selection unit 22 of the first embodiment, and further selects The left lane boundary point indicating the left lane is selected based on the coordinates of the lane boundary point and the inclination of the lane boundary line indicated by the lane boundary point group. The left lane boundary point selection unit 22L stores the selected left lane boundary point in the left lane boundary point storage unit 24L and outputs it to the left lane parameter estimation unit 26L.

  The right lane boundary point selection unit 22R also selects the right lane boundary point indicating the right lane by the same processing as the left lane boundary point selection unit 22L, and the selected left lane boundary point is stored in the right lane boundary point storage unit 24R. The data is stored and output to the right lane parameter estimation unit 26R.

  The left-side lane parameter estimating unit 26L and the right-side lane parameter estimating unit 26R estimate the lane parameters using the lane boundary points selected at two different times as in the case of the lane parameter estimating unit 26 of the first embodiment. . As described above, in the present embodiment, since the road parameter can be estimated even when only one lane is detected, the road parameter is estimated in each of the left road parameter estimation unit 26L and the right road parameter estimation unit 26R. can do.

  The left and right lane parameter estimation unit 28 estimates the lane parameter using parallelism of both lanes based on both the left lane boundary point and the right lane boundary point at one time, as in the prior art.

  The final estimator 30 estimates and outputs final lane parameters based on the estimation results of the left lane parameter estimator 26L, the right lane parameter estimator 26R, and the left and right lane parameters estimator 28. For example, the final runway parameter can be calculated by the average of three estimation results or the weighted average. Further, any one estimation result may be selected as a final estimation result in accordance with a predetermined condition.

  In addition, the difference between the estimation result of the left lane parameter estimation unit 26L and the estimation result of the right lane parameter estimation unit 26R is within a predetermined range (estimation results substantially match), and the estimation result of the left lane parameter estimation unit 26L and the right side If any of the estimation results of the lane parameter estimation unit 26R and the estimation results of the left and right lane parameter estimation unit 28 are equal to or larger than a predetermined value (the estimation results are greatly different), the left lane parameter estimation unit 26L and the right lane parameter estimation unit It is preferable to use at least one of the estimation results of 26R and calculate the final estimation result without using the estimation result of the left and right lane parameter estimation unit 28. In such a case, for example, a scene where the left and right lanes are detected but the parallelism between the left and right lanes is lost is assumed, such as an increased runway. Therefore, the estimation results of the left lane parameter estimation unit 26L and the right lane parameter estimation unit 26R that can estimate the lane parameters without being affected by the parallelism of the left and right lanes are used to influence the parallelism of the left and right lanes. By not using the estimation result of the left / right lane parameter estimation unit 28 that receives the lane parameter, the lane parameter can be estimated appropriately.

Next, with reference to FIG. 9, a runway estimation process routine executed by computer 216 of runway estimation apparatus 210 according to the present embodiment will be described. Here, the processing at t = t ₁ will be described, and the left lane boundary point storage unit 24L and the right lane boundary point storage unit 24R store the left lane boundary point and the right lane selected in the processing at t = t ₀ . Assume that boundary points are stored. In addition, about the process same as the runway estimation process routine of 1st Embodiment, the same code | symbol is attached | subjected and detailed description is abbreviate | omitted.

  In step 100, a captured image is acquired, and in step 102, feature points are extracted from the captured image.

Next, in step 200, a lane boundary point indicating a lane is selected by discriminating the shape, width, color, and the like of edge points that are continuously arranged from the feature points extracted in step 102, and the selection is further performed. The left lane boundary point indicating the left lane is selected based on the coordinates of the lane boundary point and the inclination of the lane boundary line indicated by the lane point group. The coordinates P _t1 ^Li (u _t1 ^Li , v _t1 ^Li ) of the selected left lane boundary point are stored in the lane boundary point storage unit 24.

Next, in step 202, the coordinates P _t0 ^Li (u _t0 ^Li , v _t0 ^Li ) of the left lane boundary point stored in the process of t = t ₀ are read from the left lane boundary point storage unit 24L. Next, in step 108, the speed V of the host vehicle detected by the vehicle speed sensor 14 is acquired.

Next, at step 204, using the relationship of the above equation (1) and the speed V acquired at step 108, the coordinates of the lane boundary determined by the road parameter _xt0 and the coordinates of the left lane boundary point read out at step 202 above. The left lane parameter is determined by optimizing the lane boundary determined by P _t0 ^Li and the lane parameter x _t1 and the coordinates P _t1 ^Li of the lane boundary point selected in step 200 by a conventionally known method such as the least square method. presume.

Next, in Steps 206 to 209, the right lane parameter based on the coordinates P _t0 ^Ri of the right lane boundary point is estimated as in Steps 200 to 204 described above.

  Next, in step 212, the left and right lane parameters are estimated using both the left lane boundary point and the right lane boundary point at one time.

  Next, in step 214, the final lane parameter is determined based on the left lane parameter estimated in step 204, the right lane parameter estimated in step 209, and the left and right lane parameter estimated in step 212. The estimation is performed, the estimation result is output, and the process is terminated.

  Note that the left lane parameter estimation process in steps 200 to 204, the right lane parameter estimation process in steps 206 to 209, and the left and right lane parameter estimation process in step 212 are not limited to the above processing. Either may be executed first, or three processes may be executed in parallel.

  As described above, according to the runway estimation apparatus of the second embodiment, the runway parameter estimation using the lane boundary point at a conventional time is also used, so that an appropriate runway parameter according to the situation is used. Can be estimated.

In the above embodiment, the case where the curvature ρ of the road is used as one of the road parameters has been described. However, this parameter may be a parameter indicating the shape of the road, and represents a shape such as a clothoid curve, for example. The change rate ρ ′ of the curvature may be used together. In this case, the first equation of the above equation (1) may be related to the lane parameter between two times as ρ ′ _t1 = ρ ′ _t0 + V (t ₁ −t ₀ ).

  The program of the present invention may be provided by being stored in a recording medium or provided via a wired or wireless communication means. Further, the present invention is not limited to implementation by software configuration, and may be implemented by hardware configuration or a combination of hardware configuration and software configuration.

DESCRIPTION OF SYMBOLS 10,210 Track estimation device 12 Imaging device 14 Vehicle speed sensor 16, 216 Computer 20 Feature point extraction unit 22 Lane boundary point selection unit 22R Right lane boundary point selection unit 22L Left lane boundary point selection unit 24 Lane boundary point storage unit 24L Left lane Boundary point storage unit 24R Right lane boundary point storage unit 26 Runway parameter estimation unit 26L Left runway parameter estimation unit 26R Right runway parameter estimation unit 28 Left and right runway parameter estimation unit 30 Final estimation unit

Claims (8)

Imaging means mounted on the host vehicle for imaging the periphery of the host vehicle;
Detecting means for detecting the speed of the host vehicle;
Extraction means for extracting a feature point indicating a lane from a captured image obtained by imaging by the imaging means;
Based on the feature points extracted by the extracting means, the value representing the shape of the traveling road on which the host vehicle travels, the depression angle, the yaw angle, and the lateral position of the imaging means with respect to the traveling road, and on the captured image When estimating the lane parameters that determine the lane position of the vehicle, the amount of change in the lateral position between the two times is calculated under the assumption that the value representing the shape and the yaw angle do not change between two different times. optimum of the relationship with the track parameters between the two times at a rate and the yaw angle detected by the detecting means according to Table Succoth, the lane position determined by the two-time each feature point extracted by the said runway parameters Two-time estimation means for estimating the lane parameter by
Runway estimation device including One-time estimation means for estimating the road parameter by optimizing the characteristic points indicating the left and right lanes at one time extracted by the extraction means and the lane position determined by the road parameter;
Final estimation means for estimating a final lane parameter using at least one of the estimation result of the one-time estimation means and the estimation result of the two-time estimation means;
The runway estimation apparatus according to claim 1, comprising: The two-time estimation means estimates the lane parameter using the feature point of the left lane and the feature point of the right lane as the feature points,
The final estimation means has a difference between the estimation result based on the feature point of the left lane and the estimation result based on the feature point of the right lane by the two-time estimation means, and the two-time estimation means When the difference between any one of the estimation results and the estimation result by the one-time estimation means is equal to or greater than a predetermined value, the final runway parameter is estimated using at least one of the estimation results by the two-time estimation means The runway estimation device according to claim 2.   The runway estimation device according to any one of claims 1 to 3, wherein a value representing the shape of the runway is a curvature of the runway, or a curvature of the runway and a rate of change of the curvature. Computer
An acquisition means for acquiring a captured image obtained by an imaging means mounted on the own vehicle for imaging the periphery of the own vehicle, and a speed of the own vehicle detected by a detection means for detecting the speed of the own vehicle;
An extraction unit that extracts a feature point indicating a lane from the captured image acquired by the acquisition unit; and a value that represents a shape of a traveling path on which the host vehicle travels based on the feature point extracted by the extraction unit, Two times at which the value representing the shape and the yaw angle are different when estimating the road parameter including the depression angle, the yaw angle, and the lateral position of the imaging unit with respect to the road and determining the lane position on the captured image. under the assumption that does not change between, associating the runway parameters between the two times at a rate and the yaw angle acquired by Table Succoth by the acquisition unit amount of change in the lateral position between the two time In order to function as a two-time estimation means for estimating the road parameter by optimal estimation of the feature point extracted at each of the two times and the lane position determined by the road parameter Runway estimation program. The computer,
The extraction means;
The two-time estimation means,
One-time estimation means for estimating the road parameter by optimizing the feature points indicating the left and right lanes at one time extracted by the extraction means and the lane position determined by the road parameter; and
The runway estimation program according to claim 5, which functions as a final estimation unit that estimates a final runway parameter using at least one of the estimation result of the one-time estimation unit and the estimation result of the two-time estimation unit . The two-time estimation means estimates the lane parameter using the feature point of the left lane and the feature point of the right lane as the feature points,
The final estimation means has a difference between the estimation result based on the feature point of the left lane and the estimation result based on the feature point of the right lane by the two-time estimation means, and the two-time estimation means When the difference between any one of the estimation results and the estimation result by the one-time estimation means is equal to or greater than a predetermined value, the final runway parameter is estimated using at least one of the estimation results by the two-time estimation means Do
The runway estimation program according to claim 6. The runway estimation program according to any one of claims 5 to 7, wherein a value representing the shape of the runway is a curvature of the runway, or a curvature of the runway and a rate of change of the curvature.

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