JP5055691B2 - Front object detection apparatus and front object detection method - Google Patents

Description

  The present invention relates to a forward object detection apparatus and a forward object detection method for detecting an object in front of a vehicle, and more particularly to an apparatus and method for detecting a road structure and further detecting an object other than a road structure.

As an apparatus for measuring a road shape in front of a vehicle, for example, a device described in Japanese Patent Application Laid-Open No. 07-027541 (see Patent Document 1) is known. According to this measurement apparatus, a road shape ahead is extracted from an image captured by an imaging unit mounted on a vehicle, a three-dimensional road model is calculated from map information including the three-dimensional shape of the road and vehicle position parameters, Accurately measure the road shape by comparing the road shape obtained from the image with the calculated road model and calculating the amount of displacement between the two, and estimating the position of the vehicle considering the calculated amount of displacement can do.
However, the conventional road shape measurement process uses an image picked up by the image pickup means, so that it is possible to accurately extract a road shape far away from the image pickup means with a decrease in image accuracy according to the image pickup distance. There was a problem that it was difficult. That is, there is a problem that it is difficult to accurately calculate the amount of deviation between the road shape extracted from the captured image and the road shape calculated from the map information.
JP-A-7-27541

The present invention has been made to solve such a conventional problem, and an object thereof is to accurately detect a forward object including a road structure indicating a road shape.
According to the present invention, the detection information including the detection point of the object detected based on the reflected wave of the scanning wave emitted in front of the host vehicle is acquired, the map database is accessed, and the current position of the host vehicle is acquired. Obtain road shape information ahead, calculate a road shape model of the traveling road ahead of the host vehicle based on the road shape information, and build a road structure model based on the obtained detection information and the calculated road shape model When the detection point having a predetermined positional relationship with the calculated road structure model is grouped, the object corresponding to the grouped detection point is detected as a road structure, and when the road structure is detected, Based on the calculated road shape model and the road structure detection position at the previous timing, the road structure ahead of the host vehicle at the current timing is detected. Set the detection area, and extracts a detection point serving as a starting point for the road structure model for each detection area set, the starting point that is extracted, a detection point in the origin and a predetermined positional relationship, and the A road structure model that detects one or more line segments by sequentially connecting detection points whose distance to the road shape model is less than a predetermined value, and approximates the shape of the road structure based on the detected line segments A forward object detection apparatus and a forward object detection method for calculating, grouping detection points having a predetermined positional relationship with the calculated road structure model, and determining an object corresponding to the grouped detection point as a road structure Is provided .
Thereby, a forward object including a road structure or the like can be accurately detected.

  The forward object detection device 100 of the present invention is mounted on a vehicle on which a user is boarded, and detects a road structure existing in front of the host vehicle, or another vehicle, an installation, or other movable object (an object other than a road structure). . In particular, the road structure is accurately detected by performing a predetermined grouping process on the detection point indicating the location of the forward object.

  Hereinafter, two embodiments of the forward object detection device 100 according to the present invention will be described with reference to the drawings. The first embodiment has a first detection means (road structure detection means) 30 for detecting at least a road structure, and the second embodiment detects a road structure and makes a determination by the first detection means 30. The second detection means (movable object detection means) 40 for detecting an object other than the road structure, for example, other vehicles, facilities, equipment, and other movable objects.

<First Embodiment>
FIG. 1 shows an outline of the configuration of an in-vehicle system including the front object detection device 100 of the first embodiment. As shown in FIG. 1, the front object detection device 100 of the present embodiment is communicably connected to another in-vehicle device 1000 via a wired or wireless in-vehicle LAN, and can exchange various information.

  The forward object detection device 100 includes a road shape model calculation unit 10, a detection information acquisition unit 20, and a first detection unit 30. The road shape model calculation means 10 is connected to an external navigation device 400 so as to be able to exchange information, acquires the current position from the current position detection function 410, and accesses the map database 420 to access the road ahead of the host vehicle. Shape information is acquired, and a road shape model of a traveling road ahead of the host vehicle is calculated based on the road shape information. The detection information acquisition means 20 is connected to the external laser radar 200 and the vehicle behavior detection device 300 so as to be able to exchange information, and is detected based on the reflected wave of the scanning wave emitted in front of the host vehicle. The detection information including the information of the detection points is acquired. First detection means 30 calculates a road structure model based on the acquired detection information and the calculated road shape model, groups detection points having a predetermined positional relationship with the road structure model, and performs grouped detection An object corresponding to the point is detected as a road structure.

Since the forward object detection device 100 of the present embodiment calculates a road structure model from the detection information and the road shape model obtained from the road shape information in the map database, the road structure with a small amount of deviation from the actual road structure. Since the model can be derived and the forward object is detected based on the calculated road structure model , the forward object can be accurately detected. In the present embodiment, since the object is detected without using a captured image whose accuracy decreases as the distance increases, the object can be accurately detected in a wide range of the host vehicle (a range away from the host vehicle). Further, in the present embodiment, the road structure is detected without referring to the relative speed of the detection points, so that a state in which the detection points are densely detected is detected, and it is not easy to temporally associate the detection points with each other. Even if it is not easy to calculate the relative speed of the points, the road structure can be accurately detected.

  Next, the external device constituting the in-vehicle system will be described first, and then the specific configuration of the front object detection device 100 of the present embodiment will be described.

  The in-vehicle device 1000 includes a laser radar 200 having a front object detection function, a vehicle behavior detection device 300 having a function of detecting the behavior of the vehicle during traveling, the current position of the host vehicle, the traveling road, and the shape of the traveling road. And a display device, a speaker, and other output devices 500 having a function of presenting information about the detected forward object to the user.

  FIG. 2 shows the installation position of the laser radar 200. 2A is a view of the vehicle 600 viewed from the side, and FIG. 2B is a view of the vehicle 600 viewed from the top. In the present embodiment, a case where detection information is acquired using a scanning laser radar (hereinafter referred to as “laser radar”) as a distance sensor will be described as an example, but a reflected wave of a scanning wave emitted in front of the host vehicle. If the detection information including the detection point of the object detected based on (the relative position where the detected object exists) can be detected, there is no particular limitation.

  As shown in FIGS. 2A and 2B, the laser radar 200 is installed in the front part of the host vehicle 600. Further, the laser radar 200 changes the optical axis at a predetermined angle on the scanning surface, scans the laser light (scanning light) L within a predetermined scanning range, and irradiates the laser light toward an object existing in the scanning range. . The laser radar 200 detects the reflected laser light (reflected wave) reflected by the emitted laser light applied to an object existing in front, thereby reflecting the reflected signal based on the light intensity of the reflected laser light (reflected wave). To get. Then, the laser radar 200 generates detection information including distance measurement information and the like by performing distance measurement processing based on the acquired reflection signal, and detects the detection information of the front object detection apparatus 100 as detection information acquisition means 20. Output to.

  The vehicle behavior detection device 300 is a GPS (Global Positioning System) reception sensor that acquires the position of the host vehicle 600, a shift position sensor that detects a shift position, and a wheel speed that detects the wheel speeds of the left and right rear wheels of the host vehicle. A sensor and a steering angle sensor for detecting the steering angle of the host vehicle 600 are provided. The vehicle behavior detection device 300 uses the sensor signal from the GPS reception sensor, the sensor signal from the shift position sensor, the sensor signal from the wheel speed sensor, and the sensor signal from the steering angle sensor to detect the vehicle position and the vehicle traveling direction. Then, the direction and movement distance of the host vehicle 600 are calculated. The GPS reception sensor may be provided in the navigation device 400, and the reception sensor or the like may be acquired from the navigation device 400. In addition, the vehicle behavior detection device 300 can access a map database 420 included in the navigation device 400, and the calculated vehicle position (current position), the traveling direction of the host vehicle, the direction of the host vehicle, and the moving distance are determined based on vehicle travel information. Is output toward the front object detection apparatus 100.

  The navigation device 400 has a general navigation function that detects the current position of the host vehicle and guides a route to the destination by using a GPS function (Global Positioning System) and autonomous navigation. The navigation device 400 includes a map database 420 that can be electronically processed. The map database 420 of the present embodiment includes road shape data relating to the shape of each road, and the road shape data is associated with position information. That is, if the information specifying the current position can be detected, the road shape data of the traveling road including the current position can be acquired. Furthermore, if the traveling direction of the host vehicle can be detected, road shape data of the traveling direction of the host vehicle (in front of the host vehicle) on the traveling road including the current position can be acquired.

  The output device 500 is a display device, speaker device, or other presentation device that can present information related to the forward object detected by the forward object detection device 100 to the user.

Next, the configuration of the front object detection device 100 of this embodiment will be specifically described.
FIG. 3 shows a specific block configuration according to the arithmetic unit of the front object detection apparatus 100. As shown in FIG. 3, the arithmetic unit of the forward object detection device 100 includes a road shape model calculation unit 10, a detection information acquisition unit 20, a first detection unit 30 (hereinafter referred to as “road structure detection unit 30”). And a microcomputer comprising a CPU (Central Processing Unit), a RAM (Random Access Memory), a ROM (Read Only Memory), an input / output I / F, etc. . Specifically, at least a program that calculates a road shape model, a program that calculates a road structure model, and a ROM (Read Only Memory) that stores a program that detects a road structure, and the like are stored in the ROM. By executing the program, the road shape model calculating means 10, the CPU (Central Processing Unit) functioning as the first detecting means 30, and the RAM (Random Access Memory) functioning as accessible storage means (memory) 50). In addition, although the present embodiment includes the memory 50 that stores the calculation process or the result of the calculation process of each unit, the memory 50 may be incorporated in each unit or each unit. Each configuration will be described below.

  The “road shape model calculating means 10” acquires the current position of the host vehicle, accesses the map database 420 to acquire road shape information in front of the host vehicle, and based on the acquired road shape information, A road shape model of the traveling road is calculated. Specifically, the road shape model calculation unit 10 includes at least a road shape information acquisition unit 11. The road shape information acquisition unit 11 acquires the current position of the host vehicle, accesses the map database 420 on the in-vehicle device 1000 side, and acquires road shape information ahead of the host vehicle at the current position. The current position of the host vehicle to be acquired is information detected by the current position detection function 410 of the navigation device 400. Since the map database 420 stores the positional information, the road direction (the traveling direction of the vehicle), and the road shape information in association with each other, the road shape information acquisition unit 11 indicates the road shape on the vehicle front side at the current position. Can be acquired. The traveling direction of the vehicle may be acquired from the navigation device 400 or may be acquired from the vehicle behavior detection device 300. The road shape model calculation means 10 calculates a road shape model of the traveling road ahead of the host vehicle based on the road shape information acquired from the map database 420. The road shape model calculation means 10 of this example calculates a road shape model expressed in two dimensions.

  The road shape model calculation unit 10 of the present embodiment is not particularly limited, and includes an acquisition range determination unit 12 and a data conversion unit 13. The acquisition range determination unit 12 determines an area wider than the detection area of the detection information acquired by the detection information acquisition unit 20 as a range for acquiring road shape information. Information on the detection area corresponding to the detection information is preferably acquired from the laser radar 200 in advance and stored in the memory 50. The predetermined area to be acquired may be defined with the detection area of the laser radar 200 as a reference. preferable. The road shape information acquisition unit 11 acquires road shape information of a predetermined area according to the determination of the acquisition range determination unit 12. The data conversion unit 13 converts the acquired road shape information in a predetermined area wider than the detection area of the laser radar 200 so as to fall within the detection area of the laser radar 200. In this way, by acquiring road shape information in a wider area than the area where the detection information is acquired, and performing data conversion so as to enter the detection area of the own vehicle sensor (laser radar 200, vehicle behavior detection device 300). The deviation between the road shape model obtained from the map database 420 and the road structure model calculated based on the detection information actually detected can be reduced. By calculating the road structure model and grouping the detection points based on the road shape model with a small deviation from the actual road structure model, the road structure can be detected accurately and efficiently.

  The “detection information acquisition means 20” acquires the detection information of the laser radar 200 and / or the detection information of the vehicle behavior detection device 300. The acquired detection information includes at least information on the detection point of the object detected based on the reflected wave of the scanning wave emitted in front of the host vehicle.

  The “road structure detection means (first detection means) 30” is based on the detection information acquired by the detection information acquisition means 20 and the road shape model calculated by the road shape model calculation means 10. , And the detection points having a predetermined positional relationship with the calculated road structure model are grouped, and an object corresponding to the grouped detection points is detected as a road structure. In this embodiment, the “road structure model” is a road (including road parts such as roadways, sidewalks, and bicycle roads; the same applies hereinafter) and the road is divided into road parts such as roadways, sidewalks, and bicycle roads. It is a model showing the location of the structure of curb lines, fences, center belts, shoulders, side belts, stop belts and other similar structures. It is preferable that the road structure model is expressed in a visual recognition area plane of a driver who gets on the vehicle.

  Since it is certain that the road shape information acquired from the map database 420 based on the current position detected by the GPS function and the vehicle travel information (including the traveling direction) indicates the road shape ahead of the host vehicle. By calculating the road structure model based on the road shape model calculated based on the road shape information, it is possible to accurately calculate the road shape in the present embodiment. In particular, the road shape model calculation means 10 acquires road shape information in an area wider than the detection area of the laser radar 200, and performs data conversion so that the vehicle enters the detection area of the detection information. Even if it exists, a road structure can be detected correctly.

  Based on FIG. 3, the specific structure of the road structure detection means 30 of this embodiment is demonstrated. Although not particularly limited, the road structure detection means 30 of the present embodiment includes a detection area setting unit 31, a starting point extraction unit 32, a line segment detection unit 33, a model calculation unit 34, a determination unit 35, and a detection result. And an output unit 36.

  The “detection area setting unit 31” is based on the road shape model calculated by the road shape model calculation unit 10 and is based on the detection position of the road structure at the previous timing. Set. The detection area setting criteria can be arbitrarily defined. Although not particularly limited, the detection area on the right side and the detection area on the left side based on the road shape model of the road on which the vehicle travels, or the vicinity based on the road shape model (less than a predetermined distance with respect to the road shape model) and It is possible to set a far detection area (a predetermined distance or more with respect to the road shape model). In the present embodiment, four detection areas are set for the right side, the left side, the far right side, and the far left side of the road shape model of the road on which the vehicle travels.

  The “starting point extraction unit 32” extracts a detection point that is a starting point of the road structure model for each detection region set by the detection region setting unit 31. The detection point extraction method will be specifically described later along with the control procedure.

  The “line segment detection unit 33” detects one or more line segments by sequentially connecting the start point extracted by the start point extraction unit 32 and the detection points having a predetermined positional relationship with the start point.

Although not particularly limited, the line segment detection unit 33 of the present embodiment includes a first line segment detection unit 331 and a second line segment detection unit 332. The first line segment detection unit 331 is a detection point having a predetermined positional relationship with the starting point detected by the starting point extraction unit 32, and the distance to the road shape model calculated by the road shape model calculating unit 10 is less than a predetermined value. These detection points are sequentially connected to detect one or more line segments. The second line segment detection unit 332 is a detection point having a predetermined positional relationship with the starting point extracted by the starting point extraction unit 32, and has a predetermined distance to the road shape model calculated by the road shape model calculation unit 1. One or more line segments are detected by sequentially connecting detection points that are equal to or greater than the value. That is, the line segment detection unit 33 of the present embodiment detects two types of line segments: a line segment along the calculated road shape model and a line segment that does not follow the road shape model (independent of the road shape). To do. The detected line segment, but is subjected to road structures model calculated in later processing, it is possible to detect and distinguish between segment does not comply with the line segment along a road shape model traveling, the traveling road Even in a road environment having a complicated structure with a branch ahead, a line segment corresponding to the structure can be detected, and the location of the road structure can be detected accurately.

  The “model calculation unit 34” calculates a road structure model that approximates the shape of the road structure based on the line segment detected by the line segment detection unit 33. A method for calculating the road structure model will be specifically described later together with a control procedure.

  The “determination unit 35” groups detection points having a predetermined positional relationship with the road structure model calculated by the model calculation unit 34, and whether or not the object corresponding to the grouped detection points is a road structure. Determine whether.

The determination unit 35 of the present embodiment groups the road structure model calculated by the model calculation unit 34 and detection points that are currently in a predetermined positional relationship, an object corresponding to the grouped detection point, Based on the positional relationship with the road structure model calculated by the model calculation unit 34 at the timing, it is determined whether or not the object corresponding to the detection point grouped at the current timing is a road structure. That is, the object (grouped detection point) to be determined this time is in a predetermined positional relationship with the previously determined road structure model (below a predetermined distance, on an extension of the detected line segment, etc.). In some cases, it is determined that the object to be determined is a road structure. Furthermore, the determination unit 35 compares the position of the object corresponding to the detection point grouped at the current timing with the position of the road structure model calculated by the model calculation unit 34 at the previous timing, and If it can be determined that the change is due to the movement of the host vehicle, that is, if the amount of change in the relative position approximates the value obtained by multiplying the running speed of the host vehicle and the elapsed time, grouping is performed at the current timing. The object corresponding to the detected point is determined to be a road structure. In other words, it is possible to determine whether or not the object corresponding to the detection point grouped at the latest timing is a road structure by using the determination result of the road structure previously performed. Of course, the simplest determination method, the determination unit 35 groups the detection points in the calculated road structures model and a predetermined positional relationship by the model calculating unit 34, corresponding to the grouped detection point The object may be determined to be a road structure.

Further, the determination unit 35 of the present embodiment updates the “positional relationship” serving as a determination criterion for determining whether or not the object corresponding to the grouped detection point is a road structure according to a predetermined condition. The determination reference update unit 351 is included. The determination criterion changing unit 351 determines whether a detection point other than the detection point corresponding to the road structure is on the right side or the left side with respect to the road shape model, and detects the detection point according to the determination result. Is changed to a predetermined positional relationship that is a criterion for determining whether or not to be a grouping target. Specifically, in the case of grouping the detection point is within the road structure model and a predetermined distance, "(positional relation of criterion) predetermined distance" on the side where the object other than the road structures (movable object) is located Make it smaller. That is, when it is determined that the movable object is on the right side, the determination criterion on the right side is tightened. Thereby, the road structure on the side where a movable object such as another vehicle is present can be accurately determined under more strict conditions.

The control procedure of this embodiment is demonstrated based on FIGS.
FIG. 4 is a flowchart showing a control procedure of the front object detection apparatus 100 of the present embodiment. 5 to 7 are explanatory views showing detection points indicating the detection position of the object detected based on the reflected wave of the scanning wave emitted forward of the host vehicle by the laser radar 200. FIG.
The front object detection device 100 of the present embodiment performs distance measurement processing of an object existing in front of the host vehicle 600 using the laser radar 200 when the host vehicle 600 is traveling. Here, as shown in FIG. 5, the control procedure will be described by taking as an example the case where objects A to E exist within the scan range in the forward direction of the host vehicle 600.

  The laser radar 200 mounted on the host vehicle scans a laser beam (scanning wave) within a scanning range (a range indicated by an arrow in the figure), and performs a distance measurement process based on a reflected wave from each object, Distance information (position information) from the own vehicle 600 to each object included in the scan range is acquired, and a detection point indicating the position of the object with respect to the own vehicle 600 is detected based on the obtained distance information. The detection information acquisition means 20 stores the distance measurement information generated by the laser radar 200 in the memory 50 (S1).

  In this example, as shown in FIG. 5, the laser radar 200 (denoted by LR in the figure) of this example detects detection points a to m indicating the position information of the objects A to E in front of the host vehicle 600. Then, the detection information acquisition means 20 acquires detection information including the detection points a to m. FIG. 5 is a diagram in which the detection points a to m related to the objects A to E are plotted according to the measured distance.

  The vehicle behavior detection device 300 mounted on the host vehicle calculates the host vehicle position, the host vehicle traveling direction, the direction of the host vehicle 600, and the movement distance, and sends them to the detection information acquisition unit 20. The detection information acquisition means 20 stores the acquired detection information in the memory 50 (also S1).

  The road shape information acquisition unit 11 of the road shape model calculation means 10 acquires road shape information from the accessible map database 420 within a range corresponding to the detection area of the laser radar 200. The road shape information acquisition unit 11 of this example acquires road shape information of a predetermined area wider than the detection area of the detection information acquired from the laser radar 200 (S2). Specifically, the acquisition range determination unit 12 of the road shape model calculation unit 10 refers to the map database 420 based on the vehicle position obtained from the current position detection function 410 (GPS function) mounted on the vehicle, and the laser radar 200. For example, the detection area of the laser radar 200 is 200 m ahead and the detection area of the laser radar 200 is 100 m right and left, whereas the detection area of the laser radar 200 is 150 m right and left. Coordinate information (road shape information) is acquired and stored in the memory 50 at least temporarily. The relationship between the width of the detection area and the width of the road shape information acquisition area is not particularly limited, and can be set as appropriate according to factors such as the detection accuracy of the laser radar, the calculation processing capacity, and the traveling speed of the vehicle.

  The data converter 13 of the road shape model calculation means 10 compresses the data so that the acquired wide area road shape information falls within the detection area of the laser radar 200 (S3). The data conversion unit 13 obtains coordinate information (road shape information) indicating the road shape ahead from the memory 50, and sets the coordinate values in the Z direction and the X direction as predetermined values and Z directions that are arbitrarily set in advance, for example, 7 (Predetermined value), the X direction is divided by 2 (predetermined value), for example, data compression is performed, coordinate conversion is performed so that a wide range of road shape information falls within the detection area of the laser radar 200, and the converted road shape information is Store in the memory 50. Note that the road shape information data conversion method is not particularly limited as long as a wide range of data falls within a predetermined narrow range (for example, interpolation processing).

  The road shape model calculation means 10 acquires the road shape information converted from the memory 50, calculates a multidimensional road shape model, and stores it in the memory 50 (S4). As described above, road shape information in a wider range than the detection area of the laser radar 200 is acquired from the map database 420, coordinate conversion is performed so as to enter the detection area of the laser radar 200, and then a road shape model ahead is calculated. Therefore, the amount of deviation between the road shape model calculated on the basis of the map beta base 420 and the position of the detection information obtained from the own vehicle sensor (laser radar 200, vehicle behavior detection device 300) can be reduced. As a result, the amount of deviation between the road shape model based on the map database 420 and the road structure model information calculated based on the detection information can be reduced, and an accurate road shape model can be obtained. A structure can be detected accurately. As described above, since the road structure is detected based on the data having a small deviation amount, the processing efficiency can be improved.

  Incidentally, the detection information and the road shape model of the present embodiment are stored in the memory 50 with a processing timing identifier added so that the processing timing can be specified. The processing timing identifier may be a processing count, a processing timing time, or current position information of the host vehicle. The road structure detection means 30 of the first detection means obtains a recently processed (latest) road shape model or detection information, a road shape model or detection information processed in the past (processed at the nth timing). can do.

  Similarly, the detection result of the road structure detection means 30 and the midway processing result up to the detection result are also stored in the memory 50 with the processing timing identifier added. As described above, in the present embodiment, by sequentially storing the processing history in the memory 50, it is possible to refer to past detection results or midway processing results in the road structure detection processing at the current time point.

Next, the process of the road structure detection means 30 concerning S5-S14 is demonstrated.
In S5, the detection area setting unit 31 of the road structure detection unit 30 determines the current timing based on the road shape model calculated by the road shape model calculation unit 10 and the detection position of the road structure at the previous timing. A detection area for detecting a road structure ahead of the host vehicle is set (S5). Specifically, the detection area setting unit 31 acquires the road shape model information and past plural road structure model information from the memory 50, and uses the road shape model as a reference, the right side area (area 1), the left side area ( Four detection areas are set: area 2), far right area (area 3, area outside the right area near), and far left area (area 4, area outside the left area near). Each detection area is not set in a fixed manner, but is set according to the detection result of the road structure at the previous timing.

Here, a method for setting each detection area will be described with an example.
First, a method for setting neighboring right and left regions (region 1 and region 2) will be described. In the neighborhood left and right areas, whether or not a road structure model is calculated in the vicinity of the right (or left) and distant to the right (or left) with respect to the road shape model at the previous timing (the road structure exists) Set whether or not there was a possibility. Of course, the right and left detection areas may be set depending on whether or not it is determined that a road structure exists in the vicinity of the right (or left) and the right (or left) of the road shape model. As described above, whether the road structure model has been calculated or the position of the object determined as the road structure can be obtained by referring to the memory 50 storing each processing history.

Below, the setting method of the detection area on the right side in the three cases of the setting conditions (1) to (3) will be described.
(1) When the road structure model is not detected in the vicinity of the right side of the road shape model at the previous timing, and the road structure model is not detected at the far right side of the road shape model at the previous timing, A region from the road shape model to the right side 7 m is set as a neighboring right region (region 1). FIG. 6 shows the near right region (region 1) set based on this condition (1).

  (2) When the road structure model is not detected in the vicinity of the right side of the road shape model at the previous timing, and the road structure model is detected at the far right side of the road shape model at the previous timing, A region from the left side 3 m of the detected road structure model to the road shape model is set as a neighborhood right region (region 1).

(3) If a road structure model is detected in the vicinity of the right side of the road shape model at the previous timing, the left and right 3m area of the detected road structure model is set as the right side area (area 1). .

  Hereinafter, a method for setting the detection region on the left side in the vicinity in the three cases of the setting conditions (4) to (6) will be described.

  (4) When the road structure model is not detected near the left side of the road shape model at the previous timing, and the road structure model is not detected at the far left side of the road shape model at the previous timing Sets the region from the road shape model to the left 7 m as the neighborhood left region (region 2).

(5) When the road structure model is not detected in the vicinity of the left side of the road shape model at the previous timing, and the road structure model is detected at the far left side of the road shape model at the previous timing, A region from the right side 3 m of the detected road structure model to the road shape model is set as a neighborhood left region (region 2).

  (6) If a road structure model is detected in the vicinity of the left side of the road shape model at the previous timing, the left and right 3 m regions of the detected road structure model are set as the vicinity left region (region 2). . FIG. 6 shows the vicinity left region (region 2) set based on this condition (6).

  Next, a method for setting the far left and right regions (region 3, region 4) will be described. In the far left and right areas, whether or not the road structure model is calculated in the vicinity of the right (or left) and the far right (left) with respect to the road shape model at the previous timing (the road structure may exist) Set according to whether or not there was sex. Of course, the far left and right detection regions may be set depending on whether or not it is determined that a road structure exists in the right (or left) vicinity and the right (or left) far side of the road shape model. As described above, whether the road structure model has been calculated or the position of the object determined as the road structure can be obtained by referring to the memory 50 storing each processing history.

Hereinafter, a method for setting the far right detection region in the three cases of the setting conditions (7) to (9) will be described.
(7) When the road structure model is not detected near the right side of the road shape model at the previous timing, and the road structure model is not detected at the far right side of the road shape model at the previous timing A region that is separated from the road shape model by 9 m or more on the right side is set as the far right region (region 3). The far right region (region 3) set based on this condition (7) is shown in FIG.

  However, when a starting point is extracted in the detection area on the right side of the road shape model in the starting point extraction unit to be described later, an additional distance obtained by adding 3 m to the distance from the road shape model to the starting point is calculated, and the road shape model is calculated. An area that is more than the addition distance from the area is preferentially the far right area (area 3).

(8) A road structure model is detected in the vicinity of the right side of the road shape model at the previous timing, and is detected when the road structure model is not detected in the far side of the road shape model at the previous timing. An area that is separated from the road structure model by 3 m or more on the right side is set as a far right area (area 3).

  (9) At the previous timing, when a road structure model is detected far away on the right side of the road shape model, from the left side 3 m of the detected road structure model to the right side 5 m of the detected road structure model The area is set as the far right area (area 3).

Hereinafter, a method for setting the far left detection region in the three cases of the setting conditions (10) to (12) will be described.
(10) If the road structure model is not detected in the vicinity of the left side of the road shape model and the road structure model is not detected in the far left side of the road shape model at the previous timing, it is detected. A region that is separated from the road shape model by 9 m or more on the left side is set as a far left region (region 4). However, when a starting point is extracted in the left region in the vicinity of the road shape model in the starting point extraction unit 32 to be described later, an addition distance obtained by adding 3 m to the distance from the road shape model to the starting point is calculated. A region that is separated from the model by more than the addition distance is preferentially a far left region (region 4).

  (11) When the road structure model is detected in the vicinity of the left side of the road shape model at the previous timing and the road structure model is not detected in the far side of the left side of the road shape model, the detected road structure model The left area separated from the left by 3 m or more to the left is set as the far left area (area 4). The far left region (region 4) set based on this condition (11) is shown in FIG.

  (12) When a road structure model is detected at the far left side of the road shape model at the previous timing, an area from the left 3 m to the left 5 m from the detected road structure model is set as the far left area (area 4 ).

  Next, the process proceeds to S6. The starting point extraction unit 32 of the road structure detecting means 30 extracts a detection point that is a starting point of the road structure model for each detection region set by the detection region setting unit 31 (S6). The detection areas are the four areas described with reference to FIG. 6, the right side area (area 1), the left side area (area 2), the far right area (area 3), and the far left area (area 4). The starting point extraction unit 32 of this example extracts one detection point that is a starting point in one detection region. The starting point extraction unit 32 has a small distance difference in the Z direction (distance difference is a predetermined value or less) and is adjacent to the X direction (distance is a predetermined value or less) among the detection points included in each of the four detection regions. A detection point that does not have other detection points and is closest to the host vehicle is extracted as a starting point (hereinafter referred to as a starting point) for detecting a line segment corresponding to the road structure model, and stored in the memory 50. To do. By using a detection point with a small distance difference in the Z direction as a starting point, the presence of a road structure close to the host vehicle can be detected. Further, by not using the detection points distributed along the X direction as starting points, it is possible to appropriately detect a road structure extending in the traveling direction.

  The starting points extracted in this example are shown in FIG. The starting point extraction unit 32 extracts the detection point d as the starting point 1 from the detection points included in the neighboring right region (region 1). The detection points e and f are closer to the laser radar 200 than the detection point d, but are not extracted as starting points because there are detection points adjacent in the X direction (distance is equal to or less than a predetermined value). The starting point extraction unit 32 extracts the detection point m as the starting point 2 from the detection points included in the vicinity left region (region 2). The detection points g and i are closer to the laser radar 200 than the detection point m, but are not extracted as starting points because there are detection points adjacent in the X direction (distance is equal to or less than a predetermined value). Since the far right region (region 3) does not include detection points, no starting point is extracted in this region. The starting point extraction unit 32 extracts the detection point 1 as a starting point from the detection points included in the far left region (region 4).

  Next, the line segment detection unit 33 detects one or more line segments by sequentially connecting the start point extracted by the start point extraction unit 32 and the detection point having a predetermined positional relationship with the start point ( S7, S8). Since the starting points are extracted one by one in each detection area, the line segments connected from the starting points are also detected by one line segment in each detection area.

In the present embodiment, two line segments corresponding to the relative position with respect to the road shape model are detected.
The first line segment detection unit 331 acquires a threshold value T1 (predetermined value) of the distance from each starting point and road shape model of the neighboring left and right regions (regions 1 and 2) from the memory 50. The first line segment detection unit 331 of the line segment detection unit 33 is a detection point having a predetermined positional relationship with the extracted start point, and a detection point whose distance to the road shape model is less than a predetermined value is set as the start point. The first line segment (road structure line segment 1) sequentially connected from the detection point is detected (S7).

  The detection process of the first line segment in the right vicinity region will be described as an example with respect to the road shape model. When the lateral position (the coordinate value in the X direction) of the point on the road shape model 100 m away from the own vehicle 600 is shifted to the right side with respect to the own vehicle 600 (the road corresponding to the road shape model is on the right side in the traveling direction) (When bent) is present on the far right side from the starting point, and the detected points whose distance in the X direction to the road shape model (hereinafter referred to as X1) is less than the threshold value T1 are compared with each other to obtain a road shape model. A line segment composed of detection points located on a substantially straight line is detected. Here, “the detection point is positioned on a substantially straight line” means that the vertical point from the obtained regression line to each detection point is when the inclination of the two points is within a predetermined range, or at least the inclination is substantially the same. In the case where the deviation amount is equal to or less than a predetermined value, a straight line may be derived based on the target detection point, and the present invention is not strictly limited.

Specifically, first, detection points where X1 is less than T1, are separated from the starting point in the traveling direction, and are close to the starting point (the shortest distance) are extracted as first connectable points. Furthermore, a detection point that is close to the right side of the first connectable point with X1 less than T1 is extracted as a second connection point. When the relative position relationship between the starting point, the first connection point, and the second connection point extracted as described above is derived and these three points are located on a predetermined substantially straight line, these detection points are One or more line segments are detected by connecting sequentially from the starting point.

  On the other hand, if it is determined that the three points are not located on a substantially straight line, a detection point that is separated from the starting point in the traveling direction, and that X1 is less than T1 and is close to the right side in the traveling direction of the second connectable point. Extracted as a third connectable point. If two points of the starting point and the first, second, and third connectable points are arranged on a substantially straight line, it is detected as a line segment. Also here, if there are no detection points lined up on a predetermined substantially straight line, of the first to third connectable points, the two points close to the host vehicle are left as the first and second connectable points, rather than the starting point. A detection point that is separated in the traveling direction, X1 is less than T1, and is detected on the right side is extracted as a new third connectable point, and again, 2 of the start points, the first, second, and third connectable points It is determined whether or not the point is located on a predetermined substantially straight line. Such processing is repeated until there is no detection point that may constitute a line segment.

  This process is performed on all detection points that are far from the starting point and X1 is equal to or less than T1, detects a line segment, the number of detection points that form a straight line, the detection point that is farthest from the host vehicle (hereinafter referred to as the end point), The inclination of the straight line is stored in the memory 50. Here, if there are no three detection points located on a predetermined substantially straight line, a line segment connecting the starting point and a connectable point close to the starting point (with the smallest distance) is detected, and a straight line is formed. The number of points, end point, and straight line inclination are stored in the memory 50.

  Further, when the lateral position of the 100 m point on the road shape model from the own vehicle is shifted to the left side with respect to the own vehicle 600, it is separated from the starting point in the driving direction, and X1 is less than T1, and the starting point is left in the driving direction. In the same manner as in the case where the road shape model described above is shifted to the right side, the line segments are detected by extracting the connectable points. Then, the number of detected points, the end point, and the slope of the straight line that constitute the detected straight line are stored in the memory 50.

Next, the second line segment detection unit 332 acquires the threshold value T2 (predetermined value) of the distance from each starting point of the far left and right regions (regions 3 and 4) and the road shape model from the memory 50. Then, the second line segment detection unit 332 of the line segment detection unit 33 is a detection point having a predetermined positional relationship with the extracted starting point, and a detection point whose distance to the road shape model is greater than or equal to a predetermined value is set as the starting point. A second line segment (road structure line segment 2) sequentially connected from the detection point is detected (S8).

  Here, the detection process of the second line segment in the far right region (region 3) with respect to the road shape model will be described as an example. Since the far left and right regions do not depend on the road shape, they exist on the far right side from the starting point, and detection is detected by comparing detection points with X1 equal to or greater than T2.

  First, a detection point that is X1 is equal to or greater than T2 and is separated from the starting point in the traveling direction and close to the right side in the traveling direction is extracted as a first connectable point. Next, a detection point that is separated from the starting point in the traveling direction and has X1 of T2 or more and close to the right side of the first connectable point is extracted as a second connectable point. If the starting point, the first connectable point, and the second connectable point are located on a predetermined substantially straight line, this is detected as a line segment.

  Further, if the detected three points are not aligned on a predetermined substantially straight line, a detection point that is separated from the starting point in the traveling direction, X1 is T2 or more, and is close to the right side of the second connectable point. Extracted as a third connectable point. If two of these starting points and the first, second, and third connectable points are aligned on a predetermined substantially straight line, this is detected as a line segment. On the other hand, if there is no detection point located on a predetermined substantially straight line, out of the first to third connectable points, two points close to the host vehicle are set as the first and second connectable points, and the traveling direction from the starting point , And a detection point that is closer to the right side with X1 being T2 or more is extracted as a new third connectable point. Then, it is determined again whether two of the starting point and the first, second and third connectable points are located on a predetermined substantially straight line.

  Such processing is repeated until there is no detection point that may constitute a line segment. The detection is performed for all detection points that are far from the starting point and have X1 equal to or greater than T2, and line segments are detected. For the detected line segment, the number of detected points, the end point, and the slope of the line segment are stored in the memory 50.

  The detection processing of the second line segment in the left far region (region 4) with respect to the road shape model is performed in the same manner as in the right far region. In other words, it is separated from the starting point in the traveling direction, exists on the left side with respect to the starting point, and can be connected by a method similar to that in the case where X1 is compared with the detecting points having T2 or more and located on the far right side from the starting point. Are sequentially extracted to detect line segments. The line segment with the most detection points composing the line segment is detected as the line segment of the corresponding area, the number of detection points, and the end point. The inclination of the line segment is stored in the memory 50.

  By performing the detection process according to the control procedure as described above, in this example, as shown in FIG. 7, the detection point c is connected to the first connection based on the starting point 1 (detection point d) extracted in the right vicinity region. As possible points, the detection point b is extracted as the second connectable point, the detection point a is extracted as the third connection point, and the straight line derived from the three points a, b, d located on the substantially straight line is the first. Detected as line segment 10.

  Further, based on the starting point 2 (detection point m) extracted in the left vicinity region, the detection point j is extracted as the first connectable point and the detection point h is extracted as the second connectable point, and these three points h, j , M is detected as the first line segment 11. The first line segment 10 and the first line segment 11 are line segments that depend on the road shape model.

  Further, based on the starting point 4 (detection point l) extracted in the left far region, the detection point k is extracted as the first connectable point, and the straight line derived from these two points k and l is the second line segment 12. Detect as. The second line segment 12 is a line segment that does not depend on the road shape model. Note that the detection points e, f, g, i, and c are determined not to form a line segment.

Thus, Oite origin extracted to each area set based on the road shape model obtained from the map database 420, by detecting a line segment extending from the starting point, the road shape of the traveling road of the vehicle 600 It is possible to set a region where a line segment along (depending on the road shape) is detected and a region where a line segment independent of the road shape is detected, and detect a line segment corresponding to each region. In particular, it is possible to accurately detect a road structure even when it has a complicated structure with a branch ahead. The road structure can be accurately detected by calculating the road structure model based on the detected line segment.

  Subsequently, the model calculation unit 34 calculates a road structure model that approximates the shape of the road structure based on the line segment detected by the line segment detection unit 33 (S9). Specifically, the model calculation unit 34 acquires information about the line segment detected from the memory 50 and the threshold value T3, and indicates the road shape ahead of the traveling direction of the host vehicle 600 based on the acquired line segment information. The approximate line L is calculated.

If the absolute value of the slope of each detected line segment is equal to or greater than the threshold value T3, it is determined that the road structures are arranged in a straight line, and detection points constituting the line segment (included in the line segment information) Is subjected to regression analysis to calculate an approximate curve L represented by Expression (1).
Approximate curve L: X = a1 + b1 * Z (1)
Further, when the absolute value of the slope of the line segment is less than the threshold value T3, it is determined that the road structures are arranged in a curved line, the detection points constituting the line segment are subjected to multiple regression analysis, and the equation (2) ) Is calculated.
Approximate curve L: X = a1 + b1 * Z + c1 * Z ² (2)
The approximate curve information regarding the calculated approximate curve L is stored in the memory 50.

  Next, the determination unit 35 determines the road structure based on the road structure model and the detection point expressed by the calculated approximate curve L. That is, the determination unit 35 groups detection points that have a predetermined positional relationship with the road structure model, and determines the grouped detection points as road structures.

  In this embodiment, prior to determining whether or not a road structure model, the determination criteria are changed (S10). The determination criterion changing unit 351 detects the previous road structure model information from the memory 50 and detection points corresponding to an object other than the previous road structure (hereinafter also referred to as “movable object” for convenience of explanation). Information and threshold values T4l and T4r. Here, the “detection point corresponding to an object other than the previous road structure” refers to an object other than the object that has been previously determined to be a road structure by the determination unit 35, for example, a preceding vehicle or parked on a traveling road. It is a detection point corresponding to a vehicle or the like. The information of the detection point corresponding to the object other than the road structure may be information stored in the memory 50 by the determination unit 35 in the previous road structure determination process, or detected as a moving obstacle by the laser radar 200. The detected point information may be used. Further, the second grouping information obtained in the previous process by the second grouping unit 42 of the second detection unit (movable object detection unit) 40 described in detail in the second embodiment may be used.

  The determination criterion changing unit 351 determines whether the movable object exists on the right side or the left side of the calculated road structure model, and determines the threshold value (from the road structure model) on the side where the movable object exists. Is a range narrower than the threshold on the side where no movable object exists. In this way, when a moving object such as a vehicle exists in the vicinity region of the road structure model, the threshold value used when determining the detection point detected on the side where the moving object exists is set strictly. Thus, when a road structure and a movable object such as a vehicle are close to each other, it is possible to prevent the detection point corresponding to the movable object from being erroneously determined as the detection point corresponding to the road structure.

  The determination unit 35 calculates the distance D1 from the approximate curve L (road model) calculated by the model calculation unit 34 to each detection point. When the detection point is present on the left side of the road structure model, the calculated distance D1 is compared with the threshold value T41, and the detection point whose distance D1 is less than the threshold value T41 is determined as road structure data. It is determined that the detection point is a road structure (S11). When the detection point is present on the right side of the road structure model, the calculated distance D1 is compared with the threshold value T4r, and the detection point whose distance D1 is less than the threshold value T4r is determined as road structure data. (S11). Here, the threshold values T4l and T4r are set to values slightly larger than the measurement accuracy of the laser radar 200 when the order of the approximate curve L is first order, and larger than the first order in the second order. It is preferable to set the value.

  Based on FIG. 8, the processing of this example will be described. The determination unit 35 acquires detection information (detection point distance measurement information), a road shape model (approximate curve information), and threshold values T4l and T4r from the memory 50, and based on the acquired information, the following information is obtained. Process. As shown in FIG. 8, in the present embodiment, since a movable object (an object other than a road structure) exists on the left side of the road structure model, the detection point on the left side of the road structure model is the road structure. A threshold value (predetermined value T4l) for determining whether or not the object data is a threshold value is a threshold value (predetermined value T4r) for determining whether or not the detection point on the right side of the road structure model is road structure data. Smaller than.

  The determination unit 35 determines whether or not the detection point is road structure data depending on whether or not the detection point is within a predetermined distance range from the road structure model (S12).

  Detection points determined to be road structure data are grouped (A, D, E in FIG. 8), and an object corresponding to the grouped detection points is determined to be a road structure (S13). The detection result output unit 36 outputs the detection result related to the determined road structure to the outside (S14). Detection results such as the presence and position of the road structure are output toward the output device 500 and presented to the user by a presentation device such as a display. These detection results may be output to a driving support system or the like, and further processed into driving support data.

  The determination unit 35 may recalculate the approximate curve L by performing regression analysis on the detection points determined to be road structure data. Next, road structure information related to the detected points detected as road structures and the calculated approximate curve L is generated and stored in the memory 50.

  In this example, as shown in FIG. 8, distances D1 from the approximate curve L to each of the detection points a to m are calculated, and the calculated distance D1 is compared with a threshold value T4l or T4r. As a result, as shown in FIG. 8, the detection points a, b, d, h, j, m, k, and l are determined as road structure data, and a predetermined distance range from the road structure model (approximate curve 1). The detection points a, b, and d inside are grouped and determined as a road structure A. Detection points h, j, and m within a predetermined distance range from the road structure model (approximate curve 2) are grouped and determined as road structure D. Furthermore, the detection points k and l within the predetermined distance range from the road structure model (approximate curve 3) are grouped and determined as the road structure E. In this case, it is preferable that the detection result output unit 36 outputs a detection result of an object having a road structure shape such as the objects A, D, and E shown in FIG.

  The detection point determined to be not a road structure by the determination unit 35 may be determined as an object other than the road structure, for example, a preceding vehicle, a vehicle parked in front, or the like. In the present embodiment, the detection point determined not to be a road structure is observed in more detail, and a process of determining as another object is performed (S15). The other object determination processing in S15 will be described in detail in the second embodiment.

  According to the present embodiment, the road structure model is calculated based on the road shape model and the detection information, and whether or not the detection point is a road structure based on the positional relationship with the calculated road structure model is determined. Since it judges, a road structure can be judged correctly. In particular, since the road structure is determined without using an image whose accuracy decreases with increasing distance, it is possible to accurately detect the road structure in a wider range than the conventional vehicle to the distance.

  Furthermore, since road structures are detected without referring to the relative speeds of the detection points that are sequentially detected, the detection points can be correlated in time as in the case where the detection points are densely detected. Even when it is difficult to calculate the relative speed of the detection point, the road structure can be accurately detected.

Second Embodiment
Next, a second embodiment will be described. The second embodiment is characterized by comprising second detection means (movable object detection means) 40 for detecting an object other than a road structure. FIG. 9 shows an outline of a block configuration of the forward object detection device 900 according to the second embodiment. Further, FIG. 10 shows a block configuration of the forward object detection device 900 of the present embodiment.

  As shown in FIGS. 9 and 10, the forward object detection device 900 of the present embodiment is common to the forward object detection device 100 of the first embodiment except that the second detection means (movable object detection means) 40 is provided. To do. Here, description of common parts is avoided, and different points are mainly described.

  The second detection means (movable object detection means) 40 of this embodiment is based on detection information of detection points other than the detection points determined to correspond to the road structure by the first detection means (road structure detection means) 30. Then, an object other than the road structure is detected. Although not particularly limited, the movable object detection means 40 of the present embodiment is a detection point other than the detection points corresponding to the road structure detected by the road structure detection means 30, and has a predetermined positional relationship with the host vehicle. The detected detection points are grouped, and an object corresponding to the grouped detection point is detected as an object other than the road structure. The predetermined positional relationship includes not only the positional relationship between the own vehicle and the detection point at the detection processing timing but also the relative position between the own vehicle and the detection point at the previous detection processing timing in the continuously performed detection processing. Includes changes over time in relationships. In other words, the movable object detection means 40 of the present embodiment detects the movable object from the viewpoint of the relative positional relationship between the host vehicle and the detection point, and / or continues the relative positional relationship between the host vehicle and the detection point. A technique for detecting a movable object from the viewpoint of temporal change can be used.

  The movable object detection means 40 of the present embodiment includes a first grouping unit 41 and / or a second grouping unit 42, and is detected by the first detection means (road structure detection means) 30 to correspond to a road structure. A first group that is a detection point other than a point and that groups detection points having a first positional relationship with the host vehicle and a detection point that has a second positional relationship different from the first positional relationship. When two groups are compared and the first group and the second group are in a predetermined positional relationship, an object corresponding to the detection point of the grouped first group or the second group is detected as an object other than a road structure. To do. Specifically, the movable object detection means 40 of the present embodiment generates the first group data based on the distance in the Z direction, and generates the second group data based on the width in the X direction. If the first group data has a predetermined positional relationship with the previous second group data, the object corresponding to the first group data grouped this time is detected as an object other than the road structure.

  Furthermore, the movable object detection means 40 of the present embodiment is a group of detection points grouped at the previous timing from the viewpoint of detecting a movable object based on the temporal change in the relative positional relationship between the host vehicle and the detection points. Based on the relative positional relationship with the group of detection points grouped at the subsequent timing, an object corresponding to the group of detection points at the later timing is detected as an object other than the road structure. In this way, the detection points other than the road structure are detected by grouping the remaining detection points except for the detection points that are difficult to correspond in time, such as the detection points corresponding to the road structure. An object existing ahead can be detected accurately. In addition, it is possible to accurately distinguish between objects other than road structures.

  The first grouping unit 41 acquires distance measurement information, road structure information, and various types of information on threshold values Zt1, Zt2, Zt3, Xt1, and Wt1 from the memory 50. Then, based on the acquired information, with respect to detection points other than the detection points corresponding to the road structure, the Z direction between the detection points adjacent to each other (detected within a predetermined distance) from the scanning start position to the scanning direction. A distance DZ (direction along the optical axis of the radar, see FIG. 11) is calculated, and the absolute value thereof is compared with the threshold value Zt1. Here, the threshold value Zt1 is a value slightly larger than the measurement accuracy in consideration of the measurement accuracy of the laser radar 200, and T4l, T4r <Zt1 (when the approximate curve is first order), Zt1 <T4l, It is set to be T4r (when the approximate curve is quadratic).

  When the first grouping unit 41 determines that the absolute value of the distance DZ in the Z direction is less than the threshold value Zt1, the two detection points forming the distance DZ are based on reflected laser beams (reflected waves) reflected from the same object. These are determined to be grouping targets of one group. On the other hand, in the first grouping unit 41, when the absolute value of the distance DZ in the Z direction is equal to or greater than the threshold value Zt1, detection points that are separated from each other and are adjacent (detected within a predetermined distance) are detected. In the case of an isolated detection point that does not exist, the grouping process is performed so that one group is formed by only one detection point.

The first grouping unit 41 repeats the above-described processing for each detection point, and generates one or more first group data. For each of the first group data, the distance in the Z direction to the host vehicle 600 is calculated. Specifically, each detection point included in the first group data and the Z direction distance to the host vehicle 600 are averaged, and this is used as the Z direction distance of the first group data. Further, for each of the first group data, the width in the X direction (the vehicle lateral direction orthogonal to the Z direction in the horizontal plane) is calculated. Specifically, the center point in the X direction of the first group data is calculated, and the unit scan width of the laser radar 200 is added to the positional difference between the right end detection point and the left end detection point of the first group data to obtain the first group data. The X direction width (object width) is calculated. These calculation results are stored in the memory 50 in association with the identifier of the first group data.

  Based on FIG. 11, the grouping process by the first grouping unit 41 will be described in detail. The detection information shown in FIG. 11 is detected in the situation shown in FIG. 2 described above.

The first grouping unit 41 performs a first group process on points other than the detected points detected as road structures by the road structure detection unit 30. The first group process is performed at a predetermined interval. Since the first group data resulting from each first group process is stored in the memory 50, any first group data having a different processing timing can be acquired from the memory 50. The first grouping unit 41 recognizes the detection points e and f as first group data R (1, t), and recognizes the detection points g and i as first group data R (2, t). Further, the detection point c is recognized as the first group data R (3, t) that cannot be grouped.

  In addition, when the host vehicle 600 is traveling, the first grouping unit 41 compares the first group data obtained by the first grouping process that varies in time, and these first groups that vary in time. It is confirmed whether or not the data is the same first group data. And about the 1st group data judged that it is the same group data, the relative speed with respect to the own vehicle 600 is further measured, and it is a group from a viewpoint of the temporal change of the relative positional relationship between the own vehicle and the detection point Determine the identity of the data. The first group data determined not to be the same is stored in the memory 50 for later processing. Hereinafter, a process for determining the identity of the group data from the viewpoint of the relative positional relationship using the relative speed will be described.

  The first grouping unit 41 according to the present embodiment confirms whether or not these first group data that move back and forth in time are the same first group data based on the relative speed of the first group data with respect to the host vehicle. To do. Specifically, the first grouping unit calculates a relative speed of each of the first group data that is confirmed to be the same first group and is temporally changed with respect to the host vehicle 600, and calculates the calculated relative speed or relative speed. Is compared with the current position predicted from the position at the time of measurement (past), and based on the comparison result, it is confirmed whether or not these first group data that are temporally mixed are the same first group data To do.

  Although not particularly limited, the first grouping unit 41 includes the first group data detected at the previous timing and the first group data that is the current processing target of (a1) and (a2) shown below. When two conditions are satisfied, it is determined that they correspond to the same first group data, that is, the same object.

  (A1) Z direction (optical axis) between the current position of the first group data estimated from the relative velocity of the first group data detected before in time and the current position of the first group data measured this time Direction) distance is less than or equal to the threshold value Zt2.

  (A2) The distance in the X direction from the center point in the X direction (lateral direction of the host vehicle) of the first group data detected before in time to the center point in the X direction of the first group data measured this time The absolute value is not more than the threshold value Xt1.

  Here, the threshold values Zt2 and Xt1 are preferably set to small values equivalent to the measurement accuracy of the laser radar 200 in order to calculate an accurate relative velocity.

  About the 1st group data which do not satisfy | fill conditions (a1)-(a2) mentioned above, it compares with the 1st group data which were detected before temporally and the relative speed is not calculated, and the identity is judged. This is because the relative speed is measured when it is confirmed that the group data preceding and following in time are the same group data, and therefore there is group data for which the relative speed has not been calculated. That is, for the first group data whose identity cannot be determined from the conditions (a1) to (a2) based on the relative speed, the identity is determined based on the following conditions (b1) to (b3). . In the present example, when the three conditions (b1) to (b3) are satisfied, it is determined that the first group data that changes in time are due to the same object.

The first grouping unit 41 uses the first group data detected before in time and the first group data to be processed this time.
(B1) The absolute value of the Z direction distance from the first group data detected before in time to the actually measured first group data is equal to or less than the threshold value Zt3.

  (B2) The absolute value of the distance in the X direction from the center point in the X direction of the first group data detected earlier in time to the center point in the X direction of the current first group data is less than or equal to the threshold value Xt1. is there.

  (B3) The absolute value of the difference between the object width of the first group data detected before in time and the actually measured object width of the first group data this time is equal to or less than the threshold value Wt1.

  The first grouping unit 41 calculates the relative speed with respect to the host vehicle 600 for the first group data of this time recognized as being the same object as the first group data of the previous time, and includes the first group data in the first group data. Is stored in the memory 50.

Next, the second grouping process of the second grouping unit 42 will be described.
The second grouping unit 42 performs the second grouping process after the first grouping process described above. In the second grouping process, an object other than a road structure (movable) is compared by comparing the second group data detected before in time with the first group data detected this time and the relative speed calculated. Object).

  The second grouping unit 42 determines the left and right lane width (for example, 1.75 m) as the grouping range from the center position in the X direction in the second group data detected at the immediately preceding search timing, and exists within this range. A plurality of detection points to be grouped as a second group.

  The second grouping unit 42 calculates the second group data detected earlier, the current relative velocity, the first group data in which the center position in the X direction exists in the grouping range, the threshold value Zt4, Zt5, Vt1, Wt2, Vt2, and Wt3 are acquired from the memory 39.

  Then, the second group data of the previous time and the current relative velocity are calculated, and the first group data having the center position in the X direction in the grouping range described above includes the following four (c1) to (c4). When all the determination conditions are satisfied, it is determined that the previous second group data and the current first group data correspond to the same object. Further, the first group data determined to correspond to the same object are grouped as second group data.

  (C1) The absolute value of the Z-direction distance from the current position of the second group data estimated from the previous relative speed of the second group data to the current first group data is equal to or less than the threshold value Zt4.

  (C2) The relative speed ratio between the previous second group data and the current first group data is greater than zero. However, this condition is not applied when the absolute value of the relative speed of the previous second group data is a predetermined value (for example, about 5 m / s).

  (C3) The difference between the previous relative speed of the second group data and the current relative speed of the first group data is equal to or less than the threshold value Vt1.

  (C4) The object width of the second group data after grouping is equal to or less than the threshold value Wt2.

  Here, the threshold values Zt4, Vt1, and Wt2 can be set from the viewpoint that the first group data and the second group data can be determined as the same object if the above-described distance difference is within one vehicle. preferable.

  The condition (c2) is a condition for determining whether the previous second group data and the current first group data are moving in the same direction. In addition, the proviso portion of the same condition means that group data having a small relative velocity is excluded from the determination as to whether or not they are the same object. This is because group data with a small relative speed may include a measurement error, and it is difficult to accurately determine in which direction each group data is moving.

  Furthermore, when it is determined that the above-described determination condition is not satisfied and the previous second group data and the current first group data do not correspond to the same object, grouping is performed between the first group data, as shown below ( When the three conditions shown in d1) to (d3) are satisfied, grouping is performed as new second group data.

  (D1) The absolute value of the distance difference in the Z direction between the current first group data is Zt5 or less.

  (D2) The absolute value of the relative speed difference between the current first group data is Vt2 or less.

  (D3) The object width of the second group data after grouping is equal to or less than the threshold value Wt3.

  Here, the threshold values Zt5, Vt2, and Wt3 are set from the viewpoint that the first group data may be determined as the same object if the above-described Z-direction distance difference is within 1.5 vehicles. It is preferable. That is, the determination based on the conditions (d1) to (d3) is performed again with the conditions relaxed for the first group data that does not satisfy the previous conditions (c1) to (c4).

  In addition, the second grouping unit 42, when the first group data does not satisfy any of the determination conditions (c1) to (c4) and (d1) to (d3) described above, only the first group data. Thus, the second group data is newly set. That is, the second grouping unit 42 generates second group data including detection points included in an object that exists in front of the host vehicle 600.

  Furthermore, an average value is calculated for each of the Z direction distance, the center point in the X direction, the left and right end points of the object, and the relative velocity from the first group data included in the generated second group data, and the calculated average value is , The Z direction distance, the center point in the X direction, the object width, and the relative speed of the second group data. Further, when there is past second group data corresponding to the second group data, the value of the number of detection times of the second group data (determined to be the same object) is updated.

  The second grouping unit 42 determines that the generated second group data corresponds to an object that exists in front of the host vehicle 600 and detects an object that exists in front of the host vehicle 600. In the example shown in FIG. 11, R (3, t) and R (1, t) determined as the second group data are detected as objects (movable objects) B and C other than the road structure.

  Further, based on the relative speed information of the second group data, it is determined whether or not the object is a moving object, and the result of the determination is stored in the memory 50 included in the second group data.

  The detection result output unit 43 outputs a detection result related to an object (movable object) to the outside in addition to the determined road structure. Detection results such as the presence and position of the determined road structure are output to the output device 500 and presented to the user by a presentation device such as a display. These detection results may be output to a driving support system or the like, and further processed into driving support data.

  Subsequently, a control procedure of the front object detection apparatus 900 according to the second embodiment will be described with reference to FIG. The basic control procedure of the front object detection apparatus 900 of the second embodiment is common to that of the front object detection apparatus 100 of the first embodiment. The processes from S1 to S15 shown in FIG. 4 described in regard to the first embodiment are common and will not be described. Here, the process of the movable object detection means 40, which is one of the features of the second embodiment, is mainly described. explain.

  FIG. 12 is a flowchart showing other object determination processing subsequent to S1 to S15 shown in FIG.

  After step 15, in step S21, the first grouping unit 37 acquires distance measurement information, road structure information, and data of threshold values Zt1, Zt2, Zt3, Xt1, and Wt1 from the memory 39.

  Next, based on the information, the first grouping unit 41 performs the first grouping process described above on the detection points that are not determined as road structure data in step S12 illustrated in FIG. 4 (S1).

  For example, the first grouping unit 41 calculates the Z direction (optical axis direction) distance between adjacent detection points from the right end of the traveling direction of the host vehicle 600 in the scanning range, and calculates the absolute Z direction distance obtained by the calculation. When the value is equal to or less than the threshold value Zt1, the detection point points are grouped to form first group data. The first grouping unit 41 updates the first group data by comparing the detection points up to the range of the region where the absolute value of the Z-direction distance exceeds the threshold value Zt1 and the range where there is no adjacent detection point.

  Subsequently, the distance with respect to the own vehicle of each 1st group data is calculated. Specifically, the Z direction distance from each detection point included in the first group data to the host vehicle 11 is averaged, and the average value is set as the Z direction distance of the first group data. Further, the center point in the X direction of the first group data is calculated, and the object width is calculated by adding the unit scan width by the laser radar 200 to the positional difference between the right end point and the left end point of the first group data. These calculation results are included in the first group data and stored in the memory 50, and the process proceeds to step S22.

  In step S <b> 22, the first grouping unit 41 compares the first group data that moves back and forth in time. Specifically, the first grouping unit 41 acquires from the memory 50 the first group data generated this time and the first group data for which the relative speed has been calculated among the first group data generated last time. To do.

  The first grouping unit 41 compares the first group data of this time with the first group data generated last time, determines whether or not the determination conditions (a1) to (a2) described above are satisfied, and sets the conditions. If it is satisfied, it is determined that the current first group data and the previous first group data are based on the same object. For the first group data that satisfies the determination conditions (a1) to (a2), the first grouping unit 41 includes distance information (in the past two points) on the first group data in order to calculate the relative speed in a later step. Z-direction distance and X-direction center point information) are added, and the processing after step S24 is performed.

  Further, the first grouping unit 41 acquires the first group data of the previous time when the relative speed is detected from the memory 50 for the current first group data that does not satisfy the determination conditions (a1) to (a2), It is determined whether or not the determination conditions (a1) to (a2) are satisfied in relation to the first group data of the previous time. If the current first group data does not satisfy the determination conditions (a1) to (a2) with the previous first group data, the previous first group data is deleted. On the other hand, when the determination conditions (a1) to (a2) are satisfied, the first group data of this time and the first group data of the previous time are determined as the same object. The point distance information is added, and the processing after step S24 is performed.

  The first grouping unit 41 determines the relative speed of the first group data of this time that does not satisfy the determination conditions (a1) to (a2) with the first group data of the previous time and the previous time when the relative speed is detected. The previous first group data that has not been calculated is acquired from the memory 39, and it is determined whether or not the previous first group data and the determination conditions (f1) to (f3) are satisfied.

  As a result, for the current first group data that does not satisfy the determination conditions (b1) to (b3), the processing from step S27 is performed. On the other hand, when the determination conditions (b1) to (b3) are satisfied, the first group data of this time and the first group data of the previous time are set as the same object, and the distance between the past two points for the first group data. Information is added, and the processing after step S23 is performed.

  In step S27, the first grouping unit 41 determines the Z direction distance, the center point in the X direction, and the object width of the first group data of this time determined to be not the same object as the past first group data in step S22. Store in memory 50 and return to step S1. In this case, the stored various information is used in the time series determination in step S22 after the next time.

  In step S23, the first grouping unit 41 uses the current, previous and previous distance information for the first group data that satisfies the determination condition for time-series determination with the first group data in the past. Calculate the relative speed. Next, the calculated relative speed information is added to the current first group data and stored in the memory 50.

  The first grouping unit 41 performs the processing from step S22 to step S23 described above for all the first group data detected in step S21, and proceeds to the processing after step S24.

  In step S24, the second grouping unit 42, from the memory 50, the first group data for which the relative speed is calculated in step S22, the past second group data, the threshold values Zt4, Zt5, Vt1, Wt2, Vt2 , Wt3 data is acquired.

  Next, there is first group data that satisfies the determination conditions (c1) to (c4) for determining the same object by comparing the first group data and the second group data based on the information or the like. It is determined whether or not. For the first group data determined to satisfy the determination conditions (c1) to (c4), the process after step S25 is performed, and for the first group data determined not to be satisfied, the process after step S28 is performed. . In step S28, the second grouping unit 42 compares the first group data that does not satisfy the determination condition with the past second group data in step S24, and determines whether or not they are the same object. When the determination conditions (d1) to (d3) are satisfied, grouping is performed, and new second group data is generated. On the other hand, when the determination conditions (d1) to (d3) are not satisfied, the second group data is newly generated with one first group data and stored in the memory 50, and the process proceeds to step S26.

  In step S25, the second grouping unit 42 calculates an average value for each of the Z direction distance, the center point in the X direction, the left and right end points of the object, and the relative velocity from the first group data included in the second group data. The calculated average value is included in the second group data as the Z direction distance, the center point in the X direction, the object width, and the relative speed of the second group data, and stored in the memory 50.

  Next, when there is past second group data corresponding to the second group data, the value of the number of detection times of the second group data is updated. By determining that the second group data is based on an object existing in front of the host vehicle 600, an object existing in front of the host vehicle 600 is detected. Further, based on the relative velocity information included in the second group data, it is determined whether or not the object is a moving object, and the result of the determination is included in the second group data.

  Such processes from step S24 to steps S26 and 28 are performed on all the first group data whose relative speeds are calculated in step S23, and the process proceeds to step S26.

  In step S <b> 26, the detection result output unit 43 presents the second group data obtained by the processing in steps S <b> 24 to S <b> 25 by the presentation device of the output device 500 in a form that can be recognized by the passengers of the host vehicle 600. .

  As described above, the movable object detection means 40 groups the detection points adjacent to each other among the detection points that are not detected as road structures, and generates first group data. The first group data Is detected from another viewpoint, an object ahead of the host vehicle 600 is detected. Thereby, objects other than road structures can be detected accurately. Since the behavior of a road structure that does not move is different from that of a movable object such as another vehicle, the characteristics of the obtained detection information are different. In the present embodiment, the road structure and the other objects are separately detected, so that the road structure and the other objects can be detected accurately without being misidentified.

  In addition, the first group data is generated based on the distance in the Z direction, and the second group data is generated based on the width in the X direction. The position of the detection point detected by the laser radar 200 is different from that point of view. By obtaining the commonality, it is possible to accurately detect objects other than road structures.

  Further, when the first group data generated this time is in a predetermined common relationship with the previous second group data, the first group data generated this time is defined as second group data, and the second group data It is possible to accurately detect an object other than a road structure by detecting an object in front of the host vehicle and obtaining commonality from the viewpoint of the temporal change of detection points detected at different timings. . In particular, for a moving object such as a preceding vehicle, the object can be accurately detected by adding a viewpoint of change over time.

  In the present embodiment, the front object detection devices 100 and 900 have been described. However, when the front object detection method is used, the front object detection devices 100 and 900 operate in the same manner and have the same effects.

  The embodiment described above is described for facilitating the understanding of the present invention, and is not described for limiting the present invention. Therefore, each element disclosed in the above embodiment is intended to include all design changes and equivalents belonging to the technical scope of the present invention.

It is a figure which shows the block structure outline | summary of the front object detection apparatus of 1st Embodiment. (A) (B) is a figure explaining arrangement | positioning of a laser radar. It is a block block diagram of the front object detection apparatus of 1st Embodiment. It is a flowchart figure which shows the control procedure of the front object detection apparatus which concerns on 1st Embodiment. It is a figure for demonstrating a detection point with a laser radar. It is a figure for demonstrating a detection area setting process. It is a figure for demonstrating a line segment detection process. It is a figure for demonstrating a model calculation process. It is a figure which shows the block structure outline | summary of the front object detection apparatus of 2nd Embodiment. It is a block block diagram of the front object detection apparatus of 2nd Embodiment. It is a figure for demonstrating the detection process of a movable object. It is a flowchart figure which shows the control procedure of the front object detection apparatus which concerns on 2nd Embodiment.

Explanation of symbols

DESCRIPTION OF SYMBOLS 100,900 ... Front object detection apparatus 10 ... Road shape model calculation means 11 ... Road shape information acquisition part 12 ... Acquisition range determination part 13 ... Data conversion part 20 ... Detection information acquisition means 30 ... 1st detection means, road structure detection Means 31 ... Detection area setting unit 32 ... Origin extraction unit 33 ... Line segment detection unit 34 ... Model calculation unit 35 ... Determination unit 36 ... Detection result output unit 40 ... Second detection unit, movable object detection unit 41 ... First grouping unit 42 ... 2nd grouping part 43 ... Detection result output part

Claims (16)

A forward object detection device that detects an object that is mounted on the host vehicle and exists in front of the host vehicle,
Detection information acquisition means for acquiring detection information including information on a detection point of the object detected based on a reflected wave of a scanning wave emitted forward of the host vehicle;
A road that obtains the current position of the host vehicle, obtains road shape information ahead of the host vehicle by accessing a map database, and calculates a road shape model of a traveling road ahead of the host vehicle based on the road shape information A shape model calculating means;
A road structure model is calculated based on the detection information acquired by the detection information acquisition means and the road shape model calculated by the road shape model calculation means, and the calculated road structure model and a predetermined positional relationship A road structure detection means for grouping detection points having the following, and detecting an object corresponding to the grouped detection point as a road structure,
The road structure detecting means is
Based on the detection position of the road structure at the previous timing and the road shape model, a detection region that is less than a predetermined distance with respect to the road shape model and a detection region that is more than a predetermined distance with respect to the road shape model A detection area setting section to be set;
A starting point extraction unit that extracts a detection point that is a starting point of the road structure model for each detection region set by the detection region setting unit;
One or two or more detection points are sequentially connected to a starting point extracted by the starting point extraction unit and detection points having a predetermined positional relationship with the starting point and having a distance to the road shape model less than a predetermined value . A line segment detector for detecting line segments;
Based on the line segment detected by the line segment detection unit, a model calculation unit that calculates a road structure model at the current timing for each of the set detection areas;
Determination of grouping detection points having a predetermined positional relationship with the road structure model calculated by the model calculation unit, and determining an object corresponding to the grouped detection point as a road structure ahead of the host vehicle at the current timing And a front object detection device.   The road shape model calculation means accesses the map database, acquires the road shape information in a predetermined area wider than the detection area of the detection information acquired by the detection information acquisition means, and acquires the acquired road shape information The forward object detection device according to claim 1, wherein data is converted so as to fall within a detection area of detection information, and a road shape model of a traveling road ahead of the host vehicle is calculated based on the road shape information after the conversion. The line segment detector
The starting point extracted for each detection region by the starting point extracting unit, and the distance to the road shape model that is a detection point having a predetermined positional relationship with the starting point and calculated at the current timing by the road shape model calculating means A first line segment detector that detects one or more line segments by sequentially connecting detection points that are less than a predetermined value;
The starting point extracted for each detection region by the starting point extracting unit, and the distance to the road shape model that is a detection point having a predetermined positional relationship with the starting point and calculated at the current timing by the road shape model calculating means The forward object detection device according to claim 1 , further comprising: a second line segment detection unit configured to sequentially detect detection points having a predetermined value or more and detect one or more line segments. The determination unit determines whether a detection point other than the detection point corresponding to the road structure is on the left side or the right side with respect to the road shape model, and the detection point is determined according to the determination result. The front object detection device according to any one of claims 1 to 3, further comprising a determination reference changing unit that changes a predetermined positional relationship that is a determination reference for determining whether or not to be the grouping target. Based on the detection information of the detection point other than the detection point corresponding to the road structures detected by the road structure detection means, according to claim 1 to 4, further comprising a movable object detecting means for detecting an object other than the road structures The front object detection apparatus in any one of. The movable object detection means is a detection point other than the detection points corresponding to the road structure detected by the road structure detection means, and groups detection points having a predetermined positional relationship with the host vehicle, The forward object detection device according to claim 5 , wherein an object corresponding to the grouped detection point is detected as an object other than a road structure. The movable object detection means is a detection point other than the detection points corresponding to the road structure detected by the road structure detection means, and groups detection points having a first positional relationship with the host vehicle. When one group is compared with a second group in which detection points having a second positional relationship are grouped, and the first group and the second group are in a predetermined positional relationship, the first group or the second group The front object detection device according to claim 5 , wherein an object corresponding to a detection point grouped as a group is detected as an object other than a road structure. The movable object detecting means detects an object corresponding to the detection point group at the later timing based on a positional relationship between the group of detection points grouped at the previous timing and the group of detection points grouped at the subsequent timing. The forward object detection device according to any one of claims 5 to 7, wherein the forward object detection device detects an object other than a road structure. A forward object detection method for detecting an object mounted on a host vehicle and existing in front of the host vehicle,
Obtaining detection information including information on a detection point of the object detected based on a reflected wave of a scanning wave emitted forward of the host vehicle;
Obtaining a current position of the host vehicle, accessing a map database to acquire road shape information ahead of the host vehicle, and calculating a road shape model of a traveling road ahead of the host vehicle based on the road shape information When,
A road structure model is calculated based on the acquired detection information and the calculated road shape model, and detection points having a predetermined positional relationship with the calculated road structure model are grouped, and the grouped Detecting an object corresponding to the detected point as a road structure,
The step of detecting road structures is
Based on the detection position of the road structure at the previous timing and the road shape model, a detection region that is less than a predetermined distance with respect to the road shape model and a detection region that is more than a predetermined distance with respect to the road shape model Steps to set,
Extracting a detection point that is a starting point of the road structure model for each of the set detection areas;
One or two or more line segments are obtained by sequentially connecting the extracted starting point and a detecting point having a predetermined positional relationship with the starting point and having a distance to the road shape model less than a predetermined value. Detecting step;
Calculating a road structure model at the current timing for each of the set detection areas based on the detected line segments;
Grouping detection points having a predetermined positional relationship with the calculated road structure model, and determining an object corresponding to the grouped detection points as a road structure ahead of the host vehicle at the current timing. A forward object detection method characterized by the above. The step of calculating the traveling road shape model includes accessing a map database, acquiring the road shape information in a predetermined area wider than the detection area of the acquired detection information, and acquiring the acquired road shape information as the detection information. The forward object detection method according to claim 9 , wherein data is converted so as to fall within a detection area of the vehicle, and a road shape model of a traveling road ahead of the host vehicle is calculated based on the road shape information after the conversion. Detecting the line segment comprises:
The starting point extracted for each detection area by the step of extracting the starting point, and a detection point that is in a predetermined positional relationship with the starting point, and the distance to the road shape model calculated at the current timing is less than a predetermined value Detecting one or more first line segments by sequentially connecting the detection points;
A starting point extracted for each of the detection areas, and a detection point that is in a predetermined positional relationship with the starting point, and that has a distance to the road shape model calculated at the current timing are sequentially greater than or equal to a predetermined value. The method for detecting a front object according to claim 9 or 10 , further comprising a step of detecting one or more second line segments by connecting them. The determining step determines whether a detection point other than the detection point corresponding to the road structure is on the left side or the right side with respect to the road shape model, and the detection point according to the determination result The method of detecting a front object according to any one of claims 9 to 11, further comprising a step of changing a predetermined positional relationship that is a criterion for determining whether or not a target of grouping. The method according to claim 9 , further comprising: detecting an object other than the road structure based on detection information of a detection point other than the detection point corresponding to the road structure detected by the step of detecting the road structure. The front object detection method according to any one of the above. The step of detecting an object other than the road structure is a detection point other than a detection point corresponding to the detected road structure, and groups detection points having a predetermined positional relationship with the host vehicle, The forward object detection method according to claim 13 , wherein an object corresponding to the grouped detection point is detected as an object other than a road structure. The step of detecting an object other than the road structure is a detection point other than the detection point corresponding to the detected road structure, wherein the detection points having the first positional relationship with the host vehicle are grouped. When one group is compared with a second group in which detection points having a second positional relationship are grouped, and the first group and the second group are in a predetermined positional relationship, the first group or the second group The front object detection method according to claim 13 , wherein an object corresponding to a detection point grouped as a group is detected as an object other than a road structure. The step of detecting an object other than the road structure includes the detection point group at the subsequent timing based on the relationship between the group of detection points grouped at the previous timing and the group of detection points grouped at the subsequent timing. The forward object detection method according to any one of claims 13 to 15, wherein an object corresponding to is detected as an object other than a road structure.

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