JP2012247933A - Device and method for monitoring surrounding area, and drive supporting device - Google Patents

Abstract

PROBLEM TO BE SOLVED: To provide a device and method for monitoring a surrounding area in which the calculation amount in detecting an object is reduced, and an increase of an estimation error of the object and occurrence of situation of disability in calculation can be prevented, and furthermore to provide a drive supporting device capable of performing drive support based on the existence of the object.SOLUTION: A vehicle driving ECU1 detects and recognizes obstacles around a vehicle and performs drive support such as setting of an avoidance route based on the recognized obstacles. When a grid larger than an intermediate value in recognition reliability exists between a maximum value grid which is a maximum value in the recognition reliability and a vehicle grid in detecting the obstacles, the recognition reliability of the grid is corrected to a minimum value by being regarded as the grid showing a false obstacle.

Description

  The present invention relates to a driving support device, and more particularly, to a surrounding monitoring device and a surrounding monitoring method for monitoring an object around a vehicle, and a driving support device that supports driving of a vehicle based on the presence of the object.

  2. Description of the Related Art Conventionally, there is a driving assistance device that provides assistance for avoiding a traveling vehicle colliding with an obstacle such as a pedestrian. As this type of driving support device, for example, while recognizing a change in the vehicle driving state such as a yaw angle change of a running vehicle, it recognizes a moving object such as a pedestrian or a stationary object, and based on the recognition result of both 2. Description of the Related Art A moving body detection device that obtains a displacement amount of an object is known (see, for example, Patent Document 1).

  The moving body detection device includes a vehicle motion estimation device that estimates vehicle motion in order to recognize changes in the vehicle driving state. The vehicle driving estimation apparatus recognizes a change in the vehicle driving state based on a change in coordinates associated with the lapse of a minute time of an object around the vehicle. The displacement amount of the object is obtained from the vehicle driving state recognized here and the recognition result of the object.

JP 2006-160116 A

  However, in the vehicle motion estimation device disclosed in Patent Document 1, since a minute time has elapsed and the object is re-recognized every time the vehicle moves, calculation for estimating the vehicle motion is performed. The amount becomes very large. For this reason, in order to predict the coordinates of the recognition target based on the past measurement values, the estimation error caused by the movement of the vehicle increases with time, and it becomes difficult to detect the object. In some cases, the object cannot be detected and the object cannot be detected. When it is difficult to detect an object and it is impossible to detect an object, there is a problem that it may not be possible to perform driving support based on the presence of the object.

  Accordingly, an object of the present invention is to provide a periphery monitoring device and a periphery monitoring method capable of reducing the amount of calculation when detecting an object and preventing an increase in estimation error of the object and a situation in which the calculation cannot be performed, An object of the present invention is to provide a driving support device that can perform driving support based on the presence of an object.

  A peripheral monitoring apparatus according to the present invention that has solved the above problems includes a peripheral object detection unit that detects a peripheral object that is an object in the vicinity of a mobile object, and presence information on the peripheral object based on past detection results by the peripheral object detection unit, and Based on the amount of movement of the moving object, based on the position estimation means for estimating the current relative position between the surrounding object and the moving object, based on the current detection result of the surrounding object by the surrounding object detection means and the estimation result by the position estimation means And object presence information correcting means for correcting the presence information of the surrounding objects.

  In the peripheral monitoring apparatus according to the present invention, the presence information of the peripheral object is corrected based on the current detection result of the peripheral object by the peripheral object detection unit and the estimation result by the position estimation unit. Therefore, the object presence information can be corrected without depending on whether or not the peripheral object detected in the past and the current peripheral object are the same. Therefore, compared to the case where presence information is corrected by tracking the same object using image measurement, etc., the amount of calculation when detecting the object is reduced, and the object estimation error is thereby reduced. It is possible to prevent an increase in the amount of data and a situation where calculation is impossible.

  Here, based on the detection result by the peripheral object detection means, there is an object presence reliability prediction means for predicting the object presence reliability, which is the reliability that the peripheral object exists, and the presence of the peripheral object based on the detection result of the peripheral object The information is the object existence reliability of the surrounding object, and the object existence information correcting means is based on the current detection result of the surrounding object by the surrounding object detection means and the estimation result by the position estimation means. It can be set as the aspect which corrects.

  As described above, by using the object presence reliability as the presence information, it is possible to accurately detect the surrounding objects.

  In addition, the object presence information correcting unit corrects the object existence reliability of the peripheral object to be small when the estimated relative position of the peripheral object is between the current position of the peripheral object and the position of the moving object. It can be.

  When the estimated position of the surrounding object is between the current position of the surrounding object and the position of the moving object, the estimated surrounding object is highly likely to be based on erroneous detection. For this reason, when the estimated position of the surrounding object is between the current position of the surrounding object and the position of the vehicle, the estimated position of the object is more accurately deleted by deleting the memory of the estimated surrounding object. Can be detected.

  Further, the object presence information correcting means has an object presence reliability in the current detection result that exceeds a predetermined threshold value, and the estimated relative position of the surrounding object is determined based on the current surrounding object. When the position is between the position and the position of the moving body, the object existence reliability of the surrounding objects can be corrected to be small.

  Thus, by setting a predetermined threshold value for the object presence reliability in advance, when the reliability is equal to or lower than the threshold, the reliability at the estimated position is not corrected. . For example, if the detection accuracy of the surrounding object detection means is low and the reliability of the detection result is low, and it cannot be determined whether there is an object from the detection result, the object existence reliability is set low from the beginning. . In this case, the object presence information correcting means can leave the presence reliability of the position of the surrounding object as it is without minimizing it. As a result, more reliable control can be performed in consideration of the reliability of the sensor.

  In addition, the object presence reliability predicting unit can reduce the object presence reliability of the surrounding objects with time.

  Since the object existence reliability decreases with the passage of time, the object existence reliability can be accurately predicted by lowering the object existence reliability of surrounding objects with the passage of time.

  Further, the peripheral object detection means includes a transmission means including a radar sensor that emits a laser beam and scans the periphery of the vehicle, a reception means that receives a reflected beam from the object of the laser beam emitted by the radar sensor, and a reception means And a coordinate specifying means for specifying the coordinates of the reflection point where the object is present.

  By providing such transmitting means and receiving means, and coordinate specifying means for specifying the coordinates where the object exists based on the reception result of the receiving means, the presence of the object can be detected with higher accuracy. Furthermore, it becomes possible to easily calculate the position of the object and the like performed later.

  Further, the object presence reliability predicting means predicts the object existence reliability of the surrounding object at the reflection point where the surrounding object exists higher than the object existence reliability of the surrounding object in the coordinates between the reflection point and the receiving means. It can be set as an aspect.

  As described above, the recognition processing of the scanned data is performed by predicting the recognition reliability of the object at the reflection point where the object exists higher than the recognition reliability of the object at the coordinates between the reflection point and the receiving unit. The recognition reliability of the object can be calculated without any problem. Therefore, it is possible to greatly reduce the calculation load when calculating the recognition reliability of the object.

  In addition, the driving support apparatus according to the present invention that has solved the above problems includes a peripheral object detection unit that detects a peripheral object that is an object in the vicinity of a moving body, and the presence of a peripheral object based on past detection results by the peripheral object detection unit. Based on the information and the amount of movement of the moving body, position estimation means for estimating the current relative position between the surrounding object and the moving body, the current detection result of the surrounding object by the surrounding object detection means, and the estimation result by the position estimation means An object presence information correcting unit that corrects the presence information of the surrounding object, and a driving support unit that supports driving of the vehicle based on the monitoring result of the surrounding object by the periphery monitoring unit. It is characterized by providing.

  In the driving assistance apparatus according to the present invention, driving assistance for the vehicle is performed based on the position of the object monitored by the periphery monitoring means. At this time, if an estimation error occurs in the relative position of the vehicle with respect to the object, proper driving assistance is hindered. In this respect, in the driving support device according to the present invention, based on the current detection result of the peripheral object by the peripheral object detection means and the estimation result by the position estimation means, the presence information of the peripheral object is corrected to Monitoring. For this reason, when an estimation error occurs in the relative position of the vehicle, the object in which the estimation error has occurred can be deleted. Therefore, calculation for extra objects can be omitted, so that the amount of calculation when detecting objects can be reduced, thereby preventing an increase in estimation error of objects and the situation where calculation cannot be performed, thereby preventing the existence of objects. Based driving assistance.

  On the other hand, the peripheral monitoring method according to the present invention that has solved the above problems includes a peripheral object detection step of detecting a peripheral object that is an object in the vicinity of a moving body, and the presence of a peripheral object based on past detection results of the peripheral object detection step. Based on the information and the amount of movement of the moving object, the position estimation process for estimating the current relative position between the surrounding object and the moving object, the current detection result of the surrounding object by the surrounding object detection process, and the estimation result by the position estimation process And an object presence information correction step of correcting the presence information of surrounding objects based on the above.

  According to the periphery monitoring device and the periphery monitoring method according to the present invention, it is possible to reduce the amount of calculation when detecting an object, thereby preventing an increase in the estimation error of the object and a situation where the calculation becomes impossible. Further, according to the driving support device of the present invention, the amount of calculation when detecting an object is reduced, thereby preventing an increase in the estimation error of the object and a situation in which the calculation becomes impossible, and driving based on the presence of the object. Can provide support.

It is a block block diagram which shows the structure of the driving assistance device which concerns on embodiment of this invention. (A) is a schematic diagram which shows the relationship between a vehicle and an obstruction, (b) is a graph which shows the obstruction reliability in each grid. It is a flowchart which shows the process sequence in a driving assistance device. It is a figure which shows an obstacle map. (A) is a figure which shows the obstacle map before reducing the recognition reliability of the grid which shows an imaginary obstacle, (b) is after reducing the recognition reliability of the grid which shows an imaginary obstacle. It is a figure which shows an obstacle map.

  Hereinafter, embodiments of the present invention will be described with reference to the accompanying drawings. In the description of the drawings, the same elements are denoted by the same reference numerals, and redundant description is omitted. For the convenience of illustration, the dimensional ratios in the drawings do not necessarily match those described.

  FIG. 1 is a block configuration diagram of a driving support device including a periphery monitoring device according to an embodiment of the present invention. A driving assistance ECU (Electronic Control Unit) 1 is a computer of an electronically controlled automobile device, and includes a CPU (Central Processing Unit), a ROM (Read Only Memory), a RAM (Random Access Memory), an input / output interface, and the like. ing.

  As shown in FIG. 1, a lidar (LIDAR: Laser Imaging Detection and Ranging) sensor 2, a vehicle motion sensor 3, and a driving assistance actuator 4 are connected to the driving assistance ECU 1. The driving support ECU 1 also includes an obstacle detection unit 10, a past history update unit 11, an obstacle map voting unit 12, and an obstacle map memory 13. Further, the driving support ECU 1 includes a recognition reliability calculation unit 14, a movement error ghost removal unit 15, and a support content determination unit 16. In the driving assistance ECU 1, a vehicle that is a moving body by an obstacle detection unit 10, a past history update unit 11, an obstacle map voting unit 12, an obstacle map memory 13, a recognition reliability calculation unit 14, and a movement error ghost removal unit 15. The surroundings are monitored and driving assistance is provided based on the monitoring results. In addition, as a moving body, it can also be set as other moving bodies, such as an aircraft and a ship, besides a vehicle.

  The lidar sensor 2 uses a remote sensing technology using light, measures scattered light with respect to laser irradiation that emits light in pulses, scans and searches an object at a long distance, etc. The distance is detected. The lidar sensor 2 includes a transmitting unit that includes a radar sensor that emits a laser beam and scans the periphery of the vehicle, and a receiving unit that receives a reflected beam obtained by reflecting the laser beam emitted from the radar sensor.

  In the lidar sensor 2, when the laser beam is projected horizontally and the laser reflection returns, the position where the laser is reflected is the position where an object such as an obstacle exists, the front is an empty space, the back side is It becomes an unknown space that cannot be seen from the lidar sensor 2. The lidar sensor 2 includes coordinate position specifying means for specifying the coordinates of the reflection point where the object exists based on the reception result of the receiving means. The lidar sensor 2 constitutes the object recognition means of the present invention. Further, the presence of the object can be detected with higher accuracy by including such a transmitting unit and a receiving unit, and a coordinate specifying unit that specifies the coordinates where the object exists based on the reception result of the receiving unit. Furthermore, it becomes possible to easily calculate the position on the coordinates (grid) of the obstacle performed later.

  The rider sensor 2 transmits coordinate information regarding the detected coordinates of the object to the obstacle detection unit 10 in the driving assistance ECU 1. In addition to the lidar sensor 2, a sensor that can measure a distance to an object such as a millimeter wave radar sensor or a stereo camera can be used as a sensor that detects an object around the vehicle. The lidar sensor 2 constitutes the peripheral object detection means of the present invention.

  The vehicle motion sensor 3 includes a sensor capable of estimating the amount of movement of the vehicle, such as an internal sensor such as a vehicle speed sensor or a yaw rate sensor, or an external sensor. The vehicle motion sensor 3 estimates the amount of movement of the vehicle and transmits the amount of movement of the vehicle, which is the estimation result, to the past history update unit 11 in the driving assistance ECU 1 as movement amount information.

  The obstacle detection unit 10 in the driving assistance ECU 1 detects and recognizes an object around the vehicle as an obstacle based on the coordinate information transmitted from the lidar sensor 2. Obstacles detected here include movable objects such as other vehicles and pedestrians, and stationary objects such as walls, signs, and buildings. The obstacle detection unit 10 recognizes the detected obstacle and outputs obstacle information regarding the coordinates of the recognized obstacle to the obstacle map voting unit 12.

  The past history update unit 11 acquires the movement amount of the vehicle based on the movement amount information transmitted from the vehicle motion sensor 3. The past history update unit 11 estimates the position of the past object in the obstacle map stored in the obstacle map memory and the relative position of the vehicle with respect to the object, and moves the object according to the amount of movement of the vehicle. Get the current position. When moving the position of the object, the following equation (1) can be used by using the translation amount T and the rotation amount R of the vehicle. The past history update unit 11 outputs movement amount information regarding the movement amount when the object is moved to the obstacle map voting unit 12 and the recognition reliability calculation unit 14. The past history update unit 11 constitutes position estimation means of the present invention.

NewP = P · R + T (1)
In the above equation (1), NewP: position of the object after movement
P: Position of the object before moving

  The obstacle map voting unit 12 calculates an object detection result as an obstacle detection result based on the obstacle information output from the obstacle detection unit 10 and the movement amount information output from the past history update unit 11. . The obstacle map voting unit 12 votes the calculated obstacle detection result on the obstacle map stored in the obstacle map memory 13.

  The obstacle map memory 13 stores the latest obstacle detection result written by the obstacle map voting unit 12 as an obstacle map. The obstacle detection result may be recorded in the 3D or 2D coordinate position of the scan point (search point) by the lidar sensor 2 as it is, or may be stored in a grid-like map partitioned into small areas. Thus, the memory usage can be reduced. The obstacle map memory 13 outputs the stored obstacle map to the recognition reliability calculation unit 14 and the movement error ghost removal unit 15 in response to the reading of the recognition reliability calculation unit 14 and the movement error ghost removal unit 15. . The obstacle map memory 13 functions as object storage means.

  The recognition reliability calculation unit 14 reads the obstacle map stored in the obstacle map memory 13 and recognizes an obstacle as an object corresponding to the object existence reliability of the surrounding object in the present invention based on the obstacle map. Predict confidence. The recognition reliability is expressed as a reliability regarding the presence of an obstacle. The recognition reliability calculation unit 14 writes the calculated recognition reliability in the obstacle map stored in the obstacle map memory 13. The obstacle map stored in the obstacle map memory 13 stores the recognition reliability of the obstacle together. The recognition reliability calculation unit 14 constitutes an object presence reliability prediction unit of the present invention.

  As shown in FIG. 2A, when the laser reflection irradiated horizontally from the lidar sensor provided in the vehicle M is returned, the place where the laser is reflected is a place where there is an extremely high possibility that the obstacle W exists. It becomes. For this reason, when the obstacle map voting unit 12 performs voting, the n-th grid corresponding to the location where the laser is reflected is a grid having the highest recognition reliability as shown in FIG. Is determined.

  In addition, when the obstacle map voting unit 12 performs voting, the first grid to the (n−1) -th grid corresponding to the position before the n-th grid corresponding to the location where the laser is reflected are obstructions. Since it is a place where the possibility of existing is extremely low, it is determined that the grid has the minimum recognition reliability. Furthermore, since the grid larger than the (n + 1) -th grid corresponding to the position behind the laser-reflected location is a location where the presence of an obstacle is unknown, the recognition reliability is medium, and the minimum and maximum values It is determined that the grid has an intermediate value between.

  In this manner, the recognition reliability of the obstacle at the reflection point where the obstacle exists is predicted to be higher than the recognition reliability of the obstacle at the coordinates between the reflection point and the receiving means, thereby recognizing the scanned data. The recognition reliability of the obstacle can be calculated without performing processing. Therefore, it is possible to greatly reduce the calculation load when calculating the obstacle recognition reliability.

  Furthermore, the recognition reliability calculation part 14 updates recognition reliability about grids other than the recognition reliability becoming an intermediate value with time changes, such as progress of time. The recognition reliability is updated by attenuating it so as to approach the intermediate value. The rate at which the recognition reliability is attenuated is, for example, a rate at which the recognition reliability that is the minimum value or the maximum value becomes an intermediate value after T seconds. When the recognition reliability is updated, the recognition reliability calculation unit 14 writes the updated recognition reliability in the obstacle map stored in the obstacle map memory 13. The recognition reliability calculation unit 14 constitutes an object presence information correction unit of the present invention.

  The movement error ghost removing unit 15 reads the obstacle map stored in the obstacle map memory, and is generated in the obstacle map based on the recognition reliability of the obstacle in each grid stored in the obstacle map. Eliminate moving error ghosts. The movement error ghost removing unit 15 outputs a corrected obstacle map, which is an obstacle map after correcting the movement error ghost by deleting the movement error ghost, to the support content determination unit 16. The movement error ghost removing unit 15 functions as an object erasing unit. Note that the movement error ghost removing unit 15 may reduce the recognition reliability of the object corresponding to the movement error ghost instead of deleting the movement error ghost.

  The support content determination unit 16 recognizes obstacles around the vehicle based on the corrected obstacle map output from the movement error ghost removal unit 15. The support content determination unit 16 determines the support content of the vehicle based on the obstacle around the recognized vehicle. The contents of assistance include, for example, formulating an avoidance route that avoids a collision with an obstacle, and performing acceleration / deceleration of the vehicle and correction of a steering angle so as to travel along the established avoidance route. The support content determination unit 16 transmits support content information corresponding to the determined support content to the driving support actuator 4. The driving assistance ECU 1 including the assistance content determination unit 16 constitutes driving assistance means of the present invention.

  The driving assistance actuator 4 includes a throttle actuator, a brake actuator, a steering actuator, and the like. The driving assistance actuator 4 operates these actuators according to the assistance content information transmitted from the assistance content determination unit 16 to provide driving assistance for the vehicle.

  Next, the operation of the driving support apparatus according to the present embodiment will be described. FIG. 3 is a flowchart showing a processing procedure in the driving support apparatus. As shown in FIG. 3, in the driving assistance apparatus according to the present embodiment, first, the vicinity of the vehicle is scanned and searched by the lidar sensor 2 (S1). Next, the vehicle movement sensor 3 estimates the amount of vehicle movement (S2).

  Subsequently, the obstacle detection unit 10 detects an obstacle around the vehicle from the information around the vehicle searched by the lidar sensor 2 (S3). Then, the past history update unit 11 updates the past history that moves the position of the past object based on the movement amount of the vehicle (S4).

  When the past history is updated, the obstacle map voting unit 12 detects obstacles around the vehicle, and the obstacle map in the obstacle map memory 13 is detected for the position where the obstacle that is the position of the object is detected. Vote (S5). As shown in FIG. 4, the obstacle map is a map representing a grid in which obstacles are detected. In the obstacle map shown in FIG. 4, the grid whose recognition reliability is a value larger than the intermediate value is shown in black. Further, the grid having the minimum recognition reliability is shown in white, and the region including the grid having the intermediate recognition reliability is hatched. Here, the area between the vehicle and the obstacle shown in white is an object non-existing area where the object of the present invention is absent. Furthermore, the map shown in FIG. 4 also displays the recognition reliability of obstacles recognized in the past. For this reason, a grid having a minimum recognition reliability exists behind the grid having a medium recognition reliability.

  Then, the recognition reliability in each grid of the obstacle map stored in the obstacle map memory 13 is calculated (S6). In calculating the recognition reliability in each grid, the obstacle maps before and after voting in the obstacle map voting unit 12 are compared. Here, for the grid where the obstacle map has been voted, the recognition reliability is set to the highest value to be the highest value grid, and between the highest value grid and the grid where the vehicle exists (hereinafter referred to as “vehicle grid”). For each grid in, the recognition reliability is set to the minimum value.

  In addition, for the grid other than each grid between the highest value grid and the highest value grid and the vehicle grid, the recognition reliability is attenuated so as to approach an intermediate value whose recognition reliability is unknown as time elapses. Specifically, when the recognition reliability is higher than unknown, the recognition reliability is decreased, and when the recognition reliability is lower than unknown, the recognition reliability is increased. The attenuation rate at this time is set to such an extent that the recognition reliability is attenuated to an intermediate value after T seconds from the state where the recognition reliability is the maximum value or the minimum value. Further, the recognition reliability is maintained for the grid whose recognition reliability is an intermediate value. Therefore, the recognition reliability has other values between the maximum value, the minimum value, and the intermediate value.

  After the recognition reliability is calculated in this way, it is determined whether or not the recognition reliability in each grid exceeds a predetermined threshold value (S7). The predetermined threshold here is set to a value larger than the intermediate value. As a result, the recognition reliability is maintained as it is for the grid whose recognition reliability does not exceed the predetermined threshold.

  For a grid whose recognition reliability exceeds a predetermined threshold (hereinafter referred to as “target grid”), it is determined whether or not the target grid is located between the highest value grid and the vehicle grid. (S8). As a result, if it is determined that the target grid is located between the highest value grid and the vehicle grid, the recognition reliability in the target grid is determined to be a movement error ghost and the movement error ghost is removed. (S9), the recognition reliability is set to the minimum value.

  In detecting an obstacle, in addition to using the lidar sensor 2, the vehicle movement sensor 3 such as a yaw rate sensor detects the amount of movement to detect the relative positional relationship between the vehicle and the obstacle. However, the detection by the vehicle motion sensor 3 is not necessarily complete, and the relative positional relationship between the vehicle and the obstacle is misrecognized. Based on this misrecognition, an imaginary obstacle is erroneously detected as an obstacle. May end up. In particular, when the detection accuracy of the vehicle motion sensor 3 is low, this tendency becomes remarkable.

  For false obstacles, the possibility of detecting obstacles to be detected later will be low, but false obstacles detected in the past remain recorded in the obstacle map. . When determining driving assistance contents such as the establishment of an avoidance route, if an imaginary obstacle remains, it is assumed that the formulation of an appropriate avoidance route will be hindered.

  On the other hand, in the latest scan result, as shown in FIG. 5A, when there is a grid whose reliability is greater than the intermediate value between the highest value grid BG detected as an obstacle and the vehicle grid MG. This grid is considered to be a grid GG showing an imaginary obstacle based on a movement error ghost. In addition, this grid is considered to be a grid with no obstacles. For this reason, a grid whose recognition reliability is greater than the intermediate value between the maximum value grid BG and the vehicle grid MG is determined as a grid GG indicating an imaginary obstacle based on a movement error ghost, and FIG. As shown in b), the recognition reliability is corrected to the minimum value.

  These operations are performed for all grids on the obstacle map. Then, the recognition reliability of the grid indicating the imaginary obstacle based on the movement error ghost between the maximum value grid and the vehicle grid is corrected to the minimum value. As a result, as shown in FIG. 5B, the obstacle map in which the recognition reliability of the grid indicating the imaginary obstacle based on the movement error ghost between the highest value grid and the vehicle grid is corrected to the minimum value is obtained. Can be generated.

  If it is determined in step S8 that the target grid is not located between the maximum value grid and the vehicle grid, it cannot be determined whether or not it is a movement error ghost. For this reason, about the object grid which is not located between the highest value grid and the vehicle grid, the recognition reliability is maintained as it is because the past recognition reliability is used.

  Thereafter, the support content determination unit 16 determines the support content (S10). As assistance contents here, an avoidance route to avoid collision with obstacles is formulated. And the assistance content determination part 16 transmits assistance content information with respect to the driving assistance actuator 4 so that it may drive | work the prepared avoidance route.

  In formulating an avoidance route, there is a high possibility that an obstacle exists for a grid with high recognition reliability, and it is required to formulate an avoidance route avoiding this grid. Here, if the recognition reliability of the grid indicating the imaginary obstacle based on the movement error ghost remains in a high state, the amount of calculation when formulating the avoidance route becomes enormous, resulting in an obstacle estimation error, Furthermore, it may become impossible to calculate. In this respect, in the present embodiment, by reducing the recognition reliability of the grid indicating an imaginary obstacle, the amount of calculation is reduced without greatly reducing the recognition reliability regarding the obstacle. Thus, the process in the driving support device is finished.

  Thus, in the driving assistance apparatus according to the present embodiment, driving assistance for the vehicle is performed based on the position of the obstacle according to the obstacle map stored in the obstacle map memory 13. At this time, if an estimation error occurs in the relative position of the vehicle with respect to the obstacle, proper driving assistance is hindered. In this regard, in the driving support device according to the present embodiment, the recognition reliability is corrected to the minimum value for the grid between the maximum value grid and the vehicle grid. For this reason, when an estimation error occurs in the relative position of the vehicle, the recognition reliability of the grid corresponding to the obstacle in which the estimation error has occurred can be eliminated. Therefore, since it is possible to omit calculations for extra obstacles, it is possible to reduce the amount of calculation when detecting obstacles, thereby preventing an increase in the estimation error of obstacle objects and the situation where calculation becomes impossible. Driving assistance based on the presence of objects can be performed.

  DESCRIPTION OF SYMBOLS 1 ... Driving assistance ECU, 2 ... Rider sensor, 3 ... Vehicle motion sensor, 4 ... Driving assistance actuator, 10 ... Obstacle detection part, 11 ... Past history update part, 12 ... Obstacle map voting part, 13 ... Obstacle map memory , 14 ... recognition reliability calculation unit, 15 ... movement error ghost removal unit, 16 ... support content determination unit, M ... vehicle, W ... obstacle.

Claims (9)

Peripheral object detection means for detecting a peripheral object that is an object in the vicinity of the moving body;
Position estimating means for estimating a current relative position between the peripheral object and the moving body based on presence information of the peripheral object based on past detection results by the peripheral object detecting means and a moving amount of the moving body;
Object presence information correction means for correcting presence information of the peripheral object based on a current detection result of the peripheral object by the peripheral object detection means and an estimation result by the position estimation means;
A periphery monitoring device comprising: Based on the detection result by the surrounding object detection means, comprising object presence reliability prediction means for predicting the object existence reliability, which is the reliability that the surrounding object exists,
The presence information of the peripheral object based on the detection result of the peripheral object is an object presence reliability of the peripheral object,
The object presence information correcting unit corrects the object presence reliability of the peripheral object based on a current detection result of the peripheral object by the peripheral object detection unit and an estimation result by the position estimation unit. Perimeter monitoring device.   The object presence information correction means reduces the object existence reliability of the peripheral object when the estimated relative position of the peripheral object is between the current position of the peripheral object and the position of the moving object. The periphery monitoring device according to claim 2 to be corrected.   The object presence information correcting means has an object presence reliability in a current detection result that exceeds a predetermined threshold value, and the estimated relative position of the surrounding object is the current surrounding object. The surroundings monitoring apparatus according to claim 2 or 3, wherein the object existence reliability of the surrounding objects is corrected to be small when the position is between the position of the moving object and the position of the moving body.   The periphery monitoring device according to any one of claims 2 to 4, wherein the object presence reliability predicting unit lowers the object presence reliability of the peripheral objects with time. The surrounding object detection means includes
Transmitting means including a radar sensor that emits a laser beam and scans the periphery of the vehicle;
Receiving means for receiving a reflected beam from the object of the laser beam emitted by the radar sensor;
Coordinate specifying means for specifying the coordinates of the reflection point where the object exists based on the reception result of the receiving means;
The periphery monitoring device according to any one of claims 1 to 5, further comprising:   The object existence reliability predicting means calculates the object existence reliability of the surrounding object at the reflection point where the surrounding object exists, based on the object existence reliability of the surrounding object in the coordinates between the reflection point and the receiving means. The periphery monitoring device according to claim 6, which predicts a higher value. Peripheral object detection means for detecting a peripheral object that is an object in the vicinity of the moving body;
Position estimating means for estimating a current relative position between the peripheral object and the moving body based on presence information of the peripheral object based on past detection results by the peripheral object detecting means and a moving amount of the moving body;
Object presence information correction means for correcting presence information of the peripheral object based on a current detection result of the peripheral object by the peripheral object detection means and an estimation result by the position estimation means;
Surrounding monitoring means including:
Driving support means for supporting driving of the vehicle based on the monitoring results of surrounding objects by the surrounding monitoring means;
A driving support apparatus comprising: A peripheral object detection step of detecting a peripheral object that is an object around the moving body;
A position estimation step of estimating a current relative position between the peripheral object and the moving body based on presence information of the peripheral object based on a past detection result by the peripheral object detection step and a moving amount of the moving body;
An object presence information correction step of correcting presence information of the peripheral object based on a current detection result of the peripheral object by the peripheral object detection step and an estimation result by the position estimation step;
A peripheral monitoring method comprising:

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