CN112286178B - Identification system, vehicle control system, identification method, and storage medium - Google Patents

Abstract

The present invention provides an identification system, a vehicle control system, an identification method and a storage medium that can determine the road surface more accurately. An identification system mounted on a vehicle, wherein the identification system includes: a detection unit that detects the position of an object present around the vehicle; and a determination unit that determines based on the detection result of the detection unit. The road surface surrounding the vehicle is determined, and the determination unit determines whether each individual area obtained by subdividing the detection result of the detection unit on a two-dimensional plane is a plane using a prescribed algorithm. The determination results of each of the individual areas are aggregated to determine the road surface surrounding the vehicle.

Description

Identification system, vehicle control system, identification method and storage medium

Technical field

The invention relates to an identification system, a vehicle control system, an identification method and a storage medium.

Background technique

Conventionally, there have been disclosed inventions for an autonomous vehicle that includes a road shape recognition unit that recognizes the road shape, a travel route creation unit that uses the recognized road shape to create a travel route, and a device that realizes autonomous travel according to the travel route. In the vehicle travel control device, the road shape recognition unit includes a coordinate information acquisition unit that acquires a plurality of coordinate information that associates plane coordinates with height information, and extracts a plurality of coordinate information whose height differences are equal to or greater than a predetermined value. A coordinate extraction unit for a coordinate of interest and a shape determination unit for specifying a road shape by statistically processing a plurality of extracted coordinates of interest (Patent Document 1 (Japanese Patent Application Publication No. 2010-250743)).

Contents of the invention

[The problem to be solved by the invention]

In the conventional technology, portions where the height difference is equal to or greater than a predetermined value are identified as road shoulders and ditches, for example, and portions that are not such are identified as road surfaces. However, in this method, a part of the road surface may not be recognized as a road surface due to the curvature of the road surface itself, or a small-sized obstacle may be ignored by increasing the predetermined value.

One object of the present invention is to provide an identification system, a vehicle control system, an identification method and a storage medium that can more accurately determine the road surface.

[Means used to solve problems]

The identification system, vehicle control system, identification method and storage medium of the present invention adopt the following structures.

(1): The recognition system according to one aspect of the present invention is mounted on a vehicle recognition system, wherein the recognition system includes: a detection unit that detects the position of an object present in the periphery of the vehicle; and a determination unit, It determines the road surface around the vehicle based on the detection result of the detection part, and the determination part uses for each individual area obtained by subdividing the detection result of the detection part on a two-dimensional plane. A predetermined algorithm is used to determine whether it is a plane, and the determination results for each of the individual areas are combined to determine the road surface around the vehicle.

(2): In the solution of (1) above, the detection unit is a laser radar.

(3): In the aspect of (2) above, the detection unit irradiates the laser to the periphery of the vehicle while changing the elevation angle, depression angle, and azimuth angle, and the determination unit is configured to change the elevation angle, depression angle, azimuth angle, and Each individual area obtained by subdividing the point cloud data of the two-dimensional plane by projecting the position of the object represented by the distance at least, uses the prescribed algorithm to determine whether it is a plane.

(4): In any one of the above (1) to (3), the determination unit makes the size of the individual area different based on the distance from the vehicle in the two-dimensional plane.

(5): In any one of the above (1) to (4), the determination unit obtains information indicating the distribution of objects around the vehicle, and based on the obtained information indicating the distribution of the objects, determines Change the size of said individual area.

(6): In any one of the above (1) to (5), the determining unit obtains information related to the type of road on which the vehicle exists, and the obtained information related to the type of road is When the road type indicates a specific type of road, the size of the individual area is increased compared to a case where the acquired information on the type of road does not indicate a road of the specific type.

(7): A vehicle control system including the recognition system according to any one of the above (1) to (6); and a driving control device that detects the detection result from the detection unit in the recognition system. The driving control of the vehicle is performed based on the information excluding the portion corresponding to the road surface determined by the determining unit.

(8): In the recognition method according to another aspect of the present invention, the computer mounted on the vehicle executes a process of acquiring the detection result of the detection unit that detects the position of an object present around the vehicle, and determining based on the detection result. When making the determination, a prescribed algorithm is used to determine whether the road surface around the vehicle is a plane for each individual area obtained by subdividing the detection result on a two-dimensional plane. The determination results of each of the individual areas are aggregated to determine the road surface surrounding the vehicle.

(9): The storage medium according to another aspect of the present invention stores a program that causes a computer mounted on the vehicle to execute a process of acquiring a detection result of a detection unit that detects the position of an object present around the vehicle, based on the above The detection results are used to determine the road surface surrounding the vehicle. When determining, a prescribed algorithm is used to determine whether each individual area obtained by subdividing the detection results on a two-dimensional plane is a plane. , the determination results for each of the individual areas are aggregated to determine the road surface surrounding the vehicle.

[Effects of the invention]

According to the solutions (1) to (9) above, the road surface can be determined more accurately.

Description of the drawings

FIG. 1 is a diagram showing a vehicle equipped with an identification system and a vehicle control system.

Fig. 2 is a structural diagram of the object recognition device.

FIG. 3 is a diagram showing an example of point cloud data.

FIG. 4 is a diagram showing a set grid.

FIG. 5 is a diagram showing point cloud data obtained by removing the coordinates of a portion determined to be a road surface using a method of a comparative example.

FIG. 6 is a diagram showing point cloud data obtained by removing the coordinates of a portion determined to be a road surface using the method of the embodiment.

FIG. 7 is a diagram showing point cloud data obtained by removing the coordinates of a portion determined to be a road surface using the method of the embodiment.

FIG. 8 is a flowchart showing an example of the flow of processing executed by the recognition system.

[Explanation of reference symbols]

10 lidar

50 Object recognition device

60 LiDAR Data Processing Department

61 Point Cloud Data Generation Department

62 Information Acquisition Department

63 Road surface determination part

63A Grid Setting Department

63B Plane Extraction Processing Department

64 Non-road surface object extraction part

65 Road dividing line recognition department

100 Ride Controls.

Detailed ways

Hereinafter, embodiments of the identification system, vehicle control system, identification method, and storage medium of the present invention will be described with reference to the accompanying drawings.

FIG. 1 is a diagram showing a vehicle M equipped with an identification system and a vehicle control system. The vehicle M is equipped with, for example, a LIDAR (Light Detection and Ranging: LIDAR) 10 (an example of a “detection unit”), a camera 20 , a radar device 30 , an object recognition device 50 , and a travel control device 100 . The combination of the lidar 10 and the object recognition device 50 is an example of a "recognition system", and the combination of the travel control device 100 is an example of a "vehicle control system". As the detection unit, a detection device other than laser radar may be used.

The lidar 10 irradiates light, detects reflected light, and detects the distance to an object by measuring the time from irradiation to detection. The lidar 10 can change the irradiation direction of light with respect to both an elevation angle or a depression angle (hereinafter, the irradiation direction φ in the vertical direction) and the azimuth angle (the irradiation direction θ in the horizontal direction). For example, the lidar 10 repeatedly fixes the irradiation direction φ and scans while changing the irradiation direction θ, then changes the irradiation direction φ in the vertical direction, fixes the irradiation direction φ at the changed angle, and scans while changing the irradiation direction θ. Actions. Hereinafter, the irradiation direction φ is called a “layer”, one scan performed while fixing the layer while changing the irradiation direction θ is called a “cycle”, and scanning all the layers is called a “scan”. For example, a finite number of layers are set from L1 to Ln (n is a natural number). The layer is changed discontinuously with respect to the angle such as L0→L4→L2→L5→L1... so that the light irradiated in the previous cycle does not interfere with the detection in the current cycle. It should be noted that the present invention is not limited to this, and the layer may be changed continuously with respect to the angle.

The laser radar 10 outputs a data group (lidar data) in a unit of {φ, θ, d, p} to the object recognition device 50 , for example. d is the distance and p is the intensity of the reflected light. The object recognition device 50 is installed anywhere in the vehicle M. In FIG. 1 , the lidar 10 is installed on the ceiling of the vehicle M and is capable of changing the irradiation direction θ through 360 degrees. However, this arrangement is just an example. For example, the lidar 10 may be installed on the front of the vehicle M and capable of changing the irradiation direction θ through 360 degrees. The vehicle M is equipped with a lidar that can change the illumination direction θ by 180 degrees with the front of the vehicle M as the center, and a lidar that is installed at the rear of the vehicle M and can change the illumination direction θ with 180 degrees with the rear of the vehicle M as the center.

The camera 20 is installed at an arbitrary position capable of photographing the periphery of the vehicle M (especially the front or rear). For example, the camera 20 is installed on the upper part of the front windshield. The camera 20 is a digital camera equipped with an imaging element such as a CCD (Charge Coupled Device) or a CMOS (Complementary Metal Oxide Semiconductor), and repeatedly captures images of the surroundings of the vehicle M at a predetermined cycle.

The radar device 30 radiates radio waves such as millimeter waves to the periphery of the vehicle M and detects radio waves (reflected waves) reflected by an object to detect at least the position (distance and direction) of the object. The radar device 30 is installed at any part of the vehicle M. For example, the radar device 30 is installed inside the front grille of the vehicle M.

FIG. 2 is a structural diagram of the object recognition device 50. The object recognition device 50 includes, for example, a lidar data processing unit 60, a camera image processing unit 70, a radar data processing unit 80, and a sensor fusion unit 90. The lidar data processing unit 60 includes, for example, a point cloud data generation unit 61, an information acquisition unit 62, a road surface identification unit 63 (an example of a “identification unit”), a non-road surface object extraction unit 64, and a road dividing line recognition unit 65. The road surface specifying unit 63 includes, for example, a grid setting unit 63A and a plane extraction processing unit 63B. These components are realized by, for example, a hardware processor such as a CPU (Central Processing Unit) executing a program (software). Some or all of these components can be realized by hardware (including circuitry) such as LSI (Large Scale Integration), ASIC (Application Specific Integrated Circuit), FPGA (Field-Programmable Gate Array), and GPU (Graphics Processing Unit). , can also be realized through the cooperation of software and hardware. The program can be stored in advance in a storage device (a storage device with a non-transitory storage medium) such as an HDD (Hard Disk Drive) or a flash memory, or in a removable storage medium (a non-transitory storage medium) such as a DVD or CD-ROM. ), which is installed by mounting the storage medium on the drive device.

The point cloud data generating unit 61 generates point cloud data based on lidar data. The point cloud data in this embodiment is data obtained by projecting the position in the three-dimensional space of the object recognized from the lidar data onto the position on the two-dimensional plane viewed from above. FIG. 3 is a diagram showing an example of point cloud data. The two-dimensional plane defining the point cloud data (the two-dimensional plane represented by the X-axis and the Y-axis in the figure) is, for example, an opposing two-dimensional plane observed from the lidar 10 . Although not shown in the figure, height information (displacement in the direction orthogonal to the X-axis and Y-axis) is given to each coordinate of the point cloud data. The height information is calculated by the point cloud data generating unit 61 based on continuity and dispersion between coordinates, differences in irradiation angles between layers, and the like.

The information acquisition unit 62 acquires various information used when the grid setting unit 63A sets the grid.

For example, the information acquisition unit 62 acquires information indicating the distribution of objects around the vehicle M. The distribution of objects around the vehicle M means, for example, obtaining some or all of the number of vehicles, the number of pedestrians, the number of bicycles, the number of signals, crosswalks, intersections, etc. within the recognition range of the recognition system. The value obtained by indexing (crowding index). Regarding the crowding index, for example, the higher the density of the above-mentioned elements, the higher the value. The information acquisition unit 62 may calculate the congestion index by itself, or may obtain it from the camera image processing unit 70, the radar data processing unit 80, or the like.

In addition, the information acquisition unit 62 may acquire information on the type of road on which the vehicle M is present. The information acquisition unit 62 may acquire the information related to the type of road from a navigation device (not shown) mounted on the vehicle M, or may derive the information based on the result of the camera image processing unit 70 recognizing the road sign from the camera image.

The grid setting unit 63A of the road surface specifying unit 63 virtually sets a plurality of grids that are individual areas obtained by dividing the two-dimensional plane of the predetermined point cloud data. FIG. 4 is a diagram showing the set grid G. The grid setting unit 63A sets the grid G in a rectangular shape (it may be a square or a rectangle), for example. The grid setting unit 63A may set the grids G of the same size, or may set the sizes of the grids G to be different based on the distance from the vehicle M in the two-dimensional plane. For example, as shown in the figure, the grid setting unit 63A may increase the size of the grid G as the distance from the vehicle M increases. The grid setting unit 63A may not set the grid G for unnecessary recognition areas (for example, off-road areas such as opposite sides of guardrails and buildings) obtained by referring to past recognition results of the recognition system. The grid setting unit 63A may set the grid G in any polygon (which may include two or more polygons) such as a triangle or a hexagon, or may set the grid G in an amorphous shape.

In addition, the grid setting unit 63A may determine the size of the grid G based on the congestion index. For example, the grid setting unit 63A may reduce the size of the grid G as the congestion index becomes higher.

In addition, the grid setting unit 63A may determine the size of the grid G based on the type of road. For example, when the type of the road is a specific type with few traffic participants other than vehicles, such as an expressway or a motor vehicle road, the grid setting unit 63A may enlarge the grid compared to the case where it is not a specific type. G size.

For each grid G, the plane extraction processing unit 63B performs plane extraction processing based on a robust regression estimation method such as RANSAC (Random Sample Consensus) on the point cloud data included in the grid G, and determines whether the grid G is a road surface. (no object exists on it), establish a corresponding relationship between the judgment result and each grid G. It should be noted that the plane extraction processing unit 63B may perform other types of plane extraction processing instead of RANSAC.

RANSAC is performed in the following sequence, for example. First, randomly select more than the number of samples (not all) required for model determination from the data set, derive a temporary model from the selected samples using the least squares method, etc., and fill the temporary model into the data. If the deviation value does not exist That many are added to the model candidates. This process is repeated several times, and the model candidate that best matches the entire data set is set as the correct solution model. In the present embodiment, the plane extraction processing unit 63B determines that the grid G whose correct solution model is a plane is a road surface.

The non-road surface object extraction unit 64 analyzes the point cloud data for the grid G other than the grid G determined to be a road surface by the plane extraction processing unit 63B, extracts the outline of the object existing on the grid G, and identifies the object based on the outline. The position of the object corresponding to this outline. Alternatively, the non-road surface object extraction unit 64 may extract the outline of the object based on the data corresponding to the grid G other than the grid G determined to be the road surface by the plane extraction processing unit 63B in the lidar data, and identify the object based on the outline. The position of the object corresponding to this outline.

The road dividing line identification unit 65 focuses on the intensity p of the reflected light in the lidar data, and identifies a portion with a high rate of change in the intensity p due to the color difference between the road surface and road dividing lines such as white lines and yellow lines. The outline of a road dividing line. Thereby, the road dividing line recognition unit 65 recognizes the position of the road dividing line such as a white line.

The processing results of the non-road surface object extraction unit 64 and the road dividing line recognition unit 65 are output to the sensor fusion unit 90 . The processing results of the camera image processing unit 70 and the radar data processing unit 80 are also input to the sensor fusion unit 90 .

The camera image processing unit 70 performs various image processing on the camera image acquired from the camera 20 and recognizes the position, size, type, etc. of objects existing around the vehicle M. The image processing performed by the camera image processing unit 70 may include a process of inputting a camera image of a learned model obtained by mechanical learning, and a process of identifying an object from a contour line obtained by extracting edge points and connecting the edge points.

The radar data processing unit 80 performs various object extraction processes on the radar data acquired from the radar device 30 to identify the position, size, type, etc. of objects existing around the vehicle M. The radar data processing unit 80 estimates the type of the object by estimating the material of the object based on, for example, the intensity of the reflected wave from the object.

The sensor fusion unit 90 integrates the processing results input from the lidar data processing unit 60 , the camera image processing unit 70 and the radar data processing unit 80 , determines the positions of objects and road dividing lines, and outputs the results to the travel control device 100 . The processing of the sensor fusion unit 90 may include, for example, processing of obtaining a logical sum, a logical product, a weighted sum, etc. for each processing result.

By performing the processing as described above, the recognition system can determine the road surface more accurately. FIG. 5 is a diagram showing point cloud data obtained by removing the coordinates of a portion determined to be a road surface using a method of a comparative example. The method of the comparative example is a method of applying RANSAC to the entire lidar data (without distinguishing the grid G) and removing the coordinates of the area determined to be a road surface. As shown in the figure, even in the method of the comparative example, many coordinates remain in the areas A1 and A2 corresponding to the road surface. In particular, the area A1 is uphill when viewed from the vehicle M, and becomes difficult to recognize as a road surface when RANSAC is applied to the entire data. In addition, the general road structure is such that the central portion of the road is high and becomes lower toward the end portions. Therefore, in the method of the comparative example, there is a possibility that the road surface is not determined to be a road surface due to the height difference between the central portion and the end portions of the road. sex. Furthermore, since there are fine recesses and the like on the road, there is a possibility that the part may be recognized as not being a road surface.

In contrast, FIGS. 6 and 7 are diagrams showing point cloud data obtained by removing the coordinates of a portion determined to be a road surface using the method of the embodiment. FIG. 6 shows the results when one side of the square grid G is set to X1, and FIG. 7 shows the results when one side of the square grid G is set to X2 (X1>X2). As shown in these figures, according to the method of the embodiment, most of the coordinates in the areas A1 and A2 corresponding to the road surface are removed, thereby reducing the possibility of recognizing an object that does not actually exist as an obstacle. It should be noted that the method of reducing one side of the grid G (that is, the method of reducing the size of the grid G) can improve the accuracy of determining the road surface, but the method of making the size of the grid G small increases the processing load. become higher, so they have a trade-off relationship. In view of this, by increasing the size of the grid G as the distance from the vehicle M increases, even if there is misrecognition, processing can be performed with a low load on distant places with little impact, and a good balance between recognition accuracy and processing load can be achieved. deal with. In addition, by reducing the size of the grid G as the congestion index becomes higher, the recognition accuracy is prioritized in places such as urban areas where there are many traffic participants, and the processing load is increased in places where there are not such places. By reducing priority processing, it is possible to perform processing with a good balance between recognition accuracy and processing load. In addition, when the road type is a specific type, compared with the case where the road type is not a specific type, by increasing the size of the grid G, it is possible to perform processing that prioritizes the reduction of the processing load in a place with few traffic participants. , processing that prioritizes recognition accuracy is performed in a place that is not such a place, so it is possible to perform processing with a good balance between recognition accuracy and processing load.

The driving control device 100 is, for example, an automatic driving control device that controls both acceleration, deceleration and steering of the vehicle M. Based on the positions of objects, white lines, etc. output from the object recognition device 50 , the driving control device 100 automatically causes the vehicle M to drive in the set lane without coming into contact with the objects, or performs lane changes, overtaking, etc. as necessary. Automatic control of branching, merging, stopping, etc. Alternatively, the driving control device 100 may be a driving support device that automatically stops when an object approaches, or the like. In this way, the driving control device 100 determines the position of the object recognized by the non-road surface object extraction unit 64 for the grid G other than the grid G determined to be a road surface by the plane extraction processing unit 63B (the location corresponding to the specified road surface). Partially excluded information) is output via the sensor fusion unit 90 to control the driving of the vehicle M.

FIG. 8 is a flowchart showing an example of the flow of processing executed by the recognition system. The lidar 10 detects an object and repeatedly outputs the lidar data to the lidar data processing unit 60 (step S100).

The lidar data processing unit 60 waits until one scan amount of lidar data is acquired (step S102). When one scan amount of laser radar data is acquired, the process proceeds to step S106.

On the other hand, the information acquisition unit 62 operates asynchronously with components other than the information acquisition unit 62 in the laser radar data processing unit 60 , acquires information for setting the grid G, and provides the information to the road surface determination unit. 63 is provided (step S104).

The point cloud data generating unit 61 generates point cloud data from lidar data (step S106). The grid setting unit 63A sets the grid G based on the information supplied from the information acquisition unit 62 (step S108).

The plane extraction processing unit 63B determines whether each grid G is a road surface (step S110). The non-road surface object extraction unit 64 performs object recognition on the grid G of the non-road surface that has not been identified as a road surface (step S112). Then, the lidar data processing unit 60 outputs the processing results of the non-road surface object extraction unit 64 and the road dividing line recognition unit 65 to the sensor fusion unit 90 (step S114). This completes the routine of the flowchart of FIG. 8 .

The recognition system according to the above-described embodiment includes: a detection unit (lidar 10) that detects the position of an object present around the vehicle (M); and a determination unit (road surface determination unit 63) that is based on the detection unit The determination unit uses a predetermined algorithm (RANSAC) for each individual area (grid G) obtained by subdividing the detection results of the detection unit on a two-dimensional plane to determine the road surface surrounding the vehicle. To determine whether or not it is a plane, the determination results for each individual area are aggregated to determine the road surface around the vehicle, so the road surface can be determined more accurately.

It should be noted that the recognition system may not include some or all of the camera 20 , the radar device 30 , the camera image processing unit 70 , the radar data processing unit 80 , and the sensor fusion unit 90 . For example, the recognition system may include the lidar 10 and the lidar data processing unit 60 , and output the processing results of the non-road surface object extraction unit 64 and the road dividing line identification unit 65 to the travel control device 100 .

Specific embodiments of the present invention have been described above using the embodiments. However, the present invention is not limited to such embodiments at all, and various modifications and substitutions can be made without departing from the gist of the present invention.

Claims (6)

1. An identification system mounted on a vehicle, wherein,

the identification system is provided with:

a detection unit that detects a position of an object existing in the periphery of the vehicle; and

a determination unit that determines a road surface around the vehicle based on a detection result of the detection unit,

the determination unit determines whether or not the vehicle is a plane using a predetermined algorithm for each individual region obtained by dividing the detection result of the detection unit in a two-dimensional plane, and determines a road surface around the vehicle by aggregating the determination results for each individual region,

the prescribed algorithm is the following algorithm: randomly selecting a predetermined number of samples from a data set as a detection result of the detection unit for each individual area, deriving model candidates based on the selected samples, setting a model candidate most matching the whole data set among the model candidates having a deviation value equal to or smaller than a predetermined number when the model candidates are filled with data as a forward solution model, determining that the individual area is a road surface when the forward solution model becomes a plane,

the detection unit is a laser radar that irradiates laser light to the periphery of the vehicle while changing an elevation angle, a depression angle, and an azimuth angle,

the determination unit determines whether or not the object is a plane by using the predetermined algorithm for each individual area obtained by dividing point cloud data obtained by projecting a position of the object represented by at least an elevation angle, a depression angle, an azimuth angle, and a distance onto a two-dimensional plane,

the determination section makes the sizes of the individual areas different based on the distances from the vehicle in the two-dimensional plane,

the determination unit does not set the individual area for the unnecessary recognition area obtained by referring to the past recognition result of the recognition system.

2. The identification system of claim 1, wherein,

the determination unit obtains information indicating a distribution of objects around the vehicle, and changes the size of the individual region based on the obtained information indicating the distribution of the objects.

3. The identification system according to claim 1 or 2, wherein,

the determination unit obtains information on a road type of the vehicle, and increases the size of the individual area when the obtained information on the road type indicates a road of a specific type, compared with when the obtained information on the road type does not indicate a road of a specific type.

4. A vehicle control system, wherein,

the vehicle control system includes:

the identification system of any one of claims 1 to 3; and

and a travel control device that performs travel control of the vehicle based on information excluding a portion corresponding to the road surface specified by the specifying unit from a detection result of the detecting unit in the identifying system.

5. An identification method, wherein,

the computer mounted on the vehicle performs the following processing:

a detection result of a detection unit for detecting the position of an object existing in the periphery of the vehicle is obtained,

determining a road surface of a periphery of the vehicle based on the detection result,

in the course of the determination as to what has been described,

a predetermined algorithm is used for each individual region obtained by dividing the detection result on a two-dimensional plane to determine whether or not the individual region is a plane,

the determination results for each of the individual regions are aggregated to determine the road surface of the periphery of the vehicle,

the prescribed algorithm is the following algorithm: randomly selecting a predetermined number of samples from a data set as a detection result of the detection unit for each individual area, deriving model candidates based on the selected samples, setting a model candidate most matching the whole data set among the model candidates having a deviation value equal to or smaller than a predetermined number when the model candidates are filled with data as a forward solution model, determining that the individual area is a road surface when the forward solution model becomes a plane,

the detection unit is a laser radar that irradiates laser light to the periphery of the vehicle while changing an elevation angle, a depression angle, and an azimuth angle,

in the course of the determination as to what has been described,

for each individual area obtained by dividing point cloud data obtained by projecting a position of an object represented by at least an elevation angle or depression angle, an azimuth angle, and a distance onto a two-dimensional plane, determining whether the object is a plane or not using the predetermined algorithm,

the sizes of the individual areas are made different based on the distance from the vehicle in the two-dimensional plane,

the individual area is not set for the unnecessary recognition area obtained by referring to the past recognition result of the computer.

6. A storage medium storing a program, wherein,

the program causes a computer mounted on a vehicle to execute:

a detection result of a detection unit for detecting the position of an object existing in the periphery of the vehicle is obtained,

determining a road surface of a periphery of the vehicle based on the detection result,

in the course of the said determination, the data of the said data,

a predetermined algorithm is used for each individual region obtained by dividing the detection result on a two-dimensional plane to determine whether or not the individual region is a plane,

the determination results for each of the individual regions are aggregated to determine the road surface of the periphery of the vehicle,

the prescribed algorithm is the following algorithm: randomly selecting a predetermined number of samples from a data set as a detection result of the detection unit for each individual area, deriving model candidates based on the selected samples, setting a model candidate most matching the whole data set among the model candidates having a deviation value equal to or smaller than a predetermined number when the model candidates are filled with data as a forward solution model, determining that the individual area is a road surface when the forward solution model becomes a plane,

the detection unit is a laser radar that irradiates laser light to the periphery of the vehicle while changing an elevation angle, a depression angle, and an azimuth angle,

in the course of the said determination, the data of the said data,

for each individual area obtained by dividing point cloud data obtained by projecting a position of an object represented by at least an elevation angle or depression angle, an azimuth angle, and a distance onto a two-dimensional plane, determining whether the object is a plane or not using the predetermined algorithm,

the sizes of the individual areas are made different based on the distance from the vehicle in the two-dimensional plane,

the individual area is not set for the unnecessary recognition area obtained by referring to the past recognition result of the computer.