JP3036183B2 - Automotive electronic control unit - Google Patents

Abstract

PURPOSE:To obtain the comfortable drive feeling by fuzzy-estimating the driver's intention and inclination, traveling environment, and safety standard according to the prescribed fuzzy rule from the output signals of a plurality of sensors and preparing the movement instruction value for controlling the automobile, from the outputs, through fuzzy estimation. CONSTITUTION:A fuzzy driver's intention estimating part 21 fuzzy-estimates the driver's intention and inclination according to the prescribed fuzzy rule from the detection signal of a sensor part 1 for detecting a variety of operations of a driver. Further, a fuzzy disturbance estimating part 22 fuzzy-estimates the traveling environment such as road surface friction, traveling resistance, and a side wind, and a fuzzy feeling estimating part 23 fuzzy-estimates the driver's feeling corresponding to the sensor outputs related to the movement of a vehicle. Then, the estimation results of the estimation parts 21-23 are inputted into a fuzzy controller 24 for preparing the movement instruction, and the optimum movement instruction value is prepared, taking consideration of the movement instruction value corresponding to the driver's intention, external disturbance, driver's feeling, and the contents of a memory 26 in which the safety standard of the automobile and the automobile properties are accommodated.

Description

DETAILED DESCRIPTION OF THE INVENTION

[0001]

BACKGROUND OF THE INVENTION 1. Field of the Invention The present invention relates to an electronic control unit for a motor vehicle, and more particularly, to an electronic control unit for integrally controlling a vehicle motion using fuzzy inference while complying with a driver's request and a driving environment. .

[0002]

2. Description of the Related Art Conventionally, when performing electronic control of an automobile, attempts have been made to control the control devices of each subsystem such as a brake, a suspension, and a steering by integrating the vehicle motion from the motion performance.

[0003]

However, although the above-mentioned conventional electronic control device has been tried, the driver's preference and will, the driving environment, and the performance and safety of the vehicle are recognized in real time. No attempt has been made to achieve optimum vehicle motion by comprehensively judging from these situations. For this reason, there is a problem that it is difficult to realize both comfortable and optimal exercise performance according to the preference of the person and the traveling environment.

[0004] On the other hand, even if an attempt is made to control the vehicle in accordance with such a request, the driver's preference and will will not be clearly indicated, for example, "somewhat good" or "want to accelerate considerably". After all, it was difficult to execute the control. In addition, these quantities vary from person to person, and the taste is sensual and varies depending on the situation. It was difficult to realize.

SUMMARY OF THE INVENTION The present invention provides an electronic control apparatus for a vehicle which can realize a comfortable and optimum kinetic performance by recognizing a driver's preference and intention, a driving environment, and a performance and safety of the vehicle in real time. It is an object.

[0006]

In order to achieve the above object, an electronic control unit for a vehicle according to the present invention includes a plurality of sensors for detecting various states of the vehicle and a fuzzy output signal of the plurality of sensors. Estimating means for performing fuzzy inference according to a predetermined fuzzy rule as an input, estimating a driver's intention and preference, driving environment, safety standards, comfortable and optimal athletic performance, and a predetermined fuzzy rule using the output of the estimating means as a fuzzy input Fuzzy inference according to
Includes a command value generating means for generating a motion command value for controlling the vehicle, and operating means for operating the respective parts of the vehicle based on the movement command value from the command value preparing means, wherein the command value preparing hands
The step indicates the driver's will and preference for the outputs of the various sensors.
Fuzzy rules, and the performance and safety of vehicles
The reference is stored in the memory in advance, and each
From the output of the seed sensor and the information stored in the memory,
Fuzzy inference based on preferences, driving environment, vehicle performance and safety
Creating an optimal athletic performance with more comprehensive judgment
Features .

[0007] The invention according to claim 2, which is dependent on claim 1.
The command value creating means controls the vehicle optimally
A higher-level control unit for creating a motion command value, and the higher-level motion
Lower-level operation that controls multiple operation units based on command values
And a lower-level control unit that creates motion command values
It has a hierarchical structure .

[0008] The invention according to claim 3, which is dependent on claim 1.
Means the traction device, the antilock
Brake device, suffension device, power stearin
And at least a plurality of devices among 4WS devices.
It is characterized by the following. Claim 4 dependent on claim 2
In the invention, the operation unit includes a traction device,
Anti-lock brake device, supension device, power
Steering device or 4WS device
And features.

[0009]

According to the present invention, the output signals of a plurality of sensors are used as fuzzy inputs, and fuzzy inference is performed in accordance with a predetermined fuzzy rule, so that the driver's will and preference, running environment, safety standards, and comfortable and optimal athletic performance are obtained. Presumed. Further, fuzzy inference is performed using the estimated value as a fuzzy input, a motion command value for controlling the vehicle is created, and each part of the vehicle is operated by the operation means based on the motion command value. Therefore, the driver's demand and driving environment are recognized in real time by fuzzy inference, and the vehicle motion is controlled in an integrated manner while responding to these demands. Optimum vehicle performance can be achieved.

[0010]

DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS The present invention will be described below with reference to the drawings. FIG. 1 to FIG. 16 are views showing an embodiment of an electronic control unit for a vehicle according to the present invention. FIG. 1 is a block diagram showing the configuration of an integrated vehicle control system to which the present apparatus is applied. In this figure, reference numeral 1 denotes a sensor unit for detecting various operations of the driver. As shown in FIG. 2, the sensor unit 1 includes, for example, an existing accelerator opening sensor 11, a steering wheel angle sensor 12, a brake Brake oil pressure (pressure) sensor 13 for detecting pedal operation, G sensor 1 for detecting acceleration
4. It comprises a vehicle speed sensor 15 and the like. Other sensors that can detect various operations by the driver can be used, and other sensors already installed in the vehicle can be used. For example, a shift position that detects a gear position of a manual gear can be used. A sensor may be used.

The detection signal of the sensor unit 1 is input to a fuzzy driver will inference unit 21, a fuzzy disturbance inference unit 22, and a fuzzy sense inference unit 23. Note that these inference units constitute the estimation unit 200 as a whole. The fuzzy driver intention inference unit 21 infers what kind of motion the driver intends to cause the car from the operation of the driver, and performs fuzzy inference based on an output signal of the sensor unit 1 according to a predetermined fuzzy rule. Go and infer the will and preferences of the driver.

FIG. 2 shows a hardware configuration of the fuzzy driver intention inference unit 21. 2, signals from an accelerator opening sensor 11, a steering angle sensor 12, a brake oil pressure sensor 13, a G sensor 14, and a vehicle speed sensor 15 in the sensor unit 1
And is sequentially input to an ADC (A / D converter) 202, and after being A / D converted, is input to a CPU 204 and the like via an I / O port 203. The signal of the vehicle speed sensor 15 is directly sent to the I / O port 203.
, And then to the CPU 204. I / O
The port 203 is connected to the CPU 204, the ROM 205 and the RA
M206 is mutually connected via a bus line,
Data is transmitted and received between them.

A ROM 205 stores data necessary for fuzzy inference, a control program for the CPU 204, and the like.
206 performs temporary storage of data and the like. CPU 204 is R
According to a program stored in the OM 205, data processing necessary for fuzzy inference is executed based on external data input via the I / O port 203, and data necessary for inference is output to the fuzzy chip 207. The fuzzy chip 207 makes a fuzzy inference on what kind of motion the driver intends to cause the vehicle to perform in accordance with a predetermined fuzzy rule based on a signal from the sensor unit 1, and generates inference data of the driver's will and preference.

Here, the fuzzy rules of the fuzzy driver will inference unit 21 will be described. The rule is the so-called I
F, THEN (if any). FIG. 3 shows a fuzzy inference rule for inferring a driver's intention regarding acceleration, deceleration, and turning. As shown in FIG. 3A, the fuzzy inference inputs regarding acceleration are an accelerator opening (input 1) and a time change rate of the accelerator opening (input 2).
The fuzzy inference output is the degree of acceleration demand. The membership function of the fuzzy rule for acceleration is shown in FIG.
To (c). As shown in FIG. 3B, the fuzzy inference input relating to deceleration is the brake oil pressure (input 1) and the time change rate of the brake oil pressure (input 2).
And the fuzzy inference output is the degree of deceleration request. Also,
The membership functions of the fuzzy rule regarding deceleration are set as shown in FIGS. As shown in FIG. 3 (C), the fuzzy inference input related to turning is a steering wheel steering angle (input 1) and a time change rate of the steering wheel angle (input 2), and the fuzzy inference output is a turning request degree. Also, the membership functions of the fuzzy rule regarding turning are set as shown in FIGS.

The meaning of each label is as follows. S: Small (small) M: Medium (medium) MD: Medium Big (slightly large) B: Big (large) MS: Medium Small (slightly small)

In these inferences, basically, the input is used as the rate of change, the output is determined by adding the rate of change to the absolute amount, and the response is accelerated, and at the same time, the output is added according to the absolute amount and the rate of change. It is characterized by the fact that the rate at which fuzzy inference is changed is appropriately utilized.

In the operation process of fuzzy inference, first, a fuzzy inference input (each of the above input 1 and input 2) is performed in the antecedent part.
To determine how well the membership function corresponding to the fuzzy rule is matched. Then, the one having a small degree of matching is selected and given to the consequent part. In the consequent part, the output membership function is restricted by the selected fitness. Then, the combined output is generated by the MAX combining process, and then one definite value (ie, the driver's intention) is obtained from the center of gravity of the combined output by the defuzzifier. In this way, the driver's intention regarding acceleration, deceleration, and turning is inferred.

Next, the fuzzy disturbance inference unit 22 infers a running environment such as road surface friction, running resistance, and crosswind which affect the motion of the vehicle. The fuzzy disturbance inference unit 22 outputs the output signal of the sensor unit 1 and the running condition of the vehicle 31. Based on the detected output of the sensor unit 32, fuzzy inference is performed according to a predetermined fuzzy rule to infer each of the above data. As the sensor unit 32, for example, an engine speed sensor, a G sensor, a current sensor, or the like is used.

FIG. 5 shows a configuration of disturbance estimation based on fuzzy inference.
Is shown as FIG. 5A is a diagram for inferring a disturbance by capturing a change in the motion state due to the disturbance of the vehicle. Various sensor outputs relating to the movement of the vehicle (outputs of the sensor unit 32) are used as fuzzy inference inputs, and the disturbance is used as a fuzzy inference output. I do. In this case, the fuzzy inference rule is the vehicle (automobile 21
0) and the vehicle response when a disturbance enters (automobile 2)
(Fuzzy disturbance inference 211).

FIG. 5B shows a motion model 2 of the automobile 210.
12, and a disturbance is estimated by fuzzy parameter inference 213 from the difference between the response of the model 212 and the response of the actual vehicle motion. The point that various sensor outputs related to the movement of the vehicle are used as fuzzy inference inputs and the disturbance is used as fuzzy inference outputs is the same as FIG. In this case, the fuzzy inference rule is composed of a rule indicating the relationship between the difference between the response of the model 212 and the actual response and the disturbance, and changes the parameter corresponding to the disturbance in the model 212 so that the response difference disappears. Thus, the disturbance is determined when the response difference falls within the allowable range.

The following equation shows an example of a model in which a disturbance such as weight, including the occupants of the vehicle, which is one of the road friction and running resistance, is incorporated into the parameters of the response model of the vehicle. In the equation, s is a Laplace transformer.

[0022]

(Equation 1)

Using this model, the relationship between the change in the parameter of the disturbance and the actual response error at that time is formed into a fuzzy rule in advance. In addition,
In the above equation, the parameter K takes into account the effect of road surface friction because it changes as shown in FIG. 6 depending on the road surface friction coefficient.

Further, since the vehicle response has nonlinear characteristics, it is difficult to estimate the disturbance by simply using an estimation method based on a general linear theory. Therefore, as in the present embodiment, a large number of characteristics relating to the relationship between the disturbance and the response are experimentally obtained and fuzzy rules are used to make use of the characteristics of the fuzzy inference. An example of each fuzzy inference rule is shown in FIGS. FIG. 7 is an inference rule for disturbance (coefficient of road surface friction) according to FIG. 5A, and FIG. 8 is an inference rule for disturbance (coefficient of road surface friction) according to FIG. 5B.

Here, the membership function of the inference rule of disturbance (coefficient of road surface friction) shown in FIG. 5A is set as shown in FIG. Similarly, the membership function of the inference rule of disturbance (coefficient of road surface friction) shown in FIG. 5B is set as shown in FIG.

Next, the fuzzy sense inference unit 23 is a fuzzy inference unit for associating a sensor output relating to the movement of the automobile with the driver's sense. The movement command value creation fuzzy controller 24 described below, when creating a movement command value,
The fuzzy sensation inference unit 23 is driven so that the motion command value gives the driver a preferable sensation. Each output signal of the sensor unit 1 and the sensor unit 32 is input to the fuzzy sensation inference unit 23, and the fuzzy sensation inference unit 23 infers a driver's sensation according to the driving environment and the exercise state of the automobile.
FIG. 11 shows a configuration example of the sensory inference rule.

FIG. 11A shows an example of a configuration in which the driver's operation for each sense, such as the degree of tension and the lightness of the steering wheel, is associated with the vehicle motion data at that time. 231 and the driver's operation data 232 are input to the data processing unit 233 and subjected to data processing. In the course of this processing, the speed of the vehicle, the steering frequency of the steering wheel, etc. are determined for each sense using a method such as a main factor analysis. Closely related variables are feature variables A1 to An
Is extracted as

That is, the data processing section 233 is a section for calculating a characteristic amount relating to the driver's sense from the sensor output sections 1 and 32, and this characteristic amount becomes a fuzzy input to a fuzzy sensory inference rule creating section 234 described later. Note that the data processing unit 233 may be omitted if it is not necessary to calculate the feature amount.

After the process of FIG. 11A, the process of FIG.
As shown in the figure, the relation between the extracted feature variables A1 to An and the respective senses is represented by a fuzzy sense inference rule creating unit 2.
At 34, a fuzzy inference rule is made, and this rule is matched with the experimental results of monitoring a large number of drivers. For this matching, an optimal fuzzy rule is designed by using, for example, a neural network 235. Then, similar design is performed for each sense (for example, sense O1: 236), and the designed fuzzy rule is applied to sense O.
The fuzzy sensation inference unit 23 is created by storing in the fuzzy chip 237 for every 1, O2... On.
FIG. 12 shows an example of a fuzzy inference rule for inferring a degree of tension as a driver's sensation. Further, the membership function of the fuzzy inference rule for inferring the degree of tension is set as shown in FIG.

The inference results of the fuzzy driver's will inference unit 21, fuzzy disturbance inference unit 22 and fuzzy sense inference unit 23 are input to a motion command value creation fuzzy controller 24. The output from the fuzzy failure diagnosis inference unit 25 is input. The fuzzy failure diagnosis and inference unit 25 determines whether or not the automobile 31 has failed based on the signal from the sensor unit 32 by fuzzy inference, and outputs the determination result to the motion command value creation fuzzy controller 24.

Motion command value creation fuzzy controller 24
Are motion command values, disturbances, driver sensations according to the driver's intentions inferred by the fuzzy driver's will inference unit 21, fuzzy disturbance inference unit 22, and fuzzy sense inference unit 23, respectively, and the vehicle safety standard and the vehicle An optimal motion command value is created by comprehensively considering the contents of the memory 26 storing the performance.

In this case, FIG. 14 shows an example of a fuzzy inference rule at the time of acceleration / turning motion during high-speed running. In addition, the membership function of the antecedent part in the fuzzy inference rule at the time of acceleration / turning motion is set as shown in FIG. 15, and the membership function of the consequent part is set as shown in FIG. The rule shown in FIG. 14 corresponds to a case in which a driver requests acceleration and turning during high-speed running. For example, if acceleration is dangerous, priority is given to turning motion so that limited tires can be used. In the force acting on the ground contact surface, the turning force is made larger than the driving force for effective use and safety is ensured.

Also, if the estimated road friction is moderate, the tire driving force is suppressed and slightly reduced to prevent the wheels from slipping. By taking into account the driver's intention, disturbance, driver's sensation, and safety as input, and describing the rules that output the vehicle's motion commands, the features of fuzzy inference that comprehensively determine conditions are utilized. The optimal motion command value is created.

These movement command values are sent to the sub-controller group 40 via the sub-controller command value output circuit 27. The sub-controller command value output circuit 27 generates a sub-controller command value for the sub-controller group 40 divided into a plurality of sections as described later based on the motion command value created by the motion command value creation fuzzy controller 24, and The data is appropriately divided and transmitted every 41 to 45.

Each of the sub-controllers 41 to 45 generates and outputs a control signal to a plurality of operation means 300 controlling each part of the automobile 31.
Specifically, there are a traction device, a power steering device, a suspension device, an antilock brake device, and a 4WS device. Then, sub-controllers 41 to 45 are arranged corresponding to these devices. Explaining with individual names, the sub-controllers are a traction controller 41, a power steering controller 42, a suspension controller 43, an anti-lock brake controller 44, and a 4WS controller 45.
Becomes

Each of the sub-controllers 41 to 45 generates a control signal for stably realizing the function of each device based on the sub-controller command value, and
That is, traction devices, power steering devices,
Suspension device, anti-lock brake device, 4W
Output to S device. The traction device, the power steering device, the suspension device, the anti-lock brake device, and the 4WS device are disposed in each part of the automobile 31 and control the running state of the vehicle.

A signal from the sensor section 32 is input to each of the sub-controllers 41 to 45 together with the sub-controller command value. In this case, for example, the anti-lock brake controller 44 is sent sensor information such as a road surface friction coefficient, which is an estimated disturbance, and longitudinal acceleration in the motion of the automobile 31 detected by the sensor unit 32. The brake controller 44 controls the operation of the anti-lock brake device as needed when the vehicle 31 travels. Note that such control is the same for other devices.

As described above, in the present embodiment, the fuzzy set represents an amount that it is difficult to clearly indicate preference or intention, and outputs of various sensors used for vehicle control (outputs of the sensor units 1 and 32) and driving. The relationship between the preferences and intentions of the driver is fuzzy rules, and the performance and safety standards of the vehicle 31 are stored in the memory 26 in advance.
The driver's preference and intention are fuzzy inferred from the various sensor outputs and the information stored in the memory 26 by the fuzzy driver will inference unit 21, fuzzy disturbance inference unit 22, and fuzzy sense inference unit 23, and the driving environment,
The performance and safety of the automobile 32 are comprehensively determined, and the optimal motion command value is determined by the motion command value creation fuzzy controller 24.
After that, control signals for controlling each part of the vehicle are generated by each of the sub-controllers 41 to 45 in accordance with the motion command values, and each device of the vehicle, that is, a traction device, a power steering device, and a suspension device, are generated based on the control signals. , Anti-lock brake device,
The 4WS device is controlled. The fuzzy inference operation is performed using a fuzzy chip.

Therefore, it is possible to recognize the driver's preferences and intentions in real time, and to realize safe, comfortable and optimal vehicle kinematics according to the driver's preferences and intentions according to the driving environment. Can be. That is, it is possible to achieve both comfortable and optimal exercise performance according to the preference of the person and the traveling environment. Further, when executing the above-described vehicle control, it is not necessary to use a new sensor, and it is possible to avoid an increase in cost for the vehicle comprehensive control.

Since the fuzzy inference operation is performed using a fuzzy chip, an inference result can be obtained in real time, and the effect can be achieved in a control requiring a high-speed operation such as an automobile. Further, since the motion command value is sent to the subsystem including the respective sub-controllers, it is possible to optimally match the braking / driving force and the turning force of the limited tire capacity, and the respective subsystems have the conflicting tire force. It is possible to prevent a decrease in exercise performance due to a request for use. In addition, there is an advantage that redundant use of the sensor between the respective subsystems can be eliminated.

The application of the present invention is not limited to the above-described vehicle device, but may be applied to other devices (for example, an engine device) as long as they control the running state of the vehicle. In that case, a fuzzy rule that is considered to be optimal in view of the characteristics of other devices may be created.

[0042]

According to the present invention, the relationship between the output of various sensors used for fuzzy rule-based vehicle control, the preference and intention of the driver, and the performance and safety standards of the vehicle stored in advance are used. At the time of traveling, from the various sensor outputs and the information stored in the memory, an optimal motion command value that comprehensively determines the driver's preference and intention, traveling environment, vehicle performance, and safety is created. , The following effects can be obtained.

When the control is performed in accordance with the request of the preference of the person, since the fuzzy inference is used, the preference and intention of the driver are, for example, "somewhat good" or "I want to accelerate considerably".
The vehicle control can be performed in accordance with such demands, even if the amount is not clearly indicated, such as. Further, even if there are individual differences in these amounts, or even if the taste is sensual and changes depending on the situation, it is possible to realize these recognitions in real time by fuzzy inference.

Therefore, the motion of the vehicle can be optimally controlled by comprehensively judging from the recognition information, and the safe, comfortable and optimal vehicle can be controlled according to the driver's preference and intention according to the driving environment. Athletic performance can be realized. That is, it is possible to achieve both comfortable and optimal exercise performance according to the preference of the person and the traveling environment.

[Brief description of the drawings]

FIG. 1 is a block diagram showing a configuration of an embodiment of an integrated vehicle control system to which an electronic control unit for a vehicle according to the present invention is applied.

FIG. 2 is a diagram illustrating a hardware configuration of a fuzzy driver will inference unit of the embodiment.

FIG. 3 is a diagram showing fuzzy rules used in a fuzzy driver will inference unit of the embodiment.

FIG. 4 is a diagram showing a membership function of a fuzzy rule used in a fuzzy driver will inference unit of the embodiment.

FIG. 5 is a diagram showing a configuration of disturbance estimation by fuzzy inference of the embodiment.

FIG. 6 is a diagram illustrating the influence of road surface friction in the embodiment.

FIG. 7 is a diagram showing a disturbance inference rule according to FIG. 5A of the embodiment.

FIG. 8 is a diagram showing a disturbance inference rule according to FIG. 5B of the embodiment.

FIG. 9 is a diagram showing a membership function of a disturbance inference rule according to FIG. 5A of the embodiment.

FIG. 10 is a diagram showing a membership function of a disturbance inference rule according to FIG. 5B of the embodiment.

FIG. 11 is a diagram illustrating a configuration example of a sensory inference rule according to the embodiment.

FIG. 12 is a diagram showing an example of a fuzzy inference rule for inferring a degree of tension in the embodiment.

FIG. 13 is a diagram illustrating a membership function of a fuzzy inference rule for inferring a degree of tension in the embodiment.

FIG. 14 is a diagram showing an example of a fuzzy inference rule at the time of acceleration / turning motion during high-speed running according to the embodiment.

FIG. 15 is a diagram showing a membership function of an antecedent part of a fuzzy inference rule at the time of acceleration / turning motion at the time of high-speed running in the embodiment.

FIG. 16 is a diagram illustrating a membership function of a consequent part of a fuzzy inference rule during acceleration / turning motion during high-speed running according to the embodiment.

[Explanation of symbols]

1, 32 sensor unit 11 accelerator opening sensor 12 steering wheel angle sensor 13 brake oil pressure (pressure) sensor 14 G sensor 15 vehicle speed sensor 21 fuzzy driver will inference unit 22 fuzzy disturbance inference unit 23 fuzzy sense inference unit 24 motion command value creation Fuzzy controller (command value creation means) 25 Fuzzy failure diagnosis and inference unit 26 Memory 27 Sub-controller command value output circuit 31 Automobile 40 Sub-controller group 41 Traction controller 42 Power steering controller 43 Suspension controller 44 Antilock brake controller 45 4WS controller 200 Estimation means 300 operation means 300

──────────────────────────────────────────────────続 き Continued on the front page (51) Int.Cl. ⁷ Identification code FI B62D 113: 00 137: 00

Claims (4)

(57) [Claims] 1. A plurality of sensors for detecting various states of a vehicle, and fuzzy inference is performed by using output signals of the plurality of sensors as fuzzy inputs in accordance with a predetermined fuzzy rule, and a driver's intention and preference, a driving environment, a safety standard, Estimating means for estimating comfortable and optimal athletic performance; fuzzy inference according to a predetermined fuzzy rule using an output of the estimating means as a fuzzy rule; and command value creating means for creating a motion command value for controlling the vehicle; Operating means for operating each part of the vehicle based on the motion command value from the means , wherein the command value creating means responds to the output of the various sensors.
Fuzzy rules for driver's intentions and preferences,
The performance and safety standards of the vehicle are stored in a memory in advance, and the outputs of the various sensors and the information stored in the memory are stored when the vehicle is running.
Driver's will and preferences, driving environment, vehicle performance, safety
Optimal motility by comprehensively judging motility by fuzzy inference
An electronic control unit for a motor vehicle, wherein the electronic control unit is used to create functions. 2. The vehicle according to claim 1, wherein said command value generating means controls the vehicle optimally.
A higher-level control unit that creates higher-level motion command values to be controlled, and controls a plurality of operation units based on the higher-level motion command values
From the lower control unit that creates the lower motion command value
2. A hierarchical structure according to claim 1, wherein
Onboard electronic control unit. 3. The operating means includes a traction device,
Anti-lock brake device, supension device, power
At least a plurality of steering devices and 4WS devices
2. The vehicle according to claim 1, further comprising:
Electronic control unit. 4. The traction device according to claim 1, wherein
Device, anti-lock brake device, suffension device,
Power steering device, 4WS device
3. The electronic control of a vehicle according to claim 2, wherein:
apparatus.