JP2020086489A - White line position estimation device and white line position estimation method - Google Patents

Abstract

To estimate a position of a white line even when a white line cannot be recognized.SOLUTION: In a vehicle V, a lane identification device 10 includes: a lane-related state identification section 121 for identifying position relation between a white line corresponding to a lane on which an own vehicle is traveling and the own vehicle, and a lane-related state indicating a curvature of a white line and a lane width or the like based on information indicating a traveling environment in front of the own vehicle; an operation state detection section 122 for detecting a vehicle speed and a yaw rate or the like being an operation state of the own vehicle; and a white line position estimation section 123 for estimating a position of a white line at a predetermined time interval based on an estimated lane-related state and an operation state by estimating a position of a white line within a predetermined range in a front and rear direction from the own vehicle at a predetermined time interval when a lane-related state is identified and also an operation state is detected, and by estimating position relation or the like based on an operation state when a lane-related state is not identified and an operation state is detected.SELECTED DRAWING: Figure 2

Description

The present invention relates to a white line position estimation device and a white line position estimation method.

Conventionally, a white line is recognized based on a captured image captured by a camera that captures the front of the vehicle, and the traveling of the vehicle is controlled based on the white line (for example, see Patent Document 1).

JP, 2005-141611, A

By the way, when realizing highly automated driving with lane changes, or when the driver performs lane changes safely, the vehicle travel is controlled based on the conditions of the white line and the surrounding vehicles traveling around the host vehicle. Is required. Therefore, it is required to specify the lane position of an object around the host vehicle as information used for vehicle driving control.

On the other hand, if the white line cannot be recognized due to a malfunction of the camera that images the front of the vehicle, the position of the white line cannot be specified, and the lane position of an object around the vehicle cannot be specified. Therefore, even if the white line cannot be recognized, it is required to be able to estimate the position of the white line although the reliability is reduced.

Therefore, the present invention has been made in view of these points, a white line position estimation device and a white line position estimation method that can be retained without losing the position information of the white line even when the white line cannot be recognized. The purpose is to provide.

A white line position estimation device according to a first aspect of the present invention is based on information indicating the front of the host vehicle, the positional relationship between the host vehicle and the white line corresponding to the lane in which the host vehicle is traveling, and the curvature of the white line. And a lane-related state specifying unit that specifies a lane-related state indicating a lane width, a motion state detecting unit that detects a motion state of the own vehicle, and the lane-related state is specified by the lane-related state specifying unit, and When the motion state is detected by the motion state detection unit, the position of the white line within a predetermined range from the own vehicle is estimated at predetermined intervals based on the lane-related condition and the motion condition, and the lane-related condition is estimated. When the lane-related state is not specified by the state specifying unit and the movement state is detected by the movement state detection unit, the lane-related state is estimated by estimating the positional relationship based on the movement state. Then, the white line position estimating unit that estimates the position of the white line at the predetermined time intervals based on the estimated lane-related state and the motion state.

The white lane position estimating unit, when the lane-related state is specified by the lane-related state specifying unit, when the lane-related state is not specified, when the lane-related state is not specified, the lane-related state is not specified. The position of the white line may be estimated based on the past lane-related state that is the lane-related state specified before the change, the lane-related state estimated based on the exercise state, and the exercise state.

The white line position estimation unit estimates the position of the white line by decreasing the influence degree of the past lane-related state with time and increasing the influence degree of the lane-related state estimated based on the motion state. May be.

The white line position estimation unit, if the lane-related state is not specified by the lane-related state specifying unit, and the movement state is not detected by the movement state detection unit, the movement state of the own vehicle, While estimating the motion state when the host vehicle is traveling at a constant speed on a straight line, the lane-related state is estimated and estimated as the lane-related state when the host vehicle is traveling on the center of a straight road. The position of the white line may be estimated based on the lane-related state and the estimated motion state.

The lane line position estimating unit determines whether the lane-related state is not specified by the lane-related state specifying unit and the movement state is detected by the movement-state detecting unit, and the lane-related state specifying unit determines the lane-related state. When the state is not specified and the exercise state is changed to a state in which the exercise state is not detected by the exercise state detection unit, a past exercise state that is the exercise state detected before the change in the exercise state is detected. The position of the white line may be estimated based on the estimated motion state and the estimated lane-related state.

The white line position estimation unit may estimate the position of the white line by decreasing the influence degree of the past exercise state and increasing the estimated influence degree of the exercise state over time.

The white line position estimating unit, based on the specific situation of the lane-related state in the lane-related state identifying unit, and the detection situation of the exercise state in the exercise state detection unit, the reliability of the position of the estimated white line It may be calculated.

A white line position estimation method according to a second aspect of the present invention is based on a positional relationship between a white line corresponding to a lane in which the own vehicle is traveling and the own vehicle, based on information indicating the front of the own vehicle executed by a computer. , A step of identifying a lane-related state indicating the curvature and a lane width of the white line, a step of detecting a motion state of the own vehicle, and a case in which the lane-related state is specified and the motion state is detected. , Estimating the position of the white line within a predetermined range from the own vehicle at predetermined intervals based on the lane-related state and the exercise state, when the lane-related state is not specified and the exercise state is detected A step of estimating the lane-related state by estimating the positional relationship based on the motion state, and estimating the position of the white line at the predetermined time intervals based on the estimated lane-related state and the exercise state; , Is provided.

According to the present invention, even if the white line cannot be recognized, the position information of the white line can be retained without being lost.

It is a figure explaining the outline of the lane identification device concerning this embodiment. It is a figure which shows typically the internal structure of the vehicle which concerns on this embodiment. It is a figure which shows the relationship between the X-axis and the Y-axis in a vehicle coordinate system. It is a figure which shows the example which the white line position specific part which concerns on this embodiment calculates several white line coordinates ahead of a vehicle. It is a figure which shows the example which the white line position specific|specification part which concerns on this embodiment calculates several white line coordinates behind a vehicle. It is a figure explaining the calculation method of lateral displacement and azimuth based on the vehicle motion state concerning this embodiment. It is a flowchart which shows the flow of a process in the lane identification device which concerns on this embodiment.

FIG. 1 is a diagram illustrating an outline of a lane identification device 10 according to the present embodiment. The lane identification device 10 is, for example, a computer mounted on the vehicle V. The lane identification device 10 is a device that identifies a lane in which a peripheral vehicle OV, which is an object around the vehicle V as the host vehicle, is traveling. In the present embodiment, the object around the vehicle V is described as the peripheral vehicle OV traveling around the vehicle V, but the present invention is not limited to this. The object around the vehicle V may be a peripheral vehicle OV stopped around the vehicle V or an obstacle such as a person, an animal, or a falling object.

The vehicle V is equipped with a camera that captures an area AC ahead of the vehicle V in the traveling direction. The lane identification device 10 functions as a white line position identification device, and based on the image of the area AC captured by the camera, the positional relationship between the white line and the vehicle V corresponding to the lane in which the vehicle V is traveling, the curvature of the white line, and the lane width. The vehicle speed and yaw rate, which are the motion states of the vehicle V, are detected while the lane-related state indicating the above is specified. When the lane identification device 10 can identify the lane-related state and can detect the motion state of the vehicle V, the lane identification device 10 detects the white lines before and after the vehicle V based on the lane-related state and the motion state of the vehicle V. Estimate the position. When the lane identification device 10 cannot detect the lane-related state due to a malfunction of the camera and can detect the motion state of the vehicle V, the white lines before and after the vehicle V are detected based on the motion state of the vehicle V. Estimate the position of. By doing so, the lane identification device 10 can estimate the position of the white line even if the white line cannot be recognized due to a malfunction of the camera or the like and the lane-related state cannot be identified.

The vehicle V is equipped with one or more obstacle detection sensors for identifying the surrounding vehicle OV. In the example shown in FIG. 1, the vehicle V is provided with six obstacle sensors. Each of the six obstacle sensors detects a surrounding vehicle OV as an obstacle in areas AL1 to AL6 around the vehicle V. The lane identification device 10 identifies the positions of the left and right ends of the surrounding vehicle OV based on the detection status of the surrounding vehicle OV in the surrounding areas AL1 to AL6 by the obstacle detection sensor.

The lane identification device 10 identifies the lane in which the peripheral vehicle OV is traveling, based on the estimated position of the white line and the positions of the vertices of the identified peripheral vehicle OV. By doing so, the lane identification device 10 can identify the situation of the surrounding vehicle OV traveling around the vehicle V and can assist the safe lane change. The lane identification device 10 will be described in detail below.

[Structure of vehicle V]
Before giving a detailed description of the lane identification device 10, the internal configuration of the vehicle V will be described. FIG. 2 is a diagram schematically showing the internal configuration of the vehicle V according to the present embodiment. The vehicle V includes a camera 1, a vehicle speed sensor 2, a yaw rate sensor 3, an obstacle detection sensor 4, and a lane identifying device 10.

The camera 1 is provided at the forefront of the vehicle V. The camera 1 is provided, for example, at the center position of the vehicle V in the width direction. The camera 1 photographs the front of the vehicle V. The camera 1 captures the front of the vehicle V by capturing an image of the front of the vehicle V at predetermined time intervals (for example, tens to hundreds of milliseconds), and outputs an image to the lane identifying device 10.

The vehicle speed sensor 2 detects the speed of the vehicle V. The vehicle speed sensor 2 outputs speed information indicating the detected speed of the vehicle V to the lane identifying device 10.
The yaw rate sensor 3 detects a yaw rate generated in the vehicle V. The yaw rate generated in the vehicle V is the speed of the yaw angle of the vehicle V. The yaw rate sensor 3 outputs yaw rate information indicating the detected yaw rate to the lane identifying device 10.

The obstacle detection sensor 4 is, for example, a LIDAR (Light Detection And Ranging), which emits an electromagnetic wave around the vehicle V and receives an electromagnetic wave reflected from the surrounding vehicle OV as an obstacle to receive the surrounding vehicle OV. To detect.

[Configuration of Lane Identification Device 10]
Next, the configuration of the lane identifying device 10 will be described with reference to FIG. As shown in FIG. 2, the lane identification device 10 includes a storage unit 11 and a control unit 12.

The storage unit 11 is, for example, a ROM (Read Only Memory) or a RAM (Random Access Memory). The storage unit 11 stores various programs for causing the control unit 12 to function.

The control unit 12 is a calculation resource including a processor such as a CPU (Central Processing Unit) (not shown). The control unit 12 executes the programs stored in the storage unit 11 so that the lane-related state identifying unit 121, the motion state detecting unit 122, the white line position estimating unit 123, the object identifying unit 124, and the lane identifying unit 125 can be used. Function.

The lane identification device 10 functions as a white lane position estimation device by including the lane related state identification unit 121, the motion state detection unit 122, and the white line position estimation unit 123, and the vehicle V corresponding to the lane in which the vehicle V is traveling. Estimate the front and back white line positions. First, the vehicle coordinate system, which is the coordinate system used in this embodiment, will be described. FIG. 3 is a diagram showing the relationship between the X axis and the Y axis in the vehicle coordinate system. In this embodiment, a vehicle coordinate system is defined in which the center in the width direction of the forefront portion of the vehicle V, which is the mounting position of the camera 1, is the origin, and the X axis is in the vehicle right-hand direction and the Y axis is in the vehicle front direction. In addition, in this embodiment, the white line indicates a lane marker or the like in addition to a solid line or a broken line that divides the lane in which the vehicle V is traveling.

The lane-related state identification unit 121, based on the image captured by the camera 1 as information indicating the traveling environment in front of the vehicle V, the positional relationship between the white line corresponding to the lane in which the vehicle V is traveling and the vehicle V, the white line A lane-related state indicating a curvature, a lane width, and the like is specified. The lane-related state identification unit 121 identifies the lane-related state by identifying a state variable indicating the lane-related state. Specifically, the lane-related state identification unit 121 makes a lateral displacement Uc from the center of the vehicle V to the center of the lane, a tangent line of the white line, and the Y-axis, as state variables indicating the positional relationship between the white line and the vehicle V. The azimuth angle θ of the vehicle V, which is the angle, is specified. In addition, the lane-related state identification unit 121 identifies the curvature ρ of the white line, the rate of change ε of the curvature of the white line in the vehicle front direction, and the lane width W as state variables.

The motion state detection unit 122 detects the motion state of the vehicle V. Specifically, the motion state detection unit 122 detects the speed as the motion state of the vehicle V by acquiring speed information indicating the speed of the vehicle V from the vehicle speed sensor 2. Further, the motion state detection unit 122 detects the yaw rate as the motion state of the vehicle V by acquiring the yaw rate information indicating the yaw rate of the vehicle V from the yaw rate sensor 3.

When the lane-related state identifying unit 121 identifies the state variable indicating the lane-related state and the motion state detecting unit 122 detects the motion state of the vehicle V, the white line position estimation unit 123 determines the state variable and the state variable. The positions of the white lines before and after the vehicle V within a predetermined range from the vehicle V are estimated at predetermined time intervals based on the motion state.

Specifically, first, the white line position estimation unit 123 uses the lateral displacement Uc, the lane width W, the azimuth angle θ, the white line curvature ρ, and the curvature change rate ε specified by the lane related state specification unit 121, The white line coordinates in front of the vehicle V are calculated. Assuming that the position in front of the vehicle is Y _i , the X coordinate of the white line on the left side at the position Y _i is X _Li , and the X coordinate of the white line on the right side is X _Ri , X _Li and X _Ri are, for example, a white line shape by a cubic curve. By approximating, it is shown by the following formulas (1) and (2). Here, Y _Li indicating the Y coordinate of the white line on the left side and Y _Ri indicating the Y coordinate of the white line on the right side are equal to the position Y _i . Since the rate of change ε of curvature is extremely small, the white line shape is defined by a quadratic curve for Y _Li or Y _Ri without considering the term of the rate of change ε of curvature shown in Expressions (1) and (2). You may approximate.

FIG. 4 is a diagram showing an example in which the white line position estimation unit 123 according to the present embodiment calculates a plurality of white line coordinates in front of the vehicle. As shown in FIG. 4, the white line position estimation unit 123 goes from the front of the vehicle V toward the rear, and the position coordinates P _L ⁽ⁱ⁾ (X _Li , Y _Li ) of the left white line (where i is 1 to 1). N _F ) and the position coordinates P _R ⁽ⁱ⁾ (X _Ri , Y _Ri ) of the right white line are calculated to calculate the white line coordinates in front of the vehicle. However, N _F is the number of points on the front left white line or right white line.

In addition, the white line position estimation unit 123 calculates the white line coordinates behind the vehicle V using the lateral position and the lane width of the lane-related state and the motion state of the vehicle V. FIG. 5 is a diagram showing an example in which the white line position estimation unit 123 according to the present embodiment calculates a plurality of white line coordinates behind the vehicle. First, as shown in FIG. 5A, consider a case where a point sequence indicating a white line position already exists behind the vehicle V. The coordinate values of the j-th point on the left rear white line at a certain time step k shown in FIG. 5A in the vehicle coordinate system are defined as (X ^k _Lj , Y ^k _Lj ). Further, the coordinate value of the same point in the vehicle coordinate system at time step k+1 after the vehicle V shown in FIG. 5B is set to (X ^k+1 _Lj , Y ^k+1 _Lj ). Similarly, the coordinate values of the j-th point on the right rear white line in the vehicle coordinate system at time step k and time step k+1 are (X ^k _Rj , Y ^k _Rj ), (X ^k+1 _Rj , Y ^k+1 _Rj ), respectively. And At this time, each coordinate value at time step k+1 can be expressed as in the following equations (3) and (4) by two-dimensional coordinate conversion using each coordinate value at time step k.

Here, the equation (3) corresponds to the coordinate transformation of the left rear white line, and the equation (4) corresponds to the coordinate transformation of the right rear white line. Further, Δψ is the amount of change in the yaw angle of the vehicle V between time steps. For example, the white line position estimating unit 123 calculates a multiplication value of the yaw rate γ _{k of the} vehicle V detected by the motion state detecting unit 122 and the time increment Δt _k between time steps as Δψ.

The coordinates (X ^k _D , Y ^k _D ) are the camera position after the time increment Δt _k between the time steps as seen from the vehicle coordinate system at the time step k, that is, the movement destination of the origin of the vehicle coordinate system. .. The white line position estimation unit 123 calculates coordinates (X ^k _D , Y ^k _D ) by autonomous navigation using the velocity v _k of the vehicle V detected by the motion state detection unit 122 and the yaw rate γ _{k of the} vehicle V. .. Then, when the white line position estimation unit 123 calculates Δψ and the coordinates (X ^k _D , Y ^k _D ), it calculates each coordinate value at the time step k+1 based on the equations (3) and (4).

On the other hand, if the rear coordinates are not calculated at all, such as at the start of traveling, or if the vehicle has traveled a certain distance since the last new rear lateral coordinates were calculated, the coordinates of the white line position directly beside the vehicle V are set. Add new. In this case, the white line position estimation unit 123 calculates the coordinates corresponding to each point of the white line behind at time k+1 based on the following equations (5) to (8).

Here, equations (5) and (6) represent the generation of a new point just beside the front of the vehicle. Further, U _Lk and U _Rk are lateral displacements up to the left white line and the right white line, respectively, estimated at the time step k, and the lateral displacements at the time step k are represented by the following equations (9) and (10). It is calculated based on U _Ck and lane width W _k .

Expression (7) shows the coordinate transformation associated with the vehicle motion for the existing left rear white line point, and Equation (8) shows the coordinate transformation associated with the vehicle motion for the existing right rear white line point. There is. Here, in the equations (7) and (8), the number of the point after coordinate conversion is shifted backward by one with the generation of a new point on the white line, and the equations (3) and (4) ) Different.

The white line position estimation unit 123 updates the coordinate value of the white line behind by performing the above calculation for j=(N _F +1) to (N _F +N _B ) at each time. However, N _B is the number of points on the left or right white line on the rear side. The distance of the lane behind which the lane determination is performed is, for example, up to about 300 m from the front of the vehicle. Therefore, when performing the above calculation, the white line position estimation unit 123 deletes points whose distance from the vehicle front surface is equal to or greater than a predetermined threshold value after updating the coordinates of the rear point at each time step. ..

Here, when the lane-related state identification unit 121 cannot identify the lane-related state due to a defect of the camera 1, or when the exercise state detection unit 122 cannot detect the exercise state due to a defect of the vehicle speed sensor 2 or the yaw rate sensor 3. There is.

On the other hand, when the lane-related state specifying unit 121 does not specify the lane-related state and the motion state detecting unit 122 detects the motion state, the white line position estimating unit 123 sets the state variable indicating the lane-related state as a state variable. The included lane width is regarded as a predetermined width, and the lane-related state is estimated by estimating the positional relationship between the white line and the vehicle V based on the motion state. Then, the white line position estimation unit 123 estimates the position of the white line at predetermined intervals based on the estimated lane-related state and motion state.

Specifically, the white lane position estimating unit 123 does not specify the lane-related state when the lane-related state specifying unit 121 changes the state in which the lane-related state is specified to the state in which the lane-related state is not specified. Among the state variables specified before changing to the state, the state variable specified last is the past state variable. Then, the white line position estimation unit 123 estimates the position of the white line based on the past state variable, the state variable estimated based on the exercise state detected by the exercise state detection unit 122, and the exercise state. The white line position estimating unit 123 estimates the position of the white line by decreasing the influence degree of the past state variable and increasing the influence degree of the state variable estimated based on the motion state as time passes.

More specifically, the white line position estimating unit 123 sets time t=0 at a time point when the state variable is identified by the lane-related state identifying unit 121 to a state in which the state variable is not identified. Then, the white line position estimation unit 123, based on the velocity v of the vehicle V in the motion state detected by the motion state detection unit 122 and the yaw rate γ, the curvature ρ, the azimuth θ, and the lateral displacement included in the state variables. Estimate Uc.

FIG. 6 is a diagram illustrating a method of calculating the lateral displacement Uc and the azimuth angle θ according to the present embodiment. As shown in FIG. 6, when the distance between the turning center position O and the center of gravity P of the vehicle V is R, R=v/γ and R=1/ρ, so the white line position estimating unit 123 Is calculated as ρ=γ/v. Further, as shown in FIG. 6, when a circular arc having a radius R is drawn from the turning center position O and the tangential direction of the circular arc coincides with the longitudinal direction of the vehicle at the center of gravity position P of the vehicle V, the vehicle moves from the front center of the vehicle V to the vehicle. The angle formed by the line extending toward the center of gravity P of V and the tangent to the arc on the front surface of the vehicle V is the azimuth angle θ.

As shown in FIG. 6, the offset distance between the front surface of the vehicle (the position of the camera 1) and the center of gravity P of the vehicle V is L. Since Rsin θ=L and R=1/ρ, θ=sin ⁻¹ (ρL), and if the azimuth angle is small, it can be approximated to θ≈ρL. Further, if it is assumed that the center of gravity P of the vehicle V is located at the center of the lane, the lateral displacement amount Uc with respect to the center of the lane on the front surface of the vehicle is R-Rcos θ, as shown in FIG. 6. As described above, since R=1/ρ, Uc=1/ρ·(1-cos θ). In addition, the white line position estimating unit 123 regards the lane width as a predetermined width (for example, 3.5 m) and the curvature change rate ε as 0.

The white line position estimation unit 123 sets the past state variable as A ₃ , and the state variable estimated or regarded as a predetermined value based on the motion state as A _2, and each state variable A at time t based on the following equation (11). Calculate _new . Here, w ₂ and w ₃ are weighting factors indicating the degree to which A ₂ and A ₃ are applied to the calculation of the X coordinate of the white line.

ただし、

However,

The white line position estimation unit 123 uses the weighting factors w ₂ and w corresponding to each time based on the following equations (12) and (13) until the value of w ₂ becomes 1 and the value of w ₃ becomes 0. Update ₃ . Δt is a time step, and α is a coefficient indicating the speed at which the weighting coefficient is updated. The white line position estimation unit 123 may change α based on the tendency of steering or acceleration/deceleration of the vehicle V. For example, the white line position estimation unit 123 may increase the value of α when the vehicle V tends to be steered more frequently than when the vehicle V is not steered frequently.

The white line position estimation unit 123 calculates Equations (1) to (10) by using the state variable A _{new at} each time calculated based on Equations (11) to (13) and the detected motion state, When the lane-related state identification unit 121 does not identify the lane-related state and the movement state detection unit 122 changes to a state in which the movement state is detected, the X coordinate of the front and rear white line of the vehicle V at each time is calculated. ..

In addition, in the white line position estimating unit 123, the state variable is specified and the motion state is detected from the state in which the lane-related state specifying unit 121 does not specify the state variable and the motion state detecting unit 122 detects the motion state. When the state changes, the value of w ₂ is set to 0 and the value of w ₃ is set to 1 immediately after the change, and based on the specified state variable and the detected motion state, equations (1) to (10) Is calculated to calculate the X coordinate of the white line in front of and behind the vehicle V at each time.

If the lane-related state specifying unit 121 does not specify the lane-related state and the motion state detecting unit 122 does not detect the motion state, the white line position estimating unit 123 indicates the motion state of the vehicle V as a straight line. Is estimated as the motion state when the vehicle is traveling at a constant speed, and the state variable indicated by the lane-related state is estimated as the state variable when the vehicle V is traveling in the straight lane center. Then, the white line position estimation unit 123 estimates the position of the white line based on the estimated state variable and motion state.

Specifically, in the white line position estimating unit 123, the state variable is not specified by the lane-related state specifying unit 121, and the state variable is not specified from the state in which the motion state is detected by the motion state detecting unit 122. When the state has changed to a state in which the motion state has not been detected, the motion state detected last among the motion states detected before the state has changed to the state in which the motion state has not been detected is defined as the past motion state. In addition, the white line position estimation unit 123 sets the last estimated state variable among the state variables estimated before the movement state is not detected as the past estimated state variable.

Then, the white line position estimation unit 123 estimates the position of the white line based on the past movement state, the past state variable, the estimated movement state, and the estimated state variable. The white line position estimation unit 123 estimates the position of the white line by decreasing the influence degree of the past motion state and the past estimated state variable and increasing the estimated influence degree of the motion state and the state variable over time.

More specifically, the white line position estimation unit 123 sets time t=0 when the movement state detection unit 122 changes from a state in which the movement state is detected to a state in which the movement state is not detected. Then, the white line position estimation unit 123 estimates the speed v of the vehicle V as a predetermined speed (for example, 60 km/h), and estimates the yaw rate γ as a value when traveling on a straight line, that is, 0 deg/s. The motion state is estimated by. In addition, the white line position estimation unit 123 considers that the vehicle V is traveling in the center of a straight lane, and estimates the curvature ρ to be 0 m ⁻¹ , the azimuth angle θ to be 0 deg, and the lateral displacement Uc to be 0 m. Estimate variables.

The white line position estimation unit 123 sets the past estimated state variable and the past movement state as B ₂ , and the movement state and the state variable estimated at time t=0 as B _1, and based on the following equation (14), the movement at the time t. Calculate state and state variable B _new . Here, w ₁ and w ₂ are weighting factors indicating the degree to which B ₁ and B ₂ are applied to the calculation of the X coordinate of the white line.

ただし、

However,

Further, the white line position estimation unit 123 uses the weighting factor w ₁ corresponding to each time based on the following equations (15) and (16) until the value of w 1 becomes _{1 and} the value of w ₂ becomes 0. , W ₂ are updated. Here, Δt is a time step, and β is a coefficient indicating the speed at which the weighting coefficient is updated. The white line position estimation unit 123 may change β based on the tendency of steering or acceleration/deceleration.

The white line position estimation unit 123 calculates the equations (1) to (10) using the motion state and the state variable B _new calculated at each time based on the equations (14) to (16), thereby calculating the lane-related state. When the lane-related state is not identified by the identifying unit 121 and the exercising state is not detected by the exercising state detecting unit 122, the X coordinate of the front and rear white line of the vehicle V at each time is calculated.

In the white line position estimating unit 123, the state variable is not specified by the lane-related state specifying unit 121, and the motion variable is not detected by the motion state detecting unit 122. When the state is changed to the state described above, the value of w ₁ is set to 0 and the value of w ₂ is set to 1 immediately at the time of the change, and based on the estimated state variable and the detected motion state, equations (1) to ( By calculating 10), the X coordinate of the white line in front of and behind the vehicle V at each time is calculated.

The white line position estimation unit 123 indicates the reliability of the estimation accuracy of the white line position based on the lane-related state identification situation in the lane-related state identification unit 121 and the exercise state detection situation in the exercise state detection unit 122. A reliability value may be calculated as the reliability information. For example, the reliability value is set to a numerical value from 1 to 3. When the reliability value is 3, the reliability is highest and the reliability decreases as the reliability value approaches 1.

For example, when the lane-related state identification unit 121 identifies the state variable indicating the lane-related state and the motion state detection unit 122 detects the motion state of the vehicle V, the white line position estimation unit 123 determines the reliability value. Set to 3. If the lane-related state identifying unit 121 has not identified the state variable indicating the lane-related state and the motion state detecting unit 122 has detected the motion state of the vehicle V, the reliability value is set to w ₃ . The value obtained by adding 2 is used. If the lane-related state specifying unit 121 does not specify the state variable indicating the lane-related state and the motion state detecting unit 122 does not detect the motion state of the vehicle V, the reliability value is set to w ₂ . Let 1 be the value added.

The white line position estimating unit 123 may output the calculated reliability value to the lane identifying unit 125. By doing so, the certainty can be added to the lane in which the peripheral vehicle OV specified by the lane specifying unit 125 of the lane specifying device 10 is located.

The lane identifying device 10 estimates the white line position to identify the lane in which the surrounding vehicle OV is located. When the obstacle detection sensor 4 cannot detect the surrounding vehicle OV, the reliability value is set to 0. Alternatively, the white line position may not be estimated and the lane in which the surrounding vehicle OV is located may not be specified.

In each time step, the object identifying unit 124 identifies the positions of the four vertices of the rectangle and the center position of the rectangle when the surrounding vehicle OV as an object around the vehicle V is regarded as a rectangle. For example, the object identifying unit 124 identifies the position of each vertex of the surrounding vehicle OV as shown below.

First, the object specifying unit 124, based on the obstacle detection state by the single or plural obstacle detection sensors 4, in the vehicle coordinate system of a plurality of point groups indicating obstacles as the surrounding vehicle OV in the vehicle coordinate system. Specify the coordinates. The object identifying unit 124 identifies the vehicle width, vehicle length, and direction of the surrounding vehicle OV based on the coordinates of the identified plurality of points. The object identifying unit 124 determines, based on the identified vehicle width, vehicle length, and direction of the surrounding vehicle OV, a rectangular bounding box indicating the surrounding vehicle OV at a position corresponding to the point cloud. The coordinates of each vertex are specified as the coordinates indicating the surrounding vehicle OV. Further, the object identifying unit 124 identifies the center position of the bounding box when the bounding box is arranged at the position corresponding to the point group as coordinates indicating the center position of the surrounding vehicle OV.

The lane identification unit 125 identifies the lane in which the peripheral vehicle OV traveling around the vehicle V is traveling at each time step. The lane specifying unit 125, based on the white line position estimated by the white line position estimating unit 123 and the positions of the vertices of the peripheral vehicle OV as the object specified by the object specifying unit 124, is based on the white line position as a reference. The left lane position and the right lane position of the vehicle OV are specified. Then, the lane identifying unit 125 identifies the lane in which the peripheral vehicle OV is located based on the lane position at the left end and the lane position at the right end. In addition, the lane identifying unit 125 may add the reliability calculated by the white line position estimating unit 123 to the lane in which the identified peripheral vehicle OV is located and output the lane.

[Process Flow in Lane Identification Device 10]
Subsequently, a flow of processing in the lane identifying device 10 will be described. FIG. 7 is a flowchart showing the flow of processing in the lane identification device 10 according to this embodiment. In addition, the lane identification device 10 shall perform the process which concerns on this flowchart in each time step.

First, the lane-related state identification unit 121 and the motion state detection unit 122 identify the state variable and the motion state of the vehicle V, and also calculate the reliability of the specific situation of the state variable and the motion state (S1).
Subsequently, the white line position estimation unit 123 estimates the white line position with respect to the lane in which the vehicle V is traveling, based on the state of the state variable specified in S1 and the state of detection of the motion state (S2).

Subsequently, the object identifying unit 124 identifies the coordinates of each vertex of the peripheral vehicle OV as an object (S3).
Subsequently, the lane identifying unit 125 identifies the positions of the left and right ends of the surrounding vehicle OV based on the white line position estimated in S2 and the coordinates of each vertex of the surrounding vehicle OV identified in S3 (S4).

Subsequently, the lane specifying unit 125 specifies the position of the peripheral vehicle OV on the lane based on the positions of the left and right ends of the specified peripheral vehicle OV (S5).
Subsequently, the lane identifying unit 125 outputs information indicating the position on the lane of the peripheral vehicle OV identified in S5 and reliability information indicating reliability when the white line position is estimated (S6).

[Effects of this embodiment]
As described above, the lane identifying device 10 according to the present embodiment identifies the positional relationship between the white line corresponding to the lane in which the vehicle V is traveling and the vehicle V, the curvature of the white line, and the state variable indicating the lane width. At the same time, when the motion state of the vehicle V is detected, the position of the white line within the predetermined range from the vehicle V is estimated at predetermined time intervals based on the state variable and the motion state, and the state variable is not specified. When the state is detected, the state variable is estimated by estimating the positional relationship based on the motion state, and the position of the white line is estimated at predetermined intervals based on the estimated state variable and the motion state. By doing so, the lane identifying device 10 can estimate the position of the white line even if the white line cannot be recognized, although the reliability is reduced.

Although the present invention has been described above using the embodiments, the technical scope of the present invention is not limited to the scope described in the above embodiments, and various modifications and changes are possible within the scope of the gist thereof. is there. For example, the specific embodiment of the distribution/integration of the device is not limited to the above-described embodiment, and all or a part thereof may be functionally or physically distributed/integrated in arbitrary units to be configured. You can Further, a new embodiment that occurs due to an arbitrary combination of a plurality of embodiments is also included in the embodiment of the present invention. The effect of the new embodiment produced by the combination has the effect of the original embodiment.

1... Camera 2... Vehicle speed sensor 3... Yaw rate sensor 4... Obstacle detection sensor 10... Lane identifying device 11... Storage unit 12... Control unit 121... Lane related State identification unit 122... Motion state detection unit 123... White line position estimation unit 124... Object identification unit 125... Lane identification unit V... Vehicle OV... Peripheral vehicle

Claims (8)

A lane-related state identification that identifies a lane-related state indicating the positional relationship between the white line corresponding to the lane in which the vehicle is traveling and the own vehicle, the curvature of the white line, and the lane width, based on information indicating the front of the vehicle Department,
A motion state detection unit that detects the motion state of the vehicle,
When the lane-related state is identified by the lane-related state identifying unit and the exercise state is detected by the exercise state detecting unit, the lane-related state is identified by the lane-related state and the exercise state at predetermined intervals based on the lane-related state and the exercise state. When the position of the white line within a predetermined range from the vehicle is estimated, the lane-related state is not specified by the lane-related state specifying unit, and the motion state is detected by the motion state detection unit, the motion state By estimating the lane-related state by estimating the positional relationship based on, the white line position estimation unit that estimates the position of the white line at the predetermined time intervals based on the estimated lane-related state and the motion state,
A white line position estimating device comprising: The white lane position estimating unit, when the lane-related state is specified by the lane-related state specifying unit, when the lane-related state is not specified, when the lane-related state is not specified, the lane-related state is not specified. Estimating the position of the white line based on the past lane-related state that is the lane-related state specified before changing, the lane-related state estimated based on the motion state, and the motion state,
The white line position estimating device according to claim 1. The white line position estimation unit estimates the position of the white line by decreasing the influence degree of the past lane-related state and increasing the influence degree of the lane-related state estimated based on the motion state, as time passes. ,
The white line position estimating device according to claim 2. The white line position estimation unit, if the lane-related state is not specified by the lane-related state specifying unit, and the movement state is not detected by the movement state detection unit, the movement state of the own vehicle, While estimating the motion state when the host vehicle is traveling at a constant speed on a straight line, the lane-related state is estimated and estimated as the lane-related state when the host vehicle is traveling on the center of a straight road. Estimating the position of the white line based on the lane-related state and the estimated motion state,
The white line position estimation device according to claim 1. The lane line position estimating unit determines whether the lane-related state is not specified by the lane-related state specifying unit and the movement state is detected by the movement-state detecting unit, and the lane-related state specifying unit determines the lane-related state. When the state is not specified and the exercise state is changed to a state in which the exercise state is not detected by the exercise state detection unit, a past exercise state that is the exercise state detected before the change in the exercise state is detected. Estimating the position of the white line based on the estimated motion state and the estimated lane-related state,
The white line position estimating device according to claim 4. The white line position estimation unit estimates the position of the white line by decreasing the influence degree of the past movement state and increasing the influence degree of the estimated movement state as time passes,
The white line position estimating device according to claim 5. The white line position estimating unit, based on the specific situation of the lane-related state in the lane-related state identifying unit, and the detection situation of the exercise state in the exercise state detection unit, the reliability of the position of the estimated white line calculate,
The white line position estimating device according to any one of claims 1 to 6. Computer running,
Based on information indicating the front of the host vehicle, a step of identifying a lane-related state indicating the positional relationship between the host vehicle and a white line corresponding to the lane in which the host vehicle travels, the curvature of the white line, and the lane width,
Detecting the motion state of the vehicle,
When the lane-related state is specified and the motion state is detected, the position of the white line within a predetermined range from the host vehicle is estimated at predetermined time intervals based on the lane-related state and the motion state. When the lane-related state is not specified and the motion state is detected, the lane-related state is estimated by estimating the positional relationship based on the motion state, and the estimated lane-related state and the Estimating the position of the white line at the predetermined time intervals based on a motion state,
A white line position estimating method comprising:

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