Estimation system, estimation device, estimation method, and estimation program

Abstract

To provide an estimation system for reducing a processing load for estimating a traveling position.SOLUTION: A processor of an estimation system estimates a traveling position of a host vehicle by map matching between observation point group data DO in which reflected light intensity relative to irradiation light to an external space in a host vehicle is observed by an observation device and map point group data DM obtained by mapping reflected light intensity from the external space. The processor is configured to execute assuming a plurality of road surface positions PS corresponding to a traveling road surface RS of the host vehicle in the map point group data DM as the traveling position to be a standard for collating the observation point group data DO with the map point group data DM, and estimating the traveling position based on a degree of matching between the observation point group data DO and the map point group data DM for each road surface position PS.SELECTED DRAWING: Figure 11

Description

[Technical field]

[0001] The present disclosure relates to a technology for estimating a traveling position of a moving object by map matching. [Background technology]

[0002] In general, the traveling position of a moving object can be estimated by map matching observation data from an observation device of the moving object with map data. For example, the technology disclosed in Patent Document 1 performs map matching between information corresponding to road markings in sensor information, which is observation data, and information on the positions of multiple divided cells representing the road markings in map data. [Prior art documents] [Patent documents]

[0003] [Patent Document 1] JP 2020-34322 A Summary of the Invention [Problem to be solved by the invention]

[0004] However, the technology disclosed in Patent Document 1 requires recognition of information corresponding to road markings in sensor information. Since the marking recognition process for such sensor information generates a high load in a short period of time, improvements are required as a map matching process for a moving object.

[0005] An object of the present disclosure is to provide an estimation system that reduces the processing load for estimating a traveling position. Another object of the present disclosure is to provide an estimation device that reduces the processing load for estimating a traveling position. Yet another object of the present disclosure is to provide an estimation method that reduces the processing load for estimating a traveling position. Yet another object of the present disclosure is to provide an estimation program that reduces the processing load for estimating a traveling position. [Means for solving the problem]

[0006] The technical means of the present disclosure for solving the problems will be described below. Note that the claims and the reference characters in parentheses in this section indicate the corresponding relationship with the specific means described in the embodiments described later in detail, and do not limit the technical scope of the present disclosure.

[0007] A first aspect of the present disclosure is An estimation system having a processor (12) for estimating a traveling position of a host moving body (2) by map matching between observation point cloud data (DO) obtained by observing a reflected light intensity of light irradiated to an external space (ES) by an observation device (3) in a host moving body (2) and map point cloud data (DM) obtained by mapping the reflected light intensity from the external space, The processor Assuming a plurality of road surface positions (PS) in the map point cloud data corresponding to the road surface (RS) on which the host mobile body is traveling as a reference traveling position for comparing the observation point cloud data with the map point cloud data; and estimating a driving position based on a matching degree between the observation point cloud data for each road surface position and the map point cloud data. 、 Assuming the road surface position is This includes excluding a position assumed to be inside a driving lane (LD) selected as a road surface position in the map point cloud data, if the position is assumed to be inside an expected parking space in which a mobile object is expected to be parked, from the road surface position.

[0008] A second aspect of the present disclosure is An estimation device having a processor (12) for estimating a traveling position of a host moving body (2) by map matching between observation point cloud data (DO) obtained by observing a reflected light intensity of light irradiated to an external space (ES) by an observation device (3) in a host moving body (2) and map point cloud data (DM) obtained by mapping the reflected light intensity from the external space, The processor Assuming a plurality of road surface positions (PS) in the map point cloud data corresponding to the road surface (RS) on which the host mobile body is traveling as a reference traveling position for comparing the observation point cloud data with the map point cloud data; and estimating a driving position based on a matching degree between the observation point cloud data for each road surface position and the map point cloud data. 、 Assuming the road surface position is This includes excluding a position assumed to be inside a driving lane (LD) selected as a road surface position in the map point cloud data, if the position is assumed to be inside an expected parking space in which a mobile object is expected to be parked, from the road surface position.

[0009] A third aspect of the present disclosure is An estimation method executed by a processor (12) for estimating a traveling position of a host moving body (2) by map matching between observation point cloud data (DO) obtained by observing a reflected light intensity of light irradiated to an external space (ES) by an observation device (3) in a host moving body (2) and map point cloud data (DM) obtained by mapping the reflected light intensity from the external space, the method comprising: Assuming a plurality of road surface positions (PS) in the map point cloud data corresponding to the road surface (RS) on which the host mobile body is traveling as a reference traveling position for comparing the observation point cloud data with the map point cloud data; and estimating a driving position based on a matching degree between the observation point cloud data for each road surface position and the map point cloud data. fruit, Assuming the road surface position is This includes excluding a position assumed to be inside a driving lane (LD) selected as a road surface position in the map point cloud data, if the position is assumed to be inside an expected parking space in which a mobile object is expected to be parked, from the road surface position.

[0010] A fourth aspect of the present disclosure is An estimation program stored in a storage medium (10) for estimating a traveling position of a host moving body (2) by map matching between observation point cloud data (DO) obtained by observing a reflected light intensity of light irradiated to an external space (ES) by an observation device (3) in the host moving body (2) and map point cloud data (DM) obtained by mapping the reflected light intensity from the external space, the estimation program including instructions to be executed by a processor (12), The command is, Assuming a plurality of road surface positions (PS) in the map point cloud data corresponding to the road surface (RS) on which the host mobile body is traveling as a reference traveling position for comparing the observation point cloud data with the map point cloud data; and estimating a driving position based on a matching degree between the observation point cloud data for each road surface position and the map point cloud data. fruit, Assuming the road surface position is This includes excluding a position assumed to be inside a driving lane (LD) selected as a road surface position in the map point cloud data, if the position is assumed to be inside an expected parking space in which a mobile body is expected to be parked, from the road surface position.

[0011] According to the first to fourth aspects, the running position of the host mobile body is estimated based on the degree of matching between the observation point cloud data in which the reflected light intensity of the irradiated light to the external space is observed by an observation device, and the map point cloud data in which the reflected light intensity from the external space is mapped. Therefore, in the first to fourth aspects, a plurality of road surface positions corresponding to the running road surface of the host mobile body are assumed in the map point cloud data as the running position of the host mobile body that is the reference for comparing the observation point cloud data and the map point cloud data. According to this, the assumption process of assuming a plurality of road surface positions from the map point cloud data including the road surface related information can be a low load. Furthermore, the map matching process based on the matching degree between the observation point cloud data and the map point cloud data limited to each assumed road surface position can also be a low load. From these points, it is possible to reduce the processing load for estimating the running position of the host mobile body. [Brief description of the drawings]

[0012] [Figure 1] 1 is a block diagram showing an overall configuration of an estimation system according to an embodiment. [Diagram 2] 1 is a schematic diagram showing the relationship between a host moving body, an observation device, an external space, and a traveling road surface according to one embodiment. [Diagram 3] FIG. 2 is a schematic diagram showing observation point cloud data according to an embodiment. [Figure 4] FIG. 2 is a schematic diagram illustrating map point cloud data according to one embodiment. [Diagram 5] FIG. 1 is a block diagram showing a functional configuration of an estimation system according to an embodiment. [Figure 6] 1 is a flowchart illustrating an estimation flow according to one embodiment. [Figure 7] FIG. 11 is a schematic diagram for explaining an estimation flow according to an embodiment. [Figure 8] FIG. 11 is a schematic diagram for explaining an estimation flow according to an embodiment. [Figure 9] FIG. 11 is a schematic diagram for explaining an estimation flow according to an embodiment. [Figure 10] FIG. 11 is a schematic diagram for explaining an estimation flow according to an embodiment. [Figure 11] FIG. 1 is a schematic diagram for explaining an estimation flow according to an embodiment; DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

[0013] Hereinafter, one embodiment of the present disclosure will be described with reference to the drawings.

[0014] An estimation system 1 according to an embodiment shown in Fig. 1 estimates the traveling position of a host vehicle 2, which is a host moving body equipped with an observation device 3 that observes an external space ES as shown in Fig. 2. The host vehicle 2 is a moving body, such as an automobile, that can travel on a traveling road surface RS with an occupant on board. From a viewpoint centered on the host vehicle 2, the host vehicle 2 can also be said to be an ego-vehicle.

[0015] In the host vehicle 2, an autonomous driving mode is executed so that the levels are classified according to the degree of manual intervention of the occupant in the driving task. The autonomous driving mode may be realized by autonomous driving control, such as conditional driving automation, high driving automation, or full driving automation, in which the system performs all driving tasks when activated. The autonomous driving mode may be realized by advanced driving assistance control, such as driving assistance or partial driving automation, in which the occupant performs some or all driving tasks. The autonomous driving mode may be realized by either one of the autonomous driving control and the advanced driving assistance control, or a combination of them, or by switching between them.

[0016] The host vehicle 2 is equipped with a sensor system 4, a communication system 5, an information presentation system 6, and a map database 7, all of which are shown in Figures 1 and 2. The sensor system 4 acquires sensor information usable by the estimation system 1 from the external and internal worlds of the host vehicle 2. To this end, the sensor system 4 is configured to include an external sensor 40 and an internal sensor 41.

[0017] The external sensor 40 acquires information on the external world, which is the surrounding environment of the host vehicle 2. The external sensor 40 includes an observation device 3 that acquires the external world information by observing the external space ES of the host vehicle 2. The observation device 3 is a three-dimensional LiDAR (Light Detection and Ranging / Laser Imaging Detection and Ranging) that can observe the reflected light intensity of light irradiated to the external space ES in the host vehicle 2. In addition to the observation device 3, the external sensor 40 may include at least one of a camera, a radar, a sonar, and the like, which acquires the external world information by sensing the external world of the host vehicle 2.

[0018] The observation device 3 of the external sensor 40 observes an observation area in the external space ES that is determined according to the viewing angle, and thereby acquires external information related to targets existing in the observation area. In particular, the external information acquired by the observation device 3 of this embodiment is observation point cloud data DO, which is obtained by two-dimensionally observing the reflected light intensity from targets existing in the external space ES including the traveling road surface RS, as shown in Fig. 3. Such observation point cloud data DO includes the reflected light intensity and position coordinates at the reflection points.

[0019] The internal sensor 41 shown in FIGS. 1 and 2 acquires information on the internal world, which is the internal environment of the host vehicle 2. The internal sensor 41 may acquire the internal world information by detecting a specific physical amount of motion in the internal world of the host vehicle 2. The internal sensor 41 of the physical amount detection type is, for example, at least one of a driving speed sensor, an acceleration sensor, a gyro sensor, etc. The internal sensor 41 may acquire the internal world information by detecting a specific state of an occupant in the internal world of the host vehicle 2. The internal sensor 41 of the occupant detection type is, for example, at least one of a driver status monitor (registered trademark), a biological sensor, a seating sensor, an actuator sensor, an in-vehicle equipment sensor, etc.

[0020] The communication system 5 acquires communication information usable by the estimation system 1 by wireless communication. The communication system 5 may receive a positioning signal from a satellite of a Global Navigation Satellite System (GNSS) existing in the external world of the host vehicle 2. The positioning type communication system 5 is, for example, a GNSS receiver. The communication system 5 may transmit and receive a communication signal between the host vehicle 2 and a V2X system existing in the external world of the host vehicle 2. The V2X type communication system 5 is, for example, at least one of a Dedicated Short Range Communications (DSRC) communication device and a Cellular V2X (C-V2X) communication device. The communication system 5 may transmit and receive a communication signal between the host vehicle 2 and a terminal existing in the internal world of the host vehicle 2. The terminal communication type communication system 5 is, for example, at least one of a Bluetooth (registered trademark) device, a Wi-Fi (registered trademark) device, and an infrared communication device.

[0021] The information presentation system 6 presents alarm information to the occupants of the host vehicle 2. The information presentation system 6 may present the alarm information by stimulating the occupants' vision. The visual stimulation type information presentation system 6 is at least one of a head-up display (HUD), a multi-function display (MFD), a combination meter, a navigation unit, a light-emitting unit, etc. The information presentation system 6 may present the alarm information by stimulating the occupants' hearing. The auditory stimulation type information presentation system 6 is at least one of a speaker, a buzzer, a vibration unit, etc.

[0022] The map database 7 stores map information that can be used by the estimation system 1. The map database 7 includes at least one type of non-transitory tangible storage medium, such as a semiconductor memory, a magnetic medium, or an optical medium. The map database 7 may be a database of a locator that estimates the motion state including the position of the host vehicle 2. The map database 7 may be a database of a navigation unit that navigates the driving route of the host vehicle 2. The map database 7 may be a combination of multiple types of these databases.

[0023] The map database 7 acquires and stores the latest map information by communicating with an external center via a V2X type communication system 5. In particular, the map information in this embodiment is map point cloud data DM in which the reflected light intensity from targets existing in an external space ES including a traveling road surface RS is two-dimensionally mapped as shown in Fig. 4. Such map point cloud data DM is vector data or raster data including the reflected light intensity and position coordinates at reflection points, and is preferably vector data.

[0024] The map point cloud data DM may include feature information on features that divide the driving lane LD on the traveling road surface RS. Here, the feature that divides the driving lane LD is at least one of the following that represent the road edge: road boundary line, outer side line of the road, shoulder strip (including parking and stopping road side strip and pedestrian road side strip), pedestrian lane, bicycle lane, dividing line (including white line), guard rail, curb, guard pipe, and side gutter. The map point cloud data DM may include marking information on markings that exist inside the driving lane LD on the traveling road surface RS. Here, the markings that exist inside the driving lane LD are at least one of the following: stop line, dividing line, crosswalk marking, speed marking, no parking and stopping marking, no turn marking, and right and left turn marking. The map point cloud data DM may include parking assumption information on parking assumption space where a mobile object is expected to park inside the driving lane LD on the traveling road surface RS. Here, the estimated parking information is information that indicates at least one of, for example, no parking or stopping signs, and parking frequency obtained by an external center.

[0025] 1 is connected to a sensor system 4, a communication system 5, an information presentation system 6, and a map database 7 via at least one of, for example, a LAN (Local Area Network) line, a wire harness, an internal bus, and a wireless communication line. The estimation system 1 is configured to include at least one dedicated computer.

[0026] The dedicated computer constituting the estimation system 1 may be a driving control ECU (Electronic Control Unit) that controls the driving of the host vehicle 2. The dedicated computer constituting the estimation system 1 may be a navigation ECU that navigates the driving route of the host vehicle 2. The dedicated computer constituting the estimation system 1 may be a locator ECU that estimates the motion state including the position of the host vehicle 2. The dedicated computer constituting the estimation system 1 may be an HCU (Human Machine Interface (HMI) Control Unit) that controls the information presentation by the information presentation system 6 in the host vehicle 2. The dedicated computer constituting the estimation system 1 may be a computer other than the host vehicle 2 that constitutes, for example, an external center or a mobile terminal capable of communicating with the communication system 5.

[0027] The dedicated computer constituting the estimation system 1 has at least one memory 10 and one processor 12. The memory 10 is at least one type of non-transitory tangible storage medium, such as a semiconductor memory, a magnetic medium, an optical medium, etc., that non-temporarily stores computer-readable programs, data, etc. The processor 12 includes at least one type of core, such as a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), a RISC (Reduced Instruction Set Computer)-CPU, a DFP (Data Flow Processor), and a GSP (Graph Streaming Processor).

[0028] The processor 12 executes a plurality of instructions included in an estimation program stored in the memory 10 in order to estimate the traveling position of the host vehicle 2 by map matching between the observation point cloud data DO and the map point cloud data DM. In this way, the estimation system 1 constructs a plurality of functional blocks for estimating the traveling position of the host vehicle 2. The plurality of functional blocks constructed in the estimation system 1 include an assumption block 100 and an estimation block 110, as shown in FIG. 5.

[0029] The flow of an estimation method (hereinafter referred to as an estimation flow) in which the estimation system 1 estimates the traveling position of the host vehicle 2 by cooperation of these blocks 100 and 110 will be described below with reference to Fig. 6. The algorithm cycle of the estimation flow is repeatedly executed while the host vehicle 2 is running. Note that each "S" in the estimation flow means a plurality of steps executed by a plurality of commands included in the object recognition program.

[0030] In S10, the assumption block 100 assumes a number of road surface positions PS corresponding to the traveling road surface RS in the map point cloud data DM as the traveling position of the host vehicle 2 that serves as a reference for comparing the observation point cloud data DO and the map point cloud data DM by map matching.

[0031] Specifically, the assumption block 100 in S10 sets a plurality of grids GS by dividing the external space ES in the map point cloud data DM as shown in Fig. 7. At this time, the region of the external space ES in which the plurality of grids GS are set is defined as a region of a size required for map matching, centered on the driving position estimated by the previous estimation flow. Each grid GS is defined as a two-dimensional rectangular region, and can also be said to be a so-called mesh.

[0032] The assumption block 100 in S10 assumes position coordinates x, y of a representative position PR that represents each grid GS set in the map point cloud data DM. At this time, the representative position PR is defined, for example, as a rectangular corner position or a rectangular center position of each grid GS. In addition, the position coordinate x of the representative position PR is defined as a coordinate representing the lateral position of the host vehicle 2, while the position coordinate y of the representative position PR is defined as a coordinate representing the longitudinal position of the host vehicle 2.

[0033] The assumption block 100 in S10 selects a plurality of road surface positions PS where it is predicted that the host vehicle 2 is currently traveling, from among the representative positions PR assumed for each grid GS in the map point cloud data DM. At this time, the selection of the plurality of road surface positions PS may be performed as shown in Fig. 8 by masking processing based on at least one type of road surface-related information, for example, feature information, sign information, and estimated parking information.

[0034] Here, in the case of mask processing based on feature information, in the map point cloud data DM, the representative position PR assumed to be outside the driving lane LD divided by the feature is removed from the road surface position PS (see the x mark in FIG. 8), while the representative position PR assumed to be inside the driving lane LD is selected as the road surface position PS. In the case of mask processing based on marking information, in the map point cloud data DM, the representative position PR assumed to be outside the driving lane LD in which the road marking exists is removed from the road surface position PS (see the x mark in FIG. 8), while the representative position PR assumed to be inside the driving lane LD is selected as the road surface position PS. In the case of mask processing based on parking assumption information in addition to at least one of feature information and marking information, even if the representative position PR is assumed to be inside the driving lane LD, the representative position PR assumed to be inside the parking assumption space in which the mobile object is expected to park in the map point cloud data DM is removed from the road surface position PS.

[0035] In the case where the assumption block 100 in S10 is based on feature information, instead of or in addition to the masking process, the representative position PR may be selected according to an algorithm as shown in Figures 9 and 10. In this algorithm, the reflection points V corresponding to the features that define the driving lane LD are selected. n (suffix n is an integer between 0 and k-1) are extracted for each feature on the left and right. Then, a reflection point V n Central angle θ between n Based on the formula 1, the total reflection point V n A selection index ω is calculated according to the total angle between the central angle θ n is defined as a clockwise or counterclockwise rotation around the representative position PR, one of which is a plus sign and the other is a minus sign. If the selection index ω is equal to or less than a threshold value of 0.5, the representative position PR is determined to be outside the driving lane LD and is excluded from the road surface position PS. On the other hand, if the selection index ω exceeds 0.5, the representative position PR is determined to be inside the driving lane LD and is assumed to be the road surface position PS.

number

[0036] 6, the estimation block 110 estimates the position coordinates x^, y^ of the driving position where the host vehicle 2 is currently traveling, based on the matching degree between the observation point cloud data DO and the map point cloud data DM for each road surface position PS assumed by the assumption block 100 in S10. At this time, the position coordinate x^ of the driving position is defined as a coordinate representing the lateral position of the host vehicle 2, while the position coordinate y^ of the driving position is defined as a coordinate representing the longitudinal position of the host vehicle 2.

[0037] Specifically, the estimation block 110 in S11 obtains a matching degree C(x, y) that satisfies Equation 2 as an index representing the degree of correlation between the observation point cloud data DO and the map point cloud data DM in map matching between the two data DO and DM. In Equation 2, x and y are the position coordinates of the road surface position PS (see FIG. 8). In Equation 2, N is the total number of points in the observation point cloud data DO. In Equation 2, mi (x, y) is the reflection intensity at the reflection point with the i-th suffix in the map point cloud data DM when the origin PO of the observation point cloud data DO corresponding to the running position of the host vehicle 2 is compared with the position coordinates x, y of the road surface position PS in the map point cloud data DM as shown in FIG. a (x, y) is the average reflection intensity of all reflection points in the map point cloud data DM when the origin PO of the observation point cloud data DO corresponding to the traveling position of the host vehicle 2 is compared with the position coordinates x, y of the road surface position PS in the map point cloud data DM. i is the reflection intensity at the reflection point with the i-th suffix in the observation point cloud data DO shown in FIG. a is the average reflection intensity of all reflection points in the observation point cloud data DO.

[0038]

number

number

number

[0039] For a driving lane LD that is, for example, linear, the estimation block 110 in S11 may stop the estimation based on the matching degree in the longitudinal direction of the host vehicle 2 and may limit the execution to the estimation based on the matching degree in the lateral direction of the host vehicle 2. In this case, the position coordinates of the driving position in the longitudinal direction may be estimated using at least one of, for example, dead reckoning information based on the internal information of the internal sensor 41 and positioning information acquired through the positioning type communication system 5.

[0040] The position coordinates x^, y^ of the traveling position estimated by the estimation block 110 of S11 may be stored in the memory 10 in association with a time stamp of the estimation timing. The position coordinates x^, y^ of the traveling position estimated by the estimation block 110 of S11 may be presented to an occupant in the host vehicle 2 by being superimposed on the map point cloud data DM through a visual stimulation type information presentation system 6. The position coordinates x^, y^ of the traveling position estimated by the estimation block 110 of S11 may be stored in an external center by being transmitted to the external center through a V2X type communication system 5.

[0041] (Action and effect) The effects of the present embodiment described above will be described below.

[0042] According to this embodiment, the running position of the host vehicle 2 is estimated based on the degree of matching between the observation point cloud data DO, which is obtained by observing the reflected light intensity of the light irradiated to the external space ES by the observation device 3, and the map point cloud data DM, which is obtained by mapping the reflected light intensity from the external space ES. Therefore, in the first to fourth aspects, a plurality of road surface positions PS corresponding to the running road surface RS of the host vehicle 2 are assumed in the map point cloud data DM as the running position of the host vehicle 2 that is a reference for comparing the observation point cloud data DO and the map point cloud data DM. According to this, the assumption process of assuming a plurality of road surface positions PS from the map point cloud data DM including the road surface related information can be a low load. Furthermore, the map matching process based on the matching degree between the observation point cloud data DO and the map point cloud data DM, which is limited to each assumed road surface position PS, can also be a low load. From these points, it is possible to reduce the processing load for estimating the running position of the host vehicle 2.

[0043] According to this embodiment, a position assumed outside the driving lane LD defined by features in the map point cloud data DM is excluded from the road surface position PS, while a position assumed inside the driving lane LD in the map point cloud data DM is extracted as the road surface position PS. This allows the assumption process of assuming multiple road surface positions PS limited to the inside of the driving lane LD from the map point cloud data DM including feature information to be low-load and accurate. Therefore, it is possible to reduce the processing load for estimating the driving position of the host vehicle 2 and ensure the accuracy of the estimation.

[0044] According to this embodiment, a position assumed to be outside the driving lane LD in which a road marking exists in the map point cloud data DM is excluded from the road surface position PS, while a position assumed to be inside the driving lane LD in the map point cloud data DM is extracted as the road surface position PS. According to this, the assumption process of assuming a plurality of road surface positions PS limited to the inside of the driving lane LD from the map point cloud data DM including the marking information can be low-load and accurate. Therefore, it is possible to reduce the processing load for estimating the driving position of the host vehicle 2 and ensure the accuracy of the estimation.

[0045] According to this embodiment, even if the position is inside the driving lane LD in the map point cloud data DM, the position assumed inside the assumed parking space where the mobile body is assumed to park is excluded from the road surface position PS. According to this, the assumption process of assuming a plurality of road surface positions PS limited to the outside of the assumed parking space inside the driving lane LD from the map point cloud data DM including the information of the assumed parking space where the host vehicle 2 is unlikely to travel can increase accuracy with a low load. Therefore, it is possible to realize the reduction of the processing load for estimating the traveling position of the host vehicle 2 and the improvement of the accuracy of the estimation.

[0046] According to this embodiment, a plurality of road surface positions PS are assumed from among representative positions PR each representing a plurality of grids GS obtained by dividing the external space ES in the map point cloud data DM. This allows the assumption process of the road surface position PS corresponding to the traveling road surface RS of the host vehicle 2 to be executed only for the representative positions PR of the plurality of grids GS, thereby promoting a reduction in the load of the assumption process. Therefore, it is possible to contribute to a reduction in the processing load for estimating the traveling position of the host vehicle 2.

[0047] According to this embodiment, the estimation of the traveling position based on the matching degree at each road surface position PS between the observation point cloud data DO and the map point cloud data DM may be limited to the lateral direction of the host vehicle 2. This makes it possible to reduce the estimation error of the traveling position in the longitudinal direction of the host vehicle 2 for a linear traveling lane LD, for example, while reducing the processing load. Therefore, it is possible to reduce the processing load for estimating the traveling position of the host vehicle 2 and ensure the accuracy of the estimation.

[0048] (Other embodiments) Although one embodiment has been described above, the present disclosure should not be construed as being limited to the embodiment described above, and can be applied to various embodiments within the scope not departing from the gist of the present disclosure.

[0049] In the modified example, the dedicated computer constituting the estimation system 1 may have at least one of a digital circuit and an analog circuit as a processor. Here, the digital circuit is at least one of ASIC (Application Specific Integrated Circuit), FPGA (Field Programmable Gate Array), SOC (System on a Chip), PGA (Programmable Gate Array), CPLD (Complex Programmable Logic Device), etc. Furthermore, such a digital circuit may have a memory that stores a program.

[0050] In S10 of the modified example, the assumption block 100 may set multiple positions instead of the representative position PR without setting multiple grids GS. In the modified example, the host moving body to which the estimation system 1 is applied may be, for example, a drone whose traveling can be remotely controlled by an external center.

[0051] In addition to the embodiments described above, the estimation system 1 according to the above-mentioned embodiments and modifications may be implemented as a processing device (e.g., a processing ECU) mounted on the host vehicle 2. Furthermore, the above-mentioned embodiments and modifications may be implemented as a semiconductor device (e.g., a semiconductor chip) having at least one each of the processor 12 and the memory 10 of the estimation system 1. [Explanation of symbols]

[0052] 1: Estimation system, 2: Host vehicle, 3: Observation device, 10: Memory, 12: Processor, DM: Map point cloud data, DO: Observation point cloud data, ES: External space, GS: Grid, LD: Driving lane, PR: Representative position, PS: Road surface position, RS: Driving road surface

Claims

1. An estimation system having a processor (12) for estimating a traveling position of a host moving body (2) by map matching between observation point cloud data (DO) obtained by observing a reflected light intensity of light irradiated to an external space (ES) by an observation device (3) in the host moving body (2) and map point cloud data (DM) obtained by mapping the reflected light intensity from the external space, the estimation system comprising: The processor, Assuming a plurality of road surface positions (PS) in the map point cloud data corresponding to a traveling road surface (RS) of the host moving body as the traveling position serving as a reference for comparing the observation point cloud data with the map point cloud data; and estimating the traveling position based on a matching degree between the observation point cloud data and the map point cloud data for each of the road surface positions. The step of assuming a road surface position includes: An estimation system that includes excluding a position assumed to be inside an expected parking space in which a mobile body is expected to be parked from the road surface position, even if the position is assumed to be inside a driving lane (LD) selected as the road surface position in the map point cloud data.

2. The step of assuming a road surface position includes: The estimation system according to claim 1 , further comprising excluding from the road surface position any position assumed to be outside the driving lane defined by features in the map point cloud data.

3. The step of assuming a road surface position includes: The estimation system according to claim 1 or 2, further comprising excluding from the road surface position a position assumed to be outside the driving lane in which a road marking is present in the map point cloud data.

4. The step of assuming a road surface position includes: The estimation system according to any one of claims 1 to 3, further comprising: assuming a plurality of said road surface positions from representative positions (PR) each representing a plurality of grids (GS) obtained by dividing said external space in said map point cloud data.

5. The estimating of the traveling position includes: The estimation system according to claim 1 , further comprising limiting the estimation of the traveling position based on the matching degree for each road surface position to a lateral direction of the host moving body.

6. An estimation device having a processor (12) for estimating a traveling position of a host moving body (2) by map matching between observation point cloud data (DO) obtained by observing a reflected light intensity of light irradiated to an external space (ES) by an observation device (3) in the host moving body (2) and map point cloud data (DM) obtained by mapping the reflected light intensity from the external space, the estimation device comprising: The processor, Assuming a plurality of road surface positions (PS) in the map point cloud data corresponding to a traveling road surface (RS) of the host moving body as the traveling position serving as a reference for comparing the observation point cloud data with the map point cloud data; and estimating the traveling position based on a matching degree between the observation point cloud data and the map point cloud data for each of the road surface positions. The step of assuming a road surface position includes: An estimation device that includes excluding a position assumed to be inside an expected parking space in which a mobile body is expected to be parked from the road surface position, even if the position is assumed to be inside a driving lane (LD) selected as the road surface position in the map point cloud data.

7. 1. An estimation method executed by a processor (12) for estimating a traveling position of a host moving body (2) by map matching between observation point cloud data (DO) obtained by observing a reflected light intensity of light irradiated to an external space (ES) by an observation device (3) in the host moving body (2) and map point cloud data (DM) obtained by mapping the reflected light intensity from the external space, comprising: Assuming a plurality of road surface positions (PS) in the map point cloud data corresponding to a traveling road surface (RS) of the host moving body as the traveling position serving as a reference for comparing the observation point cloud data with the map point cloud data; estimating the traveling position based on a matching degree between the observation point cloud data and the map point cloud data for each of the road surface positions, The step of assuming a road surface position includes: The estimation method includes excluding a position assumed to be inside an expected parking space in which a mobile body is expected to be parked from the road surface position, even if the position is assumed to be inside a driving lane (LD) selected as the road surface position in the map point cloud data.

8. An estimation program stored in a storage medium (10) for estimating a traveling position of a host moving body (2) by map matching between observation point cloud data (DO) obtained by observing a reflected light intensity of light irradiated into an external space (ES) by an observation device (3) in the host moving body (2) and map point cloud data (DM) obtained by mapping the reflected light intensity from the external space, the estimation program including instructions to be executed by a processor (12), The instruction: Assuming a plurality of road surface positions (PS) in the map point cloud data corresponding to a road surface (RS) on which the host vehicle is traveling as the traveling position serving as a reference for comparing the observation point cloud data with the map point cloud data; and estimating the traveling position based on a matching degree between the observation point cloud data and the map point cloud data for each of the road surface positions, The road surface position is assumed to be An estimation program that includes excluding a position assumed to be inside an expected parking space in which a mobile body is expected to be parked from the road surface position, even if the position is assumed to be inside a driving lane (LD) selected as the road surface position in the map point cloud data.

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