Source: https://patents.google.com/patent/JPWO2011114442A1/en
Timestamp: 2020-02-25 20:18:47
Document Index: 441303684

Matched Legal Cases: ['art 3', 'art 3', 'art 3', 'art 11', 'art 12', 'art 12', 'art 12', 'art 13', 'art 14', 'art 15', 'art 16', 'art 17', 'art, 3', 'art, 4', 'art, 11', 'art, 12']

JPWO2011114442A1 - Driving assistance device - Google Patents
JPWO2011114442A1
JPWO2011114442A1 JP2012505352A JP2012505352A JPWO2011114442A1 JP WO2011114442 A1 JPWO2011114442 A1 JP WO2011114442A1 JP 2012505352 A JP2012505352 A JP 2012505352A JP 2012505352 A JP2012505352 A JP 2012505352A JP WO2011114442 A1 JPWO2011114442 A1 JP WO2011114442A1
JP2012505352A
JP5316698B2 (en
深町　映夫
映夫 深町
政行 清水
2010-03-16 Application filed by トヨタ自動車株式会社 filed Critical トヨタ自動車株式会社
2013-06-27 Publication of JPWO2011114442A1 publication Critical patent/JPWO2011114442A1/en
2013-10-16 Publication of JP5316698B2 publication Critical patent/JP5316698B2/en
An object of this invention is to provide the driving assistance device which can implement | achieve the driving assistance which considered the potential risk. The driving support device is a device that performs driving support for avoiding an object that is a risk target in driving the vehicle for a vehicle driver, and an object detection unit that detects the object; A collision prediction time calculation unit that calculates a collision prediction time that is a time indicating the degree of approach of the vehicle to the object, and an estimated risk determination that determines an estimated risk that indicates the possibility that the object moves on the predicted course of the vehicle And a driving support content determination unit that determines the content of driving support based on the predicted collision time and the estimated risk. Thereby, even when the target object does not exist on the predicted course of the vehicle and the risk is not manifested, the potential risk related to the target object is considered in the determination of the content of the driving assistance. Therefore, driving support in consideration of the potential risk is possible.
Conventionally, a risk potential is calculated based on a collision prediction time (TTC: Time To Collision), which is a physical quantity indicating the current degree of proximity of the host vehicle to a preceding vehicle, and driving such as braking control and steering control is performed according to the calculated risk potential. An apparatus that performs support is known (see, for example, Patent Document 1).
JP 2004-106673 A
The TTC used for determining the control content in the conventional apparatus is calculated based on the relative speed between the own vehicle and the object to be determined, the current position of the own vehicle and the object, and the current vehicle and the object are currently It is assumed that the same movement state is maintained. For this reason, the TTC in the prior art can represent a risk that is manifested in the current state. On the other hand, for example, when the target object does not exist on the predicted course of the host vehicle, it is not assumed that the host vehicle collides with the target object, so TTC is not defined. However, when the target object is in a motion state different from the current state, there is a risk that the host vehicle will collide with the target object. That is, even if there is no object on the predicted course of the host vehicle, there may be a potential risk. With conventional devices, driving assistance based on such potential risks is not possible.
Therefore, the present invention has been made to solve the above problems, and an object of the present invention is to provide a driving support device capable of realizing driving support in consideration of a potential risk.
A driving support device according to the present invention is a driving support device that performs driving support for avoiding an object that is a risk target in driving the vehicle for a driver of the vehicle, the target for detecting the target The object detection means, the collision prediction time calculation means for calculating the collision prediction time, which is the time indicating the degree of approach of the vehicle to the object, and the estimated risk indicating the possibility that the object moves on the predicted course of the vehicle are determined. Estimated risk level determining means, and driving support content determining means for determining the content of driving support based on the predicted collision time and the estimated risk level.
In the driving support device of the present invention, when the content of driving support is determined based on the predicted collision time, the estimated risk level indicating the possibility that the object moves on the predicted course of the vehicle is considered. As a result, even when the target object does not exist on the predicted course of the vehicle and the risk is not manifested, the potential risk related to the target object is considered in the determination of the content of the driving assistance. Therefore, driving support in consideration of the potential risk is possible.
Further, in the driving support device of the present invention, the collision prediction time calculation means, when the target object is on the predicted course of the vehicle, is a definite collision that is a predicted collision time for the target object existing on the predicted path of the vehicle. If the target time is calculated and the target is located in a place other than the predicted course of the vehicle, it is assumed that the target that exists in a place other than the predicted course of the vehicle has moved on the predicted course A predicted collision prediction time that is a collision prediction time for an object is calculated.
According to this configuration, when there is an object on the predicted course of the vehicle, the determined collision prediction time is calculated assuming that the risk has become apparent. On the other hand, if the target object exists in a place other than the predicted course of the vehicle, it is assumed that there is a potential risk, and the target object is assumed to have moved on the predicted path of the vehicle. The expected collision prediction time is calculated based on the location after the movement. As a result, the collision prediction time is appropriately calculated regardless of whether the risk is actualized or not.
The driving support device of the present invention further includes traffic environment information acquisition means for acquiring traffic environment information that is information related to the traffic environment in the vicinity of the vehicle and the object, and the estimated risk determination means includes the object that the object is the vehicle. When traffic environment information indicating that it is possible to move on the predicted route is acquired, the estimated risk may be determined to be larger than when the traffic environment information is not acquired.
When there is a traffic environment in which the object can move on the predicted course of the vehicle, there is a possibility that the object will move on the predicted path of the vehicle, compared to the case where such a traffic environment does not exist. It is considered high. According to this configuration, when there is a traffic environment in which the object can move on the predicted course of the vehicle, it is determined to be larger than when there is no traffic environment that requires an estimated risk. Driving assistance that takes into account potential risks is possible. Examples of the traffic environment information include information on traffic rules, road shapes, presence / absence of predetermined structures on the road, and the like.
Further, in the driving support device of the present invention, the estimated risk determination means, when acquiring information indicating the movement of the target object on the predicted course of the vehicle, compared to the case where the information is not acquired. The estimated risk level is judged large.
When the target object moves on the predicted course of the vehicle, it can be considered that the collision risk related to the target object has become apparent. According to this configuration, in such a case, the estimated risk level is determined to be larger, so that driving assistance that appropriately considers the risk related to the object is possible.
In the driving support device of the present invention, the estimated risk determination means is information indicating a causal relationship between the target object and another target object different from the target object, and is at least one of an attribute, a position, and a speed. The estimated risk is determined based on causal relationship information, which is information based on the relationship between the target object and other target objects.
The movement of the object is affected by another object different from the object. For example, the presence of another object may increase the possibility that the object will move on the predicted course of the vehicle. According to this configuration, since the estimated risk level related to the object is determined based on the causal relationship between the target object and other objects, the determination accuracy of the estimated risk level is improved, and appropriate driving support is possible. .
According to the driving support apparatus of the present invention, it is possible to realize driving support in consideration of potential risks.
It is a lineblock diagram of a driving support device concerning an embodiment of the present invention. It is a figure which shows the positional relationship of the parked vehicle which is a vehicle and a target object, and the figure which shows the positional relationship of the pedestrian who is a vehicle and a target object. It is a flowchart which shows the estimated risk determination process in an estimated risk determination part. It is. It is a figure which shows the example of the condition of the target object for every estimated risk, and traffic environment. It is a figure which shows the example of a driving assistance content determination table. It is a figure which shows the example of a display of a display of HMI when information provision is implemented as driving assistance. It is a figure which shows the example of the target path | route based on a risk map and a risk map. It is a flowchart which shows the content of the driving assistance process in a driving assistance device.
FIG. 1 is a configuration diagram showing an embodiment of a driving support apparatus of the present invention. The driving support device 1 is a device that performs driving support for a vehicle driver to avoid an object that is a risk target in driving the vehicle.
As shown in FIG. 1, the driving support device 1 includes an object detection unit 2 (object detection unit), a traffic environment information acquisition unit 3 (traffic environment information acquisition unit), a control ECU (Electronic Control Unit) 4, an HMI ( (Human Machine Interface) 5 and various actuators 6.
The object detection unit 2 is a part that detects an object that is a risk object in driving the vehicle, and can detect the presence / absence, position, and speed of the object. The target objects of risk are pedestrians, vehicles, and other obstacles existing on and around the predicted course of the vehicle. The predicted course is a course of the vehicle when the vehicle maintains the current traveling state.
The object detection unit 2 includes, for example, a camera and an image recognition processing unit. The image recognition processing means is constituted by a computer. The object detection unit 2 may be configured by a radar device. The object detection unit 2 sends the detected position, size, etc. of the object to the control ECU 4 as object information. The objects detected by the object detection unit 2 include, for example, pedestrians, stopped vehicles, vehicles on the opposite lane, obstacles existing on the road, and the like. In other words, not only objects directly subject to risk in driving the vehicle but also objects that may affect the movement of the risk object are included in the objects detected by the object detection unit 2.
The traffic environment information acquisition part 3 is a part which acquires the traffic environment information which is the information regarding the traffic environment around the vehicle and the object. The traffic environment information acquisition unit 3 includes, for example, a camera, a radar device, an infrastructure information communication device, and the like. The infrastructure information communication device is a device that receives traffic environment information of a road on which the host vehicle is traveling from the infrastructure, and may be configured as a part of a function of a so-called car navigation device, or a single communication device It is good also as comprising.
The traffic environment information acquisition unit 3 acquires information regarding traffic rules, road shapes, presence / absence of predetermined structures on the road, and the like as traffic environment information. More specifically, the traffic environment information includes information related to all traffic environments around the vehicle, around the predicted course of the vehicle, and around the object detected by the object detection unit 2. It includes information such as signs on the roads and the presence or absence of guardrails that warn of the existence of guards. Furthermore, the predetermined structure detected as traffic environment information includes a store existing on the roadside. The traffic environment information acquisition unit 3 sends the acquired traffic environment information to the control ECU 4. In addition, although the driving assistance apparatus 1 of this embodiment is provided with the traffic environment information acquisition part 3, it is good also as not providing the traffic environment information acquisition part 3 as a minimum structure of the driving assistance apparatus of this invention.
The control ECU 4 controls the HMI 5 and the various actuators 6 based on the information acquired from the object detection unit 2 and the traffic environment information acquisition unit 3, thereby implementing driving support for avoiding the object that is a risk target. And a computer having a storage device such as a CPU, ROM, and RAM, an input / output interface, and the like. The control ECU 4 includes a collision prediction time calculation unit 10 (collision prediction time calculation unit), an estimated risk determination unit 11 (estimated risk determination unit), a driving support content determination unit 12 (driving support content determination unit), and a driving support control unit. 13 is provided.
The collision prediction time calculation unit 10 is a part that calculates a collision prediction time that is a time indicating the degree of approach of the vehicle to the object. The collision prediction time is calculated by dividing the distance from the vehicle to the object by the relative speed between the vehicle and the object. The collision prediction time calculation unit 10 according to the present embodiment calculates a confirmed collision prediction time when the object is present on the predicted course of the vehicle, and the target is present at a place other than the predicted course of the vehicle. The expected collision prediction time is calculated. The definite collision prediction time is a collision prediction time for an object existing on the predicted course of the vehicle. The predicted collision prediction time is a collision prediction time for an object when it is assumed that an object that exists at a place other than the predicted track of the vehicle has moved on the predicted track. The collision prediction time calculation unit 10 sends the calculated collision prediction time to the driving assistance content determination unit 12. With reference to FIG. 2, the confirmed collision prediction time and the expected collision prediction time will be described in detail.
FIG. 2A is a diagram showing the positional relationship between the vehicle C and the parked vehicle T that is the object. As shown in FIG. 2A, the parked vehicle T exists on the predicted course of the vehicle C. In this case, since the parked vehicle T is an object of the risk that has become apparent to the vehicle C, the collision prediction time calculation unit 10 determines the distance from the vehicle C to the parked vehicle T relative to the vehicle C and the parked vehicle T. The definite collision prediction time is calculated by dividing by the speed.
FIG. 2B is a diagram showing the positional relationship between the vehicle C and the pedestrian P that is the object. As shown in FIG. 2 (b), the pedestrian P is on a sidewalk, and therefore exists in a place other than the predicted course of the vehicle C. Even if the vehicle C travels in this state, the vehicle C does not collide with the pedestrian P. However, when the pedestrian P moves on the start of the vehicle C, the vehicle C may collide with the pedestrian P. Therefore, the pedestrian P is an object of potential risk in driving the vehicle C. In this case, the collision prediction time calculation unit 10 determines the distance from the vehicle C to the position Px of the pedestrian P when the pedestrian P has moved on the predicted course of the vehicle C. The expected collision prediction time is calculated by dividing by the relative speed with the person P.
As described with reference to FIGS. 2 (a) and 2 (b), when there is an object on the predicted course of the vehicle, the estimated collision prediction time is calculated as the risk is manifested, and the vehicle If there is an object in a place other than the predicted course of the vehicle, it is assumed that there is a potential risk, and the object has been moved on the predicted course of the vehicle. The expected collision prediction time is calculated based on the location. As a result, the collision prediction time is appropriately calculated regardless of whether the risk is actualized or not.
The estimated risk level determination unit 11 is a part that determines an estimated risk level indicating a possibility that an object that is not on the predicted course of the vehicle C moves on the predicted path of the vehicle. That is, the estimated danger level indicates the degree of danger related to an object that is a target of potential risk. The estimated risk determination unit 11 sends the determined estimated risk to the driving assistance content determination unit 12.
More specifically, the estimated risk determination unit 11 acquires the traffic environment information when the traffic environment information indicating that the object can move on the predicted course of the vehicle C is acquired. The estimated risk is determined to be larger than the case where it has not been done. In addition, when the information indicating the movement of the object on the predicted course of the vehicle C is acquired, the estimated risk determination unit 11 determines the estimated risk to be larger than when the information is not acquired. To do. Furthermore, the estimated risk determination unit 11 is information indicating a causal relationship between the target object and another target object different from the target object, and the target object related to at least one of the attribute, the position, and the speed, and the other target object. The estimated risk is determined based on causal relationship information, which is information based on the relationship with the object.
With reference to FIG.3 and FIG.4, the estimated risk determination process in the estimated risk determination part 11 is demonstrated. FIG. 3 is a flowchart showing an estimated risk determination process in the estimated risk determination unit 11. FIGS. 4A to 4D are diagrams illustrating examples of objects and traffic environment conditions for each estimated risk level. In the present embodiment, as an example, it is assumed that the estimated risk is determined in any one of four stages of R0 to R3. The estimated risk degree R0 has the smallest risk degree among the four stages of estimated risk degrees, and the estimated risk degree R3 has the largest degree of danger among the four stages of estimated risk degrees. In the present embodiment, the estimated risk level is determined in four stages, but it is shown as an example of the embodiment of the present invention and is not limited to this.
In step S10, the estimated risk determination unit 11 determines whether or not the collision time can be determined based on the relative speed and the relative distance between the vehicle C and the object. That is, the estimated risk determination unit 11 determines that the collision time can be determined when the movement of the object on the predicted course of the vehicle C is detected. If it is determined that the time of the collision can be determined, the processing procedure proceeds to step S11. On the other hand, if it is not determined that the collision time can be determined, the processing procedure proceeds to step S12.
In step S11, the estimated risk determination unit 11 determines that the estimated risk is R3. FIG. 4 (d), the estimated risk is a diagram showing an example of a condition of the vehicle C and the pedestrian P 3 in the case where it is determined in R3. As shown in FIG. 4 (d), the pedestrian P 3 is are on the sidewalk not on the predicted course of the vehicle C, and starts to move in the direction indicated by the arrow r. In this case, it can be considered that the collision risk related to the target object has been actualized. Therefore, the estimated risk determining unit 11, when detecting the movement of the pedestrian P 3 onto the predicted course of the vehicle C determines the estimated risk and R3. That is, the estimated risk determining unit 11, when acquiring the information indicating the movement of the pedestrian P 3 onto the predicted course of the vehicle C is the estimated risk as compared with the case of failure to obtain the information Judge large. In this case, the collision prediction time calculation unit 10 calculates a confirmed collision prediction time for the target object.
In step S12, the estimated risk determination unit 11 determines whether or not the object is movable. When an object that is not on the predicted course of the vehicle C is not a movable object, there is no possibility that the target object moves on the predicted course of the vehicle C. Therefore, if it is not determined that the object is movable, the processing procedure proceeds to step S13. In step S13, since there is no risk related to the target object, the estimated risk determination unit 11 determines the target object as a non-danger target object, determines the estimated risk level related to the target object, and determines the target object. The driving support process for avoiding it is terminated.
On the other hand, if it is determined that the object is movable, the processing procedure proceeds to step S14. In step S <b> 14, the estimated risk determination unit 11 determines whether there is a traffic environment in which the object can enter the predicted course of the vehicle C. The traffic environment includes, for example, a traffic rule. If it is not determined that there is a traffic environment in which the object can enter the predicted course of the vehicle C, the processing procedure proceeds to step S15.
In step S15, the estimated risk determination unit 11 determines that the estimated risk is R0. FIG. 4A is a diagram illustrating an example of the situation of the vehicle C and the pedestrian P 0 when the estimated risk is determined to be R0. As shown in FIG. 4 (a), the pedestrian P 0 is present on the trail not on the predicted course of the vehicle C, and, for example, as illustrated in crosswalk pedestrian P 0 is the vehicle C There is no traffic environment that can move on the predicted course. In this case, the predicted collision time calculation unit 10 calculates a predicted collision prediction time for the target object.
On the other hand, if it is not determined that there is a traffic environment in which the object can enter the predicted course of the vehicle C, the processing procedure proceeds to step S16. In step S <b> 16, the estimated risk determination unit 11 may cause the determination target object to enter the predicted course of the vehicle C due to a relationship with another object or obstacle different from the determination target object. Determine if it is high. If it is not determined that the object is likely to enter the predicted course of the vehicle C, the processing procedure proceeds to step S17. On the other hand, when it is determined that there is a high possibility that the object will enter the predicted course of the vehicle C, the processing procedure proceeds to step S18.
In step S17, the estimated risk determination unit 11 determines that the estimated risk is R1. FIG. 4 (b), the estimated risk is a diagram showing an example of a condition of the vehicle C and the pedestrian P 1 when it is determined to R1. As shown in FIG. 4 (b), the pedestrian P 1 as an object is present on a sidewalk not on the predicted course of the vehicle C. Further, as the traffic environment, there is a crosswalk S 1. In this case, the pedestrian P 1, due to the presence of the crosswalk S 1, is likely to move as compared with the case where crosswalk S 1 is not present on the predicted course of the vehicle C. Therefore, the estimated risk determining unit 11, when the pedestrian P 1 detects the presence of a crosswalk S1 as a traffic-related information indicating that it is possible to move on the predicted course of the vehicle C is the The estimated risk is determined to be larger than when the traffic environment information is not acquired (S14, S15). In this case, the predicted collision time calculation unit 10 calculates a predicted collision prediction time for the target object.
As described with reference to FIG. 4B, when there is a traffic environment in which the object can move on the predicted course of the vehicle C, compared to the case where such a traffic environment does not exist. Since the estimated risk level is determined to be large, driving support in which potential risks are appropriately taken into consideration becomes possible.
In step S18, the estimated risk determination unit 11 determines that the estimated risk is R2. FIG. 4 (c), the estimated risk is a diagram showing an example of a condition of the vehicle C and the pedestrian P 2 in the case where it is determined in R2. As shown in FIG. 4 (c), the pedestrian P 2 as an object is present on a sidewalk not on the predicted course of the vehicle C. Further, as the traffic environment, the crosswalk S 1 is present, the vehicle T 2 is present that is stopped on the opposite lane. This situation, where the pedestrian P 2 is about to cross the crosswalk S 2, since this is where the vehicle T 2 is stopped, the possibility of pedestrian P 2 is moved over the predicted path of the vehicle C, 4 ( Higher than the situation shown in b). That is, there is a high possibility that the pedestrian P 2 will move on the predicted course of the vehicle C based on the causal relationship among the pedestrian P 2 , the pedestrian crossing S 2 , and the vehicle T 2 stopped in the opposite lane. It is possible to judge.
The estimated risk determination unit 11 includes a pedestrian P 2 that is a risk object, another object that is different from the risk object, a pedestrian crossing S 2 that is a traffic environment, and a vehicle that is stopped in an oncoming lane. based on the causal relationship information indicating the causal relationship between T 2, it is possible to determine the estimated risk. The causal relationship information is information based on a mutual relationship regarding at least one of the risk object, another object different from the risk object, and the attribute, position, and speed of the traffic environment. Further, the estimated risk determination unit 11 has a database (not shown) in which various causal information is stored in advance. The determination process shown in step S16 is performed by referring to the database and determining whether or not the detected event corresponds to the stored causal relationship information. If the target object of risk is a pedestrian, the target object has a causal relationship on the sidewalk that faces the sidewalk where the target object exists, such as a pedestrian crossing, a vehicle in the opposite lane, a roadside store A pedestrian or the like is exemplified. As described with reference to FIG. 4C, since the estimated risk is determined based on the causal relationship between the target object and the other target object, the accuracy of determination of the estimated risk is improved and appropriate driving support is provided. Is possible.
The driving assistance content determination unit 12 is a part that determines the content of driving assistance based on the predicted collision time and the estimated risk. Specifically, the driving assistance content determination unit 12 refers to the driving assistance content determination table 12T based on the estimated collision time acquired from the estimated collision time calculation unit 10 and the estimated risk acquired from the estimated risk determination unit 11. Determine the driving assistance content. FIG. 5 is a diagram illustrating an example of the driving support content determination table 12T. The driving assistance content determination unit 12 determines the obtained collision prediction time in three stages T1 to T3 (T1: 2.5 to 3.5 sec, T2: 1.8 to 2.5 sec, T3: less than 1.8 sec). Determine either. And the driving assistance content determination part 12 extracts driving assistance content based on collision prediction time (T1-T3) and estimated risk (R0-R3). The driving support content determination unit 12 sends information regarding the extracted driving support content to the driving support control unit 13. In the present embodiment, as shown in FIG. 5, for example, driving support such as information provision, alerting, avoidance guidance, avoidance control, and alarm generation is performed.
The driving support control unit 13 is a part that implements driving support by controlling the HMI 5 and various actuators 6 based on the information regarding the driving support content acquired from the driving support content determination unit 12. The driving support control unit 13 includes an information providing control unit 14, an avoidance guidance control unit 15, an avoidance control unit 16, and an alarm control unit 17 in order to implement various types of driving support. In the present embodiment, the driving support control unit 13 provides driving support such as “information provision”, “attention”, “avoidance guidance”, “avoidance control”, and “alarm generation” to the various control units 14 to 17, for example. Let it be implemented.
In “information provision” as driving assistance, the object is highlighted on the display of the HMI 5 in order to make the driver recognize the presence of the object. In “calling attention” as driving support, the object is displayed on the display of the HMI 5 with emphasis more strongly than in the case of “information provision”, and an alarm sound is generated by the HMI 5.
In “avoidance guidance” as driving assistance, the steering wheel, brake pedal, and accelerator pedal of the vehicle C are driven in a direction in which the vehicle C avoids the object. In “avoidance control” as driving assistance, the vehicle is forcibly decelerated or rerouted. In “alarm generation” as driving assistance, an alarm sound is output from the HMI 5 speaker.
Further, even when driving assistance for the driver is not performed, sensor tracking may be performed. Sensor tracking is to continue capturing an object by various sensors (not shown) of the driving support device 1.
Moreover, since the driving assistance apparatus 1 performs the process which determines the driving assistance content for every target object which becomes a risk object, the driving assistance content determination part 12 determines the driving assistance content for every several target object simultaneously. There is a case. In this case, the driving support control unit 13 arbitrates a plurality of driving support contents. For example, the driving support control unit 13 can perform arbitration so that priority is given to a thing with a short distance to the target object or a value with a small predicted collision time among a plurality of driving support contents. Moreover, even if it is a case where the information regarding several driving assistance content is acquired from the driving assistance content determination part 12, the driving assistance control part 13 is good also as implementing all the several driving assistance contents. Next, the information provision control part 14, the avoidance guidance control part 15, the avoidance control part 16, the alarm control part 17, and various driving assistance contents are demonstrated.
The information provision control unit 14 causes the image of the target object to be a risk to be highlighted on the display of the HMI 5. FIG. 6 is a diagram illustrating a display example of the display of the HMI 5 when “information provision” is performed as driving support. In the display of HMI 5 in FIG. 6, a parked vehicle R 1 , a preceding vehicle R 2 , a pedestrian R 3 , and a counter vehicle R 4 are displayed as objects while overlapping frames of a predetermined color. Further, when “warning” is implemented as driving support, the information provision control unit 14 displays an object on the display of the HMI 5 while superimposing a frame of a color different from “information provision”. Can do.
The avoidance guidance control unit 15 controls the various actuators 6 when the “avoidance guidance” as driving assistance is performed, so that the vehicle C avoids the object, the steering wheel, the brake pedal, and the accelerator of the vehicle C. Drive the pedal. The avoidance guidance control unit 15 generates a risk map in order to obtain a target route for guidance of the vehicle C. The generation of the risk map is briefly described below.
FIG. 7 is a diagram illustrating an example of a risk map and a target route based on the risk map. The avoidance guidance control unit 15 includes information related to the object transmitted from the object detection unit 2, traffic environment information transmitted from the traffic environment information acquisition unit 3, a collision prediction time calculated by the collision prediction time calculation unit 10, and A risk map is generated based on information such as the estimated risk determined by the estimated risk determination unit 11. As shown in FIG. 7, the risk map represents an area where the risk of collision with each target object (electric pole G, oncoming vehicle E, and preceding vehicle F) is high as an elliptical collision risk area, and these collision risk areas. It is a map for generating the recommended course of vehicles which avoided. The collision risk area is set according to the risk calculated from the TTC and the estimated risk for each obstacle. The risk degree is an index indicating a collision risk, and becomes a larger value as the TTC is smaller, that is, the time until the vehicle collides with an obstacle is smaller. Further, the risk level increases as the risk level indicated in the estimated risk level increases.
As shown in FIG. 7, the collision risk area is composed of a plurality of hierarchies corresponding to the degree of risk. For example, the collision risk area of the utility pole G is composed of three layers of a low risk degree area L, a medium risk degree area M, and a high risk degree area H. The avoidance guidance control unit 15 generates the target route information of the vehicle and the optimum vehicle speed information when traveling on the target route by the arithmetic processing based on the created risk map. The avoidance guidance control unit 15 uses the generated target route information and optimum vehicle speed information for avoidance guidance.
The avoidance control unit 16 forcibly decelerates or changes the course of the vehicle by controlling the various actuators 6 when “avoidance control” as driving assistance is performed. Further, the alarm control unit 17 outputs an alarm sound from the speaker of the HMI 5 when “attention alerting” and “alarm generation” are performed as driving support.
The HMI 5 is an interface for exchanging information between the apparatus and the driver, and includes a display and a speaker. The HMI 5 displays an image and outputs a sound and a warning sound based on the control by the driving support control unit 13. The various actuators 6 drive the steering, the brake pedal, and the accelerator pedal based on the control by the driving support control unit 13.
Next, with reference to FIG. 8, the processing content implemented in the driving assistance apparatus 1 is demonstrated. FIG. 8 is a flowchart showing the contents of the driving support process in the driving support device 1.
First, in step S <b> 1, the object detection unit 2 detects an object that is a risk object in driving the vehicle, and sends information related to the detected object to the control ECU 4. Subsequently, in step S2, the traffic environment information acquisition unit 3 acquires the traffic environment information and sends the detected traffic environment information to the control ECU 4.
Next, in step S3, the estimated risk determination unit 11 determines the estimated risk. The contents of the estimated risk determination processing are as described with reference to the flowchart of FIG. The estimated risk determination unit 11 sends the determined estimated risk to the driving assistance content determination unit 12.
In step S4, the collision prediction time calculation unit 10 calculates a confirmed collision prediction time when the object is on the predicted course of the vehicle, and the target is present at a place other than the predicted course of the vehicle. The expected collision prediction time is calculated. Then, the collision prediction time calculation unit 10 sends the calculated confirmed / probable collision prediction time to the driving assistance content determination unit 12.
In step S5, the driving assistance content determination unit 12 determines the content of driving assistance based on the confirmed / probable collision prediction time and the estimated risk. In step S <b> 6, the driving support control unit 13 performs driving support by controlling the HMI 5 and the various actuators 6 based on the information regarding the driving support content acquired from the driving support content determination unit 12. The process of step S1-S6 demonstrated above is implemented for every target object used as a risk object.
In the driving support device 1 of the present embodiment described above, when the content of the driving support is determined based on the predicted collision time, the estimated risk level indicating the possibility that the object moves on the predicted course of the vehicle C is considered. Is done. Thereby, even when the target object does not exist on the predicted course of the vehicle C and the risk is not manifested, the potential risk related to the target object is considered in the determination of the content of the driving assistance. . Therefore, driving support in consideration of the potential risk is possible.
In addition, embodiment mentioned above demonstrated an example of the driving assistance apparatus which concerns on this invention, and the driving assistance apparatus which concerns on this invention is not limited to what was described in this embodiment. The driving assistance apparatus according to the present invention may be a modification of the driving assistance apparatus 1 according to the embodiment or application to other things without changing the gist described in each claim.
According to the present invention, it is possible to realize driving support in consideration of potential risks.
DESCRIPTION OF SYMBOLS 1 ... Driving assistance device, 2 ... Object detection part, 3 ... Traffic environment information acquisition part, 4 ... Control ECU, 6 ... Various actuators, 10 ... Collision prediction time calculation part, 11 ... Estimated risk degree determination part, 12 ... Driving Support content determination unit, 12T ... driving support content determination table, 13 ... driving support control unit, 14 ... information provision control unit, 15 ... avoidance guidance control unit, 16 ... avoidance control unit, 17 ... alarm control unit.
A driving support device that performs driving support for avoiding an object that is a risk target in driving the vehicle for a driver of the vehicle,
A collision prediction time calculation means for calculating a collision prediction time which is a time indicating the degree of approach of the vehicle to the object;
Estimated risk determination means for determining an estimated risk indicating the possibility that the object moves on the predicted course of the vehicle;
Driving assistance content determination means for determining the content of the driving assistance based on the predicted collision time and the estimated risk;
The collision prediction time calculation means calculates a definite collision prediction time which is the collision prediction time for the object existing on the predicted course of the vehicle when the target exists on the predicted course of the vehicle. In the case where the object exists in a place other than the predicted course of the vehicle, the object in a place other than the predicted course of the vehicle is assumed to have moved on the predicted course. Calculating a predicted collision prediction time, which is the collision prediction time for an object,
Traffic environment information acquisition means for acquiring traffic environment information that is information about the traffic environment around the vehicle and the object,
When the estimated risk level determination means has acquired the traffic environment information indicating that the object can move on the predicted course of the vehicle, the traffic risk information has not been acquired. The estimated risk is determined to be greater than
The driving support device according to claim 1 or 2.
When the information indicating the movement of the target object on the predicted course of the vehicle is acquired, the estimated risk determination means determines the estimated risk as large as compared to a case where the information is not acquired. To
The driving assistance device according to any one of claims 1 to 3.
The estimated risk determination means is information indicating a causal relationship between the object and another object different from the object, and the object and at least one of attribute, position, and speed Determining the estimated risk based on causal information, which is information based on the relationship with the target object,
The driving assistance device according to any one of claims 1 to 4.
JP2012505352A 2010-03-16 2010-03-16 Driving assistance device Active JP5316698B2 (en)
JPWO2011114442A1 true JPWO2011114442A1 (en) 2013-06-27
JP5316698B2 JP5316698B2 (en) 2013-10-16
JP2012505352A Active JP5316698B2 (en) 2010-03-16 2010-03-16 Driving assistance device
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