JP2008024108A - Collision controller for vehicle - Google Patents

Abstract

<P>PROBLEM TO BE SOLVED: To reduce the collision damage of a crew by quantitatively grasping the collision damage of a crew without asking any collision configuration. <P>SOLUTION: A three-dimensional object is detected based on a signal from a full azimuth camera 5 and a laser radar 6 by a three-dimensional object detection part 10, and the collision possibility of the three-dimensional object and its own vehicle is judged by a collision judging part 11. Then, when it is judged that it is impossible to avoid collision, the deformation quantity of the cabin of its own vehicle due to collision is estimated by a cabin deformation estimation part 12, and the injury of a crew is estimated by a crew injury estimation part 14 based on the cabin deformation quantity and the boarding status of the crew detected based on signals from a seat sensor 7 and an in-vehicle camera 8 by a crew status detection part 13, and the vehicle controlled variables of avoidance control for minimizing the risk of the injury of the crew is calculated by a vehicle controlled variable calculation part 15. Thus, it is possible to quantitatively grasp the collision damage of the crew, and to reduce the collision damage of the crew. <P>COPYRIGHT: (C)2008,JPO&INPIT

Description

  The present invention relates to a vehicle collision control device that reduces damage to passengers caused by a vehicle collision.

  Recently, vehicles such as automobiles are equipped with TV cameras, laser radars, etc. to detect vehicles and obstacles ahead, determine the risk of collision with them, and issue warnings to drivers, or automatically Collision control devices that avoid or reduce collision damage by operating brakes or steering devices have been actively developed.

Such a collision control device is disclosed in Patent Document 1, for example. In this prior art, when the target object existing in the traveling direction of the host vehicle is detected and it is determined that the collision between the host vehicle and the target object cannot be avoided, the shape of the target object, the passenger of the host vehicle, the boarding position In response to this, the traveling state of the vehicle is changed so as to suppress damage at the time of collision.
JP 2000-95130 A

  By the way, in the vehicle avoidance control for suppressing the collision damage of the vehicle as in Patent Document 1, it is preferable to execute the avoidance control only when the damage at the time of the collision is relatively large. If it is sufficient, it is preferable to leave the driving to the vehicle driver without executing the avoidance control. The technique disclosed in Patent Document 1 does not consider determining whether to execute avoidance control according to the magnitude of collision damage. Therefore, even if the damage of collision is relatively small, the avoidance control is executed and the driver causes There is a possibility that the avoidance control may be hindered.

  The present invention has been made in view of the above circumstances, and an object thereof is to provide a collision control device capable of quantitatively grasping a passenger's collision damage and reducing the passenger's collision damage.

  In order to achieve the above object, a collision control device according to the present invention includes a three-dimensional object detection unit that detects a three-dimensional object existing around the host vehicle, and a collision determination unit that determines the possibility of a collision between the three-dimensional object and the host vehicle. And, when it is determined that a collision between the three-dimensional object and the host vehicle is unavoidable, a cabin deformation amount estimating means for estimating a cabin deformation amount of the host vehicle due to the collision, and an occupant state detection for detecting a passenger's boarding state of the host vehicle Vehicle, an occupant injury estimation means for estimating the occupant's collision damage based on the cabin deformation amount and the occupant's boarding state, and a vehicle for calculating a vehicle control amount for avoidance control based on the occupant's collision damage And a control amount calculating means.

  The collision control device according to the present invention can quantitatively grasp the passenger's collision damage and reduce the passenger's collision damage.

  Embodiments of the present invention will be described below with reference to the drawings. 1 to 11 relate to a first embodiment of the present invention, FIG. 1 is a system configuration diagram of a vehicle collision control device, FIG. 2 is an explanatory diagram showing a situation immediately before a collision, and FIG. 3 is a deformation of a cabin. FIG. 4 is an explanatory diagram showing the probability distribution of cabin deformation, FIG. 5 is an explanatory diagram showing the occupant situation, FIG. 6 is an explanatory diagram showing the occupant existence probability distribution, and FIG. 7 is an occupant injury risk due to cabin deformation. FIG. 8 is an explanatory diagram showing the weighting of each occupant's injury site, FIG. 9 is a flowchart of the collision control process, FIG. 10 is an explanatory diagram showing vehicle control for a side collision, and FIG. 11 is for a single frontal collision. It is explanatory drawing which shows vehicle control.

  The vehicle collision control apparatus 1 shown in FIG. 1 monitors the situation outside the host vehicle and the boarding state of passengers in the vehicle, and collides with another vehicle on the host vehicle or on a three-dimensional object such as a utility pole or guardrail of the host vehicle. If it is determined that the vehicle is in an unavoidable situation, the vehicle control execution is instructed so that the damage to the own vehicle and the passenger is minimized under the collision unavoidable situation, and a warning is output. The vehicle collision control apparatus 1 includes a control unit 2 composed of a microcomputer or the like, a sensor for detecting a vehicle state, a sensor for monitoring a vehicle exterior situation of the host vehicle, and a sensor for detecting the interior situation of the host vehicle. Etc. are connected.

  As a sensor for detecting the vehicle state, a vehicle speed sensor 3 and a rudder angle sensor 4 are used, and as a sensor for monitoring a situation outside the vehicle of the own vehicle, a laser radar having a mechanism capable of scanning or imaging in all directions. Or using a camera. In this embodiment, an omnidirectional camera 5 capable of imaging a 360 ° range centering on the host vehicle and a laser radar 6 (which may be a millimeter wave radar) capable of omnidirectional scanning are used in combination to monitor outside conditions. It is a sensor. In addition, as a sensor for monitoring the boarding state of the occupant of the own vehicle, a seat sensor 7 for detecting the load of the occupant seated on the seat and an in-vehicle camera 8 for monitoring the interior of the vehicle are used in combination.

  In addition to the seat sensor 7 and the in-vehicle camera 8, a seat belt sensor that detects the occupant's seat belt wearing may be used in combination as a sensor that monitors the occupant's boarding state.

  On the other hand, the control unit 2 recognizes a three-dimensional object to be processed based on signals from various sensors, expresses collision damage with a single index, and executes collision determination and collision damage reduction processing. That is, the damage received by the occupant at the time of the collision differs depending on the collision mode such as a collision in the front of the vehicle (front collision), a collision in the side of the vehicle (side collision), or a collision in the rear of the vehicle (rear collision). Although it was difficult to quantitatively grasp the collision damage with this technology, as will be described later, the control unit 2 grasps the collision damage using a single index called the risk probability, thereby Quantitative comparison is possible regardless of.

  For this reason, the control unit 2 detects a three-dimensional object based on signals from the omnidirectional camera 5 and the laser radar 6, and a collision determination that determines the possibility of collision between the extracted three-dimensional object and the host vehicle. Unit 11, cabin deformation estimation unit 12 that estimates the amount of deformation of the cabin of the host vehicle due to a collision, passenger state detection unit 13 that detects a passenger's boarding state based on signals from seat sensor 7 and in-vehicle camera 8, The occupant injury estimation unit 14 that estimates occupant injury based on the occupant's boarding state and the vehicle control amount calculation unit 15 that calculates vehicle control amount for avoidance control that minimizes the risk of occupant injury are provided. Yes.

  As will be described later, the control unit 2 includes a vehicle position information detection unit 20 that detects absolute position information of the host vehicle by GPS or the like when an accident occurs, and an external communication control unit 21 that controls communication between the host vehicle and the outside. Instruct the necessary data transmission to support the lifesaving emergency system.

  The three-dimensional object detection unit 10 recognizes a three-dimensional object existing around the host vehicle by a known technique using sensor fusion between the omnidirectional camera 5 and the laser radar 6. In the recognition of the three-dimensional object by the sensor fusion, the three-dimensional object extracted by processing the image captured by the omnidirectional camera 5 and the radar image generated by receiving the reflected wave of the laser beam transmitted from the laser radar 6 are extracted. The three-dimensional object is fused, and final recognition is performed using distance information based on the time from laser light transmission to light reception.

  Then, the coordinate distribution of the three-dimensional object is calculated based on the moving amount of the vehicle obtained from the speed of the host vehicle from the vehicle speed sensor 3 and the elapsed time, and the steering angle from the steering angle sensor 4. In calculating the coordinate distribution of this three-dimensional object, straight travel and turning are determined based on the steering angle, and in the case of straight travel, the vehicle movement amount is added to the vehicle front-rear direction component of the three-dimensional object coordinates calculated up to the previous time. In this case, the rotation center and rotation angle of the vehicle are obtained from the movement amount and the steering angle, and the three-dimensional object coordinates calculated so far are rotated with respect to the rotation center.

  The collision determination unit 11 determines the coordinates based on the coordinate distribution indicating the vehicle speed from the vehicle speed sensor 3, the steering angle from the steering angle sensor 4, and the relative position between the subject vehicle and the surrounding object from the three-dimensional object detection unit 10. In the distribution, when the host vehicle is driven with the current steering angle and the vehicle speed, an object that may collide with the host vehicle is determined, and the collision condition is calculated. For example, as shown in FIG. 2, when another vehicle 150 detected as a three-dimensional object approaches the rear side of the host vehicle 100, the position and speed (speed vector) of the host vehicle 100 and the position and speed of the other vehicle 150 are detected. The possibility of a collision is determined from the (velocity vector), and collision conditions such as the collision position and size of the vehicle, the collision speed (relative speed between the host vehicle and the collision object), the weight of the collision object, and the like are calculated.

  If the collision determination unit 11 determines that there is a possibility of a collision, the determination result is output to the alarm device 22 and an alarm is given by sound or image.

  The cabin deformation estimation unit 12 estimates the cabin deformation amount of the host vehicle due to the collision between the three-dimensional object and the host vehicle based on the collision condition calculated by the collision determination unit 11. For example, when the other vehicle 150 collides with the host vehicle 100 illustrated in FIG. 2, it is estimated that the cabin side rear of the host vehicle 100 is deformed as illustrated in FIG. 3.

  The estimation of the cabin deformation amount is performed by creating a map for evaluating the risk of cabin deformation corresponding to the collision condition in advance and using this map. In order to cope with an actual collision accident, the map is created by acquiring the data of the cabin deformation amount in a wide range of collision conditions by the collision analysis by numerical simulation, and further adding the error due to the variation of the collision conditions.

  That is, the relative speed, weight, size, collision position, and the like, which are the collision conditions, are estimated values estimated before the collision, and thus some error occurs. If the amount of deformation of the cabin is estimated only with the estimated collision condition without considering the variation in conditions, if the error is large, there is a possibility that the actual deformation may be greatly different from the estimation. Therefore, when performing a numerical simulation of collision analysis, all the cabin deformation amount data when the collision conditions vary are obtained in advance, and the risk is evaluated as a probability distribution of cabin deformation based on the distribution of these data To do.

  In this embodiment, as shown in FIG. 4, the risk of cabin deformation is evaluated for each voxel using a voxel model in which a space including a cabin is divided into cubic elements (voxels). That is, when it is determined that the collision is inevitable, the collision condition is checked for each voxel, and the risk of deformation is estimated over the entire voxel model (the entire cabin).

  In FIG. 4, corresponding to FIG. 3, the corresponding three voxel groups VXa have the highest deformation probability (for example, 81%) on the rear corner side of the cabin side which is the highest risk of deformation due to collision. Then, the deformation probability of the three voxel groups VXb adjacent to the voxel group VXa on the front side of the cabin is increased (for example, 80 to 61%). In addition, the voxel group VXc around the voxel groups VXa and VXb has a low probability of deformation (for example, 60-41%), and the degree of deformation is small. The deformation probability is low (for example, 20% or less), which indicates that relatively slight deformation is sufficient.

  The deformation probability for each of these voxels is set according to the collision condition from a previously held map (a map created by performing a numerical simulation in advance), and even in the collision mode shown in FIGS. 2 and 3, For example, the deformation probability decreases as the relative position of the other vehicle 150 with respect to the host vehicle 100 increases or the relative speed decreases.

  The voxel model in FIG. 4 is expressed in two dimensions for convenience of explanation, but a three-dimensional voxel model is used for actual evaluation. This three-dimensional voxel model shows a risk space under a situation where collision is inevitable, and can be said to be a risk map that can cope with any collision mode.

  The occupant state detection unit 13 detects the occupant's boarding position using the seat sensor 7, and estimates the occupant's body shape, posture, and the like at the detected position from the image captured by the in-vehicle camera 8. Then, the occupant position, posture, and body shape variation are evaluated, and the probability distribution in which the occupant exists is set to a voxel model similar to the voxel model of cabin deformation.

  For example, as shown in FIG. 5, when two passengers 101 and 102 are in the front seat of the host vehicle 100, the body shape estimated from the boarding position detected by the seat sensor 7 and the captured image of the in-vehicle camera 8. A passenger presence probability distribution as shown in FIG. 6 is set by evaluating the position and posture. In FIG. 6, the existence probabilities of the voxels VX1 and VX2 corresponding to the boarding positions of the occupants 101 and 102 are the highest, and the existence probabilities of the surrounding voxels are set according to variations in the boarding positions depending on the occupant system and posture.

The occupant injury estimation unit 14 estimates the risk of occupant injury based on the probability distribution of cabin deformation and the probability distribution of occupant presence. In other words, the space where the cabin is deformed and the space where the occupant is present is defined as the space where the occupant is injured, and the probability that the cabin is deformed is multiplied by the probability that the occupant is present. Assess the risk. Specifically, assuming that the cabin deformation probability is Pc and the occupant existence probability is Pj, the occupant injury probability Pr due to cabin deformation is calculated for each voxel by the following equation (1).
Pr = Pc × Pj (1)

  FIG. 7 is an occupant injury voxel model calculated using a cabin deformation probability voxel model (see FIG. 4) and an occupant existence probability voxel model (see FIG. 6). In this voxel model of occupant injury, since the part having the highest cabin deformation probability Pc and the part having the highest occupant existence probability Pj are separated from each other, the voxel VX3 adjacent to the voxel VX2 at the occupant position in front of the collision part. In this case, the injury probability Pr is slightly increased, and in the voxel VX1 at the occupant position on the opposite side of the collision site, the injury probability Pr is small, indicating that serious injury can be avoided.

In this case, it is desirable to weight the occupant injury risk due to cabin deformation in consideration of various factors such as an expected injury site and individual differences among occupants. When the weighting factor K is introduced and rewritten in the equation (1), the following equation (2) is obtained, and a more precise risk evaluation can be performed. The weighting factor K can be set by multiplying weighting factors for each factor or independently.
Pr = Pc × Pj × K (2)

  For example, the occupant's riding posture, age, gender, etc. are specified from the image taken by the in-vehicle camera 8, and weighting taking into account differences in body tolerance depending on the injured site, age, and gender as shown in (a) to (c) below It can be performed. In addition, as for the age and sex of the boarding, the age, sex, etc. of an occupant who may be boarded, such as a family, may be input in advance.

(A) Weighting by injury site As shown in FIG. 8, the weight is changed for each site where the expected severity of injury is different, such as the head, neck, and leg. When the weight is 10, the weight of the leg is 1. In FIG. 8, weights are set in three stages, namely, the head and neck region H1 of the occupant H, the region H2 around the region H, and the torso and limb regions H3, and the weight of the region H1 is maximized. Next, an example in which the weight of the region H2 is increased is shown.

(B) Weighting of physical tolerance according to age Considering a decrease in physical tolerance due to aging, for example, when the weight for an occupant 65 years or older is 10, the weight for an occupant aged 20 to 40 is set to 1.

(C) Weighting of physical tolerance by gender Considering the difference in physical tolerance between women and men, weighting is performed according to the gender of the occupant. For example, when airbags are deployed, women tend to be more injured than men. Therefore, when the weight for women is 10, the weight for men is 5.

  In the present embodiment, the above risk of occupant injury (occupant injury probability Pr) is summed for all voxels, and the vehicle control amount calculation unit 15 calculates the vehicle control amount for avoidance control that minimizes the total risk ΣPr. The vehicle control amount of this avoidance control is the vehicle steering angle, acceleration control amount, brake control amount, etc., vehicle behavior simulation, gradient method (GM; Gradient-based Method), annealing method (SA; Simulated Annealing), By using a well-known optimization method such as genetic algorithm (GA), it solves the minimum design problem using the total risk ΣPr as design variables such as objective function, steering angle, acceleration control amount, and brake control amount. You can get it. Vehicle control amounts such as a steering angle, an acceleration control amount, and a brake control amount that minimize the total risk ΣPr are output to the vehicle control device 23 to minimize occupant damage by executing vehicle posture control including acceleration and deceleration. It becomes possible.

  Note that the vehicle control that minimizes the total risk sum ΣPr is an optimization based on the size of the total risk amount, and can effectively reduce the risk for any collision mode. When the high area is limited to some extent, optimization is performed mainly on the part with the highest occupant injury probability Pr including the above-mentioned weighting without taking the sum of the entire voxels to actively prevent severe injury. It may be avoided.

  Specifically, each function in the control unit 2 described above is executed as a program process of the collision control shown in the flowchart of FIG. Next, the program process of this collision control will be described.

  In this collision control process, in the first step S1, the traveling state of the host vehicle is detected based on information such as the vehicle speed sensor 3 and the rudder angle sensor 4, and in step S2, the omnidirectional camera 5 and the laser radar 6 are used. Thus, a three-dimensional object existing as a collision object around the host vehicle is detected. Next, in step S3, the surrounding situation around the host vehicle is grasped from the image from the omnidirectional camera 5 and the distance information from the laser radar 6, and in step S4, it is determined whether or not a collision is inevitable.

  As a result, when it is determined that a collision can be avoided (inevitable), the process proceeds from step S4 to step S5, and it is determined whether or not avoidance by vehicle attitude control including acceleration / deceleration is required. If it is determined that the collision can be avoided without controlling the steering and acceleration / deceleration, the process is left as it is. If it is determined that the steering or acceleration / deceleration needs to be controlled, the steering or acceleration / deceleration is performed in step S6. Is automatically controlled to execute vehicle attitude control.

  On the other hand, if it is determined in step S4 that collision avoidance is impossible (unavoidable), the process proceeds from step S4 to step S7, and the risk of collision damage is estimated. As described above, the risk of this collision damage is a passenger who uses the voxel model of the cabin deformation probability distribution and the voxel model of the passenger existence probability distribution and multiplies the cabin deformation probability Pc and the passenger existence probability Pj for each voxel. Estimated by injury probability Pr.

  After the risk of collision damage is estimated in step S7, the process proceeds to step S7-1, and it is determined whether the occupant injury probability Pr exceeds a preset threshold value Pset. If it is determined that the occupant injury probability Pr is equal to or less than the threshold value Pset, the process is exited. On the other hand, if it is determined that the occupant injury probability Pr exceeds the threshold value Pset, a vehicle posture that minimizes the risk is determined in step S8, and vehicle posture control including acceleration / deceleration is executed in step S9. As an example of the vehicle attitude control, control in a situation where a collision between the vehicle A and the vehicle B is inevitable as shown in FIG. 10 and control in a single accident as shown in FIG. 11 will be described.

  In the example of FIG. 10, only a driver is boarded in vehicle A (the right seat at the front of the vehicle), two passengers are boarded in vehicle B, and vehicle A is on the left side of vehicle B. The vehicle A and the vehicle B are in an inevitable situation. Under such circumstances, the rear risk of the vehicle B is relatively low because there is no passenger in the rear seat. Therefore, the vehicle B performs posture control such as rapid acceleration while maintaining the current rudder angle, so that the cabin deformation due to the collision is set to the rear side with a low risk, and the injury to the passenger on the front seat side is minimized. It becomes possible. On the other hand, since the vehicle A is a driver-only ride, the risk on the passenger seat side is relatively low. Accordingly, the vehicle A can perform the posture control of steering to the right while decelerating, thereby deforming the passenger seat side with low risk and minimizing the driver's injury.

  FIG. 11 shows a case where the vehicle C collides with a fixed structure 200 such as a telegraph pole from the front. Generally, the fixed structure such as a telegraph pole is higher in rigidity than the vehicle and the deformation of the vehicle is local. Damage is expected to increase. Therefore, as shown in FIG. 11A, when the vehicle C collides with the structure 200 between the front frames C2 supporting the front bumper beam C1, the risk of deformation increases.

  Therefore, in the vehicle C, as shown in FIG. 11 (b), the vehicle C is decelerated so that it collides with the structure 200 in front of the front frame C2 having high rigidity and low deformation risk. By executing the attitude control for steering, it is possible to reduce the deformation of the vehicle and to minimize the injury of the occupant.

  After executing the vehicle attitude control as described above, it is checked in step S10 whether an actual collision can be avoided. If the collision can be avoided, the process exits, and if the collision is reached, the process proceeds from step S10 to step S11, where the vehicle position information detected by the vehicle position information detection unit 20 by GPS or the like (position of the collision occurrence site), the collision Data on the injured part of the occupant and the size of the injury estimated from the speed and collision part of the vehicle are communicated to an external emergency medical center etc. via the in-vehicle external communication control unit 21 to support the occupant's lifesaving emergency. Do.

  As described above, in the present embodiment, the risk of occupant injury due to cabin deformation is expressed by a single index called a probability distribution for various types of collisions. It is possible to accurately grasp the collision damage and reduce the passenger collision damage.

  Next, a second embodiment of the present invention will be described. FIG. 12 is a system configuration diagram of a vehicle collision control apparatus according to a second embodiment of the present invention.

  The second form considers the risk due to the occupant behavior in addition to the risk due to the cabin deformation as compared with the first form described above. That is, occupant injury due to collision may cause serious injury depending on the behavior of the occupant even if the cabin deformation is slight. For example, if the passenger on the passenger's seat does not wear a seat belt, there is a risk of banging the face and head on the instrument panel, etc. at the time of a forward collision. If the method is inappropriate or the riding posture is inappropriate, there is a possibility that the chest may be damaged by a rapid deceleration due to the restraint of the belt. Therefore, in addition to the deformation of the cabin, it is possible to reduce a wider range of passenger injury by evaluating the injury to the passenger due to acceleration (acceleration / deceleration).

  For this reason, 1 A of vehicle collision control apparatuses in a 2nd form change a structure of the control part 2 of the vehicle collision control apparatus 1 of a 1st form a little. Specifically, as shown in FIG. 12, the control unit 2A of the second form estimates occupant injury estimation due to acceleration to the occupant injury estimation unit 14 that estimates occupant injury due to cabin deformation. The portion 14A is added as a second occupant injury estimation unit. In the following, differences in functions from the first embodiment will be mainly described, and details of similar functions will be omitted.

  The evaluation of the risk due to the occupant behavior in the occupant injury estimator 14A corresponds to actual collision conditions of the accident, so that data on the occupant behavior under a wide range of collision conditions are previously mapped and evaluated using this map. The map is created by performing crash analysis by numerical simulation of occupant behavior data under a wide range of collision conditions. This analysis is an analysis model (voxel model) reflecting the cabin deformation described in the first embodiment, and the generated acceleration is evaluated for each voxel.

The risk of occupant behavior due to acceleration is that the occupant behavior when the collision condition varies is obtained in advance by numerical simulation, and the generated acceleration G and the collision condition at that time are as shown in the following equation (3). The occupant injury probability Pr2 based on the appearance probability Pa is calculated for each voxel. The acceleration G is standardized data.
Pr2 = G × Pa (3)

In the second embodiment, the occupant injury probability Pr due to cabin deformation and the occupant injury probability Pr2 due to occupant behavior are input to the vehicle control amount calculation unit 15, and the occupant injury probability due to cabin deformation is expressed by the following equation (4). The occupant injury probability PrT is calculated by adding Pr and the occupant injury probability Pr2 due to the occupant behavior. In the second mode, this passenger injury probability PrT represents the total risk of passenger injury due to cabin deformation and passenger behavior.
PrT = Pr + Pr2 (4)

  The weighting of various factors with respect to the risk of occupant injury is the same as in the first embodiment, and the vehicle control amount for avoidance control that minimizes the sum ΣPrT of the occupant injury probability PrT for each voxel is determined by the vehicle behavior simulation and optimization method. It is calculated and occupant damage due to cabin deformation and occupant behavior at the time of collision can be reduced.

  In the second form, in addition to the risk due to cabin deformation, the risk due to occupant behavior is taken into account. Therefore, the risk can be grasped more precisely than in the first form, and the collision damage of the occupant in a wider range of situations Can be reduced.

  In the second embodiment, the risk due to cabin deformation and the risk due to passenger behavior are added together for each voxel to optimize the total sum of the voxels and perform vehicle attitude control. Vehicle attitude control may be performed by multi-objective optimization that is minimized.

The system block diagram of the collision control apparatus for vehicles concerning 1st Embodiment of this invention. Same as above, explanatory diagram showing the situation just before the collision Same as above, explanatory diagram showing deformation of cabin Same as above, explanatory diagram showing the probability distribution of cabin deformation Same as above, explanatory diagram showing occupant status Same as above, explanatory diagram showing occupant probability distribution As above, explanatory diagram showing the risk of passenger injury due to cabin deformation Explanatory drawing which shows weighting according to a passenger | crew's injury site | part same as the above Same as above, flowchart of collision control processing Explanatory drawing which shows vehicle control with respect to side collision as above Same as above, explanatory diagram showing vehicle control for a single frontal collision The system block diagram of the collision control apparatus for vehicles concerning 2nd Embodiment of this invention.

Explanation of symbols

DESCRIPTION OF SYMBOLS 1,1A Vehicle collision control apparatus 2,2A Control part 10 Solid object detection part 11 Collision judgment part 12 Cabin deformation estimation part 13 Passenger state detection part 14, 14A Passenger injury estimation part 15 Vehicle control amount calculation part Pc Cabin deformation probability Pj Crew presence probability Pr, Pr2, PrT Crew injury probability

Claims (6)

A three-dimensional object detecting means for detecting a three-dimensional object existing around the host vehicle;
A collision judging means for judging the possibility of collision between the three-dimensional object and the host vehicle;
When it is determined that a collision between the three-dimensional object and the host vehicle is inevitable, a cabin deformation amount estimating unit that estimates a cabin deformation amount of the host vehicle due to the collision;
Occupant state detection means for detecting the boarding state of the occupant of the own vehicle;
Occupant injury estimation means for estimating the occupant's collision damage based on the cabin deformation amount and the occupant's boarding state;
A vehicle collision control device comprising: a vehicle control amount calculation means for calculating a vehicle control amount for avoidance control based on the collision damage of the occupant. The cabin deformation amount estimating means includes:
A voxel model of the cabin of the host vehicle is set, and a probability distribution due to variation in the collision condition of the cabin deformation amount obtained in advance corresponding to the collision condition is set in the voxel model,
The occupant state detection means
The vehicle collision control device according to claim 1, wherein the cabin of the host vehicle is converted into a voxel model, and the presence probability distribution of the occupant is set in the voxel model in consideration of the detection variation of the occupant detected by a sensor. . The occupant injury estimation means is
A voxel model of the cabin of the host vehicle, and an occupant injury probability distribution in which the occupant suffers collision damage based on the probability distribution of the cabin deformation amount and the presence probability distribution of the occupant is set in the voxel model. The vehicle collision control device according to claim 2. The occupant injury estimation means is
4. The vehicle collision control device according to claim 3, further comprising setting a probability distribution of occurrence of injury caused by the behavior of the occupant and adding the probability distribution to the occupant injury probability distribution. The vehicle control amount calculation means includes:
5. The vehicle collision control device according to claim 3, wherein the vehicle control amount is calculated as a control amount that minimizes a total sum of the occupant injury probability distributions. The vehicle collision control device according to any one of claims 3 to 5, wherein the occupant injury probability distribution is weighted based on at least the occupant's expected injury site.