Driving assistance device for vehicle

Abstract

This driving assistance device for a vehicle is provided with a control unit that is provided with: a road information acquisition unit that acquires road information on the basis of travel environment information; a minimum road surface friction coefficient detection unit that detects a minimum road surface friction coefficient on the basis of the road information; a driving force calculation unit that calculates a driving force on the basis of the torque of the driving source; a road surface grip calculation unit that calculates the road surface grip of the drive wheel on the basis of the minimum road surface friction coefficient detected by the minimum road surface friction coefficient detection unit; a comparison unit that compares the driving force and the road holding force; and a travel assistance control unit that executes travel assistance control when the comparison unit determines that the driving force exceeds the road holding force, the control unit further comprising a travel distinguishing setting unit that distinguishes a travel route ahead of the host vehicle in accordance with a predetermined travel distinguishing condition, and a travel assistance control unit that executes travel assistance control when the comparison unit determines that the driving force exceeds the road holding force. The minimum road surface friction coefficient detection unit detects a minimum road surface friction coefficient within the travel area on the basis of the travel environment information.

Description

Technical Field

[0001] This invention relates to a driving assistance device for a vehicle. Background Technology

[0002] While driving this vehicle, there may be situations where the vehicle behaves unexpectedly due to changes in road conditions or weather. For example, if part of the road surface is icy, the vehicle may experience momentary skidding when driving over the icy surface.

[0003] Furthermore, momentary slippage can occur when the tires step into or pass over metal joints, snow, or fallen leaves located at bridge seams. In front-wheel-drive vehicles where the drive wheels also serve as steering wheels, front wheel slippage makes steering difficult.

[0004] If the vehicle skids while in motion, its behavior becomes unstable, which can cause unease among occupants, including the driver.

[0005] For example, regarding the technology disclosed in Japanese Patent Application Publication No. 2023-1303513, firstly, in autonomous driving mode, the driving control unit determines the tire slippage condition based on the tire's rotational speed and estimates the road surface friction coefficient. Then, based on this road surface friction coefficient, it investigates whether the road surface is slippery. If it is determined to be slippery, the driving control unit executes a low friction coefficient driving mode. In this low friction coefficient driving mode, the steering speed is slowed, the braking timing is set earlier, and the upper speed limit and upper acceleration limit are set lower.

[0006] When operating in a low-friction driving mode, the driving control unit detects the driver's level of anxiety based on factors such as the frequency of the driver's eye movement. If the system determines that the driver is experiencing anxiety, it further adjusts the steering speed and braking timing. This helps alleviate the driver's anxiety.

[0007] However, road conditions change constantly depending on the driving environment. For vehicles in motion, in order to prevent skidding, it is necessary to predict in advance the changes in the road surface friction coefficient that will occur.

[0008] However, in the technology disclosed in Japanese Patent Application Publication No. 2023-1303513, a low-friction coefficient driving mode is executed after vehicle slippage is detected. Furthermore, at this time, the degree of driver anxiety is determined to alleviate it. Therefore, momentary delays can easily occur in the control to prevent slippage. Additionally, since control to alleviate anxiety is not executed before the driver feels anxious, momentary control delays also occur in this situation.

[0009] In view of the above-mentioned problems, the present invention aims to provide a driving assistance device for a vehicle that can suppress slippage and significantly reduce the anxiety felt by occupants, including the driver. Summary of the Invention

[0010] Technical solution One embodiment of the present invention includes: a driving environment information acquisition unit that acquires driving environment information ahead of the vehicle; and a control unit, the control unit comprising: a road information acquisition unit that acquires road information based on the driving environment information acquired by the driving environment information acquisition unit; a minimum road surface friction coefficient detection unit that detects a minimum road surface friction coefficient based on the road information acquired by the road information acquisition unit; a driving force calculation unit that calculates a driving force based on the torque of a driving source; a road surface grip calculation unit that calculates the road surface grip of the driving wheels based on the minimum road surface friction coefficient detected by the minimum road surface friction coefficient detection unit; and a comparison unit. The system compares the driving force calculated by the driving force calculation unit with the road grip calculated by the road grip calculation unit; and a driving assistance control unit performs driving assistance control when the comparison unit determines that the driving force exceeds the road grip. The control unit also includes a driving zone setting unit, which distinguishes the driving route ahead of the vehicle according to predetermined driving zone conditions. The minimum road friction coefficient detection unit detects the minimum road friction coefficient within the driving zone set by the driving zone setting unit based on the driving environment information obtained by the driving environment information acquisition unit. Attached Figure Description

[0011] Figure 1 This is a schematic diagram of the vehicle's driver assistance system.

[0012] Figure 2 This is a flowchart illustrating the estimated road surface μ setting routine.

[0013] Figure 3 This is a flowchart showing the slip suppression control routines differentiated by driving mode.

[0014] Figure 4A This is a conceptual diagram based on the basic road surface μ setting mapping of road types.

[0015] Figure 4B This is a conceptual diagram of the weather correction factor setting mapping.

[0016] Figure 4C This is a conceptual diagram of the tire correction factor setting mapping.

[0017] Figure 4D This is a conceptual diagram of the mapping of pavement μ correction coefficient settings based on pavement type.

[0018] Figure 5 This is an explanatory diagram showing how the road surface μ is estimated according to each zone.

[0019] Figure 6 This is an explanatory diagram showing the relationship between the driving force of the driving tire and the road grip. Detailed Implementation

[0020] Hereinafter, an embodiment of the present invention will be described based on the accompanying drawings. It should be noted that the drawings are schematic diagrams, and the relationship between the thickness and width of each component, the proportion of the thickness of each component, etc., are different from the actual components. Of course, the drawings also include parts with different dimensional relationships or proportions.

[0021] Figure 1 The symbol 1 represents the drive control device. This drive control device 1 is mounted on the vehicle M (refer to...). Figure 5 This vehicle M is an electric vehicle. Hereinafter, this vehicle M will be referred to as electric vehicle M.

[0022] The drive control device 1 includes a vehicle integrated control unit 2. This vehicle integrated control unit 2 is a component that comprehensively performs various controls related to the drive of the electric vehicle M. A camera control unit 3 and left and right side-view cameras 71 and 7r are connected to this vehicle integrated control unit 2. It should be noted that, although not shown, various sensors are connected to the vehicle integrated control unit 2 to acquire information required for driving the electric vehicle M. Furthermore, the vehicle integrated control unit 2 and the camera control unit 3 correspond to the control unit of this invention.

[0023] Side-view cameras 7l and 7r are mounted on the left and right front sides of the vehicle body to capture images of the left and right sides of the vehicle body. The vehicle integrated control unit 2 detects the road surface movement speed and the movement speed of the tires of the drive wheels Fl and Fr at the road surface contact position based on the images captured by each side-view camera 7l and 7r.

[0024] In addition, an inverter control unit 5 is connected to the vehicle integrated control unit 2. It should be noted that, although not shown in the figure, the vehicle integrated control unit 2 can freely communicate bidirectionally with various control units.

[0025] Each of the control units 2, 3, 5, and the navigation system 8 described later consists of a microcontroller equipped with a CPU, RAM, ROM, rewritable non-volatile memory (flash memory or electrically erasable programmable read-only memory), and peripheral devices. The ROM stores programs or fixed data required for various processes to be executed in the CPU. Additionally, RAM serves as the CPU's working area, temporarily storing various data within the CPU. It should be noted that the CPU is also referred to as an MPU (Microprocessor) or processor. Alternatively, a GPU (Graphics Processing Unit) or a GSP (Graphics Streaming Processor) can be used instead of the CPU. Alternatively, a combination of CPU, GPU, and GSP can be selectively used.

[0026] An inverter 12 is connected to an electric drive system 11 via an inverter control unit 5. The electric drive system 11 includes the inverter 12, a high-voltage battery 13, and a drive motor 14 as a drive source. The inverter 12 operates according to command signals from the inverter control unit 5. The inverter 12 converts the direct current (DC) power from the high-voltage battery 13 into alternating current (AC) power to supply power to the drive motor 14, according to the command signals from the inverter control unit 5. The inverter control unit 5 controls the driving force of the drive motor 14 via vector control.

[0027] The output shaft 21 of the drive motor 14 is connected to the drive shafts 22l and 22r of the left and right drive wheels Fl and Fr via the differential Df. The drive motor 14 drives the drive wheels Fl and Fr using electricity supplied from the inverter 12, thereby powering the electric vehicle M.

[0028] In addition, a camera unit 6, serving as a driving environment detection unit, is installed at the front of the electric vehicle M. The camera unit 6 is a stereo camera consisting of a main camera 6a and a secondary camera 6b.

[0029] The camera unit 6 is connected to the input side of the camera control unit 3. The camera control unit 3 performs image processing on the images of the driving environment in front of the electric vehicle M captured by the two cameras 6a and 6b of the camera unit 6 to obtain information about the driving environment in front.

[0030] Additionally, information about the currently installed tires is stored in the non-volatile memory 3a of the camera control unit 3. This tire information is entered by the user or driver when installing the tires. This tire information includes the tire type, replacement date, etc. The tire type includes distinctions between regular tires and studless anti-skid tires, as well as tire size, etc.

[0031] Furthermore, various mappings are stored in the non-volatile memory 3a. These mappings include a basic road surface μ setting mapping read when setting an estimated initial μ value (see reference...). Figure 4A Weather correction coefficient setting mapping (refer to) Figure 4B Tire correction factor setting mapping (refer to) Figure 4C In addition, the road surface μ correction coefficient mapping is stored in the non-volatile memory 3a (see reference). Figure 4D ).

[0032] like Figure 4A As shown, the basic road surface μ mapping stores pre-defined basic road surface μ data for each road type. Road types include paved roads (concrete pavement, asphalt pavement), unpaved roads (gravel roads, dirt roads), etc. The basic road surface μ for unpaved roads is set lower than that for paved roads.

[0033] like Figure 4B As shown, a weather-related correction factor is set in the weather correction factor setting mapping (where the correction factor is ≤1). This correction factor is set to decrease in the order of sunny > cloudy > rain > snow.

[0034] like Figure 4C As shown, the tire calibration factor setting mapping includes calibration factors corresponding to the tire category (where the calibration factor is ≤1). The calibration factor for standard tires is set higher than that for studless traction tires. Furthermore, the higher the tire's aspect ratio, the higher the calibration factor value is set.

[0035] like Figure 4D As shown, the pavement μ correction coefficient setting map includes correction coefficients corresponding to the pavement category. Even for paved roads, the pavement μ for snowy or wet roads is set lower than that for dry roads. The pavement μ correction coefficient setting map also includes a percentage reduction in pavement μ determined through pre-experiments for each pavement category.

[0036] In addition, a navigation system 8 is connected to the camera control unit 3. Furthermore, a GNSS (Global Navigation Satellite System) receiver 9 and a road information transceiver unit 10 are connected to the navigation system 8.

[0037] The navigation system 8 obtains the vehicle's position (latitude, longitude, and altitude) information based on positioning signals received from multiple positioning satellites by the GNSS receiver 9. The navigation system 8 has a storage unit for storing road map data. This road map data contains both static and dynamic information. The static information includes road information such as paved roads and unpaved roads. The dynamic information includes real-time weather information.

[0038] Weather information includes conditions such as sunny / rainy / snowy weather, sunshine duration, rainfall, snowfall, temperature, humidity, and air pressure. It should be noted that weather information can also be obtained from meteorological information provided by websites.

[0039] The navigation system 8 plots the vehicle's position, estimated based on the positioning signal received by the GNSS receiver 9, onto the road map data, thereby estimating the vehicle's current position and direction of travel on the road map. Then, the navigation system 8 constructs a driving route on the road map that connects the destination input by the driver and others with the vehicle's position.

[0040] Furthermore, the navigation system 8 can access the cloud server 103 from the road information transceiver unit 10 via the base station 101 and the network 102. The navigation system 8 obtains the latest road map data (static information and dynamic information) around the vehicle from the cloud server 103. It should be noted that the camera unit 6 and the navigation system 8 correspond to the driving environment information acquisition unit of the present invention.

[0041] In addition, the navigation system 8 obtains road information around the vehicle based on road map information and sends this obtained road information to the camera control unit 3.

[0042] The camera control unit 3 sets the criteria for distinguishing driving routes defined on the road map at what distance. These distinguishing criteria are based on road information ahead of the vehicle obtained from the navigation system 8 and driving environment information ahead of the vehicle obtained from images captured by the camera unit 6. It should be noted that these distinguishing criteria are constantly updated. Therefore, while the electric vehicle M is traveling on a road with distinguishing driving routes, the driving route ahead is distinguished.

[0043] The camera control unit 3 distinguishes the driving route from the front of the vehicle to a preset distance according to the set driving route distinction conditions. Then, the camera control unit 3 sets a minimum road surface value (μmin) within each driving route distinction. The camera control unit 3 sets this minimum road surface value (μmin) for each driving route distinction.

[0044] When the electric vehicle M enters a road that passes through a driving zone, the vehicle integrated control unit 2 controls the driving force P of the drive wheels Fl and Fr based on the minimum road surface value μmin set within that driving zone.

[0045] However, when the electric vehicle M is in motion, if the driving force P applied by the drive motor 14 to the tires (called "drive tires") of the drive wheels Fl and Fr exceeds the grip force (called "road grip force") Ft from the road surface acting on the drive tires (P > Ft), slippage occurs.

[0046] As shown in Figure 7, the driving force P of the drive tires (front wheels in the figure) in the electric vehicle M is generated by... P=T / r …(1) Find the value of T. Here, T represents torque, and r represents the effective radius of the driving tire.

[0047] In addition, the road grip F is determined by F = μ·Wf …(2) Calculate μ. Here, μ is the coefficient of road friction, and Wf is the vehicle weight applied to the driving tires (front wheels). Incidentally, in Figure 7, W is the total vehicle weight, and Wr is the vehicle weight applied to the driven tires (rear wheels).

[0048] Therefore, if the road grip force F is set based on the minimum road surface μmin, and the driving force P is set in a way that always maintains the relationship P≤F, then the electric vehicle M can continue to drive without slipping.

[0049] Specifically, the camera control unit 3 sets the minimum road surface value (μmin) for each driving zone according to... Figure 2 The estimated road surface μ setting routine is shown.

[0050] The camera control unit 3 first acquires the vehicle's position information estimated by the navigation system 8 (step S1). Next, the camera control unit 3 explores the driving route ahead of the vehicle's position from road map data based on the vehicle's position, acquiring driving environment information along the driving route (step S2). This driving environment information includes road information acquired based on images captured by the camera unit 6, as well as static and dynamic information stored in the road map data. It should be noted that the processing in step S2 corresponds to the road information acquisition unit of the present invention.

[0051] Next, the camera control unit 3 sets driving differentiation conditions based on driving environment information (step S3). The driving differentiation conditions are based on the distance traveled from the position where the driving route was differentiated during the last calculation, a pre-set time interval, the lane after the electric vehicle M changes lanes, road type (paved road, unpaved road, etc.), road sunlight (sunny, shady), road surface condition (dry, wet), and snow surface condition (not cleared, cleared). Furthermore, the driving differentiation conditions select the conditions used for driving differentiation. Figure 6 The diagram shows the configuration of the driving zone setting based on whether it is a paved road or an unpaved road. This driving zone setting can be changed sequentially.

[0052] Then, the camera control unit distinguishes the driving route of the electric vehicle M according to the set driving distinction conditions (step S4). It should be noted that the processing in steps S3 and S4 corresponds to the driving distinction setting unit of the present invention.

[0053] When the driving distinction conditions are set in terms of paved roads or unpaved roads, Figure 6 The driving route shown is divided into three sections: the current driving section A is a paved road, the next driving section B is an unpaved road, and the next driving section C is a paved road. It should be noted that this driving section is divided within a range where road conditions can be identified based on images captured by camera unit 6.

[0054] Then, the camera control unit 3, based on the differentiated road categories and referring to the basic road surface μ mapping ( Figure 4A (Step S5) , and sets the basic road surface μ corresponding to the obtained road type. The basic road surface μ mapping pre-determines and stores the road surface μ set for each road type through experiments, etc.

[0055] Additionally, current weather information around the vehicle's location is obtained from dynamic information in road map data or from meteorological information provided on a website (step S6). Then, a mapping is set based on this weather information with reference to a weather correction factor. Figure 4B Set the weather correction factor (step S7).

[0056] Furthermore, the camera control unit 3 sets a mapping based on the tire information of the electric vehicle M pre-stored in the non-volatile memory 3a and with reference to the tire correction coefficient. Figure 4C To set the tire correction factor (step S8).

[0057] Then, the camera control unit 3 sets an initial value for the estimated road surface μ by multiplying the average of the weather correction coefficient and the tire correction coefficient, or the lower of either one, by the basic road surface μ set for each driving zone (step S9). It should be noted that when setting this initial value for the estimated road surface μ, information of other vehicles in the driving zone (e.g., slippage information and / or road surface μ information) can be read from the cloud server 103 and incorporated into the initial value for the estimated road surface μ.

[0058] Next, the camera control unit 3 sets the road surface category distribution within the driving zone (step S10). For example, Figure 6 Even on paved roads, driving zones A and B, as shown, have different surface types, such as asphalt or concrete, and the surface μ varies depending on the type. Furthermore, as shown in driving zone C, even on paved roads, there are areas where snow can accumulate and / or ice can form in shady locations. Additionally, as... Figure 6 As shown in driving zone B, even in unpaved roads where most of the road is gravel, there are still sections with exposed dirt, uneven gravel distribution, and muddy areas. Thus, even if the road type is paved or unpaved, the road surface μ will vary depending on the condition of the road surface.

[0059] The road surface category distribution is based on images of the road ahead captured by camera unit 6. Through image recognition, such as utilizing AI (artificial intelligence), a frame of image data is divided and identified according to each road surface category. This road surface category represents information about current road surface changes acquired in real time.

[0060] Next, the camera control unit 3 refers to the road surface μ correction coefficient mapping for each road surface category identified by AI ( Figure 4D ), set the pavement μ correction coefficient corresponding to the pavement category (step S11). In this pavement μ correction coefficient mapping, the correction coefficient for each pavement category is pre-determined and set through experiments, etc.

[0061] Then, the camera control unit 3 uses the road surface μ correction coefficient to correct the set initial value of the estimated road surface μ, and sets the estimated road surface μ according to the road surface category (step S12).

[0062] Next, the smallest estimated road surface μ is selected from the estimated road surface μ set within the driving zone (step S13). The selected smallest estimated road surface μ is set as the minimum road surface μmin (step S14). The road surface information of the driving zone including the minimum road surface μmin is sent as road information to the vehicle integrated control unit 2 (step S15), and the routine is exited. It should be noted that the processing in steps S13 and S14 corresponds to the minimum road surface friction coefficient detection unit of the present invention.

[0063] The vehicle integrated control unit 2 reads road surface information from the camera control unit 3 for each driving zone and detects whether the electric vehicle M experiences slippage during actual driving. Specifically, the vehicle integrated control unit 2 performs the detection of whether slippage has occurred in each driving zone according to... Figure 3 The slippage detection routine shown is performed according to the driving conditions.

[0064] The drive motor 14 of the electric vehicle M controls the driving force through vector control. Vector control separates the current flowing through the motor into a current component that generates torque (torque current component) and a current component that generates magnetic flux in the rotor (magnetic flux current component), and controls them independently. The vehicle integrated control unit 2 first calculates the driving force P applied to the drive wheels Fl and Fr based on the torque current component (step S21). It should be noted that the processing in step S21 corresponds to the driving force calculation unit of the present invention.

[0065] Next, the vehicle integrated control unit 2 reads the vehicle body weight Wf applied to the tires of the drive wheels Fl and Fr (step S22). This vehicle body weight Wf is pre-stored in the non-volatile memory 3a. In addition, the minimum road surface μmin of the current driving zone is read (step S23).

[0066] Then, the road grip force F of the drive tires is calculated based on the vehicle weight Wf and the minimum road surface area μmin (F = μmin·Wf: step S24). It should be noted that the processing in step S24 corresponds to the road grip force calculation unit of this pair of names.

[0067] Then, the vehicle integrated control unit 2 compares the driving force P and the road grip force F (step S25). It should be noted that the processing in step S25 corresponds to the comparison unit of the present invention.

[0068] Then, if P > F (step S25: Yes), a skid is predicted to have occurred. Conversely, if P ≤ F (step S25: No), the vehicle integrated control unit 2 determines that the probability of skid occurring is low. If skid is predicted to occur, the vehicle integrated control unit 2 executes driving assistance control (step S28). Since the road grip force F is calculated based on the minimum road surface μmin in step S24, the occurrence of skid can be quickly predicted when P > F.

[0069] In addition, when the vehicle integrated control unit 2 determines that the possibility of slippage is low, it detects the moving speed of the road surface and the moving speed of the tires of the drive wheels Fl and Fr at the road surface contact position based on the images detected by the left and right side-view cameras 7l and 7r (step S26).

[0070] The vehicle integrated control unit 2 determines whether slippage has occurred based on the road surface movement speed and the movement speed of the tires of drive wheels Fl and Fr at their contact points with the road surface (step S27). As described above, slippage can be quickly predicted when P > F. However, even when a minimum road surface speed μmin is set, unexpected slippage may still occur due to driving conditions.

[0071] Therefore, the vehicle integrated control unit 2 verifies whether slippage has actually occurred. Then, if the difference between the vehicle integrated control unit 2's moving speed on the road surface and the moving speed of the tire at the road surface contact point is less than the slippage determination speed set for determining slippage (step S27: No), it determines that there is no slippage and ends the routine.

[0072] On the other hand, if the moving speed of the tires of drive wheels Fl and Fr at the road surface contact position is faster than the moving speed of the road surface and the difference is greater than or equal to the slippage determination speed (step S27: Yes), the vehicle integrated control unit 2 determines that slippage has occurred. When slippage is determined to have occurred, the vehicle integrated control unit 2 executes driving assistance (step S28). It should be noted that the processing in step S28 corresponds to the driving assistance control unit of the present invention.

[0073] Here, the driving assistance control performed by the vehicle integrated control unit 2 is a control designed to suppress skidding. As driving assistance controls, these include limiting the upper limit of the acceleration of the electric vehicle M, extending the lower limit of the inter-vehicle distance from the vehicle in front, and setting a higher threshold for collision prevention braking intervention relative to the vehicle in front. The vehicle integrated control unit 2 combines and executes at least one, or two or more, of these controls.

[0074] Alternatively, the driving assistance control executed by the vehicle integrated control unit 2 can be a control that only notifies the driver of the occurrence of slippage, instead of the aforementioned control. This notification of slippage to the driver can be performed, for example, by applying reaction forces or vibrations to the accelerator and brake pedals.

[0075] By applying a reaction force or vibration to the accelerator and brake pedals, the driver is prevented from pressing either pedal beyond the necessary depth. This suppresses slippage. It should be noted that the reaction force or vibration is applied by installing pressure actuators on the accelerator and brake pedals, and the vehicle integrated control unit 2 actuates these actuators.

[0076] Subsequently, the vehicle integrated control unit 2 updates the road map data stored in the navigation system 8 (step S29). The updated information is the road surface information for the current driving zone. The updated road surface information includes the driving zone where the slippage occurred, the time of the slippage, and the minimum road surface depth (μmin) measured during the slippage.

[0077] Furthermore, the vehicle integrated control unit 2 sends the road information to the cloud server 103 via network 102 (step S30), ending the routine. The cloud server 103 stores the received vehicle information as vehicle information in the driving zone of the electric vehicle M and provides it to other vehicles.

[0078] Thus, in this embodiment, the driving routes of the electric vehicle M are distinguished according to pre-set driving distinction conditions. Then, the minimum road surface pressure μmin within the driving distinction is detected, and the road surface grip force F is calculated based on this minimum road surface pressure μmin. Therefore, if the vehicle travels within the driving distinction with a P≤F relationship, slippage can be prevented before it occurs.

[0079] Furthermore, even when P ≤ F, there is still a possibility that skidding may actually occur. Therefore, in this embodiment, when P ≤ F, the presence or absence of skidding is investigated based on images captured by the side-view cameras 7l and 7r. Then, if skidding is detected, driving assistance is activated, thereby suppressing skidding and achieving greater driving stability. As a result, the anxiety felt by occupants, including the driver, can be significantly reduced.

[0080] It should be noted that the electric vehicle M can be a four-wheel drive vehicle. Additionally, Figure 2 The simulated road surface μ setting routine shown can also be executed by the vehicle integrated control unit 2. Furthermore, the vehicle integrated control unit 2 can also provide notification via sound or a monitor image when it detects slippage.

Claims

1. A driving assistance device for a vehicle, characterized in that, have: The driving environment information acquisition unit acquires driving environment information in front of the vehicle; and Control Department The control unit includes: The road information acquisition unit acquires road information based on the driving environment information acquired by the driving environment information acquisition unit; The minimum road surface friction coefficient detection unit detects the minimum road surface friction coefficient based on the road information acquired by the road information acquisition unit. The driving force calculation unit calculates the driving force based on the torque of the driving source; The road grip calculation unit calculates the road grip of the drive wheel based on the minimum road friction coefficient detected by the minimum road friction coefficient detection unit; The comparison unit compares the driving force calculated by the driving force calculation unit with the road grip force calculated by the road grip force calculation unit. as well as The driving assistance control unit executes driving assistance control when the comparison unit determines that the driving force exceeds the road surface grip. The control unit also includes a driving zone setting unit, which distinguishes the driving route ahead of the vehicle according to predetermined driving zone conditions. The minimum road surface friction coefficient detection unit detects the minimum road surface friction coefficient within a driving zone set by the driving zone setting unit based on the driving environment information acquired by the driving environment information acquisition unit.

2. The driving assistance device for a vehicle according to claim 1, characterized in that, The driving differentiation conditions are at least one of the following: the distance traveled from the position where the driving route was differentiated in the last calculation, a preset time interval, the lane after the vehicle changes lanes, the road category, the road sunlight, the road surface condition, and the snow surface condition.

3. The driving assistance device for a vehicle according to claim 2, characterized in that, The driving distinction conditions can be changed.

4. The driving assistance device for a vehicle according to claim 1, characterized in that, The driving assistance control performed by the driving assistance control unit is a control for suppressing slippage.

5. The driving assistance device for a vehicle according to any one of claims 1 to 4, characterized in that, The control unit sends the road information acquired by the road information acquisition unit to the cloud server via the network.

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