Output device, control method, program, and storage medium

Abstract

To provide an output device suitable for calculating a position with high accuracy.SOLUTION: An automatic driving control unit 18 of an on-vehicle machine 1 acquires the position accuracy in the direction of travel of a vehicle and a side direction of the vehicle. The automatic driving control unit 18 refers to a map DB 10 to acquire, for every feature, normal information indicating the direction of the feature. The automatic driving control unit 18 outputs, to an electronic control unit of the vehicle, control information for controlling the vehicle to increase the accuracy of detecting a target feature that is directed in a direction with lower position accuracy of the direction of travel and the side direction of the vehicle.SELECTED DRAWING: Figure 2

Description

[Technical Field]

[0001] The present invention relates to a technique for controlling a vehicle. [Background technology]

[0002] Conventionally, there has been known a technique for detecting features installed ahead of a vehicle using a radar or a camera and calibrating the vehicle's position based on the detection results. Patent Document 1 discloses a driving assistance system that, when recognizing features using a radar or a camera, guides a traveling vehicle to a position where the features can be easily detected. [Prior art documents] [Patent documents]

[0003] [Patent Document 1] Japanese Patent Application Laid-Open No. 2014-048205 Summary of the Invention [Problem to be solved by the invention]

[0004] The features around the road are not necessarily all facing the same direction. Therefore, when using features around the road for position estimation, the accuracy of the position will be affected due to the different orientations of the faces of the features detected by radar, etc.

[0005] The present invention has been made to solve the above-mentioned problems, and a main object of the present invention is to provide an output device that is suitable for calculating a position with high accuracy. [Means for solving the problem]

[0006] The invention described in claim 1 is an output device comprising: a first acquisition unit that acquires positional accuracy in a first direction and a second direction relative to the traveling direction of a moving body; a second acquisition unit that acquires orientation information that is added to map information and indicates the orientation of each feature; and an output unit that outputs control information for controlling the moving body so as to increase the detection accuracy of target features facing in the direction with the lower positional accuracy, out of the first direction and the second direction.

[0007] The invention described in claim 10 is a control method executed by an output device, comprising a first acquisition step of acquiring the positional accuracy in a first direction and a second direction relative to the traveling direction of a moving body, a second acquisition step of acquiring orientation information that is added to map information and indicates the orientation of each feature, and an output step of outputting control information for controlling the moving body so as to increase the detection accuracy of target features facing in the direction with the lower positional accuracy out of the first direction and the second direction.

[0008] The invention described in claim 11 is a program executed by a computer, which causes the computer to function as a first acquisition unit that acquires the positional accuracy in a first direction and a second direction relative to the traveling direction of a moving body, a second acquisition unit that acquires orientation information that is added to map information and indicates the orientation of each feature, and an output unit that outputs control information for controlling the moving body so as to increase the detection accuracy of target features facing in the direction with the lower positional accuracy, out of the first direction and the second direction. [Brief explanation of the drawings]

[0009] [Figure 1] FIG. 1 is a schematic configuration diagram of a driving assistance system. [Figure 2] FIG. 2 is a block diagram showing the functional configuration of the vehicle-mounted device. [Figure 3] 10 is an example of a data structure of feature information included in a map DB. [Figure 4] This is a diagram showing a state variable vector in two-dimensional orthogonal coordinates. [Figure 5] FIG. 10 is a diagram illustrating a schematic relationship between a prediction step and a measurement update step. [Figure 6] 10 is a flowchart illustrating vehicle control based on position estimation accuracy. [Figure 7] (A) shows an overhead view of the vehicle performing a lidar scan, while (B) and (C) show close-ups of the feature, highlighting the laser beam spot. [Figure 8] This shows an overhead view of a vehicle on a three-lane road with features ahead on both the left and right sides of the road. [Figure 9] 10 shows an overhead view of a vehicle when the position estimation accuracy in the lateral direction of the vehicle is lower than that in the traveling direction of the vehicle. [Figure 10] 10 shows an overhead view of a vehicle when there are features on the left and right front of the road with normals in approximately the same direction. [Figure 11] 10 shows an overhead view of a vehicle when there are features with different normal directions on the left and right sides of the road. [Figure 12] 10 is a flowchart showing a target feature determination process. DETAILED DESCRIPTION OF THE INVENTION

[0010] According to a preferred embodiment of the present invention, the output device includes a first acquisition unit that acquires positional accuracy in a first direction and a second direction relative to the traveling direction of a moving body, a second acquisition unit that acquires orientation information that is added to map information and indicates the orientation of each feature, and an output unit that outputs control information for controlling the moving body so as to increase the detection accuracy of target features facing in the direction with the lower positional accuracy out of the first direction and the second direction.

[0011] When position estimation is performed based on the detection results of features, the position accuracy tends to be high in the direction the feature is facing. Therefore, in this aspect, the output device controls the mobile object so as to increase the detection accuracy of features facing in a direction with low position accuracy. This allows the output device to suitably improve the position accuracy in a direction with low position accuracy.

[0012] In one aspect of the output device, the output unit determines that the feature is facing in the direction with low positional accuracy when the angle between the direction with low positional accuracy and the orientation of the feature is equal to or less than a predetermined angle. The predetermined angle is preferably set to at least 45 degrees. This aspect allows the output device to control the moving object so as to suitably improve the positional accuracy in the direction with low positional accuracy.

[0013] In another aspect of the output device, the map information includes information about the size of each feature, and when there is no feature facing the direction with low positional accuracy, the output unit determines the target feature for which detection accuracy should be increased based on the information about the size. With this aspect, the output device can control the mobile object to suitably improve the positional accuracy in the direction with low positional accuracy, even when there is no feature facing the direction with low positional accuracy.

[0014] In another aspect of the output device, the output device further includes a third acquisition unit that acquires an output of a detection device that detects features around the moving object, and a position estimation unit that estimates the position of the moving object based on the output of the detection device for the target feature and position information of the target feature included in the map information. With this aspect, the output device can estimate the position based on a feature facing a direction in which positional accuracy is low, and can suitably improve the positional accuracy in the direction in which positional accuracy is low.

[0015] In another aspect of the output device, the output unit outputs control information for moving the mobile body to a lane closest to the target feature, or for moving the mobile body to a side of the lane that the mobile body is traveling in that is closer to the target feature. With this aspect, the output device can suitably move the mobile body closer to a feature facing in a direction with low positional accuracy, thereby improving the detection accuracy of the feature.

[0016] In another aspect of the output device, the output unit selects the target feature from features existing along the route of the moving object. With this aspect, the output device can improve the positional accuracy in a direction where the positional accuracy is low without substantially changing the route to the destination.

[0017] In another aspect of the output device, the output unit selects, as the target feature, a feature that forms the smallest angle between the direction of low positional accuracy and the orientation of the feature among features that exist within a predetermined distance from the moving body. With this aspect, the output device can control the moving body to suitably improve the positional accuracy in the direction of low positional accuracy.

[0018] In another aspect of the output device, the output unit selects the target feature from among features present within a predetermined distance from the mobile body based on an angle between the direction in which the positional accuracy is low and the orientation of the feature, and on the suitability of the feature as a detection target for a detection device that detects features around the mobile body. This aspect enables the output device to perform highly accurate position estimation while suitably improving the positional accuracy in the direction in which the positional accuracy is low.

[0019] In another aspect of the output device, the output unit selects, as the target feature, the feature with the highest appropriateness among features for which the angle between the direction with low positional accuracy and the orientation of the feature is equal to or less than a predetermined angle. With this aspect, the output device can perform highly accurate position estimation while suitably improving the positional accuracy in the direction with low positional accuracy.

[0020] According to another preferred embodiment of the present invention, there is provided a control method executed by an output device, the control method including: a first acquisition step of acquiring positional accuracy in a first direction and a second direction relative to a traveling direction of a moving object; a second acquisition step of acquiring orientation information added to map information and indicating the orientation of each feature; and an output step of outputting control information for controlling the moving object so as to increase the detection accuracy of a target feature facing one of the first direction and the second direction, the direction with the lower positional accuracy. By executing this control method, the output device can preferably improve the positional accuracy in the direction with the lower positional accuracy.

[0021] According to another preferred embodiment of the present invention, there is provided a computer-executable program that causes the computer to function as a first acquisition unit that acquires positional accuracy in a first direction and a second direction relative to a traveling direction of a moving object, a second acquisition unit that acquires orientation information that is added to map information and indicates the orientation of each feature, and an output unit that outputs control information for controlling the moving object so as to increase the detection accuracy of target features facing in one of the first and second directions, the direction with the lower positional accuracy. By executing this program, an output device can preferably improve the positional accuracy in the direction with the lower positional accuracy. Preferably, the program is stored in a storage medium. [Example]

[0022] Preferred embodiments of the present invention will now be described with reference to the drawings. [Schematic configuration]

[0023] Fig. 1 is a schematic diagram of a driving assistance system according to this embodiment. The driving assistance system shown in Fig. 1 includes an on-board device 1 that is mounted on a vehicle and controls driving assistance for the vehicle, a Lidar (Light Detection and Ranging or Laser Illuminated Detection and Ranging) 2, a gyro sensor 3, a vehicle speed sensor 4, and a GPS receiver 5.

[0024] The vehicle-mounted device 1 is electrically connected to a LIDAR 2, a gyro sensor 3, a vehicle speed sensor 4, and a GPS receiver 5, and estimates the position of the vehicle (also referred to as "host vehicle position") on which the vehicle-mounted device 1 is mounted based on the outputs of these sensors. Based on the estimated host vehicle position, the vehicle-mounted device 1 performs automatic driving control of the vehicle so that the vehicle travels along a route to a set destination. The vehicle-mounted device 1 stores a map database (DB) 10 that stores road data and feature information, which is information about landmark features installed near the road. The landmark features include, for example, kilometer posts, 100-meter posts, delineators, traffic infrastructure facilities (e.g., signs, directional signs, traffic lights), utility poles, streetlights, and other features that are periodically lined along the side of the road. The feature information is information that associates at least an index assigned to each feature with the location information of the feature and information about the orientation of the feature. Based on this feature information, the vehicle-mounted device 1 compares it with the output of the LIDAR 2 and the like to estimate the host vehicle position.

[0025] The LIDAR 2 emits a pulsed laser beam over a predetermined angular range in the horizontal and vertical directions to discretely measure the distance to an object in the external environment and generate three-dimensional point cloud information indicating the position of the object. In this case, the LIDAR 2 includes an irradiation unit that irradiates laser light while changing the irradiation direction, a light receiving unit that receives reflected (scattered) light from the irradiated laser beam, and an output unit that outputs scan data based on the light receiving signal output by the light receiving unit. The scan data is generated based on the irradiation direction corresponding to the laser beam received by the light receiving unit and the response delay time of the laser beam determined based on the above-mentioned light receiving signal. Generally, the closer the distance to the object, the higher the accuracy of the LIDAR's distance measurement value, and the farther the distance, the lower the accuracy. In this embodiment, the LIDAR 2 is installed facing the vehicle's traveling direction so as to scan at least the area ahead of the vehicle. The LIDAR 2, gyro sensor 3, vehicle speed sensor 4, and GPS receiver 5 each provide output data to the vehicle-mounted device 1. The vehicle-mounted device 1 is an example of the "output device" in the present invention, and the lidar 2 is an example of the "detection device" in the present invention.

[0026] 2 is a block diagram showing the functional configuration of the vehicle-mounted device 2. The vehicle-mounted device 2 mainly includes an interface 11, a storage unit 12, an input unit 14, a control unit 15, and an information output unit 16. These elements are interconnected via a bus line.

[0027] The interface 11 acquires output data from sensors such as the lidar 2, the gyro sensor 3, the vehicle speed sensor 4, and the GPS receiver 5, and supplies the data to the control unit 15. The interface 11 also supplies signals related to vehicle driving control generated by the control unit 15 to an electronic control unit (ECU) of the vehicle. The signals transmitted from the control unit 15 to the electronic control unit of the vehicle via the interface 11 are an example of "control information" in the present invention.

[0028] The storage unit 12 stores programs executed by the control unit 15 and information required for the control unit 15 to execute predetermined processes. In this embodiment, the storage unit 12 stores a map DB 10 including feature information. FIG. 3 shows an example of the data structure of the feature information. As shown in FIG. 3, the feature information is information in which information related to each feature is associated with the feature, and here includes a feature ID corresponding to an index of the feature, location information, shape information, and suitability information. The location information indicates the absolute position of the feature expressed by latitude and longitude (and altitude), etc. The shape information is information related to the shape of the feature, and includes normal information indicating the orientation of the feature (i.e., the normal direction relative to the front) and size information indicating the size of the feature. The suitability information indicates the ease of detection by the LIDAR 2, in other words, the suitability as a measurement target by the LIDAR 2. The map DB 10 may be updated periodically. In this case, for example, the control unit 15 receives partial map information relating to the area to which the vehicle position belongs from a server device that manages map information via a communication unit (not shown), and reflects the information in the map DB 10.

[0029] Here, the suitability information is information that is set in advance as information indicating the suitability of each feature as a measurement target by the LIDAR 2 and is stored as part of the feature information. The suitability information is typically information that quantifies the suitability, and is expressed, for example, as a numerical value within the range of 0 to 1.0. The suitability information for a feature with the highest suitability is set to 1.0, and the suitability information of a feature with a relatively lower suitability is set to a smaller numerical value.

[0030] Examples of setting the suitability information are as follows. For example, if the feature is a clean road sign and is easy to detect by the LIDAR 2, the suitability information is set to 1.0. If the feature is slightly dirty, reducing the reflectivity of the LIDAR 2's laser light and making it somewhat difficult to detect, the suitability information is set to 0.8. For features located near trees, some of the feature may be hidden by the leaves of the trees from spring to autumn, so the suitability information is set to 0.8 in winter, and to 0.5 in other seasons. For features that are subject to snow accumulation during snowfall, the suitability information is set to 0.1 during snowfall, and to 0.8 in other weather conditions. For features or electronic signboards that are not coated with retroreflective material, which may be difficult for the LIDAR 2 to detect, the suitability information is set to 0.2 to 0.1.

[0031] The input unit 14 is a button, touch panel, remote controller, voice input device, etc. for user operation, and accepts inputs to specify a destination for route search, inputs to specify whether autonomous driving is on or off, etc. The information output unit 16 is, for example, a display, speaker, etc. that outputs information based on the control of the control unit 15.

[0032] The control unit 15 includes a CPU that executes a program and controls the entire in-vehicle device 1. In this embodiment, the control unit 15 has a vehicle position estimation unit 17 and an autonomous driving control unit 18. The control unit 15 is an example of the "first acquisition unit," "second acquisition unit," "third acquisition unit," "position estimation unit," "output unit," and "computer" that executes a program in the present invention.

[0033] The vehicle position estimation unit 17 corrects the vehicle position estimated from the output data of the gyro sensor 3, the vehicle speed sensor 4, and / or the GPS receiver 5, based on the measurement values ​​of the distance and angle relative to the feature by the LIDAR 2 and the position information of the feature extracted from the map DB 10. In this embodiment, as an example, the vehicle position estimation unit 17 alternately executes a prediction step of estimating the vehicle position from the output data of the gyro sensor 3, the vehicle speed sensor 4, etc., based on a state estimation method based on Bayesian estimation, and a measurement update step of correcting the estimated value of the vehicle position calculated in the immediately preceding prediction step.

[0034] The autonomous driving control unit 18 refers to the map DB 10 and transmits signals necessary for autonomous driving control to the vehicle based on the set route and the vehicle position estimated by the vehicle position estimation unit 17. The autonomous driving control unit 18 sets a target trajectory based on the set route and controls the position of the vehicle by transmitting a guide signal to the vehicle so that the vehicle position estimated by the vehicle position estimation unit 17 deviates from the target trajectory by a predetermined amount or less. In this embodiment, the autonomous driving control unit 18 monitors the position estimation accuracy of the vehicle position estimation unit 17 in the traveling direction and in a direction perpendicular to the traveling direction (also referred to as the "lateral direction"). The autonomous driving control unit 18 then corrects the target trajectory of the vehicle to improve the detection accuracy by the LIDAR 2 of features suitable for improving the position estimation accuracy in a direction with low position estimation accuracy (also referred to as the "low position accuracy direction Dtag"). The traveling direction and lateral direction of the vehicle are examples of the "first direction" and "second direction" in the present invention.

[0035] Here, a supplementary explanation will be given of the process of estimating the vehicle position by the vehicle position estimation unit 17. The vehicle position estimation unit 17 estimates the vehicle position by sequentially repeating a prediction step and a measurement update step. As the state estimation filter used in these steps, various filters developed to perform Bayesian estimation can be used, such as an extended Kalman filter, an unscented Kalman filter, and a particle filter. As such, various methods have been proposed for position estimation based on Bayesian estimation. Below, a brief explanation will be given of vehicle position estimation using an extended Kalman filter as an example.

[0036] Fig. 4 is a diagram showing the state variable vector x in two-dimensional Cartesian coordinates. As shown in Fig. 4, the vehicle position on a plane defined on the two-dimensional Cartesian coordinates of xy is represented by coordinates "(x, y)" and the vehicle's orientation "θ". Here, the orientation θ is defined as the angle between the vehicle's traveling direction and the x-axis. The coordinates (x, y) indicate an absolute position equivalent to, for example, a combination of latitude and longitude.

[0037] FIG. 5 is a diagram showing a schematic relationship between the prediction step and the measurement update step. As shown in FIG. 5, the prediction step and the measurement update step are repeated to sequentially calculate and update the estimated value of the state variable vector X. Here, the state variable vector at the reference time (i.e., the current time) "t" to be calculated is calculated as "X - t " or "X ^ t " ("State variable vector X t =(x t , y t , θ t ) T " ) Note that the provisional estimates made in the prediction step are indicated by " - " is added to the character representing the value, and the more accurate estimated value updated in the measurement update step is added with " ^ " is added.

[0038] In the prediction step, the vehicle position estimation unit 17 calculates the state variable vector X ^ t-1 The vehicle's moving speed "v" and angular velocity "ω" (collectively referred to as the "control value u") t =(v t , ω t ) T ") to obtain the estimated value of the vehicle position at time t (also called the "prior estimated value") X - t At the same time, the vehicle position estimation unit 17 calculates the pre-estimated value X - t The covariance matrix Σ corresponding to the error distribution- t ” is the covariance matrix “Σ ^ t-1 " is calculated from

[0039] In the measurement update step, the vehicle position estimation unit 17 associates the position vector of the feature registered in the map DB 10 with the scan data of the LIDAR 2. When the association is established, the vehicle position estimation unit 17 updates the measurement value "Z t " and the prior estimate X - t and the estimated measurement value of the feature, "Z ^ t " and " are obtained. Measurement value Z t is a two-dimensional vector representing the distance and scan angle of the feature measured by the LIDAR 2 at time t. Then, the vehicle position estimation unit 17 calculates the measurement value Z t and the estimated measurement value Z ^ t The difference between these two is the Kalman gain "K t " and multiply this by the prior estimate X - t By adding it to the updated state variable vector (also called "posterior estimate") X ^ t Calculate. X ^ t =X - t +K t (Z t -Z ^ t ) Formula (1)

[0040] In the measurement update step, the vehicle position estimation unit 17 calculates the posterior estimated value X ^ t The covariance matrix Σ corresponding to the error distribution of ^ t is the prior covariance matrix Σ - t The Kalman gain Kt The parameters such as above can be calculated in the same manner as in a known self-location technique using, for example, an extended Kalman filter.

[0041] When the vehicle position estimation unit 17 is able to associate the position vectors of multiple features registered in the map DB 10 with the scan data of the LIDAR 2, the vehicle position estimation unit 17 may perform the measurement update step based on the measurement values ​​of any one selected feature (for example, a target feature Ltag described below), or may perform the measurement update step multiple times based on the measurement values ​​of all the associated features. When using the measurement values ​​of multiple features, the vehicle position estimation unit 17 takes into account that the farther a feature is from the LIDAR 2, the greater the measurement error of the LIDAR 2, and decreases the weighting for the feature as the distance between the LIDAR 2 and the feature increases.

[0042] Furthermore, the automatic driving control unit 18 determines a low position accuracy direction Dtag, as will be described later, and corrects the target trajectory of the vehicle in order to increase the position estimation accuracy in the low position accuracy direction Dtag.

[0043] [Vehicle control based on position estimation accuracy] (1) Processing flow Fig. 6 is a flowchart showing vehicle control based on position estimation accuracy executed by the autonomous driving control unit 18 in this embodiment. In the flowchart of Fig. 6, when the autonomous driving control unit 18 detects a low position accuracy direction Dtag, it determines a feature (also referred to as a "target feature Ltag") that is suitable for improving the position estimation accuracy of the low position accuracy direction Dtag, and controls the vehicle to approach the target feature Ltag. Note that when the flowchart of Fig. 6 is executed, it is assumed that the autonomous driving control unit 18 has set a target trajectory for the vehicle along a route to a set destination.

[0044] First, the autonomous driving control unit 18 identifies the range of error in position estimation in the traveling direction and lateral direction of the vehicle (step S101). For example, the autonomous driving control unit 18 identifies the range of error in position estimation in the traveling direction and lateral direction of the vehicle by transforming the covariance matrix of the error obtained in the calculation process of position estimation based on the extended Kalman filter with a rotation matrix using the orientation θ of the vehicle.

[0045] Next, the autonomous driving control unit 18 determines whether a low position accuracy direction Dtag exists (step S102). For example, if the range of error in the position estimation in either the traveling direction or the lateral direction identified in step S101 is longer than a predetermined threshold, the autonomous driving control unit 18 considers the direction in which the error range is longer than the predetermined threshold as the low position accuracy direction Dtag. Note that instead of performing the determination in step S102, the autonomous driving control unit 18 may compare the range of error in the position estimation in the traveling direction of the vehicle with the range of error in the lateral direction of the vehicle, and consider the direction in which the error range is longer as the low position accuracy direction Dtag. In this case, after identifying the low position accuracy direction Dtag, the autonomous driving control unit 18 executes the processes from step S103 onwards.

[0046] Then, if the autonomous driving control unit 18 determines that a low position accuracy direction Dtag exists (step S102; Yes), it refers to the map DB10 and executes a destination object determination process to determine, as the target object Ltag, an object along the route that is suitable for improving the position estimation accuracy of the low position accuracy direction Dtag (step S103).

[0047] The destination object determination process of step S103 will now be described with reference to FIG. 12. FIG. 12 is a flowchart showing the destination object determination process executed in step S103 of FIG. 6 in this embodiment. In the flowchart of FIG. 12, the autonomous driving control unit 18 refers to the map DB 10, and determines, as the target feature Ltag, features that are highly suitable for measurement by the LIDAR 2 and have a small angular difference between the low-precision direction Dtag and the normal direction from among features within a predetermined distance along the route, features whose normal direction is within a predetermined angular difference between the low-precision direction Dtag and the normal direction, i.e., features whose normal direction is similar to the low-precision direction Dtag. Note that the predetermined angular difference is set to less than 45 degrees, for example. In this case, features whose normal direction is less than 45 degrees from the low-precision direction Dtag are considered to have a normal direction similar to the low-precision direction Dtag.

[0048] Here, a supplementary explanation will be given regarding step S103. As will be described later, the closer the direction of the surface onto which the laser light of the LIDAR 2 is irradiated (i.e., the normal direction) is to the laser light of the LIDAR 2, the more it is possible to improve the accuracy of position estimation in that direction. Therefore, the smaller the angle difference between the low position accuracy direction Dtag and the normal direction of the feature, the better the estimation accuracy in the low position accuracy direction Dtag by position estimation using the feature.

[0049] First, when the destination object determination process of step S103 is started, the autonomous driving control unit 18 considers, among features registered in the map DB 10 and measurable by the LIDAR 2 from the travel route, features whose normal direction is within a predetermined angle difference from the low position accuracy direction Dtag to have a normal direction similar to the low position accuracy direction Dtag, and determines whether there are multiple such features (step S301). Then, if the autonomous driving control unit 18 determines that there are multiple features whose normal direction is within the predetermined angle difference from the low position accuracy direction Dtag (step S301; Yes), it extracts, from the multiple features, the feature with the highest suitability as a measurement target by the LIDAR 2 (step S302). For example, the autonomous driving control unit 18 references suitability information of the features whose normal direction was determined to be within the predetermined angle difference in step S301, and extracts the feature with the highest suitability indicated by the suitability information.

[0050] Next, the autonomous driving control unit 18 determines whether there is only one feature with the highest degree of suitability (step S303). That is, it determines whether multiple features with the same degree of suitability were extracted as the feature with the highest degree of suitability in step S302. If the autonomous driving control unit 18 determines that there is only one feature with the highest degree of suitability (step S303; Yes), it determines the one feature extracted in step S302 as the target feature Ltag, and proceeds to step S104. On the other hand, if it is not determined that there is only one feature with the highest degree of suitability (step S303; No), it determines the feature whose normal direction is most similar to the low position accuracy direction Dtag, among the multiple features extracted in step S304, as the target feature Ltag, and proceeds to step S104.

[0051] If the autonomous driving control unit 18 determines in step S301 that there are no features whose normal direction is within the predetermined angle difference from the low positional accuracy direction Dtag (step S301; No), it determines whether there is only one feature whose normal direction is within the predetermined angle difference from the low positional accuracy direction Dtag (step S306). If it determines that there are no features whose normal direction is within the predetermined angle difference from the low positional accuracy direction Dtag (step S306; No), the autonomous driving control unit 18 extracts and references size information of features that exist within a predetermined distance along the traveling route from the map DB 10, and determines a large feature as the target feature Ltag. On the other hand, if it determines that there is only one feature whose normal direction is within the predetermined angle difference from the low positional accuracy direction Dtag (step S306; Yes), it determines that one feature as the target feature Ltag and proceeds to step S104.

[0052] In this embodiment, the autonomous driving control unit 18 determines, as the target feature Ltag, the feature whose normal direction is closest to the low-precision direction Dtag among the features whose normal direction is within a predetermined angle difference and has the highest suitability. However, the method for determining the target feature Ltag is not limited to this. For example, if there are multiple features whose normal direction is extremely small, the feature with the highest suitability among these features may be determined as the target feature Ltag. Alternatively, the angular difference between the low-precision direction Dtag and the normal direction and the suitability of each feature may be scored according to a predetermined standard, and the target feature Ltag may be determined by comprehensively determining a feature suitable for improving the position estimation accuracy of the low-precision direction Dtag based on the angle difference score and the suitability score. Furthermore, if suitability information is not registered in the feature information of the map DB 10, the feature whose angular difference between the low-precision direction Dtag and the normal direction is smallest may be determined as the target feature Ltag.

[0053] Taking the above into consideration, the autonomous driving control unit 18 determines, as the target feature Ltag, a feature whose normal direction is similar to the low position accuracy direction Dtag among features registered in the map DB 10 and measurable by the LIDAR 2 from the traveled route. In this case, the autonomous driving control unit 18 identifies the normal direction of each feature based on the normal information of each feature registered in the feature information of the map DB 10. Then, the autonomous driving control unit 18 determines, as the target feature Ltag, for example, a feature whose normal direction is most similar to the low position accuracy direction Dtag among features present within a predetermined distance along the route, or a feature whose angular difference between the low position accuracy direction Dtag and the normal direction is within a predetermined range and which is highly suitable as a measurement target for the LIDAR 2. Furthermore, if there is no feature along the route whose normal direction is similar to the low position accuracy direction Dtag, the autonomous driving control unit 18 determines the target feature Ltag by referring to the size information of the feature information. For a specific example of setting the target feature Ltag, see "(3) Specific examples " section.

[0054] Then, the autonomous driving control unit 18 corrects the target trajectory of the vehicle so as to approach the target feature Ltag determined in step S103 (step S104). Specifically, the autonomous driving control unit 18 corrects the target trajectory so as to change lanes to the lane closest to the target feature Ltag, or corrects the target trajectory so as to bias the driving position within the lane on which the vehicle is traveling to the side closer to the target feature Ltag. In this way, by bringing the vehicle closer to the target feature Ltag, the autonomous driving control unit 18 can suitably improve the detection accuracy of the target feature Ltag by the LIDAR 2 and improve the position estimation accuracy of the low position accuracy direction Dtag. Furthermore, even when position estimation is performed based on multiple features with weighting according to the distance between each feature and the vehicle, it is possible to relatively increase the weighting related to the target feature Ltag by bringing the vehicle closer to the target feature Ltag.

[0055] On the other hand, in step S102, if the automatic driving control unit 18 determines that there is no low position accuracy direction Dtag (step S102; No), it determines that there is no need to correct the target trajectory of the vehicle, and ends the processing of the flowchart.

[0056] (2) Relationship between feature normal direction and position estimation accuracy Next, the relationship between the normal direction of an object and the accuracy of position estimation will be explained. As will be explained below, the closer the normal direction of the surface of the object onto which the laser light of the LIDAR 2 is irradiated is to the laser light of the LIDAR 2, the higher the accuracy of position estimation in that direction can be.

[0057] FIG. 7(A) shows an overhead view of a vehicle undergoing scanning by the lidar 2. In FIG. 7(A), features 51 and 52 with different normal directions are present within the measurement range of the lidar 2. FIG. 7(B) is an enlarged view of feature 51, clearly showing irradiation points "P1" to "P5" of the laser beam from the lidar 2. FIG. 7(C) is an enlarged view of feature 52, clearly showing irradiation points "P6" to "P9" of the laser beam from the lidar 2. In FIGS. 7(B) and 7(C), the center of gravity coordinates of the vehicle in the lateral direction and the traveling direction, respectively, calculated from the position coordinates of each irradiation point, are shown by dashed lines. Note that, for ease of explanation, FIGS. 7(A) to 7(C) illustrate a case where the lidar 2 emits laser beams along a predetermined scanning plane, but the lidar 2 may emit laser beams along multiple scanning planes at different heights.

[0058] As shown in Figures 7(A) and 7(B), the normal direction of the feature 51 is approximately aligned with the lateral direction of the vehicle. Therefore, the illumination points P1 to P5 are aligned along the traveling direction of the vehicle, with large variation in the traveling direction and small variation in the lateral direction. On the other hand, when determining the measurement position of the feature by the LIDAR 2 (measurement value z in Figure 5) in the position estimation process, the autonomous driving control unit 18 calculates the centroid coordinates of the two-dimensional coordinates based on the vehicle indicated by the point cloud data of the target feature. Therefore, in position estimation using the feature 51, it is predicted that the position accuracy in the lateral direction, where the variation of the illumination points P1 to P5 is small, will be high. Therefore, when the lateral direction is the low position accuracy direction Dtag, the autonomous driving control unit 18 sets the feature 51 as the target feature Ltag.

[0059] 7(A) and 7(C), the normal direction of the feature 52 is approximately aligned with the vehicle's traveling direction. Therefore, the illumination points P6 to P9 are aligned along the side of the vehicle, with large variation in the side direction and small variation in the traveling direction. Therefore, in position estimation using the feature 52, it is predicted that the position accuracy in the traveling direction, where the variation in the illumination points P6 to P9 is small, will be high. Therefore, the autonomous driving control unit 18 sets the feature 52 as the target feature Ltag when the traveling direction is the low position accuracy direction Dtag.

[0060] In this way, the closer the normal direction of the surface of the feature onto which the laser light of the LIDAR 2 is irradiated is to the laser light of the LIDAR 2, the more accurate the position estimation accuracy in that direction can be increased. Therefore, in step S103 of Fig. 6, the autonomous driving control unit 18 determines, as the target feature Ltag, an feature along the route whose normal direction is similar to the low position accuracy direction Dtag.

[0061] (3) Specific examples Next, a specific example based on the processing of the flowchart in FIG. 6 will be described with reference to FIGS.

[0062] Fig. 8 shows an overhead view of a vehicle on a three-lane road when there is a feature 53 facing forward relative to the vehicle on the left side of the road and a feature 54 facing sideways relative to the vehicle on the right side of the road. In Figs. 8 to 11, the solid arrow "L1" indicates the target trajectory before execution of the flowchart in Fig. 6, and the dashed arrow "L2" indicates the target trajectory after execution of the flowchart in Fig. 6. The dashed ellipse "EE" indicates an error ellipse corresponding to the range of error in position estimation.

[0063] In the example of FIG. 8 , the range of error in the vehicle's traveling direction, indicated by the dashed ellipse EE, is longer than the lateral direction of the vehicle and is longer than a predetermined threshold. In this case, in step S102 of the flowchart in FIG. 6 , the autonomous driving control unit 18 regards the vehicle's traveling direction as the low-position-accuracy direction Dtag. Then, in step S103, the autonomous driving control unit 18 refers to the map DB 10 and sets, as the target feature Ltag, a feature 53 whose normal direction is similar to the traveling direction, which is the low-position-accuracy direction Dtag, among features present within a predetermined distance along the traveling route. Then, in step S104, the autonomous driving control unit 18 changes the target trajectory so as to change lanes from the center lane of the traveling road to the left lane closer to the feature 53. As a result, the vehicle travels in the left lane closest to the feature 53 and passes the feature 53, so that the onboard device 1 can detect the feature 53 with high accuracy using the LIDAR 2 and suitably improve the estimation accuracy of the traveling direction, which is the low-position-accuracy direction Dtag.

[0064] FIG. 9 shows an overhead view of a vehicle traveling on the same road as in the example of FIG. 8, when the position estimation accuracy is lower in the lateral direction than in the traveling direction of the vehicle.

[0065] In this case, in step S102 of the flowchart in Fig. 6, the autonomous driving control unit 18 regards the lateral direction of the vehicle as the low-position-accuracy direction Dtag. Then, in step S103, the autonomous driving control unit 18 refers to the map DB 10 and sets, as the target feature Ltag, a feature 54 whose normal direction is similar to the lateral direction, which is the low-position-accuracy direction Dtag, among features present within a predetermined distance along the traveling route. Then, in step S104, the autonomous driving control unit 18 changes the target trajectory so as to change lanes from the center lane of the traveling road to the right lane closer to the feature 54. As a result, the vehicle travels in the right lane closest to the feature 54 and passes the feature 54, so that the onboard device 1 can detect the feature 54 with high accuracy using the LIDAR 2 and suitably improve the estimation accuracy of the lateral direction, which is the low-position-accuracy direction Dtag.

[0066] 10 shows an overhead view of a vehicle when there are no features along the road whose normal direction is similar to the low-precision direction Dtag. In the example of Fig. 10, features 55 and 56 that exist within a predetermined distance along the traveling route both have normal directions that are approximately perpendicular to the low-precision direction Dtag.

[0067] In the example of FIG. 10, the autonomous driving control unit 18 regards the lateral direction of the vehicle as the low-position-accuracy direction Dtag in step S102 of the flowchart in FIG. 6. Furthermore, in step S103, the autonomous driving control unit 18 refers to the map DB 10 and determines that, among the features present within a predetermined distance along the traveling route, there is no feature whose normal direction is similar to the low-position-accuracy direction Dtag. In this case, the autonomous driving control unit 18 extracts and refers to size information of features 55, 56 present within a predetermined distance along the traveling route from the map DB 10, and sets the large-sized feature 55 as the target feature Ltag (see step S307). Then, in step S104, the autonomous driving control unit 18 changes the target trajectory so as to change lanes from the center lane of the traveling road to the left lane closer to the feature 55, as shown by the dashed line L2.

[0068] In this way, when there is no feature whose normal direction is similar to the low-position-accuracy direction Dtag, the autonomous driving control unit 18 selects a feature that is large in size (i.e., has more points irradiated with the laser light of the LIDAR 2) as the target feature Ltag. Generally, when determining the measurement position (measurement value z) of a feature by the LIDAR 2, the greater the number of point cloud data for the feature, the greater the number of samples when calculating the center of gravity position, making it possible to set the measurement position of the target feature with high accuracy. Therefore, in the example of FIG. 10, the autonomous driving control unit 18 selects a feature from which more point cloud data can be obtained as the target feature Ltag, thereby improving the estimation accuracy of the low-position-accuracy direction Dtag as much as possible.

[0069] 11 shows an overhead view of a vehicle with two features on the left and right sides of the road, each with a different normal direction. In this example, feature 57 is tilted at an angle α1 relative to the vehicle's side, and feature 58 is tilted at an angle α2, which is greater than α1, relative to the vehicle's side.

[0070] 11, in step S102, the autonomous driving control unit 18 identifies the vehicle's traveling direction as the low-position-accuracy direction Dtag, and in step S103, determines that the normal directions of both of the features 57 and 58 are similar to the traveling direction, which is the low-position-accuracy direction Dtag. In this case, the autonomous driving control unit 18 references the appropriateness information corresponding to the features 57 and 58 from the map DB 10, and if these appropriateness information items indicate the same value or if there is no corresponding appropriateness information, sets the feature 57 along the route whose normal direction is closest to the low-position-accuracy direction Dtag as the target feature Ltag (see step S304). Then, in step S104, the autonomous driving control unit 18 changes the target trajectory so as to change lanes from the center lane of the travel road to the left lane closer to the feature 55, as shown by the dashed line L2. On the other hand, the automatic driving control unit 18 refers to the suitability information corresponding to the features 57 and 58 from the map DB 10, and if the value indicated by the suitability information of the feature 58 is greater than the value indicated by the low accuracy information of the feature 57, sets the feature 58 as the target feature Ltag (see steps S302 and S305).

[0071] 11, when there are multiple features whose normal direction is similar to that of the low positioning accuracy direction Dtag, the autonomous driving control unit 18 sets the feature whose normal direction is most similar to that of the low positioning accuracy direction Dtag or the feature whose appropriateness information value is the highest as the target feature Ltag. This makes it possible to suitably improve the position estimation accuracy of the low positioning accuracy direction Dtag.

[0072] As described above, the autonomous driving control unit 18 of the vehicle-mounted device 1 according to this embodiment acquires the positional accuracy in the vehicle's traveling direction and the vehicle's lateral direction. The autonomous driving control unit 18 also acquires normal information indicating the orientation of each feature by referring to the map DB 10. The autonomous driving control unit 18 then outputs control information to the vehicle's electronic control device to control the vehicle so as to increase the detection accuracy of features facing in the vehicle's traveling direction or lateral direction, whichever direction has lower positional accuracy. This allows the vehicle-mounted device 1 to suitably improve the positional accuracy in the direction where the positional accuracy is low.

[0073] [Variations] Modifications suitable for the embodiment will be described below. The following modifications may be applied to the embodiment in combination.

[0074] (Variation 1) Instead of storing the map DB 10 in the storage unit 12, the vehicle-mounted device 1 may have a server device (not shown) that stores the map DB 10. In this case, the vehicle-mounted device 1 acquires the necessary feature information by communicating with the server device via a communication unit (not shown).

[0075] (Variation 2) The low position accuracy direction Dtag is not limited to being set to either the vehicle's traveling direction or a side direction of the vehicle. Alternatively, the autonomous driving control unit 18 may, for example, determine the direction with the largest range of error of two predetermined directions as the low position accuracy direction Dtag. In another example, when the direction with the largest range of error among all directions is neither the traveling direction nor a side direction, the autonomous driving control unit 18 may determine the direction with the largest range of error as the low position accuracy direction Dtag.

[0076] (Variation 3) The configuration of the driving assistance system shown in Fig. 1 is an example, and the configuration of a driving assistance system to which the present invention can be applied is not limited to the configuration shown in Fig. 1. For example, instead of having an on-board device 1, the driving assistance system may have an electronic control device of the vehicle that executes the processes of the vehicle position estimation unit 17 and the automatic driving control unit 18 of the on-board device 1. In this case, the map DB 10 may be stored in, for example, a storage unit in the vehicle, and the electronic control device of the vehicle may receive update information for the map DB 10 from a server device (not shown). [Explanation of symbols]

[0077] 1 On-vehicle device 2 Rider 3 Gyro sensor 4 Vehicle speed sensor 5 GPS receiver 10 Map DB

Claims

[Claim 1] a first acquisition unit that acquires positional accuracy in a first direction and a second direction relative to a traveling direction of the moving object; a second acquisition unit that acquires orientation information that is assigned to the map information and indicates the orientation of each feature; an output unit that outputs control information for controlling the moving object so as to increase the detection accuracy of a target feature facing in one of the first direction and the second direction, the direction in which the positional accuracy is low; An output device comprising:

Images