JP2024035511A - Exterior environment recognition apparatus - Google Patents

Abstract

To accurately recognize an exterior environment situation in the surroundings of a vehicle while reducing a processing load.SOLUTION: An exterior environment recognition apparatus 50 comprises a LiDAR 5 as an in-vehicle detector irradiating the surrounding of an own vehicle with an electromagnetic wave to detect an exterior environment situation in the surrounding in time series, and a processing section 11 as a road surface information acquisition section for acquiring road surface information on a road on which the own vehicle is traveling on the basis of the information detected by the LiDAR 5. The processing section 11 includes a recognition section 111 for recognizing information indicating a predetermined detection target in a first area separated from the own vehicle on the road by a predetermined distance at different positions on the road, and a mapping section 114 for mapping the information indicating the detection target recognized in the recognition section 111 on the basis of a traveling direction and driving speed of the own vehicle.SELECTED DRAWING: Figure 2

Description

The present invention relates to an external world recognition device that recognizes the external world situation of a vehicle.

This type of device scans by changing the irradiation angle of the laser beam emitted from the lidar around a first axis parallel to the height direction and a second axis parallel to the horizontal direction, and detects each detection point. A device is known that detects the outside world of a vehicle based on position information (see, for example, Patent Document 1).

Japanese Patent Application Publication No. 2020-149079

In the above device, there are many detection points acquired by scanning, and the processing load for acquiring position information based on each detection point is heavy.

An external world recognition device that is one aspect of the present invention includes an on-vehicle detector that irradiates electromagnetic waves around the vehicle to detect the surrounding external world situation in time series, and an on-vehicle detector that detects the surrounding external world situation in time series. An external world recognition device comprising: a road surface information acquisition section that acquires road surface information of a road on which a vehicle travels; a recognition unit that recognizes information indicating a predetermined detection target in the first area; and a mapping unit that maps information indicating the detection target recognized by the recognition unit based on the traveling direction and traveling speed of the host vehicle. include.

According to the present invention, it is possible to accurately recognize the external environment surrounding the vehicle while reducing the processing load.

A diagram showing how a vehicle travels on a road. FIG. 1B is a diagram showing an example of point cloud data of the points in FIG. 1A. FIG. 1 is a block diagram schematically showing the configuration of main parts of a vehicle control device. FIG. 3 is a diagram illustrating a lidar detection area. FIG. 3 is a diagram illustrating a lidar detection area. FIG. 3B is an excerpt of the detection area seen from the perspective of FIG. 3B. The figure which showed the 1st area|region of FIG. 4A on a two-dimensional map. FIG. 3 is a schematic diagram illustrating the light projection angle of the lidar. FIG. 3 is a diagram illustrating a second area and a third area. 3 is a flowchart illustrating an example of a process executed by a CPU of the controller in FIG. 2. FIG. 7 is a flowchart illustrating the process of S10 in FIG. 6. 7 is a flowchart illustrating the process of S20 in FIG. 6. 7 is a flowchart illustrating the process of S30 in FIG. 6.

Embodiments of the invention will be described below with reference to the drawings.
The external world recognition device according to the embodiment of the invention can be applied to a vehicle having an automatic driving function, that is, an automatic driving vehicle. Note that the vehicle to which the external world recognition device according to the present embodiment is applied may be referred to as the own vehicle to distinguish it from other vehicles. The host vehicle may be an engine vehicle having an internal combustion engine as a driving source, an electric vehicle having a traveling motor as a driving source, or a hybrid vehicle having an engine and a driving motor as a driving source. The own vehicle can run not only in an automatic driving mode that does not require any driving operations by the driver, but also in a manual driving mode that requires driving operations by the driver.

When a self-driving vehicle is running in self-driving mode (hereinafter referred to as automatic driving or autonomous driving), it detects the surroundings of the vehicle based on detection data from in-vehicle detectors such as cameras and lidar (Light Detection and Ranging). Recognize the external situation. The self-driving vehicle generates a travel trajectory (target trajectory) for a predetermined period of time from the current time based on the recognition result, and controls the travel actuator so that the vehicle travels along the target trajectory.

FIG. 1A is a diagram showing how a self-driving vehicle 101, which is an autonomous vehicle, travels on a road RD. FIG. 1B is a diagram showing an example of detection data (measurement points) obtained by a lidar mounted on the own vehicle 101. A measurement point by a lidar is information about a point where an irradiated laser beam is reflected at a certain point on the surface of an object and returned to the object. Specific point information includes the distance from the laser source to the point, the intensity of the reflected laser beam, and the relative velocity of an object located at the point. Furthermore, data composed of a plurality of measurement points as shown in FIG. 1B is referred to as point cloud data. FIG. 1B shows point cloud data corresponding to the points in FIG. 1A. The host vehicle 101 recognizes the external environment around the vehicle, more specifically, the road structure and objects around the vehicle, based on point cloud data as shown in FIG. 1B, and generates a target trajectory based on the recognition results. do.

By the way, one way to fully recognize the external environment surrounding the vehicle is to increase the number of points irradiated with electromagnetic waves emitted from onboard detectors such as lidar (in other words, increase the density of points irradiated with electromagnetic waves and increase the density of point cloud data). It is possible to increase the The irradiation point may also be called a target. On the other hand, increasing the number of irradiation points increases the processing load for controlling the on-vehicle detector, and increases the volume of detection data (point cloud data) obtained by the on-vehicle detector, increasing the processing load for point cloud data. may increase. In particular, as shown in FIG. 1B, in a situation where there are many objects (trees, people, buildings, etc.) on the side of the road, the capacity of the point cloud data further increases. Moreover, if an attempt is made to deal with such a problem, there is a risk that the scale of the apparatus will increase, such as by increasing the number of lasers. Therefore, in consideration of these points, the external world recognition device is configured as follows in the embodiment.

<Summary>
The external world recognition device according to the embodiment intermittently irradiates irradiation light (a type of electromagnetic wave) from the rider 5 of the own vehicle 101 traveling on the road RD in the traveling direction of the own vehicle 101, and emits light (a type of electromagnetic wave) at different positions on the road RD. Obtain point cloud data discretely. The irradiation range of the irradiation light emitted from the lidar 5 is such that the point cloud data acquired by the lidar 5 based on the previous irradiation light and the point cloud data acquired by the lidar 5 based on the current irradiation light are The road should be shaped like a long strip in the width direction of the road that intersects with the direction of travel so that it is seamlessly connected to the direction of travel of the RD.
More specifically, at a position on the road RD that is a predetermined distance (corresponding to the depth distance described later) from the own vehicle 101 based on the vehicle speed (traveling speed) of the own vehicle 101, One area is set as an irradiation range, and a predetermined irradiation point density is set for this first area. Then, the total number of irradiation points irradiated by the lidar 5 is set by setting the irradiation point density for other areas other than the first area lower than the irradiation point density for the first area, or by stopping irradiation for other areas. suppress. In other words, it is possible to reduce the number of irradiation points of the rider 5 without reducing the recognition accuracy of the position (distance from the host vehicle 101) and size of objects etc. recognized based on point cloud data.
Such an external world recognition device will be explained in more detail.

<Configuration of vehicle control device>
FIG. 2 is a block diagram showing the main configuration of vehicle control device 100 including an external world recognition device. This vehicle control device 100 includes a controller 10, a communication unit 1, a positioning unit 2, an internal sensor group 3, a camera 4, a rider 5, and a driving actuator AC. Further, the vehicle control device 100 includes an external world recognition device 50 that constitutes a part of the vehicle control device 100. The external world recognition device 50 recognizes the external world surrounding the vehicle based on detection data from on-vehicle detectors such as the camera 4 and the lidar 5.

The communication unit 1 communicates with various servers (not shown) via networks including wireless communication networks such as the Internet and mobile phone networks, and periodically or arbitrarily transmits map information, driving history information, traffic information, etc. Obtained from the server at the timing of Networks include not only public wireless communication networks but also closed communication networks established for each predetermined management area, such as wireless LAN, Wi-Fi (registered trademark), Bluetooth (registered trademark), and the like. The acquired map information is output to the storage unit 12, and the map information is updated. The positioning unit (GNSS unit) 2 has a positioning sensor that receives positioning signals transmitted from positioning satellites. The positioning satellite is an artificial satellite such as a GPS satellite or a quasi-zenith satellite. The positioning unit 2 measures the current position (latitude, longitude, altitude) of the own vehicle 101 using the positioning information received by the positioning sensor.

The internal sensor group 3 is a general term for a plurality of sensors (internal sensors) that detect the driving state of the own vehicle 101. For example, the internal sensor group 3 includes a vehicle speed sensor that detects the vehicle speed of the host vehicle 101, an acceleration sensor that detects the longitudinal acceleration and lateral acceleration (lateral acceleration) of the host vehicle 101, and a rotation speed sensor that detects the rotation speed of the travel drive source. It includes a rotational speed sensor that detects the rotational speed, a yaw rate sensor that detects the rotational angular velocity around the vertical axis of the center of gravity of the host vehicle 101, and the like. The internal sensor group 3 also includes sensors that detect driving operations by the driver in the manual driving mode, such as accelerator pedal operations, brake pedal operations, steering wheel operations, and the like.

The camera 4 has an imaging device such as a CCD or CMOS, and images the surroundings (front, rear, and sides) of the own vehicle 101. The lidar 5 receives scattered light from the omnidirectional irradiation light of the own vehicle 101 and measures the distance from the own vehicle 101 to surrounding objects, the position, shape, etc. of the objects.

Actuator AC is a travel actuator for controlling travel of own vehicle 101. When the traveling driving source is an engine, the actuator AC includes a throttle actuator that adjusts the opening degree of a throttle valve of the engine (throttle opening degree). When the travel drive source is a travel motor, the travel motor is included in the actuator AC. The actuator AC also includes a brake actuator that operates the braking device of the host vehicle 101 and a steering actuator that drives the steering device.

The controller 10 is composed of an electronic control unit (ECU). More specifically, the controller 10 includes a computer having an arithmetic unit 11 such as a CPU (microprocessor), a storage unit 12 such as a ROM or RAM, and other peripheral circuits (not shown) such as an I/O interface. Consists of. Although a plurality of ECUs with different functions, such as an engine control ECU, a travel motor control ECU, and a braking device ECU, can be provided separately, in FIG. 2, the controller 10 is shown as a collection of these ECUs for convenience. .

The storage unit 12 stores highly accurate and detailed map information (referred to as high-precision map information). High-precision map information includes road position information, road shape (curvature, etc.) information, road slope information, intersection and branch point position information, information on the number of lanes (driving lanes), lane width and each lane. Contains location information (information on lane center positions and lane position boundaries), location information on landmarks on the map (traffic lights, signs, buildings, etc.), and information on road surface profiles such as road surface irregularities. . In addition to the two-dimensional map information described later, the storage unit 12 also contains various control programs, information such as threshold values used in the programs, and setting information for onboard detectors such as the lidar 5 (irradiation point information described later). be remembered.

The calculation unit 11 has a recognition unit 111, a demarcation unit 112, a setting unit 113, a mapping unit 114, and a travel control unit 115 as functional components. Note that, as shown in FIG. 2, the recognition unit 111, the demarcation unit 112, the setting unit 113, and the mapping unit 114 are included in the external world recognition device 50. As described above, the external world recognition device 50 recognizes the external world situation around the vehicle based on detection data from on-vehicle detectors such as the camera 4 and the lidar 5. Details of the recognition unit 111, demarcation unit 112, setting unit 113, and mapping unit 114 included in the external world recognition device 50 will be described later.

In the automatic driving mode, the driving control unit 115 generates a target trajectory based on the external environment surrounding the vehicle recognized by the external world recognition device 50, and activates the actuator AC so that the host vehicle 101 travels along the target trajectory. Control. Note that in the manual driving mode, the travel control unit 115 controls the actuator AC in accordance with a travel command (steering operation, etc.) from the driver acquired by the internal sensor group 3.

<Detection area by lidar>
The external world recognition device 50 sets a detection area for the rider 5. FIGS. 3A and 3B are diagrams illustrating a detection area of the rider 5 when detecting a three-dimensional object or the like on the road RD in the traveling direction. In the embodiment, the road surface shape including road surface irregularities, steps, undulations, etc., and three-dimensional objects located on the road RD (equipment related to the road RD (traffic lights, signs, grooves, walls, fences, guardrails, etc.)) , objects on the road RD (including other vehicles and obstacles on the road surface), and marking lines provided on the road surface are collectively referred to as three-dimensional objects. Partition lines include white lines (including lines of different colors such as yellow), curb lines, road studs, etc., and may also be called lane marks. Furthermore, a three-dimensional object or the like that is set in advance as a detection target is referred to as a detection target. Since the lidar 5 receives scattered light with respect to the irradiation light, the first area set as the irradiation range of the irradiation light becomes the detection area. That is, the first region in the embodiment is an irradiation range and a detection region.
The region RS shown in FIG. 3A is a road segment recognized by the recognition unit 111, which will be described later, while the own vehicle 101 is traveling on the road RD shown in FIG. 1A. In the embodiment, the area corresponding to the road RD sandwiched between the boundary lines RL and RB of the road RD is called a road segment.

The detection area BX shown in FIG. 3A corresponds to a first area set as an irradiation range at a point on the road RD that is a predetermined depth distance away from the own vehicle 101. The length of the arrow line in FIG. 3A represents the depth distance L. As shown by the arrow line, in the embodiment, the distance from the installation position of the rider 5 to the center of the lower side of the detection area BX will be referred to as the depth distance. The depth distance is set to a predetermined length based on the vehicle speed of the own vehicle 101.
The detection area BX is set to include an area that should be watched while driving in automatic driving mode. As an example, the center position of the detection area BX in the road width direction may be set to overlap the center position of the road segment in the road width direction. Further, the width (length in the road width direction) of the detection area BX is set to be longer than the road width at the depth L. Furthermore, the depth width of the detection area BX (width in the vertical direction of the band-shaped area forming the detection area BX) is set to a predetermined length based on the vehicle speed of the own vehicle 101.

FIG. 3B is a diagram schematically showing a detection area BX, a detection area BX1, and a detection area BX2 set on the road RD at different depth distances from the own vehicle 101. The reason for illustrating the three detection regions BX, BX1, and BX2 is to explain their variability. Moreover, FIG. 4A is a diagram which extracts the detection area BX, the detection area BX1, and the detection area BX2 seen from the viewpoint of FIG. 3B. The detection area BX corresponds to a first area set on the road RD at a point where the depth distance from the own vehicle 101 is, for example, L=180 m. The detection region BX1 corresponds to a first region set on the road RD at a point where the depth distance from the host vehicle 101 is, for example, L1=100 m. The detection area BX2 corresponds to a first area set at a point on the road RD where the depth distance from the host vehicle 101 is, for example, L2=10 m.

In the embodiment, it is assumed that the external world recognition device 50 sets the first region based on the traveling speed of the host vehicle 101. The faster the vehicle 101 travels, the longer the braking distance becomes. Therefore, in order to avoid a collision, it is necessary to increase the depth distance and recognize distant objects. Therefore, it has a function of varying the position of the detection area BX according to the traveling speed of the own vehicle 101. For example, when the own vehicle 101 is stopped, a detection area BX2 as a first area is set on the road RD at a point where the depth distance is L2=10 m. Furthermore, when the host vehicle 101 is traveling at a vehicle speed of 100 km/h, a detection area BX1 as a first area is set on the road RD at a point where the depth distance is L1=100 m. Further, when the own vehicle 101 is traveling at a vehicle speed of 180 km/h, a detection area BX as a first area is set on the road RD at a point where the depth distance is L=180 m.

As shown in FIGS. 3B and 4A, when viewed from the viewpoint of the rider 5, the farther the depth distance from the own vehicle 101 is, the smaller the detection area BX becomes. However, in a situation where the road width becomes wider as the depth distance increases, the first area may become larger as the depth distance increases.

<Width of detection area>
FIG. 4B is a diagram showing each detection area in FIG. 4A on a two-dimensional map. In FIG. 4B, the smaller the detection area seen from the viewpoint of the lidar 5, the larger the area when expressed on the two-dimensional map. The reason for this is that the farther the depth distance L from the host vehicle 101 is in the detection area BX, the wider the depth width D is set. To explain in more detail, as described above, in the embodiment, the detection area BX as the first area is set at a position where the depth distance is greater as the vehicle speed of the own vehicle 101 is faster. If the vehicle speed is high, the travel distance of the own vehicle 101 during the measurement interval of the rider 5 becomes long. Therefore, the first By widening the depth width of the detection region BX as a region, the acquisition section of point cloud data per measurement is widened.

<Projection angle of irradiation light>
FIG. 5A is a schematic diagram illustrating the light projection angle θ ₀ (angle of irradiated light with respect to the horizontal direction) of the rider 5. The external world recognition device 50 changes the irradiation direction of the irradiation light up and down by changing the projection angle θ ₀ to adjust the depth distance on the road RD where the irradiation light is irradiated.
In FIG. 5A, when the irradiation light is irradiated onto the road RD at a point where the depth distance L2 is 10 m, the road surface is irradiated at an incident angle θ ₂ . Furthermore, when the irradiation light is irradiated onto the road RD at a point where the depth distance L1 is 100 m, the road surface is irradiated at an incident angle θ ₁ . Furthermore, when the irradiation light is irradiated onto the road RD at a point where the depth distance L is 180 m, the road surface is irradiated at an incident angle θ.

Generally, when the road gradient in the traveling direction is zero, the projection angle θ ₀ and the incident angle of the irradiated light on the road surface match. The depth distance L at this time is calculated by the following equation (1).
L=1/tanθ ₀ ×H…(1)
However, the symbol H represents the height of the road surface.

The external world recognition device 50 decreases the projection angle θ ₀ when it wants to increase the depth distance, and increases the projection angle θ ₀ when it wants to shorten the depth distance. For example, when changing the depth distance from a state where the irradiation light is irradiated to a point with a depth distance of 70 m to 100 m, the external world recognition device 50 makes the projection angle θ ₀ smaller than the current value so that the depth distance is 100 m. Make sure that the irradiation light is irradiated on the spot. For example, if the road RD is on a downward slope and the irradiation light is not irradiated onto the road RD, the external world recognition device 50 increases the projection angle θ ₀ from the current value so that the irradiation light is directed onto the road RD. so that it is irradiated.

<Number of irradiation points of irradiation light>
The external world recognition device 50 calculates an irradiation point for irradiating the irradiation light of the lidar 5 within the detection area BX1 set as described above. More specifically, the external world recognition device 50 uses an angular resolution calculated based on a prespecified minimum size of the detection target (for example, 15 cm in both height and width) and depth distance (for example, L1 (100 m)). Calculate the irradiation point accordingly. In this case, the angular resolution is required to be 0.05 degrees in each of the vertical direction (also referred to as the vertical direction) and the left and right direction (also referred to as the road width direction) of the grid points, which will be described later. In addition, when detecting a detection target smaller than 15 cm or when setting the detection area at a depth distance longer than L1 (100 m), increase the number of irradiation points in the detection area to increase the angular resolution. Good too.
For example, the external world recognition device 50 calculates irradiation points arranged in a grid within the detection region BX1, and makes the vertical and horizontal intervals of the grid points correspond to the angular resolution. When increasing the angular resolution in the vertical direction, the detection area BX1 is divided vertically by a number based on the angular resolution, and the grid interval in the vertical direction is narrowed to increase the number of irradiation points. On the other hand, when lowering the angular resolution in the vertical direction, the detection area BX1 is divided vertically by a number based on the angular resolution, and the grid interval in the vertical direction is widened to reduce the number of irradiation points. The same applies to the left and right directions.
The external world recognition device 50 generates information indicating the position of the irradiation point calculated according to the angular resolution (hereinafter referred to as irradiation point information), and stores it in association with the position information indicating the current traveling position of the own vehicle 101. 12.

When the own vehicle 101 is running in the automatic driving mode, the external world recognition device 50 reads out the irradiation point information corresponding to the current traveling position of the own vehicle 101 from the storage unit 12, and adjusts the position of the rider 5 according to the irradiation point information. Set the irradiation point within the detection area. Thereby, the irradiation light from the rider 5 is irradiated toward the set irradiation point.
Note that the irradiation light from the lidar 5 may be irradiated using a raster scanning method, or the irradiation light may be irradiated intermittently so that only the irradiation points arranged in a grid within the detection area are irradiated with the irradiation light. Alternatively, the irradiation may be performed in other ways.

<Configuration of external world recognition device>
The details of the external world recognition device 50 will be explained.
As described above, the external world recognition device 50 includes the recognition section 111, the demarcation section 112, the setting section 113, the mapping section 114, and the lidar 5.
<Recognition part>
The recognition unit 111 determines the road structure in the traveling direction of the road RD on which the own vehicle 101 is traveling and the detection on the road RD in the traveling direction based on the image information captured by the camera 4 or the detection data measured by the lidar 5. Recognize the object. Road structures include, for example, straight roads, curved roads, branch roads, tunnel entrances, and the like.
Further, the recognition unit 111 detects a marking line by performing a brightness filtering process on data indicating a flat road surface, for example. In this case, the recognition unit 111 determines that the road surface is a partition line when the height of the road surface whose brightness exceeds the predetermined threshold is substantially the same as the height of the road surface whose brightness does not exceed the predetermined threshold.

A second area and a third area are defined by a defining unit 112 (described later), and from a setting unit 113 (described later) to the rider 5, in addition to the detection area BX1 as the first area, the detection area BY1 as the second area and the third area are transmitted. When the detection area BZ1 is set, the rider 5 irradiates the detection area BY1 and the detection area BZ1 with light in addition to the detection area BX1.
The recognition unit 111 generates three-dimensional point cloud data using time-series detection data (measurement points) detected in the detection area BX1 by the lidar 5, and also generates three-dimensional point cloud data using the time-series detection data (measurement points) detected in the detection area BY1 and BZ1 by the lidar 5. Three-dimensional point cloud data is generated using detected time-series detection data (measurement points).
Note that when acquiring detection data by irradiating irradiation light from the lidar 5 only to the detection region BX1, it is not necessary to generate three-dimensional point group data in the detection region BY1 and the detection region BZ1.

<Recognition of road structure>
Recognition of the road structure by the recognition unit 111 will be explained in more detail. The recognition unit 111 recognizes curbs, walls, grooves, guardrails, or partition lines of the road RD ahead in the traveling direction, which are included in the point cloud data generated based on the detection area BX1, as the boundary lines RL and RB of the road RD. do. Then, the road structure in the direction of travel indicated by boundary lines RL and RB is recognized. As described above, the marking lines include white lines (including lines of different colors), curb lines, road studs, etc., and the markings by these marking lines define the driving lanes of the road RD. In the embodiment, the boundary lines RL and RB of the road RD defined by the above markings are referred to as partition lines.

The recognition unit 111 recognizes the area between the boundary lines RL and RB as an area corresponding to the road RD (the above-mentioned road segment). Note that the method for recognizing road segments is not limited to this, and may be recognized using other methods.
In addition, the recognition unit 111 recognizes irregularities, steps, undulations, etc. exceeding 15 cm, for example, among the three-dimensional objects of the road RD in the road segment ahead in the traveling direction, which are included in the point cloud data generated based on the detection area BX1. The road surface shape and objects larger than 15 cm in length and width, for example, are recognized as detection targets.

<Recognition of detection target>
The recognition of the detection target by the recognition unit 111 will be explained in more detail. A second region and a third region are defined by a defining section 112, which will be described later, and in addition to the detection region BX1, the lidar 5 irradiates the detection region BY1 corresponding to the second region and the detection region BZ1 corresponding to the third region. is irradiated, the recognition unit 111 recognizes, for example, 100 cm of solid objects on the road RD in the road segment ahead in the traveling direction, which is included in the point cloud data generated based on the detection area BY1 and the detection area BZ1. A road surface shape of approximately 100 cm and an object approximately 100 cm in length and width, for example, are recognized as detection targets.
Note that when the lidar 5 irradiates the detection area BX1 with irradiation light to obtain detection data, the recognition unit 111 detects the road ahead in the traveling direction based on the point cloud data generated based on the detection area BX1. The road surface shape of the road RD and the detection target in the segment may be recognized.

<Demarcation section>
The defining unit 112 defines the above-mentioned first area as the irradiation range where the lidar 5 irradiates the irradiation light. More specifically, as illustrated in FIG. 4B, the demarcation unit 112 defines a belt-shaped strip having a predetermined depth width D1 at a position on the road RD that is a predetermined distance (for example, depth distance L1) away from the own vehicle 101 in the traveling direction. A first region is defined.
As described above, the demarcation unit 112 calculates the depth distance L1 from the current position of the own vehicle 101 to the first area and the depth width D1 of the first area (width of the band-shaped area forming the first area). It is calculated based on the vehicle speed of the host vehicle 101 detected by the vehicle speed sensor of the sensor group 3. Regarding the depth distance L2 from the current position of the host vehicle 101 to the first area and the depth width D2 of the first area, and the depth distance L from the current position of the host vehicle 101 to the first area and the depth width D of the first area The same is true.
With this configuration, when the rider 5 intermittently irradiates the detection area BX1 corresponding to the first area with irradiation light from the host vehicle 101 moving on the road RD, the lidar 5 It becomes possible to define the first area so that the point group data detected by the point group data and the point group data detected by the lidar 5 based on the current irradiation light are seamlessly connected in the traveling direction. This makes it possible to obtain detection data of the rider 5 without any breaks in the traveling direction.

The demarcation unit 112 calculates the length W1 in the road width direction of the band-shaped area constituting the first area (corresponding to the detection area BX1) based on the road structure recognized by the recognition unit 111. The demarcation unit 112 calculates the length W1 so as to be sufficiently longer than the road width of the road RD at the point of the depth distance L1. The same applies to the length W2 at the point of depth distance L2 and the length W at the point of depth distance L.
With this configuration, the detection data of the rider 5 includes the left and right ends (boundaries RL, RB) of the road RD.

Further, as illustrated in FIG. 5B, the demarcation unit 112 detects a road that is closer to the vehicle 101 than the first region corresponding to the detection region BX1 (in other words, the distance from the vehicle 101 is shorter than the depth distance L1). Define a second area on the road RD and a third area on the road RD which is farther from the host vehicle 101 than the first area (in other words, the distance from the host vehicle 101 is longer than the depth distance L1 + depth width D1). You may.

<Settings section>
Based on the first area defined by the defining unit 112, the setting unit 113 sets a detection area BX1 corresponding to the first area for the rider 5. As described above, in the embodiment, the detection region BX1 corresponds to the irradiation range of the irradiation light.
Further, when the second area and the third area are defined by the defining unit 112, the setting unit 113 determines the detection area corresponding to the second area for the rider 5 based on the second area and the third area. Detection areas BZ1 corresponding to BY1 and the third area are respectively set. In the embodiment, the detection regions BY1 and BZ1 also correspond to the irradiation range of the irradiation light.

<Different angular resolution>
The setting unit 113 calculates and sets the number of irradiation points (in other words, irradiation point density) in the detection area BX1 corresponding to the first area described above based on the angular resolution of measurement required for the lidar 5. . As described above, the irradiation points arranged in a grid within the detection area BX1 are calculated, and the vertical and horizontal intervals of the grid points are made to correspond to the angular resolution.
Furthermore, when setting the detection area BY1 corresponding to the second area and the detection area BZ1 corresponding to the third area for the lidar 5, the setting unit 113 sets the detection area BY1 corresponding to the second area and the detection area BZ1 corresponding to the third area to the detection area BX1. The number of irradiation points for the second resolution, which is lower in density than the first resolution, is calculated and set for the detection region BY1 and the detection region BZ1, respectively. The first resolution corresponds to, for example, the number of irradiation points required to detect a three-dimensional object exceeding 15 cm on the road RD at a point where the depth distance L1 is 100 m. The second resolution corresponds to, for example, the number of irradiation points required to detect a three-dimensional object or the like exceeding 100 cm on the road RD at a point where the depth distance L1 is 100 m.

<Mapping section>
The mapping unit 114 converts data indicating the position of the detection target (including the position of the marking line) detected based on the time-series point cloud data measured in real time by the lidar 5 into a two-dimensional map similar to that shown in FIG. 4B, for example. A series of position data is generated by mapping onto the top.
More specifically, the mapping unit 114 acquires the position information of all three-dimensional objects, etc. (excluding marking lines) on the two-dimensional map stored in the storage unit 12, and calculates the moving speed and movement of the host vehicle 101. The relative position of the three-dimensional object, etc. is calculated from the direction (for example, azimuth angle) by performing coordinate transformation around the position of the own vehicle 101. Every time point cloud data is acquired by the lidar 5 in the main measurement (step S310) described later, the mapping unit 114 calculates the relative position of a three-dimensional object, etc. based on the acquired point cloud data, with the position of the host vehicle 101 as the center. Coordinates are transformed and recorded on a two-dimensional map.
Furthermore, the mapping unit 114 acquires the position information of all the marking lines on the two-dimensional map stored in the storage unit 12, and calculates the relative position of the marking lines based on the moving speed and direction of the own vehicle 101. It is calculated by performing coordinate transformation around the position of 101. Every time point cloud data is acquired in the main measurement (step S310), which will be described later, the mapping unit 114 performs coordinate transformation on the relative position of the marking line based on the acquired point cloud data, centering on the position of the host vehicle 101. Record on a two-dimensional map.

<Explanation of flowchart>
FIG. 6 is a flowchart showing an example of a process executed by the calculation unit 11 of the controller 10 in FIG. 2 according to a predetermined program. The process shown in the flowchart of FIG. 6 is repeated at predetermined intervals, for example, while the host vehicle 101 is traveling in the automatic driving mode.

First, in step S10, the calculation unit 11 sets the vertical irradiation range of the light irradiated by the lidar 5. More specifically, the irradiation is performed by changing the projection angle θ ₀ so that a long belt-shaped irradiation light in the road width direction is irradiated onto a point at a predetermined depth distance L1 (for example, 100 m) on the road RD. The light irradiation range is adjusted in the vertical direction, and the process proceeds to step S20.
Since the irradiation range of the irradiation light is adjusted in the vertical direction in step S10, even if the depth distance to which the irradiation light is irradiated changes depending on the height H and slope of the road surface of the road RD, the irradiation range of the irradiation light is adjusted in the vertical direction. It becomes possible to correct the irradiation range of the irradiation light in the vertical direction. Details of the process in step S10 will be described later with reference to FIG.

In step S20, the calculation unit 11 sets the irradiation range of the light irradiated by the rider 5 in the horizontal direction (road width direction). More specifically, the length W1 of the strip-shaped irradiation range in the road width direction is made longer than the road width at a predetermined depth distance L1 (for example, 100 m) on the road RD, so that the irradiation light is directed to the left and right road edges. Then, the process proceeds to step S30.
Since the irradiation range of the irradiation light is adjusted in the road width direction in step S20, even if the left and right ends (boundary lines RL, RB) of the road RD deviate from the irradiation range of the irradiation light due to the curve of the road RD, the irradiation range of the irradiation light is adjusted in the road width direction. It becomes possible to correct the irradiation range of the irradiation light in the road width direction before the actual measurement. Details of the process in step S20 will be described later with reference to FIG.

In step S30, the calculation unit 11 measures road surface information indicating the unevenness of the road surface, etc., based on the point cloud data acquired by the rider 5. More specifically, a predetermined three-dimensional object or the like as a predetermined detection target is detected, and the process proceeds to step S40. Details of the process in step S30 will be described later with reference to FIG.

In step S40, the calculation unit 11 generates continuous two-dimensional position data by mapping the relative positions of solid objects, etc. based on the point cloud data measured in step S30 on a two-dimensional map. The process advances to step S50.
More specifically, each time point cloud data is acquired by the lidar 5 in the main measurement (step S310), which will be described later, the mapping unit 114 of the calculation unit 11 calculates the relative position of a three-dimensional object, etc. based on the acquired point cloud data. The coordinates of the position are converted around the position of the own vehicle 101 and recorded on a two-dimensional map.

In step S50, the calculation unit 11 determines whether or not to end the process. If the host vehicle 101 is continuing to travel in the automatic driving mode, the calculation unit 11 makes a negative determination in step S50, returns to step S10, and repeats the above-described process. By returning to step S10, measurements of three-dimensional objects and the like based on point group data are periodically and repeatedly performed while the own vehicle 101 is traveling. On the other hand, when the host vehicle 101 has finished traveling in the automatic driving mode, the calculation unit 11 makes an affirmative determination in step S50 and ends the process shown in FIG. 6.

FIG. 7 is a flowchart illustrating details of the process of step S10 (FIG. 6) executed by the calculation unit 11.
In step S110, the calculation unit 11 uses the detection data detected by the lidar 5 to obtain three-dimensional point group data, and proceeds to step S120. The measurement of the lidar 5 performed in step S110 to obtain point cloud data may be referred to as a first preliminary measurement. The first preliminary measurement is for determining whether or not the irradiation light from the lidar 5 is irradiating the detection area BX1 (corresponding to the first area) on the road RD at the depth distance L1 (for example, 100 m). be.

In step S120, the calculation unit 11 performs a separation process and proceeds to step S130. More specifically, data such as three-dimensional objects on the road RD are detected and separated from the point cloud data generated in step S110 using a known method, and point cloud data indicating a flat road surface is obtained. . The three-dimensional objects include, for example, curbs, walls, grooves, guardrails, etc. provided at the left and right ends of the road RD, as well as other vehicles such as running motorcycles.

In step S130, the calculation unit 11 determines whether road surface data is included in the point cloud data obtained in step S120. As an example, the calculation unit 11 calculates a histogram of height data from the point group data, and determines that road surface data is present when the total number of points of a mountain having a peak top is equal to or greater than a predetermined threshold. Further, the calculation unit 11 obtains the height H of the road surface based on the position of the mountain having the peak top. Other methods may be used to determine the presence or absence of road surface data and to obtain the height H of the road surface.
When there is road surface data (in other words, there is flat data with a value higher than the noise level in the road width direction), the calculation unit 11 makes an affirmative determination in step S130 and proceeds to step S140. If there is no road surface data (in other words, only noise level data exists), the calculation unit 11 makes a negative determination in step S130 and proceeds to step S170.

The case where the process proceeds to step S170 is when the irradiation light is not irradiated onto the road RD due to the above-mentioned downward slope or the like. In step S170, the calculation unit 11 sends an instruction to the rider 5 to make the projection angle θ ₀ larger than the current one, so that the irradiation light is irradiated onto the road RD, and the process returns to step S110 to repeat the above-described process. .

If the process proceeds to step S140, it is a case where the lidar 5 receives scattered light due to the irradiation light being irradiated onto the road RD. In step S140, the calculation unit 11 calculates the height H of the road surface as described above based on the point cloud data obtained in step S120, and proceeds to step S150.

In step S150, the calculation unit 11 calculates the depth distance by substituting the road surface height H calculated in step S140 and the projection angle θ ₀ set for the rider 5 into the above equation (1). The process advances to step S160.

In step S160, the calculation unit 11 adjusts the irradiation range of the light irradiated by the lidar 5 in the vertical direction (also referred to as the depth direction), and ends the process shown in FIG. 7. More specifically, regarding the irradiation range to be set for the next irradiation for the lidar 5, by changing the projection angle θ ₀ , the detection area BX1 (first area) at the point of depth distance L1 (for example, 100 m) (corresponding) so that the irradiation light is irradiated.

FIG. 8 is a flowchart illustrating details of the process of step S20 (FIG. 6) executed by the calculation unit 11.
In step S210, the calculation unit 11 uses the detection data detected by the lidar 5 to obtain three-dimensional point group data, and proceeds to step S220. The measurement of the lidar 5 performed in step S210 to obtain point cloud data may be referred to as a second preliminary measurement. The second preliminary measurement is for determining whether the irradiation range of the lidar 5 includes the road edge on the road RD at the depth distance L1 (for example, 100 m).

In step S220, the calculation unit 11 performs a separation process and proceeds to step S230. More specifically, data such as a three-dimensional object on the road RD is detected from the point cloud data generated in step S210 using a known method and separated from the road surface data, thereby indicating the three-dimensional object. Obtain point cloud data. The three-dimensional objects include the above-mentioned curbs, walls, grooves, guardrails, and the like, as well as other vehicles such as running motorcycles.

In step S230, the calculation unit 11 determines whether the separated point group data includes data indicating a three-dimensional object or the like. If there is data indicating a three-dimensional object or the like, the calculation unit 11 makes an affirmative determination in step S230 and proceeds to step S240. If there is no data indicating a three-dimensional object or the like (in other words, only flat road surface data exists in the road width direction intersecting the direction of travel), the calculation unit 11 makes a negative determination in step S230 and proceeds to step S250.

In step S240, the calculation unit 11 extracts data indicating a road edge based on point cloud data indicating a three-dimensional object, etc., and proceeds to step S250. As an example, while data indicating curbs, walls, ditches, guardrails, etc. that are approximately parallel to the direction of travel of the host vehicle 101 are extracted, data indicating three-dimensional objects in the road width direction that intersect with the direction of travel are not extracted. .

In step S250, the calculation unit 11 adjusts the irradiation range of the light irradiated by the lidar 5 in the horizontal direction (road width direction), and ends the process shown in FIG. 8. Specifically, regarding the irradiation range to be set for the next irradiation for the rider 5, the length W1 of the detection area BX1 (corresponding to the first area) in the road width direction is determined by the length W1 of the road RD at the point of the depth distance L1. By setting it to be longer than the width, the left and right ends (boundary lines RL, RB) of the road RD at the point of the depth distance L1 are included in the next irradiation range.
For example, if data indicating one of the left and right ends is not extracted in step S240, the next irradiation will be performed based on the positions of both ends in the road width direction and the length W1 of the currently set detection area BX1 (corresponding to the first area). The position of the end in the road width direction of the detection area BX1 for which the above-mentioned data is not extracted is set to be enlarged by a predetermined amount.

Further, if there is no data indicating a three-dimensional object, etc. and a negative determination is made in step S230 (for example, only flat road surface data exists in the road width direction intersecting the direction of travel, and data indicating the left and right ends of the road RD exists). If not, in step S250, the calculation unit 11 determines the length of the detection area BX1 for the next irradiation based on the positions of both ends in the road width direction and the length W1 of the currently set detection area BX1 (corresponding to the first area). The area W1 is set to be enlarged at a predetermined magnification from both end positions of the currently set detection area BX1 to both the left and right sides.

Note that the length W1 in the road width direction of the detection area BX1 (corresponding to the first area) set in step S250 may be determined even if filtering processing such as a state space model or a Kalman filter is performed to ensure time series continuity. good. The position of the detection area BX1 in the road width direction (left end, right end position) may be set based on position information measured by the positioning unit 2 and high-precision map information stored in the storage unit 12.

FIG. 9 is a flowchart illustrating details of the process of step S30 (FIG. 6) executed by the calculation unit 11.
In step S310, the calculation unit 11 uses the detection data detected by the lidar 5 to obtain three-dimensional point group data, and proceeds to step S320. The measurement of the lidar 5 performed in step S310 to obtain point cloud data may be referred to as the main measurement. This measurement is for detecting a predetermined three-dimensional object or the like as a predetermined detection target using the lidar 5.

In step S320, the calculation unit 11 performs a separation process and proceeds to step S330. More specifically, by detecting data such as a three-dimensional object on the road RD from the point cloud data generated in step S310 using a known method or the like and separating the road surface data, the three-dimensional object is indicated. Obtain point cloud data. Three-dimensional objects include, for example, curbs, walls, grooves, guardrails, etc. exceeding 15 cm, as well as other vehicles such as running motorcycles.

In step S330, the calculation unit 11 determines whether the point group data separated in step S320 includes data indicating a three-dimensional object or the like. If there is data indicating a three-dimensional object or the like, the calculation unit 11 makes an affirmative determination in step S330 and proceeds to step S340. If there is no data indicating a three-dimensional object or the like (in other words, there is only flat road surface data in the road width direction intersecting the direction of travel), the calculation unit 11 makes a negative determination in step S330 and returns to step S310.

In step S340, the calculation unit 11 separates data indicating a predetermined detection target based on the point group data indicating a solid object, etc., ends the process in FIG. 9, and proceeds to step S40 in FIG. 6. As an example, data indicating objects, etc. that are three-dimensional objects in a direction that intersects the traveling direction of the host vehicle 101 and whose height from the road surface is within a predetermined range (for example, less than 100 cm) may be separated and left. Furthermore, three-dimensional objects that are oriented substantially parallel to the direction of travel are separated and left as separate data. Furthermore, even if the three-dimensional object is oriented perpendicular to the direction of travel, data indicating the three-dimensional object whose height is not within a predetermined range may not be left.

To explain in more detail, from three-dimensional objects that are oriented perpendicular to the direction of travel and whose height is within a predetermined range, to static and small obstacles such as uneven road surfaces and fallen objects on the road surface. It becomes possible to separate only . Furthermore, it is possible to limit the separation to three-dimensional objects related to road edges, such as curbs, walls, grooves, guardrails, etc., from three-dimensional objects oriented substantially parallel to the direction of travel.
This is because, for example, many moving objects such as vehicles and people on the road are three-dimensional objects that are oriented perpendicular to the direction of travel, and their heights are not within a predetermined range (for example, less than 100 cm). In order to prevent such a moving three-dimensional object from being reflected in the subsequent 2D map, processing can be performed so that the data is not separated and left behind.

According to the embodiment described above, the following effects are achieved.
(1) The external world recognition device 50 includes a lidar 5 as an on-vehicle detector that irradiates light as electromagnetic waves around the host vehicle 101 to detect the surrounding external world situation in time series, and information detected by the lidar 5. The calculation unit 11 is provided as a road surface information acquisition unit that acquires road surface information of the road RD on which the host vehicle 101 travels based on the following. The calculation unit 11 is a recognition unit that recognizes information indicating a predetermined detection target in a first area (corresponding to detection area BX1) on the road RD that is a predetermined distance away from the host vehicle 101 at different positions on the road RD. 111, and a mapping unit 114 that maps information indicating the detection target recognized by the recognition unit 111 based on the traveling direction and traveling speed of the own vehicle 101.
Since the calculation unit 11 is configured to acquire the road surface information for the first region on the road RD, the irradiation time when the lidar 5 irradiates the irradiation light is lower than the case where the road surface information for the entire road surface of the road RD is acquired at once. It becomes possible to reduce the number of points. Furthermore, since the mapping unit 114 maps the information recognized by the recognition unit 111 constituting the calculation unit 11 based on the traveling direction and traveling speed of the host vehicle 101, the travel of the host vehicle 101 is regarded as substantial scanning travel. becomes possible. Thereby, even if the first region does not cover the entire road surface in the traveling direction of the road RD, it is possible to cover the road surface information regarding the entire road surface of the road RD.
As described above, the number of irradiation points of the lidar 5 can be reduced without reducing the recognition accuracy of the position and size of the object to be detected by the external world recognition device 50. Reducing the number of irradiation points also leads to a reduction in the processing load on the calculation unit 11.

(2) In the external world recognition device 50 of (1) above, the recognition unit 111 further performs the detection on the road RD closer to the own vehicle 101 than the first area (corresponding to the detection area BX1) at different positions on the road RD. Information indicating a predetermined detection target in a second region (corresponding to detection region BY1) and a third region on road RD (corresponding to detection region BZ1) farther from host vehicle 101 than the first region is recognized.
With this configuration, compared to the case where the calculation unit 11 acquires road surface information only from the first area on the road RD (corresponding to the detection area BX1), the range for acquiring road surface information can be adjusted in the traveling direction of the road RD. It becomes possible to expand. Even in this case, the number of irradiation points to which the rider 5 irradiates the irradiation light can be reduced compared to the case where road surface information for the entire road surface of the road RD is acquired at once.
Further, for example, in a scene where a flying object suddenly falls on the path of the host vehicle 101 at a position outside the detection area BX1, it becomes possible to recognize this if it falls into the detection area BY1 or BZ1.

(3) In the external world recognition device 50 of (2) above, the recognition unit 111 recognizes the information indicating the detection target with the first resolution for the first region (corresponding to the detection region BX1), and Information indicating the detection target is recognized with respect to the region (corresponding to detection regions BY1 and BZ1) at a second resolution lower than the first resolution.
With this configuration, for example, while ensuring higher recognition accuracy in the first area, the recognition accuracy is lowered in the second and third areas compared to the first area, and the lidar 5 irradiates the irradiation light. It becomes possible to appropriately reduce the number of points.

(4) In the external world recognition device 50 of (1) above, the calculation unit 11 further calculates a predetermined distance based on the vehicle speed of the own vehicle 101, and calculates a predetermined distance on the road RD that is a predetermined distance away from the own vehicle 101 in the traveling direction. It includes a defining section 112 that defines, as a first region, a strip-shaped region that intersects with the RD, has a depth based on the vehicle speed, and is long in the road width direction.
With this configuration, the defining unit 112 can define the first area (corresponding to the detection area BX1) so that the left and right road ends of the road RD are irradiated with the irradiation light. Furthermore, when the rider 5 intermittently irradiates the detection area BX1 corresponding to the first area with irradiation light from the own vehicle 101 moving on the road RD, the demarcation unit 112 calculates the calculation unit 11 based on the previous irradiation light. It becomes possible to define the first region so that the road surface information acquired by the first area and the road surface information acquired by the calculation unit 11 based on the current irradiation light are seamlessly connected in the direction of travel.

(5) In the external world recognition device 50 of (4) above, the calculation unit 11 further irradiates the rider 5 so that the irradiation light scattered on the road surface in at least the first region (corresponding to the detection region BX1) is detected. It includes a setting section 113 that sets the direction of light irradiation.
With this configuration, for example, when the depth distance over which the irradiation light is irradiated changes depending on the height H or slope of the road surface of the road RD, it becomes possible to modify the irradiation range of the irradiation light. Further, even when the left and right ends (boundary lines RL, RB) of the road RD deviate from the irradiation range of the irradiation light due to a curve of the road RD, it is possible to correct the irradiation range of the irradiation light.

(6) In the external world recognition device 50 of (5) above, the recognition unit 111 further recognizes road surface data in the traveling direction of the own vehicle 101 based on information detected in preliminary measurement before the main measurement of the rider 5, The setting unit 113 sets the irradiation range in the vertical direction and the road width direction for irradiating the first region on the road RD with the irradiation light, based on the predetermined distance and the road surface data.
With this configuration, it is possible to correct the irradiation range of the irradiation light in the vertical direction and the road width direction before the actual measurement.

(7) In the external world recognition device 50 of (6) above, the setting unit 113 includes each grid obtained by dividing the first region (corresponding to the detection region BX1) in the road width direction based on the angular resolution as the first resolution. The irradiation position of the lidar 5 is associated with the position of the point.
With this configuration, it is possible to appropriately reduce the number of irradiation points in the road width direction at which the rider 5 irradiates irradiation light within the detection area BX1.

(8) In the external world recognition device 50 of (7) above, the setting unit 113 further divides the first region (corresponding to the detection region BX1) in the vertical direction based on the angular resolution as the first resolution, and each grid is obtained by The irradiation position of the lidar 5 is associated with the position of the point.
With this configuration, it is possible to appropriately reduce the number of irradiation points in the vertical direction at which the lidar 5 irradiates irradiation light within the detection region BX1.

(9) In the external world recognition device 50 of (8) above, the setting unit 113 divides the second and third regions (corresponding to the detection regions BY1 and BZ1) in the road width direction based on the angular resolution as the second resolution. The irradiation position of the lidar 5 is associated with the position of each grid point obtained.
With this configuration, it is possible to appropriately reduce the number of irradiation points in the road width direction at which the lidar 5 irradiates irradiation light within the detection regions BY1 and BZ1.

(10) In the external world recognition device 50 of (9) above, the setting unit 113 further divides the second and third regions (corresponding to the detection regions BY1 and BZ1) in the vertical direction based on the angular resolution as the second resolution. The irradiation position of the lidar 5 is associated with the position of each grid point obtained.
With this configuration, it is possible to appropriately reduce the number of irradiation points in the vertical direction at which the lidar 5 irradiates irradiation light within the detection regions BY1 and BZ1.

The embodiments described above can be modified into various forms. Modifications will be described below.
(Modification 1)
The mapping unit 114 maps data indicating the height H of the road surface detected based on time-series point cloud data acquired by the rider 5 at different positions on the road RD onto a one-dimensional map to form a continuous map. Road surface slope data may also be generated.
More specifically, the mapping unit 114 calculates the relative position of the point of depth L1 on the road RD at each measurement time based on the moving speed and direction (for example, azimuth) of the own vehicle 101. Calculate by converting the coordinates to the center. Every time point cloud data is acquired by the lidar 5 in the main measurement (step S310), the mapping unit 114 calculates the height H of the road surface at the point with the depth distance L1 based on the acquired point cloud data on the one-dimensional map. Record above. The one-dimensional map information may be stored in the storage unit 12.

(Modification 2)
The reflection intensity of the light irradiated by the rider 5 is weak on a road surface far away from the own vehicle 101, and there are cases where it is not possible to detect a sufficiently strong reflected light. The depth distance at which the intensity of reflection from the road surface decreases to a detectable level will be referred to as the maximum road surface detection distance L'.

In modification example 2, when the depth distance L to the detection area BX calculated from the vehicle speed of the own vehicle 101 exceeds the maximum road surface detection distance L' (for example, when the depth distance is L = 150 m, the maximum road surface detection distance When L'=110 m), the height at the point of the predetermined depth distance L is estimated from the height at the point of the maximum road surface detection distance L', and the detection area BX at that time is set. Furthermore, the detection area BX' at the point of the maximum road surface detection distance L' is also set at the same time.
Note that the depth width of the detection area BX' may be increased to make the detection area BX' and the detection area BX a continuous detection area.
According to modification example 2, even in situations where it is difficult to detect the road surface itself due to the distance, it is possible to detect irregularities on the road surface, three-dimensional objects, etc. where the level of reflected light is higher than that of the reflected light from the road surface. become.

The above description is merely an example, and the present invention is not limited to the embodiments and modifications described above unless the characteristics of the present invention are impaired. It is also possible to arbitrarily combine the above embodiment and one or more of the modifications, and it is also possible to combine the modifications.

1 communication unit, 2 positioning unit, 3 internal sensor group, 4 camera, 5 lidar, 10 controller, 11 calculation unit, 12 storage unit, 111 recognition unit, 112 demarcation unit, 113 setting unit, 50 external world recognition device, 100 vehicle control device, 101 host vehicle, 114 mapping section, 115 travel control section, AC actuator

Claims (11)

An on-vehicle detector that irradiates electromagnetic waves around the vehicle to detect the surrounding external environment in time series, and acquires road surface information of the road on which the vehicle is traveling based on the information detected by the on-vehicle detector. An external world recognition device comprising: a road surface information acquisition unit;
The road surface information acquisition unit includes:
a recognition unit that recognizes information indicating a predetermined detection target in a first area on the road that is a predetermined distance away from the host vehicle at different positions on the road;
a mapping unit that maps information indicating the detection target recognized by the recognition unit based on the traveling direction and traveling speed of the own vehicle;
An external world recognition device comprising: The external world recognition device according to claim 1,
The recognition unit further includes a second area on the road that is closer to the host vehicle than the first area and a second area on the road that is farther from the host vehicle than the first area at different positions on the road. An external world recognition device characterized by recognizing information indicating predetermined detection targets in three areas. The external world recognition device according to claim 2,
The recognition unit recognizes information indicating the detection target at a first resolution for the first region, and recognizes information indicating the detection target for the second and third regions at a second resolution lower than the first resolution. An external world recognition device characterized by recognizing information indicating. The external world recognition device according to claim 1,
The road surface information acquisition unit further calculates the predetermined distance based on the vehicle speed of the host vehicle, and calculates the predetermined distance based on the vehicle speed while intersecting the road on the road that is the predetermined distance away from the host vehicle in the traveling direction. An external world recognition device comprising: a defining section that defines a strip-shaped region having a depth and being long in the road width direction as the first region. The external world recognition device according to claim 4,
The road surface information acquisition unit further includes a setting unit that sets the irradiation direction of the electromagnetic waves to the on-vehicle detector so that at least electromagnetic waves scattered on the road surface in the first area are detected. recognition device. The external world recognition device according to claim 5,
The recognition unit further recognizes road surface data in the traveling direction of the own vehicle based on information detected by the on-vehicle detector in a preliminary measurement before the main measurement,
The setting unit may set an irradiation range in the vertical direction and the road width direction for irradiating the first area on the road with electromagnetic waves, based on the predetermined distance and the road surface data. External world recognition device. The external world recognition device according to claim 6,
The external world recognition device is characterized in that the setting unit associates the irradiation position of the on-vehicle detector with the position of each grid point obtained by dividing the first region in the road width direction based on the first resolution. . The external world recognition device according to claim 7,
The external world recognition device is characterized in that the setting unit associates the irradiation position of the vehicle-mounted detector with the position of each grid point obtained by further dividing the first region in the vertical direction based on the first resolution. The external world recognition device according to claim 8,
The setting unit is characterized in that the setting unit associates the irradiation position of the on-vehicle detector with the position of each grid point obtained by dividing the second and third regions in the road width direction based on the second resolution. External world recognition device. The external world recognition device according to claim 9,
The setting unit associates the irradiation position of the vehicle-mounted detector with the position of each grid point obtained by further dividing the second and third regions in the vertical direction based on the second resolution. recognition device. The external world recognition device according to any one of claims 1 to 10,
The external world recognition device is characterized in that the vehicle-mounted detector is a lidar.

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