JPH05124529A - Electronic controller for automobile - 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 an automobile, and more particularly to an electronic control unit for integrally controlling vehicle motion using fuzzy reasoning while meeting the driver's requirements and driving environment. ..

[0002]

2. Description of the Related Art Conventionally, when electronically controlling an automobile, attempts have been made to control a control device of each subsystem such as a brake, a suspension, and a steering by integrally capturing a vehicle motion from a motion performance.

[0003]

However, even though the above-mentioned conventional electronic control device has been tried, it is possible to recognize the driver's preference and will, the driving environment, and the performance and safety of the vehicle in real time. Moreover, there has been no example of trying to achieve optimum car movement by making a comprehensive judgment based on those situations. Therefore, there is a problem in that it is difficult to realize both comfortable and optimal exercise performance in accordance with people's preferences and driving environment.

On the other hand, even if an attempt is made to perform control in accordance with such a demand, the driver's preference and will are not clearly shown, for example, "somewhat good" or "want to accelerate considerably". After all, it was difficult to carry out the control. In addition, these amounts vary from person to person and at the same time, their tastes are sensual and change according to the situation. Was difficult to achieve.

Therefore, the present invention provides an electronic control device for a vehicle, which recognizes the driver's preference and will, the driving environment, the performance and safety of the vehicle in real time, and can realize comfortable and optimal exercise performance. It is an object.

[0006]

In order to achieve the above object, an electronic control unit for an automobile according to a first aspect of the invention uses a plurality of sensors for detecting various states of the vehicle and fuzzy output signals of the plurality of sensors. Estimating means for estimating the driver's will and preference, driving environment, safety standards, comfortable and optimal motor performance by performing fuzzy inference according to a predetermined fuzzy rule as input, and the output of the estimating means as fuzzy input for the predetermined fuzzy rule Make fuzzy inference according to
It is characterized in that it is provided with command value creating means for creating a motion command value for controlling the vehicle, and operating means for operating each part of the vehicle based on the motion command value from the command value creating means.

In a preferred mode, the command value creating means fuzzy rules the driver's intentions and preferences for the outputs of the various sensors, and stores the vehicle performance and safety standards in a memory in advance. When driving, fuzzy reasoning is used to comprehensively judge the driver's will and preference, running environment, vehicle performance, and safety from the outputs of the various sensors and the memory information stored in the memory to create the optimal exercise performance. To do. The command value creating means is a host control unit that creates a host motion command value for optimally controlling the vehicle,
It is characterized in that it has a hierarchical structure composed of a lower control unit that creates a lower motion command value for controlling the plurality of operation units based on the higher motion command value.

The operating means includes at least a plurality of devices among a traction device, an anti-lock brake device, a sufficiency device, a power steering device, and a 4WS device, and the operating unit includes a traction device and an anti-lock device. It is characterized by being any one of a brake device, a suspension device, a power steering device, and a 4WS device.

[0009]

In the present invention, the output signals of a plurality of sensors are used as fuzzy inputs to perform fuzzy inference according to predetermined fuzzy rules, and the driver's will and preference, driving environment, safety standards, and comfortable and optimal exercise performance can be obtained. Presumed. Fuzzy inference is performed using the estimated value as a fuzzy input, a motion command value for controlling the vehicle is created, and each unit of the vehicle is operated by the operation means based on the motion command value. Therefore, the driver's request and the driving environment are recognized in real time by fuzzy reasoning, and the vehicle motion is controlled in an integrated manner in accordance with these requests, so that the driver's preference and intention can be considered safe, comfortable and It is possible to realize optimal motor performance.

[0010]

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

The detection signal of the sensor unit 1 is input to the fuzzy driver willing reasoning unit 21, the fuzzy disturbance reasoning unit 22 and the fuzzy sense reasoning unit 23. In addition, these inference units constitute the estimation means 200 as a whole. The fuzzy driver intention deduction unit 21 deduces from the operation of the driver what kind of motion the driver is trying to make the vehicle perform, and based on the output signal of the sensor unit 1, a fuzzy inference is performed according to a predetermined fuzzy rule. Go and infer the driver's will and preferences.

The hardware configuration of the fuzzy driver willingness inference unit 21 is shown in FIG. In FIG. 2, signals from the accelerator opening sensor 11, the steering wheel angle sensor 12, the brake oil pressure sensor 13, the G sensor 14, and the vehicle speed sensor 15 in the sensor unit 1 are multiplexed by the multiplexer 201.
Are alternately selected and sequentially input to the ADC (A / D converter) 202, A / D converted, and then input to the CPU 204 or the like via the I / O port 203. Further, the signal from the vehicle speed sensor 15 is directly transmitted to the I / O port 203.
Is input to the CPU 204 and then to the CPU 204. I / O
Port 203 is CPU 204, ROM 205 and RA
It is mutually connected to M206 via a bus line,
Data is exchanged between them.

A ROM 205 stores data necessary for fuzzy inference, a control program of the CPU 204, etc., and a RAM
206 temporarily stores data and the like. CPU204 is R
According to the program stored in the OM 205, data processing required for fuzzy inference is executed based on external data input via the I / O port 203, and the data required for inference is output to the fuzzy chip 207. The fuzzy chip 207 performs a fuzzy inference based on a signal from the sensor unit 1 according to a predetermined fuzzy rule about what motion the driver intends to make the vehicle, and generates inference data of the driver's intention and preference.

The fuzzy rules of the fuzzy driver willingness inference unit 21 will be described. The rule is so-called I
It is expressed in the form 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. 3 (A), the fuzzy inference inputs regarding acceleration are the accelerator opening (input 1) and the time rate of change of the accelerator opening (input 2),
The fuzzy reasoning output is the acceleration demand. Further, the membership function of the fuzzy rule regarding acceleration is shown in Fig. 4 (a).
It is set as shown in (c). As shown in FIG. 3 (B), the fuzzy inference input regarding deceleration is the brake hydraulic pressure (input 1) and the temporal change rate of the brake hydraulic pressure (input 2).
And the fuzzy inference output is the deceleration demand. Also,
The membership function of the fuzzy rule regarding deceleration is set as shown in FIGS. As shown in FIG. 3 (C), the fuzzy inference input regarding turning is the steering wheel steering angle (input 1) and the time change rate of the steering wheel steering angle (input 2), and the fuzzy inference output is the turning demand degree. Further, the membership function of the fuzzy rule regarding turning is set as shown in FIGS. 4 (g) to 4 (i).

The meaning of each label is as follows. S: Small M: Medium MD: Medium Big B: Big MS: Medium Small

These inferences are basically such that the input is the rate of change, the output is determined by adding the rate of change to the absolute amount, the response is speeded up, and at the same time the rate of addition is added according to the absolute amount and the rate of change. The characteristic is that the rate of change is changed, and the nonlinearity of fuzzy reasoning is appropriately used.

In the operation process of fuzzy inference, first, in the antecedent part, fuzzy inference input (each input 1 and input 2)
And to find how well it fits the membership function corresponding to the above fuzzy rule. Then, the one with the smaller matching degree is selected and given to the consequent part. In the consequent part, the output membership function is restricted based on the selected goodness of fit. Then, the combined output is generated by the MAX combining processing, and then, one definite value (that is, 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 friction, running resistance, and cross wind that influences the motion of the automobile. The fuzzy disturbance inference unit 22 outputs the output signal of the sensor unit 1 and the running condition of the automobile 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.

The structure of disturbance estimation by fuzzy inference is shown in FIG.
As shown. FIG. 5 (a) is for inferring a disturbance by grasping a change in a motion state due to a disturbance of the vehicle. Various sensor outputs (outputs of the sensor unit 32) related to the movement of the vehicle are used as fuzzy inference inputs, and the disturbance is referred to as fuzzy inference output. To do. In this case, the fuzzy inference rule is
0) input and vehicle response in the event of disturbance (automobile 2
10), and a rule (fuzzy disturbance inference 211) indicating a relationship with the response output.

FIG. 5B is a motion model 2 of the automobile 210.
12, the disturbance is estimated by the fuzzy parameter inference 213 from the difference between the response of the model 212 and the response of the actual vehicle motion. As in the case of FIG. 10A, various sensor outputs related to the movement of the automobile are used as fuzzy inference inputs and disturbances are used as fuzzy inference outputs. In this case, the fuzzy inference rule is composed of a rule showing the relationship between the difference between the response of the model 212 and the actual response and the disturbance, and changing the parameter corresponding to the disturbance in the model 212 so as to eliminate the response difference. Then, the disturbance is determined when the response difference is within the allowable range.

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

[0022]

[Equation 1]

Using this model, a fuzzy rule is created in advance for the relationship representing the characteristics of the change in the disturbance parameter and the actual response error at that time. In addition,
In the above mathematical expression, the parameter K takes into consideration the influence of road surface friction because it changes depending on the road surface friction coefficient as shown in FIG.

Further, since the vehicle response has a non-linear characteristic, it is difficult to realize disturbance estimation simply by using an estimation method based on a general linear theory. Therefore, as in the present embodiment, it is possible to utilize the characteristics of fuzzy inference by experimentally obtaining a large number of characteristics regarding the relationship between the disturbance and the response and forming them into fuzzy rules. An example of each fuzzy inference rule is shown in FIGS. FIG. 7 is an inference rule for disturbance (road friction coefficient) according to FIG. 5A, and FIG. 8 is an inference rule for disturbance (road friction coefficient) according to FIG. 5B.

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

Next, the fuzzy sense inference unit 23 is a fuzzy reasoning unit that associates the sensor output related to the motion of the automobile with the driver's sense. The motion command value creation fuzzy controller 24, which will be described later,
The fuzzy sense inference unit 23 is driven so that the motion command value gives the driver a preferable sense. The output signals of the sensor unit 1 and the sensor unit 32 are input to the fuzzy sense inference unit 23, and the fuzzy sense inference unit 23 infers the driver's sense according to the running environment and the exercise state of the automobile.
A configuration example of the sense inference rule is shown in FIG.

FIG. 11A shows an example of a configuration in which the driver's operation for each classified feeling 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 operation data 232 are input to the data processing unit 233 for data processing, and in the process of this processing, the speed of the vehicle, the steering frequency of the steering wheel, etc. are measured using a method such as main factor analysis for each sense. Variables with deep relationships are characteristic variables A1 to An
Is extracted as.

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

After the processing of FIG. 11A, the processing of FIG.
As shown in FIG. 5, the relationship between the extracted characteristic variables A1 to An and the respective senses is determined by the fuzzy sense inference rule creating unit 2
At 34, a fuzzy inference rule is created, and this rule is matched with the experimental result 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, the same design is performed for each sense (for example, sense O1: 236), and the designed fuzzy rules are applied to the sense O.
The fuzzy sense inference unit 23 is created by storing in the fuzzy chip 237 every 1, O2 ... On.
FIG. 12 shows an example of a fuzzy inference rule that infers the degree of tension as a driver's sense. 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 willingness inference unit 21, the fuzzy disturbance inference unit 22 and the fuzzy sense inference unit 23 are input to the motion command value creation fuzzy controller 24, and the motion command value creation fuzzy controller 24 further includes The output from the fuzzy fault diagnosis and inference unit 25 is input. The fuzzy fault diagnosis and inference unit 25 determines based on a signal from the sensor unit 32 whether or not the automobile 31 is out of order by fuzzy inference, and outputs the determination result to the motion command value creation fuzzy controller 24.

Motion command value creation fuzzy controller 24
Is the motor command value, the disturbance, the driver's sensation according to the driver's will, which is inferred by the fuzzy driver will inference unit 21, the fuzzy disturbance inference unit 22 and the fuzzy sense inference unit 23, respectively, and the safety standard of the automobile and the automobile. An optimum motion command value is created by comprehensively considering the contents of the memory 26 in which the performance is stored.

In this case, FIG. 14 shows an example of fuzzy inference rules during acceleration / turning motion during high-speed traveling. The membership function of the antecedent part in the fuzzy inference rule during 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 of FIG. 14 corresponds to a request from the driver for acceleration and turning when traveling at high speed. For example, if acceleration is dangerous, priority is given to the turning motion to limit the tires. With respect to 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.

Further, if the estimated road surface friction is medium, the tire driving force is restrained so as to prevent the wheels from slipping. By taking into account the driver's intention, disturbance, driver's feeling, and safety as described above, and describing the rules for outputting motor vehicle motion commands, the features of fuzzy reasoning that comprehensively judge conditions are utilized. The optimum motion command value is created.

These motion 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 creates sub-controller command values for a plurality of sub-controller groups 40, which will be described later, based on the motion command values created by the motion command value creation fuzzy controller 24, and each sub-controller command value is created. The data is appropriately divided into 41 to 45 and transmitted.

Each of the sub-controllers 41 to 45 is for generating and outputting 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, the sub-controllers 41 to 45 are arranged corresponding to these respective devices. Explaining by individual names, each sub-controller is a traction controller 41, a power steering controller 42, a suspension controller 43, an anti-lock brake controller 44, 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 each device,
That is, a traction device, a power steering device,
Suspension device, anti-lock brake device, 4W
Output to S device. The traction device, the power steering device, the suspension device, the antilock brake device, and the 4WS device are arranged in each part of the automobile 31 to 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 with sensor information such as a road surface friction coefficient which is an estimated disturbance and a longitudinal acceleration in the motion of the automobile 31 detected by the sensor unit 32, and the anti-lock brake controller 44 is based on these input information. The brake controller 44 controls the operation of the antilock brake device as needed when the automobile 31 is running. Note that such control is the same for other devices.

As described above, in this embodiment, the fuzzy set represents the amount of preference or intention which is difficult to clearly show, and the outputs of the various sensors (outputs of the sensor units 1 and 32) used for vehicle control and the driving. Fuzzy rules are created based on fuzzy rules regarding personal preferences and intentions, and the performance and safety standards of the automobile 31 are stored in the memory 26 in advance.
From the various sensor outputs and the information stored in the memory 26, the fuzzy driver intention inference unit 21, the fuzzy disturbance inference unit 22 and the fuzzy sensory inference unit 23 fuzzy infer the driver's preference and intention, and the running environment,
The performance and safety of the automobile 32 are comprehensively judged, and the optimum motion command value is generated as a motion command value fuzzy controller 24.
The control signal for controlling each part of the vehicle is generated by the sub-controllers 41 to 45 according to the motion command value, and each device of the vehicle, that is, the traction device, the power steering device, the suspension device is generated based on the control signal. , Anti-lock brake device,
The 4WS device is controlled. The fuzzy inference operation is performed using a fuzzy chip.

Therefore, it becomes possible to recognize the driver's preferences and intentions in real time, and to realize safe, comfortable and optimal motor vehicle performance according to the driver's preferences and intentions according to the driving environment. You can That is, it is possible to realize both comfortable and optimal exercise performance in accordance with the preference of the person and the traveling environment. In addition, it is not necessary to use a new sensor when executing the vehicle control, and it is possible to avoid an increase in cost for the vehicle total control.

Since the fuzzy inference operation is performed using the fuzzy chip, the inference result can be obtained in real time, and the effect can be achieved in the control that requires high-speed operation such as an automobile. Furthermore, since the above-mentioned motion command value is sent to the subsystem including each sub-controller, it is possible to optimally match the braking / driving force and the turning force of the limited tire capacity, and the tire forces of the tires that are in conflict with each other can be obtained. It is possible to prevent deterioration of exercise performance due to demanding use. In addition, there is an advantage that the duplicated use of the sensor between each subsystem can be eliminated.

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

[0042]

According to the present invention, the relationship between various sensor outputs used for fuzzy rule-based vehicle control and the driver's preferences and intentions, and the previously stored performance and safety standards of the vehicle are used. At the time of driving, the optimum motion command value that comprehensively judges the driver's preferences and intentions, the driving environment, the performance of the vehicle, and the safety is created from the outputs of the various sensors and the information stored in the memory. Since the control is performed, the following effects can be obtained.

Since fuzzy reasoning is used when performing control in accordance with the demands of the person's preference, the driver's preference or will, for example, is "somewhat good" or "I want to accelerate considerably".
Vehicle control can be performed in line with such requirements, even if the amount is not explicitly stated, such as. Further, even if there are individual differences in these amounts, or even if the preference is sensual and changes depending on the situation, it is possible to realize these recognitions in real time by fuzzy reasoning.

Therefore, it is possible to optimally control the movement of the vehicle by comprehensively judging from the recognition information, and to select a safe, comfortable and optimal vehicle according to the preference and intention of the driver according to the driving environment. Exercise performance can be realized. That is, it is possible to realize both comfortable and optimal exercise performance in accordance with the preference of the person and the traveling environment.

[Brief description of drawings]

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

FIG. 2 is a diagram showing a hardware configuration of a fuzzy driver willingness inference unit of the embodiment.

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

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

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

FIG. 6 is a diagram for explaining the influence of road surface friction in the same 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.

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

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 showing a configuration example of a sense inference rule according to the same embodiment.

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

FIG. 13 is a diagram showing a membership function of a fuzzy inference rule that infers the degree of tension in the example.

FIG. 14 is a diagram showing an example of a fuzzy inference rule during acceleration / turning motion during high-speed traveling in the same embodiment.

FIG. 15 is a diagram showing the membership function of the antecedent part of the fuzzy inference rule during acceleration / turning motion during high-speed traveling in the same example.

FIG. 16 is a diagram showing a membership function of a consequent part of a fuzzy inference rule during acceleration / turning motion during high-speed traveling in the same example.

[Explanation of symbols]

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

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Claims (5)

[Claims] 1. A plurality of sensors for detecting various states of a vehicle, and fuzzy inference according to a predetermined fuzzy rule, using output signals of the plurality of sensors as fuzzy inputs, and a driver's will and preference, driving environment, safety standards, Estimating means for estimating comfortable and optimum motion performance, command value creating means for creating a motion command value for controlling the vehicle by performing fuzzy inference according to a predetermined fuzzy rule using the output of the estimating means as fuzzy input, and command value creation An electronic control unit for an automobile, comprising: an operating unit that operates each unit of the vehicle based on a motion command value from the unit. 2. The command value creating means fuzzy rules the driver's intention and preference for the outputs of the various sensors, stores the performance and safety standards of the vehicle in a memory in advance, and stores the information when the vehicle is running. The optimum exercise performance is created by comprehensively determining the driver's will and preference, traveling environment, vehicle performance, and safety from the outputs of various sensors and memory information by fuzzy reasoning. The electronic control unit for a vehicle according to 1. 3. The command value creating means includes a host control unit for creating an upper motion command value for optimally controlling the vehicle, and a lower motion command value for controlling a plurality of operation units based on the upper motion command value. The electronic control device for an automobile according to claim 1, wherein the electronic control device has a hierarchical structure including a lower control unit to be created. 4. The electronic device for an automobile according to claim 1, wherein the operation means includes at least a plurality of devices among a traction device, an antilock brake device, a sufficiency device, a power steering device, and a 4WS device. Control device. 5. The operation unit includes a traction device, an anti-lock brake device, a sufficiency device,
4. The electronic control device for a vehicle according to claim 3, wherein the electronic control device is one of a power steering device and a 4WS device.