JP2020026189A - Travel support method and travel support device - Google Patents

Abstract

To provide a travel support method capable of reducing control delay of a vehicle, even when a vehicle behavior is changed kinetically.SOLUTION: In a travel support method, a travel plan of a vehicle is acquired S1, an initial value of a controlled variable of an actuator is determined S3, a vehicle behavior when traveling along the travel plan is predicted S4, an evaluation value to the predicted vehicle behavior is calculated based on the travel plan S6, and calculation of a candidate of the controlled variable is repeated until the controlled variable of the actuator for controlling the vehicle, based on the evaluation value S4-S8.SELECTED DRAWING: Figure 8

Description

  The present invention relates to a driving support method and a driving support device.

  2. Description of the Related Art Conventionally, as a control method of a vehicle active suspension, it is known to perform control by calculating an optimum control output that minimizes an evaluation function based on the current behavior of a vehicle (see Patent Document 1).

JP 2010-188772 A

  However, in the method of Patent Document 1, since the future behavior of the vehicle is not taken into account, a delay occurs in the control of the vehicle when the behavior of the vehicle dynamically changes.

  In view of the above problems, an object of the present invention is to provide a driving support method and a driving support device that can reduce a delay in control of a vehicle even when the behavior of the vehicle changes dynamically.

  In the driving support method and the driving support device according to one aspect of the present invention, the behavior of the vehicle when traveling according to the traveling plan of the own vehicle is predicted, and an evaluation value for the predicted behavior of the vehicle is calculated based on the traveling plan. And calculating a control amount of an actuator for controlling the vehicle based on the evaluation value.

  According to the aspect of the present invention, the vehicle can be controlled in consideration of the future behavior of the vehicle based on the travel plan of the vehicle. Therefore, even when the behavior of the vehicle changes dynamically, the control of the vehicle is delayed. It is possible to provide a driving support method and a driving support device that can reduce the load.

It is a block diagram of an example of a run support device concerning an embodiment of the present invention. It is a schematic structure figure of an example of an active type suspension concerning an embodiment of the present invention. FIG. 4 is a characteristic diagram illustrating a relationship between a command current and a control pressure in the pressure control valve according to the embodiment of the present invention. It is a block diagram showing an example of functional composition of a controller concerning an embodiment of the present invention. It is a schematic diagram showing an example of a run scene concerning an embodiment of the present invention. 6 is a graph illustrating a control ratio of weight of an evaluation function according to the embodiment of the present invention. It is a graph showing the change of the control amount in a prediction section. It is a graph showing the change of the state quantity in a prediction area. It is a graph showing the change of the cost in optimization control. 5 is a flowchart illustrating an example of a driving support method according to an embodiment of the present invention. It is a schematic diagram showing an example of a run scene concerning a 1st modification of an embodiment of the present invention. It is a graph which shows the change of the root mean square of the acceleration concerning a 1st modification. It is a graph which shows the change of the root mean square of the speed which concerns on a 1st modification. It is a schematic diagram showing an example of the run scene concerning a 2nd modification of an embodiment of the present invention. 9 is a graph showing a relationship between a road surface roughness and a maximum stroke amount according to a second modification.

  Hereinafter, embodiments of the present invention will be described with reference to the drawings. Note that the drawings are schematic and may differ from actual ones. The embodiments of the present invention described below exemplify an apparatus and a method for embodying the technical idea of the present invention. Is not specified as: The technical idea of the present invention can be variously modified within the technical scope defined by the claims described in the claims.

  As shown in FIG. 1, the driving support device 1 according to the embodiment of the present invention includes an external environment sensor 2, a vehicle sensor 3, a navigation system 4, a driving control device 5, an actuator 6, and a controller 7. The driving support device 1 according to the embodiment of the present invention can be mounted on a vehicle, for example. Hereinafter, a vehicle on which the driving support device 1 according to the embodiment of the present invention is mounted is referred to as “own vehicle”.

  The external environment sensor 2 is a sensor that detects an external environment outside the host vehicle. The type and number of the outside environment sensors 2 can be set as appropriate. The external environment sensor 2 includes a distance measuring device such as a laser range finder (LRF) and a radar. The ranging device detects, for example, an object existing around the own vehicle, a relative position between the own vehicle and the object, and a distance between the own vehicle and the object. Further, the distance measuring device may detect a distance between the host vehicle and a road surface in front of the host vehicle. By scanning the road surface ahead of the host vehicle as the host vehicle advances, road surface roughness (road surface undulation) can be measured. The distance measurement device outputs the detected distance measurement data to the travel control device 5 and the controller 7 as the outside environment information.

  The external environment sensor 2 includes a camera such as a stereo camera or a monocular camera. The camera performs travel control by using, as exterior environment information, objects existing around the vehicle, road markings such as lane boundaries (for example, white lines), terrain such as curbs and guardrails, and road surface images in front of the vehicle as environment information outside the vehicle. Output to the device 5 and the controller 7.

  The vehicle sensor 3 is a sensor that detects the current state (behavior) of the host vehicle. The type and number of the vehicle sensors 3 can be set as appropriate. The vehicle sensor 3 detects, for example, the six-axis motion of the vehicle body on the spring of the own vehicle and the three-axis motion of the unsprung portion in the vertical direction, the left-right direction, and the front-back direction as the current state (behavior) of the own vehicle. Hereinafter, the three-axis movement in the up-down direction, the left-right direction, and the front-back direction is referred to as “bounce”. In addition, the behavior of the host vehicle includes, for example, displacement of a bounce on a spring, displacement of a pitch angle, displacement of a roll angle, and displacement of a yaw angle on a spring, displacement of a bounce under a spring, and displacement of a bounce on a spring. Speed, pitch angular velocity on rolls, roll angular velocity, and yaw angular velocity, unsprung bounce velocity, spring bounce acceleration, and spring pitch angular acceleration, roll angular acceleration, and yaw angular acceleration , At least one of unsprung bounce acceleration.

  For example, the vehicle sensor 3 may detect the displacement of the bounce on the spring, the displacement of the pitch angle, the displacement of the roll angle, the displacement of the yaw angle on the spring, and the displacement of the bounce under the spring. Further, the vehicle sensor 3 may detect the bounce speed on the spring, the pitch angular speed, the roll angular speed, and the yaw angular speed on the spring, and the bounce speed under the spring. Further, the vehicle sensor 3 may detect the bounce acceleration on the spring, the pitch angular acceleration, the roll angular acceleration, and the yaw angular acceleration on the spring, and the bounce acceleration under the spring.

  The vehicle sensor 3 may include a wheel speed sensor that detects a wheel speed of the own vehicle. Further, the vehicle sensor 3 may include a camera or a pressure sensor that detects the arrangement, weight, and movement of the occupant in the vehicle compartment of the host vehicle, and the arrangement, weight, and movement of luggage in the vehicle compartment. The vehicle sensor 3 outputs vehicle state information indicating the current state of the host vehicle to the travel control device 5 and the controller 7.

  The navigation system 4 includes a navigation controller, a positioning device, a map database, and a communication unit. The navigation controller is an electronic control unit (ECU) that controls the information processing operation of the navigation system 4. The positioning device measures the current position of the host vehicle. The positioning device is, for example, a global positioning system (GNSS) receiver such as a GPS receiver that obtains the current position of the vehicle by receiving radio waves from a plurality of navigation satellites. The positioning device may be an inertial navigation device.

  The map database stores map information such as a high-precision map. The map information includes information on a road map indicated by nodes and links, a road type (for example, a general road or an expressway) in road map coordinates, a road width, a road shape, a road gradient, the number of lanes, and a legal speed (speed limit). And at least are included. For example, a road in a road map is identified by a link number for each road, and information on a road type, a road width, a road shape, a road gradient, the number of lanes, and a legal speed (speed limit) is associated with each link number. I have. Further, a link number is set for each lane of each road.

  The communication unit performs wireless communication with a communication device outside the own vehicle. The communication method by the communication unit may be, for example, wireless communication using a public mobile phone network, vehicle-to-vehicle communication, road-to-vehicle communication, or satellite communication. The navigation system 4 may acquire road map data and information on the environment outside the host vehicle from the external device by the communication unit. The navigation system 4 may acquire information on the direction and strength of the wind around the own vehicle, for example, as the environment outside the vehicle. The navigation system 4 outputs information on the environment outside the vehicle acquired from the external device by the communication unit to the controller 7.

  The navigation system 4 sets a traveling route from the current position of the own vehicle to the destination, and provides route guidance to the occupant according to the set traveling route. The traveling route may be set for each lane or for each road. Further, the navigation system 4 outputs information of the set traveling route to the traveling control device 5 and the controller 7 as a traveling plan of the own vehicle. For example, the navigation system 4 outputs map information of the set traveling route to the traveling control device 5 and the controller 7.

  The travel control device 5 is an ECU that automatically drives the own vehicle. Here, the automatic driving in the embodiment of the present invention includes running control for automatically controlling at least one of acceleration, steering, and braking, and also includes running control for automatically controlling all of acceleration, steering, and braking. The traveling control device 5 travels the traveling route set by the navigation system 4 based on the outside environment information from the outside environment sensor 2 and the vehicle state information from the vehicle sensor 3 to the own vehicle during automatic driving of the own vehicle. A traveling trajectory (trajectory) to be generated is generated.

  When generating the traveling trajectory, the traveling control device 5 first determines a driving behavior plan for the vehicle to automatically travel on the traveling route set by the navigation system 4. The driving behavior plan is a plan of driving behavior at a lane level in a middle-long distance range, which defines a lane in which the own vehicle travels and a driving behavior required to travel in this lane. For example, the driving action plan is a plan that defines a driving action such as how many meters before the intersection to change lanes to a right-turn lane in a scene where a right turn is made at an intersection existing ahead.

  Then, the travel control device 5 generates a track candidate for causing the own vehicle to travel according to the driving action plan based on the motion characteristics of the own vehicle and the like. The travel control device 5 evaluates the future risk of each of the track candidates, selects an optimal track, and sets the track as a travel track for the vehicle to travel. The traveling track includes a target steering angle of the own vehicle, a speed plan of the own vehicle, and acceleration / deceleration for that purpose. The travel control device 5 may control the actuator 6 by outputting a control signal to the actuator 6 based on the selected travel trajectory.

  Further, the traveling control device 5 outputs the driving action plan and the traveling trajectory to the controller 7 as the traveling plan of the own vehicle. Note that all or a part of the functions of the travel control device 5 may be built in the navigation system 4 or the controller 7. Further, instead of the travel control device 5, the navigation system 4 may determine the driving action plan and set the travel trajectory. In this case, the navigation system 4 may output the driving action plan and the traveling trajectory to the controller 7 as the traveling plan of the own vehicle.

  Further, the travel control device 5 outputs an automatic operation level scheduled when traveling according to the travel plan to the controller 7 as a travel plan. Autonomous driving levels can be categorized, for example, according to criteria established by the U.S. Department of Transportation Road Traffic Safety Administration (NHTSA). Specifically, at the automatic driving level 1, one of acceleration, steering, and braking is automatically performed. In the automatic driving level 2, a plurality of operations out of acceleration, steering, and braking are automatically performed, and an occupant is obliged to monitor. In the automatic driving level 3, acceleration, steering and braking are all performed automatically, and the occupant is not obligated to monitor during automatic driving, but the occupant responds to the request of the automatic driving system. In the automatic driving level 4, acceleration, steering and braking are all performed automatically, and the occupant does not participate in driving at all.

  The actuator 6 is a drive device that controls an own vehicle by converting an electrical control signal from the travel control device 5 or the controller 7 into a mechanical motion. The type and number of the actuators 6 can be set as appropriate. For example, the actuator 6 includes a steering actuator, an accelerator opening actuator, a brake control actuator, and an active suspension.

  The steering actuator controls the steering angle of the host vehicle according to a control signal from the travel control device 5. The accelerator opening actuator controls the accelerator opening of the host vehicle according to a control signal from the traveling control device 5. The brake control actuator controls the braking amount of the host vehicle according to a control signal from the travel control device 5. The active suspension is interposed between the vehicle-side member and the wheel-side member, and the operating pressure of a hydraulic cylinder provided in the active suspension is adjusted according to a control signal from the controller 7.

  FIG. 2 shows an example of the active suspension. The active suspension 8 includes hydraulic cylinders 18FL, 18FR, 18RL, and 18RR as actuators interposed between the vehicle body-side member 10 and the wheel-side members 14 of the wheels 11FL, 11FR, 11RL, and 11RR, respectively. Pressure control valves 20FL, 20FR, 20RL, and 20RR for individually adjusting the operating pressures of 18FL, 18FR, 18RL, and 18RR are provided.

  Further, the active suspension 8 supplies hydraulic oil of a predetermined pressure to the pressure control valves 20FL, 20FR, 20RL, and 20RR via the supply pipe 21S, and returns oil from the pressure control valves 20FL, 20FR, 20RL, and 20RR. And a pressure accumulator 24F, 24R interposed in the supply pipe 21S between the hydraulic source 22 and the pressure control valves 20FL, 20FR, 20RL, 20RR.

  Each of the hydraulic cylinders 18FL, 18FR, 18RL, 18RR has a cylinder tube 18a. A lower pressure chamber L is formed in the cylinder tube 18a and is separated by a piston 18c having a through hole in the axial direction. The lower pressure chamber L generates a thrust corresponding to the pressure receiving area difference between the upper and lower surfaces of the piston 18c and the internal pressure. The lower end of the cylinder tube 18a is attached to the wheel side member 14, and the upper end of the piston rod 18b is attached to the vehicle body side member 10.

  Each of the pressure chambers L is connected to an output port of a pressure control valve 20FL, 20FR, 20RL, 20RR via a hydraulic pipe 38. Further, each of the pressure chambers L of the hydraulic cylinders 18FL, 18FR, 18RL, 18RR is connected to an accumulator 34 for absorbing unsprung vibration via a throttle valve 32. A coil spring 36 having a relatively low spring constant and supporting the static load of the vehicle body is disposed between the upper and lower portions of each of the hydraulic cylinders 18FL, 18FR, 18RL, 18RR. Each of the pressure control valves 20FL, 20FR, 20RL, and 20RR is constituted by a 3-port proportional electromagnetic pressure reducing valve having a cylindrical valve housing having a spool slidably mounted therein and a proportional solenoid provided integrally therewith. ing.

  By adjusting the command current i (control amount) supplied to the exciting coil of the proportional solenoid, the moving distance of the poppet accommodated in the valve housing, that is, the position of the spool is controlled. Thereby, the hydraulic oil flowing between the hydraulic source 22 and the hydraulic cylinders 18FL, 18FR, 18RL, 18RR via the supply port and the output port or the output port and the return port is controlled. Thus, in the active suspension 8, the thrust generated by the hydraulic cylinders 18FL, 18FR, 18RL, 18RR is controlled by adjusting the command current i (control amount) of the pressure control valves 20FL, 20FR, 20RL, 20RR. In addition, the vehicle body behavior of the own vehicle is suppressed.

  FIG. 3 shows the relationship between the command current i (: iFL, iFR, iRL, iRR) applied to the excitation coil and the control pressure P output from the output ports of the pressure control valves 20FL, 20FR, 20RL, 20RR. At the time of the minimum current value iMIN in consideration of noise, the control pressure P becomes the minimum control pressure PMIN. When the current value i is increased from this state, the control pressure P increases linearly in proportion to the current value i, When the current value is iMAX, the maximum control pressure PMAX is equivalent to the set line pressure of the hydraulic power source 22. Reference numeral iN indicates a neutral command current, and reference numeral PN indicates a neutral control pressure.

  The controller 7 shown in FIG. 1 is an ECU including a processor such as a CPU (Central Processing Unit) and an MPU (Micro-Processing Unit) and peripheral components such as a storage device. The storage device may include any of a semiconductor storage device, a magnetic storage device, and an optical storage device. The storage device may include a register, a cache memory, and a memory such as a ROM (Read Only Memory) and a RAM (Random Access Memory) used as a main storage device. The processor realizes functions of the controller 7 described below by executing a computer program stored in the storage device. The controller 7 may be realized by a functional logic circuit set in a general-purpose semiconductor integrated circuit. For example, the controller 7 may include a programmable logic device (PLD) such as a field programmable gate array (FPGA).

  The controller 7 outputs the external environment information output from the external environment sensor 2, the vehicle state information output from the vehicle sensor 3, the travel plan and the external environment information output from the navigation system 4, and the output from the travel control device 5. The actuator 6 is controlled based on the travel plan thus set. In the embodiment of the present invention, a case where the controller 7 controls a steering actuator, an accelerator opening actuator, a brake control actuator, and an active suspension as the actuator 6 is illustrated.

  As shown in FIG. 4, the controller 7 includes a vehicle information acquisition unit 41, an outside information acquisition unit 42, a travel plan acquisition unit 43, and a model prediction control unit 44. The vehicle information acquisition unit 41 acquires vehicle state information output from the vehicle sensor 3. The vehicle state information may include, for example, lateral acceleration, and information on displacement, velocity, and acceleration around the roll axis and the yaw axis.

  When the vehicle state information output from the vehicle sensor 3 is displacement information, the vehicle information acquisition unit 41 can calculate speed information and acceleration information by differentiating the displacement information. When the vehicle state information output from the vehicle sensor 3 is speed information, the vehicle information obtaining unit 41 can calculate displacement information by integrating the speed information and calculate acceleration information by differentiating the speed information. When the vehicle state information output from the vehicle sensor 3 is acceleration information, the vehicle information acquisition unit 41 can calculate displacement information and speed information by integrating the acceleration information. The vehicle information acquisition unit 41 outputs the acquired vehicle state information to the model prediction control unit 44.

  The outside-of-vehicle information acquisition unit 42 acquires outside-of-vehicle environment information that is output from the outside-of-vehicle environment sensor 2 and indicates the outside environment of the host vehicle. The external environment information may include, for example, the road surface roughness of the road ahead of the host vehicle, the road surface vibration occurring on the road surface, and the road surface friction coefficient of the road ahead of the host vehicle. Furthermore, the outside-of-vehicle information acquisition unit 42 may acquire outside-of-vehicle environment information indicating the direction and strength of the wind around the vehicle output from the navigation system 4. The outside-of-vehicle information acquisition unit 42 outputs the acquired outside-of-vehicle environment information to the model prediction control unit 44.

  The travel plan acquisition unit 43 acquires a travel plan for the host vehicle to travel. For example, the travel plan acquisition unit 43 may acquire the set route set by the navigation system 4 and its map information as a travel plan. Further, the traveling plan acquisition unit 43 may acquire the driving action plan and the traveling trajectory determined by the traveling control device 5 as the traveling plan. Further, the travel plan acquisition unit 43 may acquire, as a travel plan, an automatic operation level of automatic operation control scheduled to be performed by the travel control device 5. Further, the travel plan acquisition unit 43 may acquire, as a travel plan, a road shape included in the map information on which the own vehicle is going to travel, and a vehicle speed plan when the own vehicle is about to travel. The travel plan acquisition unit 43 outputs the acquired travel plan to the model prediction control unit 44.

  The model prediction control unit 44 performs model prediction control. The model predictive control predicts an output of a control target and determines a control amount for minimizing an evaluation function indicating a control performance in a future finite section (prediction section) from the current time to a predetermined time using an optimization problem or the like. To search. The model prediction control unit 44 is a model based on the vehicle state information acquired by the vehicle information acquisition unit 41, the outside environment information acquired by the outside information acquisition unit 42, and the travel plan acquired by the travel plan acquisition unit 43. Predictive control is performed to calculate the control amounts of the steering actuator, accelerator opening actuator, brake control actuator, and active suspension as the actuator 6, respectively.

  The model prediction control unit 44 includes a state predictor 51, a weight setting unit 52, an evaluation unit 53, and an optimization unit 54. The state predictor 51 includes a current state of the own vehicle acquired by the vehicle information acquisition unit 41, an outside environment acquired by the outside information acquisition unit 42, and a travel plan of the own vehicle acquired by the travel plan acquisition unit 43. The state (behavior) of the own vehicle in the prediction section in the future is predicted on the basis of the control amount candidate of the actuator 6 calculated by the optimization unit 54. For this reason, the state predictor 51 includes the current state of the own vehicle acquired by the vehicle information acquisition unit 41, the outside environment acquired by the outside information acquisition unit 42, and the own vehicle acquired by the travel plan acquisition unit 43. And a prediction model in which the behavior of the own vehicle generated according to the travel plan of the vehicle and the candidate of the control amount of the actuator 6 calculated by the optimization unit 54 is modeled.

  The prediction model includes, for example, the current state of the own vehicle acquired by the vehicle information acquisition unit 41, the outside environment acquired by the outside information acquisition unit 42, and the travel plan of the own vehicle acquired by the travel plan acquisition unit 43. The behavior of the vehicle generated according to the control amount candidate of the actuator 6 calculated by the optimizing unit 54 may be a model represented by a differential equation, such as learning of a deep neural network (DNN) or the like. It may be a model composed of containers.

  For example, the state predictor 51 determines, as the future state (behavior) of the vehicle body of the own vehicle in the prediction section, the displacement, speed, and acceleration of the bounce on the spring, and the displacement, speed, and velocity around the pitch axis, the roll axis, and the yaw axis. Acceleration, unsprung bounce displacement, velocity and acceleration may be predicted. The state predictor 51 outputs the predicted future state quantities of the vehicle body to the evaluation unit 53.

  The weight setting unit 52 includes a traveling plan in which the own vehicle acquired by the traveling plan acquiring unit 43 is to travel, the external environment information acquired by the external information acquiring unit 42, and the vehicle information predicted by the state estimator 51. An evaluation value for the behavior of the own vehicle is calculated based on the behavior. For example, the weight setting unit 52 sets the weight of each state variable of the evaluation function for evaluating the control amount of the actuator 6 as an evaluation value.

For example, in the model prediction control by the model prediction control unit 44, an evaluation function J1 expressed by the following equation (1) can be used.

J1 = Wa * Roll_dis + Wb * Roll_vel + Wc * Roll_acc
+ Wd * Pitch_dis + We * Pitch_vel + Wf * Pitch_acc
+ Wg * Yaw_dis + Wh * Yaw_vel + Wi * Yaw_acc
+ Wj * Bounce_dis + Wk * Bounce_vel + Wl * Bounce_acc
… (1)

  Here, Roll_dis is the state variable of the roll angular displacement, Roll_vel is the state variable of the roll angular velocity, Roll_acc is the roll angular acceleration, Pitch_dis is the state variable of the pitch angular displacement, Pitch_vel is the state variable of the pitch angular velocity, and Pitch_acc is the state of the pitch angular acceleration. Variables, Yaw_dis is the yaw angular displacement state variable, Yaw_vel is the yaw angular velocity state variable, Yaw_acc is the yaw angular acceleration state variable, Bounce_dis is the bounce displacement state variable, Bounce_vel is the bounce speed state variable, and Bounce_acc is the bounce acceleration state Indicates a variable.

  Also, Wa is the weight of roll angular displacement, Wb is the weight of roll angular velocity, Wc is the weight of roll angular acceleration, Wd is the weight of pitch angular displacement, We is the weight of pitch angular velocity, Wf is the weight of pitch angular acceleration, Wg is The weight of yaw angular displacement, Wh is the weight of yaw angular velocity, Wi is the weight of yaw angular acceleration, Wj is the weight of bounce displacement, Wk is the weight of bounce speed, and Wk is the weight of bounce acceleration.

  In the model predictive control, control is performed so as to minimize the evaluation function. Therefore, the larger the weight set by the weight setting unit 52, the smaller the amount of suppression of the behavior of the vehicle corresponding to the state variable weighted by the weight. growing. For this reason, the weight setting unit 52 adjusts the weight of each state variable so as to increase the weight of the state variable corresponding to the behavior of the host vehicle that is to be preferentially suppressed when traveling from now on according to the travel plan. For example, when the vehicle travels in accordance with the travel plan, the weight of the state variable predicted to increase the state quantity of the own vehicle is increased, and the weight of the state variable predicted to decrease the state quantity of the own vehicle is adjusted to be small. Is also good.

  For example, when the vehicle turns to change lanes or turn left or right, the roll angular displacement, roll angular velocity, roll angular acceleration, yaw angular displacement, yaw angular velocity, yaw angular acceleration, etc. Becomes relatively large. For this reason, when the weight setting unit 52 recognizes that the travel plan is scheduled to make a turn based on the travel route, the driving action plan, the travel trajectory, the road shape, and the like included in the travel plan, the weight setting unit 52 performs the turning operation. Roll angular displacement, roll angular velocity, roll angular acceleration, yaw angular displacement, yaw angular velocity, yaw angular velocity, yaw angular velocity, yaw angular velocity , The weights of the yaw angular acceleration may be adjusted in advance so as to be relatively large as compared to before the start of the turning operation, and the other weights may be relatively small.

  Also, when the traveling control of the own vehicle includes acceleration / deceleration, the pitch angle displacement, the pitch angular velocity, the pitch acceleration, the bounce displacement, the bounce speed, the bounce acceleration, and the like are relatively smaller than when the vehicle is traveling at a constant speed. Become larger. For this reason, when the weight setting unit 52 recognizes that the travel plan is scheduled to accelerate or decelerate based on the vehicle speed plan or the like included in the travel plan, the weight setting unit 52 determines the pitch angle displacement, pitch, Angular velocity, pitch acceleration, bounce displacement, bounce velocity, and weights of pitch angle displacement, pitch angular velocity, pitch acceleration, bounce displacement, bounce velocity, and bounce acceleration of the section where acceleration / deceleration occurs so as to suppress the bounce acceleration May be adjusted in advance so as to be relatively large as compared with a section in which no acceleration or deceleration occurs, and to relatively reduce other weights.

  For example, as shown in FIG. 5, consider a case where the own vehicle 100 is scheduled to make a right turn (turn right) as schematically shown by an arrow D1 when the own vehicle 100 travels according to a travel plan. The weight setting unit 52 adjusts and sets a weight at the time of the right turn different from the weight before the start of the right turn from the start of the right turn based on the travel plan.

  For example, as shown in FIG. 6, the weight Wa of the roll angular displacement is 14.6%, the roll angular velocity Wb is 10.7%, and the weight Wc of the roll angular acceleration is 8.1 in association with the state variable of the evaluation function J1 of the above equation (1). %, Pitch angular displacement weight Wd is 7.5%, pitch angular velocity weight We is 11.6%, pitch angular acceleration weight Wf is 0.9%, yaw angular displacement weight Wg is 13.1%, yaw angular velocity weight Wh is 5.5%, The yaw angular acceleration weight Wi is set to 9.2%, the bounce displacement Wj is set to 10.3%, the bounce speed weight Wk is set to 8.4%, and the bounce acceleration weight Wl is set to 0.1%. For example, the weight Wa of the roll angular displacement, the roll angular velocity Wb, the weight Wc of the roll angular acceleration, the weight Wg of the yaw angular displacement, the weight Wg of the yaw angular velocity, the weight Wh of the yaw angular velocity, and the weight Wi of the yaw angular acceleration at the time of the right turn start the right turn. Each has been adjusted larger than before.

  The evaluation unit 53 calculates the value (cost) of the evaluation function based on the future state quantity of the own vehicle predicted by the state predictor 51 and the weight set by the weight setting unit 52, thereby obtaining the prediction section. Then, the control performance of the candidate of the control amount of the actuator 6 is evaluated. For example, the evaluation unit 53 sets the state variable corresponding to the future state quantity of the own vehicle predicted by the state predictor 51 using the evaluation function J1 of the above equation (1) by the weight setting unit 52. The cost is calculated by weighting with the weight. The evaluation unit 53 outputs the calculated cost to the optimization unit 54.

  The optimizing unit 54 optimizes the candidate of the control amount of the actuator 6 so as to minimize the cost calculated by the evaluating unit 53. The optimization unit 54 may optimize the control amount candidate based on, for example, an iterative method using a differential value of the evaluation function. Further, for example, the optimization unit 54 may optimize the control amount candidate based on a heuristic method of directly searching for an optimal solution by trial and error. As a heuristic, for example, a genetic algorithm, particle swarm optimization, artificial bee colony algorithm may be used. The optimizing unit 54 outputs the control amount of the actuator 6 optimized by the search to the actuator 6.

  The model prediction control unit 44 repeats a series of processes of calculating the control amount candidates of the actuator 6, predicting the state of the own vehicle, and calculating the cost a predetermined number of times until the control amount candidates of the actuator 6 are optimized. Thus, the final control amount minimizing the cost is determined. For example, the curves L1 and L2 in FIG. 7A are examples of the control amounts of the actuator 6 in the prediction section Tp from the current time t0 to the predetermined time tp calculated by the optimization unit 54 in the first and second processing cycles. Is shown. Curves L3 and L4 in FIG. 7B show an example of the state quantity of the own vehicle in the prediction section Tp from the current time t0 to the predetermined time tp, which is predicted by the state predictor 51 in the first and second processing cycles, respectively. . FIG. 7C illustrates a change in cost calculated by the evaluation unit 53 for each processing cycle.

(Driving support method)
Next, an example of the driving support method according to the embodiment of the present invention will be described with reference to the flowchart of FIG.

  In step S1, the navigation system 4 and the travel control device 5 determine a travel plan of the host vehicle. In step S2, the vehicle sensor 3 detects the current state of the own vehicle. The external environment sensor 2 detects an external environment outside the host vehicle.

  In step S3, the optimization unit 54 determines an initial value of a candidate for the control amount of the actuator 6 in the prediction section. In step S <b> 4, the state predictor 51 determines the current state of the own vehicle detected by the vehicle sensor 3, the external environment detected by the external environment sensor 2, and the prediction determined by the navigation system 4 and the travel control device 5. Based on the travel plan of the own vehicle in the section and the candidate for the control amount of the actuator 6 in the prediction section determined by the optimization unit 54, the state quantity of the vehicle body in the prediction section when the own vehicle travels according to the travel plan is predicted. I do.

  In step S <b> 5, the weight setting unit 52 is configured based on the travel plan of the own vehicle in the prediction section determined by the navigation system 4 and the travel control device 5 and the state quantity of the vehicle body in the prediction section predicted by the state predictor 51. Then, an evaluation value for the future behavior of the own vehicle is calculated. For example, the weight setting unit 52 uses the evaluation function J1 of the above equation (1) to reduce the weight of each state variable of the evaluation function J1 so as to suppress the behavior occurring on the vehicle body of the host vehicle when traveling according to the travel plan. Is set as the evaluation value.

  In step S6, the evaluation unit 53 determines the cost of the evaluation function based on the state quantity predicted by the state predictor 51 and the weight set by the weight setting unit 52, and is determined by the optimization unit 54. The control performance of the candidate for the control amount of the actuator 6 in the predicted section is evaluated.

  In step S7, the optimization unit 54 determines whether or not the candidate for the control amount of the actuator 6 in the prediction section has been optimized based on the cost calculated by the evaluation unit 53. For example, the optimization unit 54 may determine that the candidate for the control amount has been optimized when the cost has become equal to or less than the threshold value or when the cost has become the minimum value.

  If it is determined in step S7 that the control amount candidate of the actuator 6 has not been optimized, the process proceeds to step S8. In step S8, the optimizing unit 54 recalculates a candidate for the control amount of the actuator 6 using a predetermined optimization algorithm. Thereafter, the procedure returns to the step S4.

  On the other hand, when it is determined in step S7 that the candidate for the control amount of the actuator 6 has been optimized, the optimizing unit 54 determines the candidate for the control amount of the actuator 6 at the time when it is determined that the optimization has been performed as the final candidate. It outputs to the actuator 6 as a control amount. The actuator 6 controls the own vehicle so as to suppress the behavior of the own vehicle according to the control amount output by the optimizing unit 54.

(Effects of the embodiment)
According to the embodiment of the present invention, a travel plan in which the own vehicle will travel is acquired, and behavior of the own vehicle when traveling in accordance with the travel plan is predicted. Then, it calculates as an evaluation value for the predicted behavior of the host vehicle based on a travel plan for the host vehicle to run, and calculates a control amount of the actuator 6 for controlling the vehicle based on the evaluation value. Thus, even when the behavior of the host vehicle changes dynamically, the characteristics of the actuator 6 can be adjusted in advance in consideration of the future change in the behavior of the host vehicle. For this reason, even when the behavior of the host vehicle changes dynamically immediately after the behavior of the host vehicle changes dynamically, it is possible to reduce a delay in control of the host vehicle. Therefore, the characteristics of the actuator 6 can be set so as to suppress the behavior of the host vehicle in accordance with the traveling scene on the traveling plan, and the riding comfort of the occupant can be improved.

  Further, for example, a plurality of evaluation values corresponding to a plurality of behaviors of the host vehicle are calculated, and a control ratio of the plurality of behaviors of the host vehicle is determined based on the plurality of evaluation values. Thus, the characteristics of the actuator 6 can be set so as to suppress a plurality of behaviors of the host vehicle, and the riding comfort of the occupant can be improved.

  Furthermore, since the travel plan used when predicting the behavior of the own vehicle includes the vehicle motion plan calculated for the automatic travel of the own vehicle, the ride comfort when performing automatic driving based on the vehicle motion plan is improved. Can be improved. In general, when performing automatic driving, the occupant perceives the riding comfort differently than when performing manual driving. Further, even when the vehicle travels on the same road, there is a possibility that the control is executed such that a behavior different from that during the manual driving occurs during the automatic driving. On the other hand, since the characteristics of the actuator 6 can be set in advance in accordance with the control of the automatic driving, the uncomfortable feeling given to the occupant during the execution of the automatic driving can be reduced.

  Furthermore, the travel plan used when predicting the behavior of the own vehicle includes a travel locus calculated for the vehicle to travel automatically. Thus, the characteristics of the actuator 6 can be set in advance in accordance with the control of the automatic driving, so that the uncomfortable feeling given to the occupant during the execution of the automatic driving can be reduced.

  Further, the travel plan used when estimating the behavior of the own vehicle includes a road shape obtained from the map, and a vehicle speed plan when the vehicle is about to travel. Thus, the characteristics of the actuator 6 can be set in advance in accordance with the control of the automatic driving, so that the uncomfortable feeling given to the occupant during the execution of the automatic driving can be reduced.

  Further, a weight for the state variable of the evaluation function for evaluating the behavior of the vehicle is set as an evaluation value, and the control amount is calculated so as to minimize the evaluation function weighted by the set weight. Accordingly, in the model predictive control using the evaluation function, the weight of the evaluation function is adjusted in advance with respect to a future environmental change, so that the performance of the model predictive control can be improved.

(First Modification)
As a first modified example of the embodiment of the present invention, a case where the control ratio of each weight of the evaluation function is adjusted according to the automatic driving level when the automatic driving level is scheduled to be switched according to the travel plan will be described. .

  For example, the automatic driving level may be switched according to the presence or absence of a high-accuracy map, or the complexity (difficulty) of different driving situations on an expressway, city area, or the like. The presence or absence of an occupant's obligation to monitor outside the vehicle differs depending on the level of automatic driving. Then, even with the same behavior of the own vehicle, the riding comfort felt by the occupant changes depending on whether or not the occupant is obliged to monitor outside the vehicle. Therefore, if the autonomous driving level is switched in the future according to the driving plan and the presence or absence of the occupant's obligation to monitor outside the vehicle is scheduled to change, the weight of the evaluation function is changed to improve the occupant's riding comfort. Let it.

  In the first modified example of the embodiment of the present invention, an evaluation function J2 which is an evaluation index of the control amount of the active suspension is used as the actuator 6. The evaluation function J2 can be represented, for example, by the following equation (2).

              J2 = Wa * a1 + Wv * v1 ... (2)

  Here, a1 is a state variable of sprung acceleration, and v1 is a state variable of sprung speed. Wa indicates the weight of the spring acceleration component, and Wv indicates the weight of the spring speed component.

  For example, as shown in FIG. 9, a case is considered where the automatic driving level is changed in the travel plan in the sections S11 and S12 of the road L11 where the host vehicle 100 is to travel. In the travel plan, the section S11 of the road L11 is set to the automatic driving level 4, and the occupant is not obliged to monitor outside the vehicle. When the occupant is not obliged to monitor, it is presumed that the occupant has a better ride comfort when the sprung acceleration is smaller. For this reason, the weight setting unit 52 shown in FIG. 4 controls the sprung acceleration from the present to the end of the section S11 so as to further suppress the sprung acceleration as compared with the case where the occupant is obliged to monitor. The weight Wa of the acceleration is set to be large, and the weight Wv of the sprung speed is set to be small.

  On the other hand, in the section S12, the automatic driving level is set to 3 in the traveling plan, the duty of monitoring the outside of the vehicle is imposed on the occupant, and the automatic driving system requests the occupant to pay attention to the outside of the vehicle. When the occupant monitors outside the vehicle, it is estimated that the lower the sprung speed, the easier the occupant can monitor outside the vehicle. For this reason, the weight setting unit 52 illustrated in FIG. 4 further suppresses the sprung speed from the start of the section S11 in which the occupant is not obliged to monitor and the section S12 in which the occupant is obliged to monitor. Thus, the weight of the sprung speed Wv is increased and the weight Wa of the sprung acceleration is reduced.

  According to the first modification of the embodiment of the present invention, the ride comfort of the occupant can be improved by adjusting the weight of the evaluation function according to whether or not the occupant has a duty to monitor outside the vehicle. For example, if the occupant is obliged to monitor outside the vehicle and the automatic driving system requires the occupant to pay attention to the outside of the vehicle, increasing the weight of the sprung speed reduces the sprung speed, making it easier for the occupant to monitor the outside of the vehicle. can do. The weight of the pitch angular velocity may be increased while increasing the weight of the sprung velocity, or instead of increasing the weight of the sprung velocity. Increasing the weight of the pitch angular velocity and reducing the pitch angular velocity can also make it easier for the occupant to monitor outside the vehicle.

  Furthermore, even when the presence or absence of the duty of the occupant to monitor outside the vehicle changes in the travel plan, the weight of the evaluation function can be adjusted in advance based on the travel plan. Control delay can be reduced.

(Second Modification)
As a second modified example of the embodiment of the present invention, when the road surface roughness (road surface undulation state) of a road traveling according to a travel plan is scheduled to change, the weight of the evaluation function is determined according to the road surface roughness. The case of adjusting the ratio of is described.

  For example, the outside-of-vehicle information acquisition unit 42 illustrated in FIG. 4 may acquire information on the road surface roughness of a road on which the host vehicle is to travel according to the travel plan, which is included in the map database of the navigation system 4. Alternatively, the outside-of-vehicle information acquisition unit 42 may acquire information on the road surface roughness by detecting the road surface roughness of the road ahead of the vehicle using the outside-of-vehicle environment sensor 2.

  For example, as shown in FIG. 12, consider a case where the road surface roughness changes in the sections S21 and S22 of the road R12 on which the vehicle 100 is to travel according to the travel plan. For example, road surface roughness is classified into levels according to ISO 8608, and is defined such that the rougher the road surface (the larger the undulation of the road surface), the higher the road surface roughness level. The section S21 shown in FIG. 12 is a road surface roughness level 7 based on ISO8608, and the section S22 is rougher than the section S21 and has a road surface roughness level 9 based on ISO8608.

  For example, in the second modification of the embodiment of the present invention, as the actuator 6, the evaluation function J2 of the above equation (2), which is an evaluation index of the control amount of the active suspension, is used. The weight setting unit 52 adjusts the weight of the sprung speed of the evaluation function J2 relatively small and the weight of the sprung acceleration relatively large in the section S21 compared to the case of the section S22. In this case, the maximum stroke amount of the suspension of the host vehicle changes as shown by a thick solid line in FIG. 13 according to the road surface roughness level. At the road surface roughness level 7 in the section S21, the maximum stroke of the suspension of the host vehicle is about 0.06 m.

  As shown by the thick solid line in FIG. 13, when the road surface roughness level changes to 9, the maximum stroke of the suspension is expected to exceed the upper limit of 0.08 m. Therefore, when the maximum stroke amount of the suspension is expected to exceed the upper limit when entering the section S22, the weight setting unit 52 adjusts the weight of the evaluation function in advance so as to suppress the stroke amount. For example, the weight setting unit 52 decreases the weight of the sprung acceleration and increases the weight of the sprung speed. Accordingly, the maximum stroke amount of the suspension of the host vehicle changes as shown by a thin solid line in FIG. 13 according to the road surface roughness level. At the road surface roughness level 9 in the section S22, the maximum stroke amount of the suspension of the host vehicle can be suppressed to about 0.08.

  According to the second modification of the embodiment of the present invention, the weights of the evaluation function, such as sprung speed and sprung acceleration, are adjusted in advance based on information on the road surface roughness of the road on which the host vehicle is to travel according to the travel plan. By doing so, it is possible to appropriately adjust the maximum stroke amount of the suspension without delay in control from the point in time when the road surface roughness of the road on which the vehicle travels changes.

(Other embodiments)
As described above, the present invention has been described by the embodiments. However, it should not be understood that the description and drawings forming part of the present disclosure limit the present invention. From this disclosure, various alternative embodiments, examples, and operation techniques will be apparent to those skilled in the art.

  For example, in the embodiment of the present invention, the case where the state predictor 51 predicts the state of the own vehicle using the travel plan used for automatic driving is exemplified, but the present invention is not limited to this. That is, the state predictor 51 may predict the state of the host vehicle using a travel plan used for route guidance to the occupant during manual driving.

  Further, in the embodiment of the present invention, the evaluation function J1 of the above equation (1) and the evaluation function J2 of the above equation (2) are exemplified. However, the evaluation function is not limited to this, and can be set as appropriate. For example, as an element of the evaluation function J1 of the above equation (1), the state variables and weights of the roll angle jerk, the pitch angle jerk, the yaw angle jerk, and the bounce jerk may be added. Further, a part of the state variables of the evaluation function J1 in the above equation (1) may not be provided.

  DESCRIPTION OF SYMBOLS 1 ... Driving assistance device, 2 ... Outside environment sensor, 3 ... Vehicle sensor, 4 ... Navigation system, 5 ... Travel control device, 6 ... Actuator, 7 ... Controller, 8 ... Active suspension, 10 ... Body side member, 11FL ... Wheel, 11RR: Wheel, 14: Wheel side member, 18a: Cylinder tube, 18b: Piston rod, 18c: Piston, 18FL, 18FR, 18RL, 18RR: Hydraulic cylinder, 20FL: Pressure control valve, 20RR: Pressure control valve, 21R ... side pipe, 21S ... supply side pipe, 22 ... hydraulic power source, 24F ... accumulator, 24R ... accumulator, 32 ... valve, 34 ... accumulator, 36 ... coil spring, 38 ... hydraulic pipe, 41 ... vehicle information acquisition unit, 42 ... Out-of-vehicle information acquisition unit, 43: travel plan acquisition units 43, 44: model prediction control unit, 51 State predictor, 52 ... weight setting unit, 53 ... evaluation unit, 54 ... optimization section

Claims (7)

Obtain the driving plan of the vehicle,
Predict the behavior of the vehicle when traveling according to the traveling plan,
Based on the travel plan, calculate an evaluation value for the predicted behavior of the vehicle,
A driving support method, comprising: calculating a control amount of an actuator that controls the vehicle based on the evaluation value. Calculating a plurality of evaluation values for the plurality of behaviors of the vehicle,
The driving support method according to claim 1, wherein a control ratio of the plurality of behaviors is determined based on the plurality of evaluation values.   The travel support method according to claim 1, wherein the travel plan includes a vehicle motion plan when the vehicle travels automatically.   The travel support method according to any one of claims 1 to 3, wherein the travel plan includes a travel locus when the vehicle travels automatically.   5. The travel plan according to claim 1, wherein the travel plan includes a road shape included in the map information on which the vehicle is to travel, and a vehicle speed plan when the vehicle travels. 6. Driving support method. A weight for a state variable of an evaluation function for evaluating the control amount of the actuator is set as the evaluation value,
The driving support method according to any one of claims 1 to 5, wherein the control amount is calculated so as to minimize the evaluation function obtained by weighting the state variable with the weight. An actuator for controlling the vehicle,
Obtain the travel plan of the vehicle, predict the behavior of the vehicle when traveling according to the travel plan, calculate an evaluation value for the predicted behavior of the vehicle based on the travel plan, and based on the evaluation value And a controller for calculating a control amount of the actuator.

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