Parking assistance method and parking assistance device

A parking assistance method includes: storing positions of target objects detected around the target parking position as learned target object positions; when the own vehicle travels in a vicinity of the target parking position after the learned target object positions are stored, counting the number of times that the learned target object position coincides with a surrounding target object position of a target object detected around the own vehicle and providing a higher degree of reliability to the learned target object position having a larger number of times of coincidence with the surrounding target object position; by comparing the learned target object position having a degree of reliability greater than or equal to a predetermined threshold degree of reliability with a position of a target object detected around the own vehicle, calculating a relative position of the own vehicle with respect to the target parking position.

TECHNICAL FIELD

The present invention relates to a parking assistance method and a parking assistance device.

BACKGROUND

As a technology relating to parking assistance to assist parking of an own vehicle at a target parking position, JP 2017-138664 A described below has been known. In JP 2017-138664 A, a target object around the target parking position is detected and stored, a relative position of the own vehicle with respect to the target parking position is calculated, based on a position of a target object detected around the own vehicle at the time of autonomous parking and a position of the stored target object, and the own vehicle is caused to autonomously move to the target parking position, based on the relative position.

SUMMARY

However, when the stored target object is a movable object, there is a risk that the position of the target object changing after the target object is stored causes calculation precision of a relative position of the own vehicle with respect to the target parking position to deteriorate.

An object of the present invention is to, in parking assistance in which a relative position of an own vehicle with respect to a target parking position is calculated based on a pre-stored position of a target object around the target parking position, prevent calculation precision of a relative position from deteriorating due to the stored target object position changing.

According to an aspect of the present invention, there is provided a parking assistance method including: storing positions of a plurality of target objects detected around a target parking position when an own vehicle is parked at the target parking position as learned target object positions; when the own vehicle travels in a vicinity of the target parking position after the learned target object positions are stored, counting, with respect to each of the learned target object positions, a number of times that the learned target object position coincides with a surrounding target object position, the surrounding target object position being a position of a target object detected around the own vehicle; providing a higher degree of reliability to the learned target object position having a large number of times of coincidence with the surrounding target object position than to the learned target object position having a small number of times of coincidence with the surrounding target object position; by comparing the learned target object position having a degree of reliability greater than or equal to a predetermined threshold degree of reliability among the learned target object positions with a position of a target object detected around the own vehicle, calculating a relative position of an own vehicle with respect to the target parking position; based on the calculated relative position, calculating a target travel trajectory from a current position of the own vehicle to the target parking position; and performing parking assistance control to assist movement of the own vehicle along the calculated target travel trajectory.

According to an aspect of the present invention, it is possible to, in parking assistance in which a relative position of an own vehicle with respect to a target parking position is calculated based on a pre-stored position of a target object around the target parking position, prevent calculation precision of a relative position from deteriorating due to the stored target object position changing.

DETAILED DESCRIPTION

FIG.1is a diagram illustrative of an example of a schematic configuration of a parking assistance device of an embodiment. An own vehicle1includes a parking assistance device10configured to assist parking of the own vehicle1at a target parking position. The parking assistance device10calculates a target travel trajectory from a current position of the own vehicle1to the target parking position and assists the own vehicle in traveling along the target travel trajectory.

In parking assistance performed by the parking assistance device10, various forms of assisting the own vehicle1in traveling along the target travel trajectory are included. For example, the parking assistance device10may, by performing autonomous driving to control the own vehicle to travel to the target parking position along the target travel trajectory of the own vehicle1, assist parking of the own vehicle1. Note that the autonomous driving to control the own vehicle1to travel to the target parking position along the target travel trajectory of the own vehicle1means control to control all or some of a steering angle, a driving force, and a braking force of the own vehicle, autonomously perform all or a portion of travel along the target travel trajectory of the own vehicle1, and thereby assist parking operation of a passenger.

In addition, for example, the parking assistance device10may, by displaying the target travel trajectory and the current position of the own vehicle1on a display device that the passenger of the own vehicle1can visually recognize, assist parking of the own vehicle1.

The parking assistance device10includes a positioning device11, an object sensor12, vehicle sensors13, human-machine interfaces (HMIs)15, actuators16, and a controller17.

The positioning device11measures a current position of the own vehicle1. The positioning device11may include, for example, a global navigation satellite system (GNSS) receiver. The GNSS receiver may be, for example, a global positioning system (GPS) receiver or the like.

The object sensor12detects an object in a predetermined distance range from the own vehicle1(for example, a detection range of the object sensor12). The object sensor12detects a surrounding environment around the own vehicle1, such as a relative position between an object existing in surroundings around the own vehicle1and the own vehicle1, distance between the own vehicle1and the object, and a direction in which the object exists. The object sensor12may include, for example, a camera to capture the surrounding environment around the own vehicle1. The camera may be, for example, cameras that capture images of the surroundings around the own vehicle1and generate captured images to be converted to a bird's eye view (around view monitoring image). The object sensor12may include a ranging device, such as a laser range finder (LRF), a radar, a light detection and ranging (LiDAR), or a laser radar.

The vehicle sensors13detect various information (vehicle information) that can be acquired from the own vehicle1. The vehicle sensors13include, for example, a vehicle speed sensor configured to detect traveling speed (vehicle speed) of the own vehicle1, wheel speed sensors configured to detect rotational speed of respective tires that the own vehicle1has, a triaxial acceleration sensor configured to detect acceleration (including deceleration) in three axial directions of the own vehicle1, a steering angle sensor configured to detect a steering angle of a steering wheel, a turning angle sensor configured to detect a turning angle of steered wheels, a gyro sensor configured to detect angular velocity generated in the own vehicle1, and a yaw rate sensor configured to detect a yaw rate.

The human-machine interfaces15are interface devices that give and receive information between the parking assistance device10and the passenger. The human-machine interfaces15include a display device (such as a display screen of a navigation system and a display device installed close to a meter in front of a driver seat) that the passenger of the own vehicle1can visually recognize. The human-machine interfaces15also include an operation element configured to accept an operation input by the passenger to the parking assistance device10. For example, the operation element may be a button, a switch, a lever, a dial, a keyboard, a touch panel, or the like.

The actuators16include a steering actuator, an accelerator actuator, and a brake control actuator. The steering actuator controls a steering angle of a steering mechanism of the own vehicle1in accordance with a control signal from the controller17. The accelerator actuator controls accelerator opening of a drive device, which is an engine or a drive motor, in accordance with a control signal from the controller17. The brake actuator causes a braking device to operate in accordance with a control signal from the controller17.

The controller17is an electronic control unit that performs parking assistance control of the own vehicle1. The controller17includes a processor18and peripheral components, such as a storage device19. The processor18may be, for example, a CPU or an MPU.

The storage device19may include a semiconductor storage device, a magnetic storage device, an optical storage device, and the like. The storage device19may include registers, a cache memory, and a memory, such as a ROM or a RAM, that is used as a main storage device. Functions of the controller17, which will be described below, may be achieved by, for example, the processor18executing computer programs stored in the storage device19.

Note that the controller17may be formed using dedicated hardware for performing various types of information processing that will be described below. For example, the controller17may include a functional logic circuit that is implemented in a general-purpose semiconductor integrated circuit. For example, the controller17may include a programmable logic device, such as a field-programmable gate array, and the like.

Next, an example of the parking assistance control performed by the parking assistance device10will be described.FIG.2is now referred to. When use of the parking assistance performed by the parking assistance device10is started, first, a relative positional relationship between a target object position of a target object existing around a target parking position2and the target parking position2is stored in the storage device19. The target parking position2is a target position at which the own vehicle1is to be parked. The target object is a ground object serving as a mark for specifying the current position of the own vehicle1. The target object may be, for example, a pavement marking (a lane boundary line3a, a stop line3b, a road sign, or the like), a road boundary (curbs3cto3e, a guardrail, or the like), or an obstacle (a house3f, a wall3g, an objet3h, or the like).

When a relative positional relationship between a target object position and the target parking position2is stored in the storage device19, an operation mode of the parking assistance device10is set to a “target object learning mode”. Subsequently, the own vehicle1is parked at the target parking position2by manual driving. Note that when the own vehicle1is to be parked at the target parking position2by manual driving, the parking assistance device10may automatically set the operation mode to the “target object learning mode”. When the own vehicle1is to be parked at the target parking position2by manual driving, a driver may select whether or not the operation mode of the parking assistance device10is set to the “target object learning mode”.

While the own vehicle1is moved to be parked at the target parking position2by manual driving, a target object position of a target object around the own vehicle1is detected by the object sensor12. The object sensor12is a sensor to detect a target object position existing in a detection region within a predetermined detection distance range from the object sensor12.

For example, the parking assistance device10may detect edges or corner portions of pavement markings (in the example inFIG.2, the lane boundary line3aand the stop line3b), road boundaries (in the example inFIG.2, ground contacting portions of the curbs3cto3e), and obstacles (in the example ofFIG.2, ground contacting portions of the house3f, the wall3g, and the objet3h) in a captured image obtained by image capturing using the camera of the object sensor12, as feature points and define positions of the feature points as target object positions.

The parking assistance device10calculates a feature amount of a detected feature point (for example, a shade, an attribute, or the like of the feature point). For detection of a feature point and calculation of a feature amount, various methods, such as SIFT, SURF, ORB, BRIAK, KAZE, and AKAZE, can be made use of. Note that the parking assistance device10may detect a feature point of point group information acquired by the laser range finder, the radar, or the LiDAR. Herein, an example in which a feature point is detected from a captured image will be described. Note that the detection of a feature amount of a feature point is not essential and it is only required to detect at least the position of a feature point.

The parking assistance device10calculates a relative positional relationship between a target object position (the position of a feature point) detected by the object sensor12and the target parking position2. For example, the parking assistance device10calculates, based on a position of a feature point in an image and camera information relating to an attachment state (an attachment position, an optical axis angle, and an angle of view) of the camera to the own vehicle1, a relative position of the feature point with respect to the own vehicle1. Next, the parking assistance device10estimates a current position of the own vehicle1in a fixed coordinate system at a time point at which a target object position is detected by the object sensor12and calculates, based on the estimated current position and the relative position of the target object with respect to the own vehicle1, a target object position in the fixed coordinate system. As used herein, the fixed coordinate system is a coordinate system with the origin set at a specific point (for example, a map coordinate system).

The current position of the own vehicle1in the fixed coordinate system may be estimated by, for example, the positioning device11, odometry, or dead reckoning. The current position of the own vehicle1in the fixed coordinate system may be estimated by map mapping between a target object position detected by the object sensor12and a known target object position or high-definition map information.

Next, the parking assistance device10identifies the target parking position2in the fixed coordinate system. For example, the parking assistance device10may detect a position of the own vehicle1when the own vehicle1is positioned at the target parking position2, as the target parking position2. The target object position and the position of the target parking position2being determined in the fixed coordinate system causes a relative positional relationship between the target object position and the target parking position2to be determined.

The parking assistance device10stores a relative positional relationship between a target object position and the target parking position2in the storage device19. For example, the parking assistance device10may individually store a target object position and the position of the target parking position2in the fixed coordinate system in the storage device19. Alternatively, the parking assistance device10may calculate a target object position in a relative coordinate system with the origin set at the target parking position2and store the calculated target object position in the storage device19. Herein, an example in which the parking assistance device10stores a target object position and the position of the target parking position2in the fixed coordinate system in the storage device19will be described.

Note that in the following description, a target object stored in the storage device19is sometimes referred to as a “learned target object”. In addition, a target object position of a learned target object is sometimes referred to as a “learned target object position”. Circular marks inFIG.2represent learned target object positions stored in the storage device19.

Next, a parking assistance method performed by the parking assistance device10will be described with reference toFIG.3. In order to use the parking assistance, the operation mode of the parking assistance device10is set to a “parking assistance mode”. In the parking assistance mode, the parking assistance device10retrieves the target object positions of learned target objects and the position of the target parking position2stored in the storage device19. Circular marks inFIG.3represent the target object positions of learned target objects retrieved from the storage device19in the parking assistance mode.

The parking assistance device10detects a relative position with respect to the own vehicle1of a target object around the own vehicle1as a target object position of the target object around the own vehicle1by the object sensor12. A detection method of a target object position is the same as the detection method in the target object learning mode. A target object position that the object sensor12detects in the parking assistance mode is indicated by a triangular mark. In the example inFIG.3, corner portions of the lane boundary line3a, the curbs3cand3e, and the wall3gare detected as target object positions.

The parking assistance device10associates the same target object positions with each other by matching the respective target object positions detected by the object sensor12in the parking assistance mode (triangular marks) with the learned target object positions retrieved from the storage device19(circular marks). For example, the parking assistance device10may determine target object positions having the same or similar feature amounts to be the same target object positions. Alternatively, regardless of feature amounts, the parking assistance device10may associate the target object positions detected by the object sensor12(triangular marks) with the target object position of the learned target objects retrieved from the storage device19(circular marks) by matching relative positional relationships between the target object positions detected by the object sensor12(triangular marks) with relative positional relationships between the target object positions of the learned target objects retrieved from the storage device19(circular marks). Still alternatively, the parking assistance device10may associate the target object positions detected by the object sensor12(triangular marks) with the target object positions of the learned target objects retrieved from the storage device19(circular marks), using both the above-described feature amounts of feature points and relative positional relationships. In the example inFIG.3, corner portions of each of the lane boundary line3a, the curbs3cand3e, and the wall3gare associated with each other.

The parking assistance device10calculates, based on relative positional relationships between the target object positions detected in the parking assistance mode (triangular marks) and the own vehicle1and relative positional relationships between learned target object positions (circular marks) associated with the detected target objects (triangular marks) and the target parking position2, a relative position of the current position of the own vehicle1with respect to the target parking position2.

For example, the parking assistance device10may calculate the position of the target parking position2in a relative coordinate system with reference to the current position of the own vehicle1(hereinafter, referred to as a “vehicle coordinate system”). Alternatively, the parking assistance device10may calculate, based on the relative positional relationships between the respective target object positions detected in the parking assistance mode (triangular marks) and the own vehicle1and the learned target object positions in the fixed coordinate system (circular marks), the current position of the own vehicle1in the fixed coordinate system. The position of the own vehicle1and the position of the target parking position2in the fixed coordinate system being determined causes the relative position of the current position of the own vehicle1with respect to the target parking position2to be determined.

The parking assistance device10calculates, based on the relative position of the current position of the own vehicle1with respect to the target parking position2, a target travel trajectory starting from the current position of the own vehicle1and reaching the target parking position2. The parking assistance device10performs parking assistance control to assist movement of the own vehicle1along the calculated target travel trajectory. For example, the parking assistance device10performs autonomous driving to control the own vehicle to travel to the target parking position along the calculated target travel trajectory. In addition, for example, the parking assistance device10displays the target travel trajectory and the current position of the own vehicle1on the display device that a user of the own vehicle1can visually recognize.

However, when a learned target object position is a target object position of a movable object (such as a bicycle placed around the target parking position2and a potted plant grown in a plant pot), the object is sometimes moved after the learned target object position is stored. In this case, since a target object position detected at the time of parking assistance is caused to be displaced from a learned target object position stored in the storage device19or a target object itself disappearing causes the target object position not to be detected at the time of parking assistance, there is a risk that calculating a relative position of the current position of the own vehicle1with respect to the target parking position2, based on learned target object positions including such target object positions causes calculation precision of the relative position to deteriorate.

Thus, the parking assistance device10of the embodiment detects the target object position of a target object around the own vehicle1on an occasion when the own vehicle1travels in a vicinity of the target parking position2after the learned target object positions are stored. A target object position that is detected on the occasion when the own vehicle1travels in the vicinity of the target parking position2after the learned target object positions are stored is sometimes referred to as a “surrounding target object position” in the following description.

The parking assistance device10counts, with respect to each of the learned target object positions stored in the storage device19, the number of times that the learned target object position coincides with a surrounding target object position on every occasion when the own vehicle1travels in the vicinity of the target parking position2after the learned target object positions are stored. Alternatively, the parking assistance device10may count after the learned target object positions are stored, with respect to each of the learned target object positions stored in the storage device19, the number of times that the learned target object position coincides with a surrounding target object position every time the number of occasions when the own vehicle1travels in the vicinity of the target parking position2reaches a predetermined number (for example, once every two times or once every three times). Still alternatively, the parking assistance device10may count, with respect to each of the learned target object positions stored in the storage device19, the number of times that the learned target object position coincides with a surrounding target object position on every occasion when the own vehicle1travels in the vicinity of the target parking position2after the learned target object positions are stored and every predetermined specific scene (for example, on every specific scene, such as a scene in which the own vehicle1leaves the target parking position2or a scene in which the own vehicle1enters the target parking position2). That is, the parking assistance device10counts, with respect to each of the learned target object positions stored in the storage device19, the number of times that the learned target object position coincides with a surrounding target object position at an arbitrary timing when the own vehicle1travels in the vicinity of the target parking position2after the learned target object positions are stored.

The parking assistance device10provides a higher degree of reliability to a learned target object position having a large number of times of coincidence with a surrounding target object position than to a learned target object position having a small number of times of coincidence with a surrounding target object position. The parking assistance device10, by comparing a learned target object position having a degree of reliability greater than or equal to a predetermined threshold degree of reliability among the learned target object positions with the position of a target object detected around the own vehicle1, calculates a relative position of the own vehicle1with respect to the target parking position2.

Because of this configuration, when positions of a fixed object and a movable object are stored as learned target object positions and subsequently the movable object is moved before a time point at which the parking assistance control is performed, the parking assistance device10can provide a higher degree of reliability to the learned target object position of the fixed object (an immovable object or an immobile object) than to the learned target object position of the movable object. As a result, since among the learned target object positions, only the learned target object position of a fixed object can be selected and used for the calculation of the relative position of the own vehicle1with respect to the target parking position2, it is possible to prevent calculation precision of the relative position from deteriorating.

With reference toFIGS.4A to4D, an example of a method for providing a degree of reliability to a learned target object position will be described. First, in the target object learning mode, the parking assistance device10, when parking the own vehicle1at the target parking position2, detects target object positions of a plurality of target objects around the own vehicle1. The parking assistance device10stores the detected positions of the plurality of target objects in the storage device19as learned target object positions.

Circular marks30ato30einFIG.4Arepresent learned target object positions stored in the storage device19. In the example inFIG.4A, the circular marks30aand30brepresent learned target object positions of curbs, the circular mark30crepresents a learned target object position of a lane boundary line, and the circular mark30drepresents a learned target object position of the house. On the other hand, the circular mark30crepresents a learned target object position of a movable object3j(for example, a bicycle).

Each of the learned target object positions30ato30eis provided with a degree of reliability. Immediately after the learned target object positions30ato30eare stored, all the degrees of reliability of the learned target object positions30ato30eare the same value (initial value). Numerical values “1” on the lower left side of the learned target object positions30ato30einFIG.4Aindicate the degrees of reliability of the learned target object positions30ato30c. The parking assistance device10stores the learned target object positions and the degrees of reliability in association with each other in the storage device19as learned target object data20.

FIG.4Bis now referred to. The parking assistance device10detects the target object position of a target object around the own vehicle1(that is, detects a surrounding target object position) on the occasion when the own vehicle1travels in a vicinity of the target parking position2after the learned target object positions30ato30eare stored. Triangular marks31ato31erepresent surrounding target object positions. The triangular marks31aand31brepresent surrounding target object positions of the curbs, the triangular mark31crepresents a surrounding target object position of the lane boundary line, the triangular mark31drepresents a surrounding target object position of the house, and the triangular mark31erepresents a surrounding target object position of the movable object3j.

An occasion of detecting the surrounding target object positions31ato31emay be any occasion as long as the occasion is an occasion when the own vehicle1travels in the vicinity of the target parking position2after the learned target object positions30ato30eare stored. In the example inFIG.4B, the surrounding target object positions31ato31eare detected on the occasion when the own vehicle1leaves the target parking position2.

The occasion when the own vehicle1leaves the target parking position2may be, for example, an occasion when the driver causes the own vehicle1to leave the target parking position2by manual driving. In addition, when the parking assistance device10has a function of parking space-leaving assistance control, the occasion of detecting the surrounding target object positions31ato31emay be a time when carrying out the parking space-leaving assistance control.

The parking space-leaving assistance control may be control to calculate a target travel trajectory to cause the own vehicle1to leave the target parking position2that is the current position of the parked own vehicle1, based on the target object positions detected around the own vehicle1and the learned target object positions and cause the own vehicle1to move along the target travel trajectory.

In addition, for example, the occasion of detecting the surrounding target object positions31ato31emay be an occasion when the own vehicle1is to be parked at the target parking position2by the parking assistance control performed by the parking assistance device10after the learned target object positions30ato30eare stored.

The parking assistance device10counts the number of times that the learned target object positions30ato30ecoincide with the surrounding target object positions31ato31e, with respect to each of the learned target object positions30ato30con every occasion when the own vehicle1travels in the vicinity of the target parking position2. For example, when the feature amounts of a learned target object position and a surrounding target object position are the same as or similar to each other, the parking assistance device10may determine that the target object positions coincide with each other. In the example inFIG.4B, the learned target object positions30ato30dcoincide with the surrounding target object position31ato31d, respectively. Thus, the parking assistance device10increases the numbers of times that the learned target object positions30ato30dcoincide with surrounding target object positions by one. On the other hand, since the movable object3jis moved after the learned target object position30eis stored, the learned target object position30edoes not coincide with the surrounding target object position31c. Thus, the parking assistance device10does not increase the number of times that the learned target object position30ecoincides with a surrounding target object position.

The parking assistance device10provides higher degrees of reliability to the learned target object positions30ato30dhaving large numbers of times of coincidence with surrounding target object positions than to the learned target object position30ehaving a small number of times of coincidence with a surrounding target object position.

For example, the parking assistance device10may provide a degree of reliability according to the number of times that each of the learned target object positions30ato30ecoincides with a surrounding target object position. Numerical values “2” on the lower left side of the learned target object positions30ato30dinFIG.4Bindicate the degrees of reliability of the learned target object positions30ato30d, and a numerical value “1” on the lower left side of the learned target object position30cindicates the degree of reliability of the learned target object position30c. As described above, each of the learned target object positions30ato30dis provided with a degree of reliability higher than the degree of reliability of the learned target object position30c.

FIG.4Cis now referred to. The parking assistance device10, when performing the parking assistance control after storing the learned target object positions30ato30c, detects the target object position of a target object around the own vehicle1(that is, a surrounding target object position). Square marks32ato32erepresent surrounding target object positions that the parking assistance device10detects when performing the parking assistance control. The square marks32aand32brepresent the surrounding target object positions of the curbs, the square mark32crepresents the surrounding target object position of the lane boundary line, the square mark32drepresents the surrounding target object position of the house, and the square mark32erepresents the surrounding target object position of the movable object3j.

The parking assistance device10selects only a learned target object position having a degree of reliability greater than or equal to a predetermined threshold degree of reliability among the learned target object positions30ato30c. Herein, a case where the threshold degree of reliability is “2” is assumed for the purpose of description. Thus, only the learned target object positions30ato30dof the fixed objects are selected, and the learned target object position30eof the movable object3jis not selected.

The parking assistance device10calculates, based on the selected learned target object positions30ato30dand the surrounding target object positions32ato32dcorresponding to the learned target object positions30ato30d, a relative position of the current position of the own vehicle1with respect to the target parking position2.

Since as described above, the parking assistance device10is capable of selecting only the learned target object positions30ato30dobtained by detecting fixed objects and making use of the selected learned target object positions30ato30dfor calculation of the relative position of the own vehicle1with respect to the target parking position2, it is possible to prevent calculation precision of the relative position from deteriorating.

FIG.4Dis now referred to. The parking assistance device10counts the number of times that the learned target object positions30ato30ecoincide with the surrounding target object positions32ato32c, with respect to each of the learned target object positions30ato30eafter the calculation of the relative position of the current position of the own vehicle1with respect to the target parking position2is completed (for example, after the own vehicle1has moved to the target parking position2by the parking assistance control).

In the example inFIG.4D, the learned target object positions30ato30dcoincide with the surrounding target object position32ato32d, respectively. Thus, the parking assistance device10increase the numbers of times that the learned target object positions30ato30dcoincide with surrounding target object positions by one. On the other hand, the movable object3jis further moved after the surrounding target object position31eis detected, and the learned target object position30edoes not coincide with the surrounding target object position32e. Thus, the parking assistance device10does not increase the number of times that the learned target object position30ecoincides with a surrounding target object position.

The parking assistance device10updates the degrees of reliability according to the number of times that the learned target object positions30ato30ecoincide with the surrounding target object positions32ato32c, respectively. As a result, the degrees of reliability of the learned target object positions30ato30dhave increased to “3”. On the other hand, the degree of reliability of the learned target object position30eremains at “1”.

Repeating detection of a surrounding target object position and update of a degree of reliability as described above enables a higher degree of reliability to be provided to the learned target object position of a fixed object than to the learned target object position of a movable object. Thus, since among the learned target object positions, only the learned target object position of a fixed object can be selected and used for the calculation of the relative position of the own vehicle1with respect to the target parking position2, it is possible to prevent calculation precision of the relative position from deteriorating.

Note that the parking assistance device10may detect the surrounding target object positions31ato31eand provide the learned target object positions30ato30ewith degrees of reliability on an occasion when the own vehicle1“first” travels in the vicinity of the target parking position2after the learned target object positions30ato30eare stored. For example, the parking assistance device10may provide the learned target object positions30ato30ewith degrees of reliability when first causing the own vehicle1to leave the target parking position2.

Because of this configuration, when the movable object3jis moved during a period from when the learned target object positions30ato30eare stored until subsequently the own vehicle1first travels in the vicinity of the target parking position2, a target travel trajectory can be calculated excluding the learned target object position of the movable object3jfrom when the own vehicle1is first parked at the target parking position2after the learned target object positions are stored.

A functional configuration of the controller17will be described in more detail below.FIG.5is now referred to. The controller17functions as an image conversion unit40, a self-position calculation unit41, a feature point detection unit43, a learned target object data generation unit44, a degree-of-reliability provision unit45, a relative position estimation unit46, a target trajectory generation unit47, a steering control unit48, a vehicle speed control unit49, and an assistance image generation unit50.

The image conversion unit40converts captured images captured by the camera of the object sensor12to a bird's eye image (an around view monitoring image) that is an image viewed from a virtual viewpoint directly above the own vehicle1as illustrated inFIGS.2and3. Hereinafter, a bird's eye image after conversion by the image conversion unit40is sometimes referred to as a “surrounding image”.

The self-position calculation unit41calculates a current position of the own vehicle1in the fixed coordinate system by dead reckoning or the like based on vehicle information output from the vehicle sensors13. The self-position calculation unit41may correct the calculated current position by map mapping or the like between a target object position detected by the object sensor12and a known target object position or the high-definition map information.

The feature point detection unit43detects a feature point of a target object around the own vehicle1from the surrounding image output from the image conversion unit40and calculates a feature amount of the feature point. The feature point detection unit43outputs the detected feature point and the feature amount of the feature point to the learned target object data generation unit44and the relative position estimation unit46in conjunction with the current position of the own vehicle1received from the self-position calculation unit41.

The learned target object data generation unit44calculates a position of a feature point in the fixed coordinate system, based on a feature point output from the feature point detection unit43and the current position of the own vehicle1.

In the target object learning mode, the learned target object data generation unit44stores the position of a feature point detected by the feature point detection unit43in the storage device19as a learned target object position. For example, the learned target object data generation unit44sets the degree of reliability of a learned target object position to an initial value and stores the learned target object position and the degree of reliability in association with each other in the storage device19as learned target object data20.

On the other hand, when the operating mode of the parking assistance device10is not the target object learning mode, the learned target object data generation unit44outputs feature point information including the position of a feature point and feature amount information to the degree-of-reliability provision unit45.

The degree-of-reliability provision unit45determines whether or not the own vehicle1is traveling in the vicinity of the target parking position2. For example, the degree-of-reliability provision unit45may determine whether or not the own vehicle1is traveling in the vicinity of the target parking position2, based on the current position of the own vehicle1calculated by the self-position calculation unit41.

For example, when the own vehicle1is caused to leave the target parking position2, the degree-of-reliability provision unit45may determine that the own vehicle1is traveling in the vicinity of the target parking position2. For example, when distance between the current position of the own vehicle1and the target parking position2is less than or equal to a predetermined value and the own vehicle1is moving away from the target parking position2or a parking space-leaving assistance function is being performed, the degree-of-reliability provision unit45may determine that the own vehicle1is caused to leave the target parking position2.

In addition, for example, when the parking assistance control to assist parking to the target parking position2is being performed, the degree-of-reliability provision unit45may determine that the own vehicle1is traveling in the vicinity of the target parking position2. For example, when the distance between the current position of the own vehicle1and the target parking position2is less than or equal to a predetermined value and the operation mode of the parking assistance device10is the parking assistance mode, the degree-of-reliability provision unit45may determine that the parking assistance control to assist parking at the target parking position2is being performed.

The degree-of-reliability provision unit45uses the position of a feature point that is output from the learned target object data generation unit44when the own vehicle1is traveling in the vicinity of the target parking position2, as a surrounding target object position.

The degree-of-reliability provision unit45counts, with respect to each learned target object position stored in the storage device19, the number of times that the learned target object position coincides with a surrounding target object position and updates the degree of reliability of each learned target object position according to the number of times that the learned target object position coincides with a surrounding target object position every time the own vehicle1travels in the vicinity of the target parking position2.

Specifically, the degree-of-reliability provision unit45provides a degree of reliability in such a way that the degree of reliability of a learned target object position having a large number of times of coincidence with a surrounding target object position is higher than the degree of reliability of a learned target object position having a small number of times of coincidence with a surrounding target object position. For example, the degree-of-reliability provision unit45may provide a degree of reliability in such a manner that the larger the number of times that a learned target object position coincides with a surrounding target object position is, the higher the degree of reliability of the learned target object position is. For example, the degree-of-reliability provision unit45may provide the number of times that a learned target object position coincides with a surrounding target object position as a degree of reliability of the learned target object position.

The relative position estimation unit46retrieves learned target object positions and degrees of reliability of the learned target object positions that are stored in the storage device19as the learned target object data20. The relative position estimation unit46selects only a learned target object position having a degree of reliability greater than or equal to a predetermined threshold degree of reliability among the retrieved learned target object positions. The predetermined threshold degree of reliability may be, for example, a value obtained by subtracting a predetermined value from a highest degree of reliability among the degrees of reliability of the learned target object positions, may be a degree of reliability ranked at a predetermined place in the descending order from a maximum value among the degrees of reliability of the learned target object positions, or may be set based on an average of the degrees of reliability of the learned target object positions.

The relative position estimation unit46, by matching the selected learned target object position with a target object position detected in the parking assistance mode, associates target object positions detected with respect to the same object with each other.

The relative position estimation unit46estimates, based on relative positional relationships between the target object positions detected in the parking assistance mode and the own vehicle1and relative positional relationships between learned target object positions associated with the detected target object positions and the target parking position2, a relative position of the current position of the own vehicle1with respect to the target parking position2.

For example, target object positions detected in the parking assistance mode are denoted by (xi, yi), and target object positions of learned target objects each of which is associated with one of the target object positions (xi, yi) are denoted by (xmi, ymi) (i=1 to N). For example, the relative position estimation unit46may calculate an affine transformation matrix Maffine, using the following equation, based on a least-square method.

The relative position estimation unit46may calculate a column vector (a1, a2, a3, a4)Tas in the following equation, using a least-square method.

The relative position estimation unit46converts a position (targetxm, targetym) of the target parking position2in the fixed coordinate system that is included in the learned target object data20to a position (targetx, targety) in the vehicle coordinate system, using the following equation.

The target trajectory generation unit47generates a target travel trajectory starting from the current position of the own vehicle1in the vehicle coordinate system (that is, the coordinate origin) and reaching the position (targetx, targety) in the vehicle coordinate system of the target parking position2.

Although a well-known method employed in an autonomous parking device that has already generally been known can be applied to calculation of a target travel trajectory starting from the current position of the own vehicle1and reaching the target parking position2, a target travel trajectory can be calculated by, for example, connecting a clothoid curve from the current position of the own vehicle1to the target parking position2, as an example. When the target travel trajectory includes a turning-back point, the target travel trajectory can be calculated by, for example, connecting clothoid curves from the current position of the own vehicle to the turning-back point, and from the turning-back point to the target parking position2in turn.

In addition, the target trajectory generation unit47calculates a target vehicle speed profile in which movement speed at each position on the target travel trajectory from the current position of the own vehicle to the target parking position2is set. For example, the target vehicle speed profile causes the own vehicle1to basically travel at a predetermined prescribed set speed, and a vehicle speed profile that causes the own vehicle1to accelerate to the set speed from the current position of the own vehicle1and subsequently come to a stop at the target parking position2can be calculated. When the target travel trajectory includes a turning-back point, the target trajectory generation unit47may calculate a vehicle speed profile that causes the own vehicle1to decelerate before the turning-back point and stop at the turning-back point, accelerate to the set speed from the turning-back point, and decelerate before the target parking position2and stop at the target parking position2. The target trajectory generation unit47may set a set speed in the calculation of a speed profile, based on curvature of the calculated target travel trajectory in such a manner that the larger the curvature is, the lower the speed becomes.

The steering control unit48controls the steering actuator in the actuators16in such a way that the own vehicle1travels along the target travel trajectory.

In addition, the vehicle speed control unit49controls the accelerator actuator and the brake actuator in the actuators16in such a way that the vehicle speed of the own vehicle1changes in accordance with the movement speed plan that the target trajectory generation unit47calculated. Because of this configuration, the own vehicle1is controlled to travel along the target travel trajectory.

The assistance image generation unit50generates a parking assistance image that represents the target travel trajectory calculated by the target trajectory generation unit47and the current position of the own vehicle1. For example, the parking assistance image may be an image obtained by superimposing the target travel trajectory and the current position of the own vehicle1on a bird's eye view or an aerial view in which the surroundings of the own vehicle1is viewed from above. The assistance image generation unit50displays the parking assistance image on the display device in the human-machine interfaces15.

FIG.6is a flowchart of an example of processing in which a target object position is stored in the target object learning mode. In step S1, the feature point detection unit43detects a target object position around the target parking position2from a surrounding image acquired by capturing the surroundings of the own vehicle, while the own vehicle1is moved to be parked at the target parking position2by manual driving.

In step S2, the learned target object data generation unit44stores the target object position detected by the feature point detection unit43in the storage device19as a learned target object position. On this occasion, the learned target object data generation unit44stores a learned target object position and a degree of reliability in association with each other in the storage device19as the learned target object data20. Subsequently, the process terminates.

FIG.7is a flowchart of an example of processing in which the degree of reliability of a target object position is updated on every occasion when the own vehicle1travels in a vicinity of the target parking position2after the learned target object position is stored. However, among occasions when the own vehicle1travels in the vicinity of the target parking position2, an example of a case where the own vehicle1is parked at the target parking position2in the parking assistance mode will be illustrated inFIG.8. For example, the processing inFIG.7is performed when the own vehicle1is caused to leave the target parking position2.

In step S11, the feature point detection unit43detects a surrounding target object position that is a target object position around the own vehicle1from a surrounding image acquired by capturing the surroundings of the own vehicle1.

In step S12, the degree-of-reliability provision unit45determines whether or not a learned target object position stored in the storage device19coincides with the target object position detected in step S11. The degree-of-reliability provision unit45increases, for a learned target object position that coincides with the target object position detected in step S11, a number obtained by counting the number of times that the learned target object position coincides with the surrounding target object position by one. For a learned target object position that does not coincide with the target object position detected in step S11, the degree-of-reliability provision unit45does not increase the number obtained by counting the number of times that the learned target object position coincides with the surrounding target object position. The degree-of-reliability provision unit45updates the degree of reliability of each learned target object position according to the number of times that the learned target object position coincides with a surrounding target object position. Subsequently, the process terminates.

FIG.8is a flowchart of an example of the parking assistance control of the embodiment. In step S21, the feature point detection unit43detects a surrounding target object position that is a target object position around the own vehicle1from a surrounding image acquired by capturing the surroundings of the own vehicle.

In step S22, the relative position estimation unit46estimates, based on only a learned target object position having a degree of reliability greater than or equal to a predetermined threshold degree of reliability among the learned target object positions stored in the storage device19as the learned target object data20and the surrounding target object position detected in step S21, a relative position of the current position of the own vehicle1with respect to the target parking position2.

In step S23, the target trajectory generation unit47generates, based on the relative position of the own vehicle1with respect to the target parking position2, a target travel trajectory that the own vehicle is caused to travel from the current position of the own vehicle1to the target parking position2and a target vehicle speed profile.

In step S24, the steering control unit48controls a steering angle in such a way that the own vehicle1travels along the target travel trajectory. The vehicle speed control unit49controls movement speed of the own vehicle1in accordance with the target vehicle speed profile. Through this processing, the steering control unit48and the vehicle speed control unit49cause the own vehicle1to move to the target parking position2.

In step S25, the degree-of-reliability provision unit45determines whether or not a learned target object position stored in the storage device19coincides with the target object position detected in step S21. The degree-of-reliability provision unit45increases, for a learned target object position that coincides with the target object position detected in step S21, a number obtained by counting the number of times that the learned target object position coincides with the surrounding target object position by one. For a learned target object position that does not coincide with the target object position detected in step S21, the degree-of-reliability provision unit45does not increase the number obtained by counting the number of times that the learned target object position coincides with the surrounding target object position. The degree-of-reliability provision unit45updates the degree of reliability of each learned target object position according to the number of times that the learned target object position coincides with a surrounding target object position. Subsequently, the process terminates.

Advantageous Effects of Embodiment

(1) The controller17: stores positions of a plurality of target objects detected around the target parking position2when the own vehicle1is parked at the target parking position2as learned target object positions; when the own vehicle1travels in a vicinity of the target parking position2after the learned target object positions are stored, counts, with respect to each of the learned target object positions, the number of times that the learned target object position coincides with a surrounding target object position that is a position of a target object detected around the own vehicle1; provides a higher degree of reliability to a learned target object position having a large number of times of coincidence with a surrounding target object position than to a learned target object position having a small number of times of coincidence with a surrounding target object position; by comparing a learned target object position having a degree of reliability greater than or equal to a predetermined threshold degree of reliability among the learned target object positions with a position of a target object detected around the own vehicle1, calculates a relative position of the own vehicle1with respect to the target parking position2; based on the calculated relative position, calculates a target travel trajectory from the current position of the own vehicle1to the target parking position2; and performs parking assistance control to assist movement of the own vehicle1along the target travel trajectory.

Because of this configuration, when positions of a fixed object and a movable object are stored as learned target object positions, and when the movable object is subsequently moved before a time point at which the parking assistance control is performed, a higher degree of reliability can be provided to the learned target object position of the fixed object than to the learned target object position of the movable object. Since among the learned target object positions, only the learned target object position of the fixed object can be selected and used for the calculation of the relative position of the own vehicle1with respect to the target parking position2, it is possible to prevent calculation precision of the relative position from deteriorating.

(2) Occasions when the own vehicle1travels in the vicinity of the target parking position2after the learned target object positions are stored may include an occasion when the own vehicle1first travels in the vicinity of the target parking position2after the learned target object positions are stored. Because of this configuration, when the movable object is moved during a period from when the learned target object positions are stored until subsequently the own vehicle1first travels in the vicinity of the target parking position2, a target travel trajectory can be calculated excluding the learned target object position of the movable object from when the own vehicle1is first parked at the target parking position2after the learned target object positions are stored.

(3) In addition, the occasions when the own vehicle1travels in the vicinity of the target parking position2after the learned target object positions are stored may include a time when carrying out the parking assistance control to assist parking at the target parking position2. Because of this configuration, the degrees of reliability of the learned target object positions can be updated every time the parking assistance control is performed.

(4) The occasions when the own vehicle1travels in the vicinity of the target parking position2after the learned target object positions are stored may include an occasion when the own vehicle1is caused to leave the target parking position2. Because of this configuration, the degrees of reliability of the learned target object positions can be updated every time the own vehicle1is caused to leave the target parking position2.

(5) The predetermined threshold degree of reliability may be a value obtained by subtracting a predetermined value from a highest degree of reliability among the degrees of reliability of a plurality of learned target object positions, may be a degree of reliability ranked at a predetermined place in the descending order from a maximum value among the degrees of reliability of the learned target object positions, or may be set based on an average of the degrees of reliability of the learned target object positions. Because of this configuration, the threshold degree of reliability can be appropriately set.

(6) The parking assistance control is, for example, control for the own vehicle1in which the own vehicle1is caused to move along the target travel trajectory from the current position of the own vehicle1to the target parking position2. Because of this configuration, parking assistance to control the own vehicle in such a way that the own vehicle1travels to the target parking position2can be achieved.

(7) The parking assistance control may be control to display a target travel trajectory and the position of the own vehicle1on the display device that a user of the own vehicle can visually recognize. Because of this configuration, the passenger can visually recognize a target travel trajectory that the own vehicle1is caused to travel to the target parking position2.

REFERENCE SIGNS LIST

40Image conversion unit41Self-position calculation unit43Feature point detection unit44Learned target object data generation unit45Degree-of-reliability provision unit46Relative position estimation unit47Target trajectory generation unit48Steering control unit49Vehicle speed control unit50Assistance image generation unit