Patent Description:
A technique that is mounted on a moving body such as an automobile or an automated guided vehicle (AGV) for monitoring the periphery of the moving body has been proposed (e.g., Patent Documents <NUM> and <NUM>).

Patent Document <NUM> discloses an obstacle recognition device that recognizes an obstacle in front of an own vehicle. The obstacle recognition device of Patent Document <NUM> includes a camera and a radar, detects a blind spot region for an own vehicle, and estimates the attribute of an obstacle that may exist in the blind spot region based on the size of the detected blind spot region. The obstacle recognition device causes the camera to search the blind spot region when it is estimated that the attribute of the obstacle that may exist in the blind spot region is a pedestrian, and causes the radar to search the blind spot region when it is estimated that the attribute of the obstacle is another vehicle.

Patent Document <NUM> discloses a vehicle environment estimation device for the purpose of accurately estimating a traveling environment around an own vehicle. The vehicle environment estimation device of Patent Document <NUM> detects the behavior of other vehicles around the own vehicle, and estimates, based on the behavior of the vehicles, the existence of another vehicle traveling in a blind spot region from the own vehicle. In this way, a vehicle traveling environment that cannot be recognized by the own vehicle and can be recognized by other vehicles on the periphery is estimated.

Patent Document <NUM> discloses representative implementations of devices and techniques which provide adjustable parameters for imaging devices and systems; dynamic adjustments to one or more parameters of an imaging component may be performed based on changes to the relative velocity of the imaging component or to the proximity of an object to the imaging component.

Further relevant prior art can be found in <CIT>.

In Patent Document <NUM>, prior to the search in a blind spot region by the camera or the like, it is estimated whether an object that may exist in the region is a pedestrian or another vehicle based on the size of the region. In Patent Document <NUM>, the inside of a blind spot region is estimated based on a detection result around an own vehicle. However, they do not estimate whether the situation needs to detect the inside of a blind spot in the first place, or whether the necessity is high or low. Thus, in the prior art, efficiently performing the detection of an object in a blind spot according to a situation is difficult.

An object of the present disclosure is to provide a sensing device, a sensing method, and a moving body system according to the claims capable of efficiently detecting an object in a blind spot in a surrounding environment of a moving body.

A sensing device according to claim is for detecting an object in a blind spot in a surrounding environment of a moving body. The sensing device includes a distance measurer, a detector, and a controller. The distance measurer is configured to acquire distance information indicating a distance from the moving body to the surrounding environment. The detector is configured to detect the object in the blind spot. The controller is configured to control operation of the detector. The controller is configured to detect the blind spot in the surrounding environment, based on the distance information acquired by the distance measurer. The controller is configured to control precision at which the detector is caused to detect the object in the blind spot, according to a distance to the detected blind spot.

A moving body system according to an aspect of the present disclosure includes: the sensing device according; and a control device arranged on the moving body to execute operation according to a detection result of the object in the blind spot by the sensing device.

A sensing method according to claim <NUM> is a sensing method of detecting an object in a blind spot in a surrounding environment of a moving body. The sensing method includes: acquiring, by a distance measurer, distance information indicating a distance from the moving body to the surrounding environment; and detecting, by a controller, the blind spot in the surrounding environment, based on the distance information. The present method includes: controlling, by the controller, precision at which a detector is caused to detect an object in the blind spot, according to a distance to the detected blind spot; and detecting, by the detector, the object in the blind spot at the precision.

The sensing device, the sensing method, and the moving body system according to the claims is capable of efficiently detecting an object in a blind spot in a surrounding environment of a moving body.

Hereinafter, embodiments of the sensing device and method, and the moving body system according to the present disclosure will be described with reference to the accompanying drawings. Note that, in each of the embodiments below, the same reference numerals are given to the same constituents.

An example to which the sensing device and method, and the moving body system according to the present disclosure can be applied will be described with reference to <FIG> and <FIG>. <FIG> is a diagram for describing an application example of the sensing device <NUM> according to the present disclosure. <FIG> is a diagram exemplifying a situation different from <FIG> of a moving body in the present application example.

The sensing device <NUM> according to the present disclosure can be applied to, for example, in-vehicle use, and constitutes a moving body system in a moving body such as an automobile. <FIG> exemplifies a traveling state of a vehicle <NUM> on which the sensing device <NUM> is mounted. The moving body system according to the present application example uses, for example, the sensing device <NUM> to monitor the surrounding environment that changes around the own vehicle <NUM> that is traveling. The surrounding environment includes, for example, structures such as a building and a utility pole existing around the own vehicle <NUM>, and various objects such as moving objects such as a pedestrian and another vehicle.

In the example of <FIG>, a wall <NUM> of a structure near an intersection <NUM> blocks a range that can be monitored from the own vehicle <NUM>, and thus a blind spot occurs. The blind spot indicates a place that cannot be seen directly geometrically depending on the surrounding environment from a moving body such as the own vehicle <NUM>. In the present example, another vehicle <NUM> approaching an intersection <NUM> from a side road exists in the blind spot region R1 which is a region of the blind spot from the own vehicle <NUM>. In the above case, it is concerned that the vehicle <NUM> from a blind spot and the own vehicle <NUM> might collide with each other at the crossing.

In view of the above, the sensing device <NUM> of the present embodiment detects an object existing in the blind spot region R1 (hereinafter, may be referred to as a "blind spot object") such as the vehicle <NUM>, and determines a risk level based on a detection result of the blind spot object <NUM>. The risk level relates to the possibility that the own vehicle <NUM> and the blind spot object <NUM> collide with each other, for example. The sensing device <NUM> can perform various control of driving support or driving control for warning to avoid a collision at the crossing or the like in accordance with a determination result of the risk level.

<FIG> illustrates an example of a situation in which the positional relation between the own vehicle <NUM> and a blind spot is different from that of the intersection <NUM> in <FIG>, an intersection <NUM>' having a wider distance between the wall <NUM> and a road, and a better view than the intersection <NUM>. For example, a vehicle <NUM>', which has the same positional relation as the blind spot object <NUM> in <FIG> with the own vehicle <NUM>, is located outside the blind spot region R1 in the present example, and can be visually recognized without detection of the inside of the blind spot.

In the example of <FIG>, the distance from the own vehicle <NUM> to the blind spot is longer than that of the example of <FIG>. Owing to the far blind spot from the own vehicle <NUM>, even when an object exists in the blind spot (inside the blind spot region R1), a case is expected where the possibility for the object to collide with the own vehicle <NUM> is sufficiently low. If, even in such a case, the detection of the inside of the blind spot is executed with the same precision as that in the case of <FIG> and the like, the processing load due to the precise detection is applied while a risk level obtained from a detection result being low, resulting in inefficient.

In view of the above, the sensing device <NUM> of the present embodiment controls the precision for detection in a blind spot according to the distance to the blind spot. The precision indicates what degree the sensing device <NUM> detects the blind spot object <NUM> precisely in the blind spot region R1 (hereinafter, also referred to as "sensing density"). By controlling the sensing density in the sensing device <NUM>, it is possible to reduce the processing load in a situation where there is a low possibility of a collision at the crossing and to efficiently detect the blind spot object <NUM>. Further, excessive warnings and the like can be avoided, and the driving of the own vehicle <NUM> can be made smooth.

Hereinafter, embodiments as a configuration example of the moving body system including the sensing device <NUM> will be described.

A configuration and operation of the moving body system according to the first embodiment will be described below.

The configuration of the moving body system according to the present embodiment will be described with reference to <FIG> is a block diagram exemplifying the configuration of the present system.

As exemplified in <FIG>, the present system includes the sensing device <NUM> and a vehicle control device <NUM>. The sensing device <NUM> of the present embodiment includes a radar <NUM>, a camera <NUM>, and a controller <NUM>. For example, the sensing device <NUM> further includes a storage <NUM>, a navigation device <NUM>, and an in-vehicle sensor <NUM>. The vehicle control device <NUM> includes various in-vehicle devices mounted on the own vehicle <NUM>, and is used for driving support or automatic driving, for example.

In the sensing device <NUM>, the radar <NUM> includes, for example, a transmitter 11a, a receiver 11b, and a radar control circuit 11c. The radar <NUM> is an example of a detector in the present embodiment. The radar <NUM> is installed on the front grill, the windshield, or the like of the own vehicle <NUM> so as to transmit and receive a signal toward the front (see <FIG>) in the traveling direction of the own vehicle <NUM>, for example.

The transmitter 11a includes, for example, an antenna having variable directivity (phased array antenna or the like), a transmission circuit for causing the antenna to transmit the physical signal Sa to the outside, and the like. The physical signal Sa includes, for example, at least one of a millimeter wave, a microwave, a radio wave, and a terahertz wave. The physical signal Sa is an example of an output signal by the detector in the present embodiment.

The receiver 11b includes, for example, an antenna having variable directivity, a receiving circuit for receiving the wave signal Sb from the outside by the antenna, and the like. The wave signal Sb is set in the same wavelength band as the physical signal Sa so as to include the reflected wave of the physical signal Sa. Note that the transmitter 11a and the receiver 11b may use a shared antenna or may be integrally configured, for example.

The radar control circuit 11c controls the transmitting and receiving of a signal by the transmitter 11a and the receiver 11b. The radar control circuit 11c starts transmitting and receiving of a signal by the radar <NUM> or controls the direction in which the physical signal Sa is radiated from the transmitter 11a, for example, by a control signal from the controller <NUM>. Further, the radar control circuit 11c radiates the physical signal Sa from the transmitter 11a to scan a predetermined range such as the surrounding environment, and detects the wave signal Sb indicating the reflected wave of the physical signal Sa in a receiving result of the receiver 11b.

The radar <NUM> operates according to a modulation system such as a continuous wave (CW) system or a pulse system, and measures the distance, direction, speed, and the like of an external object. The CW system includes a two-wave CW system, an FM-CW system, a spread spectrum system, and the like. The pulse system may be a pulse-Doppler system, or may use pulse compression of a chirp signal or pulse compression of a PN sequence. The radar <NUM> uses, for example, coherent phase information control. The radar <NUM> may use an incoherent system.

The camera <NUM> is installed at a position where, for example, a range superimposed on a range in which the physical signal Sa can be radiated from the radar <NUM> in the own vehicle <NUM> can be imaged. For example, the camera <NUM> is installed on the windshield or the like of the own vehicle <NUM> with an orientation frontward for the own vehicle <NUM>, for example (see <FIG>). For a blind spot in the sensing device <NUM>, the installation position of the camera <NUM> may be used as a geometrical reference or the installation position of the radar <NUM> may be used as a reference.

The camera <NUM> captures an external image from the installation position and generates a captured image. The camera <NUM> outputs image data indicating the captured image to the controller <NUM>. The camera <NUM> is, for example, an RGB-D camera, a stereo camera, or a distance image sensor. The camera <NUM> is an example of a distance measurer (or monitor) in the present embodiment.

The controller <NUM> includes a CPU, a RAM, a ROM, and the like, and controls each constituent according to information processing. The controller <NUM> is composed of, for example, an electronic controller (ECU). The controller <NUM> loads a program stored in the storage <NUM> into a RAM, and the CPU interprets and executes the program loaded into the RAM. As a software module realized in this way, for example, the controller <NUM> realizes a blind-spot-region estimator <NUM>, a blind-spot-object measurer <NUM>, and a risk-level determiner <NUM>. Each of the modules <NUM> to <NUM> will be described later.

The storage <NUM> stores a program executed by the controller <NUM>, various data, and the like. For example, the storage <NUM> stores structural information D1 described later. The storage <NUM> includes, for example, a hard disk drive or a solid state drive. Further, the RAM and the ROM may be included in the storage <NUM>.

The above programs and the like may be stored in a portable storage medium. The storage medium is a medium that stores information such as a program and the like by an electrical, magnetic, optical, mechanical, or chemical action so that a computer, other devices and machines can read the stored information of the program and the like. The sensing device <NUM> may acquire the program and the like from the storage medium.

The navigation device <NUM> is an example of a distance measurer (or monitor) including, for example, a memory for storing map information and a GPS receiver. The in-vehicle sensor <NUM> is various sensors mounted on the own vehicle <NUM>, and includes, e.g., a vehicle speed sensor, an acceleration sensor, a gyro sensor, and the like. The in-vehicle sensor <NUM> detects the speed, acceleration, angular velocity, and the like of the own vehicle <NUM>.

The above configuration is an example, and the sensing device <NUM> is not limited to the above configuration. For example, the sensing device <NUM> does not have to include the navigation device <NUM> or the in-vehicle sensor <NUM>. Further, the controller <NUM> of the sensing device <NUM> may be composed of a plurality of hardware resources that separately execute each of the above units <NUM> to <NUM>. The controller <NUM> may be composed of various semiconductor integrated circuits such as a CPU, an MPU, a GPU, a microcomputer, a DSP, an FPGA, and an ASIC.

The vehicle control device <NUM> is an example of a control device of the moving body system according to the present embodiment. The vehicle control device <NUM> includes, for example, a vehicle driving device <NUM> and a notification device <NUM>. The vehicle driving device <NUM> is composed of, for example, an ECU, and drives and controls each unit of the own vehicle <NUM>. For example, the vehicle driving device <NUM> controls the brake of the own vehicle <NUM> to realize automatic braking.

The notification device <NUM> notifies the user of various information by means of an image, a sound, or the like. The notification device <NUM> is, for example, a display device such as a liquid crystal panel or an organic EL panel mounted on the own vehicle <NUM>. The notification device <NUM> may be a voice output device that outputs an alarm or the like by voice.

In the sensing device <NUM> of the present embodiment, a detector may be configured by the cooperation of a radar <NUM> and a controller <NUM> (blind-spot-object measurer <NUM>). Further, a distance measurer may also be configured in cooperation with the controller <NUM>.

The operation of the moving body system and the sensing device <NUM> configured as described above will be described below.

The moving body system according to the present embodiment operates the sensing device <NUM> to monitor the surrounding environment, when the own vehicle <NUM> is driven, for example. The vehicle control device <NUM> of the present system performs various control for driving support, automatic driving, or the like of the own vehicle <NUM> based on the detection result by the sensing device <NUM>.

The sensing device <NUM> of the present embodiment captures an image around the own vehicle <NUM> with a camera <NUM> and monitors the surrounding environment of the own vehicle <NUM>, for example. A blind-spot-region estimator <NUM> of the sensing device <NUM> in turn detects a region where a blind spot is estimated in the current surrounding environment, based on distance information indicating various distances in the monitoring result and the like, for example.

In the sensing device <NUM>, when the blind-spot-region estimator <NUM> finds a blind spot, the blind-spot-object measurer <NUM> measures an internal state of the blind spot region R1 using the radar <NUM>. It is expected that the physical signal Sa radiated from the radar <NUM> of the own vehicle <NUM>, which has a wave-like property, may generate the propagation in which the physical signal Sa causes multiple reflections, diffractions, or the like to reach the blind spot object <NUM> in the blind spot region R1, and further returns to the own vehicle <NUM>. The sensing method of the present embodiment detects the blind spot object <NUM> by utilizing a wave propagating as described above.

A risk-level determiner <NUM> of the present embodiment determines a risk level of the blind spot object <NUM> that may exist in the blind spot region R1 based on a measurement result of the blind-spot-object measurer <NUM>. The risk level indicates the degree to which a collision between the blind spot object <NUM> and the own vehicle <NUM> is expected to be possible, for example.

For example, when the sensing device <NUM> determines a risk level that is presumed to require a warning, the present system can notify the driver or the like by the notification device <NUM> or execute vehicle control for enhancing the safety such as automatic braking by the vehicle driving device <NUM>.

The sensing device <NUM> of the present embodiment dynamically sets the sensing density, which is the precision when the radar <NUM> is made to perform the detection in the blind spot region R1 in the sensing method as described above. The details of the operation of the sensing device <NUM> in the present system will be described below.

The operation of the sensing device <NUM> according to the present embodiment will be described with reference to <FIG>.

<FIG> is a flowchart for describing the operation of the sensing device <NUM> according to the present embodiment. Each processing shown in the flowchart of <FIG> is executed by the controller <NUM> of the sensing device <NUM>. The present flowchart is started at a predetermined cycle, for example, while the vehicle <NUM> is driven.

At first, the controller <NUM> acquires a captured image of one or more frames from the camera <NUM> (S1). In step S1, the controller <NUM> may acquire a distance image as the captured image, or may generate a distance image based on the acquired captured image. The distance image is an example of distance information indicating various distances for monitoring the surrounding environment.

Next, the controller <NUM> performs various image analysis for peripheral monitoring on the acquired captured image (S2). For example, the controller <NUM> generates structural information D1 regarding the current surrounding environment of the own vehicle <NUM>. The structural information D1 is information indicating various object structures in the surrounding environment, and includes distances to various structures, for example. Further, the controller <NUM>, operating as the blind-spot-region estimator <NUM> in step S2, performs detection of a blind spot by image analysis on the acquired captured image. <FIG> exemplifies an image to be analyzed in step S2.

In an example of <FIG>, an image is captured from the own vehicle <NUM> as a distance image (S1), and the image shows walls <NUM> and <NUM> formed by a plurality of structures in the vicinity of the intersection <NUM>. In the present example, the blind spot region R1 exists on the back side of the wall <NUM> due to shielding by the wall <NUM> in the vicinity of the own vehicle <NUM>. Further, the wall <NUM> on the back side of the blind spot region R1 faces the own vehicle <NUM>. Hereinafter, the wall <NUM> is referred to as a "shielding wall", and the wall <NUM> is referred to as a "facing wall". A boundary between the blind spot region R1 and the outside is formed between the shielding wall <NUM> and the facing wall <NUM> (see <FIG>).

In step S2, for example, the controller <NUM> extracts distance values of the various walls <NUM> and <NUM> in the distance image as structural information D1 for each pixel and holds them in a storage <NUM>. In the case of <FIG>, the distance value changes continuously from the side of the own vehicle <NUM> for the size of the shielding wall <NUM> along a direction d1, and changes discontinuously from an end portion (i.e., a blind spot end 31a (<FIG>)) of the shielding wall <NUM> to the facing wall <NUM>. The controller <NUM> can estimate the existence of the blind spot region R1 by analyzing the change in the distance value as described above.

Returning to <FIG>, the controller <NUM> as the blind-spot-region estimator <NUM> determines whether or not the blind spot region R1 is detected in the current surrounding environment of the own vehicle <NUM> according to an estimation result by image analysis, for example (S3). When determining that the blind spot region R1 is not detected (NO in S3), the controller <NUM> periodically repeats the processing of steps S1 to S3, for example,.

When determining that, the blind spot region R1 is detected (YES in S3), the controller <NUM> performs setting processing of the sensing density, for example (S4). The setting processing of the sensing density is processing of setting the sensing density in the detection in the blind spot region R1. In the present embodiment, the processing of step S4 sets the sensing density lower, as the distance to the blind spot end 31a is farther. The details of the processing in step S4 will be described later.

For example, in a case where a sensing density higher than "<NUM>" is set (YES in S5), the controller <NUM> executes detection processing of a blind spot object in the set sensing density (S6). In the present embodiment, the blind spot object <NUM> in the blind spot region R1 is detected by utilizing a multiple reflected wave in the wave signal Sb of the radar <NUM>.

In step S6, the controller <NUM> radiates the physical signal Sa from the radar <NUM> in a manner scanning a range of the facing wall <NUM> and the like in the vicinity of the boundary of the blind spot region R1, based on the analysis result of <FIG>, for example. <FIG> exemplify a propagation path of the physical signal Sa in step S6 in cases where there is and there is not the blind spot object <NUM>, respectively.

In the example of <FIG>, the physical signal Sa from the radar <NUM> of the own vehicle <NUM> is repeatedly reflected between the facing wall <NUM> and a wall <NUM> on the opposite side via the blind spot region R1 of a side road, and propagates as a multiple reflected wave. In the example of <FIG>, the multiple reflected wave does not come toward the own vehicle <NUM> corresponding to the absence of the blind spot object <NUM>.

In contrast, in the example of <FIG>, as the blind spot object <NUM> exists, the physical signal Sa from the radar <NUM> is reflected by the blind spot object <NUM> in addition to the walls <NUM> and <NUM>, respectively, so as to be a multiple reflected wave Rb1 directed toward the own vehicle <NUM>. Therefore, the wave signal Sb received by the radar <NUM> includes a signal component of the multiple reflected wave Rb1 having the information of the blind spot object <NUM>.

A signal component of the multiple reflected wave Rb1 (<FIG>) has information according to the speed of the blind spot object <NUM> as a reflection source and the length of the propagation path by the Doppler shift, the phase and the propagation time. By analyzing such a signal component, the detection processing of a blind spot object (S6) detects the speed, position, and the like of the blind spot object <NUM> that reflects the multiple reflected wave Rb1. The details of the processing in step S6 will be described later.

Returning to <FIG>, the controller <NUM> performs determination processing of a risk level based on a detection result (S6) of the blind spot object <NUM> (S7). In the determination processing of a risk level, a risk level is determined as the necessity of a warning regarding the detected blind spot object <NUM>, and various control signals are output to a vehicle control device <NUM> according to a determination result, for example. When determining in step S7 that a warning is required, the controller <NUM> generates a control signal for causing a notification device <NUM> to notify the warning or controlling a vehicle driving device <NUM>.

In a case where information of the movement, distance, type, shape, and the like of the blind spot object <NUM> is detected in step S6, the risk level may be determined using such information in step S7. The details of the processing in step S7 will be described later.

When, outputting a control signal (S8), the controller <NUM> ends the processing shown in the flowchart of <FIG>, for example.

Further, when the sensing density is set to "<NUM>" in step S4 (YES in S5), the controller <NUM> ends the processing according to the present flowchart without performing the detection processing of a blind spot object (S6) or the like. The controller <NUM> executes the present flowchart again after a period of a predetermined cycle elapses, for example.

According to the above processing, with performing peripheral monitoring of the own vehicle <NUM> (S1 to S3), the sensing device <NUM> detects the blind spot object <NUM> (S6) when a blind spot is found (YES in S3), so as to perform various actions according to a risk level (S7). At this time, the farther the distance to the blind spot end 31a , the lower the sensing density for the detection processing of a blind spot object (S6) is set (S4). Consequently, the processing efficiency of the sensing device <NUM> can be improved.

In the above processing, the camera <NUM> is used for the peripheral monitoring. However, the navigation device <NUM> may be used. The present variation is shown in <FIG>. As shown in <FIG>, the navigation device <NUM> calculates various distances to the own vehicle <NUM> in map information D2 of the surrounding environment of the own vehicle <NUM> and monitors the current position of the own vehicle <NUM>. The controller <NUM> can use the monitoring result of the navigation device <NUM> as described above for various processing shown in <FIG>. The controller <NUM> can acquire the structural information D1 and detect the blind spot region R1 based on the monitoring result of the navigation device <NUM>, based on a structure <NUM> in the map information D2, for example (S2). Further, the controller <NUM> may appropriately use a detection result of the in-vehicle sensor <NUM> in the processing of <FIG>.

In the above processing, in a case where an obstacle is detected outside a blind spot in peripheral monitoring (S2), the controller <NUM> may determine a risk level regarding the obstacle outside the blind spot and issue various warnings according to a determination result, for example.

The setting processing of the sensing density (S4 in <FIG>) will be described with reference to <FIG>.

<FIG> is a flowchart exemplifying the setting processing of the sensing density according to the present embodiment. <FIG> is a diagram for describing the setting processing of the sensing density according to the present embodiment. The processing by the flowchart of <FIG> is executed by the controller <NUM> in step S4 of <FIG>.

At first, the controller <NUM> calculates a distance to a blind spot regarding the blind spot detected in step S3 of <FIG> (S11). <FIG> exemplifies a blind spot direction d10 from the own vehicle <NUM> toward the blind spot end 31a, a traveling direction d11 of the own vehicle <NUM>, and a crossing direction d12 orthogonal to the traveling direction d11. For example, the controller <NUM> calculates a distance L in the blind spot direction d10 with reference to the blind spot end 31a based on the distance information obtained in steps S1 and S2 of <FIG>.

Next, the controller <NUM> determines whether or not the calculated distance L exceeds a preset upper limit value (S12). For example, the upper limit value is a value indicating an upper limit of a range of the distance for which it is presumed that the blind spot object <NUM> needs to be detected from the possibility of collision between the blind spot object <NUM> and the own vehicle <NUM> (e.g., <NUM>).

When determining that the distance L exceeds the upper limit value (YES in S12), the controller <NUM> sets the sensing density to "<NUM>" (S13), and ends the processing of step S4 in <FIG>. In this case, the controller <NUM> proceeds to "YES" in subsequent step S5, and the detection processing of a blind spot object (S6) is omitted.

On the other hand, when determining that the distance L does not exceed the upper limit value (NO in S12), the controller <NUM> sets the sensing density in a range higher than "<NUM>" (S14 to S16). Hereinafter, a processing example of setting the sensing density according to the distance L from two levels M1 and M2 will be described.

For example, the controller <NUM> determines whether or not the distance L exceeds a preset reference value (S14). The reference value is set at a positive value smaller than the above upper limit value in consideration of the necessity of precisely detecting the blind spot object <NUM> according to the distance L, for example.

When determining that the distance L does not exceed the reference value (NO in S14), the controller <NUM> sets the sensing density to a standard level M2 (S15). The standard level M2 indicates the standard sensing density for detecting the blind spot object <NUM> precisely.

On the other hand, when determining that the distance L exceeds the reference value (YES in S14), the controller <NUM> sets the sensing density to a low level M1 (S16). The low level M1 indicates a lower sensing density than standard level M2.

By setting the sensing density (S15 and S16), the controller <NUM> ends the processing of Step S4 in <FIG>. In this case, the controller <NUM> proceeds to "NO" in subsequent step S5, and executes the detection processing of a blind spot object (S6).

According to the above processing, the sensing density for detecting the blind spot object <NUM> is dynamically controlled according to the distance L from the own vehicle <NUM> to a blind spot. Further, in a case where the distance L is sufficiently large, detection of the blind spot object <NUM> is controlled not to be performed. In this manner, the processing efficiency of the sensing device <NUM> can be improved.

In the above description, the example (S14 to S16) is described in which the sensing density is set in the two levels M1 and M2, but not limited to this. For example, the sensing density may be set in three or more levels. For example, the controller <NUM> may use a level higher than the standard level according to a case where the distance L is small. Further, the sensing density may be set as a continuous value. The controller <NUM> may calculate the sensing density based on the distance L sequentially.

Further, in the above description, the example is described in which the sensing density is set using the distance L in the blind spot direction d10. The sensing density setting processing of the present embodiment is not limited to this, and for example, the distance L2 in the crossing direction d12 may be used.

As exemplified in <FIG>, the traveling direction d11 and the blind spot direction d10 of the own vehicle <NUM> form an angle θ. Then, the distance L in the blind spot direction d10 can be orthogonally decomposed into a distance L1 in the traveling direction d11 and a distance L2 in the crossing direction d12. The controller <NUM> can calculate the respective distances L1 and L2 and the angle θ together with or separately from the distance L by acquiring the traveling direction d11 of the own vehicle <NUM> from the in-vehicle sensor <NUM>, for example (S11).

For the determination in step S14, the controller <NUM> may set the magnitude of the sensing density by using the distance L2 of the crossing direction d12 instead of the distance L of the blind spot direction d10, for example. When the distance L2 in the crossing direction d12 is sufficiently large, it is expected that the visibility of the intersection <NUM> and the like is fine, and that the influence of the blind spot is small. In view of the above, the controller <NUM> sets the sensing density smaller as the distance L2 in the crossing direction d12 is larger (S14, S16). In this manner as well, the processing efficiency of the sensing device <NUM> can be improved. The sensing density may be set using both the distances L and L2.

The detection processing of a blind spot object (S6 in <FIG>) will be described with reference to <FIG>.

<FIG> is a flowchart exemplifying the detection processing of a blind spot object according to the present embodiment. <FIG> is a diagram for describing the detection processing of a blind spot object. The processing according to the flowchart of <FIG> is executed by the controller <NUM> that operates as the blind-spot-object measurer <NUM> in step S6 of <FIG>.

At first, according to the sensing density set in the setting processing of the sensing density (S4 in <FIG>), the controller <NUM> as the blind-spot-object measurer <NUM> controls the radar <NUM> to radiate the physical signal Sa to the blind spot region R1 (S21). For example, as the sensing density is higher, the controller <NUM> controls the radar <NUM> to shorten the time interval in which the radiation of the physical signal Sa is repeated for scanning or re-measurement more.

In step S21, the radar <NUM> radiates the physical signal Sa and receives the wave signal Sb to perform various measurement based on the reflected wave of the physical signal Sa. The controller <NUM> acquires a measurement result from the radar <NUM> (S22).

The controller <NUM> removes an environmental component showing a reflected wave from the surrounding environment from the acquired measurement result of the radar <NUM>, to extract a signal component for analyzing the blind spot object (S23). The processing of step S23 is performed using the structural information D1 acquired in step S2 for example,.

In the example of <FIG>, referring to the structural information D1 of the intersection <NUM>, the controller <NUM> predicts a reflected wave due to direct reflection from various structures around the intersection <NUM>, and subtracts a predicted environmental component from the measurement outcome of the radar <NUM>, for example (S23). According to this, the influence of the reflected wave by the structure under an environment can be reduced, and it can be facilitated to obtain only the signal component of the object in the blind spot.

Next, the controller <NUM> performs signal analysis for detecting the blind spot object <NUM> based on the signal component obtained by removing the environmental component (S24). The signal analysis in step S24 may include various analysis such as frequency analysis, analysis on the time axis, spatial distribution, and signal strength.

In step S24, the controller <NUM> determines the presence or absence of the blind spot object <NUM> by analyzing whether or not a wave source is observed on the further side of the blind spot (facing wall <NUM>) under the assumption that a propagation path of a wave is linear, for example. For example, a wave source <NUM> of the multiple reflected wave from the blind spot object <NUM> is observed to be on the further side than the facing wall <NUM> in the example of <FIG>. Thus, the wave source <NUM> is at a position not predicted as an environmental component from the structural information D1. It can be expected that such a situation is caused by the multiple reflection of a wave from the object <NUM> in the blind spot. That is, the controller <NUM> can determine that the blind spot object <NUM> is present in a case where a reflected wave is observed at a distance exceeding the facing wall <NUM> in the direction of the detected blind spot.

Further, in a case where the blind spot object <NUM> is determined to be present, the controller <NUM> can calculate various measured values such as the distance to the blind spot object <NUM> and its speed, according to the propagation path in which refraction is predicted due to multiple reflections, for example. For example, by using information indicating a road width of the blind spot portion (width of the blind spot region R1) in the structural information D1, the controller <NUM> can calculate the position of the blind spot object <NUM> closer to the actual position with correction of a path length to the blind spot object <NUM>, which is found from the signal component, in a manner folding back the path as exemplified in <FIG>.

After the signal analysis (S24) of the blind spot object <NUM>, the controller <NUM> ends the process of step S6 of <FIG>. After that, the controller <NUM> executes the determination processing of a risk level (S7 in <FIG>) for the blind spot object <NUM> for which signal analysis is performed.

According to the above processing, the blind spot object <NUM> can be detected by using a signal component generated inside the blind spot region R1, based on the property of multiple reflections in the physical signal Sa of the radar <NUM>.

In step S21, the controller <NUM> controls the radar <NUM> according to the set sensing density. Accordingly, in a case where the sensing density is set to the low level M1 (S16 in <FIG>), the processing load can be reduced as compared with the case of the standard level M2 (S15), for example. Thus, the blind spot object <NUM> can be detected efficiently.

The control according to the sensing density in step S21 is not limited to the above, and various control may be performed. For example, the controller <NUM> may increase the magnitude of output (i.e., the magnitude of the output signal) of the physical signal Sa radiated from the radar <NUM>, or sharpen the directivity of radiation of the physical signal Sa as the sensing density increases. By controlling the directivity, it is possible to improve the substantial output for detecting the inside of a blind spot and to suppress a component that undergoes excessive multiple reflections.

In step S21, the controller <NUM> may widen a frequency band of the physical signal Sa or lengthen a signal length of the physical signal Sa as the sensing density increases. According to the control of the frequency band, the time resolution in a received wave can be improved, for example. Controlling the signal length can improve the frequency resolution for analyzing the Doppler shift.

In step S23, referring to a distance to the intersection in the vicinity of the blind spot in the structural information D1, the controller <NUM> may remove a signal component of a received wave obtained in the reciprocating propagation time of a signal or less, with respect to a linear distance from the intersection. Such a received wave is a directly reflected wave (i.e., a wave with one reflection) and does not include information on the blind spot object <NUM>. Thus, is can be excluded from the object to be analyzed. Further, the controller <NUM> can separate a reflected wave arriving from the blind spot and a reflected wave arriving from another angle, based on an azimuth angle of the blind spot from the own vehicle <NUM>.

The processing of step S23 does not need to use the structural information D1 of the surrounding environment. For example, the controller <NUM> may restrict the object to be analyzed to a moving object, by subtracting a position change of the own vehicle <NUM> from a signal obtained along the time axis. The present processing may be performed in the signal analysis in step S24.

In step S24, the controller <NUM> may analyze whether or not to find a characteristic that appears due to the behavior of a specific object, e.g., Doppler shift due to reflection on a moving object or fluctuation of the behavior peculiar to a person or a bicycle in the signal component to be analyzed. Further, the controller <NUM> may analyze whether area-measured signal distribution with spatial expanse has distribution peculiar to an automobile, a bicycle, a person or the like, or includes reflection by an automobile-sized metal body based on reflection intensity, or the like. The above analysis may be performed in combination as appropriate, or may be performed as a multidimensional feature quantity using machine learning instead of explicitly analyzing each.

The determination processing of a risk level (S7 in <FIG>) will be described with reference to <FIG>.

<FIG> is a flowchart exemplifying the determination processing of a risk level. <FIG> is a diagram for describing the determination processing of a risk level. The processing according to the flowchart of <FIG> is executed by the controller <NUM> that operates as the risk-level determiner <NUM> in step S7 of <FIG>.

At first, the controller <NUM> calculates a risk level index D based on the detection result of the blind spot object <NUM> in step S6 (S31). The risk level index D indicates an index for determining a risk level of collision between the detected blind spot object <NUM> and the own vehicle <NUM>. As exemplified in <FIG>, the speed v<NUM> at which the blind spot object <NUM> approaches the own vehicle <NUM> can be set as the risk level index D.

Next, by using a preset threshold value Va, the controller <NUM> determines whether or not the calculated risk level index D exceeds the threshold value Va, for example (S32). The threshold value Va is set in consideration of the magnitude of the risk level index D that requires a warning regarding the blind spot object <NUM>, for example. For example, when the risk level index D exceeds the threshold value Va when D = v<NUM>, the controller <NUM> proceeds to "YES" in step S32.

When determining that the risk level index D exceeds the threshold value Va (YES in S32), the controller <NUM> performs various warning control as a result of determining the risk level at which a warning is required (S33). The warning control includes output of a control signal for causing the notification device <NUM> to issue a warning or the vehicle driving device <NUM> to perform specific control.

The controller <NUM> ends the determination processing of the risk level (S7 in <FIG>) by performing the warning control (S33).

On the other hand, when determining that the risk index D does not exceed the threshold value Va (NO in S32), the controller <NUM> ends step S7 in <FIG> without particularly performing the warning control (S33), as a result of determining that no warning is required. Then, the controller <NUM> executes the flowchart of <FIG> again, for example.

According to the above processing, a risk level of the blind spot object <NUM> approaching the own vehicle <NUM> or the intersection <NUM> is determined according to the corresponding risk level index D. For example, binary determination is performed according to the necessity of a warning.

Note that the determination processing of a risk level is not limited to the binary determination, and for example, a ternary determination may be performed for determining whether or not attention calling in a case where a warning is unnecessary. For example, using a threshold value Vb (< Va) for attention calling, the controller <NUM> may determine whether or not D > Vb when proceeding to "NO" in step S32.

In the above processing, the risk level index D is not limited to the speed v<NUM>, and can be set by (measured values of) various quantities related to the blind spot object <NUM>. For example, the risk level index D may be set to an acceleration dv<NUM>/dt instead of the speed v<NUM>.

Further, the risk level index D may be set to a distance Lh between the own vehicle <NUM> and the blind spot object <NUM>. It is presumed that as the distance Lh is smaller, the risk level of collision between the own vehicle <NUM> and the blind spot object <NUM> is higher. In view of the above, in step S32, the controller <NUM> may proceed to "YES" in the case of the risk level index D (= Lh) falling below the threshold value Va, and may proceed to "NO" in the case of the risk level index D not falling below the threshold value Va, for example.

Further, the risk level index D may be set by a combination of various quantities. The risk level index D of such an example is shown in the following equation (<NUM>): <MAT>.

In the above equation (<NUM>), Lh1 is a distance from a reference position P0 to the blind spot object <NUM> (<FIG>). The reference position P0 is set to a position where collision between the blind spot object <NUM> and the own vehicle <NUM> is expected, such as the center of an intersection. For example, the predetermined time width Δt is set in the vicinity of a time width that the own vehicle <NUM> is predicted to take to reach the reference position P0. The distance Lh0 is a distance from the reference position P0 to the own vehicle <NUM>. The speed v<NUM> of the own vehicle <NUM> can be acquired from the in-vehicle sensor <NUM> and the like.

The risk level index D in the above equation (<NUM>) is the sum of a distance between the blind spot object <NUM> and the reference position P0 and a distance between the reference position P0 and the own vehicle <NUM> estimated after the time width Δt elapses (<FIG>). According to the above equation (<NUM>), it can be estimated that when the risk level index D is smaller than a predetermined value, the possibility that the own vehicle <NUM> and the blind spot object <NUM> reach the reference position P0 at the same time is sufficiently high. As determination of a risk level corresponding to the above estimation with the above equation (<NUM>), the controller <NUM> may proceed to "YES" in step S32 in the case of the risk level index D falling below the threshold value Va, and may proceed to "NO" in the case of the risk level index D not falling below the threshold value Va, as in the case of D = Lh.

Further, the risk level index D may be set as in the following equation (<NUM>) or equation (<NUM>'): <MAT> <MAT>.

In each of the above equations (<NUM>) and (<NUM>'), Δt = Lh0/v<NUM> is set for example. The time width Δt may be set within an allowable range in consideration of a fluctuation of the speed v<NUM> of the own vehicle <NUM> or an estimation error of the reference position P0.

When the risk level index D in the equation (<NUM>) is smaller than the predetermined value (including a negative value), it can be estimated that the possibility, for which the blind spot object <NUM> crosses the front of the own vehicle <NUM> before the own vehicle <NUM> reaches the reference position P0, is sufficiently high. Further, when the risk level index D (an absolute value in the case of the equation (<NUM>)) of equation (<NUM>') is smaller than the predetermined value, it can be estimated that the possibility, for which the own vehicle <NUM> and the blind spot object <NUM> exist at the reference position P0 at the same time, is sufficiently high. In response to the above estimation, the controller <NUM> can use the risk level index D of the equation (<NUM>) or the equation (<NUM>') to determine a risk level as in the case of the equation (<NUM>).

In the above determination processing of a risk level, the threshold value Va may be dynamically changed according to states of the own vehicle <NUM> and the blind spot object <NUM>. For example, in a case where Lh0 described above is small, dv<NUM>/dt or dv<NUM>/dt is large, or the blind spot object <NUM> is estimated to be a person, it is considered that the determination of a risk level should be performed more strictly. In view of the above, when such a case is detected, the controller <NUM> may increase the threshold value Va with respect to the risk level index D of the above equation (<NUM>), for example.

As described above, the sensing device <NUM> according to the present embodiment detects an object in a blind spot, that is the blind spot object <NUM>, in the surrounding environment of the own vehicle <NUM> which is an example of a moving body. The sensing device <NUM> includes the camera <NUM> as a distance measurer, the radar <NUM> as a detector, and the controller <NUM>. The camera <NUM> acquires distance information indicating a distance from the own vehicle <NUM> to the surrounding environment. The radar <NUM> detects an object in a blind spot. The controller <NUM> controls operation of the radar <NUM>. The controller <NUM> detects a blind spot in the surrounding environment, based on the acquired distance information (S3). The controller <NUM> controls the precision at which the detector detects an object in the blind spot, that is, the sensing density, in accordance with the distance to the blind spot (S4).

According to the above sensing device <NUM>, by controlling the sensing density according to the condition information related to the distance to the blind spot, it is possible to efficiently detect an object in a blind spot in the surrounding environment of the own vehicle <NUM>.

In the sensing device <NUM> of the present embodiment, as the distance L is larger, the controller <NUM> sets the sensing density to be smaller (S14 to S16). When the blind spot is far from the own vehicle <NUM>, the inside of the blind spot can be detected efficiently with the processing load being reduced. The controller <NUM> may set the sensing density smaller as the distance L2 between the own vehicle <NUM> and the blind spot in the crossing direction d12 is larger.

In the sensing device <NUM> of the present embodiment, the controller <NUM> controls the radar <NUM> so as not to detect the blind spot object <NUM> when the distance L to the blind spot is larger than a predetermined upper limit value (S12 to S13). In this manner, in a situation where the possibility of a collision between the blind spot object <NUM> and the own vehicle <NUM> is expected to be sufficiently small, the detection of the blind spot object <NUM> can be omitted and the processing efficiency can be improved.

In the sensing device <NUM> of the present embodiment, the radar <NUM> radiates the physical signal Sa as an output signal having a wave characteristic from the own vehicle <NUM> to the surrounding environment, and the blind spot object <NUM> is detected based on a component of a wave arriving from the blind spot in the reflected wave of the radiated physical signal Sa. In this manner, a wave characteristic of the physical signal Sa from the radar <NUM> can be utilized to detect an object existing in a blind spot in the surrounding environment from the own vehicle <NUM>. The wave to be utilized is not limited to a multiple reflected wave, and may include a diffracted wave or a transmitted wave.

In the sensing device <NUM> of the present embodiment, the sensing density is set corresponding to at least one of magnitude, time interval, directivity, frequency band, and signal length of the output signal of the radar <NUM> (S21). Reduction in the processing load can be achieved by control of various parameters of the radar <NUM> according to the sensing density.

In the sensing device <NUM> of the present embodiment, when detecting the blind spot region R1 in the surrounding environment, the controller <NUM> may control the radar <NUM> to radiate the physical signal Sa toward the detected blind spot region R1 (S21). In this manner, the physical signal Sa can be concentrated in the vicinity of the blind spot region R1, and the multiple reflected waves Rb1 and the like can be easily obtained from the blind spot object <NUM> in the blind spot region R1. Note that the physical signal Sa from the radar <NUM> does not need to be concentrated in the blind spot region R1. For example, the physical signal Sa may be radiated in a range that can be detected by the radar <NUM> in a timely manner.

The sensing device <NUM> of the present embodiment may further include the storage <NUM> that stores structural information D1 indicating an object structure of the surrounding environment. Referring to the structural information D1, the controller <NUM> may analyze a wave signal including a component of a wave arriving from the blind spot region R1 in the detection result of the radar <NUM>. By using the structural information D1, the detection of the blind spot object <NUM> can be made accurate. The controller <NUM> may generate the structural information D1 based on the detection result of the camera <NUM> and hold the structural information D1 in the storage <NUM> (S2). The structural information D1 can be generated sequentially so that the blind spot object <NUM> can be detected with high accuracy.

The moving body system according to the present embodiment includes the sensing device <NUM> and the vehicle control device <NUM>. The vehicle control device <NUM> is mounted on the own vehicle <NUM> and executes operation according to the detection result of the blind spot object <NUM> by the sensing device <NUM>. In the moving body system, the sensing device <NUM> can efficiently detect an object in a blind spot in the surrounding environment of the own vehicle <NUM>.

The sending method according to the present embodiment is a method of detecting an object in a blind spot in the surrounding environment of a moving body such as the own vehicle <NUM>. The present method includes step S1 in which the distance measurer acquires distance information indicating a distance from the moving body to the surrounding environment, and steps S2 and S3 in which the controller <NUM> detects a blind spot in the surrounding environment based on the distance information. The present method includes step S4 in which the controller <NUM> controls the sensing density in which the detector is caused to detect an object in the blind spot according to the distance to the blind spot, and step S6 in which the detector detects an object in the blind spot in the sensing density.

In the present embodiment, a program for causing the controller <NUM> to execute the above sensing method is provided. According to the sensing method of the present embodiment, it is possible to efficiently detect an object existing in a blind spot in the surrounding environment of a moving body such as the own vehicle <NUM>.

In the first embodiment, the operation example is described in which the sensing density is controlled using the distance L. In the present embodiment, operation of the sensing device <NUM> that controls the sensing density by using the speed of the own vehicle <NUM> will be further described.

<FIG> is a flowchart exemplifying the setting processing of the sensing density according to a second embodiment. <FIG> is a diagram for describing the setting processing of the sensing density according to the present embodiment.

In the present embodiment, in addition to processing same as that in the first embodiment (<FIG>), the controller <NUM> acquires a speed v<NUM> of the own vehicle <NUM> from the in-vehicle sensor <NUM> (S17a), and calculates a braking distance La by the following equation (<NUM>), for example (S17b).

In the above equation (<NUM>), g indicates a gravitational acceleration, and, e.g., g = <NUM>. The friction coefficient µ indicates the friction between a tire of the own vehicle <NUM> and the road surface 3a. As the friction coefficient µ, predetermined value may be used, or the friction coefficient µ may be calculated in real time. For example, the controller <NUM> can calculate the friction coefficient µ based on the detection result of the in-vehicle sensor <NUM> or the like.

Furthermore, as shown in <FIG>, in a case where a moving object such as a vehicle exists in a blind spot to be detected, the controller <NUM> calculates a predicted position P20 at which the moving object is predicted to collide with the own vehicle <NUM> (S17c), and calculates a predicted distance L20 from the own vehicle <NUM> to the predicted position P20 (S17d). For example, the controller <NUM> can calculate the predicted position P20 and the predicted distance L20 based on the structural information D1 acquired in real time.

The controller <NUM> determines whether or not the predicted distance L20 exceeds the braking distance La, for example (S17). When the predicted distance L20 exceeds the braking distance La, the own vehicle <NUM> can stop before reaching the predicted position P20. In view of the above, when determining that the predicted distance L20 exceeds the braking distance La (YES in S17), the controller <NUM> sets the sensing density to be smaller than that in the case of "NO", for example (S16). At this time, the controller <NUM> may set the sensing density to "<NUM>" and control the detection in a blind spot not to be performed.

As described above, the sensing device <NUM> of the present embodiment further includes the in-vehicle sensor <NUM> which is an example of a speed receiver. The in-vehicle sensor <NUM> acquires the speed v<NUM> of the own vehicle <NUM>. The controller <NUM> sets the sensing density based on the braking distance La of the own vehicle <NUM> according to the speed v<NUM> and the distance to the blind spot. In this manner, the inside of the blind spot is detected in consideration of the braking distance La of the own vehicle <NUM>, and the safety can be improved.

In a third embodiment, the operation of the sensing device <NUM> that detects a plurality of blind spots and controls the sensing density for detection in each of the blind spots will be described.

<FIG> is a flowchart for describing the operation of the sensing device <NUM> according to the third embodiment. In the sensing device <NUM> of the present embodiment, the controller <NUM> executes the flowchart of <FIG> instead of the flowchart of <FIG>. By image analysis of peripheral monitoring, and the like (S2), the controller <NUM> can detect a plurality of blind spots (S3).

In a case where a plurality of blind spots are detected in the present embodiment, the controller <NUM> selects one blind spot in order from a blind spot having a shorter distance L , for example (S41). With respect to the selected blind spot, the controller <NUM> performs the processing of Steps S4 to S7 in the same manner as described above. When warning control is performed in Step S7 (YES in S7b), the present processing ends.

On the other hand, when warning control is not performed (NO in S7b) and an unselected blind spot exists (YES in S42), the controller <NUM> newly selects an unselected blind spot (S41) and performs processing of Step S4 and subsequent processing.

According to the above processing, for example, in a case where a plurality of blind spots are found during travel of the own vehicle <NUM>, the sensing device <NUM> performs detection of the inside of a blind spot and the like (S4 to S7), by using the radar <NUM> at each sensing density in order from blind spots having a shorter distance L (S41). When a risk level of the detected blind spot is low to the extent that no warning is required (NO in S7b), detection is performed for a blind spot with a next shortest distance L (S42 and S41).

Claim 1:
A sensing device (<NUM>) for detecting an object (<NUM>) in a blind spot (R1) in a surrounding environment of a moving body (<NUM>), the sensing device (<NUM>) comprising:
a distance measurer (<NUM>) configured to acquire distance information indicating a distance from the moving body (<NUM>) to the surrounding environment;
a detector (<NUM>) configured to detect the object (<NUM>) in the blind spot (R1); and
a controller (<NUM>) configured to control operation of the detector (<NUM>),
wherein the controller (<NUM>) is configured to:
detect the blind spot (R1) in the surrounding environment and a distance to the blind spot (R1), based on the distance information acquired by the distance measurer (<NUM>);
control precision indicating what degree the detector (<NUM>) precisely detects the object (<NUM>) in the blind spot (R1), according to the detected distance to the blind spot (R1); and
cause the detector (<NUM>) to detect the object (<NUM>) in the blind spot (R1), according to the controlled precision.