JP2008217094A - Collision decision device - Google Patents

Abstract

<P>PROBLEM TO BE SOLVED: To provide a collision decision device for precisely deciding collision using the measurement value of a yaw rate sensor. <P>SOLUTION: A collision decision device 10 is provided with: a course prediction part 13 for predicting the traveling direction of the own vehicle by using a yaw rate; a collision decision part 14 for deciding collision by using monitor information and the traveling direction in front of the own vehicle; and a smoothing processing part 11 for smoothing the yaw rate of the own vehicle acquired at every predetermined intervals. When the curve curvature of a road on which the own vehicle travels is large, the smoothing processing part 11 reduces the level of the smoothing of the yaw rate in comparison with when the curve curvature is small. Thus, even when a vehicle enters a curve spot, the smoothed yaw rate sensor measurement value is changed according to the change of the traveling direction of the vehicle without generating any delay, so that it is possible to precisely predict the traveling direction of the vehicle by using the yaw rate sensor measurement value, and to precisely decide collision. <P>COPYRIGHT: (C)2008,JPO&INPIT

Description

  The present invention relates to a collision determination device.

2. Description of the Related Art Conventionally, there has been known an apparatus that detects an obstacle on a vehicle path with a millimeter wave radar or the like, and performs an alarm or braking (for example, see Patent Document 1). This apparatus uses the information obtained from the yaw rate sensor to predict the course direction of the host vehicle and detects a collision with an obstacle.
Japanese Patent Laid-Open No. 2004-227122

  However, in such a device, for example, even if the driver is driven so as to go straight along a straight path, the measurement value obtained from the yaw rate sensor is deviated from an accurate value due to noise or the like. It is difficult to predict the course of the vehicle and determine whether an obstacle exists on the course of the vehicle.

  Such measurement value fluctuation can be avoided by smoothing (for example, averaging) the measurement value obtained from the yaw rate sensor. However, if the measured value of the yaw rate sensor is smoothed, for example, when the vehicle enters the curve point, even if the actual measured value of the yaw rate sensor changes, the measured value of the smoothed yaw rate sensor turns. There is a possibility that information that the vehicle is present cannot be acquired, and there is a risk that the roadside object at the curve point is determined as an obstacle on the course of the host vehicle.

  Accordingly, the present invention has been made to solve such a technical problem, and an object of the present invention is to provide a collision determination device that can accurately perform a collision determination using a measurement value of a yaw rate sensor.

  That is, the collision determination device according to the present invention includes a course direction prediction unit that predicts the course direction of the host vehicle using the yaw rate, and a collision determination unit that performs the collision determination using the monitoring information and the course direction in front of the host vehicle. And a smoothing means for smoothing the yaw rate of the host vehicle acquired every predetermined time, and the smoothing means has a large curve curvature of the road on which the host vehicle travels, Compared with the case where the curve curvature is small, the degree of smoothing of the yaw rate is reduced.

  With such a configuration, when the course direction of the vehicle is predicted using the smoothed measurement value of the yaw rate sensor, the degree of smoothing of the measurement value of the yaw rate sensor can be reduced as the curve curvature increases. Thus, by smoothing the measured value of the yaw rate sensor corresponding to the curve curvature, even if the vehicle enters the curve point, the measured value of the smoothed yaw rate sensor is the direction of the vehicle Therefore, the course direction of the vehicle can be accurately predicted using the measurement value of the yaw rate sensor. Thereby, the precision of collision determination can be improved.

  In the collision determination apparatus, it is preferable that the smoothing unit stops the smoothing when the curve curvature is larger than a predetermined value. If the curve curvature is large to some extent, there will be a large delay between the change in the direction of the vehicle and the change in the measurement value of the smoothed yaw rate sensor. It is possible to avoid the prediction of the course direction using the measured value of the generated yaw rate sensor. Therefore, it is possible to avoid making an erroneous collision determination.

  According to the present invention, the collision determination using the measurement value of the yaw rate sensor can be performed with high accuracy.

  Hereinafter, embodiments of the present invention will be described in detail with reference to the accompanying drawings. In the description of the drawings, the same elements are denoted by the same reference numerals, and redundant description is omitted.

  FIG. 1 is a schematic configuration diagram of a vehicle including a collision determination device according to the present embodiment. The collision determination device according to the present embodiment is suitably used for a vehicle that performs a collision determination by predicting a course direction of the vehicle from a measurement value of a yaw rate sensor.

  As shown in FIG. 1, the travel control ECU 100 includes a collision determination device 10 and a travel control unit 15. The collision determination device 10 includes a route prediction unit (route direction prediction unit) 13, an obstacle detection unit 12, and a collision determination unit 14 (collision determination unit). The travel control ECU 100 is connected to the yaw rate sensor 20, the millimeter wave radar 23, and the image sensor 24, and inputs information from each sensor. The travel control ECU 100 is connected to the actuator 30, the warning device 31, and the occupant protection device 32, and outputs information and commands to each device.

  First, each sensor connected to the travel control ECU 100 will be described. The yaw rate sensor 20 connected to the travel control ECU 100 is a sensor that detects the rotational speed of the vehicle in the horizontal direction, that is, the yaw rate, every predetermined time. For example, the distortion generated by the rotational force of the vehicle is measured by a piezoelectric element. What detects a yaw rate is used. The yaw rate sensor 20 has a function of outputting a yaw rate signal to the travel control ECU 100.

  The millimeter wave radar 23 is used to detect an obstacle ahead of the vehicle as a forward monitoring means. For example, the millimeter wave radar 23 emits a radio wave from the own vehicle side, receives a radio wave reflected from the obstacle, and propagates. A device that measures the position of an obstacle or the relative speed with respect to the host vehicle based on time or a frequency difference caused by the Doppler effect is used. Here, the obstacle includes not only objects existing on the road but also guardrails, road signs, walls, and the like provided around the road. The image sensor 24 is used to detect an obstacle ahead of the vehicle as a forward monitoring unit, and for example, an in-vehicle camera capable of recognizing an object from an acquired image is used. The millimeter wave radar 23 and the image sensor 24 have a function of outputting the acquired information to the obstacle detection unit 12 as forward monitoring information.

  Next, the internal structure of the travel control ECU 100 will be described. The course prediction unit 13 provided in the collision determination device 10 has a function of predicting the course direction of the host vehicle based on the yaw rate of the host vehicle. For example, the course prediction unit 13 inputs the yaw rate of the host vehicle from the yaw rate sensor 20, predicts the course direction using the yaw rate, and outputs the prediction result to the collision determination unit 14.

  The obstacle detection unit 12 has a function of detecting an obstacle existing in front of the host vehicle by inputting image information on which the obstacle is projected, distance information to the obstacle, and the like. And the obstacle detection part 12 is provided with the function to grasp | ascertain obstacle information, for example, a magnitude | size and a location. The obstacle detection unit 12 inputs image information and position information from, for example, the millimeter wave radar 23 and the image sensor 24, and outputs information on an obstacle with a possibility of collision to the collision determination unit 14 as forward monitoring information. .

  The collision determination unit 14 has a function of determining whether or not to collide with an obstacle using the size and distance information of the obstacle in the course direction and the course direction of the host vehicle predicted at the current location. Yes. The collision determination unit 14 inputs, for example, the obstacle information and the course direction as the forward monitoring information from the obstacle detection unit 12 and the course prediction unit 13, respectively, and the traveling control unit 15, the warning device 31, and the occupant protection device 32. The judgment result is output to

  The travel control unit 15 has a function of controlling the travel of the vehicle based on the yaw rate. The traveling control unit 15 has a function of inputting a determination result that the vehicle collides with an obstacle and controlling the traveling of the vehicle so as to avoid the collision. For example, the yaw rate and the collision determination result are input from the collision determination unit 14 and a traveling control command is output to the actuator 30. The travel control unit 15 is not an essential component and may be provided as necessary.

  Next, the actuator 30, the warning device 31, and the occupant protection device 32, to which the traveling control ECU 100 outputs information, will be described. The actuator 30 is a mechanical component that controls traveling of the vehicle, and is, for example, a brake actuator, an engine throttle valve, a handle, or the like. The actuator 30 has a function of operating based on a control command input from the travel control unit 15.

  The warning device 31 has a function of notifying the driver of a vehicle collision. This function is, for example, a function for outputting a buzzer sound from a speaker or lighting a warning lamp.

  The occupant protection device 32 has a function of protecting the occupant from the impact of the collision in the event of a collision. This function is a function of protecting the occupant by absorbing and diffusing an impact applied to the occupant by operating an air bag, a pre-crash seat belt, or the like, for example.

  The travel control ECU 100 configured as described above acquires the yaw rate from the yaw rate sensor 20 to predict the course direction of the host vehicle, and collides based on the forward monitoring information obtained from the millimeter wave radar 23 and the image sensor 24. It is possible to perform collision avoidance, collision warning, and passenger protection at the time of collision.

  Here, the collision determination device 10 of the travel control ECU 100 according to the present embodiment includes a yaw rate smoothing processing unit (smoothing means) 11. The yaw rate smoothing processing unit 11 has a function of smoothing a yaw rate that is blurred from an accurate value due to noise or the like. For example, the yaw rate sensor 20 is connected to smooth a predetermined number of yaw rates input from the yaw rate sensor 20. Furthermore, it has a function of changing the degree of smoothing based on the curve curvature as road information on which the host vehicle is traveling. For example, road information is acquired from the white line recognition sensor 21 and the navigation device 22 and the degree of smoothing is changed based on the estimated curve curvature of the road.

  The white line recognition sensor 21 is used for estimating a curve curvature, and has a function of determining information on a road shape such as a curve or a straight line from the shape of the white line. The navigation device 22 is a device that can provide road shape information. For example, the navigation device 22 can provide road information stored in a map DB.

  Next, the operation of the travel control ECU 100 including the collision determination device 10 according to the present embodiment will be described.

  2 and 3 are flowcharts showing the operation of the travel control ECU 100 according to the present embodiment. The flowchart shown in FIG. 2 is repeatedly executed at a predetermined timing after the ignition is turned on, for example. In addition, a control start switch or the like may be provided so that the process is repeatedly executed after the user presses the control start switch. In the following description, it is assumed that the curve curvature is a value greater than or equal to 0, and in the case of 0, it indicates a straight line. Further, description will be made using n, A, B, and C as predetermined values of the curve curvature. These predetermined values have a relationship of n> A> B> C> 0.

  The travel control ECU 100 starts the operation from the measurement value input process shown in FIG. 2 (S10). The processing of S10 is executed by the yaw rate smoothing processing unit 11 and the obstacle detection unit 12, and is a process of inputting measurement values from each sensor. For example, the travel control ECU 100 inputs the yaw rate from the yaw rate sensor 20 in the yaw rate smoothing processing unit 11, and inputs road information, for example, the curve curvature of the road on which the vehicle travels, from the white line recognition sensor 21 and the navigation device 22. In addition, for example, the obstacle control unit 12 inputs the position information of the obstacle from the millimeter wave radar 23 and the image sensor 24 in the obstacle control unit 100. When the process of S10 ends, the process proceeds to a road shape determination process (S12).

  The process of S12 is a process that is executed by the yaw rate smoothing processing unit 11 and determines whether the road shape does not cause a phase delay due to smoothing even if the yaw rate smoothing process is performed. For example, it is determined whether or not the road shape input in the process of S10 is a value smaller than a predetermined curve curvature n. If the curve curvature input in the process of S10 is equal to or less than the predetermined curve curvature n, it is determined that there is no deviation between the actual vehicle course direction and the course direction calculated from the smoothed yaw rate, and the process proceeds to the smoothing process. (S14).

  The process of S14 is a process that is executed by the yaw rate smoothing processing unit 11 and smoothes the predetermined number of yaw rates input in the process of S10. Details of the yaw rate smoothing process will be described later. When the process of S14 ends, the process proceeds to a course direction prediction process (S16).

  The process of S16 is a process executed by the course prediction unit 13 to predict the course direction of the vehicle. The course direction is determined based on the latest yaw rate obtained in S10 or the smoothed yaw rate obtained in S14. When the process of S16 ends, the process proceeds to a collision determination process (S18).

  The process of S18 is a process that is executed by the collision determination unit 14 and determines whether or not the vehicle collides with an obstacle. For example, it is determined whether the obstacle is on the course of the vehicle from the obstacle position obtained in S10 and the course direction obtained in S16. Further, it may be determined whether the vehicle collides within a predetermined time using the current vehicle speed. In the process of S18, when an obstacle exists on the course, the process proceeds to a warning process (S20).

  The process of S20 is executed by the warning device 31 and the occupant protection device 32, and is a process for notifying the occupant of a collision and protecting the occupant from the impact of the collision. When the process of S20 ends, the control process of FIG. 2 ends.

  On the other hand, in the process of S12, when the curve curvature input in the process of S10 is larger than the predetermined curve curvature n, it is determined that a deviation occurs between the actual vehicle course direction and the course direction calculated from the smoothed yaw rate, The process proceeds to the course direction prediction process without performing the smoothing process (S16). That is, in S16, the course direction is predicted using the latest unsmoothed yaw rate.

  In the process of S18, if no obstacle exists on the course, the control process of FIG. 2 is terminated without performing the warning process (S20).

  As described above, by executing the execution of the yaw rate smoothing process using the curve curvature as a threshold, for example, when entering a sharp curve, the smoothing process is not performed and the yaw rate that has not been smoothed is used. The direction of the course can be determined. Therefore, it is possible to avoid inputting a yaw rate that is delayed with respect to the direction of the vehicle.

  Next, the smoothing process will be described with reference to FIGS.

  FIG. 3 is a flowchart showing a yaw rate smoothing process, and FIGS. 4 to 7 show a smoothing coefficient map used for the smoothing process. The smoothing process of FIG. 3 shows the details of the operation of S14 which is the yaw rate smoothing process of FIG.

  As shown in FIG. 3, the yaw rate smoothing process is started from the curve curvature determination process in S22. The process of S22 is a process of determining whether or not the curve curvature input in the process of S10 is equal to or less than a predetermined curve curvature C. When it is determined that the curve curvature input in the process of S10 is equal to or less than the curve curvature C, the process proceeds to the first smoothing coefficient input process (S28).

  The process of S28 is a process of inputting a coefficient that determines the degree of smoothing. Here, the smoothing coefficient map used for the smoothing process will be described. The smoothing performed in the smoothing process is an average operation, and the degree of smoothing is determined by changing the number of data of the yaw rate to be smoothed and the ratio of each data to the smooth value. Further, the ratio to the smooth value is determined for each acquired data point. A graph of these data points and the ratio to the smooth value is shown in FIGS.

  4 to 7 are smoothing coefficient maps, and the horizontal axis indicates data points according to the acquisition timing of the yaw rate. Data 1 is a value input at the latest timing, and data 20 is a value at which the most time has elapsed after acquisition. The data acquisition timing is, for example, 50 ms. The vertical axis indicates the ratio to the smooth value. Thus, since the ratio with respect to the smooth value corresponding to the data point of 1-20 is mapped, smoothing which weighted according to the acquisition timing of a yaw rate is performed with respect to the past 20 yaw rate measurement values. Can do.

  The smoothing coefficient map of FIG. 4 indicates that the yaw rate acquired at any timing is smoothed by weighting at a constant value ratio. In this case, since all values are used for smoothing, it is the same as a simple averaging operation.

  The smoothing coefficient map in FIG. 5 indicates that smoothing is performed using the latest data 1-17. Further, as the acquisition timing becomes the latest, the ratio to the smooth value is increased. In this way, the degree of smoothing can be reduced by reducing the number of objects to be smoothed compared to FIG. Since FIG. 6 and FIG. 7 are the same as FIG. 6 is less smoothed than FIG. 5, and FIG. 7 is less smoothed than FIG.

  Returning to the process of S28, a process of inputting a smoothing coefficient map is performed. Since this process is executed when it is determined that the curve curvature of the road to be traveled is C or less, it is applied when the road shape is a straight line shape or a relatively gentle curve. In such a case, the first smoothing map shown in FIG. 4 is input. When the process of S28 ends, the process proceeds to a smoothing process (S36).

  The process of S36 is a process of smoothing based on the smoothing map. Smoothing is performed by multiplying the coefficient assigned to each data point by the yaw rate measurement and summing. When the process of S36 ends, the yaw rate smoothing process ends.

  On the other hand, in the process of S22, when it is determined that the curve curvature input in the process of S10 is larger than the curve curvature C, the process proceeds to the curve curvature determination process (S24).

  The process of S24 is a process of determining whether or not the curve curvature input in the process of S10 is equal to or less than a predetermined curve curvature B. When it is determined that the curve curvature input in the process of S10 is equal to or less than the curve curvature B, the process proceeds to the second smoothing coefficient input process (S30).

  The process of S30 is a process for inputting a smoothing coefficient map. This process is executed when it is determined that the curve curvature of the road to be traveled is B or less, and the second smoothing map shown in FIG. 5 having a smoothing degree smaller than that in FIG. 4 is input. When the process of S30 ends, the process proceeds to a smoothing process (S36).

  Further, in the process of S24, when it is determined that the curve curvature input in the process of S10 is larger than the curve curvature B, the process proceeds to the curve curvature determination process (S26).

  The process of S26 is a process of determining whether or not the curve curvature input in the process of S10 is equal to or less than a predetermined curve curvature A. When it is determined that the curve curvature input in the process of S10 is equal to or less than the curve curvature A, the process proceeds to the third smoothing coefficient input process (S32).

  The process of S32 is a process of inputting a smoothing coefficient map. This process is executed when it is determined that the curve curvature of the road to be traveled is A or less, and the third smoothing map shown in FIG. 6 having a smoothing degree smaller than that in FIG. 5 is input. When the process of S30 ends, the process proceeds to a smoothing process (S36).

  Further, in the process of S26, when it is determined that the curve curvature input in the process of S10 is larger than the curve curvature A, the process proceeds to the fourth smoothing coefficient input process (S34).

  The process of S34 is a process of inputting a smoothing coefficient map. This process is executed when it is determined that the curve curvature of the road to be traveled is larger than A, and the third smoothing map shown in FIG. 6 having a smaller degree of smoothing than FIG. 5 is input. When the process of S30 ends, the process proceeds to a smoothing process (S36).

  Thus, the smoothing process of the yaw rate can be performed according to the curve curvature using the smoothing maps of FIGS. For example, as shown in FIG. 9, in the conventional collision determination device, when the vehicle R travels on a straight line (R1), the course direction of the vehicle and the course direction (r11) calculated from the yaw rate sensor are the same. However, when entering the curve (R2), the smoothed yaw rate was not able to follow the change in the course direction of the vehicle and was delayed, so the course direction (r22) calculated from the smoothed yaw rate and the course direction of the vehicle were shifted. It was determined that the vehicle collides with the obstacle X.

  Compared to this, in the collision determination device 10 of the present embodiment, as shown in FIG. 8, when the vehicle S travels on a straight line (S1), the course direction calculated from the course direction of the vehicle and the yaw rate sensor 20 (s11). ) Are the same, and even when entering the curve (S2), the course direction (s22) calculated from the yaw rate deviates from the course direction of the vehicle in order to change the degree of smoothing according to the curve curvature. And determination that the vehicle collides with the obstacle X can be avoided. Thereby, the precision of collision determination can be improved.

  As described above, according to the collision determination device 10 according to the present embodiment, when the course direction of the vehicle is predicted using the smoothed yaw rate, the degree of smoothing of the yaw rate is reduced as the curve curvature increases. be able to. By smoothing the yaw rate according to the curve curvature, even if the vehicle enters the curve point, the smoothed yaw rate changes without delay as the vehicle direction changes, so the yaw rate Can be used to accurately predict the course direction of the vehicle. Thereby, the precision of collision determination can be improved.

  Further, according to the collision determination device 10 according to the present embodiment, when the curve curvature is large to some extent, a large delay occurs between the change in the vehicle course direction and the change in the smoothed yaw rate. Since the smoothing itself is stopped, it is possible to avoid the prediction of the course direction using the measured value of the yaw rate sensor in which the delay has occurred. Therefore, it is possible to avoid making an erroneous collision determination.

  Each embodiment mentioned above shows an example of the collision judging device concerning the present invention. The collision determination device according to the present invention is not limited to the above, and the collision determination device according to the embodiment is modified or applied to other devices so as not to change the gist described in each claim. It may be a thing.

  For example, in the embodiment, an example in which collision determination is performed using four yaw rate smoothing maps has been described, but the number is not limited to four, and a plurality of yaw rate smoothing maps may be used.

1 is a schematic configuration diagram of a vehicle including a collision determination device according to an embodiment. It is a flowchart which shows operation | movement of traveling control ECU containing the collision determination apparatus of FIG. It is a flowchart which shows the yaw rate smoothing process of FIG. It is explanatory drawing of the smoothing coefficient map used for the smoothing process of FIG. It is explanatory drawing of the smoothing coefficient map used for the smoothing process of FIG. It is explanatory drawing of the smoothing coefficient map used for the smoothing process of FIG. It is explanatory drawing of the smoothing coefficient map used for the smoothing process of FIG. It is a schematic diagram explaining the effect of the collision determination apparatus of FIG. It is a schematic diagram explaining the effect of the conventional collision determination apparatus.

Explanation of symbols

  DESCRIPTION OF SYMBOLS 10 ... Collision determination apparatus, 11 ... Yaw rate smoothing process part, 12 ... Obstacle detection part, 13 ... Course prediction part, 14 ... Collision determination part, 15 ... Travel control part, 20 ... Yaw rate sensor, 21 ... White line recognition sensor, DESCRIPTION OF SYMBOLS 22 ... Navigation apparatus, 23 ... Millimeter wave radar, 24 ... Image sensor, 30 ... Actuator, 31 ... Warning apparatus, 32 ... Passenger protection apparatus, 100 ... Traveling control ECU.

Claims (2)

A course direction prediction means for predicting a course direction of the host vehicle using a yaw rate, and a collision determination means for performing a collision judgment using the monitoring information of the obstacle ahead of the host vehicle and the predicted course direction. A collision determination device,
Comprising a smoothing means for smoothing the yaw rate of the host vehicle acquired every predetermined time;
The smoothing means reduces the degree of smoothing of the yaw rate when the curve curvature of the road on which the host vehicle is traveling is large compared to when the curve curvature is small;
A collision determination device characterized by the above.   The collision determination apparatus according to claim 1, wherein the smoothing unit stops smoothing when the curve curvature is larger than a predetermined value.

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