Mobile object control system and mobile object control method

Abstract

To improve continuity of driving assist control to be performed based on a result of localizing processing in a mobile body.SOLUTION: A mobile body control system executes localizing processing to estimate a position of a mobile body based on sensor detection information detected by sensors mounted on the mobile body and feature object map information. The mobile body control system also performs driving assist control based on the estimated position of the mobile body. A first range is the range which includes the estimated position of the mobile body and becomes larger as reliability of the localizing processing becomes lower. A first future range is at least a part of a prediction passing range of the first range. The mobile body control system allows the mobile body to decelerate or stop when there is an obstacle in the first range or the first future range. The mobile body control system continues the driving assist control when there is no obstacle in the first range or the first future range.SELECTED DRAWING: Figure 4

Description

[Technical field]

[0001] The present disclosure relates to a technique for controlling a moving object that executes a localization process (self-position estimation process). [Background technology]

[0002] Patent Document 1 discloses a control system for an autonomous vehicle. In this technology, an autonomous driving reliability value is calculated based on the self-location estimation accuracy, and when the autonomous driving reliability value falls below a reference value, a request to end autonomous driving is notified to the driver. [Prior art documents] [Patent documents]

[0003] [Patent Document 1] JP 2018-032321 A Summary of the Invention [Problem to be solved by the invention]

[0004] A localization process for estimating the position of a moving object is known. The position of the moving object estimated by the localization process is used, for example, in driving assistance control (e.g., automatic driving control) that assists the driving of the moving object. Therefore, when the accuracy of the localization process (i.e., the reliability of the localization process) decreases, it is considered to interrupt the driving assistance control. However, interrupting the driving assistance control more than necessary leads to a decrease in the traveling efficiency of the moving object.

[0005] One object of the present disclosure is to provide a technology capable of improving the continuity of driving assistance control performed in a moving object based on the results of localization processing. [Means for solving the problem]

[0006] A first aspect of the present disclosure relates to a mobile object control system for controlling a mobile object. The mobile control system is one or more processors; one or more storage devices for storing feature map information indicating the positions of features; Equipped with. The one or more processors Acquire sensor detection information detected by a sensor mounted on the moving object; A localization process is executed to estimate a position of the moving object based on the sensor detection information and the feature map information; Executing driving assistance control to assist driving of a moving object based on an estimated position of the moving object It is configured as follows. The first range includes the estimated position of the moving object, and becomes larger as the reliability of the localization process becomes lower. The first future range is at least a portion of the expected passage range of the first range. The one or more processors may further comprise: determining whether an obstacle is present in the first range or the first future range; If an obstacle is present in the first range or the first future range, the moving body is slowed down or stopped. If no obstacle is present within the first range or the first future range, driving assistance control is continued.

[0007] The second aspect relates to a moving object control method for controlling a moving object. The moving object control method includes: Acquiring sensor detection information detected by a sensor mounted on the moving object; Executing a localization process to estimate a position of a moving object based on the sensor detection information and feature map information indicating the positions of the feature objects; Executing a driving assistance control for assisting driving of the moving body based on the estimated position of the moving body; Includes. The first range includes the estimated position of the moving object, and becomes larger as the reliability of the localization process becomes lower. The first future range is at least a portion of the expected passage range of the first range. The moving object control method further includes: Determining whether an obstacle is present in the first range or the first future range; slowing down or stopping the moving body when an obstacle is present in the first range or the first future range; If no obstacle is present within the first range or the first future range, continuing the driving assistance control; Includes. Effect of the Invention

[0008] According to the present disclosure, the lower the reliability of the localization process, the larger the first range or the first future range is set. If an obstacle is present in the first range or the first future range, safety control is executed to decelerate or stop the moving body. On the other hand, if no obstacle is present in the first range or the first future range, the driving assistance control is continued. In other words, even if the reliability of the localization process is reduced, the driving assistance control is continued as long as the traveling safety of the moving body is ensured. Since the driving assistance control is not interrupted more than necessary, the continuity of the driving assistance control is improved. Furthermore, since the continuity of the driving assistance control is improved, the traveling efficiency of the moving body is also improved. [Brief description of the drawings]

[0009] [Figure 1] 1 is a block diagram showing an overview of a vehicle control system according to an embodiment; [Diagram 2] FIG. 13 is a conceptual diagram for explaining the reliability of the localization process. [Diagram 3] 4 is a conceptual diagram for explaining a first example of a continuation determination process for driving assist control according to an embodiment. FIG. [Figure 4] 5 is a flowchart showing a first example of a continuation determination process for driving assist control according to an embodiment. [Diagram 5] FIG. 11 is a conceptual diagram for explaining a second example of the continuation determination process of the driving assist control according to the embodiment. [Figure 6] 6 is a flowchart showing a second example of the continuation determination process for driving assist control according to the embodiment. DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

[0010] The present disclosure relates to control of a moving body. Examples of the moving body include a vehicle, a robot, and the like. The moving body may be capable of autonomous driving. The vehicle may be a self-driving vehicle. As an example, in the following description, a case where the moving body is a vehicle will be considered. When generalizing, "vehicle" in the following description will be read as "moving body".

[0011] 1. Overview of the vehicle control system 1-1. Configuration example 1 is a block diagram showing an overview of a vehicle control system 10 according to the present embodiment. The vehicle control system 10 controls a vehicle 1. The vehicle control system 10 includes an internal sensor 20, an external sensor 30, a GNSS (Global Navigation Satellite System) sensor 40, a traveling device 50, and a control device 100.

[0012] The internal sensor 20 is mounted on the vehicle 1 and detects the traveling state of the vehicle 1. Examples of the internal sensor 20 include a vehicle speed sensor (wheel speed sensor), a steering angle sensor, an acceleration sensor, and a yaw rate sensor.

[0013] The external sensor 30 is mounted on the vehicle 1 and detects the surrounding conditions of the vehicle 1. Examples of the external sensor 30 include a LIDAR (Laser Imaging Detection and Ranging), a camera, and a radar.

[0014] The traveling device 50 includes a steering device, a drive device, and a brake device. The steering device steers the wheels. The drive device generates a drive force. The brake device generates a braking force.

[0015] The control device 100 is a computer that controls the vehicle 1. The control device 100 includes one or more processors 110 (hereinafter simply referred to as processor 110) and one or more storage devices 120 (hereinafter simply referred to as storage device 120). The processor 110 executes various processes. For example, the processor 110 includes a CPU (Central Processing Unit). The storage device 120 stores various information. Examples of the storage device 120 include a volatile memory, a non-volatile memory, an HDD (Hard Disk Drive), and an SSD (Solid State Drive). Typically, the control device 100 is mounted on the vehicle 1. Alternatively, a part of the control device 100 may be disposed in an external device outside the vehicle 1 and control the vehicle 1 remotely.

[0016] The storage device 120 stores a vehicle control program 210, vehicle driving information 220, sensor detection information 230, map information 240, localization result information 250, and the like.

[0017] The vehicle control program 210 is a computer program for controlling the vehicle 1. The processor 110 executes the vehicle control program 210 to realize various processes by the control device 100. The vehicle control program 210 may be recorded in a computer-readable recording medium.

[0018] The vehicle driving information 220 is information detected by the internal sensor 20, and indicates the driving state of the vehicle 1. The driving state of the vehicle 1 includes the vehicle speed, the steering angle, the acceleration, the yaw rate, and the like.

[0019] The sensor detection information 230 is information detected by the external sensor 30. For example, the sensor detection information 230 includes point cloud information obtained by a LIDAR. As another example, the sensor detection information 230 may include an image captured by a camera.

[0020] Furthermore, the sensor detection information 230 includes object information on objects around the vehicle 1. Examples of objects around the vehicle 1 include pedestrians, other vehicles, white lines, road structures, obstacles, and feature objects FE. The feature objects FE are objects (landmarks) used in the localization process described later. Examples of feature objects FE include lanes, curbs, poles, telephone poles, signs, signs, and building corners. The feature objects FE can also be called "feature amounts." The object information indicates the relative position and relative speed of an object with respect to the vehicle 1. For example, an object can be recognized based on point cloud information obtained by LIDAR, and the relative position and relative speed of the object can be obtained. As another example, an object can be recognized and identified by analyzing an image obtained by a camera, and the relative position of the object can be calculated.

[0021] The map information 240 includes a general navigation map. The map information 240 may indicate lane arrangements and road shapes. Furthermore, the map information 240 includes "feature object map information MAP-FE" and "stationary object map information MAP-ST". The feature object map information MAP-FE indicates the position (absolute position) of the feature object FE in an absolute coordinate system. This feature object map information MAP-FE is used in a localization process described later. The stationary object map information MAP-ST indicates the position (absolute position) of a stationary object ST in an absolute coordinate system. The stationary object ST is, for example, an immovable road structure such as a wall or a guardrail. This stationary object map information MAP-ST is used in a continuation determination process of the driving assistance control described later.

[0022] 1-2. Localization processing (self-position estimation processing) The processor 110 executes a localization process to estimate the position (absolute position) of the vehicle 1. The position of the vehicle 1 will hereinafter be simply referred to as the “vehicle position.” An initial value of the vehicle position is obtained by the GNSS sensor 40, for example.

[0023] The processor 110 acquires the vehicle driving information 220, calculates the amount of movement (displacement) of the vehicle 1 based on the steering angle and vehicle speed of the vehicle 1, and estimates a rough vehicle position based on the amount of movement. The method of estimating the vehicle position based on the steering angle and vehicle speed is also called "dead reckoning."

[0024] Furthermore, the processor 110 acquires the sensor detection information 230, and recognizes (extracts) a feature FE around the vehicle 1 based on the sensor detection information 230. The object information indicates the relative position of the recognized feature FE with respect to the vehicle 1. The absolute position of the recognized feature FE is calculated by combining the vehicle position obtained by dead reckoning with the relative position of the recognized feature FE. Meanwhile, the absolute position of the feature FE is registered in the feature map information MAP-FE. The processor 110 corrects the vehicle position so that the absolute position of the recognized feature FE and the absolute position of the feature FE obtained from the feature map information MAP-FE match as closely as possible. In other words, the processor 110 corrects the vehicle position by comparing the recognition result of the feature FE by the external sensor 30 with the feature map information MAP-FE.

[0025] The processor 110 can continuously obtain a highly accurate vehicle position by repeatedly performing the position estimation by dead reckoning and the above-described position correction.

[0026] The localization result information 250 indicates the result of the localization process. Specifically, the localization result information 250 indicates the vehicle position estimated by the localization process. The localization result information 250 may further indicate the reliability of the localization process, which will be described later.

[0027] 1-3. Driving assistance control The processor 110 executes vehicle driving control for controlling the driving of the vehicle 1. The vehicle driving control includes steering control, acceleration control, and deceleration control. The processor 110 executes the vehicle driving control by controlling the driving device 50 (steering device, driving device, braking device).

[0028] The processor 110 also executes driving assistance control to assist the driving of the vehicle 1. The driving assistance control automatically performs at least one of steering, acceleration, and deceleration without the need for driving operation by the driver. Examples of such driving assistance control include automatic driving control, risk avoidance control, and lane keeping control. In the driving assistance control, the processor 110 generates a target route (target trajectory) of the vehicle 1 based on the vehicle driving information 220, the sensor detection information 230 (object information), the map information 240, and the localization result information 250 (vehicle position information). For example, the target route is generated so as to follow the center position of the lane. As another example, the target route is generated so as to avoid an obstacle in front of the vehicle 1. Then, the processor 110 executes vehicle driving control so that the vehicle 1 follows the target route.

[0029] In order to ensure the accuracy of driving assistance control, the accuracy of the vehicle position, that is, the reliability of the localization process, is important.

[0030] 2. Reliability of localization process FIG. 2 is a conceptual diagram for explaining the reliability of the localization process. Various methods for calculating the reliability have been proposed. In the example shown in FIG. 2, a probability density distribution is defined for the vehicle position estimated by the localization process. The smaller the variance of the probability density distribution, the higher the reliability of the localization process. Conversely, the larger the variance of the probability density distribution, the lower the reliability of the localization process.

[0031] For example, a filter (e.g., particle filter, Kalman filter, UKF, etc.) is used in the localization process. For example, when a particle filter is used, a large number of particles are scattered within the error range of the internal sensor 20. Furthermore, based on the matching result when it is assumed that the vehicle 1 is located at the position of each particle, the likelihood is calculated for each particle. Then, the weighted average of the particle positions calculated with a weight according to the likelihood for each particle is estimated as the vehicle position. Furthermore, the variance of the distribution of the particle positions is calculated. The variance reflects the reliability of the localization process.

[0032] The "first range R1" is a range that reflects the reliability of the localization process. More specifically, the first range R1 includes the vehicle position estimated by the localization process, and becomes larger as the reliability of the localization process becomes lower. For example, when the variance of the probability density distribution of the vehicle position estimated by the localization process is σ 2 The variance σ 2 The smaller the first range R1, the smaller the variance σ 2 The larger the θ is, the larger the first range R1 is. For example, the first range R1 is a range of 3σ from the vehicle position. It can be said that the first range R1 is a range that includes the estimation error of the vehicle position.

[0033] 3. Determination of continuation of driving assistance control When the reliability of the localization process is reduced, it is possible to interrupt the driving assistance control. However, interrupting the driving assistance control more than necessary leads to a decrease in the driving efficiency of the vehicle 1. Therefore, in this embodiment, even if the reliability of the localization process is reduced, the processor 110 does not interrupt the driving assistance control uniformly. The processor 110 takes into consideration driving safety in addition to the reliability of the localization process in order to determine whether to continue or interrupt the driving assistance control. Specifically, the processor 110 checks whether driving safety is ensured, and continues the driving assistance control if driving safety is ensured even if the reliability of the localization process is reduced. Since the driving assistance control is not interrupted more than necessary, the continuity of the driving assistance control is improved. In addition, since the continuity of the driving assistance control is improved, the driving efficiency of the moving body is also improved.

[0034] The process of determining whether or not to continue the driving assist control will hereinafter be referred to as a “continuation determination process.” Below, some specific examples of the continuation determination process will be described.

[0035] 3-1. First Example 3 is a conceptual diagram for explaining a first example of the continuation determination process. In the first example, the continuation determination process is performed based on the above-mentioned first range R1 and the positions of stationary objects ST existing around the vehicle 1.

[0036] Specifically, the processor 110 recognizes a stationary object ST existing around the vehicle 1 as an obstacle. The position (absolute position) of the stationary object ST in the absolute coordinate system is obtained from the stationary object map information MAP-ST. Therefore, the processor 110 can recognize the stationary object ST existing around the vehicle 1 as an obstacle based on the vehicle position (estimated position) and the stationary object map information MAP-ST. Note that when there are multiple stationary objects ST, the processor 110 may recognize the stationary object ST that is closest to the vehicle 1 among the multiple stationary objects ST as an obstacle.

[0037] In case (A) of FIG. 3, no stationary object ST (obstacle) exists in the first range R1. The first range R1 includes an estimation error of the vehicle position, and the absence of a stationary object ST in the first range R1 means that the driving safety of the vehicle 1 is sufficiently ensured. Therefore, in this case, the processor 110 continues the driving assistance control without interruption. The higher the reliability of the localization process, the smaller the first range R1 becomes, and the lower the possibility that a stationary object ST exists in the first range R1. Therefore, the higher the reliability of the localization process, the higher the possibility that the driving assistance control will continue. However, even if the reliability of the localization process decreases and the first range R1 becomes larger, if no stationary object ST exists in the first range R1, the driving safety is ensured, and the driving assistance control will continue. This improves the continuity of the driving assistance control.

[0038] In cases (B) and (C) of Fig. 3, a stationary object ST (obstacle) is present in the first range R1. The first range R1 includes an estimation error of the vehicle position, and the presence of a stationary object ST in the first range R1 means that the traveling safety of the vehicle 1 is not necessarily sufficient. Therefore, the processor 110 executes "safety control" at an early stage to decelerate or stop the vehicle 1. This improves safety.

[0039] In order to determine whether to slow down or stop the vehicle 1 in safety control, a second range R2 narrower than the first range R1 may be used. For example, the first range R1 is a range of 3σ from the vehicle position, and the second range R2 is a range of σ from the vehicle position. In case (B), a stationary object ST is present in the first range R1 but not in the second range R2. In this case (B), the processor 110 executes safety control to slow down the vehicle 1. On the other hand, in case (C), the stationary object ST is present in the second range R2. In this case (C), the processor 110 executes safety control to stop the vehicle 1 early. The processor 110 may stop the vehicle 1 by evacuating it to a safe place such as a road shoulder.

[0040] 4 is a flowchart showing a first example of a continuation determination process. A flow related to a localization process is executed separately.

[0041] In step S100, the processor 110 acquires various information such as stationary object map information MAP-ST, localization result information 250 (vehicle position information, localization processing reliability information), etc. Then, the process proceeds to step S110. The localization processing reliability information includes information on the first range R1 and the second range R2.

[0042] In step S110, the processor 110 recognizes a stationary object ST existing around the vehicle 1 as an obstacle based on the vehicle position information and the stationary object map information MAP-ST. Then, in step S120, the processor 110 determines whether or not a stationary object ST exists in the first range R1. If it is determined that a stationary object ST exists in the first range R1 (step S120; Yes), the process proceeds to step S130. Otherwise (step S120; No), the process proceeds to step S160.

[0043] In step S130, the processor 110 determines whether or not a stationary object ST exists in the second range R2. If it is determined that a stationary object ST exists in the second range R2 (step S130; Yes), the process proceeds to step S140. Otherwise (step S130; No), the process proceeds to step S150.

[0044] In step S140, the processor 110 executes safety control to stop the vehicle 1. In step S150, the processor 110 executes safety control to decelerate the vehicle 1. In step S160, the processor 110 continues the driving assistance control.

[0045] 3-2. Second Example FIG. 5 is a conceptual diagram for explaining a second example of the continuation determination process. The "expected passing range RE" of the first range R1 is a range that the first range R1 is expected to pass through within a certain time from the current time. The first range R1 here is, for example, a range of σ from the vehicle position. The certain time is, for example, several to tens of seconds. The expected passing range RE is obtained based on the traveling direction or target route and speed of the vehicle 1.

[0046] The "first future range RF" is at least a part of the expected passing range RE. For example, the first future range RF is a range of the expected passing range RE that extends beyond the lane in which the vehicle 1 is located. The lane in which the vehicle 1 is located can be recognized based on the white lines on the left and right of the vehicle 1. The positions of the white lines on the left and right of the vehicle 1 are obtained from the sensor detection information 230 (object information). Alternatively, the range of the lane in which the vehicle 1 is located may be obtained from the map information 240.

[0047] In the second example, the continuation determination process is performed based on the first future range RF and the position of an obstacle OBJ existing around the vehicle 1. The obstacle OBJ is, for example, a moving object such as a pedestrian or another vehicle. The processor 110 can recognize the obstacle OBJ existing around the vehicle 1 based on the sensor detection information 230 (object information).

[0048] In cases (A) and (B) in Fig. 5, the first future range RF is the range of the predicted passing range RE that extends beyond the lane in which the vehicle 1 is located. Possible obstacles OBJ include moving objects such as pedestrians on the sidewalk and oncoming vehicles in the oncoming lane.

[0049] In case (A), no obstacle OBJ exists in the first future range RF. In this case, the processor 110 determines that the driving safety of the vehicle 1 is ensured, and continues the driving assistance control. As the reliability of the localization process increases, the first future range RF becomes smaller, and the possibility that an obstacle OBJ exists in the first future range RF becomes lower. Therefore, as the reliability of the localization process increases, the possibility that the driving assistance control will continue increases. However, even if the reliability of the localization process decreases and the first future range RF becomes larger, if no obstacle OBJ exists in the first future range RF, driving safety is ensured, and therefore the driving assistance control will continue. This improves the continuity of the driving assistance control.

[0050] In case (B), an obstacle OBJ exists in the first future range RF. In this case, the processor 110 determines that the traveling safety of the vehicle 1 is not necessarily sufficient, and executes "safety control" to decelerate or stop the vehicle 1 early. For example, the processor 110 stops the vehicle 1 in a safe place such as a road shoulder. This improves safety.

[0051] As shown in case (C), it is also assumed that a pedestrian will enter the roadway. Therefore, the first future range RF may include the lane in which the vehicle 1 exists. That is, the first future range RF may be the same as the expected passing range RE of the first range R1. In general, the first future range RF may be at least a part of the expected passing range RE of the first range R1. At least an effect can be obtained by performing the continuation determination process based on whether or not an obstacle OBJ exists in at least a part of the expected passing range RE of the first range R1.

[0052] 6 is a flowchart showing a second example of the continuation determination process. A flow related to the localization process is executed separately.

[0053] In step S200, the processor 110 acquires various information such as the sensor detection information 230, the localization result information 250 (vehicle position information, localization processing reliability information), etc. Then, the process proceeds to step S210. The localization processing reliability information includes information on the first range R1.

[0054] In step S210, the processor 110 calculates the predicted passing range RE and the first future range RF of the first range R1 based on various information. Then, in step S220, the processor 110 recognizes an obstacle OBJ existing around the vehicle 1 based on the sensor detection information 230 (object information). Then, the process proceeds to step S230.

[0055] In step S230, the processor 110 determines whether or not an obstacle OBJ exists in the first future range RF. If it is determined that an obstacle OBJ exists in the first future range RF (step S230; Yes), the process proceeds to step S240. Otherwise (step S230; No), the process proceeds to step S250.

[0056] In step S240, the processor 110 executes safety control to decelerate or stop the vehicle 1. Preferably, the processor 110 stops the vehicle 1. In step S250, the processor 110 continues the driving assistance control. [Explanation of symbols]

[0057] 1...vehicle, 10...vehicle control system, 20...internal sensor, 30...external sensor, 100...control device, 110...processor, 120...storage device, 230...sensor detection information, FE...feature, ST...stationary object, OBJ...obstacle, MAP-FE...feature map information, MAP-ST...stationary object map information, R1...first range, R2...second range, RE...expected passing range, RF...first future range

Claims

1. A mobile object control system for controlling a mobile object, one or more processors; one or more storage devices for storing feature map information indicating the positions of features; Equipped with The one or more processors: Recognizing features around the moving body based on sensor detection information detected by an external sensor mounted on the moving body, and acquiring object information indicating a relative position of the recognized features with respect to the moving body; executing a localization process for estimating a position of the moving body by comparing a position of the feature obtained from the object information with a position of the feature obtained from the feature map information; A driving assistance control is executed to generate a target route for the moving body based on the estimated position of the moving body and to control the traveling of the moving body so as to follow the target route. It is configured as follows: the first range includes the estimated position of the moving object, and becomes larger as the reliability of the localization process becomes lower; The first future range is at least a part of the predicted passing range of the first range, The one or more processors further include: determining whether an obstacle is present in the first range or the first future range; When the obstacle is present in the first range or the first future range, the moving body is decelerated or stopped; If the obstacle is not present within the first range or the first future range, the driving assistance control is continued. Mobile control system.

2. The mobile object control system according to claim 1, The one or more storage devices further store stationary object map information indicating the positions of stationary objects; The one or more processors: Recognizing the stationary object existing around the moving object as the obstacle based on the estimated position of the moving object and the stationary object map information; determining whether the stationary object is present in the first range; When the stationary object is present in the first range, the moving object is decelerated or stopped; When the stationary object is not present within the first range, the driving assistance control is continued. Mobile control system.

3. The mobile object control system according to claim 2, The second range is narrower than the first range, The one or more processors further include: When the stationary object is present in the second range, the moving object is stopped; When the stationary object is present in the first range and not present in the second range, the moving object is decelerated. Mobile control system.

4. The mobile object control system according to claim 1, the first future range is a range of the predicted passing range of the first range that extends beyond a lane in which the moving object is present, The one or more processors: Recognizing the obstacle present around the moving object based on the sensor detection information; determining whether the obstacle is present in the first future range; When the obstacle is present in the first future range, the moving body is decelerated or stopped; If the obstacle does not exist in the first future range, the driving assistance control is continued. Mobile control system.

5. A mobile object control method for controlling a mobile object, comprising: Recognizing features around the moving body based on sensor detection information detected by an external sensor mounted on the moving body, and acquiring object information indicating a relative position of the recognized features with respect to the moving body; executing a localization process for estimating a position of the moving object by comparing a position of the feature obtained from the object information with a position of the feature obtained from feature map information; generating a target route for the moving object based on the estimated position of the moving object, and executing driving assistance control to control traveling of the moving object so as to follow the target route; Including, the first range includes the estimated position of the moving object, and becomes larger as the reliability of the localization process becomes lower; The first future range is at least a part of the predicted passing range of the first range, The moving object control method further comprises: determining whether an obstacle is present in the first range or the first future range; slowing down or stopping the moving body when the obstacle is present in the first range or the first future range; If the obstacle is not present within the first range or the first future range, continuing the driving assistance control; Includes A method for controlling a moving object.

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