Parking assistance method and parking assistance device

Abstract

This parking assistance method: detects an object marker in the vicinity of a host vehicle and an object marker position, which is the relative position of the object marker with respect to the host vehicle (S2); reads, from a prescribed storage device, a known object marker and a relative positional relationship between the known object marker and a target parking position, which are stored in advance in the storage device (S3); estimates the relative position of the target parking position with respect to the current position of the host vehicle on the basis of the object marker position as detected by a sensor and the relative positional relationship between the known object marker and the target parking position (S5); calculates a target travel path from the current position of the host vehicle to the relative position of the target parking position on the basis of the estimated relative position (S8); determines the estimation accuracy of the estimated relative position (S6); and, when the estimation accuracy is low, limits the travel speed of the host vehicle traveling along the target travel path to a lower speed than when the estimation accuracy is high (S10).

Description

[Technical field]

[0001] The present invention relates to a parking assistance method and a parking assistance device. [Background technology]

[0002] The following Patent Document 1 describes a technology that recognizes and stores characteristic points around a target parking position, calculates the relative position of the target parking position with respect to the vehicle based on the relative position of the vehicle with respect to the stored characteristic points, and automatically moves the vehicle to the target parking position based on the calculated relative position. [Prior art documents] [Patent documents]

[0003] [Patent Document 1] JP 2017-138664 A Summary of the Invention [Problem to be solved by the invention]

[0004] However, if the calculation accuracy of the relative position between the target parking position and the vehicle is low, there is a risk that the occupants will feel uncomfortable if the vehicle is moved based on the relative position. The present invention aims to reduce the discomfort felt by occupants when moving the vehicle to a target parking position based on a target detected by a sensor mounted on the vehicle and a known relative positional relationship between the target parking position and the target. [Means for solving the problem]

[0005] In a parking assistance method according to one aspect of the present invention, a sensor mounted on the vehicle detects targets around the vehicle and target positions, which are the relative positions of the targets with respect to the vehicle, and reads from the storage device a known target and a relative positional relationship between the known target and the target parking position, which are pre-stored in a predetermined storage device. Based on the target position detected by the sensor and the relative positional relationship between the known target and the target parking position, a relative position of the target parking position with respect to a current position of the vehicle is estimated. Based on the estimated relative position, a target driving trajectory from the current position of the vehicle to the relative position of the target parking position is calculated. The estimation accuracy of the estimated relative position is determined. When the estimation accuracy is low, a speed limit for the travel speed of the vehicle traveling along the target driving trajectory is limited to a speed lower than when the estimation accuracy is high. Effect of the Invention

[0006] According to the present invention, the discomfort felt by occupants when moving the vehicle to a target parking position can be reduced based on a target detected by a sensor mounted on the vehicle and a known relative positional relationship between the target parking position and the target. The objects and advantages of the invention will be realized and attained by means of the elements and combinations recited in the appended claims. It is to be understood that both the foregoing general description and the following detailed description are exemplary and explanatory only and are not restrictive of the invention as claimed. [Brief description of the drawings]

[0007] [Figure 1] 1 is a diagram illustrating an example of a schematic configuration of a parking assistance device according to an embodiment; [Figure 2A] FIG. 2 is an explanatory diagram of an example of a learned target. [Figure 2B] FIG. 2 is an explanatory diagram of an example of a parking assistance method. [Diagram 3] 2 is a block diagram illustrating an example of a functional configuration of a controller illustrated in FIG. 1. [Figure 4] 4 is a flowchart of an example of a parking assistance method according to an embodiment. DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

[0008] (composition) FIG. 1 is a diagram showing an example of a schematic configuration of a parking assistance device according to an embodiment. A vehicle 1 includes a parking assistance device 10 that supports parking of the vehicle 1 at a target parking position. In parking assistance by the parking assistance device 10, a target travel trajectory from the current position of the vehicle 1 to the target parking position is calculated. Then, automatic driving is performed to control the vehicle 1 to travel along the target travel trajectory to the target parking position. The parking assistance device 10 includes a positioning device 11, an object sensor 12, a vehicle sensor 13, a controller 14, and an actuator 15.

[0009] The positioning device 11 measures the current position of the vehicle 1. The positioning device 11 may include, for example, a Global Positioning System (GNSS) receiver. The GNSS receiver is, for example, a Global Positioning System (GPS) receiver, and receives radio waves from multiple navigation satellites to measure the current position of the vehicle 1. The object sensor 12 detects objects within a predetermined distance range from the host vehicle 1 (for example, a detection area of ​​the object sensor 12). The object sensor 12 detects the surrounding environment of the host vehicle 1, such as the relative position between the host vehicle 1 and objects present around the host vehicle 1, the distance between the host vehicle 1 and the objects, and the direction in which the objects exist. The object sensor 12 may include, for example, a camera that captures the surrounding environment of the host vehicle 1. The object sensor 12 may include a distance measuring device such as a laser range finder (LRF), a radar, or a laser radar of a LiDAR (Light Detection and Ranging).

[0010] The vehicle sensor 13 detects various information (vehicle information) obtained from the host vehicle 1. The vehicle sensor 13 may include, for example, a vehicle speed sensor that detects the traveling speed (vehicle speed) of the host vehicle 1, a wheel speed sensor that detects the rotational speed of each tire equipped on the host vehicle 1, a three-axis acceleration sensor (G sensor) that detects the acceleration (including deceleration) in three axial directions of the host vehicle 1, a steering angle sensor that detects the steering angle (including the turning angle), a gyro sensor that detects the angular velocity generated in the host vehicle 1, and a yaw rate sensor that detects the yaw rate. The controller 14 is an electronic control unit (ECU) that performs parking assistance control of the vehicle 1. The controller 14 includes a processor 14a and peripheral components such as a storage device 14b. The processor 14a may be, for example, a central processing unit (CPU) or a micro-processing unit (MPU). The storage device 14b may include a semiconductor storage device, a magnetic storage device, an optical storage device, or the like. The functions of the controller 14 described below are realized, for example, by the processor 14a executing a computer program stored in the storage device 14b. The controller 14 may be formed of dedicated hardware for executing each information processing described below.

[0011] The actuator 15 operates the steering wheel, accelerator opening, and brake device of the host vehicle 1 in response to control signals from the controller 14 to generate vehicle behavior of the host vehicle 1. The actuator 15 includes a steering actuator, an accelerator opening actuator, and a brake control actuator. The steering actuator controls the steering direction and steering amount of the host vehicle 1. The accelerator opening actuator controls the accelerator opening of the host vehicle 1. The brake control actuator controls the braking operation of the brake device of the host vehicle 1.

[0012] Next, an example of parking assist control by the parking assist device 10 of the embodiment will be described. Please refer to Fig. 2A. When the driver uses parking assistance provided by the parking assistance device 10, first, the driver stores in the storage device 14b the landmarks existing near the target parking position 2, which is the target position for parking the vehicle 1, and the relative positional relationship between these landmarks and the target parking position 2. The landmarks are objects on the ground that serve as markers for identifying the current position of the vehicle 1, and may be, for example, road markings (lane boundary lines 3a, stop lines 3b, road signs, etc.) painted on the road surface, road boundaries (curbs 3c to 3e, guardrails, etc.), and obstacles (houses 3f, fences 3g, objects 3h, etc.). In this manner, data combining the known targets stored in the memory device 14b and the relative positional relationships between these known targets and the target parking position 2 may be referred to as "parking assistance data" in the following description.

[0013] To store the parking assistance data in the storage device 14b, the operation mode of the parking assistance device 10 is set to the "target learning mode." Then, the driver parks the host vehicle 1 at the target parking position 2 by manually driving the host vehicle 1. While the host vehicle 1 is being manually parked at the target parking position 2, the parking assistance device 10 detects targets around the host vehicle 1 and target positions, which are the relative positions of the targets with respect to the host vehicle 1, using the object sensor 12.

[0014] For example, the parking assistance device 10 detects, as feature points, the edges and corners of objects such as road markings (in the example of FIG. 2A, lane boundary line 3a and stop line 3b), road boundaries (in the example of FIG. 2A, the grounding points of curbs 3c to 3e), and obstacles (in the example of FIG. 2A, the grounding points of house 3f, fence 3g, and object 3h). The parking assistance device 10 also calculates the feature amount of the detected feature point. Here, the feature amount may be, for example, vector data calculated from the neighborhood area of ​​the feature point on the image. For example, SIFT, SURF, ORB, BRIAK, KAZE, AKAZE, or other methods can be used to detect the feature point and calculate the feature amount. In addition, the parking assistance device 10 calculates the relative position of the characteristic point with respect to the vehicle 1 based on the position of the characteristic point on the image and camera information regarding the mounting state of the camera on the vehicle 1 (mounting position, optical axis angle, and angle of view). The parking assistance device 10 may detect feature points from point cloud information obtained by a laser range finder, a radar, or a LiDAR. In this specification, an example of detecting feature points from a captured image will be described.

[0015] Next, the parking assistance device 10 obtains a relative positional relationship between the target (feature point) detected by the object sensor 12 and the target parking position 2. For example, the parking assistance device 10 estimates the current position of the host vehicle 1 in a fixed coordinate system at the time when the target is detected by the object sensor 12, and calculates the position of the target in the fixed coordinate system based on the estimated current position and the relative position of the target with respect to the host vehicle 1. Here, the fixed coordinate system is a coordinate system (e.g., a map coordinate system) with a specific point as the coordinate origin. Next, the parking assistance device 10 detects the position of the host vehicle 1 when the host vehicle 1 is located at the target parking position 2 as the position of the target parking position 2 in the fixed coordinate system. The relative positional relationship between the target and the target parking position 2 is determined by determining the position of the target and the target parking position 2 in the fixed coordinate system. The current position of the vehicle 1 in the fixed coordinate system may be estimated by, for example, the positioning device 11, odometry, or dead reckoning. The current position of the vehicle 1 in the fixed coordinate system may also be estimated by map mapping between a target detected by the object sensor 12 and a known target position or high-precision map information in which a target position is stored in advance.

[0016] In addition, the parking assistance device 10 may obtain the relative positional relationship between the target and the target parking position 2 based on the target position detected by the object sensor 12 when the host vehicle 1 is located at the target parking position 2. The parking assistance device 10 stores parking assistance data, which is a combination of target information (i.e., feature amounts of feature points) and a relative positional relationship between the target and the target parking position 2, in the storage device 14b. As the data on the relative positional relationship, the position of the target in a fixed coordinate system and the position of the target parking position 2 may be stored, or the position of the target in a fixed coordinate system with the target parking position 2 as the coordinate origin may be stored. Hereinafter, the target stored in the storage device 14b may be referred to as a "learned target". The circle plots in FIG. 2A represent learned targets stored in the storage device 14b. The parking assistance device 10 may store, for each of a plurality of different target parking positions, parking assistance data for performing parking assistance at each of these target parking positions in the storage device 14b.

[0017] When the parking assistance data is stored in the storage device 14b, it becomes possible to use parking assistance by the parking assistance device 10. To use parking assistance, the operation mode of the parking assistance device 10 is set to "parking assistance mode." In the parking assistance mode, the parking assistance device 10 reads out parking assistance data from the storage device 14b. If parking assistance data is stored for a plurality of different target parking positions, the parking assistance device 10 selects and reads out the parking assistance data stored for the target parking position where the host vehicle 1 is to be parked. For example, the parking assistance device 10 may read out parking assistance data corresponding to the current position of the vehicle 1 measured by the positioning device 11. For example, the parking assistance data stored for the target parking position closest to the current position of the vehicle 1 may be read out. In addition, attribute data of the parking assistance data (e.g., "parking lot at home" or "parking lot at work") may be stored in memory device 14b, and a passenger (e.g., the driver) may select the parking assistance data to be read from memory device 14b based on the attribute data. The circle plots in FIG. 2B represent learned targets read out from the storage device 14b in the parking assistance mode.

[0018] The parking assistance device 10 detects targets around the vehicle 1 and target positions, which are positions of the targets relative to the vehicle 1, using the object sensor 12. The method of detecting targets and target positions is the same as the detection method in the target learning mode. In the parking assistance mode, the targets detected by the object sensor 12 are shown in triangular plots. In the example of Fig. 2B, the lane boundary line 3a, the curbs 3c and 3e, and the corners of the fence 3g are detected as feature points of the targets. The parking assistance device 10 associates each target (triangle plot) detected by the object sensor 12 in the parking assistance mode with the learned target (circle plot) stored in the storage device 14b, which are identical targets. For example, the parking assistance device 10 may determine that targets with feature points having the same or similar feature amounts are the same target. In the example of FIG. 2B, the lane boundary line 3a, the curbs 3c and 3e, and the corners of the fence 3g are associated with each other.

[0019] The parking assistance device 10 calculates the relative position of the target parking position 2 with respect to the current position of the vehicle 1 based on the relative positional relationship between each target (triangle plot) detected during the parking assistance mode and the vehicle 1, and the relative positional relationship between the learned target (circle plot) associated with these targets (triangle plot) and the target parking position 2. For example, the parking assistance device 10 may calculate the position of the target parking position 2 on a relative coordinate system (hereinafter referred to as a "vehicle coordinate system") based on the current position of the vehicle 1. Alternatively, the parking assistance device 10 may calculate the current position of the vehicle 1 on a fixed coordinate system based on the relative positional relationship between each target (triangle plot) detected during the parking assistance mode and the vehicle 1, and the learned target (circle plot) on the fixed coordinate system. The relative position of the target parking position 2 with respect to the current position of the vehicle 1 is determined by determining the position of the vehicle 1 and the position of the target parking position 2 in the fixed coordinate system.

[0020] The parking assistance device 10 calculates a target driving trajectory from the current position of the vehicle 1 to the target parking position 2 based on the relative position of the target parking position 2 with respect to the current position of the vehicle 1. The parking assistance device 10 performs automatic driving to control the vehicle 1 so that the vehicle 1 travels along the calculated target driving trajectory to the target parking position. In this way, if the relative position of the target parking position 2 with respect to the vehicle 1 (hereinafter also simply referred to as the relative position of the target parking position 2) is estimated based on the target detected by a sensor mounted on the vehicle 1 and the known relative positional relationship between the target parking position 2 and the target, and the vehicle 1 is controlled to travel to the target parking position 2, the occupants may feel uncomfortable. For example, if the estimation accuracy of the relative position of the target parking position 2 is low, the accuracy of the control for traveling the vehicle 1 to the target parking position 2 decreases, which may cause the occupants to feel uncomfortable. Therefore, the parking assistance device 10 judges the estimation accuracy of the relative position of the target parking position 2, and when the estimation accuracy is low, limits the moving speed of the vehicle 1 along the target driving trajectory to a lower speed than when the estimation accuracy is high. As a result, when the estimation accuracy of the relative position of the target parking position 2 is low, the moving speed to move to the target parking position 2 is lowered, so that the discomfort felt by the occupants can be suppressed.

[0021] The functional configuration of the controller 14 will be described in more detail below with reference to Fig. 3. The controller 14 functions as an image conversion unit 40, a self-position calculation unit 41, a feature point detection unit 42, a map generation unit 43, a relative position estimation unit 45, an estimation accuracy determination unit 46, a target trajectory generation unit 47, a steering control unit 48, and a vehicle speed control unit 49. The image conversion unit 40 converts the image captured by the camera of the object sensor 12 into a bird's-eye view image (around view monitor image) seen from a virtual viewpoint directly above the vehicle 1 as shown in Figures 2A and 2B. Hereinafter, the bird's-eye view image converted by the image conversion unit 40 may be referred to as a "surrounding image." Figure 2A is a surrounding image in the target learning mode, and Figure 2B is a surrounding image in the parking assistance mode. The self-position calculation unit 41 calculates the current position of the vehicle 1 on a fixed coordinate system by dead reckoning or the like based on the vehicle information output from the vehicle sensor 13. The self-position calculation unit 41 may correct the calculated current position by map mapping or the like between the target detected by the object sensor 12 and a known target position or high-precision map information.

[0022] The feature point detection unit 42 detects feature points of targets around the vehicle 1 from the surrounding image output from the image conversion unit 40, and calculates feature amounts of the feature points. The feature point detection unit 42 outputs the detected feature points and their feature amounts together with the current position of the vehicle 1 received from the self-position calculation unit 41 to the map generation unit 43 and the relative position estimation unit 45. In the target learning mode, the map generating unit 43 stores, in the storage device 14b, feature point information including the feature points output from the feature point detecting unit 42 while the driver is manually parking the vehicle 1 at the target parking position 2, the current position of the vehicle 1 synchronized therewith, and the feature amounts of the feature points, as parking assistance data 44. Based on the current position of the vehicle 1, the positions of the feature points on a fixed coordinate system may be calculated and stored as feature point information. Ma Ta,When the current position of the vehicle 1 reaches the target parking position 2, the map generating unit 43 receives the current position of the vehicle 1 on the fixed coordinate system from the positioning device 11 or the self-position calculating unit 41, and stores the target parking position 2 as parking assistance data 44 in the storage device 14b. For example, the driver may operate a human-machine interface (not shown) of the parking assistance device 10 to indicate that the current position of the vehicle 1 is the target parking position 2. As a result, the storage device 14b stores the relative positional relationship between the position of the feature point detected in the target learning mode and the position of the target parking position 2, and the feature amount of the feature point, as parking assistance data 44, in the storage device 14b. Note that the parking assistance data 44 stored in the storage device 14b only needs to include at least the relative positional relationship between the position of the feature point and the position of the target parking position 2, and does not necessarily need to include the feature amount of the feature point. Hereinafter, information including the position of the feature point or the position of the feature point and the feature amount will also be referred to as feature point information.

[0023] When the operation mode of the parking assistance device 10 is switched to the parking assistance mode, the relative position estimation unit 45 reads out the parking assistance data 44 from the storage device 14b. The relative position estimation unit 45 matches the feature point information of the learned target stored as the parking assistance data 44 (circle plot in FIG. 2A) with the feature point information output by the feature point detection unit 42 from the captured image output by the camera of the object sensor 12 during the parking assistance mode (triangle plot in FIG. 2B), and associates the feature point information detected for the same target with each other. The relative position estimation unit 45 estimates the relative position of the target parking position 2 with respect to the current position of the vehicle 1 based on the relative positional relationship between the feature points (triangular plots) detected in the parking assistance mode and the vehicle 1, and the relative positional relationship between the feature point information (circular plots) of learned targets associated with these feature points (triangular plots) and the target parking position 2.

[0024] For example, the feature points detected in the parking assistance mode are (x i ,y i ) and the feature points (x i ,y i ) are the feature points of the learned targets corresponding to each of (x mi ,ymi ) (i=1 to N). For example, the relative position estimation unit 45 calculates the affine transformation matrix M affine may be calculated.

number

[0025] Using the weighted least squares method, we find the column vector (a1, a2, a3, a4) as follows: T may be calculated.

number

[0026] The relative position estimation unit 45 calculates the position of the target parking position 2 on the fixed coordinate system stored in the parking assistance data 44 by the following equation: m ,targety m ) is converted to a position (targetx, targety) in the vehicle coordinate system.

number

[0027] Instead, the relative position estimation unit 45 estimates the position (x mi ,y mi ) (i = 1 to N), the feature points (x i ,y i ) and feature points (x i ,y i ) are associated with the feature points (x mi ,y mi ) the distance d between i (i.e., the position error) and calculate the distance d i The average S=(d1+d2+…d N The position of the feature point of the learned target on the vehicle coordinate system may be determined so that θ(i) / N is minimized. The relative position estimation unit 45 may calculate the position (targetx, targety) of the target parking position 2 on the vehicle coordinate system based on the position of the feature point of the learned target on the determined vehicle coordinate system and the relative positional relationship between the position of the feature point of the learned target and the target parking position 2.

[0028] The estimation accuracy determination unit 46 determines the estimation accuracy of the relative position of the target parking position 2 with respect to the current position of the vehicle 1 estimated by the relative position estimation unit 45. For example, the estimation accuracy determination unit 46 may determine that the estimation accuracy is lower when the number of feature points detected in the parking assistance mode is small than when the number of feature points detected is large. For example, the estimation accuracy may be determined to be lower as the number of feature points detected in the parking assistance mode is small. Also, for example, the estimation accuracy determination unit 46 may determine that the estimation accuracy is lower when the number of learned targets is small than when the number of learned targets is large. For example, the estimation accuracy may be determined to be lower as the number of learned targets is small. That is, when the number of feature points or learned targets detected in the parking assistance mode is small, the number of learned targets associated with the feature points detected in the parking assistance mode will be small, and there is a possibility that the estimation accuracy of the relative position of the target parking position 2 with respect to the current position of the vehicle 1 will decrease. For this reason, the estimation accuracy may be estimated assuming a case in which the feature points or learned targets detected in the parking assistance mode, whichever is fewer in number, are all associated with the other, and it may be determined that the estimation accuracy is lower as the number of the one, which is fewer in number, decreases.

[0029] Also, for example, the estimation accuracy determination unit 46 may determine that the estimation accuracy is lower when the number of learned targets associated with the feature points detected in the parking assistance mode is small than when the number of learned targets is large. For example, the estimation accuracy may be determined to be lower as the number of learned targets associated with the feature points detected in the parking assistance mode is smaller. Also, for example, the estimation accuracy determination unit 46 may determine that the estimation accuracy is lower when the minimum value of the average S=(d1+d2+...dN) / N of the distances (position errors) di between the positions of the detected targets and the positions of the feature points of the learned targets after matching (aligning) the targets detected in the parking assistance mode with the feature points of the learned targets is large than when the minimum value is small. The estimation accuracy may be determined to be lower as the average S is larger. For example, the estimation accuracy determination unit 46 may determine the following based on the least squares method: TeA When the residual error in calculating the fin transformation matrix Maffine is large, it may be determined that the estimation accuracy is lower than when the residual error is small. The larger the residual error, the lower the estimation accuracy may be determined.

[0030] The target trajectory generating unit 47 generates a target driving trajectory from the current position of the host vehicle 1 on the vehicle coordinate system (i.e., the coordinate origin) to a position (targetx, targety) on the vehicle coordinate system of the target parking position 2. The target driving trajectory can be generated, for example, by connecting the current position of the host vehicle 1 to the target parking position 2 with a clothoid curve. Furthermore, the target trajectory generating unit 47 calculates a moving speed plan (moving speed profile) that sets the moving speed at each position on the target traveling trajectory from the current position of the vehicle to the target parking position 2. For example, the target trajectory generating unit 47 calculates the moving speed at each position on the target traveling trajectory so that the vehicle accelerates from a stationary state at the current position of the vehicle to a predetermined speed limit at a predetermined acceleration, and then decelerates from the speed limit at a predetermined deceleration to stop at the target parking position 2. The target trajectory generating unit 47 may set a lower moving speed for a point with a larger curvature of the target traveling trajectory.

[0031] The steering control unit 48 controls the steering actuator of the actuator 15 so that the host vehicle 1 travels along the target travel trajectory. In addition, the vehicle speed control unit 49 controls the accelerator actuator and the brake actuator of the actuator 15 so that the vehicle speed of the host vehicle 1 changes according to the travel speed plan calculated by the target trajectory generation unit 47. In this way, the host vehicle 1 is controlled so as to travel along the target travel trajectory.

[0032] When the estimation accuracy of the relative position of the target parking position 2 determined by the estimation accuracy determination unit 46 is low, the vehicle speed control unit 49 limits the speed limit of the travel speed of the host vehicle 1 along the target travel trajectory to a speed lower than when the estimation accuracy is high. For example, the lower the estimation accuracy, the lower the speed limit is set. For example, the vehicle speed control unit 49 may calculate a corrected travel speed plan by correcting, to the speed limit, a travel speed at a position where a travel speed higher than the speed limit set according to the estimation accuracy is set in the travel speed plan calculated by the target trajectory generating unit 47. The vehicle speed control unit 49 may control the accelerator actuator and the brake actuator of the actuator 15 so that the vehicle speed of the host vehicle 1 changes according to the corrected travel speed plan.

[0033] Furthermore, the vehicle speed control unit 49 may determine whether or not the moving speed of the host vehicle 1 detected by the vehicle speed sensor of the vehicle sensor 13 is higher than a speed limit set according to the estimation accuracy when the speed of the host vehicle 1 is controlled according to the moving speed plan calculated by the target trajectory generation unit 47. If the moving speed of the host vehicle 1 is higher than the speed limit, the moving speed of the host vehicle may be controlled to be equal to or lower than the speed limit. Furthermore, the vehicle speed control unit 49 may determine whether the distance from the host vehicle 1 to an obstacle around the host vehicle 1 is less than a predetermined threshold, and may stop the host vehicle 1 when the distance from the host vehicle 1 to the obstacle is less than the predetermined threshold. Distance information from the host vehicle 1 to the obstacle around the host vehicle 1 may be detected by the object sensor 12, for example.

[0034] The target trajectory generating unit 47 may calculate the target driving trajectory only once when parking assistance by the parking assistance device 10 starts, or may regenerate the target driving trajectory and update the target driving trajectory after the host vehicle 1 starts traveling along the target driving trajectory to the target parking position 2. For example, the target trajectory generating unit 47 may repeatedly update the target driving trajectory at a predetermined period. When updating the target driving trajectory, the feature point detection unit 42 detects feature points of targets around the vehicle 1 from images captured by the camera of the object sensor 12 after the vehicle starts driving to the target parking position 2. The relative position estimation unit 45 associates the feature points detected by the feature point detection unit 42 after starting travel to the target parking position 2 with the feature points of the learned targets stored as the parking assistance data 44, and re-estimates the relative position of the target parking position 2 with respect to the current position of the vehicle 1. The estimation accuracy determination unit 46 also determines the estimation accuracy of the re-estimated relative position of the target parking position 2. In general, the closer the vehicle 1 gets to the target parking position 2, the more targets that can be associated with learned targets are detected, and therefore the higher the estimation accuracy tends to be.

[0035] The target trajectory generating unit 47 regenerates a target travel trajectory from the current position of the vehicle 1 to the target parking position 2 based on the re-estimated relative position of the target parking position 2. The target trajectory generating unit 47 also updates the travel speed plan based on the regenerated target travel trajectory. At this time, the target trajectory generating unit 47 sets the current vehicle speed of the vehicle 1 detected by the vehicle sensor 13 to the travel speed (initial value) at the current position. The steering control unit 48 controls the steering actuator so that the host vehicle 1 travels along the regenerated target travel trajectory. The vehicle speed control unit 49 controls the actuator 15 so that the vehicle speed of the host vehicle 1 changes according to the updated travel speed plan. Depending on the estimation accuracy of the re-estimated relative position of the target parking position 2, the vehicle speed control unit 49 limits the moving speed of the vehicle 1 along the target driving trajectory to a lower speed when the estimation accuracy is low than when the estimation accuracy is high.

[0036] (operation) FIG. 4 is a flowchart of an example of the operation of the parking assistance device 10 in the parking assistance mode. In step S1, the positioning device 11 measures the current position of the host vehicle 1 on a fixed coordinate system. In step S2, the object sensor 12 detects feature points of targets around the host vehicle 1. In step S3, the relative position estimation unit 45 of the controller 14 reads out the parking assistance data corresponding to the current position of the host vehicle 1 from the storage device 14b.

[0037] In step S4, the relative position estimation unit 45 determines the correspondence between the characteristic points of the target detected in step S2 and the characteristic points of the known target in the parking assistance data read in step S3. In step S5, the relative position estimation unit 45 estimates the relative position of the target parking position 2 with respect to the vehicle 1 based on the relative positions of the feature points of the target detected in step S2 with respect to the vehicle 1 and the relative positional relationship between the feature points of a known target corresponding to the feature points of the target detected in step S2 and the target parking position 2. In step S6, the estimation accuracy determination unit 46 determines the accuracy of the estimation of the relative position in step S5. In step S7, when the estimation accuracy determined in step S6 is low, the vehicle speed control unit 49 limits the travel speed of the host vehicle 1 to the target parking position 2 to a lower speed than when the estimation accuracy is high.

[0038] In step S8, the target trajectory generating unit 47 generates a target driving trajectory for driving the host vehicle from the current position of the host vehicle 1 to the target parking position 2 based on the relative position of the target parking position 2 with respect to the host vehicle 1 estimated in step S5. In step S9, the target trajectory generating unit 47 calculates a travel speed plan in which the travel speed at each position on the target travel trajectory is set. In step S10, the vehicle speed control unit 49 limits the travel speed plan calculated in step S9 to the speed limit set in step S7. In step S11, the steering control unit 48 controls the steering angle so that the host vehicle 1 travels along the target travel trajectory generated in step S8. The vehicle speed control unit 49 controls the travel speed of the host vehicle 1 according to the travel speed plan limited in step S10. When the host vehicle 1 reaches the target parking position 2, the process ends.

[0039] (Effects of the embodiment) (1) The object sensor 12 detects targets around the vehicle 1 and target positions, which are the relative positions of the targets with respect to the vehicle 1. The controller 14 reads out from the storage device 14b known targets and the relative positional relationship between the known targets and the target parking position, which are stored in advance in the storage device 14b, estimates the relative position of the target parking position with respect to the current position of the vehicle 1 based on the target position detected by the object sensor 12 and the relative positional relationship between the known targets and the target parking position, calculates a target driving trajectory from the current position of the vehicle 1 to the relative position of the target parking position based on the estimated relative position, determines the estimation accuracy of the estimated relative position, and when the estimation accuracy is low, limits the speed limit of the travel speed at which the vehicle 1 travels along the target driving trajectory to a speed lower than when the estimation accuracy is high. As a result, when the estimation accuracy of the relative position of the target parking position 2 is low, the moving speed to the target parking position 2 is slowed down, so that the discomfort felt by the occupants can be suppressed. That is, for example, when the estimation accuracy of the relative position of the target parking position 2 is low, the calculation accuracy of the target travel trajectory from the current position of the vehicle 1 to the relative position of the target parking position is also low, and there is a possibility that an optimal target travel trajectory cannot be generated. In such a case, if the moving speed of the vehicle 1 is fast, the driver may feel uncomfortable, but by lowering the moving speed, the discomfort felt by the occupants can be suppressed.

[0040] (2) The controller 14 may determine that the estimation accuracy is lower when the number of targets detected by the object sensor 12 is small than when the number of targets is large. The controller 14 may determine that the estimation accuracy is lower when there are fewer known targets stored in the storage device 14b than when there are more. The controller 14 estimates the relative position of the target parking position with respect to the current position of the vehicle 1 by matching the targets detected by the object sensor 12 with known targets stored in the memory device 14b, and may determine that the estimation accuracy is lower when the number of known targets matched with the targets detected by the object sensor 12 is small than when the number of known targets matched with the targets detected by the object sensor 12 is large. The controller 14 may estimate the relative position of the target parking position with respect to the current position of the vehicle 1 so that the average value of the distances between the multiple targets detected by the object sensor 12 and the multiple known targets stored in the memory device 14b is small, and may determine that the estimation accuracy is lower when the average value is large than when the average value is small. This makes it possible to determine the estimation accuracy of the relative position of the target parking position with respect to the current position of the vehicle 1.

[0041] (3) The controller 14 may calculate a movement speed plan that sets the movement speed at each position on the target driving trajectory from the current position of the vehicle 1 to the target parking position, calculate a corrected movement speed plan by correcting the movement speed at positions where a movement speed higher than the speed limit is set in the movement speed plan to the speed limit, and control the movement speed at which the vehicle 1 travels along the target driving trajectory based on the corrected movement speed plan. This makes it possible to limit the travel speed at which the host vehicle 1 travels along the target travel trajectory in accordance with the estimation accuracy of the relative position. (4) The vehicle sensor 13 detects the moving speed of the host vehicle 1. When the moving speed of the host vehicle 1 traveling along the target traveling trajectory is higher than the speed limit, the controller 14 may control the moving speed of the host vehicle 1 to be equal to or lower than the speed limit. This makes it possible to limit the travel speed at which the host vehicle 1 travels along the target travel trajectory in accordance with the estimation accuracy of the relative position. (5) The distance from the host vehicle 1 to an obstacle around the host vehicle 1 may be detected by the object sensor 12, and the host vehicle may be stopped if the detected distance is less than a predetermined distance. This reduces the discomfort felt by occupants when the vehicle 1 approaches an obstacle too closely.

[0042] All examples and conditional terms described herein are intended for educational purposes to help the reader understand the present invention and the concepts provided by the inventor for the advancement of technology, and should be interpreted without being limited to the above specifically described examples and conditions, and the configuration of the examples in this specification with respect to showing the advantages and disadvantages of the present invention. Although the embodiments of the present invention have been described in detail, it should be understood that various changes, substitutions and modifications can be made thereto without departing from the spirit and scope of the present invention. [Explanation of symbols]

[0043] 1... host vehicle, 2... target parking position, 3a... lane boundary, 3b... stop line, 3c to 3e... curb, 3f... house, 3g... fence, 3h... object, 10... parking assistance device, 11... positioning device, 12... object sensor, 13... vehicle sensor, 14... controller, 14a... processor, 14b... storage device, 15... actuator, 40... image conversion unit, 41... host position calculation unit, 42... feature point detection unit, 43... map generation unit, 44... parking assistance data, 45... relative position estimation unit, 46... estimation accuracy determination unit, 47... target trajectory generation unit, 48... steering control unit, 49... vehicle speed control unit

Claims

1. Detecting targets around the host vehicle and target positions, which are relative positions of the targets with respect to the host vehicle, using a sensor mounted on the host vehicle; A known target object and a relative positional relationship between the known target object and the target parking position, which are stored in advance in a predetermined storage device, are read from the storage device; Based on the target position detected by the sensor and the relative position relationship between the known target and the target parking position, a relative position of the target parking position with respect to a current position of the vehicle is estimated; Calculating a target driving trajectory from the current position of the vehicle to the relative position of the target parking position based on the estimated relative position; determining an estimation accuracy of the estimated relative position; When the estimation accuracy is low, a speed limit of the travel speed at which the host vehicle travels along the target travel trajectory is limited to a speed lower than that when the estimation accuracy is high. A parking assistance method comprising:

2. 2. The parking assistance method according to claim 1, wherein the estimation accuracy is determined to be lower when the number of targets detected by the sensor is small than when the number of targets detected by the sensor is large.

3. 3. The parking assistance method according to claim 1, wherein the estimation accuracy is determined to be lower when the number of known targets stored in the storage device is small than when the number of known targets stored in the storage device is large.

4. By associating the target object detected by the sensor with the known target object stored in the storage device, a relative position of the target parking position with respect to a current position of the vehicle is estimated; The parking assistance method according to any one of claims 1 to 3, characterized in that when the number of known targets associated with the targets detected by the sensor is small, it is determined that the estimation accuracy is lower than when the number of known targets associated with the targets detected by the sensor is large.

5. estimating a relative position of the target parking position with respect to a current position of the vehicle so that an average value of each distance between a plurality of targets detected by the sensor and a plurality of known targets stored in the storage device becomes small; 5. The parking assistance method according to claim 1, wherein the estimation accuracy is determined to be lower when the average value is large than when the average value is small.

6. Calculating a travel speed plan in which a travel speed at each position on the target travel path from the current position of the vehicle to the target parking position is set; Calculating a corrected travel speed plan by correcting a travel speed at a position where a travel speed higher than the speed limit is set in the travel speed plan to the speed limit; controlling a travel speed at which the host vehicle travels along the target travel trajectory based on the corrected travel speed plan; 6. The parking assistance method according to claim 1,

7. Detecting a moving speed of the vehicle; When a moving speed of the host vehicle traveling along the target traveling path is higher than the speed limit, the moving speed of the host vehicle is controlled to be equal to or lower than the speed limit.

6. The parking assistance method according to claim 1,

8. The parking assistance method according to any one of claims 1 to 7, characterized in that the sensor detects a distance from the host vehicle to an obstacle around the host vehicle, and stops the host vehicle when the detected distance is less than a predetermined distance.

9. A sensor mounted on the host vehicle for detecting targets around the host vehicle and target positions relative to the host vehicle; A storage device that stores a known target object and a relative positional relationship between the known target object and a target parking position; a controller that estimates a relative position of the target parking position with respect to a current position of the vehicle based on a target position detected by the sensor and a relative positional relationship between the known target and the target parking position, calculates a target driving trajectory from the current position of the vehicle to the relative position of the target parking position based on the estimated relative position, determines an estimation accuracy of the estimated relative position, and when the estimation accuracy is low, limits a moving speed of the vehicle along the target driving trajectory to a speed lower than when the estimation accuracy is high; A parking assistance device comprising:

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