JP2008074401A - Collision prediction apparatus - Google Patents

Abstract

<P>PROBLEM TO BE SOLVED: To provide a collision prediction apparatus for accurately recognizing a front obstacle which highly likely collides with one's own vehicle, and therefore has improved accuracy in collision prediction and judgment. <P>SOLUTION: A collision prediction ECU 11 of the collision prediction apparatus 10 estimates the presence state of a detected front obstacle. In this case, the ECU 11 estimates the presence state based on road shape data supplied from a navigation ECU 21 of a navigation apparatus 20. Also, the ECU 11 confirms and corrects the calculated amounts of the road gradient. In this case, the ECU 11 corrects the amounts of the gradient based on road gradient data supplied from the ECU 21. Also, the ECU 11 changes a collision avoidance time Tc based on traveling environment data supplied from the ECU 21. Furthermore, the ECU 11 judges whether or not its own vehicle has passed a gate by acquiring an ETC gate passage signal from the ECU 21. Then, the ECU 11 performs collision prediction based on the corrected value. <P>COPYRIGHT: (C)2008,JPO&INPIT

Description

  The present invention relates to a collision prediction apparatus that predicts and determines a collision between a host vehicle and a forward obstacle existing on a course.

  Conventionally, as shown in Patent Document 1, for example, a vehicle occupant protection device that operates based on a determination result of a collision possibility is known. This vehicle occupant protection device acquires predetermined data such as position information and vehicle speed information as information relating to the host vehicle, and determines the possibility of collision of the host vehicle based on the predetermined data. When the possibility of a collision increases, the highly accurate operation of the occupant protection device of the counterpart vehicle can be secured by transmitting the predetermined data to the colliding counterpart vehicle. Yes.

  For example, as shown in Patent Document 2, there is known an occupant protection support device that monitors the running state of a vehicle and the surrounding environment and operates based on the monitoring result. This occupant protection support apparatus also monitors the running state of the host vehicle and the surrounding environment by a radar that detects the inter-vehicle distance, a G sensor that detects deceleration, an image detection device, and the like. Then, by processing the monitoring results, the possibility of collision of the host vehicle is predicted, and on the basis of the prediction results, the occupant protection device and the warning device are operated, and the communication for the rescue request after the accident occurs is performed. It has come to be.

Furthermore, for example, as shown in Patent Document 3, a vehicle occupant protection device that receives a collision prediction signal and executes a predetermined operation is known. This vehicle occupant protection device also confirms whether there is an obstacle ahead of the host vehicle or a moving body approaching behind based on detection signals from an ultrasonic sensor, an infrared sensor, or a radar, It is determined whether an obstacle or a moving body collides with the host vehicle. Then, based on the front collision prediction signal or the rear collision prediction signal that is output when it is determined that the possibility of a collision is high, a specific operation of the occupant is limited, and the occupant protection device is reliably operated. ing.
JP-A-11-263190 JP 2000-142321 A JP 2000-247210 A

  By the way, in each of the conventional devices described above, data (parameters) used for determining the possibility of collision of the host vehicle is used without checking whether or not it is detected from each detection device, that is, whether it is a false detection. To determine the possibility of collision. However, depending on the shape of the road on which the host vehicle is traveling and the environment around the host vehicle (for example, tunnels, iron bridges, etc.), the collision object may be erroneously recognized. In this case, passenger protection (support) The device may perform useless operation. For this reason, it is desired to accurately recognize a collision target, particularly a front obstacle present on the course of the host vehicle, and further improve the accuracy of collision determination.

  The present invention has been made to address the above-described problems, and its purpose is to accurately recognize a front obstacle that is highly likely to collide with the host vehicle and improve the accuracy of collision prediction determination. It is to provide a prediction device.

  A feature of the present invention is that the vehicle includes a front obstacle detection unit that detects a front obstacle present on the course of the host vehicle, and whether the detected front obstacle collides with the host vehicle as the host vehicle travels. In the collision prediction apparatus that predicts whether or not the vehicle travel environment information acquisition means for acquiring travel environment information related to the travel environment of the host vehicle, parameter value detection means for detecting a parameter value representing the travel state of the host vehicle, and the parameter The parameter value detected by the value detection means is compared with a predetermined collision determination reference value used for predicting and determining a collision between the front obstacle and the host vehicle, and the parameter value is the predetermined collision determination reference value. Based on the travel environment information acquired by the collision prediction determination means for predicting the collision between the front obstacle and the host vehicle when the vehicle is satisfied, and the travel environment information acquisition means. In further comprising a changing means for changing the predetermined collision determination reference value.

  In this case, the traveling environment information is traveling environment information representing a traveling environment in which the front obstacle detection unit detects a continuous stationary object existing around the host vehicle as the host vehicle travels, and the change The means may change the predetermined collision determination reference value to a collision determination reference value that makes it difficult to select the front obstacle as a collision target according to the traveling environment information. Further, the traveling environment information is traveling environment information representing a traveling environment in which the host vehicle starts a right turn at an intersection, and the changing means determines the predetermined collision determination reference value according to the traveling environment information, The collision determination reference value may be changed to make it difficult to select the front obstacle as a collision target. Further, the traveling environment information is traveling environment information representing a traveling environment in which the host vehicle travels on an expressway, and the changing means sets the predetermined collision determination reference value according to the traveling environment information to the front It is good to change to the collision judgment reference value which makes it easy to select an obstacle as a collision target.

  In these cases, the travel environment information may be acquired from a navigation device in which the travel environment information acquisition unit is mounted on the host vehicle. In particular, the travel environment information in which the host vehicle travels on a highway is the travel environment information. The information acquisition means may be acquired from an ETC terminal device mounted on the host vehicle.

  According to these, the collision prediction apparatus can acquire the driving environment information related to the driving environment of the host vehicle from the navigation device or the ETC terminal device, for example, by the driving environment information acquisition means. Here, the traveling environment is an environment including a situation around a road on which the host vehicle is traveling, a traveling pattern of the host vehicle, and the like. In addition, the collision prediction device predicts and determines the collision between the front obstacle and the host vehicle by comparing the detected parameter value indicating the traveling state of the host vehicle with a predetermined collision determination reference value by the collision prediction determination unit. be able to. Further, the collision prediction apparatus can appropriately change the predetermined collision determination reference value based on the acquired traveling environment information by the changing unit. Thereby, the collision prediction device can accurately select a front obstacle that is highly likely to collide, and can prevent erroneous determination of the collision prediction determination.

  In addition, if the change unit acquires travel environment information representing a travel environment in which the front obstacle detection unit detects a continuous stationary object existing around the host vehicle as the host vehicle travels, The collision determination reference value can be changed to a collision determination reference value that makes it difficult to select a front obstacle as a collision target. Here, the continuous stationary object existing around the host vehicle is, for example, an inner wall of a tunnel. Further, if the traveling environment information representing the traveling environment in which the host vehicle starts a right turn at the intersection is acquired, the changing unit makes it difficult to select a predetermined obstacle determination reference value as a front obstacle as a collision object. It can be changed to the collision judgment reference value.

  In this way, the changing means changes the predetermined collision determination reference value to the collision determination reference value that makes it difficult to select the front obstacle as the collision object, so that the stationary object or right turn that is continuously detected by the front obstacle detection means is changed. It is possible to reduce the number of collision prediction determinations with a front obstacle that has a low possibility of collision, such as a vehicle that sometimes crosses the front of the host vehicle. Therefore, the erroneous determination in the collision prediction with these front obstacles can be greatly reduced.

  In addition, if the change unit acquires travel environment information representing a travel environment in which the host vehicle travels on a highway, a collision determination that makes it easy to select a predetermined collision determination reference value as a front obstacle as a collision target. The reference value can be changed. As described above, when the vehicle is traveling at a high speed on the highway by changing the predetermined collision determination reference value to the collision determination reference value that makes it easy to select a front obstacle as a collision target. Can detect a forward obstacle with a possibility of collision at an early stage and appropriately execute the collision prediction determination. Therefore, based on the collision prediction determination, for example, the occupant protection device can be operated at an appropriate timing.

  A first embodiment of the present invention will be described below with reference to the drawings. FIG. 1 is a block diagram schematically illustrating an entire collision prediction system according to the first embodiment, which predicts and determines a collision between the host vehicle and a front obstacle and operates an occupant protection device based on the prediction determination. is there. This collision prediction system includes a collision prediction device 10 that predicts a collision and determines the possibility of a collision, and a navigation device 20 that provides route guidance to the driver and supplies various information.

  The collision prediction device 10 and the navigation device 20 are communicably connected to each other via a bus 30, a gateway computer 40, and a LAN (Local Area Network) 50. Here, the gateway computer 40 is a computer that comprehensively controls the flow of various data shared between the collision prediction device 10 and the navigation device 20 and the flow of control signals for controlling the cooperation between these devices 10 and 20. The bus 30 is connected to an occupant protection device 60 including a device for avoiding a vehicle collision and a device for reducing damage at the time of the vehicle collision.

  As shown in FIG. 2, the collision prediction apparatus 10 includes a collision prediction electronic control unit 11 (in the following description, simply referred to as a collision prediction ECU 11). The collision prediction ECU 11 includes a microcomputer including a CPU, a ROM, a RAM, a timer, and the like as main components. And collision prediction ECU11 acquires each signal supplied from the navigation apparatus 20 and each sensor, and runs the program shown in FIGS. For this reason, a vehicle speed sensor 12, a steering angle sensor 13, a yaw rate sensor 14, a radar sensor 15, and an acceleration sensor 16 are connected to the collision prediction ECU 11. Here, the detection values output from these sensors are output to the bus 30 via the bus interface 17 described later, and can be used also by the navigation device 20 and the occupant protection device 60.

  The vehicle speed sensor 12 detects and outputs the vehicle speed V based on a pulse signal corresponding to the vehicle speed. The steering angle sensor 13 outputs a signal corresponding to the steering angle of the front wheels. Then, the steering angle δ of the front wheels is detected by the collision prediction ECU 11. The yaw rate sensor 14 detects and outputs the yaw rate γ of the host vehicle based on a signal corresponding to the rotational angular velocity around the center of gravity of the host vehicle.

  The radar sensor 15 is assembled at the front end of the host vehicle (for example, near the front grille), and based on the time required for transmitting and receiving millimeter waves, the radar sensor 15 is connected to a front obstacle existing in a predetermined range in front of the host vehicle. A relative distance L representing a relative distance and a relative speed VR representing a relative speed are detected and output. Further, the radar sensor 15 detects the presence direction (vertical direction, left-right direction) of the front obstacle with reference to the own vehicle, and also outputs the presence direction information indicating the presence direction. The acceleration sensor 16 is installed at a substantially center of gravity position of the host vehicle, and detects and outputs an acceleration G in the vertical direction based on a signal corresponding to the vertical displacement speed of the host vehicle.

  A bus interface 17 is connected to the collision prediction ECU 11. The bus interface 17 is connected to the bus 30, and supplies various information from the navigation device 20 to the collision prediction ECU 11, and each detection of each sensor 12, 13, 14, 15, 16 supplied from the collision prediction ECU 11. Values and various information are output to the navigation device 20 and the occupant protection device 60.

  As shown in FIG. 3, the navigation device 20 includes a navigation electronic control unit 21 (hereinafter simply referred to as a navigation ECU 21). The navigation ECU 21 also includes a microcomputer including a CPU, a ROM, a RAM, a timer, and the like as main components. The navigation ECU 21 is connected to a GPS (Global Positioning System) receiver 22, a gyroscope 23, a storage device 24, and a LAN interface 25.

  The GPS receiver 22 receives a radio wave for detecting the current location of the host vehicle from a satellite, and detects and outputs the current location of the host vehicle as, for example, coordinate data. The gyroscope 23 detects and outputs the turning speed of the vehicle for detecting the traveling direction of the host vehicle. And navigation ECU21 acquires the vehicle speed V output from the vehicle speed sensor 12, in addition to acquisition of each detected value output from the GPS receiver 22 and the gyroscope 23, and detects the present location of the own vehicle.

  The storage device 24 includes a recording medium such as a hard disk, a CD-ROM, and a DVD-ROM and a drive device for the recording medium, and stores various data including a program (not shown) executed by the navigation ECU 21 and road data. ing. Here, the road data includes road type data representing road types (express roads, national roads, prefectural roads, etc.), road environment data representing environments around the roads (tunnels, iron bridges, etc.), road shapes (number of lanes, curve radii, etc.) Road shape data representing road, road surface state data representing road surface conditions (such as a friction coefficient μ of the road surface), and road gradient data representing road gradient values and road lengths having the same gradient values.

  The LAN interface 25 is connected to the LAN 50 built in the vehicle, and enables communication between the navigation ECU 21 and the gateway computer 40. Thereby, the LAN interface 25 supplies various information from the navigation ECU 21 to the gateway computer 40 via the LAN 50, or acquires various information supplied from the collision prediction device 10 from the gateway computer 40 and supplies it to the navigation ECU 21. To do.

  The occupant protection device 60 includes a device that controls the traveling state of the host vehicle to avoid a collision based on the collision prediction of the collision prediction device 10 and a device that reduces damage to the occupant during a vehicle collision. Yes. The occupant protection device 60 includes, for example, a device that controls the deceleration of the vehicle speed of the host vehicle, a device that assists the driver's brake pedal force, an automatic steering device that automatically steers steering to avoid a collision, and an occupant during a collision. A device for preventing the vehicle from moving forward, a device for optimizing the operation of the airbag and the impact absorption efficiency during the airbag operation, a device for changing the shock energy absorption load, a device for moving the operating pedal, and an occupant protection device 60 In addition, there is a shut-off circuit that shuts off power supply to devices other than the traveling state control device of the host vehicle.

  In addition, each apparatus which comprises these passenger | crew protection apparatuses 60 operate | moves immediately before the collision of the own vehicle or immediately after a collision, and is not directly related to this invention. Therefore, in this specification, detailed description of the operation of each of these devices will be omitted, but will be briefly described below.

  The device that controls the deceleration of the vehicle speed of the host vehicle automatically brakes in order to ensure an appropriate relative distance and relative speed when the detected relative distance and relative speed to the front obstacle are out of the predetermined range. It is a device that operates the device to reduce the vehicle speed of the host vehicle. The device that assists the driver's brake pedal force assists the driver's pedal force when the driver operates the brake pedal to stop the vehicle in order to avoid a collision. This is a device that reliably operates the brake device of the host vehicle by maintaining the pressure-increasing state. An automatic steering device that automatically steers steering to avoid a collision is a device that assists the steering operation of the driver or reliably steers the steering in a collision avoidance direction when the collision is avoided.

  As a device that prevents the occupant from moving forward in the event of a collision, for example, there is a seat belt retractor. The seat belt retractor prevents the occupant from moving forward due to inertia when the host vehicle collides with a front obstacle. That is, when the seat belt retractor detects a collision of the host vehicle, the seat belt is retracted and locked at the retracted position to prevent the seat belt from being pulled out. In order to realize this function, a device that locks the seat belt by using an electric motor or compressed gas has been implemented.

  As a device for optimizing the operation of the air bag and the impact absorption efficiency at the time of the air bag operation, for example, there is a column moving device that moves the steering column depending on whether the occupant is wearing a seat belt or the physique (weight) of the occupant. is there. In this column moving device, the steering column is moved in order to efficiently absorb an impact with the distance between the occupant and the steering as a distance necessary for deploying the airbag. In order to realize this function, a device that changes the angle of the steering column, a device that changes the distance between the steering wheel and the occupant, a device that moves the seat in the front-rear direction, and the like have been implemented.

  As an apparatus for changing the impact energy absorption load, for example, there is an impact energy absorption apparatus that relieves a driver's collision with a steering wheel by absorbing energy accompanying deformation of a steering column. Even if the driver collides with the steering wheel due to the collision of the vehicle, the impact energy absorbing device can appropriately reduce the impact energy generated by the collision by absorbing the energy accompanying the deformation of the steering column. In order to realize this function, for example, a conical pin is inserted from the outer peripheral surface direction of the steering column, and the deformation resistance when the pin inserted by a predetermined amount relatively moves while tearing the outer peripheral surface of the steering column. The impact energy absorber to be used has been implemented.

  As an apparatus that moves the operation pedal, for example, there is a pedal movement apparatus that moves the operation pedal forward of the vehicle immediately before or at the time of the vehicle collision. When a vehicle collision is detected, the pedal moving device moves the operation pedal forward of the vehicle in order to avoid a collision between a driver's leg thrown out by inertia and an operation pedal (for example, an accelerator pedal, a brake pedal, etc.). It is supposed to let you. In order to realize this function, for example, a pedal moving device that moves the operation pedal by the driving force of the electric motor or changes the movement timing of the accelerator pedal and the brake pedal is used.

  A shut-off circuit that shuts off power supply to devices other than the occupant protection device 60 and the traveling state control device of the host vehicle is provided in the occupant protection device 60 and the vehicle travel control device (for example, ABS, vehicle stability control device, etc.) In order to supply power preferentially, it is a shut-off circuit that shuts off power supply to other devices. That is, the cutoff circuit cuts off the power supply to a device that is not necessary for vehicle collision or collision avoidance, such as an audio device.

  Next, the operation of the collision prediction system according to this embodiment configured as described above will be described in detail. By turning on an ignition switch (not shown), the collision prediction ECU 11 of the collision prediction apparatus 10 starts to repeatedly execute the collision prediction program of FIG. 4 every predetermined short time.

  The execution of the collision prediction program is started in step S10, and in step S11, the collision prediction ECU 11 determines the relative distance L from the front end of the host vehicle to the front obstacle output from the radar sensor 15, and the host vehicle and the front obstacle. The relative speed VR between objects is acquired. Then, the acquired relative distance L and relative speed VR are set as the current relative distance Lnew and the current relative speed VRnew, which indicate that the current program has been input.

  After setting the current relative distance Lnew and the current relative speed VRnew, the collision prediction ECU 11 determines in step S12 whether or not the current relative speed VRnew is positive. If the relative speed VRnew is not positive this time, “No” is determined in step S12, the process proceeds to step S24, and the execution of the program is temporarily terminated. This means that when the relative speed VRnew is not positive this time, the relative distance L from the front end of the host vehicle to the front obstacle does not change or increases. In this case, the host vehicle moves forward. This is because there is no possibility of collision with an obstacle, so there is no need to predict collision.

  If the relative speed VRnew is positive this time, “Yes” is determined in step S12, and the process proceeds to step S13. In step S13, the collision prediction ECU 11 determines whether or not the absolute value | δ | of the steering angle δ detected based on the output signal from the steering angle sensor 13 is larger than a preset steering angle δs. By this determination, the collision prediction ECU 11 can determine whether or not the host vehicle is currently traveling on a curve. That is, if the absolute value | δ | of the steering angle δ is equal to or smaller than the preset steering angle δs, the host vehicle is not traveling in a curve, so the collision prediction ECU 11 determines “No” and proceeds to step S16. .

  On the other hand, if the absolute value | δ | of the steering angle δ is larger than the preset steering angle δs, the collision prediction ECU 11 determines “Yes” and proceeds to step S14 because the vehicle is traveling on a curve. . In step S14, the collision prediction ECU 11 executes an obstacle presence confirmation routine on the curved road. The obstacle confirmation routine on the curved road is a forward obstacle that is less likely to actually collide when the host vehicle travels on a curve even if it is a forward obstacle detected by the radar sensor 15 (for example, This is a routine for accurately selecting a front obstacle that has a high possibility of a collision, except for vehicles traveling in the opposite lane and signs installed on the opposite lane.

  As shown in FIG. 5, this obstacle confirmation routine on the curved road is started in step S100, and in step S101, a curve radius R1 in which the host vehicle is currently traveling on a curve is calculated. That is, the collision prediction ECU 11 calculates the curve radius R1 of the host vehicle using the steering angle δ based on the output signal from the steering angle sensor 13 and the yaw rate γ from the yaw rate sensor 14, and proceeds to step S102.

  In step S102, the collision prediction ECU 11 determines whether or not the relative distance between the host vehicle and the front obstacle, that is, the current relative distance Lnew is equal to or less than a predetermined distance (for example, 50 m). In other words, if the current relative distance Lnew is 50 m or less, the collision prediction ECU 11 determines “Yes” and proceeds to step S103. On the other hand, if the current relative distance Lnew is greater than 50 m, the collision prediction ECU 11 determines “No” and proceeds to step S107. Here, in this comparative determination of the relative distance Lnew, the collision prediction ECU 11 can also determine the relative distance by adding other conditions. That is, for example, when the current relative speed VRnew is 120 km / h or less, the collision prediction ECU 11 determines the relative distance or the collision time between the host vehicle and the front obstacle is within 1.5 sec. The relative distance can also be determined based on conditions for determining the relative distance.

  In step S103, the collision prediction ECU 11 estimates the curve radius R2 where the forward obstacle exists. More specifically, the collision prediction ECU 11 acquires road shape data and road surface state data of a road on which a forward obstacle exists from the navigation device 20. Here, an operation in which the collision prediction ECU 11 communicates with the navigation ECU 21 of the navigation device 20 to acquire road shape data and road surface state data will be described. First, the collision prediction ECU 11 supplies the gateway computer 40 with the relative distance to the front obstacle, that is, the current relative distance Lnew via the bus 30.

  The gateway computer 40 supplies the supplied current relative distance Lnew to the navigation ECU 21 of the navigation device 20 via the LAN 50. The navigation ECU 21 acquires the current relative distance Lnew via the LAN interface 25 and temporarily stores it in a RAM (not shown). Moreover, navigation ECU21 acquires each detection value from GPS receiver 22, the gyroscope 23, and the vehicle speed sensor 12, and detects the present location and the advancing direction of the own vehicle. Then, the navigation ECU 21 uses the detected current location of the host vehicle, the traveling direction, and the stored current relative distance Lnew to confirm the position where the forward obstacle exists. Subsequently, the navigation ECU 21 searches the storage device 24 to obtain road shape data and road surface state data of the road on which the front obstacle whose existence position has been confirmed is present. To the gateway computer 40.

  As described above, when the road shape data and the road surface state data are supplied from the navigation ECU 21, the gateway computer 40 supplies the supplied data to the collision prediction ECU 11 via the bus 30. The collision prediction ECU 11 acquires the supplied road shape data and road surface state data, and temporarily stores them in a RAM (not shown). Then, the collision prediction ECU 11 uses the stored road shape data to estimate the presence state of the front obstacle, that is, the road curve radius R2. At this time, the collision prediction ECU 11 includes data indicating the number of lanes of the road in the stored road shape data. Therefore, the collision prediction ECU 11 uses the current relative distance Lnew and the existing direction information, and the lane where the forward obstacle exists. Is identified.

  Then, the collision prediction ECU 11 estimates the curve radius of the identified lane as the curve radius R2 where the front obstacle exists. Here, when the front obstacle is moving, the collision prediction ECU 11 corrects the curve radius R2 estimated based on the road shape data using the stored road surface state data, and more accurately curves. It is also possible to estimate the radius R2. When the curve radius R2 is estimated in this way, the collision prediction ECU 11 proceeds to step S104.

  In step S104, a curve connecting the curve radius R1 calculated in step S101 and the curve radius R2 estimated in step S103 with the current relative distance Lnew, for example, a clothoid curve, is calculated using a known calculation method. To do. The clothoid curve calculation processing is calculated by a well-known method, and a detailed description thereof will be omitted in the present specification, but will be briefly described below.

The clothoid curve as a curve is a curve whose curvature is proportional to the arc length, and is represented by R × L = A ² as a basic formula. Here, L is a curve length from the clothoid origin to an arbitrary point P, R is a radius of curvature at the arbitrary point P, and A is a clothoid parameter. Then, using this unique characteristic, it is used, for example, in road design as a curve connecting arcs, line segments, or line segments and arcs.

  Using this clothoid curve, the collision prediction ECU 11 connects the curve radius R1 and the curve radius R2. That is, the collision prediction ECU 11 uses the basic equation of the clothoid curve, L is the distance between the host vehicle and the front obstacle, that is, the current relative distance Lnew, R is the radius of curvature at the position of the front obstacle, The clothoid curve is calculated using the clothoid parameter. The curve is not limited to being calculated as a clothoid curve. For example, the curve may be calculated as a curve in which the curve radius R1 changes to the curve radius R2 at a point half the relative distance Lnew this time. . And collision prediction ECU11 will progress to step S105, if a curve is calculated.

  In step S105, the collision prediction ECU 11 calculates a curve radius R3 that passes through a point where the host vehicle has traveled the current relative distance Lnew according to the curve calculated in step S104. That is, the collision prediction ECU 11 selects a point on the curve calculated in step S104 that is away from the current vehicle by the current relative distance Lnew. Then, the collision prediction ECU 11 calculates a curve radius R3 passing through the selected point and the reference point of the host vehicle, for example, and proceeds to step S106.

  In step S106, the collision prediction ECU 11 calculates a relative lateral position between the front obstacle and the host vehicle, that is, a relative lateral position Xr when the host vehicle travels for the current relative distance Lnew at the curve radius R3. Hereinafter, although the calculation of the relative lateral position Xr will be described, there are various calculation methods for this calculation. Therefore, in the present embodiment, the relative lateral position Xr will be described as an offset amount between the central axis of the host vehicle and the side surface of the front obstacle. A case where the relative lateral position Xr is calculated using the curve radius R3 on which the host vehicle travels and the current relative distance Lnew to the front obstacle will be described.

  In calculating the relative lateral position Xr, the collision prediction ECU 11 first detects an instantaneous relative lateral position X between the center axis of the host vehicle and the side surface of the front obstacle. That is, the collision prediction ECU 11 uses the current relative distance Lnew to the front obstacle and the curve radius R2 where the front obstacle exists estimated in step S103 as a reference, based on the position and traveling direction of the host vehicle. The relative lateral position X is detected based on the forward obstacle presence direction (traveling direction).

Since the detected relative lateral position X is the relative lateral position X at the moment when the program is executed this time, it is the relative lateral position X when it is assumed that the host vehicle goes straight ahead and approaches the front obstacle. . However, when the host vehicle is traveling while curving with the curve radius R3, the host vehicle goes straight ahead and does not approach the front obstacle. For this reason, the collision prediction ECU 11 calculates a correction amount of Lnew ² / (2 × R3) using the detected relative lateral position X using the curve radius R3 and the current relative distance Lnew to the front obstacle. The relative lateral position X is corrected by the correction amount, and the relative lateral position Xr is calculated. When the relative lateral position Xr is calculated, the process proceeds to step S108.

If the current relative distance Lnew is greater than 50 m in step S102, the collision prediction ECU 11 determines “No”, and proceeds to step S107. In step S107, based on the curve radius R1 calculated in step S101, the correction amount is calculated as Lnew ² / (2 × R1) in the same manner as in step S106, and the relative lateral position X is corrected and the relative lateral position is corrected. The position Xr is calculated. When the relative lateral position Xr is calculated, the process proceeds to step S108.

  After the calculation process of step S106 or step S107, the collision prediction ECU 11 compares the predetermined distance ΔW with the relative lateral position Xr calculated in step S106 or step S107 in step S108, and calculates the relative lateral position. It is determined whether Xr is equal to or less than a predetermined distance ΔW. Here, the predetermined distance ΔW is determined as ½ of a preset area width (own lane) necessary for the host vehicle to travel without colliding with a front obstacle. That is, if the relative lateral position Xr is equal to or smaller than the predetermined distance ΔW, the collision prediction ECU 11 determines “Yes” because the front obstacle exists in the own lane, and proceeds to step S109. In step S109, the collision prediction ECU 11 recognizes the front obstacle as a collision object with a high possibility of colliding, and proceeds to step 111.

  On the other hand, if the relative lateral position Xr is larger than the predetermined distance ΔW, the front obstacle does not exist in the own lane, so the collision prediction ECU 11 determines “No” and proceeds to step S110. In step S110, the collision prediction ECU 11 recognizes that the front obstacle is unlikely to collide with the front obstacle, excludes it from the collision object, and proceeds to step S111. In step S111, the collision prediction ECU 11 finishes the execution of the obstacle confirmation routine on the curved road.

  Returning to the flowchart of FIG. 4 again, in step S15, the collision prediction ECU 11 determines whether or not a forward obstacle on the curve road has been recognized as a collision object. That is, the collision prediction ECU 11 determines “No” when the obstacle on the curved road is excluded from the collision target by executing the obstacle existence confirmation routine on the curved road in step S14, and proceeds to step S24. The program execution is temporarily terminated. Further, if the collision prediction ECU 11 recognizes the front obstacle as the collision target, it determines “Yes” and proceeds to step S16.

  In step S16, the collision prediction ECU 11 determines whether or not the absolute value | G | of the acceleration G output from the acceleration sensor 16 is greater than a preset acceleration Gs. By this determination, the collision prediction ECU 11 can determine whether or not the host vehicle is currently traveling on a slope. That is, if the absolute value | G | of the acceleration G is equal to or less than the preset acceleration Gs, the collision prediction ECU 11 determines “No” and proceeds to step S19 because the vehicle is not traveling on the slope.

  On the other hand, if the absolute value | G | of the acceleration G is larger than the preset acceleration Gs, the collision prediction ECU 11 determines “Yes” because the vehicle is traveling on the slope, and the process proceeds to step S17. In step S17, the collision prediction ECU 11 executes an obstacle existence confirmation routine on the hill. This obstacle confirmation routine on the hill may cause a collision detected when, for example, a vertical movement (vibration) associated with a step of the own vehicle occurs among the front obstacles detected by the radar sensor 15. This is a routine for accurately selecting a front obstacle having a high possibility of collision by excluding a low front obstacle (for example, a signboard or a bridge).

  As shown in FIG. 6, the slope obstacle confirmation routine is started in step S150, and in step S151, the slope value of the slope on which the host vehicle is currently traveling is calculated. More specifically, the collision prediction ECU 11 prepares a slope gradient value map in which slope slope values having a large value when the acceleration G is large are stored in association with the acceleration G. Then, the collision prediction ECU 11 calculates a slope gradient value corresponding to the acceleration G by referring to the slope slope value map, and proceeds to step 152.

  In step S152, the collision prediction ECU 11 uses the road gradient data of the navigation device 20 to check whether the slope gradient value calculated in step S151 is appropriate. That is, the collision prediction ECU 11 acquires the road gradient data of the road where the vehicle is currently present from the navigation device 20, and whether or not the acceleration G output from the acceleration sensor 16 is detected as the hill runs. Check if it is detected as a result of vertical movement caused by stepping or acceleration / deceleration.

  Here, an operation in which the collision prediction ECU 11 acquires road gradient data from the navigation device 20 will be described. The navigation ECU 21 of the navigation device 20 repeatedly detects the current location as the host vehicle travels, and identifies the road on which the host vehicle is present. Thereby, navigation ECU21 searches the memory | storage device 22, and acquires the road gradient data of the identified road. The navigation ECU 21 supplies the acquired road gradient data to the gateway computer 40 via the LAN 50. The gateway computer 40 supplies the supplied road gradient data to the collision prediction ECU 11 via the bus 30.

  When the collision prediction ECU 11 acquires the supplied road gradient data, the collision prediction ECU 11 compares the gradient value included in the road gradient data with the slope gradient value calculated in step S151, and the vehicle is present on the slope. Make sure that That is, the collision prediction ECU 11 confirms that the host vehicle exists on the slope when the calculated slope value is within a predetermined range as compared to the obtained slope value. If it is confirmed by this confirmation that the host vehicle is surely present on the slope, the collision prediction ECU 11 executes the subsequent processing using the calculated slope gradient value. At this time, if the calculated road gradient value is outside the predetermined range compared to the gradient value acquired from the navigation ECU 21 even though the host vehicle exists on the slope, the collision prediction ECU 11 calculates The slope slope value thus corrected is corrected so as to coincide with the obtained slope value.

  On the other hand, when the acceleration G is detected on the basis of the gradient value included in the road gradient data, the collision prediction ECU 11 determines that the vehicle is on the flat road. If it exists, the subsequent processing is executed. That is, when the collision prediction ECU 11 confirms that the host vehicle is clearly not on the slope based on the slope value included in the road slope data, the detected acceleration G is determined based on, for example, a step present on the road surface. The acceleration generated due to the vertical movement (vibration) caused by the passing of the host vehicle is recognized, and the host vehicle recognizes that the host vehicle exists on a flat road. Thereby, it can be confirmed accurately whether the own vehicle exists on the slope.

  After the confirmation processing in step S152, the collision prediction ECU 11 determines in step S153 whether or not a forward obstacle exists on the slope. More specifically, the collision prediction ECU 11 acquires the presence direction information output from the radar sensor 15 to identify the direction in which the front obstacle is present, and the current relative distance Lnew to the front obstacle. Compare road length data with road length.

  According to this comparison, if the current relative distance Lnew to the forward obstacle existing in the specified direction is smaller than the road length, the forward obstacle exists on the slope, so the collision prediction ECU 11 determines “Yes”. The process proceeds to step S154. In step S154, the collision prediction ECU 11 recognizes a front obstacle present on the slope as a collision object with a high possibility of collision, and proceeds to step S156.

  On the other hand, if the current relative distance Lnew to the forward obstacle existing in the specified direction is longer than the road length, the forward obstacle does not exist on the slope, so the collision prediction ECU 11 determines “No”, and the step The process proceeds to S155. In step S155, the collision prediction ECU 11 recognizes that the front obstacle is unlikely to collide, excludes it from the collision object, and proceeds to step S156. The forward obstacles excluded in this way are, for example, bridges and signboards provided above the road. In step S156, the collision prediction ECU 11 ends the execution of the on-hill obstacle presence confirmation routine.

  Returning to the flowchart of FIG. 4 again, in step S18, the collision prediction ECU 11 determines whether or not a forward obstacle on the slope is recognized as a collision target. That is, the collision prediction ECU 11 determines “No” and proceeds to step S24 if the forward obstacle is excluded from the collision object by executing the obstacle existence confirmation routine on the slope in step S17. The program execution is temporarily terminated. Further, if the collision prediction ECU 11 recognizes the front obstacle as the collision target, the collision prediction ECU 11 determines “Yes” and proceeds to step S19.

  In step S19, the collision prediction ECU 11 executes a collision avoidance time change routine. As shown in FIG. 7, the collision avoidance time changing routine is started in step S200, and in step S201, it is determined whether or not the host vehicle is passing through an iron bridge or a tunnel. More specifically, the collision prediction ECU 11 communicates with the navigation device 20 via the bus 30, the gateway computer 40, and the LAN 50. Then, the collision prediction ECU 11 acquires the road environment data of the road on which the host vehicle is currently traveling from the navigation ECU 21 of the navigation device 20. Here, in supplying the road environment data, the navigation ECU 21 detects the current location of the host vehicle, and searches the road environment data in the storage device 24 using the detected current location.

  The collision prediction ECU 11 determines “Yes” based on the acquired road environment data, and proceeds to step S <b> 203 if the host vehicle passes through an iron bridge or a tunnel. On the other hand, if the host vehicle does not pass through the iron bridge or tunnel, it is determined as “No”, and the process proceeds to step S202. In step S202, the collision prediction ECU 11 determines whether the host vehicle is waiting for a right turn at an intersection.

  More specifically, the collision prediction ECU 11 communicates with the navigation device 20 via the bus 30, the gateway computer 40, and the LAN 50. Then, the collision prediction ECU 11 checks with the navigation ECU 21 of the navigation device 20 whether or not the host vehicle is currently located at the intersection. Further, the collision prediction ECU 11 acquires the vehicle speed V output from the vehicle speed sensor 12 and confirms whether or not the host vehicle is currently stopped.

  Then, the collision prediction ECU 11 determines whether the host vehicle is waiting for a right turn at the intersection based on these confirmations. If the host vehicle is waiting for a right turn at the intersection, the collision prediction ECU 11 determines “Yes” and proceeds to step S203. In step S203, the collision prediction ECU 11 sets a collision avoidance time Tc representing a collision determination reference value, that is, a time necessary for avoiding a collision between the host vehicle and the front obstacle. This is because, when it is determined as “Yes” in the determination process of step S201 or step S202, a front obstacle with a low possibility of collision is detected, and the front obstacle is selected as a collision object. This is to make it difficult.

  That is, an inner wall of an iron bridge or a tunnel or a vehicle passing in front of the host vehicle while waiting for a right turn is always detected by the radar sensor 15 even though the possibility of collision is low. For this reason, there is a possibility that an occupant protection device 60 malfunctions due to an erroneous determination by a collision determination process in step S22 described later. For this reason, in step S203, the collision prediction ECU 11 sets the collision avoidance time Tc used for the collision prediction determination process to be long in order to prevent erroneous determination and malfunction, and executes the collision avoidance time change routine in step S206. Exit.

  On the other hand, if the vehicle is not waiting for a right turn at the intersection in step S202, the collision prediction ECU 11 determines “No” and proceeds to step S204. In step S204, it is determined whether or not the host vehicle is traveling on a highway. More specifically, the collision prediction ECU 11 communicates with the navigation device 20 via the bus 30, the gateway computer 40, and the LAN 50. Then, the collision prediction ECU 11 acquires the road type data on which the host vehicle is currently traveling from the navigation ECU 21 of the navigation device 20. Here, in supplying the road type data, the navigation ECU 21 detects the current location of the host vehicle, and searches the road type data in the storage device 24 using the detected current location.

  Based on the acquired road type data, the collision prediction ECU 11 determines “Yes” if the host vehicle is traveling on an expressway, and proceeds to step S205. In step S205, the collision prediction ECU 11 sets the collision avoidance time Tc to be short, and proceeds to step S206. This is because when the host vehicle is traveling on the highway by the determination process in step S204, the vehicle speed V is large, so the collision avoidance time Tc needs to be set short, and the front obstacle is set as the collision object. This is to facilitate selection.

  That is, since the vehicle speed V is high when traveling on a highway, it is necessary to detect a collision target early and appropriately execute a collision prediction determination process in step S22 described later. For this reason, the collision avoidance time Tc used for the collision prediction determination process is set short. On the other hand, if the own vehicle is not traveling on the highway based on the acquired road type data, the determination is “No”, the process proceeds to step S206, and the execution of the collision avoidance time change routine is terminated.

  Returning to the flowchart of FIG. 4 again, in step S20, the collision prediction ECU 11 executes an automatic fee settlement, that is, an ETC (Electronic Toll Collection) gate confirmation routine. This ETC gate confirmation routine confirms whether or not the forward obstacle detected by the execution of the above steps is an ETC stop instruction ETC gate bar. This is because when the front obstacle as the collision object is an ETC gate bar, the ETC gate bar automatically jumps up and avoids collision when the host vehicle passes through the ETC gate. This is because it needs to be excluded.

  This ETC gate confirmation routine is started in step S250, and in step S251, the collision prediction ECU 11 determines whether or not an ETC passing signal is received from the navigation device 20. This will be specifically described. The navigation ECU 21 of the navigation device 20 determines whether the host vehicle is currently on the ETC gate use lane by detecting the current location of the host vehicle. When the host vehicle is on the ETC gate use lane, an ETC passing signal indicating that the vehicle is passing through the ETC gate is output. The output ETC passing signal is supplied to the collision prediction ECU 11 via the LAN 50, the gateway computer 40, and the bus 30.

  If the collision prediction ECU 11 has not received the ETC passing signal, the collision prediction ECU 11 determines “No” and proceeds to step S254. On the other hand, if the ETC passing signal has been received, the collision prediction ECU 11 determines “Yes” and proceeds to step S252. In step S252, it is determined whether or not the front obstacle is in front of the host vehicle and is a stop within a predetermined distance (for example, 30 m) range. That is, it is determined whether or not the forward obstacle detected when the host vehicle passes through the ETC lane is an ETC gate bar.

  More specifically, when the host vehicle passes through the ETC gate, the ETC gate bar is positioned in front of the host vehicle in a closed state until the toll fee settlement process is completed. For this reason, the collision prediction ECU 11 confirms a forward obstacle based on the current relative distance Lnew, the current relative speed VRnew, and the vehicle speed V. That is, using the relative distance Lnew this time, it is confirmed whether the distance from the front end of the host vehicle to the front obstacle is within a predetermined distance range. Further, the collision prediction ECU 11 uses the current relative speed VRnew and the vehicle speed V to check whether the front obstacle is stopped.

  Then, when it is confirmed that the front obstacle is within a predetermined distance range from the front end of the host vehicle and is stopped, the collision prediction ECU 11 determines “Yes”, and proceeds to step S253. In step S253, the collision prediction ECU 11 determines that the front obstacle is an ETC gate bar. If it is confirmed that the front obstacle is not within the predetermined distance range from the front end of the host vehicle or has not stopped, the collision prediction ECU 11 determines “No” and proceeds to step S254. In step S254, the collision prediction ECU 11 determines that the front obstacle is not an ETC gate bar. After the processing of step S253 or step S254, the process proceeds to step S255, and the execution of the ETC gate confirmation routine is terminated in step S255.

  Returning to the flowchart of FIG. 4 again, in step S21, the collision prediction ECU 11 determines whether or not the front obstacle is determined as the ETC gate bar. That is, if the front obstacle is determined to be an ETC gate bar as a result of executing the ETC gate confirmation routine in step S20, the determination is “No”, the process proceeds to step S24, and the execution of the program is temporarily terminated. This is because the front obstacle is an ETC gate bar, so that a collision with the host vehicle is automatically avoided.

  On the other hand, if the front obstacle is not the ETC gate bar, the collision prediction ECU 11 determines “Yes” and proceeds to step S22. In step S22, the collision prediction ECU 11 determines a collision between the host vehicle and the detected front obstacle. That is, the collision prediction ECU 11 compares the distance calculated by multiplying the collision avoidance time Tc and the current relative speed VRnew and the current relative distance Lnew to determine whether or not the front obstacle collides with the host vehicle. To do.

  That is, the collision prediction ECU 11 multiplies the collision avoidance time Tc and the current relative speed VRnew to predict the travel distance of the host vehicle, and based on the comparison between the predicted travel distance and the current relative distance Lnew, Predict collisions with objects. If the current relative distance Lnew is greater than the predicted movement distance, the ECU 10 determines “No”, proceeds to step S24, and temporarily ends the execution of the program. On the other hand, if the current relative distance Lnew is smaller than the predicted movement distance, it is determined as “Yes” and the process proceeds to step S23. The collision prediction determination is not limited to the above-described collision prediction determination, and various collision prediction determination methods are conceivable. Therefore, it goes without saying that the collision prediction determination can be executed by employing another collision prediction determination method.

  In step S23, the collision prediction ECU 11 sets the collision determination flag FRG for instructing the operation of the occupant protection device 60 to “1” indicating the operation of the occupant protection device 60 and is set to “1”. The collision determination flag FRG is output to the bus 30. This is because it is necessary to operate the occupant protection device 60 in order to avoid a collision between the host vehicle and a front obstacle or to protect the occupant when the collision cannot be avoided.

  Thus, when the collision prediction ECU 11 outputs the collision determination flag FRG set to “1” to the bus 30, the occupant protection device 60 acquires the collision determination flag FRG. The occupant protection device 60 controls, for example, the ABS and traction control so as to control the traveling state of the vehicle to avoid a collision, and controls the operation of the pedal moving device, the operation of the shut-off circuit, and the like. , Operate each device to reduce the damage to the passengers due to collision. After the process of step S23, the ECU 10 proceeds to step S24 and temporarily ends the execution of the program.

  As can be understood from the above description, according to the first embodiment, the collision prediction ECU 11 of the collision prediction apparatus 10 accurately determines the presence state of the front obstacle, that is, the curve radius R2 where the front obstacle exists. Can be estimated. Further, it is possible to confirm and accurately correct the parameter representing the running state of the host vehicle, that is, the acceleration G detected by the acceleration sensor 16. This estimation and correction can be corrected based on various information supplied from the navigation device 20, that is, road shape data, road surface state data, and road gradient data.

  In addition, the collision prediction ECU 11 determines whether or not the collision as a collision determination reference value when the host vehicle is traveling on a road on which a stationary object (such as an inner wall of a tunnel) exists or starts traveling from a situation of waiting for a right turn at an intersection. The avoidance time Tc can be changed longer. In this way, by increasing the collision avoidance time Tc, it is possible to make it difficult to select a front object for collision that is highly likely to collide with a front obstacle in the collision prediction determination. Accordingly, a stationary object that continues to be detected as a front obstacle or a vehicle that crosses the front of the host vehicle when turning right can be excluded as a front obstacle that has a low possibility of colliding with the host vehicle. Therefore, it is possible to greatly reduce erroneous determinations in the prediction of collision with these front obstacles.

  The collision prediction ECU 11 can change the collision avoidance time Tc to be shorter when the host vehicle is traveling on the highway. In this way, by shortening the collision avoidance time Tc, it is possible to easily select a front object of collision that is highly likely to collide with a front obstacle in the collision prediction determination. As a result, when the host vehicle is traveling at a high speed on the highway, a front obstacle that may collide can be detected at an early stage, and the collision prediction determination can be appropriately executed. Therefore, based on the collision prediction determination, for example, the occupant protection device 60 can be operated at an appropriate timing.

  Further, the collision prediction ECU 11 can detect the ETC gate bar for instructing the vehicle stop existing in front of the host vehicle when the host vehicle passes the ETC gate. Since collision prediction is prohibited by determining the ETC gate bar, an erroneous determination that the ETC gate bar and the host vehicle collide can be prevented, and the accuracy of the collision prediction determination can be improved.

  In the above-described embodiment, the collision prediction ECU 11 uses the road data stored in advance in the storage device 24 of the navigation device 20 mounted on the host vehicle, and the presence of a front obstacle present on the curve road. Confirmation was made to confirm the slope value of the road where the host vehicle is located and the traveling environment of the host vehicle.

  On the other hand, the navigation device 20 communicates with the outside (for example, an information providing center or a traffic information providing center) to acquire the latest traffic information, and supplies the acquired latest traffic information to the collision prediction ECU 11. It is also possible to carry out. Hereinafter, although this 2nd Embodiment is described in detail, the same code | symbol is attached | subjected to the same part as the said 1st Embodiment, and the detailed description is abbreviate | omitted.

  As shown in FIG. 9, the navigation device 20 in the second embodiment includes a communication device 26 for enabling communication with the outside. The communication device 26 is connected to the navigation ECU 21 and communicates with the outside at a predetermined cycle to obtain the latest traffic information. For this reason, the communication device 26 includes an antenna 26a for wireless communication with the outside. Here, as the latest traffic information, for example, weather information and traffic jam information around the current location of the own vehicle, or accident information indicating that many accidents occur on the road on which the own vehicle is currently traveling, etc. is there. For communication with the outside of the communication device 26, for example, communication between vehicles (between vehicles) or communication between roads and vehicles (between roads and vehicles) can be employed.

  Then, the latest traffic information acquired by the communication device 26 is supplied to the navigation ECU 21. The navigation ECU 21 temporarily stores the supplied latest traffic information in a predetermined storage position in a RAM (not shown) and supplies the storage device 24 with the storage information. The storage device 24 stores the latest traffic information supplied, for example, in a predetermined storage location of the hard disk.

  Also in the second embodiment configured as described above, the collision prediction ECU 11 executes the collision prediction program shown in FIGS. 4 to 8 and executes an obstacle presence confirmation routine on the curve road in step S14. Then, the collision prediction ECU 11 acquires road shape data, road surface state data, and the latest traffic information from the navigation ECU 21 of the navigation device 20 in step S103 of the obstacle existence confirmation routine on the curved road shown in FIG. Thus, by acquiring the latest traffic information, the collision prediction ECU 11 calculates the curve radius R2 in step S103, calculates the curve radius R3 and the relative lateral position Xr from step S104 to step S106, and forwards in step S108. The object presence determination process can be executed more accurately by using, for example, weather information included in the latest information.

  As can be understood from the above description, according to the second embodiment, the collision prediction ECU 11 can correct the parameter representing the traveling state of the host vehicle based on the latest traffic information (for example, weather information). Therefore, the front obstacle can be recognized more accurately. Therefore, the possibility of collision between the front obstacle and the host vehicle can be predicted with higher accuracy.

  While the embodiments of the present invention have been described above, the present invention is not limited to the above-described embodiments and modifications thereof, and various modifications can be made without departing from the object of the present invention. is there.

  For example, in the first embodiment and the second embodiment, the collision prediction device 10 and the navigation device 20 are provided and implemented. However, in a vehicle that does not have the navigation device 20, the collision prediction device 10 performs navigation. It is also possible to add functions and form them integrally.

  This will be specifically described with reference to FIG. 10. A GPS receiver 22, a gyroscope 23, a storage device 24, and a communication device 26 of the navigation device 20 are connected to the collision prediction ECU 11 of the collision prediction device 10. Thus, the collision prediction apparatus 10 is configured. Thereby, the collision prediction ECU 11 can directly acquire the current location of the own vehicle, road data, latest information, and the like, and can execute the collision prediction program shown in FIGS. 4 to 8.

  Therefore, also in this case, the same effect as the first embodiment and the second embodiment can be obtained. Moreover, it is not necessary to secure a mounting space for mounting the navigation device 20 on the host vehicle, and the collision prediction system can be made compact.

  Moreover, in the said 1st Embodiment and 2nd Embodiment, when the collision prediction apparatus 10 acquires the ETC passing signal from the navigation apparatus 20, it determines whether the own vehicle is passing the ETC gate now. Was carried out as follows. However, it is also possible to implement the collision prediction apparatus 10 so as to acquire an ETC passing signal from an ETC terminal apparatus that is mounted on the host vehicle and automatically settles the toll. Hereinafter, this modification will be described in detail with reference to FIG. 11, but detailed operation of the ETC terminal device is not directly related to the present invention, and thus detailed description thereof is omitted.

  When the host vehicle approaches the ETC gate to a predetermined distance, the ETC terminal device 70 receives radio waves transmitted from the ETC main body computer provided in the ETC gate. Then, the ETC terminal device 70 transmits information necessary for the settlement of the toll to the ETC main body computer (for example, toll section information and settlement card balance information). The ETC terminal device 70 is connected to the bus 30 in order to supply an ETC passing signal to the collision prediction device 10.

  When the ETC terminal device 70 receives the radio wave transmitted from the ETC main body computer, the ETC terminal device 70 supplies an ETC passing signal to the collision prediction ECU 11 via the bus 30. The collision prediction ECU 11 acquires the ETC passing signal supplied from the ETC terminal device 70. Thereby, collision prediction ECU11 can determine whether the own vehicle is passing the ETC gate. Therefore, also in this modification, the same effect as the first embodiment and the second embodiment can be obtained.

It is the schematic which shows the whole collision prediction system which concerns on 1st Embodiment of this invention. It is a block diagram which shows roughly the collision prediction apparatus of FIG. FIG. 2 is a block diagram schematically showing the navigation device of FIG. 1. It is a flowchart of the collision prediction program performed by collision prediction ECU (microcomputer) of FIG. It is a flowchart of the obstacle presence confirmation routine on a curve road performed by collision prediction ECU (microcomputer) of FIG. FIG. 3 is a flowchart of a slope obstacle confirmation routine executed by a collision prediction ECU (microcomputer) in FIG. 1. FIG. It is a flowchart of the collision avoidance time change routine performed by collision prediction ECU (microcomputer) of FIG. 3 is a flowchart of an ECT gate confirmation routine executed by a collision prediction ECU (microcomputer) in FIG. 1. It is the block diagram which showed roughly the navigation apparatus which concerns on 2nd Embodiment of this invention. It is the block diagram which showed roughly the collision prediction apparatus which concerns on the modification of 1st Embodiment of this invention, and 2nd Embodiment. It is the block diagram which showed roughly the collision prediction system which concerns on the modification of 1st Embodiment of this invention, and 2nd Embodiment.

Explanation of symbols

DESCRIPTION OF SYMBOLS 10 ... Collision prediction apparatus, 11 ... Collision prediction ECU, 12 ... Vehicle speed sensor, 13 ... Steering angle sensor, 14 ... Yaw rate sensor, 15 ... Radar sensor, 16 ... Acceleration sensor, 17 ... Bus interface, 20 ... Navigation device, 21 ... Navigation ECU, 22 ... GPS receiver, 23 ... Gyroscope, 24 ... Storage device, 25 ... LAN interface, 30 ... Bus, 40 ... Gateway computer, 50 ... LAN, 60 ... Occupant protection device, 70 ... ETC terminal device

Claims (6)

A front obstacle detection means for detecting a front obstacle present on the course of the host vehicle is provided, and it is predicted whether or not the detected front obstacle and the host vehicle collide with the traveling of the host vehicle. In the collision prediction device,
Driving environment information acquisition means for acquiring driving environment information related to the driving environment of the host vehicle;
Parameter value detecting means for detecting a parameter value representing the running state of the host vehicle;
The parameter value detected by the parameter value detecting means is compared with a predetermined collision determination reference value used for predicting and determining a collision between the front obstacle and the host vehicle, and the parameter value is determined as the predetermined collision determination. A collision prediction determination means for predicting a collision between the front obstacle and the host vehicle when a reference value is satisfied;
A collision prediction apparatus comprising: a changing unit that changes the predetermined collision determination reference value based on the driving environment information acquired by the driving environment information acquiring unit. The travel environment information is
Travel environment information representing a travel environment in which the front obstacle detection means detects a continuous stationary object present around the host vehicle as the host vehicle travels,
2. The collision prediction according to claim 1, wherein the changing unit changes the predetermined collision determination reference value to a collision determination reference value that makes it difficult to select the front obstacle as a collision target according to the traveling environment information. apparatus. The travel environment information is
It is the driving environment information representing the driving environment where the vehicle starts making a right turn at an intersection.
2. The collision prediction according to claim 1, wherein the changing unit changes the predetermined collision determination reference value to a collision determination reference value that makes it difficult to select the front obstacle as a collision target according to the traveling environment information. apparatus. The travel environment information is
Travel environment information representing the travel environment in which the vehicle travels on a highway,
2. The collision prediction according to claim 1, wherein the changing unit changes the predetermined collision determination reference value to a collision determination reference value that makes it easier to select the front obstacle as a collision target according to the traveling environment information. apparatus. The travel environment information is
The collision prediction device according to any one of claims 1 to 4, wherein the traveling environment information acquisition unit acquires from a navigation device mounted on the host vehicle. The travel environment information is
The collision prediction apparatus according to claim 4, wherein the traveling environment information acquisition unit acquires from an ETC terminal device mounted on the host vehicle.

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