Conflict avoidance support device

Abstract

Provided is a collision avoidance assistance device provided with an algorithm for adjusting the balance between prevention of over-actuation and prevention of non-actuation. This collision avoidance assistance device is provided with: a recognition sensor 106; a first collision estimation unit 131 and a second collision estimation unit 132 that have different characteristics (response characteristics), the first collision estimation unit 131 having higher responsiveness than the second collision estimation unit 132, and the second collision estimation unit 132 having higher noise tolerance than the first collision estimation unit 131; a first collision risk calculation unit 141 that calculates a first collision risk on the basis of a first estimation result; a second collision risk calculation unit 142 that calculates a second collision risk on the basis of a second estimation result; and a collision estimation arbitration unit 150, 250 that selects either the first estimation result or the second estimation result.

Description

[Technical Field]

[0001] The present invention relates to a collision avoidance assistance device that assists in avoiding a collision between a moving body and a target. [Background technology]

[0002] As a conventional technology for a collision avoidance support device that supports collision avoidance between a moving body (host vehicle) and a target around the moving body, the technology described in Patent Document 1 calculates the speed and position of the target using optical flow and then determines whether the moving body will collide with the target. This target speed and position calculation method is characterized by a processing method that searches for corresponding points from image data. [Prior art documents] [Patent documents]

[0003] [Patent Document 1] Japanese Patent Application Laid-Open No. 2012-160128 Summary of the Invention [Problem to be solved by the invention]

[0004] However, in optical flow, collision estimation is performed based on the relative speed between the vehicle and the target, and this relative speed is affected by the behavioral components of the vehicle, noise from the sensor itself, sudden changes in direction of the target, etc., making it difficult to determine and separate which frequency components are to be used for control, resulting in a decrease in collision prediction accuracy.

[0005] The degradation of collision prediction accuracy can be caused by degradation of the sensor accuracy of the recognition sensor, degradation of accuracy due to driver operation, the vehicle's status, and vehicle parameter identification errors. Accuracy degradation due to driver operation occurs when a change in driver operation affects the future vehicle position when the vehicle's future position is predicted assuming that the current driver operation will continue. The vehicle's status refers to when the vehicle is subjected to acceleration or vibration, causing it to pitch or roll. Vehicle parameter identification errors occur when physical values ​​such as the center of gravity and weight required to estimate the vehicle's future position differ from the actual values. In such cases, the accuracy of the vehicle's predicted position decreases, resulting in a degradation of collision prediction accuracy.

[0006] When the collision determination accuracy is reduced, the following two events can occur. Excessive activation occurs when, even though the vehicle and the target are in a positional relationship that would prevent a collision, a collision is determined to have occurred due to a decrease in collision prediction accuracy, and the collision avoidance assistance device is activated. The inoperative state occurs when, even though the vehicle and the target are in a positional relationship that would cause a collision, the collision avoidance support device is not activated because it is determined that a collision will not occur due to a decrease in collision prediction accuracy.

[0007] There is a trade-off between preventing excessive operation and preventing inactivity, and it is difficult to completely achieve both prevention of excessive operation and prevention of inactivity.

[0008] Since collision avoidance support systems are devices that assist the driver in driving operations, there is a tendency that inactivation is acceptable, but excessive activation is not. This is because even if the driver is driving carefully, excessive activation of the control can interfere with the driver's driving operations, and in addition, sudden control intervention can cause secondary damage, so it is important for collision avoidance support systems to strike a balance between preventing excessive activation and preventing inactivation.

[0009] In order to address such problems, the present invention provides a technology that removes noise from both the vehicle speed and the target speed, thereby improving collision prediction accuracy (solving the problem of reduced collision prediction accuracy) and reducing the risk of excessive operation. However, if too much emphasis is placed on preventing excessive operation, it becomes difficult to prevent inactivity.

[0010] The present invention has been made in consideration of the above circumstances, and an object of the present invention is to provide a collision avoidance assistance device equipped with an algorithm that adjusts the balance between preventing excessive operation and preventing inoperation. [Means for solving the problem]

[0011] In order to solve the above problem, a collision avoidance assistance device according to the present invention is a collision avoidance assistance device that is mounted on a moving body and avoids a collision between the moving body and a target object around the moving body, and is characterized by comprising: a recognition sensor that acquires information on the position and speed of the target; a first collision estimation unit and a second collision estimation unit that have different characteristics and that use the information acquired by the recognition sensor as input to output an estimation result regarding a collision between the moving body and the target, the first collision estimation unit having higher responsiveness than the second collision estimation unit and the second collision estimation unit having higher noise resistance than the first collision estimation unit; a first collision risk calculation unit that calculates a first collision risk based on the first estimation result output by the first collision estimation unit; a second collision risk calculation unit that calculates a second collision risk based on the second estimation result output by the second collision estimation unit; and a collision estimation arbitration unit that selects either the first estimation result or the second estimation result. [Effects of the Invention]

[0012] According to the present invention, it is possible to balance the prevention of excessive operation and the prevention of inoperation even when the vehicle is turning, for example, and to assist in collision avoidance.

[0013] Problems, configurations, and effects other than those described above will become apparent from the following description of the embodiments. [Brief explanation of the drawings]

[0014] [Figure 1] Example 1 of a scene where excessive operation occurs. [Figure 2] Example 2 of a scene where excessive operation occurs. [Figure 3] 1 is a control block diagram of a collision avoidance assistance device according to a first embodiment. [Figure 4] An explanatory diagram of coordinate definitions. [Figure 5] An explanatory diagram of collision risk. [Figure 6] FIG. 4 is an explanatory diagram of the arbitration logic of the collision estimation arbitration unit for the estimation results according to the first embodiment. [Figure 7] 4 is a flowchart of a collision estimation arbitration unit according to the first embodiment. [Figure 8] 10 shows an example scene 1 using the first embodiment. [Figure 9] 2 shows an example scene 2 using the first embodiment. [Figure 10] 10 shows scene example 3 using embodiment 1. [Figure 11] FIG. 10 is a control block diagram of a collision avoidance assistance device according to a second embodiment. [Figure 12] FIG. 10 is a diagram illustrating the weighting of collision prediction accuracy. [Figure 13] FIG. 10 is an explanatory diagram of the arbitration logic of the collision estimation arbitration unit for the estimation results according to the second embodiment. [Figure 14] 10 is a flowchart of a collision estimation arbitration unit according to the second embodiment. [Figure 15] 10 shows scene example 3 using embodiment 2. DETAILED DESCRIPTION OF THE INVENTION

[0015] In the present invention, an estimation result of the target and the vehicle, which is a moving body, is output from a sensor input signal. The present invention relates to a collision avoidance support device equipped with this vehicle control.

[0016] Prior to describing specific embodiments of the present invention, specific examples of scenes in which excessive operation occurs are shown in FIGS.

[0017] Figure 1 shows an example of an over-detection scenario where sensor noise momentarily indicates an abnormal value for a pedestrian's movement speed information. At time T=T1, a pedestrian is walking on the sidewalk at a constant speed, and the vehicle begins to turn at an intersection. At time T=T2, the pedestrian is actually walking on the sidewalk where there is no risk of collision with the vehicle, but because sensor noise momentarily indicates an abnormal value for the pedestrian's movement speed information (speed detected by the sensor), this could lead to a false determination that the pedestrian is moving in the direction of crossing the intersection.

[0018] Generally, when sensor detection values ​​are used for control, noise removal is performed to suppress the effects of electromagnetic noise from the sensor itself, communication paths, connectors, and external sources. A low-pass filter is often used for noise removal.

[0019] If noise is removed from the speed and position detection results of a target from a recognition sensor using a filter with low noise resistance (in other words, high responsiveness), there is a risk that the system will over-determine that there will be a collision between the vehicle and the target, as shown by the velocity vector of the sensor-detected speed in Figure 1, and erroneously take action to avoid the collision, such as automatic braking. The action to avoid the collision may be an alarm sound, vibration, warning light, or automatic steering.

[0020] Figure 2 shows an example of an over-detection scenario where a target moves suddenly. At time T=T1, a pedestrian is walking on the sidewalk toward the roadway at a constant speed, and the vehicle begins to turn at the intersection. If both vehicles continue to move in this manner, they will collide. However, at time T=T2, the pedestrian turns back in the opposite direction just before entering the roadway. At this time, if noise is removed from the target's speed and position detection results from the recognition sensor using a low-response (in other words, highly noise-resistant) filter, the velocity vector of the sensor-detected speed will lag behind the actual speed, resulting in an over-detection that there will be a collision between the vehicle and the target, and the vehicle may mistakenly take action to avoid the collision, such as applying automatic braking.

[0021] From the above two over-operation scenarios, it is clear that it is difficult to suppress over-operation while suppressing under-operation by simply adjusting the gain of a single speed filter or the like.

[0022] Hereinafter, an embodiment of the present invention will be described with reference to the drawings.

[0023] (Embodiment 1) The first embodiment is a mode for preventing excessive operation while preventing non-operation. The control block configuration of the collision avoidance assist device of the first embodiment is shown in FIG.

[0024] The collision avoidance support device 100 of the first embodiment is a device that is mounted on a vehicle (host vehicle) and that avoids collision between the vehicle (host vehicle) and objects around the vehicle (host vehicle). The collision avoidance support device 100 includes a vehicle state sensor 101 and a recognition sensor 106.

[0025] The vehicle state sensor 101 is a sensor that acquires information relating to the vehicle state (vehicle state information) of the vehicle itself. The vehicle state sensor 101 is composed of a vehicle speed sensor 102 that acquires the speed of the vehicle itself, a steering angle sensor 103 that acquires the steering angle of the vehicle itself, a yaw rate sensor 104 that acquires the yaw rate of the vehicle itself, and the like.

[0026] The recognition sensor 106 is a sensor that recognizes the surrounding environment of the vehicle. The recognition sensor 106 is composed of a camera, millimeter wave radar, laser radar, etc. that are mounted on the vehicle. In this example, the recognition sensor 106 acquires information on the relative positions and relative speeds of targets around the vehicle.

[0027] As shown in FIG. 3, the collision avoidance assistance device 100 includes, as functional blocks, a ground speed / ground position calculation unit 110, noise removal processing units 120, 121, 122, collision estimation units 131, 132, collision risk calculation units 141, 142, and a collision estimation arbitration unit 150.

[0028] The ground speed / ground position calculation unit 110 calculates the ground position and ground speed of the target based on the vehicle state information acquired by the vehicle state sensor 101 and the information on the relative position and relative speed of the target acquired by the recognition sensor 106.

[0029] The definitions of the coordinate axes are shown in Figure 4. The coordinate axes have the bumper of the vehicle as the reference origin, with the x-axis representing the longitudinal direction of the vehicle and the y-axis representing the lateral direction of the vehicle. In this case, the ground speed of the target [m / s] (vtx, vty) is calculated using the following formula (1) from the relative speed of the target [m / s] (vtx', vty') obtained from the recognition sensor information, the speed of the vehicle [m / s] (vhx, vhy) obtained from the vehicle speed sensor and yaw rate sensor, and the peripheral speed components [m / s] (-γ·yt, γ·xt) where γ is the yaw rate [rad / s] of the vehicle.

number

[0030] On the other hand, when converting the target's ground position (tx, ty), if the coordinate axes are defined as shown in Figure 4, the target's relative position (tx', ty') obtained from the recognition sensor information will be equal to the ground position (tx, ty).

[0031] The vehicle itself cannot change direction suddenly at high speed, but targets such as pedestrians can change direction suddenly at low speed, so the operating frequency ranges are different. For this reason, separate noise removal processing blocks are provided for the vehicle itself and targets (pedestrians, etc.), as shown in Figure 3.

[0032] The noise removal processing unit 120, which is a noise removal processing block for the host vehicle, uses, for example, a low-pass filter to perform noise removal processing on the position and speed of the host vehicle obtained from the vehicle state information.

[0033] The target noise removal processing block is provided with multiple blocks so that it can follow the high-response movement of the target and remove high-frequency noise. In the example of Fig. 3, there are provided noise removal processing unit 121, which is a high-response noise removal processing block that can follow the sudden movement of the target, and noise removal processing unit 122, which is a noise removal processing block with high noise resistance that is robust against instantaneous noise.

[0034] The position and velocity of the target calculated by the ground speed / ground position calculation unit 110 are input to a noise removal processing unit 121 and a noise removal processing unit 122. The noise removal processing unit 121 performs high-response noise removal processing on the position and velocity of the target, and the noise removal processing unit 122 performs high-noise resistance noise removal processing on the position and velocity of the target.

[0035] The position and velocity of the host vehicle after noise removal processing by the noise removal processing unit 120 and the position and velocity of the target after noise removal processing by the high-response noise removal processing unit 121 are input to a collision estimation unit 131, which is a collision estimation block. The position and velocity of the host vehicle after noise removal processing by the noise removal processing unit 120 and the position and velocity of the target after noise removal processing by the high-noise resistance noise removal processing unit 122 are input to a collision estimation unit 132, which is a collision estimation block. The collision estimation units 131 and 132 estimate a collision between the host vehicle and the target from the input information. The collision estimation unit 131 has higher responsiveness than the collision estimation unit 132 by using information after high-response noise removal processing, and the collision estimation unit 132 has higher noise resistance than the collision estimation unit 131 by using information after high-noise resistance noise removal processing, and the two units have different response characteristics.

[0036] The collision estimation units 131 and 132 predict the future position of the host vehicle, for example, based on the steering angle and vehicle speed of the host vehicle after noise removal processing, assuming that the vehicle will make a turn with a constant radius. Similarly, the future position of the target is predicted based on the target speed and target position after noise removal processing, assuming that the target moves at a constant speed. Then, a collision between the two is estimated based on the future positions of the host vehicle and the target.

[0037] The estimated collision results include the time until collision between the vehicle and the target (hereinafter referred to as TTC (Time to Collision)), the relative longitudinal speed between the vehicle and the target at the time of collision (at the predicted collision point) (hereinafter referred to as collision relative speed), the overlap ratio between the vehicle range and the target range (at the predicted collision point) at the time of collision (hereinafter referred to as overlap rate), and whether or not a collision will occur.

[0038] From these estimation results, collision risks are calculated by collision risk calculation units 141 and 142, which are collision risk calculation blocks. That is, estimation result 1 from high-response collision estimation unit 131 is input to collision risk calculation unit 141, which calculates collision risk 1 based on estimation result 1. Similarly, estimation result 2 from high-noise resistance collision estimation unit 132 is input to collision risk calculation unit 142, which calculates collision risk 2 based on estimation result 2.

[0039] The collision risk is expressed as a single scale using the accuracy of collision prediction, the extent of damage, and the urgency (margin) until a collision occurs, as shown in the following equation (2), and the risk of a collision can be grasped with a single parameter. [Number 2] Collision risk = (accuracy of collision prediction) × (extent of damage) × (imminence of collision) Equation (2)

[0040] Here, the accuracy of collision prediction is determined according to the overlap rate (Fig. 5), the extent of damage is determined according to the relative collision speed (Fig. 5), and the imminence of collision is determined according to the distance between the vehicle and the target (calculated according to the TTC) (Fig. 5).

[0041] Then, based on the collision risk and the estimation result, the collision estimation arbitration unit 150, which is a collision estimation arbitration block, determines whether to select the result of the estimation result indicating whether or not there is a collision, 1 or 2. In other words, the collision estimation arbitration unit 150 arbitrates the collision result based on the magnitude of the estimation result 1 or 2 and the collision risk 1 or 2. The arbitration logic of this collision estimation arbitration unit 150 is shown in FIG. 6.

[0042] The collision estimation arbitration unit 150 determines that there is no collision if either estimation result 1 or 2 determines that there is no collision. Also, if the collision estimation arbitration unit 150 determines that there is a collision in both estimation results 1 and 2, it selects the reliable collision risk indicated by both estimation results. In other words, in the above case, it selects the collision result with the low collision risk (the estimation result corresponding to the low collision risk when comparing collision risks 1 and 2).

[0043] In this way, excessive operation can be prevented by redundantly determining a collision as in the logic of the collision estimation arbitration unit 150 in Figure 6, and arbitrating the estimation results using the collision risk when there is a risk of collision on both sides.

[0044] The collision estimation arbitration unit 150 of the first embodiment is shown in the flowchart of FIG. As described above, if there is no collision determination in collision presence / absence 1 or 2 (of estimation result 1 or 2), i.e., if the flowchart returns "No" in S151 or S152, the estimation result that determines there is no collision is selected (S154, S155). Similarly, if there is a collision determination in collision presence / absence 1 or 2 (of estimation result 1 and 2), i.e., if the flowchart returns "Yes" in S151 and S152, the collision risk levels 1 and 2 are compared (S153), and the estimation result with the lowest collision risk is selected (S156, S157). The selected estimation result (TTC: time to collision, LAP: overlap rate, ΔV: collision relative velocity) is then output to the outside for use in braking control, etc. (S158).

[0045] 8 and 9 show the case where the first embodiment is applied to the scenes of FIGS. 1 and 2. FIG.

[0046] In Figure 8, when instantaneous noise is present in the speed detected by the sensor, estimation result 1 has high response and therefore high tracking ability to the noise, resulting in an overdetermined collision, whereas estimation result 2 has low tracking ability to the actual speed and therefore results in a collision-free determination due to its noise resistance (high noise tolerance). According to the logic of the collision estimation arbitration unit 150, if either estimation result 1 or 2 indicates no collision, the arbitration result can be correctly determined as no collision. This makes it possible to prevent erroneous implementation of collision avoidance actions.

[0047] In Fig. 9, in response to a sudden change in direction of a target such as a pedestrian, estimation result 1 determines that there is no collision due to high response, but estimation result 2 determines that there is a collision due to noise resistance (high noise tolerance). According to the logic of the collision estimation arbitration unit 150, if either estimation result 1 or 2 indicates that there is no collision, the arbitration result can be correctly determined as no collision. This makes it possible to prevent erroneous implementation of collision avoidance action.

[0048] A different scene from those in FIGS. 8 and 9 has been added to FIG. 10. In this scene, a pedestrian walking on the sidewalk suddenly changes direction, enters a crosswalk, and crosses the roadway, predicting a collision. In this scene, both estimation result 1 and estimation result 2 predict a collision (determine that a collision will occur), and the collision estimation arbitration unit 150 selects estimation result 2, which indicates a lower risk of collision. Therefore, in this case, it is possible to take action to avoid the collision.

[0049] In this way, according to the first embodiment, it is possible to prevent non-operation while suppressing excessive operation.

[0050] (Embodiment 2) In the first embodiment, it is possible to prevent inactivation while suppressing excessive operation, but because the balance between excessive operation prevention and inactivation prevention is fixed in favor of excessive operation prevention, there is an issue of delaying the timing of braking by the collision avoidance support device 100. Specifically, in the scene of Fig. 10, the collision determination position in estimation result 2 is farther away from the actual collision position, and the collision point is determined to be a position farther away than the actual collision point. In the second embodiment, an algorithm is shown that automatically adjusts the balance between excessive operation prevention and inactivation prevention to solve the issue of the first embodiment.

[0051] In addition to the configuration of embodiment 1, embodiment 2 defines a weight for collision prediction accuracy, which corresponds to the likelihood of collision prediction depending on the driving environment and sensing environment, and adds to embodiment 1 an algorithm that changes the arbitration method between estimation result 1 and estimation result 2 depending on the magnitude of the weight for collision prediction accuracy.

[0052] The driving environment and sensing environment affect collision prediction accuracy. When the driving environment and sensing environment are good, the weighting of collision prediction accuracy is increased to select an estimation result that allows for earlier collision avoidance (an estimation result with a high collision risk) and initiate braking, and when the driving environment and sensing environment deteriorate, the weighting of collision prediction accuracy is decreased to select a more reliable estimation result (an estimation result with a low collision risk) and initiate braking.

[0053] FIG. 11 shows a control block diagram of a collision avoidance assistance device according to the second embodiment. The collision avoidance assistance device 200 according to the second embodiment is obtained by adding a weighting unit 260 to the components of the first embodiment. In the example shown in FIG. 11, the weighting unit 260 calculates a weight (the magnitude of the weight) for the collision prediction accuracy based on the vehicle state information obtained from the vehicle state sensor 101 and the recognition state information obtained from the recognition sensor 106. The weighting unit 260 inputs this weight to the collision estimation arbitration unit 250, which then arbitrates the collision result according to this weight.

[0054] As shown in Figure 12, the weights include, for example, a vehicle state weight Wcar and a recognition state weight Wsens. For the vehicle state weight Wcar, the smaller of the vehicle acceleration weight Wacc and the steering angle speed weight Wstr is selected to reduce the risk of excessive operation. As the vehicle acceleration and steering angle speed increase, the vehicle state fluctuates significantly, making it difficult to predict the vehicle's future position. Therefore, Wacc and Wstr are reduced. Wacc and Wstr are extracted from a map as shown in Figure 12. Furthermore, for the recognition state weight Wsens, for example, due to the radar's directivity and the camera parallax principle of a stereo camera, the central field of view has the highest recognition accuracy and the peripheral field of view has lower recognition accuracy. Therefore, the weight Wsens is set to be the largest when the field of view relative to the target position is 0°, and decreases as the field of view moves away from 0°. Wsens is extracted from a map as shown in Figure 12. The weight W is normalized to 1 when the driving environment and sensing environment are optimal and to 0 when the driving environment and sensing environment are worst.

[0055] FIG. 13 shows the arbitration logic of the collision estimation arbitration unit 250 according to the second embodiment.

[0056] Similar to the collision estimation arbitration unit 150 in the first embodiment, the collision estimation arbitration unit 250 determines that there is no collision when either the estimation result 1 or 2 indicates that there is no collision. When both the estimation result 1 and 2 indicate that there is a collision, the collision estimation arbitration unit 250 outputs the estimation result 1 and the estimation result 2 according to the weights.

[0057] The collision estimation arbitration unit 250 of the second embodiment is shown in the flowchart of FIG. 14. As described above, if there is no collision determination in collision presence / absence 1 or 2 (of estimation result 1 or 2), i.e., if the flowchart returns "No" in S251 or S252, the estimation result that determines there is no collision is selected (S255, S256). Similarly, if there is a collision determination in collision presence / absence 1 or 2 (of estimation result 1 and 2), i.e., if the flowchart returns "Yes" in S251 and S252, the vehicle behavior weight Wcar and the sensing weight Wsens are calculated (S253c, S253s). To reduce the risk of excessive operation, the smaller of the vehicle behavior weight Wcar and the sensing weight Wsens is selected to calculate the final weight W (S253w). The estimation result is weighted by W and output.

[0058] For example, collision risk levels 1 and 2 are compared (S254), and if collision risk level 1 obtained by the high-response noise removal processing (noise removal processing unit 121) is greater than collision risk level 2 obtained by the high-noise resistance noise removal processing (noise removal processing unit 122), when W=1, the sensing environment is the best, so estimation result 1 with a high collision risk (corresponding to collision risk level 1) is output (TTC=TTC1, LAP=LAP1, δV=δV1), and when W=0, the sensing environment is the worst, so estimation result 2 with a low collision risk (corresponding to collision risk level 2) is output (TTC=TTC2, LAP=LAP2, δV=δV2) (S258). In addition, the allocation of estimated result 1 and estimated result 2 is determined based on the magnitude of this weight W; more specifically, as W becomes larger (approaching 1), the allocation of estimated result 1 with a high collision risk (corresponding to collision risk level 1) becomes larger and the allocation of estimated result 2 with a low collision risk (corresponding to collision risk level 2) becomes smaller; alternatively, as W becomes smaller (approaching 0), the allocation of estimated result 1 with a high collision risk (corresponding to collision risk level 1) becomes smaller and the allocation of estimated result 2 with a low collision risk (corresponding to collision risk level 2) becomes larger. Estimated result 1 and estimated result 2 are then arbitrated, and an estimated result according to the sensing environment can be output (S258).

[0059] Similarly, if collision risk 1 is lower than collision risk 2 (collision risk 2 is higher than collision risk 1), when W=1, the sensing environment is the best, so estimation result 2 with a high collision risk (corresponding to collision risk 2) is output (TTC=TTC2, LAP=LAP2, δV=δV2), and when W=0, the sensing environment is the worst, so estimation result 1 with a low collision risk (corresponding to collision risk 1) is output (TTC=TTC1, LAP=LAP1, δV=δV1) (S257). In addition, the allocation of estimated result 1 and estimated result 2 is determined based on the magnitude of this weight W; more specifically, as W increases (approaching 1), the allocation of estimated result 2 with a high collision risk (corresponding to collision risk level 2) increases and the allocation of estimated result 1 with a low collision risk (corresponding to collision risk level 1) decreases; alternatively, as W decreases (approaching 0), the allocation of estimated result 2 with a high collision risk (corresponding to collision risk level 2) decreases and the allocation of estimated result 1 with a low collision risk (corresponding to collision risk level 1) increases; and an estimated result according to the sensing environment can be output by arbitrating estimated result 1 and estimated result 2 (S257).

[0060] Then, the calculated estimation results (TTC: time to collision, LAP: overlap rate, ΔV: collision relative velocity) are output to the outside for use in braking control etc. (S259).

[0061] Fig. 15 shows the same scene as Fig. 10. By selecting the estimation result (weighted) of the second embodiment, braking by the collision avoidance assistance device 200 can be performed at more appropriate timing within a range that does not cause excessive operation. In other words, by determining a situation in which the accuracy of collision determination is improved, if the accuracy of collision determination is improved, control can be performed earlier than the timing of the first embodiment that is set from the viewpoint of preventing excessive operation, and therefore collision avoidance can be improved while suppressing the risk of excessive operation.

[0062] (Summary of Embodiments 1 and 2) As described above, the collision avoidance support devices 100 and 200 of the first and second embodiments are collision avoidance support devices that are mounted on a moving body and that avoid collisions between the moving body and targets around the moving body, and include a recognition sensor 106 that acquires information on the position and speed of the target, a first collision estimation unit 131 and a second collision estimation unit 132 that receive information acquired by the recognition sensor 106 as input and output an estimation result regarding a collision between the moving body and the target, and have different characteristics (response characteristics), and the first collision estimation unit 131 has a higher responsiveness than the second collision estimation unit 132. a second collision estimation unit 132 having higher noise resistance than the first collision estimation unit 131; a first collision risk calculation unit 141 that calculates a first collision risk based on the first estimation result output by the first collision estimation unit 131; a second collision risk calculation unit 142 that calculates a second collision risk based on the second estimation result output by the second collision estimation unit 132; and a collision estimation arbitration unit 150, 250 that selects either the first estimation result or the second estimation result.

[0063] Furthermore, the first estimation result and the second estimation result include at least one of the time until collision, the overlap rate which is the ratio of overlap between the moving body range and the target range at the collision prediction point, and the relative speed in the forward / backward direction at the collision prediction point, and the first collision risk calculation unit 141 and the second collision risk calculation unit 142 calculate the first collision risk and the second collision risk from at least one of the margin of error (urgency) until collision, the accuracy of the collision prediction with the target, and the extent of damage, and the collision estimation arbitration units 150, 250 select either the first estimation result or the second estimation result based on the first collision risk and the second collision risk.

[0064] According to the first and second embodiments, it is possible to balance the prevention of excessive operation and the prevention of inoperation even during turning of the vehicle, for example, and to assist in avoiding a collision.

[0065] The present invention is not limited to the above-described embodiments and includes various modifications. For example, the above-described embodiments have been described in detail to clearly explain the present invention, and are not necessarily limited to those including all of the described configurations. Furthermore, it is possible to replace part of the configuration of one embodiment with the configuration of another embodiment, or to add the configuration of another embodiment to the configuration of one embodiment. Furthermore, it is possible to add, delete, or replace part of the configuration of each embodiment with other configurations.

[0066] Furthermore, the above-described configurations, functions, processing units, processing means, etc. may be partially or entirely implemented in hardware, for example, by designing them as integrated circuits. The above-described configurations, functions, etc. may also be implemented in software, with a processor interpreting and executing a program that implements each function. Information such as the programs, tables, and files that implement each function can be stored in a memory, a storage device such as a hard disk or SSD (Solid State Drive), or a recording medium such as an IC card, SD card, or DVD.

[0067] In addition, the control lines and information lines shown are those that are considered necessary for the explanation, and do not necessarily show all the control lines and information lines in the product. In reality, it can be assumed that almost all components are interconnected. [Explanation of symbols]

[0068] 100 Collision avoidance support device (first embodiment) 101 Vehicle status sensor (moving body status sensor) 102 Vehicle speed sensor 103 Steering angle sensor 104 Yaw rate sensor 106 Recognition Sensor 110 Ground speed and ground position calculation unit 120 Noise removal processing unit (own vehicle) 121 Noise removal processing unit (high response) (target) 122 Noise removal processing unit (high noise resistance) (target) 131 Collision estimation unit (first collision estimation unit) 132 collision estimation unit (second collision estimation unit) 141 Collision risk calculation unit (first collision risk calculation unit) 142 collision risk calculation unit (second collision risk calculation unit) 150 Collision Estimation Mediation Department 200 Collision Avoidance Assistance Device (Embodiment 2) 250 Collision estimation arbitration unit (Embodiment 2) 260 Weighting Unit (Embodiment 2)

Claims

1. A collision avoidance support device mounted on a moving body to avoid a collision between the moving body and a target around the moving body, a recognition sensor for acquiring information on the position and velocity of the target; a first collision estimation unit and a second collision estimation unit which receive information acquired by the recognition sensor as an input and output an estimation result regarding a collision between the moving body and the target and have different characteristics, the first collision estimation unit having higher responsiveness than the second collision estimation unit, and the second collision estimation unit having higher noise resistance than the first collision estimation unit; a first collision risk calculation unit that calculates a first collision risk based on the first estimation result output by the first collision estimation unit; a second collision risk calculation unit that calculates a second collision risk based on the second estimation result output by the second collision estimation unit; a collision estimation arbitration unit that selects either the first estimation result or the second estimation result, The collision estimation arbitration unit If either the first estimation result or the second estimation result indicates no collision, it is determined that no collision has occurred between the moving body and the target; If both the first estimation result and the second estimation result indicate a collision, the first collision risk is compared with the second collision risk, and the estimation result corresponding to the lower collision risk is selected; The collision avoidance assistance device A collision avoidance support device, characterized in that the collision avoidance between the moving body and the target is supported by using the estimation result selected by the collision estimation arbitration unit.

2. The collision avoidance assistance device according to claim 1, the first estimation result and the second estimation result include at least one of a time until collision, an overlap rate which is a ratio of an overlap between a moving object range and a target range at a collision prediction point, and a relative speed in a longitudinal direction at the collision prediction point; the first collision risk calculation unit and the second collision risk calculation unit calculate the first collision risk and the second collision risk from at least one of a margin of error until a collision, a degree of accuracy of a collision prediction with the target, and a magnitude of damage; a collision avoidance assistance device characterized in that the collision estimation arbitration unit selects either the first estimation result or the second estimation result based on the first estimation result, the second estimation result, and the first collision risk and the second collision risk.

3. The collision avoidance assistance device according to claim 1, the collision estimation arbitration unit further selects the first estimation result or the second estimation result based on outputs of the recognition sensor and a mobile body state sensor that acquires a state of the mobile body; the collision estimation arbitration unit arbitrates the first estimation result and the second estimation result in accordance with a weight of collision prediction accuracy calculated based on outputs of the recognition sensor and the moving object state sensor; The collision estimation arbitration unit As the sensing environment obtained from the output of the recognition sensor or the driving environment obtained from the output of the moving body state sensor improves, the weight of the collision prediction accuracy is increased; If either the first estimation result or the second estimation result indicates no collision, it is determined that no collision has occurred between the moving body and the target; a collision avoidance support device that performs arbitration between the first and second estimation results so that, if both the first and second estimation results indicate a collision, as the sensing environment or the driving environment improves, the first collision risk and the second collision risk are compared, and the distribution of estimation results corresponding to a high collision risk becomes larger and the distribution of estimation results corresponding to a low collision risk becomes smaller; or, as the sensing environment or the driving environment deteriorates, the first collision risk and the second collision risk are compared, and the distribution of estimation results corresponding to a high collision risk becomes smaller and the distribution of estimation results corresponding to a low collision risk becomes larger.

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