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`EP 2 549 456 A1
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`Patentamt
`V Euwpa‘tsclzes
`European
`{latent Office
`Office européen 3
`do: urcvcts
`
`EUROPEAN PATENT APPLICATION
`published in accordance with Art. 153(4) EPC
`
`(43) Date of publication:
`23.01.2013 Buiietin 2013/04
`
`(51) int CL:
`GOBG #107006“)
`
`860R 211001200601)
`
`(21) Appiication number: 10847858]
`
`(22) Date of filing: 16.03.2010
`
`(86) international application number:
`PCT/JP2010/054433
`
`(87) international publication number:
`WO 2011/114442 (22.09.2011 Gazette 2011/38)
`
`(84) Designated Contracting States:
`AT BE BG CH CY CZ DE DK EE ES Ft FR GB GR
`HR HU iE IS [T LI LT LU LV MC MK MT NL NO PL
`PT RO SE Si SK SM TR
`
`(71) Appiicant: Toyota Jidosha Kabushiki Kaisha
`Toyota-shi, Aichi 471 -8571 (JP)
`
`(72) inventors:
`- SAKUGAWA Jun
`
`Toyota-shi
`Aichi 471-8571 (JP)
`
`. FUKAMACHI Hideo
`
`Toyota-shi
`Aichi 471-8571 (JP)
`' SHiMiZU Masayuki
`Toyota-shi
`Aichi 471-8571 (JP)
`- NAGATA Shinichi
`
`Toyota-shi
`Aichi 471-8571 (JP)
`
`(74) Representative: TBK
`Bavariaring 4-6
`80336 Mijnchen (DE)
`
`
`
`(54)
`
`DRlVlNG ASSiSTANCE DEViCE
`
`The object of the invention is to provide a driving
`(57)
`assistance device that is able to perform driving assist-
`ance considering potential risks. A device for providing
`driving assistance for a driver of the vehicie to avoid the
`object of the risk subject when driving the vehicle, which
`includes : an object determination unit that detects the
`object; a collision prediction time caiculation unit that cal—
`culates a time to coilision which is a time indicating a
`degree to which the vehicle approaches to the object: an
`estimated risk level determination unit that determines
`
`an estimated risk ievel indicating a possibility ofthe object
`
`moving onto a predicted traveiiing path of the vehicie:
`and a driving assistance content determination unit that
`determines driving assistance content based on the col-
`lision prediction time and the estimated risk ievei. There—
`by, even in a case where the risk is not being manifested,
`that is, a case where the object is not present on the
`predicted travelling path of the vehicle C, the potential
`risk related to the object is considered in determining the
`driving assistance content. Accordingiy, it is possible to
`provide the driving assistance considering the potential
`risk.
`
`1
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`/,,
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`/
`
`'
`Fig. 1
`
`rd
`4
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`CONTROL ecu
`r" 13
`DRIVING ASSlSTANCE
`CONTROL UNIT
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`r4 12
`re 14
`DRlVleS ASSISTANCE
`PROVISION OF
`
`
`
`agiggigfgm
`H 5
`r“ 2
`r4 0
`Dietitian um
`
`OBJECT
`COLLISION FRED
`121.
`
`(.4 15
`—>
`HMI
`DETECTION » CTlON TIME
`H
`UNIT
`CALCULATlON UNIT W AVOIDANCE
`r4 11 a AggayéugE » lNDUCTION
`3
`TRAFFIC
`ESTIMATED ”'
`DETERMWAWN
`CONTROL UNIT
`ENVlRONMENT
`RISK LEVEL
`lNFORMAT‘ON a DETERMlNA‘nDN
`1““
`16
`ACOU’SlTlON UNlT
`UNIT
`AVOIDANCE
`CONTROL UNiT
`
`r4 17
`ALARM
`CONTROL UNIT
`
`
`
`
`
`
`
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`
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`H 6
`VARIOUS
`’ ACTUATORS
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`Printed by Jouve, 75001 PARES (FR)
`
`EP2549456A1
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`

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`EP 2 549 456 A1
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`2
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`Description
`
`Technical Field
`
`[0001] This invention relates to a driving assistance
`device.
`
`Background Art
`
`ln the related art, a device that calculates a po-
`[0002]
`tential risk based on a collision prediction time (Time To
`Collision: TTC) which is a physical quantity indicating the
`degree to which the current host vehicle approaches to
`a preceding vehicle, and performs a driving assistance
`such as a braking control and a steering control in re-
`sponse to a calculated potential risk has been well known
`(referring to Patent Literature 1. for example).
`
`Citation List
`
`Patent Literature
`
`[Patent Literature 1] Japanese Unexamined
`[0003]
`Patent Application Publication No. 2004406673
`
`Summary of lnvention
`
`Technical Problem
`
`[0004] The TTC is used to determine control contents
`in the device of the prior art and is calculated based on
`relative speed between the host vehicle and an object to
`be determined, and current positions of the host vehicle
`and the object, and is obtained on the consumption that
`the host vehicle and the object maintain the same moving
`state as the current state. Accordingly, the TTC in the
`prior art can represent a risk manifested in the current
`state. Meanwhile, for example, in a case where the object
`is not present on the predicted travelling path ofthe host
`vehicle, since it is not assumed that the host vehicle col—
`lides with the object, the TTC is not defined. However, in
`a case where the object enters moving state different
`from the current state, a risk that the host vehicle collides
`with the object occurs. That is, even in a case where an
`object is not present on the predicted travelling path of
`the host vehicle, there is a case where there is a potential
`risk. With the device of the prior art. driving assistance
`based on such potential risk cannot be performed.
`[0005] Accordingly, the present invention is made to
`resolve the problem mentioned above and an object
`thereof is to provide a driving assistance device that is
`able to perform the driving assistance considering the
`potential risk.
`
`Solution to Problem
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`device which provides driving assistance for the driver
`of the vehicle to avoid the object of risk subject when
`driving the vehicle, the device including: object detection
`means for detecting the object; collision prediction time
`calculation means for calculating the collision prediction
`time which is a time indicating a degree to which the
`vehicle approaches to the object; estimated risk level de-
`termination means for determining an estimated risk level
`indicating a possibility of the object moving onto a pre-
`dicted travelling path of the vehicle; and driving assist-
`ance content determination means for determining driv—
`ing assistance content based on the and the estimated
`risk level.
`
`in the driving assistance device of the present
`{0007]
`invention, when the driving assistance content is deter—
`mined, based on the collision prediction time, there is
`consideration ofthe estimated risk level which indicates
`
`the possibility of the object moving onto the predicted
`travelling path of the vehicle. Thereby. even in a case
`where a risk is manifested, that is, a case where the object
`is not present on the predicted travelling path of the ve-
`hicle, the potential risk related to the object is considered
`in determining the driving assistance content. According-
`ly. it is possible to provide the driving assistance consid—
`ering the potential risk.
`{0008]
`Further the collision prediction time calculation
`means calculates a fixed collision prediction time which
`is the collision prediction time with respect to the object
`which presents on the predicted travelling path of the
`vehicle in a case where the object presents on the pre-
`dicted travelling path of the vehicle. and in a case where
`the object presents at a location other than the predicted
`travelling path ofthe vehicle, calculates an estimated col—
`lision prediction time which is the collision prediction time
`with the object in a case where it is assumed that the
`object presenting at a location other than the predicted
`travelling path of the vehicle has moved on the predicted
`travelling path.
`{0009] According to this configuration, in a case where
`the object presents on the predicted travelling path of the
`vehicle, the fixed collision prediction time is calculated
`regarding the risk as being manifested. Meanwhile, in a
`case where the object presents at a location other than
`the predicted travelling path of the vehicle, on the as-
`sumption that the object has moved onto the predicted
`travelling path of the vehicle regarding that there is the
`potential risk, the estimated collision prediction time is
`calculated based on a location after movement of the
`
`object. Thereby, even in either a case where the risk is
`being manifested or a case where the risk is not being
`manifested, the collision prediction time is calculated
`properly.
`Further, the driving assistance device of the
`{0010]
`present invention further includes a traffic environment
`information acquisition means for acquiring the traffic en-
`vironment information which is information related to the
`
`[0006] The driving assistance device according to an
`aspect of the present invention is a driving assistance
`
`traffic environment in the vicinity of the vehicle and the
`object. wherein an estimated risk level determination
`
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`EP 2 549 456 A1
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`4
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`means may determine the estimated risk level as higher
`in a case when acquiring the traffic environment informa-
`tion indicating a possibility of the object moving onto the
`predicted travelling path of the vehicle, compared with a
`case when acquiring the traffic environment information.
`(0011]
`In a case where there is the traffic environment
`in which the object may move onto the predicted travelling
`path of the vehicle, it is considered that the possibility of
`the object moving onto the predicted travelling path of
`the vehicle is high. According to this configuration, since
`the estimated risk level of a case where there is the traffic
`
`environment in which the object may move onto the pre-
`dicted travelling path of the vehicle is determined as high—
`er compared with a case where such environment is not
`present, it is possible to provide a driving assistance ap-
`propriately considering the potential risk. Further, as the
`traffic environment information, there is exemplified in—
`formation related to a traffic law, a road shape and pres-
`ence of certain structures on the road.
`
`in the driving assistance device
`Furthermore,
`(0012]
`of the present invention, the estimated risk level deter—
`mination means determines the estimated risk level as
`
`higher in a case when acquiring information indicating
`the movement of the object onto the predicted travelling
`path of the vehicle, compared with a case when not ac-
`quiring the information.
`(0013] The case where the object moves onto the pre-
`dicted travelling path of the vehicle can be regarded as
`a state where the collision risk related to the object has
`been manifested. According to this configuration, in such
`case, since it is determined that the estimated risk level
`is higher, it is possible to provide the driving assistance
`appropriately considering the risk related to the object.
`(0014]
`Further, in the driving assistance device of the
`present invention, the estimated risk level determination
`means determines the estimated risk level on the basis
`
`of cause-and-effect relationship information which is in-
`formation based on the relationship between an object
`and another object relating to at least one of attributes,
`position and speed, as information indicating the cause—
`and-effect relationship between an object and the other
`object different from the object.
`(0015] The movement of the object is affected from the
`other object different from the object. For example, de-
`pending on the presence of the other object, there is a
`case of high possibility ofthe object moving onto the pre-
`dicted travelling path of the vehicle. According to this con-
`figuration, since the estimated risk level related io the
`object is determined based on the cause-and-effect re-
`lationship between the object and the other object, it is
`possible to improve the determination accuracy of the
`estimated risk level as well as to provide appropriate driv-
`ing assistance.
`
`Advantageous Effects of lnvention
`
`[0016] According to the driving assistance device of
`the present invention, it is possible to perform the driving
`
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`assistance considering the potential risk.
`
`Brief Description of Drawings
`
`{0017]
`
`(HG. 1} is a configuration diagram of a driving as-
`sistance device related to an embodiment of the
`
`present invention.
`(HG. 2) is a diagram illustrating a positional relation-
`ship between a vehicle and a parked vehicle of an
`object, and is a diagram illustrating the positional re-
`lationship between a vehicle and a pedestrian of the
`object.
`(HG. 3} is a flowchart illustrating an estimated risk
`level determination process in an estimated risk level
`determination unit.
`
`(HG. 4] is a diagram illustrating examples of situa-
`tions of the object and the traffic environment for
`each estimated risk level.
`
`(HG. 5} is a diagram illustrating an example of a driv-
`ing assistance content determination table.
`(HG. 6) is a diagram illustrating a display example
`of a HMI display when the driving assistance infor—
`mation is provided for the driving assistance.
`(HG. 7} is a diagram illustrating an example of a risk
`map and a target travelling path based on the risk
`map.
`(HG. 8] is a flowchart illustrating contents of the driv—
`ing assistance process in the driving assistance de-
`vrce.
`
`Description of Embodiments
`
`(0018] Hereinafter. a preferred embodiment of the
`present invention with reference to the attached drawings
`will be described in detail. Further, in the following de-
`scription, the same or the corresponding subject is de—
`noted by the same reference numeral or symbol, and the
`description for the corresponding portion is omitted.
`(0019] HG. t is a configuration diagram of an embod-
`iment of the driving assistance device of the present in—
`vention. The driving assistance device 1 is a device for
`providing driving assistance for the driver of a vehicle to
`enable avoiding the object ofthe risk subjectwhen driving
`the vehicle.
`
`(0020] The driving assistance device ‘l as illustrated in
`HG 1 includes an object detection unit2 (objectdeteciion
`means), a traffic environment information acquisition unit
`3 (traffic information acquisition means), a control ECU
`(Electronic Control Unit) 4, a HMl (Human Machine ln-
`terface) 5, and various actuators 6.
`(0021] The object detection unit 2 is a portion for de-
`tecting an object of the risk subject when driving the ve-
`hicle and can detect presence or absence, position and
`speed ofthe object. The object ofthe risk subject includes
`a pedestrian, a vehicle, other obstacles and the like which
`present on the predicted travelling path ofthe vehicle and
`
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`EP 2 549 456 A1
`
`6
`
`around the predicted travelling path thereof. in addition,
`the predicted travelling path is the travelling path of the
`vehicle in a case where the vehicle maintains the current
`
`travelling state.
`[0022] The object detection unit 2 is constituted by a
`camera and image recognition process means, for ex-
`ample. The image recognition process means is consti-
`tuted by a computer. Further, the object detection unit 2
`may be constituted by a radar device. The object detec—
`tion unit 2 sends the object information of position, size
`or the like with regard to the detected object to the control
`ECU 4. The object detected by the object detection unit
`2 includes, for example, pedestrians, stopped vehicles,
`vehicles in the opposite lane, obstacles on the road and
`the like. That is, the object detected in the objectdetection
`unit 2 includes not only the object having the direct risk
`subjects when driving the vehicle, but also the object hav—
`ing a possibility of exerting an influence on the movement
`of the risk object.
`[0023] The traffic environment information acquisition
`unit 3 is a portion for obtaining the traffic environment
`information which is information related to the traffic en—
`
`vironment in the vicinity of the vehicle and object. The
`traffic environment information acquisition unit 3 is con-
`stituted by, for example, a camera, a radar device, an
`infrastructure information communication device and the
`like. The infrastructure information communication de—
`
`vice is a device for receiving the traffic environment in-
`formation of a road from the infrastructure during the host
`vehicle is travelling, and may be constituted as a part of
`the functions of a so-called car—navigation device or may
`be constituted as a single communication device.
`[0024] The traffic environment information acquisition
`unit 3 acquires information related to traffic law, road
`shape, presence of certain structures on the road and
`the like, as the traffic environment information. More spe—
`cifically, the traffic environment information includes in-
`formation which is related to all possible traffic environ-
`ments in the vicinity of the vehicle, predicted travelling
`path of the vehicle and objects detected by the object
`detection unit 2, and for example, includes the presence
`of crosswalks, sign on the road warn of the presence of
`a crosswalk, and the presence ofguardrails. Further, cer-
`tain structures which are detected as traffic environment
`
`information include shops and the like which are located
`on both sides of the road. The traffic environment infor-
`
`mation acquisition unit 3 sends out the acquired traffic
`environment information to the control ECU 4. ln addition,
`the driving assistance device 1 of the embodiment in-
`cludes the traffic environment information acquisition unit
`3, but the traffic environment information acquisition unit
`3 may not be included in its minimum configuration.
`[0025] The control ECU 4 is a device for performing
`the driving assistance to avoid the object which is the risk
`subject by controlling a HMl 5 and various actuators 6,
`based on the information acquired from the object detec-
`tion unit 2 and the traffic environment information acqui-
`sition unit 3, and is constituted by a computer including
`
`a storage device such as CPU, ROM or RAM, an input—
`output interface and the like. The ECU 4 includes a col—
`lision prediction time calculation unit 10 (collision predic-
`tion time calculation means), an estimated risk level de-
`termination unit 1t (estimated risk level determination
`means), a driving assistance content determination unit
`12 (driving assistance contentdetermination means) and
`a driving assistance control unit 13.
`{0026] The collision prediction time calculation unit 10
`is a portion for calculating the time to collision which is a
`time indicating a degree to which the vehicle approaches
`to the object. The collision prediction time is calculated
`by dividing the distance from the vehicle to the object by
`the relative speeds of a host vehicle and an object. The
`collision prediction time calculation unit to calculates a
`fixed collision prediction time in a case where an object
`presents on the predicted travelling path of the vehicle,
`and calculates an estimated collision prediction time in
`a case where the object presents at a location other than
`the predicted travelling path of the vehicle. The fixed col—
`lision prediction time is a fixed time to collision with an
`object which presents on the predicted travelling path of
`the vehicle. Further, the estimated collision prediction
`time is a time to collision with the object in a case where
`it is assumed that the object presenting at a location other
`than the predicted travelling path of the vehicle has
`moved onto the predicted travelling path. The collision
`prediction time calculation unit 10 sends out the calcu-
`lated collision prediction time to the driving assistance
`content determination unit 12. Referring to FIG. 2, the
`fixed collision prediction time and the estimated collision
`prediction time will be described in detail.
`{0027] HS. 2(a) is a diagram illustrating a positional
`relationship between a vehicle C and a parked vehicle T
`of the object. As shown in FlG. 2(a), the parked vehicle
`T presents on the predicted travelling path of the vehicle
`C. ln this case, since the parked vehicle T is a risk subject
`manifested with respect to the vehicle C, the collision
`prediction time calculation unit 10 calculates a fixed col-
`lision prediction time by dividing the distance from the
`vehicle C to the parked vehicle T by the relative speeds
`of the vehicle C and the parked vehicle T.
`{0028]
`FIG. 2(b) is a diagram illustrating a positional
`relationship between a vehicle C and a pedestrian P that
`is the object. As shown in HS. 2(b), since the pedestrian
`P being on the sidewalk presents at a location other than
`the predicted travelling path of the vehicle, even if the
`vehicle C travels in this state, the vehicle C will not collide
`with the pedestrian P. However, in a case where the pe-
`destrian P has moved onto the travelling range of the
`vehicle C, there is a possibility of the vehicle colliding
`with the pedestrian P. Thus, the pedestrian P is a poten—
`tial risk subject in driving the vehicle C. in this case, the
`collision prediction time calculation unit to calculates the
`estimated collision prediction time by dividing the dis-
`tance from the vehicle C to a position PX of the pedestrian
`P, assuming that the pedestrian P moves onto the pre-
`dicted travelling path of the vehicle C, by the relative
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`speeds of the vehicle C and the pedestrian P.
`[0029] As described referring to FlG. 2(a) and 2(b), in
`a case where an object presents on the predicted trav-
`elling path of the vehicle, the fixed collision prediction
`time is calculated as if the risk were manifested, and in
`a case where an object presents at a location other than
`the predicted travelling path of the vehicle, on the as-
`sumption that the object has moved onto the predicted
`travelling path of the vehicle as if the potential risk pre—
`sented, the estimated collision prediction time is calcu-
`lated based on a location after movement of the object.
`Accordingly, even in either of a case where the risk is
`being manifested or a case where the risk is not being
`manifested, the collision prediction time may be appro-
`priately calculated.
`[0030] The estimated risk level determination unit 11
`is a portion for determining the estimated risk level indi—
`cating a possibility of the object which is not present on
`the predicted travelling path of the vehicle c moving onto
`the predicted travelling path ofthe vehicle. In otherwords,
`the estimated risk level indicates a risk level related to
`
`the object ofthe potential risk subject. The estimated risk
`level determination unit 11 sends out the estimated risk
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`level as determined above to the driving assistance con-
`tent determination unit 12.
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`[0031] More specifically, the estimated risk level deter-
`mination unit 11 determines the estimated risk level as
`
`higher in a case when acquiring the traffic environment
`information indicating that the object may move onto the
`predicted travelling path ofthe vehicle, compared with a
`case when not acquiring the traffic environment informa-
`tion. Further, the estimated risk level determination unit
`11 determines the estimated risk level as higher in a case
`when acquiring information indicating movement of the
`object onto the predicted travelling path of the vehicle (3,
`compared with a case when not acquiring the informa—
`tion. Furthermore, the estimated risk level determination
`unit 11 determines the estimated risk level on the basis
`
`of cause-and—eftect relationship information which is in-
`formation based on a relationship between an object and
`anotherobject relating to at least one ofattribute, position
`and speed, as information indicating cause—and-effect re-
`lationship between the object and the other object differ-
`ent from the object.
`[0032] An estimated risk level determination process
`in the estimated risk level determination unit 11 will be
`described with reference to HS. 3 and HS. 4. FIG. 3 is
`
`a flowchart illustrating an estimated risk level determina-
`tion process in the estimated risk level determination unit
`11. Further, FlGS. 4(a) to 4(d) are diagrams illustrating
`examples of situations of the object and the traffic envi-
`ronmentfor each estimated risk level. ln the embodiment,
`as an example, the estimated risk level is determined to
`be any of 4 steps R0 to R3. Among the 4 steps of esti-
`mated risk level, the estimated risk level RO is the level
`at which the danger is the lowest and the estimated risk
`level R3 is the level at which the danger is the highest.
`Further, in the embodiment, the estimated risk level is
`
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`determined in 4 steps. However, this is an example of
`the embodiment ofthe present invention and is notlimited
`thereto.
`
`in STEP 810, the estimated risk level determi-
`{0033]
`nation unit 11 determines whether or not the collision
`
`prediction time can be fixed based on relative speed and
`relative distance between the vehicle C and the object.
`That is, the estimated risk level determination unit 11
`determines that the collision prediction time can be fixed
`in a case where movement of the object onto the predict-
`ed travelling path of the vehicle C is detected. if it is de—
`termined that the collision prediction time can be fixed,
`the processing procedure is advanced to STEP S11.
`Meanwhile, if it is determined that the collision prediction
`time cannot be fixed, the processing procedure is ad—
`vanced to STEP S12.
`
`in STEP 811, the estimated risk level determi-
`{0034]
`nation unit 11 determines that the estimated risk level is
`
`R3. FIG. 4(d) is a diagram illustrating an example of a
`situation of the vehicle 0 and the pedestrian P3 in a case
`where it is determined that the estimated risk level is R3.
`
`As shown in FIG. 4(d), the pedestrian P3 is not present
`on the predicted travelling path of the vehicle C and
`presents on the sidewalk, and starts to move in a direction
`shown by an arrow r. This case can be regarded as a
`state where the collision risk related to the object has
`been manifested. Accordingly, the estimated risk level
`determination unit 11 determines that the estimated risk
`
`level is R3 in a case where the movement of the pedes—
`trian P3 onto the predicted travelling path of the vehicle
`C is detected. That is, the estimated risk level determi-
`nation unit 11 determinesthe estimated risk level as high-
`er in a case when acquiring the information indicating the
`moving of the pedestrian P3 to the predicted travelling
`path of the vehicle C, compared with a case when not
`acquiring the information. Further, in this case, the colli-
`sion prediction time calculation unit 10 calculates the
`fixed collision prediction time for the object.
`{0035]
`in STEP S12, the estimated risk level determi-
`nation unit 11 determines whether or not the object is in
`movable state.
`in a case where the object that is not
`present on the predicted travelling path of the vehicle 0
`is nota movable object, there is no possibility ofthe object
`moving onto the predicted travelling path of the vehicle
`0. Accordingly, in a case where it is determined that the
`object is not in a movable state, the processing procedure
`is advanced to STEP 813. in STEP 813, since there is
`no risk related to the object, the estimated risk level de-
`termination unit 11 determines the object as an object
`other than the risk object, and terminates the process for
`the driving assistance in orderto determine the estimated
`risk level related to the object and to avoid the object.
`{0036] Meanwhile, in a case where it is determined that
`the object is in movable state, the processing procedure
`is advanced to STEP 814. in STEP S14, the estimated
`risk level determination unit 11 determines whether or
`
`not there is in traffic environment that the object can enter
`the predicted travelling path ofthe vehicle 0. Further, the
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`traffic environment includes the traffic law, for example.
`in a case where it is not determined that a traffic envi-
`
`ronment presents where the vehicle can enter the pre-
`dicted travelling path of the vehicle C, the processing
`procedure is advanced to STEP 8 15.
`[0037]
`in STEP 815, the estimated risk level determi-
`nation unit 11 determines that the estimated risk level is
`
`R0. FlG. 4(a) is a diagram illustrating an example of a
`situation of the vehicle C and the pedestrian P5 in a case
`where it is determined that the estimated risk level is R0.
`
`As shown in FlG. 4(a), the pedestrian Po presents on the
`sidewalk outside of the predicted travelling path of the
`vehicle C, and thus, for example, the traffic environment,
`illustrated as a crosswalk for example, where the pedes-
`trian PO can move onto the predicted travelling path of
`the vehicle C is not present. Further,
`in this case, the
`collision prediction time calculation unit 10 calculates the
`estimated time to collision with the object.
`[0038] Meanwhile. in a case where it is determined that
`a traffic environment in which the object can enter the
`predicted travelling path of the vehicle C is not present,
`the processing procedure is advanced to STEP 816. ln
`STEP S16, the estimated risk level determination unit 11
`determines whether or not there is a high possibility of
`the object of the determined target entering the predicted
`travelling path of the vehicle C, considering the relation-
`ship between the other object different from the object of
`a determined target and the obstacle. in a case where it
`is not determined thatthere is high possibility of the object
`of the determined target entering the predicted travelling
`path of the vehicle,
`the processing procedure is ad—
`vanced to STEP S17. Meanwhile, in a case where it is
`determined that there is high possibility of the object en-
`tering the predicted travelling path of the vehicle C, the
`processing procedure is advanced to STEP S 18.
`[0039]
`ln STEP 817, the estimated risk level determi—
`nation unit 11 determines that the estimated risk level is
`
`R1. FIG. 4(b) is a diagram illustrating an example of a
`situation of the vehicle C and pedestrian P1 in a case
`where it is determined that the estimated risk level is R1.
`
`As shown in FlG. 4(b), the pedestrian P1 of the object is
`not present on the predicted travelling path of the vehicle
`C, but presents on the sidewalk. Further, as the traffic
`environment, there presents a crosswalk S1. in this case,
`due to the presence of the crosswalk S1, the possibility
`that the pedestrian P1 may move onto the predicted trav-
`elling path of the vehicle C is high compared with a case
`where there is no crosswalk S1 . Accordingly, the estimat-
`ed risk level determination unit 11 determines the esti-
`
`mated risk level as high when detecting the presence of
`crosswalk 81 which is the traffic environment information
`indicating that pedestrian P1 may move onto the predict-
`ed travelling path of the vehicle C, compared with a case
`(814, 515) when the traffic environment information is
`not acquired. Further, in this case, the collision prediction
`time calculation unit 10 calculates the estimated time to
`
`collision with the object.
`[0040] As described referring to FlG. 4(b), since the
`
`estimated risk level of a case when a traffic environment
`
`presents in which the object can move onto the predicted
`travelling path of the vehicle is higher than that of a case
`when such traffic environment is not present, it is possible
`to provide driving assistance appropriately considering
`the potential risk.
`{0041]
`in STEP 818, the estimated risk level determi-
`nation unit 11 determines that the estimated risk level is
`
`R2. FlG. 4(c) is a diagram illustrating an example of a
`situation of the vehicle C and the pedestrian P2 in a case
`when there is determined that the estimated risk level is
`
`R2. As shown in FIG. 4(c), the pedestrian P2 ofthe object
`is not present on the predicted travelling path, but
`presents on the sidewalk. Further, as such traffic envi-
`ronment. there presents crosswalk 81 as well as a vehicle
`T2 stopped in the opposite lane. This situation means
`that if the pedestrian P2 goes across the crosswalk 82,
`since the vehicle T2 is stopped, the possibility of pedes-
`trian P2 moving onto the predicted travelling path of the
`vehicle C is higher comparing with a situation showing
`in FIG. 4(b). That is, based on a cause-and-effect rela-
`tionship between a pedestrian P2, a crosswalk S2, and
`a vehicle T2 stopped in the opposite lane, it is possible
`to determine thatthere is high possibility ofthe pedestrian
`P2 moving onto the predicted travelling path of the vehicle
`C.
`
`{0042] The estimated risk level determination unit 11
`can determine the estimated risk level based on the
`
`cause-and-effect relationship information indicating the
`cause—and—effect relationship between the pedestrian P2
`of the risk subject, the crosswalk 82 which is the other
`object differentfrom the object of the risk subject or traffic
`environment. and the vehicle T2 stopped in the opposite
`lane. The cause-and-effect relationship information is in—
`formation based on the relationship relating among a risk
`object, another object different from the risk object, and
`at least one of attribute, position and speed of the traffic
`environment. Further. the estimated risk level determi—
`nation unit 11 has a database (not shown) that a variety
`of cause-and—effect relationship information is stored in
`advance. The determination process shown in the STEP
`8 16 is performed by determining whether or not the de—
`tected event co