Traction control based on friction coefficient estimation

Abstract

The present disclosure discloses a method and apparatus for traction control based on friction coefficient estimation. An exemplary vehicle includes multiple sensors for measuring road surface quality and an anti-lock braking system module. The anti-lock braking system module (a) estimates confidence values ​​for different road surface types based on the road surface quality, (b) estimates the friction coefficient between the road and the vehicle's tires based on the confidence values, and (c) adjusts the traction control system by varying a target slip ratio based on the friction coefficient.

Description

Technical Field

[0001] The present disclosure relates generally to traction control systems in vehicles and, more particularly, to traction control based on friction coefficient estimation. Background Art

[0002] The stability and traction control system detects a loss of traction at a driven wheel. This is typically caused by a mismatch between engine torque and throttle input and road conditions. The stability and traction control system applies the brakes to that wheel to prevent it from spinning faster than the other wheels. However, when the stability and traction control system fails to take into account the environment in which it is driving, the system reacts to slip rather than proactively managing and anticipating loss of traction. Summary of the Invention

[0003] The appended claims define the present application. This disclosure summarizes aspects of the embodiments and should not be used to limit the claims. Other embodiments are contemplated based on the techniques described herein, as will be apparent to one of ordinary skill in the art upon examination of the following figures and detailed description, and are intended to fall within the scope of this application.

[0004] A method and apparatus for traction control based on friction coefficient estimation is disclosed. An exemplary vehicle includes a plurality of sensors for measuring road surface quality and an anti-lock braking system module. The anti-lock braking system module (a) estimates confidence values ​​for different road surface types based on the quality of the road surface, (b) estimates a friction coefficient between the road and the vehicle's tires based on the confidence values, and (c) adjusts the traction control system by varying a target slip based on the friction coefficient.

[0005] A method includes measuring a quality of a road surface in front of a vehicle using a first sensor and a second sensor different from the first sensor. The method further includes (a) generating first confidence values ​​for different road surface types based on the quality of the road surface measured by the first sensor, and (b) generating second confidence values ​​for different road surface types based on the quality of the road surface measured by the second sensor. Additionally, the method includes estimating a coefficient of friction between the road and a tire of the vehicle based on a sum of the first confidence value and the second confidence value. The method includes controlling torque applied to a wheel of the vehicle based on the coefficient of friction using an anti-lock braking system.

[0006] According to the present invention, there is provided a vehicle comprising:

[0007] a plurality of sensors for measuring road surface quality;

[0008] Anti-lock braking system module for:

[0009] Estimate confidence values ​​for different road surface types based on road surface quality;

[0010] estimating a coefficient of friction between a road surface and a tire of the vehicle based on the confidence value; and

[0011] The vehicle's wheels are controlled by changing the target slip ratio based on the friction coefficient.

[0012] According to an embodiment of the invention, each of the confidence values ​​represents a likelihood that the road surface corresponds to a specific one of the different road surface types.

[0013] According to an embodiment of the present invention, the plurality of sensors include cameras.

[0014] According to one embodiment of the present invention, the anti-lock braking system module implements the following steps to estimate confidence values ​​for different road surface types:

[0015] capturing an image of the road surface in front of the vehicle; and

[0016] Compare the image to reference images of different road surface types.

[0017] According to one embodiment of the present invention, the anti-lock braking system module implements the following steps to estimate confidence values ​​for different road surface types:

[0018] capturing a series of images of the road surface in front of the vehicle; and

[0019] Photometric values ​​and changes in photometric values ​​across a series of images are analyzed to determine confidence values ​​for different road surface types.

[0020] According to an embodiment of the present invention, the plurality of sensors include ultrasonic sensors.

[0021] According to one embodiment of the present invention, the anti-lock braking system module implements the following steps to estimate confidence values ​​for different road surface types:

[0022] broadcasting a wave signal in front of the vehicle; and

[0023] The reflection and refraction patterns of the wave signals leaving the road surface are analyzed to determine confidence values ​​for different road surface types.

[0024] According to an embodiment of the present invention, the plurality of sensors include at least one of a suspension vibration sensor or an accelerometer.

[0025] According to one embodiment of the present invention, the anti-lock braking system module implements the following steps to estimate confidence values ​​for different road surface types:

[0026] applying a plurality of filters to a signal generated by at least one of the plurality of sensors, the filters representing different ones of the different road surface types; and

[0027] A confidence value is generated based on an amount by which the filter attenuates the signal representing different types of road surface types.

[0028] According to an embodiment of the present invention, the anti-lock braking system module applies a weighting coefficient to the confidence value based on environmental data information received from a remote weather server.

[0029] According to the present invention, there is provided a method comprising:

[0030] measuring a quality of a road surface in front of the vehicle with a first sensor and a second sensor different from the first sensor;

[0031] generating, with a processor in the vehicle, first confidence values ​​for different road surface types based on the quality of the road surface measured by the first sensor;

[0032] generating, with a processor in the vehicle, second confidence values ​​for different road surface types based on the quality of the road surface measured by the second sensor;

[0033] estimating a coefficient of friction between the road and a tire of the vehicle based on a sum of the first confidence value and the second confidence value; and

[0034] Anti-lock braking systems are used to control the torque applied to the wheels of a vehicle based on the coefficient of friction.

[0035] According to an embodiment of the present invention, the first sensor is a camera and the second sensor is one of a suspension vibration sensor or an accelerometer.

[0036] According to one embodiment of the present invention, generating the first confidence value includes:

[0037] capturing an image of the road surface in front of the vehicle; and

[0038] Compare the image to reference images of different road surface types.

[0039] According to one embodiment of the present invention, generating the first confidence value includes:

[0040] capturing a series of images of the road surface in front of the vehicle; and

[0041] Photometric values ​​and changes in photometric values ​​within a series of images are analyzed to determine confidence values ​​for different road surface types.

[0042] According to one embodiment of the present invention, generating the first confidence value includes:

[0043] applying a plurality of filters to a signal generated by at least one of the plurality of sensors, the filters representing different ones of the different road surface types; and

[0044] A confidence value is generated based on an amount by which the filter attenuates the signal representing different types of road surface types. BRIEF DESCRIPTION OF THE DRAWINGS

[0045] For a better understanding of the present invention, reference may be made to the embodiments illustrated in the following drawings. The components in the drawings are not necessarily drawn to scale and related elements may be omitted or, in some cases, the proportions may be exaggerated in order to emphasize and clearly illustrate the novel features described herein. Furthermore, as is known in the art, the system components may be arranged differently. Furthermore, in the drawings, like reference numerals designate corresponding components throughout the several views.

[0046] Figure 1 A vehicle operating in accordance with the teachings of the present disclosure is shown;

[0047] Figure 2 yes Figure 1 A block diagram of a friction estimator of a traction control system of a vehicle;

[0048] Figure 3 yes Figure 1 A block diagram of the vehicle's electronic components;

[0049] Figure 4 is a flow chart of a method for controlling a traction control system based on estimated friction, which may be Figure 3 Electronic components are implemented. DETAILED DESCRIPTION

[0050] While the present invention may be embodied in various forms, certain exemplary and non-limiting embodiments are shown in the drawings and will be described below, with the understanding that this disclosure is to be considered as illustrative of the invention and is not intended to limit the invention to the particular embodiments shown.

[0051] As used herein, a traction control-based system refers to any vehicle system that controls the forces applied by a vehicle's wheels to a road surface. Examples of traction control-based systems include traction control systems, electronic stability systems, and roll stability systems. The coefficient of friction between a vehicle's tires and the road surface is determined by the tire's properties (e.g., tire grip, surface area of ​​the tire / road interface, etc.) and the characteristics of the road surface. For example, the coefficient of friction between a tire and an icy or slippery road is lower than that on dry asphalt. As described below, using the estimated friction coefficient, a traction control-based system adjusts the slip ratio target to control the force applied by the wheel on the road in response to changes in the friction environment. For example, when the road surface is icy, the traction control system may control wheel torque differently than when the road surface is covered in gravel. Slip ratio is the ratio of vehicle speed to wheel rotational speed. For example, when vehicle speed and wheel rotational speed are equal, the slip ratio is 0%, and when vehicle speed is zero and wheel rotational speed is greater than zero, the slip ratio is 100%. A slip ratio of 100% means that the vehicle is not moving despite the wheels spinning. Different surfaces have different ideal amounts of slip (e.g., based on the coefficient of friction) for a vehicle to move safely over the surface. For example, without at least some slip, a vehicle may become stuck in sand. Traditionally, for example, traction control systems are used to eliminate all slip without knowing what the coefficient of friction is. In this case, the vehicle would not move in sand. The traction control-based system of the present disclosure sets a target slip based on the coefficient of friction. The traction control-based system uses one or more methods to estimate the coefficient between the vehicle's tires and the surface on which the vehicle is currently traveling or is expected to travel based on the vehicle's current trajectory. When multiple methods are used, the results of each method are multiplied by a confidence factor to produce a confidence estimate of the likely road condition. Higher confidence estimates are given more weight when estimating the coefficient of friction in the vehicle's path.

[0052] One approach involves analyzing signals from sensors (e.g., measuring suspension oscillations, road noise, tire pressure, etc.) to determine whether the signals are characteristic of a vehicle traveling on a specific road surface. Using this approach, the traction control system applies multiple filters designed to filter out signals from specific road conditions (e.g., asphalt, snow, mud, gravel, etc.). The amount by which a particular filter smooths (e.g., attenuates) the signal increases confidence that the vehicle is traveling on that road type. For example, if a filter tuned to a gravel surface results in a substantially smoothed signal, the traction control system can be confident that the vehicle is traveling on gravel.

[0053] Another approach involves analyzing visual data from a camera (e.g., a standard camera, an RGB camera, an infrared camera, etc.) and comparing the image's reflectivity, color, and smoothness to reference images of different surfaces. The confidence with which the vehicle is traveling on a particular surface is related to the extent to which the image matches the reference image of that surface. For example, an image of a white reflective surface can match a reference image of a snow-covered surface. This approach helps determine the quality of a surface by features of different sizes. For example, the approach can distinguish between small and large particles to distinguish a sand or gravel surface from a brick or rock surface. Additionally, using this approach, the vehicle can construct a photometric map to determine the properties of the surface. Different photometric values ​​(μ) represent different surfaces. For example, dry asphalt may have one characteristic photometric value, while wet asphalt may have another. The measured photometric values ​​are compared to a table that gives input photometric values ​​to represent the confidence level of different types of road surfaces.

[0054] In another approach, a vehicle uses a distance detection sensor (e.g., LiDAR, RADAR, ultrasonic, etc.) to broadcast a wave signal at regular intervals. The wave signal reflects and refracts off the surface. The vehicle analyzes the pattern of reflection and / or refraction to determine the type of surface. For example, using this approach, the vehicle can determine whether the road surface is smooth (e.g., indicating asphalt, ice, etc.) or rough (e.g., indicating gravel or snow).

[0055] In another method, the vehicle analyzes changes in steering current draw at a constant speed. When steering current draw increases, the vehicle can determine that the surface corresponds to a surface with higher traction, and vice versa.

[0056] Additionally, in some examples, the vehicle also analyzes environmental data (e.g., weather data, ambient temperature data, humidity data, precipitation data, etc.) to change the confidence level of certain road types. For example, environmental data indicating that it is cold outside can increase the confidence level that the road surface is snowy or icy.

[0057] As described below, the vehicle uses one or more of these methods to estimate the surface type of the road the vehicle is traveling on. In some examples, multiple methods are used to generate confidence levels for multiple surface types, and a surface type for the road is selected based on which surface type has the highest confidence level. For example, if a wave signal indicates that the road surface is reflective, a signal from one of the sensors indicates that the vehicle is traveling on an asphalt road surface, and environmental data indicates that it has rained within the past 24 hours, the confidence level may indicate that the vehicle is traveling on wet asphalt.

[0058] Figure 1A vehicle 100 is shown operating in accordance with the teachings of the present disclosure. The vehicle 100 can be a standard gasoline-powered vehicle, a hybrid vehicle, an electric vehicle, a fuel cell vehicle, and / or any other type of vehicle. The vehicle 100 includes components related to mobility, such as a powertrain having an engine, a transmission, a suspension, a drive shaft, and / or wheels. The vehicle 100 can be non-autonomous, semi-autonomous (e.g., some conventional power functions are controlled by the vehicle 100), or autonomous (e.g., power functions are controlled by the vehicle 100 without direct driver input). In the example shown, the vehicle 100 includes sensors 102, an on-board communications module (OBCM) 104, and an anti-lock brake system (ABS) module 106.

[0059] Sensors measure characteristics of the vehicle 100 and the surroundings of the vehicle 100. Sensors 102 can be arranged in and around the vehicle 100 in any suitable manner. Sensors 102 can be installed to measure characteristics of the surroundings outside the vehicle 100. For example, such sensors 102 can include cameras (e.g., standard cameras, RGB cameras, infrared cameras) and distance detection sensors (e.g., RADAR, LiDAR, ultrasonic, etc.). In addition, some sensors 102 can be installed within the cabin of the vehicle 100 or within the body of the vehicle 100 (e.g., the engine compartment, wheel wells, etc.) to measure properties within the vehicle 100. For example, such sensors 102 can include accelerometers, odometers, tachometers, pitch and yaw sensors, wheel speed sensors, microphones, tire pressure sensors, biometric sensors, suspension vibration sensors, etc. These sensors 102 generate signals that can be analyzed to determine the type of surface the vehicle 100 is currently traveling on.

[0060] The vehicle communication module 104 includes a wired or wireless network interface to enable communication with the external network 108. The vehicle communication module 104 also includes hardware (e.g., a processor, memory, storage, antenna, etc.) and software to control the wired or wireless network interface. In the example shown, the vehicle communication module 104 includes one or more communication controllers for standard-based networks (e.g., Global System for Mobile Communication (GSM), Universal Mobile Telecommunications System (UMTS), Long Term Evolution (LTE), Code Division Multiple Access (CDMA), WiMAX (IEEE 802.16m), local area wireless networks (including IEEE 802.11a / b / g / n / ac or others), Dedicated Short Range Communication (DSRC), and Wireless Gigabit (IEEE 802.11ad), etc.). In some examples, the vehicle communication module 104 includes a wired or wireless interface (e.g., an auxiliary port, a Universal Serial Bus (USB) port, a Bluetooth wireless node, etc.) to communicatively couple with a mobile device (e.g., a smartphone, a smartwatch, a tablet, a police mobile computer, etc.). In such an example, the vehicle communication module 104 can communicate with the external network 108 via the networked mobile device.

[0061] The external network 108 can be a public network such as the Internet, a private network (e.g., an intranet), or a combination thereof, and can utilize various network protocols now available or later developed, including but not limited to TCP / IP-based network protocols. In the illustrated example, the external network 108 includes a weather server 110. The vehicle 100 receives environmental data (e.g., weather data, ambient temperature data, humidity data, precipitation data, etc.) from the weather server 110.

[0062] The ABS module 106 controls the brakes of the vehicle 100 to control the torque applied to each wheel. The ABS module 106 includes a stability and traction control system that detects when one or more wheels have lost traction (e.g., through a mismatch in measurements from wheel speed sensors). In the example shown, the ABS module 106 includes a friction estimator 112. As shown in conjunction with Figure 2As disclosed, the friction estimator 112 estimates the coefficient of friction between the tires of the vehicle 100 and the road. The anti-lock braking system module 106 uses the estimated coefficient of friction to change the target slip ratio for the wheels of the traction control-based system, taking into account the surface on which the vehicle 100 is traveling, to reduce the frequency with which one or more wheels lose traction, causing adverse driving events (e.g., binding, fishtailing, etc.).

[0063] Figure 2 yes Figure 1 FIG2 is a block diagram of a friction estimator 112 for a traction control system of a vehicle 100. In the illustrated example, the friction estimator 112 receives vehicle operation data and / or images from various sensors 102 and environmental data from a weather server 110 (e.g., via the onboard communication module 104). The friction estimator 112 includes one or more coefficient generators 202a-202d. The coefficient generators 202a-202d generate confidence values ​​associated with different road types based on the vehicle operation data from the sensors 102. For example, one of the coefficient generators 202a-202d may generate a confidence value for a surface as 90% likely dry asphalt, 7% likely wet asphalt, and 3% likely gravel. The coefficient generators 202a-202d include reference tables 204a-204e that correlate vehicle operation data with known samples to generate confidence levels. For example, one coefficient generator 202a-202d can include a reference image in its reference table 204a-204d to compare the image captured by the RGB camera with the reference image to determine a confidence value for the road surface type. Confidence generator 206 compiles the confidence values ​​from coefficient generators 202a-202d and estimates the friction coefficient based on the confidence levels. In some examples, confidence generator 206 selects the type of road surface with the highest aggregate confidence value. In some examples, confidence generator 206 includes a lookup table that associates the type of road surface with the friction coefficient.

[0064] The filter coefficient generator 202a receives a signal from the vehicle operating data and applies multiple filters to the signal separately. In some examples, the signal comes from a suspension vibration sensor and / or a tire pressure monitoring system (TPMS). In some examples, the filter coefficient generator 202a applies a pre-filter to the signal to filter out normal vehicle behavior (e.g., turning, engine vibration, wheel speed, etc.) and environmental factors (e.g., changes based on wind, rain, etc.). Each filter is designed to attenuate signals with characteristics of a specific type of road condition. For example, one filter can be designed to attenuate signals generated by a vehicle 100 passing through gravel, and another filter can be designed to attenuate signals generated by a vehicle 100 passing through asphalt. The filter coefficient generator 202a selects confidence values ​​for different road types based on the amount of signal attenuation.

[0065] Image coefficient generator 202b compares images captured by a camera of vehicle 100 with reference images to determine confidence values ​​for different types of road surfaces. In some examples, camera (a) captures an image of the road ahead of vehicle 100 based on the current trajectory of vehicle 100 (e.g., two feet ahead, four feet ahead, etc.). The confidence value is based on the percentage of matches between the image from the camera and one of the reference images. For example, reference table 204b may include one or more images for each of grass, dry asphalt, wet asphalt, snow, ice, mud, and / or gravel surfaces. In some examples, image coefficient generator 202b determines the confidence value based on the size of features identified in the image. For example, image coefficient generator 202b may determine that an image with small features is more likely to contain gravel, sand, or dirt, while an image with larger features is more likely to contain tiles or rocks, etc. In some examples, image coefficient generator 202b also uses color analysis to determine the likelihood that the road surface is a particular type of road surface. For example, when the image indicates that the road surface has few or no distinguishable features (eg, the road surface is asphalt, concrete, snow, mud, etc.), the image coefficient generator 202b may use the color of the surface to determine the confidence value.

[0066] In some examples, image coefficient generator 202b maps (e.g., via LiDAR, etc.) reflective surfaces in an image or a series of images. A flickering or moving bright spot indicates a wet road surface. A spotty, stationary surface indicates a rough surface. A consistently bright surface indicates a uniform surface, such as ice, snow, or mud. In some examples, reference table 204b associates photometric value measurements with possible road surface types.

[0067] Reflection coefficient generator 202c analyzes the waveform patterns broadcast by the distance detection sensor to determine the degree of surface roughness or smoothness. When a wave signal is broadcast from the distance detection sensor, the waves reflect and refract off the surface in question, and some of the waves return to the sensor. The reflected / refracted signals are analyzed to determine confidence levels for various surface types. For example, a smooth surface may have a relatively high confidence level for asphalt, ice, and snow, while a rough surface may have a relatively high confidence level for gravel, and so on.

[0068] As the vehicle travels at a near-constant speed, current coefficient generator 202d analyzes changes in steering current draw (e.g., from a current sensor in an Electronic Power Steering (EPS) system). For example, when current coefficient generator 202d detects that the steering control unit draws more current to turn the wheels, current coefficient generator 202d determines that the surface vehicle 100 is traveling on is rough. Reference table 204d correlates current draw with confidence levels for various types of road surfaces.

[0069] Environment detector 208 receives environmental data via vehicle communication module 104. Based on road conditions and weather data near vehicle 100, environment detector 208 generates weights to apply to the confidence values ​​generated by coefficient generators 202a-202d. For example, for a threshold period of time following rain, environment detector 208 may generate weighting coefficients for road surface types associated with "wetness" (e.g., mud, wet asphalt, etc.), which increase the corresponding confidence value and correspondingly decrease the weighting coefficients for road surface types associated with "dryness" (e.g., dry asphalt, dirt, etc.), thereby lowering the corresponding confidence value. The weighting coefficients may be influenced by other environmental factors, such as altitude, road trajectory, and recent temperature history. For example, rain on a dirt road will cause the weighting coefficient to increase the confidence value for mud in a valley due to the potential for mud to form; while snow falling outside when the ambient temperature is below freezing will cause the weighting coefficient to increase the confidence value associated with snow due to the potential for snow accumulation on the road. As another example, environment detector 208 may use snowplow data to further refine the predicted road conditions.

[0070] Figure 3 yes Figure 1 FIG. 1 is a block diagram of electronic components 300 of the vehicle 100 . In the example shown, the electronic components 300 include the sensors 102 , the onboard communication module 104 , the anti-lock braking system module 106 , and a vehicle data bus 302 .

[0071] The ABS module 106 includes a processor or controller 304 and a memory 306. In the example shown, the ABS module 106 is configured to include the friction estimator 112. The processor or controller 304 can be any suitable processing device or collection of processing devices, such as, but not limited to, a microprocessor, a microcontroller-based platform, a suitable integrated circuit, one or more field programmable gate arrays (FPGAs), and / or one or more application-specific integrated circuits (ASICs). Memory 306 may be a volatile memory (e.g., RAM (Random Access Memory), which may include non-volatile RAM, magnetic RAM, ferroelectric RAM, and any other suitable form), a non-volatile memory (e.g., disk memory, FLASH memory, EPROM (erasable programmable read-only memory), EEPROM (electrically erasable programmable read-only memory), a non-volatile solid-state memory, etc.), an immutable memory (e.g., EPROM), a read-only memory, and / or a high-capacity storage device (e.g., a hard disk drive, a solid-state drive, etc.). In some examples, memory 306 includes multiple types of memory, particularly volatile memory and non-volatile memory.

[0072] Memory 360 is a computer-readable medium on which one or more sets of instructions (e.g., software for operating the methods of the present disclosure) may be embedded. The instructions may embody one or more methods or logic as described herein. In certain embodiments, the instructions may reside completely or at least partially within any one or more of memory 306, the computer-readable medium, and / or the processor 304 during execution of the instructions.

[0073] The terms "non-transitory computer-readable medium" and "tangible computer-readable medium" should be understood to include a single medium or multiple media, such as a centralized or distributed database, and / or associated caches and servers that store one or more sets of instructions. The terms "non-transitory computer-readable medium" and "tangible computer-readable medium" also include any tangible medium that can store, encode, or carry a set of instructions for execution by a processor or cause a system to perform any one or more methods or operations disclosed herein. As used herein, the term "tangible computer-readable medium" is expressly defined to include any type of computer-readable storage device and / or storage disk and to exclude propagating signals.

[0074] In the example shown, the vehicle data bus 302 is communicatively coupled to the onboard communication module 104 and the anti-lock brake system module 106. In some examples, the vehicle data bus 302 includes one or more data buses. The vehicle data bus 202 can be configured in accordance with the Controller Area Network (CAN) bus protocol defined by the International Organization for Standardization (ISO) 11898-1, the Media Oriented Systems Transport (MOST) bus protocol, the CAN flexible data (CAN-FD) bus protocol (ISO 11898-7), and / or the K-line bus protocol (ISO 9141 and ISO 14230-1), and / or Ethernet. TM It is implemented by bus protocols such as IEEE 802.3 (since 2002).

[0075] Figure 4 is a flow chart of a method for controlling an anti-lock braking system based on estimated friction, which may be Figure 3 The electronic component 300 is implemented as shown in FIG. Initially, at block 402, the friction estimator 112 selects the next coefficient generator 202a-202d. At block 404, the friction estimator 112 takes measurements using the sensor 102 associated with the selected coefficient generator 202a-202d. For example, if the image coefficient generator 202b is selected, the friction estimator 112 captures an image from a camera. At block 406, the friction estimator 112 analyzes the measurements obtained at block 404 to generate a confidence value for the road surface type. At block 408, the friction estimator 112 determines whether there is another coefficient generator 202a-202d to select. If there is another coefficient generator 202a-202d to select, the method returns to block 402. Otherwise, if there is no other coefficient generator 202a-202d to select, the method continues to block 410. At block 410, the friction estimator 112 calculates the sum of the confidence values ​​for the road surface type. In some examples, friction estimator 112 applies a weighting factor to the confidence value based on ambient or photometric data. At block 412, friction estimator 112 determines whether the sum of the confidence values ​​associated with any type of road surface meets (e.g., is greater than or equal to) a confidence threshold. For example, the confidence threshold may be 70%. When the sum of the confidence values ​​associated with any type of road surface meets the confidence threshold, the method continues at block 416. Otherwise, when no sum of the confidence values ​​associated with any type of road surface meets the confidence threshold, the method continues at block 414.

[0076] At block 414, the ABS module 106 controls the traction control system, the roll control system, and / or the stability control system based on a default target slip ratio (e.g., 0% slip ratio, etc.). At block 416, the friction estimator 112 estimates the friction between the road surface and the tires of the vehicle 100 based on the sum of the confidence values ​​for the road surface types. At block 418, the ABS module 106 controls the traction control system, the roll control system, and / or the stability control system based on the estimated friction coefficient. For example, the ABS module 106 may change the torque applied to one or more wheels, change the relationship between the accelerator pedal input and the delivered torque and / or wheel slip ratio (e.g., as represented by the slip ratio target, etc.).

[0077] Figure 4 The flowchart represents a flow chart stored in a memory (eg Figure 3 The memory 306 of the processor 306) contains machine-readable instructions of one or more programs, which, when executed by the processor (e.g. Figure 3 The processor 304 of the embodiment of the present invention, when executed, causes the vehicle 100 to implement the example friction estimator 112, and / or more specifically, to implement Figure 1 、 Figure 2 and Figure 3 ABS module 106. In addition, although reference Figure 4 While the flowchart shown depicts an example procedure, many other methods of implementing the example friction estimator 112 may alternatively be used, and / or more specifically, the anti-lock braking system module 106 may alternatively be used. For example, the order of execution of the blocks may be changed and / or some of the blocks described may be changed, eliminated, or combined.

[0078] In this application, the use of transitional conjunctions is intended to include conjunctions. The use of clear or unambiguous subject matter is not intended to indicate cardinality. In particular, references to "the" object or "a" and "an" objects are also intended to indicate one of a possible plurality of such objects. In addition, the conjunction "or" can be used to convey features that exist simultaneously rather than mutually exclusive alternatives. In other words, the conjunction "or" should be understood to include "and / or". As used herein, the terms "module" and "unit" refer to hardware having circuits that are typically combined with sensors to provide communication, control and / or monitoring capabilities. "Module" and "unit" may also include firmware executed on the circuit. The terms "includes", "including" and "include" are inclusive and have the same scope as "comprises", "comprising" and "comprise", respectively.

[0079] The above embodiments, and particularly any "preferred" embodiments, are possible examples of implementations and are presented merely for a clear understanding of the principles of the invention. Many variations and modifications may be made to the above embodiments without substantially departing from the spirit and principles of the technology described herein. All modifications are intended to be included within the scope of this disclosure and protected by the following claims.

Claims

1. A vehicle comprising: a plurality of sensors for measuring road surface quality; An anti-lock braking system module, wherein the anti-lock braking system module is used to: receiving a first signal from a first sensor of the plurality of sensors; applying a first filter to the first signal, the first filter configured to filter out signals from a predetermined type of road condition from the first signal; estimating a first confidence value that the road on which the vehicle is traveling is the predetermined type of road condition based on an amount of smoothing of the first signal caused by applying the first filter to the first signal; estimating a coefficient of friction between the road surface and a tire of the vehicle based on a final confidence value, the final confidence value being based on the first confidence value; as well as The wheels of the vehicle are controlled by changing a target slip ratio based on the friction coefficient. 2 . The vehicle of claim 1 , wherein each of the first confidence values ​​represents a likelihood that the road surface corresponds to a particular one of different road surface types.

3. The vehicle of claim 1 or 2, wherein the plurality of sensors comprises cameras.

4. The vehicle of claim 3, wherein the ABS module implements the following steps to estimate second confidence values ​​for different road surface types: capturing an image of the road surface in front of the vehicle; and The image is compared to a reference image of the different road surface types.

5. The vehicle of claim 3, wherein the ABS module implements the following steps to estimate second confidence values ​​for different road surface types: capturing a series of images of the road surface in front of the vehicle; and The photometric values ​​and changes in the photometric values ​​in the series of images are analyzed to determine second confidence values ​​for different road surface types.

6. The vehicle of claim 1 , wherein the plurality of sensors includes a second sensor, the anti-lock braking system module being configured to: generating second confidence values ​​for different road surface types based on the quality of the road surface measured by the second sensor; A coefficient of friction between a road and a tire of the vehicle is estimated based on a sum of the first confidence value and the second confidence value.

7. The vehicle of claim 1 , wherein the plurality of sensors include ultrasonic sensors, the ABS module implementing the following steps to estimate second confidence values ​​for different road surface types: broadcasting a wave signal in front of the vehicle; and Reflection and refraction patterns of the wave signal off the road surface are analyzed to determine the second confidence values ​​for the different road surface types.

8. The vehicle of claim 1 or 2, wherein the plurality of sensors include at least one of a suspension vibration sensor or an accelerometer.

9. The vehicle of claim 1 or 2, wherein the anti-lock braking system module applies a weighting factor to the first confidence value based on environmental data information received from a remote weather server.

10. A traction control method, comprising: measuring a quality of a road surface in front of the vehicle with a first sensor and a second sensor different from the first sensor; Utilizing the processor in the vehicle: receiving a first signal from the first sensor; applying a first filter to the first signal, the first filter configured to filter out signals from a predetermined type of road condition from the first signal; estimating a first confidence value that the road on which the vehicle is traveling is the predetermined type of road condition based on an amount of smoothing of the first signal caused by applying the first filter to the first signal; estimating a coefficient of friction between the road surface and a tire of the vehicle based on a final confidence value, the final confidence value being based on the first confidence value; as well as Torque applied to wheels of the vehicle is controlled using an anti-lock braking system based on the coefficient of friction.

11. The method of claim 10, wherein the first sensor is one of a suspension vibration sensor or an accelerometer and the second sensor is a camera, the method further comprising: generating, with the processor in the vehicle, second confidence values ​​for different road surface types based on the quality of the road surface measured by the second sensor; A coefficient of friction between a road and a tire of the vehicle is estimated based on a sum of the first confidence value and the second confidence value.

12. The method of claim 11 , wherein generating the second confidence value comprises: capturing an image of a road surface in front of the vehicle; as well as The image is compared to a reference image of the different road surface types.

13. The method of claim 11 , wherein generating the second confidence value comprises: capturing a series of images of the road surface in front of the vehicle; as well as Photometric values ​​and variations in said photometric values ​​within a series of images are analyzed to determine said second confidence values ​​for said different road surface types.

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