JP4924207B2 - Own vehicle risk acquisition device - Google Patents

Description

  The present invention relates to a host vehicle risk acquisition apparatus that acquires a risk that a driver who drives the host vehicle collides the host vehicle with an obstacle such as another vehicle.

2. Description of the Related Art Conventionally, there is known a risk level acquisition device that detects an obstacle around a host vehicle, determines the possibility of collision between the host vehicle and the host vehicle, and outputs the possibility of collision as a risk level. As a technique using this risk level acquisition device, for example, there is a collision prevention device. This collision prevention device avoids a collision by notifying the driver of the danger of collision when there is a possibility of collision between the host vehicle and an obstacle, or by automatically decelerating the host vehicle. Yes (see, for example, Patent Document 1).
JP-A-7-104062

  However, in the collision prevention device disclosed in Patent Document 1, when the obstacle is a moving body such as another vehicle, only one predicted course of the obstacle is calculated. For this reason, for example, when the host vehicle or an obstacle travels on a road with many branches in the course of the obstacle, such as an intersection, it is difficult to calculate the possibility of collision, and the risk level of the host vehicle is determined based on the possibility of collision. There is a problem that the accuracy of the risk level is low when calculating.

  Moreover, the risk level of the host vehicle changes from moment to moment according to the surrounding situation. However, since the collision prevention apparatus disclosed in Patent Document 1 only calculates the course of an obstacle, when a surrounding situation changes with the passage of time, a stable risk is obtained. There was a problem that could not.

  Therefore, an object of the present invention is to provide a host vehicle risk level acquisition device that can stably determine a risk level even when there is a change in surrounding conditions with the passage of time.

The own vehicle risk acquisition device according to the present invention that has solved the above problems includes an actual route acquisition unit that acquires an actual route that is an actual route taken by the own vehicle, and a risk that the own vehicle in the realized route collides with an obstacle. A risk level acquisition means for acquiring a risk level, a risk level storage means for storing the risk level acquired by the risk level acquisition means, a statistical processing means for statistically processing the risk level stored in the risk level storage means, and the own vehicle Self-vehicle possible route acquisition means for acquiring a plurality of possible routes that are taken routes, and safest route acquisition means for acquiring the safest route with the lowest risk of collision of the vehicle with an obstacle among the plurality of possible routes And acquiring the risk of the host vehicle based on the degree of deviation between the risk of the host vehicle colliding with the obstacle and the risk of the host vehicle colliding with the obstacle on the safest path. Is.

  Here, the own vehicle possible route acquisition means for acquiring a plurality of possible routes that are the routes that the host vehicle has been able to acquire, and among the plurality of possible routes, the safest route with the lowest risk of collision of the host vehicle with an obstacle is acquired. And a safest route acquisition means for determining whether or not the own vehicle on the realized route collides with an obstacle and the degree of deviation between the risk of the own vehicle on the safest route and the obstacle. It can be set as the aspect which acquires a risk degree.

  In this way, by acquiring the risk level of the host vehicle based on the degree of deviation between the risk level on the realized path and the risk level on the safest path, the driving behavior of the host vehicle in various traffic environments can be evaluated. At the same time, the amount of calculation can be reduced.

  In addition, the own vehicle risk acquisition device according to the present invention that has solved the above-described problems includes an own vehicle possible route acquisition unit that acquires a plurality of possible routes that are routes taken by the own vehicle, and the own vehicle among the plurality of possible routes. A safest route acquisition unit that acquires the safest route with the lowest risk of collision with an obstacle, a safest risk level storage unit that stores the risk in the safest route acquired by the safest route acquisition unit, Statistical processing means for statistically processing the highest safety risk stored in the safety risk storage means.

  In this way, even by statistically processing the safest risk level, the risk level can be obtained stably even when there is a change in the surrounding situation over time.

  Further, the statistical processing means may be configured to perform statistical processing using a statistical quantity obtained from the n-th moment with respect to a plurality of risk levels stored in the risk level storage means.

  In this way, stable statistical processing can be performed by performing statistical processing using the statistical amount obtained from the n-th moment.

  According to the own vehicle risk level acquisition apparatus according to the present invention, the risk level can be obtained stably even when there is a change in the surrounding situation over time.

  Hereinafter, embodiments of the present invention will be described 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. For the convenience of illustration, the dimensional ratios in the drawings do not necessarily match those described.

  FIG. 1 is a block diagram showing the configuration of the host vehicle risk acquisition ECU according to the first embodiment of the present invention. As shown in FIG. 1, a host vehicle risk level acquisition ECU 1 that is a host vehicle risk level acquisition device is a computer of an automobile device that is electronically controlled, and includes a CPU (Central Processing Unit), a ROM (Read Only Memory), a RAM (Random). Access Memory) and an input / output interface. The own vehicle risk acquisition ECU 1 includes an obstacle information temporary storage unit 11, an obstacle possible route calculation unit 12, an own vehicle route recording unit 13, an own vehicle route reading unit 14, an own vehicle position reading unit 15, and an own vehicle possible route calculation. Unit 16, realized track collision probability calculation unit 17, best host vehicle track collision probability calculation unit 18, host vehicle risk level calculation unit 19, host vehicle risk degree temporary storage unit 20, and statistical processing unit 21. In addition, an obstacle sensor 2 is connected to the own vehicle risk acquisition ECU 1 via an obstacle extracting unit 3 and an own vehicle sensor 4 is connected.

  The obstacle sensor 2 includes a millimeter wave radar sensor, a laser radar sensor, an image sensor, and the like, and detects obstacles such as other vehicles and passers-by around the host vehicle. The obstacle sensor 2 transmits obstacle-related information including information on the detected obstacle to the obstacle extraction unit 3.

  The obstacle extraction unit 3 extracts obstacles from the obstacle-related information transmitted from the obstacle sensor 2 and temporarily stores obstacle information in the vehicle risk acquisition ECU 1 as obstacle information such as the position and moving speed of the obstacles. Output to the storage unit 11. For example, when the obstacle sensor 2 is a millimeter wave radar sensor or a laser radar sensor, the obstacle extraction unit 3 detects an obstacle based on the wavelength of a reflected wave reflected from the obstacle. When the obstacle sensor 2 is an image sensor, for example, another vehicle is extracted from the captured image as an obstacle by a technique such as pattern matching.

  The own vehicle sensor 4 includes a speed sensor, a yaw rate sensor, and the like, and detects information related to the traveling state of the own vehicle. The own vehicle sensor 4 transmits the detected traveling state information related to the traveling state of the own vehicle to the own vehicle course recording unit 13 in the own vehicle risk acquisition ECU 1. The traveling state information of the host vehicle here includes, for example, the speed and yaw rate of the host vehicle.

  The obstacle information temporary storage unit 11 stores the obstacle information transmitted from the obstacle extraction unit 3 for a predetermined time, for example, 5 seconds. The obstacle possible course calculating unit 12 reads the obstacle information for the past 5 seconds stored in the obstacle information temporary storage unit 11, and based on the obstacle information for 5 seconds, the obstacle for a certain period of time thereafter. A plurality of courses predicted to move are calculated and acquired. Note that, instead of the past 5 seconds, an appropriate time (for example, a time between 3 seconds and 10 seconds) can be set. The obstacle possible course calculation unit 12 outputs the obstacle course information regarding the calculated course of the obstacle to the realized course collision probability calculation unit 17 and the best own vehicle course collision probability calculation unit 18.

  The own vehicle course recording unit 13 records the course history of the own vehicle based on the traveling state signal of the own vehicle transmitted from the own vehicle sensor 4. The own vehicle route reading unit 14 reads the history of the own vehicle track for a predetermined time, for example, 5 seconds, which is recorded in the own vehicle route recording unit 13. The predetermined time set in advance here is the same as the time of the obstacle information stored in the obstacle information temporary storage unit 11. The own vehicle course reading unit 14 outputs, to the realized course collision probability calculation unit 17, the actual course information related to the actual course that the host vehicle has actually taken, based on the read course history of the host vehicle.

  The own vehicle position reading unit 15 reads the position of the own vehicle for a predetermined time, for example, 5 seconds, from the history of the own vehicle recorded in the own vehicle route recording unit 13, and the own vehicle position related to the position of the own vehicle Information is output to the own vehicle possible route calculation unit 16. The own vehicle possible route calculating unit 16 can move the own vehicle from the position where the own vehicle is recorded based on the own vehicle position information output from the own vehicle position reading unit 15. Calculate and obtain multiple courses. The own vehicle possible route calculation unit 16 outputs the own vehicle possible route information regarding the calculated possible route of the own vehicle to the best own vehicle route collision probability calculation unit 18.

  Based on the obstacle route information output from the obstacle possible route calculation unit 12 and the realized route information output from the own vehicle route reading unit 14, the actual route collision probability calculation unit 17 includes the own vehicle during the past 5 seconds. Calculates and acquires the actual course collision probability that may have collided with an obstacle in the actual course. The actual track collision probability is the “degree of risk that the host vehicle in the actual track will collide with an obstacle” in the present invention. The realized course collision probability calculation unit 17 outputs the realized course collision probability information related to the calculated realized course collision probability to the own vehicle risk calculation unit 19.

  Based on the obstacle route information output from the obstacle possible route calculation unit 12 and the own vehicle possible route information output from the own vehicle possible route calculation unit 16, the best own vehicle route collision probability calculation unit 18 The best own vehicle course that minimizes the probability of collision with another vehicle is calculated. The best own vehicle course collision probability calculation unit 18 also has the possibility that the own vehicle may collide with an obstacle in the best own vehicle course during the past 5 seconds based on the calculated best own vehicle course. Calculate and obtain the course collision probability. The best own vehicle course is the “safest course” of the present invention, and the best own vehicle course collision probability is the “risk of collision of the own vehicle on the safest path with an obstacle” of the present invention. The best host vehicle course collision probability calculation unit 18 outputs the best host vehicle course collision probability information based on the calculated best host vehicle course collision probability to the host vehicle risk calculation unit 19.

  The own vehicle risk calculation unit 19 divides the realized course collision probability information output from the realized course collision probability calculation unit 17 and the best own vehicle course collision probability information output from the best own vehicle course collision probability calculation unit 18. Based on the above, the own vehicle risk, which is the risk of the own vehicle, is calculated. For example, the degree of discrepancy between the actual course collision probability based on the actual course collision probability information and the best own vehicle course collision probability based on the best own vehicle course collision probability information should be the ratio of the two. Can do. The own vehicle risk calculation unit 19 outputs own vehicle risk information related to the calculated own vehicle risk to the own vehicle risk temporary storage unit 20.

  The host vehicle risk temporary storage unit 20 temporarily stores the host vehicle risk based on the host vehicle risk information output from the host vehicle risk calculation unit 19 as a stored host vehicle risk along a time series. Yes. An appropriate number can be determined as the number of host vehicle risk levels stored here. The statistical processing unit 21 reads a plurality of stored host vehicle risk levels stored in the host vehicle risk temporary storage unit 20, and performs a statistical process using these stored host vehicle risk levels. By performing this statistical processing, the own vehicle risk statistical processing value is calculated.

  Next, a processing procedure in the own vehicle risk acquisition apparatus according to the present embodiment will be described. FIG. 2 is a flowchart showing a processing procedure of the own vehicle risk obtaining apparatus according to the present embodiment.

  As shown in FIG. 2, in the own vehicle risk acquisition device according to the present embodiment, the obstacle extraction unit 3 uses the obstacle extraction unit 3 based on the obstacle related information transmitted from the obstacle sensor 2. Is extracted (S1). Here, another vehicle is extracted as an obstacle. When a plurality of other vehicles are included, all of the plurality of other vehicles are extracted.

  When the other vehicle as the obstacle is extracted, the other vehicle information regarding the extracted other vehicle is stored in the obstacle information temporary storage unit 11 and based on the other vehicle information for the past 5 seconds stored in the obstacle information temporary storage unit 11. Thus, the possible course where the other vehicle can move is calculated by the obstacle possible course calculation unit 12 as a trajectory on time and space constituted by time and space for each other vehicle (S2). Here, as a possible course in which the other vehicle can move, a certain destination point is not defined and the possible course to the destination point is not calculated, but until a predetermined movement time in which the other vehicle moves passes. Find the course of In general, there is no place where safety is guaranteed in advance on the road on which the host vehicle travels, so in order to determine the possibility of collision between the host vehicle and the other vehicle, the arrival point between the host vehicle and the other vehicle However, it cannot be said that the collision can be surely avoided.

  For example, as shown in FIG. 3, on a three-lane road R, the host vehicle M travels in the first lane r1, the first other vehicle H1 travels in the second lane r2, and the third lane travels in the second lane. Assume that H2 is traveling. At this time, in order to avoid collision with the other vehicles H1 and H2 that the vehicle M travels in the second and third lanes r2 and r3, respectively, the vehicle M reaches positions Q1, Q2, and Q3, respectively. It is considered preferable to travel. However, when the second other vehicle H2 takes the route B3 so as to change the route to the second lane r2, the first other vehicle H1 takes the route B2 in order to avoid a collision with the second other vehicle H2. It is conceivable that the vehicle enters the first lane r1. In this case, if the host vehicle M travels so as to reach the positions Q1, Q2, and Q3, there is a risk of collision with the first other vehicle H1.

  Therefore, the positions of the own vehicle and other vehicles are not determined in advance, but the courses of the own vehicle and other vehicles are predicted each time. By predicting the course of the host vehicle and the other vehicle each time, for example, the course B1 as shown in FIG. 4 can be used as the course of the host vehicle, so that the danger of the host vehicle M traveling can be avoided accurately. Safety.

  In addition, instead of prescribing until a predetermined travel time for the other vehicle to move has elapsed, an aspect in which the possible course of the other vehicle is obtained until the travel distance traveled by the other vehicle reaches a predetermined distance may be adopted. it can. In this case, the predetermined distance can be appropriately changed according to the speed of the other vehicle (or the speed of the host vehicle).

  The possible routes of other vehicles are calculated for each other vehicle as follows. An initialization process is performed in which the value of the counter k for identifying another vehicle is set to 1, and the value of the counter n indicating the number of possible course generations for the same other vehicle is set to 1. Subsequently, the position and moving state (speed and moving direction) of the other vehicle based on the other vehicle information transmitted from the obstacle sensor 2 and extracted from the other vehicle related information are set as the initial state.

  Subsequently, one behavior is selected from a plurality of selectable behaviors according to a behavior selection probability given in advance to each behavior as a behavior of the other vehicle assumed during the subsequent fixed time Δt. The behavior selection probability when selecting one behavior is defined, for example, by associating elements of a selectable behavior set with a predetermined random number. In this sense, a different behavior selection probability may be given for each behavior, or an equal probability may be given to all elements of the behavior set. Moreover, it is also possible to adopt a mode in which the behavior selection probability depends on the position and traveling state of another vehicle and the surrounding road environment.

  The selection of the behavior of the other vehicle assumed during a certain time Δt based on the behavior selection probability is repeatedly performed, and the behavior of the other vehicle is selected up to a time that is a predetermined movement time for the other vehicle to move. One possible course of the other vehicle can be calculated based on the behavior of the other vehicle thus selected.

  When one possible route of another vehicle is calculated, a plurality (N) of possible routes of the other vehicle are calculated by the same procedure. Even when a similar procedure is used, since one behavior is selected according to a behavior selection probability given in advance to each behavior, different possible routes are calculated in most cases. The number of possible routes calculated here is determined in advance and can be set to 1000 (N = 1000), for example. Of course, it can also be set as the aspect which calculates another some possible course, for example, it can be set as the number between hundreds-tens of thousands. The possible course calculated in this way is set as the predicted course of the other vehicle.

  Furthermore, when there are a plurality of other vehicles extracted, possible routes are calculated for each of the plurality of other vehicles.

  When the possible course of the other vehicle has been calculated, the own-vehicle course reading unit 14 reads the course of the host vehicle recorded in the own-vehicle course recording unit 13 for the past 5 seconds (S3). The own vehicle route reading unit 14 outputs the realized route information related to the realized route of the own vehicle for the past 5 seconds to the realized route collision probability calculating unit 17.

  Subsequently, the host vehicle position reading unit 15 reads the position of the host vehicle recorded in the host vehicle course recording unit 13 in the past 5 seconds. Then, based on the position of the host vehicle in the past 5 seconds, the host vehicle possible path calculation unit 16 calculates a plurality of possible paths from which the host vehicle can move (S4). The possible course of the host vehicle is calculated as a trajectory on time and space composed of time and space by the same calculation procedure as the possible course of the other vehicle. When the own vehicle possible route calculating unit 16 acquires the possible route of the own vehicle, the own vehicle possible route calculating unit 16 outputs it to the best own vehicle route collision probability calculating unit 18 as own vehicle possible route information.

  Thus, after the possible course of the host vehicle has been calculated, the actual course collision probability calculation unit 17 calculates the actual course that the host vehicle has moved based on the actual course information output from the host vehicle course reading unit 14 ( S5). Then, the actual course collision probability calculation unit 17 calculates the actual course collision probability that the host vehicle allowed to collide with another vehicle (S6). Here, the predicted courses of a plurality of other vehicles in the past 5 seconds based on the obstacle course information output from the obstacle course calculation unit 12 are compared with the actual course obtained in step S5, and in the past 5 seconds, The collision probability allowed by the host vehicle is calculated.

  Now, examples of the predicted course of the other vehicle and the actual course of the host vehicle obtained in steps S2 and S3 are represented by the three-dimensional space shown in FIG. In the three-dimensional space in FIG. 5, the position of the vehicle is shown on the xy plane indicated by the x axis and the y axis, and the t axis is set as the time axis. Therefore, the predicted courses of the other vehicle and the host vehicle can be indicated by (x, y, t) coordinates, and the trajectory obtained by projecting each course of the host vehicle and the other vehicle on the xy plane This is a travel locus on the road of the travel route and the predicted travel route predicted by the other vehicle to travel.

  In this way, by expressing the predicted courses of the host vehicle and other vehicles in the space shown in FIG. 5, a plurality of vehicles (own vehicle and other vehicles) existing within a predetermined range of the three-dimensional space-time are taken. A spatiotemporal environment consisting of a set of possible prediction paths is formed. The spatiotemporal environment Env (M, H) shown in FIG. 5 is a set of the actual course of the own vehicle M and the predicted course of the other vehicle H, and the actual course {M (n1)} of the own vehicle M and the other vehicle H It consists of a predicted course set {H (n2)}. More specifically, the spatiotemporal environment (M, H) is when the host vehicle M and the other vehicle H are moving in a + y-axis direction on a flat and straight road R such as an expressway. It shows the spatial environment. Here, when the predicted course of the other vehicle is obtained, the predicted course is independently obtained for each of the own vehicle M and the other vehicle H without considering the correlation between the own vehicle M and the other vehicle H. Paths may intersect in space and time.

Thus, when the actual course of the own vehicle M and the predicted course of the other vehicle H are obtained, the probability that the own vehicle M has allowed the own vehicle M and the other vehicle H to collide is obtained. Now, when the actual course of the own vehicle M and the predicted course of the other vehicle H intersect, the own vehicle M and the other vehicle H will collide, but the predicted course of the own vehicle M and the other vehicle H is predetermined. It is obtained based on the behavior selection probability. Therefore, the collision probability between the own vehicle M and the other vehicle H can be determined by the number of the predicted courses of the other vehicle H that intersect the predicted course of the own vehicle M. For example, the case of 1000 calculates a predicted route of the vehicle H, if five of which intersects the predicted course of the vehicle M is 0.5% chance of a collision (collision possibility) P _A Can be calculated. In other words, the remaining 99.5% can be a probability that the own vehicle M and the other vehicle H do not collide (non-collision possibility).

Further, as the other vehicle H, when a plurality of other vehicles are extracted, the collision probability P _A that allows to collide with at least one of the plurality of other vehicles be determined by the following equation (1) it can.

Here, k: number of other vehicles extracted P _A k: probability of collision with the kth vehicle In this way, a plurality of predicted routes of the other vehicle H are calculated, and the own vehicle is calculated using the plurality of predicted routes. By predicting the possibility of collision between M and the other vehicle H, the routes that the other vehicle can take are widely calculated. Accordingly, the collision probability can be calculated taking into account the case where there is a large change in the course of another vehicle, such as when an accident occurs at a place where there is a branch such as an intersection.

  When the collision probability between the own vehicle and the other vehicle is obtained in this way, the best own vehicle route collision probability calculating unit 18 selects the route having the lowest collision probability with the other vehicle among the possible routes in which the own vehicle can move. The best own vehicle course is selected (S7). In the best own vehicle course collision probability calculation unit 18, the predicted courses of a plurality of other vehicles based on the obstacle course information output from the obstacle possible course calculation unit 12 and the own vehicle output from the own vehicle possible course calculation unit 16. The best own vehicle route is selected by comparing with possible routes of a plurality of own vehicles based on the possible route information.

  Now, an example of the predicted course of the other vehicle obtained in step S2 and the actual course of the subject vehicle obtained in step S4 are represented by the three-dimensional space shown in FIG. The spatiotemporal environment Env (M, H) shown in FIG. 6 is a set of possible courses of the own vehicle M and predicted courses of the other vehicle H. The possible course set {M (n1)} of the own vehicle M and the other vehicle H Of the predicted course set {H (n2)}.

From the possible course set of the own vehicle M and the predicted course set of the other vehicle H, it is confirmed that the own vehicle M collided with the other vehicle H for each time the own vehicle M moved along the possible course of each own vehicle The probability that the host vehicle M has allowed is obtained. Collision probability P _A of that vehicle M had collided with another vehicle H vehicle M had acceptable can be determined by the above equation (1).

Thus, after each determined collision probability P _A plurality of possible tracks of the vehicle M, to select the most collision probability it can lower course as the best vehicle track. The best own vehicle course collision probability calculation unit 18 calculates the collision probability in the selected best own vehicle course as the best own vehicle course collision probability, and outputs the best own vehicle course collision probability information to the own vehicle risk calculation unit 19. To do.

  When the actual course collision probability is calculated, the host vehicle risk calculation unit 19 calculates the host vehicle risk (S8). The own vehicle risk calculation unit 19 realizes the actual course collision probability based on the actual course collision probability information output from the actual course collision probability calculation unit 17 and the best own vehicle course output from the best own vehicle course collision probability calculation unit 18. Based on the degree of deviation from the collision probability, the host vehicle risk is calculated. The own vehicle risk here is obtained by a ratio of the actual course collision probability and the best own vehicle course collision probability. The closer the ratio of the actual course collision probability and the best own vehicle course collision probability is to 1, the closer the own vehicle risk is Will be low.

  As described above, in the own vehicle risk acquisition apparatus according to the present embodiment, a plurality of own vehicle possible paths obtained by the own vehicle are acquired, and the degree of divergence between the risk in the plurality of possible paths and the actual course of the own vehicle is acquired. Based on the above, the risk level of the own vehicle is acquired. For this reason, it is possible to evaluate the risk level of the host vehicle with high accuracy and according to the traffic situation based on the actual course of the host vehicle. By evaluating the risk level of the host vehicle, it is possible to use it as an index of safety directivity that indicates how close the driver driving the host vehicle is to safe driving. Moreover, the load concerning calculation can be reduced by acquiring the risk level of the host vehicle based on the degree of deviation from the risk level on the safest path among the possible paths of the host vehicle.

  When the risk level of the host vehicle is acquired in this way, the acquired host vehicle risk level is temporarily stored in the host vehicle risk temporary storage unit 20 (S9). Then, the statistical processing unit 21 performs statistical processing using the own vehicle risk stored in the own vehicle risk temporary storage unit 20 (S10).

  The statistical processing in the statistical processing unit 21 can be performed by an appropriate method on the time series data of the host vehicle risk stored in the host vehicle risk temporary storage unit 20. The statistical processing can be performed using, for example, a Gaussian filter or a median filter.

  In addition, as statistical processing, the average value of the statistics obtained from the first moment, the variance and standard deviation of the statistics obtained from the second moment, the skewness of the statistics obtained from the third moment, and the fourth moment A statistic obtained from an n-th moment such as the kurtosis of the statistic obtained can be used. Alternatively, statistical processing using the maximum value and the minimum value of the host vehicle risk can be performed. Furthermore, statistical processing using a combination of these statistics and maximum / minimum values as appropriate can be performed. When the statistical process is finished, the process by the own vehicle risk obtaining apparatus is finished.

  In the own vehicle risk acquisition apparatus according to the present embodiment, such statistical processing is performed. For this reason, it is possible to obtain the own vehicle risk that is stable with respect to time changes. As a result, even when there is a change in the surrounding situation with the passage of time, the degree of danger can be obtained stably. Further, even when the host vehicle risk level is updated, the host vehicle risk level can be obtained at a cycle shorter than the cycle of the update process.

  Next, a second embodiment of the present invention will be described. FIG. 7 is a block diagram showing the configuration of the host vehicle risk acquisition ECU according to the second embodiment of the present invention. Compared with the first embodiment, the present embodiment stores the best own vehicle risk based on the best own vehicle course collision probability without calculating the own vehicle risk, and uses the best own vehicle risk. This is mainly different in that statistical processing is performed.

  As shown in FIG. 7, the host vehicle risk acquisition ECU 30 according to the present embodiment includes an obstacle information temporary storage unit 11, an obstacle possible route calculation unit 12, a host vehicle route recording unit 13, a host vehicle position reading unit 15, The own vehicle possible course calculation unit 16, the best own vehicle course collision probability calculation unit 18, the best own vehicle risk degree temporary storage unit 31, and the statistical processing unit 32 are provided. Among these, the obstacle information temporary storage unit 11, the obstacle possible route calculation unit 12, the own vehicle route recording unit 13, the own vehicle position reading unit 15, the own vehicle possible route calculation unit 16, and the best own vehicle route collision probability calculation unit 18 has the same function as in the first embodiment. However, the best own vehicle course collision probability calculation unit 18 outputs the best own vehicle risk information that uses the calculated best own vehicle course collision probability as the best own vehicle risk level to the best own vehicle risk temporary storage unit 31.

  The best own vehicle risk temporary storage unit 31 stores the best own vehicle risk based on the best own vehicle risk information output from the best own vehicle course collision probability calculation unit 18 as a stored best own vehicle risk along a time series. I remember it temporarily. The number of stored best vehicle risk levels can be determined as appropriate. The statistical processing unit 32 reads a plurality of stored best own vehicle risk levels stored in the best own vehicle risk degree temporary storage unit 31 and performs a statistical process using these stored best own vehicle risk levels. By performing this statistical processing, the best own vehicle risk statistical processing value is calculated.

  Next, a processing procedure in the own vehicle risk acquisition apparatus according to the present embodiment will be described. FIG. 8 is a flowchart showing a processing procedure of the own vehicle risk acquiring apparatus according to the present embodiment.

  As shown in FIG. 8, in the own vehicle risk level acquisition device according to the present embodiment, obstacles are extracted by the same procedure as in the first embodiment (S11), and the other vehicle possible course is calculated (S12). . Next, the host vehicle position reading unit 15 reads the host vehicle position recorded in the host vehicle course recording unit 13 (S13). Then, the own vehicle possible course is calculated by the same procedure as in the first embodiment (S14), and the predicted course of the other vehicle based on the obstacle course information output from the obstacle possible course calculation unit 12 and the own vehicle are possible. The best own vehicle that compares the possible routes of the plurality of own vehicles based on the own vehicle possible route information output from the route calculation unit 16 and has the lowest collision probability with other vehicles among the possible routes of the plurality of own vehicles. A course is selected (S15).

  If the best own vehicle course is selected, the best own vehicle risk is acquired (S16). As the best own vehicle risk, the collision probability (best own vehicle course collision probability) in the selected best own vehicle can be used as it is. If the best own vehicle risk is acquired, the acquired best own vehicle risk is temporarily stored in the best own vehicle risk temporary storage unit 31 (S17). Then, the statistical processing unit 32 performs a statistical process using the best own vehicle risk stored in the best own vehicle risk temporary storage unit 31 (S18). The statistical processing procedure can be the same as that in the first embodiment. In this way, the process by the own vehicle risk acquisition device is completed.

  As described above, by performing statistical processing using the best-performed host vehicle risk level, the host vehicle risk level that is stable with respect to time changes can be obtained. As a result, even when there is a change in the surrounding situation with the passage of time, the degree of danger can be obtained stably. Further, even when the host vehicle risk level is updated, the host vehicle risk level can be obtained at a cycle shorter than the cycle of the update process.

  The preferred embodiments of the present invention have been described above, but the present invention is not limited to the above embodiments. For example, in the above-described embodiment, the “risk of collision of the host vehicle with the obstacle on the safest path” is used as the “risk of collision of the host vehicle with a plurality of possible paths”. An aspect may be sufficient. For example, it is possible to use all of a plurality of possible routes or to use several low-risk routes. Moreover, in each said embodiment, although other vehicles are assumed as an obstruction, living organisms, such as a passerby, can also be assumed, for example.

  In the first embodiment, the own vehicle risk, which is the risk of the own vehicle, is calculated based on the degree of deviation between the actual track collision probability and the best own vehicle track collision probability. Although statistical processing is performed by temporarily storing, it is also possible to adopt an aspect in which the actual course collision probability is set as the own vehicle risk level as it is. Even in this case, when there is a change in the surrounding situation with the passage of time, the degree of danger can be obtained stably. Further, even when the host vehicle risk level is updated, the host vehicle risk level can be obtained at a cycle shorter than the cycle of the update process.

It is a block block diagram which shows the structure of the own vehicle risk acquisition apparatus which concerns on 1st Embodiment. It is a flowchart which shows the operation | movement procedure of the own vehicle risk acquisition apparatus which concerns on 1st Embodiment. It is a schematic diagram which shows typically the driving state of the own vehicle and another vehicle. It is a schematic diagram which shows typically the driving | running route which the own vehicle can take. It is a graph which shows the structure of the spatiotemporal environment where the prediction course of the own vehicle is one. It is a graph which shows the structure of the spatiotemporal environment where the prediction course of the own vehicle is multiple. It is a block block diagram which shows the structure of the own vehicle risk degree acquisition apparatus which concerns on 2nd Embodiment. It is a flowchart which shows the operation | movement procedure of the own vehicle risk acquisition apparatus which concerns on 2nd Embodiment.

Explanation of symbols

  DESCRIPTION OF SYMBOLS 1,30 ... Own vehicle risk acquisition ECU, 2 ... Obstacle sensor, 3 ... Obstacle extraction part, 4 ... Own vehicle sensor, 11 ... Obstacle information temporary storage part, 12 ... Obstacle possible course calculation part, 13 ... Own vehicle course recording unit, 14 ... Own vehicle course reading unit, 15 ... Own vehicle position readout unit, 16 ... Own vehicle possible course calculation unit, 17 ... Realized course collision probability calculation unit, 18 ... Best own vehicle course collision probability calculation unit , 19 ... Own vehicle risk level calculation unit, 20 ... Own vehicle risk level temporary storage unit, 21, 32 ... Statistical processing unit, Env ... Spatio-temporal environment, H, H1, H2 ... Other vehicles, M ... Own vehicle.

Claims (3)

An actual route acquisition means for acquiring an actual route that is a route actually taken by the host vehicle;
A degree-of-risk acquisition means for acquiring a degree of risk that the host vehicle collides with an obstacle on the realization path;
Risk storage means for storing the risk acquired by the risk acquisition means;
Statistical processing means for statistically processing the risk stored in the risk storage means;
Own vehicle possible route acquisition means for acquiring a plurality of possible routes that are routes that the own vehicle has taken;
Of the plurality of possible routes, the safest route acquisition means for acquiring the safest route with the lowest risk of collision of the host vehicle with an obstacle, and
The risk level of the host vehicle is acquired based on a degree of divergence between the risk of the host vehicle colliding with the obstacle on the realization route and the risk of the host vehicle colliding with the obstacle on the safest route. The own vehicle risk acquisition apparatus characterized by the above. Own vehicle possible route acquisition means for acquiring a plurality of possible routes that are routes that the own vehicle has taken;
Of the plurality of possible routes, the safest route acquisition means for acquiring the safest route with the lowest risk of collision of the own vehicle with an obstacle, and
Safest risk storage means for storing the risk in the safest course acquired by the safest course acquisition means;
Statistical processing means for statistically processing the safest risk stored in the safest risk storage means;
A vehicle risk acquisition device comprising: The host vehicle risk according to claim 1 , wherein the statistical processing means performs statistical processing using a statistical quantity obtained from an n-th moment with respect to a plurality of risks stored in the risk storage means. Degree acquisition device.

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