JP2011063107A - Vehicle controller - Google Patents

Abstract

<P>PROBLEM TO BE SOLVED: To provide a vehicle controller capable of generating two-dimensional road surface μ distribution with high accuracy by a simple method. <P>SOLUTION: A driving plan generation ECU 18 extracts a road surface μ, having reliability of a degree of non-linearity of a vehicle and tire and/or reliability of wheel slip which are higher than predetermined values, out of calculated road surfaces μ, wherein the road surface μ at a right side wheel position and left side wheel position is expressed as, μ=(longitudinal Gx<SP>2</SP>+lateral Gy<SP>2</SP>)<SP>1/2</SP>. Then, the two-dimensional road surface μ distribution is generated by interpolating in a longitudinal direction and lateral direction of the road based on the extracted road surface μ. <P>COPYRIGHT: (C)2011,JPO&INPIT

Description

  The present invention relates to a vehicle control device, and more particularly to a vehicle control device that generates a two-dimensional road surface friction coefficient distribution in the traveling direction and width direction of a road.

  In recent years, with the advancement of intelligence technology and information technology of vehicles such as automobiles, vehicle control technology that supports driving and traveling of vehicles has been developed to a higher degree than before, and the driving burden on the driver of the vehicle has been reduced. Reduced and improved vehicle safety. For example, using on-vehicle radar, inter-vehicle communication technology, etc., automatically discovers leading vehicles and obstacles that exist in the traveling direction of the vehicle, and avoids them appropriately or maintains an appropriate inter-vehicle distance By using a technology that issues a warning to a person or controls the running / motion of the vehicle and a car navigation system using GPS (Global Positioning System), information on the current position of the vehicle and information on the surrounding road conditions are acquired. In addition, a technique for generating an operation plan (travel locus) by referring to such information and long-term desires of the driver (destination, arrival time, etc.) has been proposed. In order to generate an operation plan and control vehicle behavior, it is necessary to grasp a road surface friction coefficient (hereinafter referred to as “μ”) representing a road surface state.

  For example, in Patent Document 1, the control unit calculates a target behavior (a target longitudinal force Fx, a target yaw moment Mz) estimated mainly from a driver operation on the host vehicle, and sets the host vehicle 1 after a preset Δt seconds. An objective function L that represents a minimum value including the friction circle utilization rate μr (Δt), the total contact probability Rt (Δt) for all three-dimensional objects to be determined, and the target behavior correction amount δFv is set in advance. A technique has been proposed in which a target behavior correction amount δFv that minimizes the objective function L is calculated, a control amount is determined based on the target behavior and the target behavior correction amount δFv, and automatic brake control is executed based on the control amount. ing.

  By the way, the value of the road surface μ generally defines only the front-rear direction of the road, that is, the one-dimensional distribution. However, the road surface condition may differ in the lateral direction of the road (for example, there is a pool of water near the road shoulder, residual snow, etc., and the road surface μ is low, but the road center is dry and the road surface μ is relatively high. In the case of the driving plan and vehicle control based on the one-dimensional distribution of the road surface μ, there is a problem that the driving plan and vehicle control that accurately reflects the road surface condition cannot be performed. In addition, since a person determines a planar state (two-dimensional) visually, a driver feels uncomfortable in driving plans and vehicle control based on a one-dimensional distribution of road surface μ.

JP 2007-99166 A

  The present invention has been made in view of the above, and an object thereof is to provide a vehicle control device capable of generating a highly accurate two-dimensional road surface μ distribution by a simple method.

  In order to solve the above-described problems and achieve the object, the present invention provides a travel information input unit that inputs travel information with a track record of own vehicle or another vehicle, and travel information input from the travel information input unit. And a two-dimensional road surface μ distribution generating means for generating a two-dimensional road surface friction coefficient distribution (hereinafter referred to as “μ”) in the longitudinal direction and the lateral direction of the road. .

  Further, according to a preferred aspect of the present invention, the traveling information includes right wheel position information, left wheel position information, front and rear Gx, and lateral Gy, and the two-dimensional road surface μ distribution generating means includes a right wheel position. It is desirable to calculate the road surface μ of the left wheel position based on the front and rear Gx and the lateral Gy.

Further, according to a preferred aspect of the present invention, it is desirable that the two-dimensional road surface μ distribution generating means sets the road surface μ of the right and left wheel positions = (front and rear Gx ² + lateral Gy ² ) ^1/2 .

  Further, according to a preferred aspect of the present invention, the two-dimensional road surface μ distribution generating means includes the reliability of the non-linear degree of the vehicle and the tire and / or the wheel slip among the calculated road surface μ of the right wheel position and the left wheel position. It is desirable to extract a vehicle whose reliability is greater than or equal to a predetermined value, and generate a two-dimensional road surface μ distribution by interpolating in the longitudinal direction and lateral direction of the road based on the extracted road surface μ.

  Further, according to a preferred aspect of the present invention, it is desirable to generate a travel locus or control a vehicle based on the two-dimensional road surface μ distribution generated by the two-dimensional road surface μ distribution generation unit.

According to the present invention, on the basis of the travel information input means for inputting travel information with a track record of own vehicle or other vehicle, and the travel information input from the travel information input means, the longitudinal and lateral directions of the road Since it has a two-dimensional road surface μ distribution generating means for generating a two-dimensional road surface μ distribution,
There is an effect that it is possible to provide a vehicle control device capable of generating a highly accurate two-dimensional road surface μ distribution by a simple method.

FIG. 1 is a block diagram showing an example of a schematic configuration of a vehicle control device according to Embodiment 1 of the present invention. FIG. 2 is a diagram for explaining an example of the yaw rate reliability map. FIG. 3 is a diagram for explaining an example of the wheel slip μ reliability map. FIG. 4 is a flowchart for explaining a method in which the operation plan generation ECU creates a two-dimensional road surface μ distribution map. FIG. 5 is a diagram for explaining wheel slip. FIG. 6 is a flowchart for explaining a specific method of calculating the two-dimensional road surface μ distribution. FIG. 7 is a diagram for explaining a specific method of calculating the two-dimensional road surface μ distribution. FIG. 8 is a flowchart for explaining a method in which the operation plan generation ECU according to the second embodiment creates a two-dimensional road surface μ distribution map.

  Embodiments of a vehicle control device according to the present invention will be described below in detail with reference to the drawings. Note that the present invention is not limited to the embodiments. In addition, constituent elements in the following embodiments include those that can be easily assumed by those skilled in the art or that are substantially the same.

(Embodiment 1)
FIG. 1 is a block diagram illustrating an example of a schematic configuration of a vehicle control device mounted on a vehicle according to the first embodiment. The vehicle according to Embodiment 1 is configured to be capable of automatic driving (autonomous driving). As shown in FIG. 1, the vehicle control device 1 according to the first embodiment includes a camera device 11, a radar device 12, a host vehicle travel information input unit 14, a road information storage unit 15, and a car navigation device 16. A communication unit (another vehicle travel information input unit) 17, an operation plan generation ECU 18, a vehicle motion control ECU 19, an EPS (electric power steering) 20, an ECB (electronic brake device) 21, and an engine device 22. Yes.

  The camera device 11 includes a camera and an image processing device, detects left and right white lines on both sides of the travel lane, and outputs the detected positions (coordinates) of the left and right white lines to the driving plan generation ECU 18. . The operation plan generation ECU 18 calculates a line (center line) passing through the center of the vehicle from the positions of the left and right white lines, a curve radius of the center line, and the like.

  The radar device 12 uses a millimeter wave radar to detect the relative distance between the host vehicle and the perceptual object, and outputs the relative distance to the driving plan generation ECU 18. The millimeter wave radar emits a millimeter wave within a predetermined range in the traveling direction from the front surface of the host vehicle CA, and receives the millimeter wave reflected by the perceptual object TA existing in the traveling direction of the host vehicle CA. Then, the millimeter wave radar detects the actual relative distance by calculating the distance from the millimeter wave radar to the perceived object of the host vehicle by measuring the time from emission to reception, and outputs it to the driving plan generation ECU 18. To do. The distance sensor is not limited to the millimeter wave radar. For example, the actual relative distance is based on image data obtained by imaging the traveling direction of the host vehicle with an imaging device such as a radar or a CCD camera using a laser or infrared ray. An image recognition device that calculates Dr may be used.

  The host vehicle travel information detection unit 14 includes, for example, a G sensor that detects front and rear Gx and lateral Gy of the vehicle, a vehicle speed sensor that detects a travel speed (vehicle speed) of the host vehicle, an R-side wheel RL, and the like. A wheel speed sensor that detects the wheel speed of the L-side wheel LL (front wheel and / or rear wheel), a yaw rate sensor that detects a yaw rate (rotational angular velocity around the vertical axis of the center of gravity of the vehicle), a steering angle sensor that detects a steering angle, For example, a GPS receiver that receives a positioning signal such as a GPS (Global Positioning System) signal for measuring the position of the vehicle using an artificial satellite and detects position information that is an absolute position coordinate of the own vehicle is provided. The detected travel information of the host vehicle is output to the driving plan generation ECU 18. The traveling information includes front and rear Gx, lateral Gy, vehicle speed, R side, L side wheel speed, yaw rate, steering angle, position information (absolute position coordinates of the vehicle, R side, absolute position coordinates of the L side wheels), and the like. .

  The road information storage unit 15 stores map data, node information related to the road, and curve information as road data. The node information is, for example, coordinate point data for grasping the road shape. For example, in addition to the start and end points of a curve set on a link (that is, a line connecting each node), information on the curvature of the curve (for example, the curvature and radius R and polarity of the curve) and the curve It consists of information relating to the depth (for example, the turning angle θ required for passing the curve, the length of the curve, etc.).

  The car navigation device 16 detects the current position and the traveling direction of the host vehicle based on the current position information of both the current position information of the host vehicle input from the GPS receiver, and the road information storage unit 15 based on the detection result. Map matching is performed on road information stored in the vehicle, and the display position of the current position of the host vehicle on the display screen is controlled, and the current position of the detected vehicle is detected or operated via various switches and keyboards. The map display on the display screen is controlled for the appropriate vehicle position input by the person.

  The communication unit 17 is configured to be able to receive travel information of other vehicles. For example, the communication unit 17 is configured to be able to communicate with the service center 31 via the base station 31, and is configured to be able to communicate with other vehicles by inter-vehicle communication. The service center 31 collects vehicle travel information. The communication unit 17 transmits the travel information of the host vehicle to the other vehicle 41 and the base station 31, receives the travel information of the other vehicle 41 transmitted from the other vehicle 41 and the base station 31, and drives the received travel information. It outputs to plan generation ECU18.

  The operation plan generation ECU 18 includes a CPU, a ROM, a RAM, a non-volatile memory, an A / D converter, a D / A converter, a port, and the like, and a two-dimensional road surface μ distribution in the longitudinal direction and the lateral direction of the road. , A yaw rate μ reliability map 18b (see FIG. 2) and a wheel slip μ reliability map 18c (see FIG. 3) used when generating the two-dimensional road surface μ distribution map 18a. ). The driving plan generation ECU 18 determines the two-dimensional road surface μ distribution based on the traveling information of other vehicles having a traveling record received by the communication unit 17 and the traveling information of the own vehicle having the traveling record detected by the own vehicle traveling information input unit 14. A map 18a is generated, an ideal travel locus is generated based on the two-dimensional road surface μ distribution map 18a, and is output to the operation control ECU 19.

  The vehicle motion control ECU 19 is connected to vehicle motion devices such as an EPS (electric power steering) 20, an ECB (brake device) 21, and an engine device 22, and controls the operation of actuators of these vehicle motion devices. The vehicle motion control ECU 19 controls the actuator of the vehicle motion device based on the travel locus input from the operation plan generation ECU 18.

  For example, the engine device 22 includes a throttle actuator that opens and closes a throttle valve in an electronic throttle device and adjusts the throttle opening, and the operation control ECU 19 operates the throttle actuator in response to an engine control signal, Adjust the opening. Further, for example, the ECB (brake device) 21 includes a brake actuator that adjusts the control hydraulic pressure to the wheel cylinder, and the operation control ECU 19 outputs a brake control signal to operate the brake actuator 22 to brake the wheel cylinder. Adjust hydraulic pressure. In addition, for example, the EPS (electric power steering) 20 includes a steering actuator that applies a rotational driving force by a motor as a steering torque to the steering mechanism via a speed reduction mechanism, and the operation control ECU 19 outputs a steering control signal to perform steering. The actuator is operated and the steering torque is adjusted by the motor.

  Next, the generation process of the two-dimensional road surface μ distribution map 18a of the operation plan generation ECU 18 will be described in detail with reference to FIGS. When creating an ideal travel locus of the vehicle, the road surface μ is necessary. However, there is no method for reliably estimating the road surface μ, and conventionally, the limit road surface μ or the high road surface μ (for example, “1”) is fixed and used for vehicle control. The method of fixing to the limit road surface μ is not practical because the detection frequency is very low on general roads. There is a safety problem in the method of fixing to the high road surface μ.

  Conventionally, since the road surface μ is defined only in the front-rear direction of the road, that is, the one-dimensional distribution, the road surface state may be different in the lateral direction of the road. In vehicle control, it is not possible to perform a travel plan or vehicle control that accurately reflects the road surface condition.

Therefore, in the present embodiment, the driving plan generation ECU 18 uses the front and rear Gx and the lateral Gy that have been traveled for each of the collected absolute position coordinates (X, Y) of the right wheel RL and the left wheel LL to obtain the road surface. μ = (front and rear Gx ² + lateral Gy ² ) ^1/2 is calculated. Next, in order to generate a highly reliable two-dimensional road surface μ distribution map 18a, the operation plan generation ECU 18 generates reliability of the degree of nonlinearity between the vehicle and the tire and / or reliability due to wheel slip in the calculated road surface μ. That have a value greater than or equal to a predetermined value. The operation plan generation ECU 18 interpolates the extracted road surface μ in the X direction and the Y direction to generate a two-dimensional road surface μ map 18a. Thereafter, the operation plan generation ECU 18 generates an ideal travel locus based on the two-dimensional road surface μ map 18a.

  The travel locus is composed of a number of parameters necessary for traveling of the vehicle such as a position, a speed pattern, an acceleration pattern, a steering angle pattern, a yaw angle, and a yaw rate. When such a travel locus is generated, a predetermined section of the road may be set as a block unit, and the travel locus in each block may be generated in a mesh unit that is a section obtained by dividing the travel path. Further, when generating a travel locus, an evaluation function may be used.

  As a method of collecting travel information having a track record, for example, a method of acquiring travel information from another vehicle (for example, a preceding vehicle) 41 by inter-vehicle communication, a travel information of the own vehicle on a road with a high frequency of travel, etc. There are a method of collecting, a method of collecting traveling information of the other vehicle 41 from the service center 32 for a target road section, and the like.

  FIG. 2 is a diagram for explaining an example of the yaw rate μ reliability map 18b. In the figure, the vertical axis represents the yaw rate μ reliability, and the horizontal axis represents the measured value γ of the yaw rate—the reference yaw rate γ *. Here, the reference yaw rate γ * is a yaw rate determined by the speed Vx and the steering angle δ. The measured value γ of the yaw rate—the reference yaw rate γ * indicates the degree of nonlinearity (yaw rate deviation) between the vehicle and the tire. The yaw rate μ reliability is constant when the measured value γ of the yaw rate−the reference yaw rate γ * is 0 to T1, increases linearly during the interval T1 to T2, and is constant when the interval is greater than T2. When the two-dimensional road surface μ distribution map 18a is generated, a road surface μ that satisfies the yaw rate μ reliability ≧ n1 (where 0 <n1 <1) is extracted from the calculated road surface μ as an effective road surface μ.

  FIG. 3 is a diagram for explaining an example of the wheel slip μ reliability map. In the figure, the vertical axis represents the wheel slip μ reliability, and the horizontal axis represents the slip ratio S. Here, the slip ratio S can be calculated by [(vehicle speed V-R side or L side wheel speed Vw) / vehicle speed V] × 100. The wheel slip μ reliability is constant in a section where the slip ratio S is 0 to T3, linearly increases in a section T3 to T4, and is constant in a section larger than T4. When the two-dimensional road surface μ distribution map 18a is generated, a road surface μ that satisfies the wheel slip μ reliability ≧ n2 (where 0 <n2 <1) is extracted from the calculated road surface μ as an effective road surface μ.

  FIG. 4 is a flowchart for explaining a method by which the operation plan generation ECU 18 creates the two-dimensional road surface μ distribution map 18a. FIG. 5 is a diagram for explaining wheel slip.

In FIG. 4, for each point on the road that is the target of the driving plan, traveling information having a traveling record from the own vehicle or another vehicle (for example, front and rear Gx, lateral Gy, vehicle speed, wheel speed V on the R side and L side, yaw rate) γ, steering angle, R side (X, Y) which is the absolute coordinate of the R side wheel RL, L side (X, Y) which is the absolute coordinate of the L side wheel LL, collection time) in a predetermined time unit or a predetermined travel distance Collected in units (step S1). For the R side (X, Y) and L side (X, Y), the road surface μ = (Gx ² + Gy ² ) ^1/2 is calculated based on the collected front and rear Gx and lateral Gy, and (R side (X, Y, Y), R side μ, R side auxiliary information, L side (X, Y), L side μ, L side auxiliary information) = (R side (X, Y), road surface μ, collection time, L side (X, Y) Y), road surface μ, and collection time) are stored in the memory (step S2). Here, since the front and rear Gx and lateral Gy on the R side and L side collected at the same time are the same, the road surface μ on the R side and L side collected at the same time has the same value.

  Next, referring to the yaw rate μ reliability map 18b, R side μ and L side μ satisfying yaw rate μ reliability ≧ n1 (where 0 <n1 <1) are extracted from the R side μ and L side μ. (Step S3). Thereafter, R side μ and L side μ satisfying wheel slip μ reliability ≧ n2 (where 0 <n2 <1) are extracted with reference to wheel slip μ reliability map 18c (step S4). For example, in FIG. 5, when there is a low μ region of AR1 to AR3 on the road, if the L-side wheel LL slips at AR1 and AR3, the L-side μ can be extracted, and the R-side wheel RL at AR3 When slipping, the R side μ can be extracted.

  The extracted R-side μ and L-side μ are continuously interpolated to create a two-dimensional road surface μ distribution map 18a (step S5). A travel locus is generated based on the created two-dimensional μ distribution map 18a (step S6).

  FIG. 6 is a flowchart for explaining a specific calculation method of the two-dimensional road surface μ distribution in step S5 of FIG. FIG. 7 is a diagram for explaining a specific calculation method of the two-dimensional μ distribution in step S5 of FIG. FIG. 7 shows a straight road having a predetermined width, where the front-rear direction of the road is the X direction and the lateral direction of the road is the Y direction. A specific method for calculating the two-dimensional road surface μ distribution will be described with reference to FIG. 7 according to the flowchart of FIG.

  First, by plotting the R-side μ and L-side μ coordinate points extracted in step S5 of FIG. 4, discrete points with unequal intervals as shown in FIG. 7 can be obtained. In FIG. 6, Hermite interpolation is executed in the X direction (step S11). Thereby, for example, as shown in FIG. 7, a continuous μ value μx in the X direction is obtained by interpolation from X1 to X6. Next, Hermite interpolation is executed in the Y direction (step S12). Thereby, as shown in the figure, a continuous μ value μy in the Y direction is obtained by interpolation from Y1 to Y6.

  For the selected coordinate point (X, Y) on the road to obtain a two-dimensional distribution, the road surface μ is calculated by (X, Y) = μx (X) × k + μy (Y) × (1-k). In step S13, the selected coordinate point (X, Y) is associated with the calculated road surface μ to generate a two-dimensional road surface μ map 18a (step S14). However, k (0.ltoreq.k.ltoreq.1) is variable depending on the situation. For example, k is increased when the lateral positioning accuracy is poor, and k is decreased when ensuring stability on a crossing road or the like. The initial value of k can be set to 0.5 to 0.8 (preferably in the front-rear direction).

  As described above, according to the first embodiment, the operation plan generation ECU 18 determines the two-dimensional road surface μ distribution map in the front-rear direction and the lateral direction of the road on the basis of the travel information with a travel record of the own vehicle or the other vehicle. Since 18a is generated, a highly accurate two-dimensional road surface μ distribution map 18a can be generated by a simple method. As a result, when calculating which position should be traveled in the lateral direction of the road, calculation options are increased, and a safer trajectory can be traced. In addition, by using the two-dimensional road surface μ distribution, it is possible to approximate human motion in human operation assistance, greatly reducing the driver's uncomfortable feeling, and improving vehicle safety performance (for example, braking by straddling road surface) (Distance reduction) and the like.

Further, according to the first embodiment, the driving plan generation ECU 18 sets the road surface μ of the right wheel position and the left wheel position to be ^1/2 (front and rear Gx ² + lateral Gy ² ) ^1/2, and among the calculated road surface μ, Extract tires with a non-linear degree of reliability and / or wheel slip reliability of a predetermined value or more, and based on the extracted road surface μ, interpolate in the longitudinal and lateral directions of the road to obtain a two-dimensional road surface μ distribution. Because it is generated, not only vehicle safety performance but also vehicle responsiveness to driver operations can be improved.

(Embodiment 2)
FIG. 8 is a flowchart for explaining a method of creating the two-dimensional road surface μ distribution map 18a of the operation plan generation ECU 18 according to the second embodiment. 8, parts having the same functions as those in FIG. 4 are denoted by the same reference numerals, description of common steps is omitted, and only different points will be described.

  In step S21 of FIG. 8, the operation plan generation ECU 18 determines the R side μ, L on the (R side (X, Y), R side μ, collection time, L side (X, Y), L side μ, collection time). The side μ is corrected based on the yaw rate μ reliability of the yaw rate reliability map 18b (step S21). To correct the R side μ and the L side μ, for example, a road surface μ whose yaw rate μ reliability of the road surface μ is greater than or equal to the threshold value n1 is used, and the road surface μ whose yaw rate μ reliability is less than the threshold value n is Can be interpolated.

  Next, the operation plan generation ECU 18 calculates the R-side wheel μ and the L-side wheel μ (step S22). The R-side wheel μ and the L-side wheel μ can be calculated by correcting the R-side μ and the L-side μ based on the wheel slip μ reliability of the wheel slip reliability map 18c. R side μ and L side μ are interpolated by Hermite interpolation, etc., using R side μ and L side μ where the wheel slip μ reliability of R side μ and L side μ is greater than or equal to threshold n2. be able to.

  The operation plan generation ECU 18 sets (R side μ, L side μ) = (MIN (current value, R side wheel μ), MIN (current value, L side wheel μ) (step S23), where MIN ( Current value, R-side wheel μ) selects the smaller one between the current value and R-side wheel μ, and MIN (current value, L-side wheel μ) selects the smaller one between the current value and L-side wheel μ.

  The operation plan generation ECU 18 corrects (updates) the R-side μ and the L-side μ based on the elapsed time from the collection time (step S24). The correction of the R side μ and the L side μ is calculated when the vehicle uses the two-dimensional road surface μ distribution map 18a. Specifically, (R side μ, L side μ) = stored (R side μ, L side μ) + (estimated macro μ−stored (R side μ, L side μ)) × elapsed time Time / forgetting time can be set. However, the estimated macro μ and the forgetting setting time can be arbitrarily set according to the outside air temperature and the surrounding situation, and can be set for each region. As a result, the road surface μ can be updated according to the outside air temperature and the surrounding situation.

  In the above-described embodiment, the case where the travel locus is generated based on the two-dimensional road surface μ distribution map 18a has been described. However, the vehicle control may be performed based on the two-dimensional road surface μ distribution map 18a.

  As described above, the vehicle control device and the vehicle control method according to the present invention are useful when generating a driving plan with a high degree of freedom and performing vehicle control in consideration of vehicle responsiveness and driving safety with respect to driver operations. is there.

DESCRIPTION OF SYMBOLS 1 Vehicle control apparatus 11 Camera apparatus 12 Radar apparatus 14 Own vehicle travel information input part 15 Road information storage part 16 Car navigation apparatus 17 Communication part (other vehicle travel information input part)
18 Operation plan generation ECU
18a Two-dimensional road surface μ distribution map 18b Yaw rate reliability map 18c Wheel slip reliability map 19 Vehicle motion control ECU
20 EPS (Electric power steering)
21 ECB (Electronic Brake Device)
22 Engine device 31 Base station 32 Service center 41 Other vehicle

Claims (5)

Traveling information input means for inputting traveling information with a traveling record of the own vehicle or another vehicle;
Two-dimensional road surface μ distribution generation for generating a two-dimensional road surface friction coefficient distribution (hereinafter referred to as “μ”) in the front-rear direction and the lateral direction of the road based on the travel information input from the travel information input means Means,
A vehicle control device comprising: The travel information includes right wheel position information, left wheel position information, front and rear Gx, and lateral Gy,
2. The vehicle control device according to claim 1, wherein the two-dimensional road surface μ distribution generating unit calculates the road surface μ of the right wheel position and the left wheel position based on the front and rear Gx and the lateral Gy. 3. The vehicle control device according to claim 2, wherein the two-dimensional road surface μ distribution generating unit sets the road surface μ of the right wheel position and the left wheel position = (front and rear Gx ² + lateral Gy ² ) ^1/2 .   The two-dimensional road surface μ distribution generating means extracts the road surface μ at the right wheel position and the left wheel position that have a reliability of non-linear degree of vehicle and tire and / or a reliability of wheel slip of a predetermined value or more. 4. The vehicle control device according to claim 2, wherein the two-dimensional road surface μ distribution is generated by interpolating in the longitudinal direction and the lateral direction of the road based on the extracted road surface μ. 5. The traveling locus is generated or the vehicle is controlled based on the two-dimensional road surface μ distribution generated by the two-dimensional road surface μ distribution generating unit. Vehicle control device.

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