JP2017206117A - Drive support apparatus - Google Patents

Abstract

PROBLEM TO BE SOLVED: To execute a drive support with a potential risk considered.SOLUTION: A drive support apparatus (20) includes: obstacle detection means (21) for detecting an obstacle on a road ahead of an own vehicle (1); potential risk estimation means (22) for estimating a potential risk that is a mobile body assumed to exist in a blind spot of the obstacle; risk information obtainment means (23) for obtaining risk information indicating plural risk degrees that are values based on respective speeds of collision between the own vehicle and potential risks, and that are respectively correlated with different plural virtual positions and speeds of the own vehicle; and locus setup means (24) for setting a vehicular locus of the own vehicle on the basis of the risk of collision between the potential risk and the own vehicle on the road ahead estimated from the risk information.SELECTED DRAWING: Figure 1

Description

  The present invention relates to a technical field of a driving support device that supports a driver of a vehicle such as an automobile.

  As this type of device, for example, the obstacle generated by generating a plurality of traveling tracks for moving from the start position to the end position of the own vehicle and taking into account the relative speed between the own vehicle and the surrounding obstacles. A plurality of generated traveling trajectories having a minimum sum of parameters indicating the smoothness of vehicle behavior obtained in each of the front-rear direction and the left-right direction of the host vehicle is low. Has been proposed (see Patent Document 1).

JP2015-058890A

  However, the technique described in Patent Document 1 does not take into account risks that are not apparent (that is, potential risks) such as pedestrians jumping out from blind spots of obstacles, and there is room for improvement.

  This invention is made | formed in view of the said fact, and makes it a subject to provide the driving assistance device which can perform the assistance which considered the potential risk.

  In order to solve the above-described problem, the driving support device of the present invention includes an obstacle detection unit that detects an obstacle on a road ahead of the host vehicle, and a virtual moving body that is assumed to exist in the blind spot of the obstacle. The potential risk estimating means for estimating a certain potential risk is associated with each of a plurality of different virtual positions and speeds of the own vehicle, and is a value based on the collision speed between the own vehicle and the potential risk. A risk information acquisition means for acquiring risk information indicating a plurality of risk degrees, a collision risk between the host vehicle and the potential risk on the front road estimated from the position and speed of the host vehicle and the risk information And a track setting means for setting the vehicle track of the host vehicle.

  According to the driving support device of the present invention, since the vehicle trajectory of the host vehicle is set based on the collision risk on the road ahead, for example, the potential risk of a pedestrian or the like jumping forward from the blind spot of the obstacle The vehicle trajectory can be set in consideration of the above. Therefore, according to the driving support device, it is possible to perform support in consideration of potential risks.

  The “obstacle” is not limited to an obstacle that obstructs the traveling of the host vehicle such as a parked vehicle that exists on the lane in which the host vehicle travels, for example, a vehicle traveling in a lane different from the lane in which the host vehicle travels It is also a concept that includes objects that obstruct the driver's field of view, such as fences, block fences, and roadside trees. In other words, “obstacle” means an object that creates a blind spot. The “blind spot of an obstacle” means a blind spot viewed from the own vehicle (for example, a blind spot viewed from the driver of the own vehicle, a blind spot for sensors used for obstacle detection, etc.).

  The “virtual position and speed of the host vehicle” means an arbitrary position and speed determined independently of the actual position and speed of the host vehicle. The “risk degree” may be the collision speed itself, for example, a value obtained by squaring the collision speed (that is, equivalent to the collision energy), a parameter calculated from the collision speed, or the like. In addition, when the own vehicle and the potential risk do not collide, the risk degree is “0”.

  The “position and speed of the host vehicle” is a concept including not only the current position and current speed of the host vehicle but also the future position and future speed of the host vehicle.

  In one aspect of the driving support apparatus of the present invention, the moving body is a moving body that enters forward of the host vehicle. According to this aspect, it is possible to provide support in consideration of a realistic potential risk that is likely to actually collide with the host vehicle.

  In another aspect of the driving support apparatus of the present invention, the track setting means includes a value based on the front and rear jerk of the own vehicle, a value based on the lateral jerk of the own vehicle, and a value based on the longitudinal acceleration of the own vehicle and The vehicle behavior of the host vehicle on the road ahead is estimated from at least one of the values based on the lateral acceleration of the host vehicle, and the vehicle trajectory is determined based on the estimated vehicle behavior in addition to the collision risk. Set.

  According to this aspect, it is possible to set a vehicle trajectory that achieves both avoidance of potential risks and vehicle behavior. “At least one of a value based on front and back jerk and a value based on lateral jerk, and a value based on longitudinal acceleration and a value based on lateral acceleration” means “value based on front and back jerk and value based on lateral jerk”, “front and back “Value based on acceleration and value based on lateral acceleration” or “value based on longitudinal jerk, value based on lateral jerk, value based on longitudinal acceleration and value based on lateral acceleration”.

  The “value based on the front and rear jerk” may be the front and rear jerk itself, or may be a value obtained by squaring the front and rear jerk, a physical quantity or parameter calculated from the front and rear jerk, or the like. The “value based on the horizontal jerk” may be the horizontal jerk itself, for example, a value obtained by squaring the horizontal jerk, or a physical quantity or parameter calculated from the horizontal jerk. The “value based on the longitudinal acceleration” may be the longitudinal acceleration itself, for example, a value obtained by squaring the longitudinal acceleration, or a physical quantity or parameter calculated from the longitudinal acceleration. The “value based on the lateral acceleration” may be the lateral acceleration itself, for example, a value obtained by squaring the lateral acceleration, or a physical quantity or parameter calculated from the lateral acceleration.

  In this aspect, the track setting means may set a vehicle track that minimizes a cost function including a risk component indicating the collision risk and a vehicle behavior component indicating the vehicle behavior as the vehicle track. With this configuration, it is possible to set an optimal vehicle trajectory relatively easily.

  In an aspect using a cost function, each of the risk component and the vehicle behavior component is given a weight, and the track setting means may change the weight based on the road structure of the front road.

  For example, on a main road having a sidewalk and a plurality of lanes, it is considered that there are few pedestrians or the like jumping out ahead of the host vehicle. For this reason, on the main road, the risk component may be relatively smaller than the vehicle behavior component, assuming that the potential risk is relatively small. Alternatively, it is considered that there is a relatively high possibility that there is a pedestrian or the like jumping out ahead of the host vehicle on a relatively narrow road having no sidewalk and only a roadside belt. For this reason, on a relatively narrow road, the potential risk may be relatively large, and the weight of the risk component may be set larger than the weight of the vehicle behavior component. If comprised in this way, the suitable vehicle track | orbit according to the road where the own vehicle is drive | working can be set.

  In another aspect of the driving assistance apparatus of the present invention, the risk information changes according to a road surface state of the front road. According to this aspect, for example, even when it is raining or snowing, an appropriate vehicle trajectory can be set based on the risk degree distribution.

  The effect | action and other gain of this invention are clarified from the form for implementing demonstrated below.

It is a block diagram which shows the structure of the vehicle which concerns on embodiment. It is a conceptual diagram which shows the calculation concept of a risk degree. It is a flowchart which shows the calculation process of a risk field. It is a figure which shows an example of a risk field. It is a figure which shows an example of the speed area | region and orbital area | region which can be produced | generated with an acceleration model. It is a figure which shows an example of each set speed pattern and vehicle track.

  An embodiment according to a driving assistance apparatus of the present invention will be described with reference to FIGS. 1 to 6. Below, after explaining the outline | summary of the vehicle by which a driving assistance device is mounted, and a driving assistance device, a risk field and a vehicle track are explained in full detail.

(Vehicle configuration)
A configuration of a vehicle on which the driving support apparatus according to the embodiment is mounted will be described with reference to FIG. FIG. 1 is a block diagram illustrating a configuration of a vehicle according to the embodiment.

  In FIG. 1, a vehicle 1 includes a camera 11, a radar 12, a GPS (Global Positioning System) 13, a sensor 14, an ECU (Electronic Control Unit) 20, various actuators 30, and a notification unit 40. Yes.

  The sensor 14 includes, for example, a vehicle speed sensor, an acceleration sensor, a yaw rate sensor, a steering angle sensor, an accelerator pedal sensor, a brake pedal sensor, and the like. The various actuators 30 include, for example, a brake actuator, a throttle actuator, a steering actuator, an accelerator pedal actuator, a brake pedal actuator, and the like.

  The camera 11 is configured to be able to capture an image around the vehicle 1. The camera 11 may be a single camera, or may be two or more cameras such as a stereo camera in which two cameras are arranged in a horizontal direction separated by a predetermined distance. The radar 12 may be, for example, a millimeter wave radar or an infrared laser radar.

  The ECU 20 as an example of the driving support apparatus according to the present embodiment includes an obstacle detection unit 21, a potential risk estimation unit 22, a risk field acquisition as a logical processing block or a physical processing circuit realized therein. A unit 23, a vehicle track setting unit 24, and a travel support unit 25 are provided.

  The obstacle detection unit 21 detects an obstacle existing on the road ahead of the vehicle 1 based on a captured image by the camera 11, a detection result by the radar 12, and the like (specifically, for example, the position and type of the obstacle, Detect size etc.). The obstacle detection unit 21 detects, for example, an object existing around the vehicle 1 by the camera 11 and the radar 12, and creates a three-dimensional digital map by modeling the detected object into a three-dimensional shape. Obstacles may be detected. In addition, since the existing technique can be applied to the obstacle detection method, a detailed description thereof will be omitted.

  The latent risk estimation unit 22 estimates a potential risk that is a virtual moving body that is assumed to exist in the blind spot of the obstacle detected by the obstacle detection unit 21. Specifically, for example, the potential risk estimation unit 22 calculates the relative position of the virtual moving body as the potential risk with respect to the obstacle, and the approach speed at which the virtual moving body enters the road ahead of the vehicle 1. presume. That is, in the present embodiment, the “potential risk” is assumed to be a virtual moving body that exists in the blind spot of the obstacle and is assumed to enter the front of the vehicle 1. Examples of the “moving body” include pedestrians, bicycles, and other vehicles.

  For example, which of the pedestrian, the bicycle, the other vehicle, and the like is set as the potential risk may be estimated based on the road structure around the vehicle 1. Specifically, for example, when there is a relatively narrow side road that intersects the road on which the vehicle 1 is traveling in front of the vehicle 1, the potential risk estimation unit 22 is a virtual bicycle that enters in front of the vehicle 1. May be estimated as a potential risk. Alternatively, when there is an entrance / exit of a parking lot of a commercial facility in front of the vehicle 1, the potential risk estimation unit 22 may estimate a virtual other vehicle entering the front of the vehicle 1 as a potential risk.

  The risk field acquisition unit 23 acquires a risk field as an example of “risk information” according to the present invention. Details of the risk field will be described later. The risk field acquisition unit 23 calculates a risk degree that is a value based on a collision speed at a virtual collision point where the vehicle 1 and the potential risk are expected to collide with each other on the road ahead of the vehicle 1 while the vehicle 1 is traveling. May be acquired by generating a risk field, or may be acquired by selecting one risk field from a plurality of risk fields generated in advance and stored in a memory (not shown) of the ECU 20. .

  The vehicle trajectory setting unit 24 is, for example, a vehicle on the road ahead of the vehicle 1 estimated from the position of the vehicle 1 based on the output of the GPS 13, the speed of the vehicle 1 based on the output of the vehicle speed sensor included in the sensor 14, and the risk field. Based on the collision risk between 1 and the potential risk, a vehicle trajectory that can avoid the obstacle detected by the obstacle detection unit 21 is set while considering the potential risk. Details of the vehicle trajectory setting will be described later.

  The driving support unit 25 performs various types of support control by controlling the various actuators 30 so that the actual track of the vehicle 1 approaches the vehicle track set by the vehicle track setting unit 24. Or the driving | running | working assistance part 25 is alert | reported by the image display alerting | reporting by a display, or audio | voice alerting | reporting by a speaker or a buzzer so that the driver | operator of the vehicle 1 may drive the vehicle 1 along the said set vehicle track | orbit. The unit 40 is controlled. In addition, since the existing technique is applicable to support control and control of the alerting | reporting part 40, the description about the detail is omitted.

  The driving support unit 25 further controls the brake actuator included in the various actuators 30 based on the image taken by the camera 11 and the detection result by the radar 12 to decelerate and stop the vehicle 1 so-called automatic emergency. It has a brake (Autonomous Emergency Breaking: AEB) function.

(Risk field)
The risk field will be described with reference to FIGS. Here, as a potential risk, a virtual pedestrian who has the highest possibility of entering (or jumping out) from the blind spot of an obstacle to the front of the vehicle 1 is taken as an example. FIG. 2 is a conceptual diagram showing a concept of calculating the risk degree. FIG. 3 is a flowchart showing risk field calculation processing. FIG. 4 is an example of a risk field.

  As shown in FIG. 2, a pedestrian as a potential risk is 1.5 m / s along the approach path from a position 1.5 m (meters) away from an obstacle (here, a parked vehicle) in the x-axis direction. It is assumed that the vehicle enters the front of the vehicle 1 (for example, jumps out).

When the vehicle 1 arrives at a certain point (in FIG. 2, a point where the position in the x-axis direction is “x _i ” and the position in the y-axis direction is “y _i ”), the driver enters the blind spot of the parked vehicle. Assuming that an existing pedestrian notices without time delay, the position where the driver's line of sight and the pedestrian's approach path intersect is set as the initial position in the y-axis direction of the pedestrian. At this time, the distance between the vehicle 1 and the parked vehicle in the y-axis direction is “d _lat ”, and the distance in the x-axis direction from the front end of the vehicle 1 to the pedestrian approach path is “d _lon ” ( (See FIG. 2).

Under such conditions, it is first determined whether or not the vehicle 1 and the pedestrian collide, assuming that the vehicle 1 continues to move at a constant speed at the speed v _i . When it is determined that the vehicle 1 and the pedestrian do not collide (specifically, when the vehicle 1 passes before the pedestrian enters the course of the vehicle 1, or when the vehicle 1 enters the approach path of FIG. 2) Risk level is set to “0”.

  If it is determined that the vehicle 1 and the pedestrian collide, then it is determined whether or not the vehicle 1 and the pedestrian collide, assuming that the vehicle 1 decelerates. Specifically, whether the vehicle 1 and the pedestrian collide with a delay time from when the vehicle 1 reaches a certain point as described above until the start of deceleration is τ seconds and the average deceleration is aG (gravity acceleration). It is determined whether or not.

  The delay time corresponds to a delay due to “recognition / judgment / operation (ie, depression of a brake pedal)” in deceleration by the driver, and corresponds to a system delay in deceleration by AEB. When there is a possibility of both deceleration by the driver and deceleration by AEB, the larger value of the delay due to “recognition / judgment / operation” and the system delay may be used as the delay time.

  When it is determined that the vehicle 1 and the pedestrian do not collide due to deceleration of the vehicle 1 (specifically, when the vehicle 1 stops before reaching the approach path of FIG. 2, or the vehicle 1 of FIG. 2 When the pedestrian passes before reaching the approach path), the risk degree is set to “0”. On the other hand, when it is determined that the vehicle 1 and the pedestrian collide, for example, the magnitude of the collision speed is set as the risk degree.

The degree of risk obtained as a result of this series of processes is set as the degree of risk for the state of the vehicle 1 at the certain point described above (that is, x _i , y _i and v _i ). A risk field that is a distribution of the degree of risk is calculated by repeatedly executing the above-described processing while changing the state of the vehicle 1. Here, “the state (x _i , y _i , v _i ) of the vehicle 1” is not limited to the actual state of the vehicle 1, but is an arbitrary state determined independently of the actual state of the vehicle 1 (that is, Virtual state).

  The risk field calculation process described above will be described with reference to the flowchart of FIG. The flowchart in FIG. 3 is an example of a calculation process based on the assumption that the vehicle 1 is decelerated by AEB. However, the driver may be assumed to decelerate the vehicle 1.

  As described above, the risk field may be calculated in advance or may be calculated while the vehicle 1 is traveling. When the risk field is calculated in advance, it is not necessarily calculated by the risk field acquisition means 23. Therefore, in the description of FIG. 3, the description of the processing subject is omitted in principle.

  In FIG. 3, first, a delay time and an average deceleration when the vehicle 1 decelerates are set (step S101). Here, the delay time and average deceleration due to the AEB performance are set. When the risk field is calculated in advance, the delay time and the average deceleration may be set based on, for example, a catalog value of the AEB scheduled to be mounted on the vehicle 1.

  Next, the state of the potential risk (here, pedestrian), specifically, the position of the potential risk and the approach speed (in FIG. 2, 1.5 m from the parked vehicle in the x-axis direction, 1.5 m per second), It is set (step S102). When the risk field is calculated while the vehicle 1 is traveling, the value estimated by the potential risk estimation unit 22 is used. On the other hand, when the risk field is calculated in advance, the position and size of the obstacle set in advance (or assumed) and the type and state of the potential risk are used.

Next, the state of the vehicle 1 (that is, x _i , y _i and v _i ) is acquired and set (step S103). Here, “acquiring the state of the vehicle 1” is not limited to actually detecting the state of the vehicle 1, but acquiring one data set from a data set indicating the state of the vehicle 1 configured in advance. It is a concept that includes.

  Next, it is determined whether or not the vehicle 1 collides with the pedestrian assuming that the vehicle 1 continues to move at a constant speed (step S104). In this determination, when it is determined that the vehicle 1 and the pedestrian do not collide (step S104: No), the risk degree is set to “0” (non-collision state) (step S105).

  On the other hand, if it is determined in step S104 that the vehicle 1 and the pedestrian collide (step S104: Yes), whether or not the vehicle 1 and the pedestrian collide assuming that the vehicle 1 is decelerated by AEB. Is determined (step S106). In this determination, when it is determined that the vehicle 1 and the pedestrian do not collide (step S106: No), the risk degree is set to “0” (stoppable state) (step S107).

  On the other hand, if it is determined in step S106 that the vehicle 1 and the pedestrian collide with each other (step S106: Yes), since the collision occurs, for example, the magnitude of the collision speed is set as the risk degree (step S108). ).

  After the process of step S105, S107, or S108, it is determined whether or not the calculation of the risk level is completed in all the states of the vehicle 1 for which the risk level is to be calculated (step S109). In this determination, when it is determined that the calculation of the risk degree has not ended (step S109: No), a new state of the vehicle 1 is acquired and set again in the process of step S103, and the processes after step S104 are performed. To be implemented. On the other hand, when it is determined that the calculation of the risk degree has been completed (step S109: Yes), the calculation process is ended.

  As shown in FIG. 4, for example, the risk field is expressed as a risk degree distribution in a coordinate space having the coordinate axis of the vehicle 1 speed v, the longitudinal position x of the vehicle 1 in the front-rear direction, and the lateral position y of the vehicle 1 in the lateral direction. Is done. In FIG. 4, black squares on the bottom surface of the coordinate space indicate obstacles, and each black dot indicates a state of the vehicle 1 that is determined to be a collision occurrence state.

  Although not clearly shown in FIG. 4, each black dot has a risk degree value (in this case, a collision speed). A region without a black spot is a region having a risk degree of “0”. In the risk field, it can be said that the degree of risk and the state of the vehicle 1 (that is, the speed v, the vertical position x, and the horizontal position y of the vehicle 1) are associated with each other.

  In the present embodiment, the case where the pedestrian is at a potential risk has been described as an example, but the risk field when the bicycle or other vehicle is at a potential risk can also be obtained by the calculation process described above. Further, by changing the average deceleration when the vehicle 1 decelerates, for example, a risk field at the time of raining or snowing can be obtained. Alternatively, by changing the delay time when the vehicle 1 decelerates, it is possible to obtain a risk field at the time of poor visibility such as when fog is generated.

  In the present embodiment, the degree of risk when the vehicle 1 and the potential risk collide is the magnitude of the collision speed. For example, the value obtained by squaring the collision speed or a parameter calculated from the collision speed is the risk degree. May be.

  When the risk field is calculated in advance, a plurality of risk fields are calculated by variously changing the position and size of the obstacle, the potential risk, the delay time when the vehicle 1 decelerates and the average deceleration, etc. It is desirable. In this case, the risk field acquisition unit 23 determines the conditions such as the position and size of the obstacle detected by the obstacle detection unit 21, the potential risk estimated by the potential risk estimation unit 22, and the speed and position of the vehicle 1. The risk field to be satisfied may be selected from a plurality of risk fields.

(Vehicle trajectory setting)
Next, the setting of the vehicle track by the vehicle track setting unit 24 will be described with reference to FIGS. 5 and 6. FIG. 5 is a diagram illustrating an example of a velocity region and a trajectory region that can be generated by the acceleration model. FIG. 6 is a diagram illustrating an example of each set speed pattern and vehicle trajectory.

  The vehicle trajectory setting unit 24 includes the risk field acquired by the risk field acquisition unit 23, the current position of the vehicle 1 based on the output of the GPS 13, the current speed of the vehicle 1 based on the output of the vehicle speed sensor included in the sensor 14, From the following evaluation function (for example, cost function), a set of the position and speed of the vehicle 1 that minimizes the evaluation function is set as the vehicle track of the vehicle 1.

The current position and current speed of the vehicle 1 correspond to (x ₀ , y ₀ , v ₀ ) in the evaluation function, that is, the initial value. W ₁ and w ₂ in the evaluation function are weights. In this “vehicle trajectory setting”, the outline of the vehicle trajectory setting will be described without referring to the weights w ₁ and w ₂ (further, the weight w ₃ described later). The weight will be described in “Significance of weight” described later.

In the above evaluation function, the value of the risk component "Risk" about the state in which a vehicle _{1 (x i, y i,} v i) is in the risk field, corresponding to the state _{(x i, y i, v} i) Equal to the risk level of the condition. The sum total of risk components “Risk” corresponds to an example of “collision risk” according to the present invention.

  In particular, the evaluation function includes a vehicle behavior component “Motion” in addition to the risk component “Risk”. For this reason, the vehicle trajectory obtained by minimizing the evaluation function not only can avoid an obstacle or a collision between the potential risk and the vehicle 1, but also can realize a relatively good vehicle behavior. .

  The vehicle trajectory setting unit 24 according to the present embodiment does not directly minimize the evaluation function, but generates a plurality of trajectory candidates from the acceleration model and selects an optimal vehicle trajectory from the plurality of trajectory candidates. To indirectly minimize the evaluation function.

Here, in order to introduce the acceleration model, it is necessary to set the operation cycle T in advance. In the present embodiment, the distance from the front end of the vehicle 1 to the entry position of the potential risk (the approach path in FIG. 2) is “dist”, the current speed of the vehicle 1 is “v _x (0)”, and the longitudinal acceleration of the vehicle 1 The operation cycle T is defined as follows, assuming that the amplitude of (ie, longitudinal acceleration) is “A _x ”.

Further, assuming that the amplitude of the lateral acceleration of the vehicle 1 is “A _y ”, the temporal changes of the longitudinal acceleration and the lateral acceleration are defined as follows.

Since the longitudinal acceleration and the lateral acceleration are defined, the jerk (the jerk), the speed, and the position of the vehicle 1 can be expressed as follows.

When the acceleration model is used, the evaluation function can be expressed as follows (hereinafter, the following expression is referred to as “modified evaluation function” as appropriate). The vehicle track setting unit 24 indirectly minimizes the evaluation function by minimizing the modified evaluation function.

Here, the vehicle behavior component “Motion” in the evaluation function is replaced with a combination of the longitudinal jerk component “j _x (i · Δt) ² ” and the lateral jerk component “j _y (i · Δt) ² ”. Please pay attention to. That is, “w ₂ · Motion (x _i , y _i , v _i )” = “w ₂ · j _x (i · Δt) ² + w ₃ · j _y (i · Δt) ² ” = “w ₂ · {j _x (i · Δt) ² + r ₁ · j _y (i · Δt) ² } ". Note that “w ₃ = w ₂ × r ₁ ” (where “r ₁ ” is an arbitrary real number).

The magnitude of the longitudinal jerk component changes mainly due to acceleration or deceleration of the vehicle 1. The magnitude of the lateral jerk component changes mainly due to the turning of the vehicle 1. For example, a relatively sudden deceleration of the vehicle 1 results in an increase in the vertical jerk component, and a relatively rapid steering of the vehicle 1 mainly results in an increase in the lateral jerk component. Therefore, in order to minimize the modified evaluation function, relatively slow acceleration / deceleration and turning of the vehicle 1 are required.

First, the vehicle trajectory setting unit 24 changes the values of the amplitudes “A _x ” and “A _y ” in various ways to generate a plurality of trajectory candidates. As described above, when the amplitudes “A _x ” and “A _y ” are determined, the operation period, the vertical acceleration, the lateral acceleration, the vertical jerk, the horizontal jerk, the vertical speed, the horizontal speed, the vertical position, and the horizontal position are automatically determined. Is done. For this reason, when the values of the amplitudes “A _x ” and “A _y ” are changed, trajectory candidates (and velocity pattern candidates) are generated.

By introducing the acceleration model, for example, as shown in FIG. 5, the speed region and the trajectory region that the vehicle 1 can take are limited (see the shaded portion in FIG. 5). That is, the values that the amplitudes “A _x ” and “A _y ” can take are limited. Therefore, a plurality of velocity pattern candidates and a plurality of trajectory candidates are included in the velocity region and the trajectory region.

Vehicle trajectory setting unit 24, then, a plurality of amplitude variously changed _{"A x"} and _{"A y"} from a combination of amplitude to minimize the modified evaluation function _{"A x"} and _"A y" Select a combination. The vehicle trajectory setting unit 24 then selects the combination of the selected amplitudes “A _x ” and “A _y ” (that is, the amplitude “A _x with a hat” and “A with a hat” that minimizes the modified evaluation function). _y ") is used to find the optimum trajectory and further the optimum velocity pattern. This series of processing is synonymous with the vehicle trajectory setting unit 24 obtaining an optimal trajectory and an optimal speed pattern from a plurality of trajectory candidates and a plurality of speed pattern candidates.

Here, the value of the risk component “Risk” has a plurality of sampling points (N in this case) from the vertical velocity, the vertical position, and the horizontal position that are naturally determined when the amplitudes “A _x ” and “A _y ” are determined. The state of the vehicle 1 at each sampling point) (ie, x ₀ , y ₀ , v _{0 to} x _N , y _N , v _N ) is obtained, and the state of the plurality of vehicles 1 and the risk field acquisition unit 23 It is calculated from the acquired risk field.

  An example of the optimal trajectory and the optimal speed pattern is shown in FIG. The optimum speed pattern and the optimum trajectory shown in FIG. 6 are respectively expressed as follows.

As described above, in order to minimize the modified evaluation function, relatively slow acceleration / deceleration and turning of the vehicle 1 are required. For this reason, the traveling of the vehicle 1 according to the optimal speed pattern and the optimal trajectory pattern obtained by the vehicle trajectory setting unit 24 does not give a sense of incongruity to the passenger such as the driver of the vehicle 1 or suppresses the sense of incongruity of the passenger. Can do.

(Significance of weight)
First, the significance of the weights w ₁ and w ₂ of the evaluation function in [Equation 1] will be described. In the calculation of the risk field according to the present embodiment, it is assumed that a potential risk (pedestrian in FIG. 2) always enters the front of the vehicle 1 (for example, jumps out). However, in reality, the possibility that a potential risk enters the front of the vehicle 1 varies depending on the road structure such as the presence or absence of a guardrail. Specifically, on a road with a guardrail at the boundary between the sidewalk and the roadway, it is considered that fewer pedestrians enter the front of the vehicle 1 from the blind spot of the obstacle than a road with only a roadside belt. .

If the weights of the risk component “Risk” and the vehicle behavior component “Motion” are uniform on all roads (ie, w ₁ and w ₂ are always 1), for example, there is a guardrail, When the vehicle 1 is traveling on a road where the possibility that a pedestrian enters the front of the vehicle 1 from the blind spot, the risk component may be overestimated. Therefore, if the weights w ₁ and w ₂ are variable values, overestimation of risk components can be prevented.

The vehicle trajectory setting unit 24 obtains information (for example, the number of lanes and pedestrian separation status) related to the structure of the road on which the vehicle 1 is currently traveling from, for example, the output of the GPS 13 or an existing navigation device (not shown). and, based on the obtained information, to increase the one weight of weights w ₁ and changing at least one of w ₂ (w ₁ and w _2, reduce the other weights w ₁ and w ₂ It ’s synonymous with Specifically, for example, the vehicle track setting unit 24 divides the initial value of the weight w _{1 by} the number of lanes based on the number of lanes on the road on which the vehicle 1 is currently traveling, or sets the initial value of the weight w _2. Multiply the number of lanes.

Next, the significance of the weights w ₂ and w ₃ of the modified evaluation function in [Equation 5] will be described. As described above, “w ₂ · Motion (x _i , y _i , v _i )” = “w ₂ · {j _x (i · Δt) ² + r ₁ · j _y (i · Δt) ² }” weight w ₂ is a common value for the longitudinal jerk component and the lateral jerk components. Here, the significance of changing the value r ₁ will be described as the significance of the weights w ₂ and w ₃ .

  As a method for avoiding a collision between the vehicle 1 and the potential risk, there are a method of decelerating the vehicle 1 and a method of turning the vehicle 1 so as to swell relatively large on the side opposite to the side having the potential risk. . The deceleration of the vehicle 1 causes a change in the vertical jerk, and the turning of the vehicle 1 mainly causes a change in the lateral jerk.

In other words, by changing the value r _1, deceleration (i.e., velocity) in preference to any and steering, it is possible to determine to avoid a collision of the vehicle 1 and the potential risks. Specifically, when the value r ₁ is larger than 1 (ie, when the value of the weight w ₃ is larger than the value of the weight w ₂ ), the vertical jerk component “ Since the value of j _x (i · Δt) ² ″ is smaller than the value of the horizontal jerk component “r ₁ · j _y (i · Δt) ² ”, the vertical jerk can be changed relatively greatly. As a result, the collision between the vehicle 1 and the potential risk is avoided mainly by changing the speed of the vehicle 1. On the other hand, if the value r ₁ is smaller than 1 (that is, the value of the weight w ₃ is smaller than the value of the weight w ₂ ), the horizontal jerk component “r _1. Since the value of j _y (i · Δt) ² ″ is smaller than the value of the longitudinal jerk component “j _x (i · Δt) ² ”, the lateral jerk can be changed relatively large. As a result, the collision between the vehicle 1 and the potential risk is avoided by mainly turning the vehicle 1.

The vehicle trajectory setting unit 24 acquires information on the structure of the road on which the vehicle 1 is currently traveling from, for example, the output of the GPS 13 or an existing navigation device, and calculates the value r ₁ based on the acquired information. change. Specifically, for example, when the vehicle 1 is traveling on a road without a center line, the vehicle track setting unit 24 avoids a collision between the vehicle 1 and a potential risk by mainly decelerating the vehicle 1. As a result, the value r ₁ is made larger than ₁ (as a result, the value of the weight w ₃ becomes larger than the value of the weight w ₂ ).

(Technical effect)
In the present embodiment, when the vehicle track of the vehicle 1 is set, a risk field that is a risk degree distribution based on the collision speed between the vehicle 1 and the potential risk is considered. For this reason, it is possible to set the vehicle trajectory in consideration of the potential risk existing in the blind spot of the obstacle. In addition, the evaluation function for obtaining the vehicle trajectory includes a vehicle behavior component “Motion” in addition to the risk component “Risk”. For this reason, the driving support device according to the present embodiment sets a vehicle trajectory that achieves both safety capable of avoiding obstacles and potential risks and smoothness of vehicle behavior capable of suppressing a sense of incongruity of the driver of the vehicle 1. can do.

  The “ECU 20”, the “obstacle detection unit 21”, the “latent risk estimation unit 22”, the “risk field acquisition unit 23”, and the “vehicle trajectory setting unit 24” according to the present embodiment, respectively, It is an example of “support device”, “obstacle detection means”, “latent risk estimation means”, “risk information acquisition means”, and “trajectory setting means”.

<First Modification>
A first modification of the driving support apparatus according to the above-described embodiment will be described. In the present modification, the risk field acquisition unit 23 changes the risk field according to the road surface state of the road ahead of the vehicle 1. The road surface condition affects the average deceleration of the vehicle 1 when calculating the risk field. Specifically, for example, even if the operation amount of the brake actuator is the same, the average deceleration when the vehicle 1 decelerates on a wet road is the average deceleration when the vehicle 1 decelerates on a dry road Smaller than. If the average deceleration of the vehicle 1 changes, the risk degree distribution in the risk field also changes.

  Therefore, if the risk field is changed according to the road surface state of the road ahead of the vehicle 1 by the risk field acquisition unit 23, a more appropriate vehicle track reflecting the road surface state is set by the vehicle track setting unit 24. Will be. If comprised in this way, an appropriate vehicle track | orbit can be set not only at the time of fine weather but at the time of raining or snowing, for example.

  Here, “change the risk field” means that when the risk field is calculated while the vehicle 1 is traveling, the average deceleration of the vehicle 1 estimated from the road surface condition of the road ahead is used to check the obstacle. This means that the risk field is calculated based on the position and size of the obstacle detected by the detection unit 21, the potential risk estimated by the potential risk estimation unit 22, the speed and position of the vehicle 1, and the like. On the other hand, when the risk field is calculated in advance, the average deceleration of the vehicle 1 estimated from the road surface condition of the road ahead, the position and size of the obstacle detected by the obstacle detection unit 21, and the potential risk estimation unit This means that a risk field that satisfies the conditions such as the potential risk estimated by the speed 22 and the speed and position of the vehicle 1 is selected.

  In addition, since the existing technique can be applied to the road surface state detection method of the road ahead, a detailed description thereof will be omitted.

<Second Modification>
A second modification of the driving support apparatus according to the above-described embodiment will be described. In this modification, the following evaluation function is used instead of the modified evaluation function in [Equation 5]. The vehicle track setting unit 24 indirectly minimizes the evaluation function in [Equation 1] by minimizing the following equation.

In the following evaluation function, the vehicle behavior component “Motion” of the evaluation function in [Equation 1] is a longitudinal jerk component “j _x (i · Δt) ² ”, a lateral jerk component “j _y (i · Δt) ² ”, It is replaced by a combination of the longitudinal acceleration component “a _x (i · Δt) ² ” and the lateral acceleration component “a _y (i, Δt) ² ”.

That is, “w ₂ · Motion (x _i , y _i , v _i )” = “w ₂ · j _x (i · Δt) ² + w ₃ · j _y (i · Δt) ² + w ₄ · a _x (i · Δt) ² + w ₅ · a _y (i · Δt) ² ″ = “w ₂ · {j _x (i · Δt) ² + r ₁ · j _y (i · Δt) ² + r ₂ · a _x (i · Δt) ² + r ₃ · a _y (i · Δt) ² } ”. It should be noted that “w ₃ = w ₂ × r ₁ ”, “w ₄ = w ₂ × r ₂ ” and “w ₅ = w ₂ × r ₃ ” (where “r ₁ ”, “r ₂ ” and “r” ₃ "is an arbitrary real number).

When the vehicle track of the vehicle 1 is set, similarly to the above-described embodiment, the vehicle track setting unit 24 uses the above-described combinations of a plurality of amplitudes “A _x ” and “A _y ” that are changed in various ways. A combination of amplitudes “A _x ” and “A _y ” that minimizes the evaluation function is selected. The vehicle trajectory setting unit 24 then selects the combination of the selected amplitudes “A _x ” and “A _y ” (that is, the amplitudes “A _x with a hat” and “A _y with a hat that minimize the evaluation function). )) Is used to find the optimal trajectory and further the optimal speed pattern.

If the weights w ₄ and w ₅ of the evaluation function are “0”, the evaluation function is equal to the modified evaluation function in [Equation 5].

<Third Modification>
A third modification of the driving support apparatus according to the above-described embodiment will be described. In this modification, the following evaluation function is used instead of the modified evaluation function in [Equation 5]. The vehicle track setting unit 24 indirectly minimizes the evaluation function in [Equation 1] by minimizing the following evaluation function.

In the following formula, the vehicle behavior component “Motion” of the evaluation function in [Equation 1] is a combination of the longitudinal acceleration component “a _x (i · Δt) ² ” and the lateral acceleration component “a _y (i, Δt) ² ”. Has been replaced.

That is, “w ₂ · Motion (x _i , y _i , v _i )” = “w ₄ · a _x (i · Δt) ² + w ₅ · a _y (i · Δt) ² ” = “w ₂ · {r ₂ · a _x (i · Δt) ² + r ₃ · a _y (i · Δt) ² } ”. Note that “w ₄ = w ₂ × r ₂ ” and “w ₅ = w ₂ × r ₃ ” (where “r ₂ ” and “r ₃ ” are arbitrary real numbers).

When the vehicle track of the vehicle 1 is set, similarly to the above-described embodiment, the vehicle track setting unit 24 uses the above-described combinations of a plurality of amplitudes “A _x ” and “A _y ” that are changed in various ways. A combination of amplitudes “A _x ” and “A _y ” that minimizes the evaluation function is selected. The vehicle trajectory setting unit 24 then selects the combination of the selected amplitudes “A _x ” and “A _y ” (that is, the amplitudes “A _x with a hat” and “A _y with a hat that minimize the evaluation function). )) Is used to find the optimal trajectory and further the optimal speed pattern.

When the values of the weights w ₂ and w ₃ of the evaluation function in [Equation 8] are “0”, the evaluation function is equal to the above-described evaluation function.

  The present invention is not limited to the above-described embodiment, and can be changed as appropriate without departing from the scope or spirit of the invention that can be read from the claims and the entire specification. Is also included in the technical scope of the present invention. The present invention is not limited to the driving support device, and can be applied to the field of automatic driving technology for vehicles.

  DESCRIPTION OF SYMBOLS 1 ... Vehicle, 11 ... Camera, 12 ... Radar, 13 ... GPS, 14 ... Sensor, 20 ... ECU, 21 ... Obstacle detection part, 22 ... Potential risk estimation part, 23 ... Risk field acquisition part, 24 ... Vehicle trajectory setting , 25 ... Travel support part, 30 ... Various actuators, 40 ... Notification part

Claims (6)

Obstacle detection means for detecting obstacles on the road ahead of the vehicle;
A potential risk estimating means for estimating a potential risk which is a virtual moving body assumed to exist in the blind spot of the obstacle;
Risk information indicating a plurality of risk levels, each of which is associated with a virtual position and speed of a plurality of different own vehicles and is a value based on a collision speed between the own vehicle and the potential risk, is obtained. Risk information acquisition means;
A trajectory setting means for setting the trajectory of the host vehicle based on a collision risk between the host vehicle and the potential risk on the road ahead estimated from the position and speed of the host vehicle and the risk information;
A driving support apparatus comprising:   The driving support apparatus according to claim 1, wherein the moving body is a moving body that enters in front of the host vehicle. The trajectory setting means includes
The front road from at least one of a value based on the longitudinal jerk of the host vehicle and a value based on the lateral jerk of the host vehicle, a value based on the longitudinal acceleration of the host vehicle and a value based on the lateral acceleration of the host vehicle. Estimating the vehicle behavior of the vehicle in
The driving support device according to claim 1, wherein the vehicle trajectory is set based on the estimated vehicle behavior in addition to the collision risk.   The trajectory setting means sets a vehicle trajectory that minimizes a cost function including a risk component indicating the collision risk and a vehicle behavior component indicating the vehicle behavior as the vehicle trajectory. The driving support device according to 1. A weight is given to each of the risk component and the vehicle behavior component,
The driving support apparatus according to claim 4, wherein the trajectory setting unit changes the weight based on a road structure of the front road.   The driving support apparatus according to any one of claims 1 to 5, wherein the risk information changes according to a road surface state of the front road.