Rough road detection for vehicles

Abstract

Methods and systems are provided that include: wheel speed sensors of a vehicle configured to obtain wheel speed sensor data regarding the speed of the vehicle's wheels; and a processor coupled to the wheel speed sensors and configured to enable at least the following: determining a variability of the wheel speed sensor data; and determining a measure of the roughness of a path on which the vehicle operates, based on the variability.

Description

Introduction

[0001] The technical field generally concerns vehicles and, in particular, methods and systems for detecting the roughness of a road or path on which a vehicle is traveling and for controlling vehicle actions based on the roughness of the surface.

[0002] Certain vehicles today have control systems to manage various vehicle actions, such as braking or other vehicle movements. These actions can be affected by the roughness of the road or surface on which the vehicle is traveling.

[0003] Accordingly, it is desirable to provide improved methods and systems for detecting the roughness of a road or path on which a vehicle is traveling and for controlling vehicle actions based on the roughness of the road or path. Furthermore, other desirable features and characteristics of the present description will become apparent from the following detailed description and the attached claims in conjunction with the attached drawings and the preceding technical field and background. Description

[0004] In an exemplary embodiment, a method is provided which includes: obtaining, via one or more wheel speed sensors of a vehicle, wheel speed sensor data relating to a speed of the vehicle's wheels; determining, via a processor of the vehicle, a variability of the wheel speed sensor data; and determining, via the processor, a measure of the roughness of a path on which the vehicle is operated, based on the variability.

[0005] In an exemplary embodiment, the method also includes taking a vehicle control action according to instructions provided by the processor, based on the degree of roughness.

[0006] Even in an exemplary embodiment, the step of taking the vehicle control action includes controlling the braking of the vehicle via a braking system of the vehicle based on the degree of roughness according to instructions provided by the processor.

[0007] In an exemplary embodiment, the degree of roughness is also determined by the processor based on a moving average of the variability of the wheel speed sensor data.

[0008] In an exemplary embodiment, the degree of roughness is also determined by the processor based on curve detection and rejection of the wheel speed sensor data.

[0009] In an exemplary embodiment, the moving average is also calculated by the processor according to the following equation: Xn = X n-1 + (X n - X n-1 ) / n, where “X n“represents the wheel speed at a current time, represents it as a current moving average at the current time, X n-1 represents a previous moving average and "n" represents a number of samples in a mean window; and the moving variance of a mean of the wheel speed sensor data is calculated by the processor according to the following equation: σ 2 n = (σ 2 n-1 + [(X n - X n-1 ) - (σ 2 n-1 ] / n, where σ 2 n represents a current variance, σ 2 n-1 represents a previous variance and X n represents a current wheel noise scan.

[0010] In one exemplary embodiment, the calculation of the moving average is also implemented by the processor using the Welford algorithm.

[0011] In an exemplary embodiment, the method further includes classifying, via the processor, the degree of roughness for the vehicle into one of the following categories: (i) smooth, if the variability is less than a first predetermined threshold; (ii) low, if the variability is greater than the first predetermined threshold and less than a second predetermined threshold; (iii) medium, if the variability is greater than the second predetermined threshold and less than a third predetermined threshold; (iv) high, if the variability is greater than the third predetermined threshold and less than a fourth predetermined threshold; and (v) extreme, if the variability is greater than the fourth predetermined threshold.and wherein the vehicle control action involves setting a braking threshold value for a braking system of the vehicle according to instructions provided by the processor to the braking system, based on the classification.

[0012] In another exemplary embodiment, a system is provided that includes one or more wheel speed sensors of a vehicle and a processor. The one or more wheel speed sensors are configured to receive wheel speed sensor data relating to the speed of the vehicle's wheels. The processor is coupled to the one or more wheel speed sensors and is configured to enable at least the following: determining a variability of the wheel speed sensor data; and determining a measure of the roughness of a path on which the vehicle operates, based on the variability.

[0013] In an exemplary embodiment, the processor is further configured to enable at least the initiation of a vehicle control action via the control of one or more of the following: torque distribution, drive control, and drive and suspension type according to instructions provided by the processor based on the degree of roughness.

[0014] In an exemplary embodiment, the processor is further configured to enable at least the control of the vehicle's braking via a vehicle braking system based on the degree of roughness according to instructions provided by the processor.

[0015] In an exemplary embodiment, the processor is further configured to enable at least the determination of the roughness measure based on a moving average of the variability of the wheel speed sensor data.

[0016] In an exemplary embodiment, the processor is further configured to enable at least the determination of the roughness measure based on a moving variance of the wheel speed sensor data.

[0017] In an exemplary embodiment, the processor is further configured to enable at least the determination of the roughness measure based on curve detection and rejection of the wheel speed sensor data.

[0018] In an exemplary embodiment, the processor is further configured to at least enable the calculation of the moving average according to the following equation: Xn = X n-1 + (X n X n-1 ) / n, where “X n “ represents the wheel speed at a current time, X n represents a current moving average at the current time, X n-1represents a previous moving average and "n" represents a number of samples in a mean window; and the calculation of the moving variance of a mean of the wheel speed sensor data according to the following equation: σ 2 n = σ 2 n-1 + [(X n - X n-1 ) - (σ 2 n-1 ] / n, where (σ 2 n represents a current variance, σ 2 n-1 represents a previous variance and X n represents a current wheel noise scan.

[0019] In an exemplary embodiment, the processor is further configured to allow at least the classification of the roughness level for the vehicle into one of the following categories: (i) smooth, if the variability is less than a first predetermined threshold; (ii) low, if the variability is greater than the first predetermined threshold and less than a second predetermined threshold; (iii) medium, if the variability is greater than the second predetermined threshold and less than a third predetermined threshold; (iv) high, if the variability is greater than the third predetermined threshold and less than a fourth predetermined threshold; and (v) extreme, if the variability is greater than the fourth predetermined threshold.and setting a braking threshold value for a vehicle's braking system according to instructions provided to the braking system by the processor, based on the classification.

[0020] In another exemplary embodiment, a vehicle is provided that includes a body, a drive system, a braking system, one or more wheel speed sensors, and a processor. The drive system is configured to move the body. The one or more wheel speed sensors are configured to obtain wheel speed sensor data regarding the speed of the vehicle's wheels.The processor is coupled to the one or more wheel speed sensors and the braking system, and the processor is configured to enable at least the following: determining a variability of the wheel speed sensor data; determining a measure of the roughness of a path on which the vehicle operates, based on the variability, including based on a moving average of the variability of the wheel speed sensor data; a moving variance of the wheel speed sensor data; and curve detection and rejection of the wheel speed sensor data; and controlling the braking of the vehicle based on the measure of roughness according to instructions provided by the processor and implemented by the braking system.

[0021] In an exemplary embodiment, the processor is further configured to enable at least the automatic control of each of the following: torque distribution, drive control, and drive and suspension type according to instructions provided by the processor, based on the degree of roughness.

[0022] In an exemplary embodiment, the processor is further configured to at least enable the calculation of the moving average according to the following equation: X n = X n-1 + (X n - X n-1 ) / n, where “X n “ represents the wheel speed at a current time, X n represents a current moving average at the current time, X n-1where represents a previous moving average and "n" represents a number of samples within a mean window; calculating the moving variance of a mean of the wheel speed sensor data according to the following equation: σ 2 n = σ 2 n-1 + [(X n - X n-1 ) - (σ 2 n-1 ] / n, where σ 2 n represents a current variance, σ 2 n-1 represents a previous variance and X na current wheel noise scan; classifying the degree of roughness for the vehicle into one of the following categories: (i) smooth, if the variability is less than a first predetermined threshold; (ii) low, if the variability is greater than the first predetermined threshold and less than a second predetermined threshold; (iii) medium, if the variability is greater than the second predetermined threshold and less than a third predetermined threshold; (iv) high, if the variability is greater than the third predetermined threshold and less than a fourth predetermined threshold; and (v) extreme, if the variability is greater than the fourth predetermined threshold; and setting a braking threshold for the vehicle's braking system according to instructions provided to the braking system by the processor, based on the classification. Brief description of the drawings

[0023] The present description is further described below in conjunction with the following drawing figures, where identical reference symbols denote identical elements and where: Fig. 1 a functional block diagram of a vehicle which includes a control system for detecting a roughness of a road or path on which the vehicle is traveling and for controlling vehicle actions based on the roughness of the road or path according to exemplary embodiments; Fig. 2. A flowchart of a process for detecting the roughness of a road or path on which a vehicle is traveling, and for controlling vehicle actions based on the roughness of the road or path, and which, in conjunction with the vehicle, Fig. 1 including the tax system Fig. 1 can be implemented; Fig. 3. A flowchart of one step of the process. Fig. 2 is, namely for calculating a moving average of wheel speed sensor noise according to exemplary embodiments; Fig. 4. A flowchart of another step in the process. Fig. 2 is, namely for calculating a moving average of wheel speed sensor noise according to exemplary embodiments; Fig. 5A and Fig. 5B (also collectively known as “ Fig. 5” denotes) a flowchart of a further step in the process Fig. 2 provide, namely for detecting and rejecting curve outliers according to exemplary embodiments; Fig. 6A and Fig. 6B (here also collectively referred to as “ Fig. 6” denotes) a flowchart of a further step in the process from Fig. 2. provide, namely for classifying output results for categorizing surface roughness according to exemplary embodiments; and Fig. 7-16 Illustrations of exemplary implementations of the process from Fig. 2 and the steps from Fig. 2-6 in various exemplary situations of a vehicle traveling at different speeds and on paths of different roughness characteristics, according to exemplary embodiments. Detailed description

[0024] The following detailed description is merely exemplary and is not intended to limit the description, application, or uses thereof. Furthermore, there is no intention to be bound to any theory presented in the preceding background or the following detailed description.

[0025] Fig. Figure 1 illustrates a vehicle 100 according to an exemplary embodiment. As described in more detail below, the vehicle 100 includes a control system 102 configured to detect the roughness of a road or path on which a vehicle is traveling, based on a variability of road wheel sensor data as determined by the control system 102, and to control vehicle actions, including vehicle braking, based on the roughness of the road or path according to exemplary embodiments.

[0026] As used in this application, the terms “path”, “road”, and “track” (and any variations thereof) refer to a path (including a road or other path) of a surface on which the vehicle 100 travels. As described herein, the path and its surface may include dirt, gravel, asphalt, concrete, and / or other types of paths with various different roughnesses, as determined by the control system 102 (as mentioned above and described in more detail below).

[0027] In various embodiments, Vehicle 100 includes an automobile. Vehicle 100 can be any of a number of different types of automobiles, such as a sedan, station wagon, truck, or SUV, and in certain embodiments can be two-wheel drive (2WD) (i.e., rear-wheel drive or front-wheel drive), four-wheel drive (4WD), or all-wheel drive (AWD), and / or various other types of vehicles. In certain embodiments, Vehicle 100 can also include a motorcycle or other vehicle, such as an aircraft, spacecraft, watercraft, and so on, and / or one or more other types of mobile platforms (e.g., a robot and / or another mobile platform).

[0028] The vehicle 100 comprises a body 104 mounted on a chassis 106. The body 104 essentially encloses other components of the vehicle 100. The body 104 and the chassis 106 can together form a frame. The vehicle 100 also includes a plurality of wheels 112. The wheels 112 are each rotatably coupled to the chassis 106 near a respective corner of the body 104 to enable the movement of the vehicle 100. In one embodiment, the vehicle 100 includes four wheels 112, although this may vary in other embodiments (for example, for trucks and certain other vehicles).

[0029] As in Fig. As shown in Figure 1, the vehicle 100 includes a braking system 108 in various embodiments. In exemplary embodiments, the braking system 108 controls the braking of the vehicle 100 using braking components that are controlled by inputs provided by a driver (e.g., via a brake pedal in certain embodiments) and / or automatically by the control system 102. In certain embodiments, the braking system 108 is automatically controlled by the control system 102 in different ways (including different anti-lock braking thresholds in certain embodiments) based on the roughness of the path, as determined by the control system 102.

[0030] In exemplary embodiments, the vehicle 100 also includes a steering system 109 that controls the steering of the vehicle 100. In various embodiments, the steering system 109 controls the steering of the vehicle 100 via steering components and is controlled by inputs that, in certain cases, are provided by a driver via a steering wheel, and in certain other cases automatically via the control system 102. In certain embodiments, the steering system 109 can be automatically controlled by the control system 102 in different ways (including different steering threshold values ​​in certain embodiments) based on the roughness of the path, as determined by the control system 102.

[0031] In exemplary embodiments, a drive system 110 is mounted on the chassis 106 and drives the wheels 112, for example via axles 114. In certain embodiments, the drive system 110 comprises a drive unit. In certain exemplary embodiments, the drive system 110 comprises an internal combustion engine and / or an electric motor / generator coupled to it via a transmission. In certain embodiments, the drive system 110 can vary and / or two or more drive systems 110 can be used. Also in exemplary embodiments, the drive system 110 controls the propulsion of the vehicle 100 according to inputs provided by a driver (e.g., via an accelerator pedal) and / or automatically via the control system 102.In certain embodiments, the drive system 110 can be automatically controlled by the control system 102 in different ways (including different drive threshold values ​​in certain embodiments) based on the roughness of the path as determined by the control system 102.

[0032] In the Fig. In the embodiment shown in Figure 1, the control system 102 is coupled to the steering system 109, the braking system 108, and the drive system 110. In certain embodiments, the control system 102 can also be coupled to one or more other vehicle systems and / or components.

[0033] In various embodiments, as mentioned above, the control system 102 detects the roughness of a road or path on which the vehicle 100 is traveling and controls vehicle actions based on the roughness of the road or path. In various embodiments, the control system 102 performs these functions according to process 200. Fig. 2 and implementations from Fig. 3-16 ready, and as described in more detail below in connection therewith. In certain embodiments, the control system 102 can also control one or more other systems of the vehicle 100.

[0034] As in Fig. As shown in Figure 1, the control system 102, in various embodiments, includes a sensor arrangement 120 and a controller 140, as described in more detail below. In certain embodiments, the control system 102 also includes a positioning system 128 (e.g., GPS) and a transceiver 130.

[0035] In various embodiments, the sensor arrangement 120 includes different sensors that receive sensor data regarding the wheel speed of the wheels 112 of the vehicle 100. Also in various embodiments, one or more variations of the sensor data (including variations relating to wheel speeds) are used by the control system 102 to determine the roughness of a road or path on which the vehicle 100 is traveling, including for use in controlling one or more vehicle actions (e.g., including braking in various embodiments). In the illustrated embodiment, the sensor arrangement 120 includes one or more wheel speed sensors 122. In certain embodiments, the sensor arrangement 120 may also include one or more other sensors 124.

[0036] In one exemplary embodiment, the one or more wheel speed sensors 122 receive wheel speed sensor data relating to the wheels 112 of the vehicle 100. In certain embodiments, the wheel speed sensors 122 receive wheel speed sensor data relating to the speed of each of the wheels 112 of the vehicle 100.

[0037] In addition, the sensor arrangement 120 may in certain embodiments also include one or more other sensors 124, such as one or more transmission sensors (e.g., to determine when the vehicle 100 is activated for a current vehicle journey, etc.), one or more user input sensors (e.g., for braking, steering, driving, or the like), and / or other types of other sensors 124 in various embodiments.

[0038] In various embodiments, the control unit 140 is coupled with the sensor arrangement 120, the positioning system 128, and the braking system 108, and in certain embodiments also with the steering system 109 and the drive system 110. In various embodiments, the control unit 140 can also be coupled with one or more other vehicle systems, as mentioned above. Also in various embodiments, the control unit 140 comprises a computer system (here also referred to as computer system 140) and includes a processor 142, a memory 144, an interface 146, a storage device 148, and a computer bus 150. In various embodiments, the control unit (or the computer system) detects the roughness of a road or path on which the vehicle 100 is traveling and controls vehicle actions based on the roughness of the road or path.In various embodiments, the control unit 140 displays these and other functions according to the steps of process 200. Fig. 2 and implementations from Fig. 3-16 ready.

[0039] In various embodiments, the control unit 140 (and in certain embodiments the control system 102 itself) is arranged within the body 104 of the vehicle 100. In one embodiment, the control system 102 is mounted on the chassis 106.

[0040] It goes without saying that the control unit 140 differs in some other way from the one in Fig. The embodiment shown in Figure 1 may differ. For example, the control unit 140 may be coupled to or otherwise used with one or more remote computer systems and / or other control systems, for example as part of one or more of the devices and systems of the vehicle 100 identified above.

[0041] In the illustrated embodiment, the computer system of the controller 140 comprises a processor 142, a memory 144, an interface 146, a storage device 148, and a bus 150. The processor 142 performs the calculation and control functions of the controller 140 and can comprise any type of processor or multiple processors, individual integrated circuits such as a microprocessor, or any suitable number of integrated circuit devices and / or printed circuit boards that work together to perform the functions of a processing unit. During operation, the processor 142 executes one or more programs 152 contained in the memory 144 and, as such, controls the general operation of the controller 140 and the computer system of the controller 140, generally when executing the processes described herein, such as process 200. Fig. 2 and implementations from Fig. 3-16.

[0042] The memory 144 can be any type of suitable memory. For example, the memory 144 can include various types of dynamic random-access memory (DRAM) such as SDRAM, the various types of static RAM (SRAM), and the various types of non-volatile memory (PROM, EPROM, and Flash). In certain examples, the memory 144 is located on the same computer chip as the processor 142 and / or is located together on the same chip. In the embodiment shown, the memory 144 stores the aforementioned program 152 together with stored values ​​157 (e.g., thresholds for the process 200 from Fig. 2 and implementations from Fig. 3-16 in various embodiments).

[0043] The bus 150 serves to transmit programs, data, status, and other information or signals between the various components of the controller's computer system 140. The interface 146 enables communication with the controller's computer system 140, for example, from a system driver and / or another computer system, and can be implemented using any suitable method and device. In one embodiment, the interface 146 receives various data from the sensor array 120, among other possible data sources. The interface 146 can include one or more network interfaces for communication with other systems or components.Interface 146 may also include one or more network interfaces for communication with technicians, and / or one or more storage interfaces for connection to storage devices such as the storage device 148.

[0044] The storage device 148 can be any suitable type of storage device, including various different types of random-access memory and / or other storage devices. In an exemplary embodiment, the storage device 148 comprises a program product from which the memory 144 can receive a program 152 executing one or more embodiments of one or more processes of the present description, such as the steps of process 200, which are described below in conjunction with Fig. 2 will be discussed. In another exemplary embodiment, the program product can be stored directly in memory 144 and / or a disk (e.g. disk 156) and / or accessed in another way, such as the one mentioned below.

[0045] Bus 150 can be any suitable physical or logical means for connecting computer systems and components. This includes, but is not limited to, direct hard-wired connections, fiber optic, infrared, and wireless bus technologies. During operation, program 152 is stored in memory 144 and executed by processor 142.

[0046] It is understood that, while this exemplary embodiment is described in the context of a fully functioning computer system, the person skilled in the art will recognize that the mechanisms of the present description are capable of being distributed as a program product with one or more types of non-volatile, computer-readable signal-carrying media used to store the program and its instructions and to carry out its distribution, such as a non-volatile, computer-readable medium carrying the program and containing computer instructions stored therein to instruct a computer processor (such as Processor 142) to execute and run the program. Such a program product can take a variety of forms, and the present description applies equally regardless of the specific type of computer-readable signal-carrying media used to carry out the distribution.Examples of signal-carrying media include: writable media such as floppy disks, hard drives, memory cards, and optical discs, and transmission media such as digital and analog communication links. It is understood that cloud-based storage and / or other technologies may also be used in certain embodiments. Likewise, it is understood that the computer system of the control unit 140 may also differ in other ways from the one described in [reference missing]. Fig. 1 can differ from the embodiment shown, for example in that the computer system of the controller 140 can be coupled with one or more remote computer systems and / or other control systems or can otherwise be used.

[0047] Fig. Figure 2 is a flowchart of a process 200 for detecting the roughness of a road or path on which a vehicle is traveling and for controlling vehicle actions based on the roughness of the road or path according to exemplary embodiments. The process 200 can also be used in conjunction with the vehicle 100 in various embodiments. Fig. 1 including the tax system 102 from Fig. 1 and its components will be implemented.

[0048] As in Fig. As shown in Figure 2, process 200 begins at step 202 in various embodiments. In one embodiment, process 200 begins when a driving or ignition cycle of the vehicle begins, for example, when a driver enters the vehicle to operate it (e.g., as in certain embodiments from one or more of the other sensors 124 of the sensor arrangement 120). Fig. 1 detected). In one embodiment, the steps of process 200 are carried out continuously during the operation of the vehicle.

[0049] In various embodiments, sensor data is obtained (step 204). In various embodiments, wheel speed sensor data is obtained from the wheel speed sensors 122. Fig. 1 regarding values ​​of the speed of the wheels 112 from Fig. 1. In certain embodiments, location data relating to a geographical position of the vehicle 100 are also obtained (e.g., via the location system 128, such as a GPS system, from Fig. 1).

[0050] In various embodiments, a moving average is calculated (step 206). In various embodiments, the processor calculates 142 from Fig. 1 during step 206 a moving average for the wheel speed values ​​of the wheel speed sensor data obtained from the wheel speed sensors 122 in step 204.

[0051] In certain embodiments, the processor 142 calculates the moving average according to the following equation: x¯n=x¯n−1+xn−x¯n−1n where “X n “ represents the wheel speed at a current time, X n represents a current moving average at the current time, X n-1 The previous moving average is represented, and "n" represents the number of samples in the mean window. In an exemplary embodiment, this is done using the Welford online algorithm, which significantly reduces the memory required to calculate moving averages and variances.

[0052] An additional flowchart is included in Fig. 3 provides additional details of the calculation of the moving average from step 206 and is described in more detail below in connection with it.

[0053] With further reference to Fig. 2. In one exemplary embodiment, moving variances are calculated (step 208). In various embodiments, the processor 142 calculates from Fig. 1 a moving variance for the wheel speed values ​​of the wheel speed sensor data based on the moving mean calculated in step 206.

[0054] In certain embodiments, the processor 142 calculates the moving variance according to the following equation: σn2=σn−12+(xn−x¯n−1)(xn−x¯n)−σn−12n where σ 2 n represents the current variance, σ 2 n-1the previous variance (which can be assumed to be zero for “n = 1” in certain embodiments, but which may have different values ​​in other embodiments) and X n represents the current wheel noise scan.

[0055] An additional flowchart is included in Fig. 4 provides additional details of the calculation of the moving variance from step 208 and is described in more detail below in connection with it.

[0056] With further reference to Fig. In one exemplary embodiment, curve outliers are determined and controlled for (step 210). In various embodiments, the processor 142 determines from Fig. 1. The outliers in the curves are identified by repeating steps 206 and 208 (and their equations) for each curve of the vehicle 100 (i.e., for a front driver's side wheel 112, a front passenger's side wheel 112, a rear driver's side wheel 112, and a rear passenger's side wheel 112). Also in exemplary embodiments, as part of step 210, mean values ​​for the variances are taken, and the variance of each curve is subtracted from the mean to determine a difference for that particular curve. Also in various embodiments, if the difference for the particular curve exceeds a predetermined threshold (which, for example, is stored in memory 144), Fig. 1 (where a stored value is stored), the variance for that specific curve is removed from the mean of the second stage. In various embodiments, the denominator (e.g., in equation 2 above) is also adjusted when the variance of a specific curve is removed. Also in various embodiments, the resulting output value comprises a single vehicle level roughness signal for vehicle 100.

[0057] An additional flowchart is included in Fig. 5 provides additional details on determining and controlling the curve outliers from step 210 and is described in more detail below in connection with this.

[0058] With further reference to Fig. 2 In an exemplary embodiment, a classification is performed (step 212). In various embodiments, the processor 142 categorizes from Fig. 1. The roughness of the road surface is classified into one of five categories based on vehicle level variance. In various embodiments, the roughness is characterized into one of the following categories (i.e., classifications): (i) smooth (e.g., a smooth asphalt or concrete surface) when the vehicle level variance is less than a first predetermined threshold; (ii) low (e.g., a light gravel or dirty road) when the vehicle level variance is greater than the first predetermined threshold and less than a second predetermined threshold; (iii) medium (e.g., a road with a relatively larger amount of gravel compared to the "low" level) when the vehicle level variance is greater than the second predetermined threshold and less than a third predetermined threshold; (iv) high (e.g.,(v) bumps or rumble strips on a road) if the vehicle level variance is greater than the third predetermined threshold and less than a fourth predetermined threshold; and (v) extreme (e.g. a cobblestone road) if the vehicle level variance is greater than the fourth predetermined threshold.

[0059] In various embodiments, the fourth predetermined threshold from step 212 is greater than the third predetermined threshold, which is greater than the second predetermined threshold, which is greater than the first predetermined threshold. Also in various embodiments, each of these predetermined thresholds from step 211 is stored in memory 144. Fig. 1 as stored values, 154 of which are stored.

[0060] An additional flowchart is included in Fig. 6 with additional details of the classification from step 212 is provided and is described in more detail below in connection with it.

[0061] With further reference to Fig. 2. One or more vehicle control actions are taken (step 214). In various embodiments, the processor 142 controls Fig. 1. The braking of the vehicle 100 is controlled via instructions provided to the braking system 108 based on the roughness of the path surface (i.e., based on the classification output from step 212), wherein the instructions are implemented via the braking system 108 when controlling the braking of the vehicle 100. In certain embodiments, the processor 142 sets one or more predetermined braking threshold values ​​(e.g., for anti-lock brakes) based on the roughness of the path surface (i.e., based on the classification output from step 212). In certain embodiments, the processor 142 can also use the roughness value to control the steering system 109, the drive system 110, and / or one or more other vehicle systems.In certain embodiments, the vehicle control actions include one or more of the following (and in certain embodiments include each of the following): taking a vehicle control action via the control of one or more of the following: torque distribution, drive control, and drive and suspension type according to instructions provided by the processor, based on the degree of roughness.

[0062] In various embodiments, the process ends at 220.

[0063] As mentioned above, they represent Fig. 3-6 Flowcharts of exemplary implementations of certain steps of process 200 from Fig. 2 according to exemplary embodiments.

[0064] With reference to Fig. Section 3 provides an illustration of an exemplary implementation of calculating the moving average from step 206 according to an exemplary embodiment. As in Fig. As shown in Figure 3, the inputs in an exemplary embodiment include the wheel speed sensor data noise “u” 302 together with a reference speed (u1) 304 and a previous value of the wheel speed sensor noise (u2) 306, together with calibratable values ​​of a normal (or expected) standard noise 308 in certain embodiments (e.g., in certain embodiments, this may be used for development purposes to turn the normalization filing on or off based on feedback from testing), a normal (or expected) gain 310, and a variance window 311.

[0065] In various embodiments, the reference velocity (u1) 304 is multiplied by the normal (or expected) gain 310 in step 312. Also in various embodiments, a Boolean determination is made in step 316 to determine whether various conditions of the product from step 312, the normal (or expected) standard noise 308, and an additional single input value 314 are present, and the result of the Boolean determination from step 316 is multiplied by the wheel speed sensor data noise “u” 302 in step 318.

[0066] In various embodiments, the resulting product from step 318 yields a normalized wheel speed sensor noise value (y) 326.

[0067] Additionally, in various embodiments, the quotient from step 318 is subtracted from the previous value of the wheel speed sensor noise (u2) 306 in step 320. Also in various embodiments, the product from step 320 is then divided by the variance window 311 in step 322, and the product from step 322 is then added to the wheel speed sensor noise (u2) 306 in step 324.

[0068] In various embodiments, the resulting sum from step 324 yields a moving average (y1) 328.

[0069] Fig. Figures 7-16 present various implementations of graphical representations relating to the RF variance compared to the RF curve velocity (both on the y-axis)-RF variance (both on the y-axis) over time (on the x-axis).

[0070] Fig. Figure 7 shows a first graphical representation 700 of the right front wheel's cornering speed 702 and its RF variance 704 in a first scenario of a smooth road (e.g., a flat, smooth highway) at approximately ten meters per second (i.e., twenty-two miles per hour). As shown in this graphical representation 700, the RF cornering speed 702 is almost completely flat under this scenario, even with variations in the RF variance 704.

[0071] Fig. Figure 8 shows a second graphical representation 800 of the RF curve speed 802 and the RF variance 804 in a second scenario of a smooth road (e.g., a flat, smooth highway) at approximately 25 meters per second (i.e., 56 miles per hour). As shown in this graphical representation 800, the RF curve speed 802 remains almost completely constant under this scenario, even with variations in the RF variance 804.

[0072] Fig. Figure 9 shows a third graph 900 of the RF curve speed 902 and the RF variance 904 in a third scenario of a light gravel road (e.g., a lightly smooth road) at about ten meters per second (i.e., twenty-two miles per hour). As shown in this graph 900, the RF curve speed 902 remains fairly constant under this scenario, but with a greater degree of variance compared to Fig. 7 and Fig. 8, with variations in RF variance 904.

[0073] Fig. Figure 10 shows a fourth graph 1000 of the RF curve speed 1002 and the RF variance 1004 in a fourth scenario of a light gravel road (e.g., a lightly smooth road) at about twenty-five meters per second (i.e., fifty-six miles per hour). As shown in this graph 1000, the RF curve speed 1002 remains relatively flat under this scenario, but also with a greater degree of variance compared to Fig. 7 and Fig. 8, with variations in RF variance 1004.

[0074] Fig. Figure 11 shows a fifth graph 1100 of the RF curve speed 1102 and the RF variance 1104 in a fifth scenario of a heavy gravel road at about ten meters per second (i.e., twenty-two miles per hour). As shown in this graph 1100, the RF curve speed 1102 remains fairly flat under this scenario, but with a greater degree of variance compared to Fig. 7-10, with variations in RF variance 1104.

[0075] Fig. Figure 12 shows a sixth graph 1200 of the RF curve speed 1202 and the RF variance 1204 in a sixth scenario of a heavy gravel road at about twenty-five meters per second (i.e., fifty-six miles per hour). As shown in this graph 1200, the RF curve speed 1202 remains fairly flat under this scenario, but also with a greater degree of variance compared to Fig. 7-10, with variations in RF variance 1204.

[0076] Fig. Figure 13 shows a seventh graph 1300 of the RF curve speed 1302 and the RF variance 1304 in a seventh scenario of a road with severe roughness (e.g., bumps) at about ten meters per second (i.e., twenty-two miles per hour). As shown in this graph 1300, the RF curve speed 1302 exhibits a significant degree of variance under this scenario, with a much greater degree of variance compared to Fig. 7-12, with variations in the RF variance 1304. In an exemplary embodiment, the road undulations are arranged in four different sections with smooth roadway in between, resulting in the four different peaks in the variance.

[0077] Fig. Figure 14 shows an eighth graph 1400 of the RF curve speed 1402 and the RF variance 1404 in an eighth scenario of a road with severe roughness (e.g., bumps) at about twenty-five meters per second (i.e., fifty-six miles per hour). As shown in this graph 1400, the RF curve speed 1402 shows a significant degree of variance under this scenario, with a much greater degree of variance compared to Fig. 7-12, with variations in the RF variance 1404. In an exemplary embodiment, the bumps are arranged in four different sections with smooth roadway in between, resulting in the four different peaks in the variance.

[0078] Fig. Figure 15 shows a ninth graph 1500 of the RF curve speed 1502 and the RF variance 1504 in a ninth scenario of a road with extreme roughness (e.g., paving or cobblestones) at varying speeds. As shown in this graph 1500, the RF curve speed 1502 exhibits a significant degree of variance under this scenario, with a much greater degree of variance compared to Fig. 7-12, with variations in RF variance 1504.

[0079] Fig. Figure 16 shows a tenth graphical representation 1600 of the RF curve speed 1602 and the RF variance 1604 in a tenth split-roughness scenario, including a left side of the vehicle 100 traveling over bumps while the other side of the vehicle 100 travels on a smooth road surface. As shown in this graphical representation 1600, the (right) RF curve speed 1602 varies relatively less with the (right) RF curve speed 1604 under this scenario; whereas the (left) variance 1606 varies relatively more with respect to the (left) NF curve speed 1608. In an exemplary embodiment, the bumps are arranged in four distinct sections with smooth road surfaces in between, resulting in the four distinct peaks in the variance.

[0080] Accordingly, methods, systems, and vehicles are provided for detecting the roughness of a road or path on which a vehicle is traveling and for controlling vehicle actions based on the roughness of the road or path. In various embodiments, the roughness of the path is determined based on a variance of wheel speed sensor values ​​from the wheel speed sensors of the vehicle 100, together with various calculations and steps performed by the processor 142 of the vehicle 100, as described herein. Also in various embodiments, the processor 142 uses the roughness determination to control one or more vehicle actions, including the control of the braking system 108 of the vehicle 100.

[0081] It is understood that the systems, vehicles, and procedures may differ from those depicted in the figures and described herein. For example, vehicle 100 may be made of Fig. 1, the tax system 102 from Fig. 1 and / or components thereof vary in different embodiments. Likewise, it is understood that the steps of process 200 differ from those in Fig. 2-6 shown can be distinguished and / or that different steps of the process 200 occur simultaneously and / or in a different order than that shown Fig. 2-6 can take place. It is also understood that the implementations in certain embodiments may differ from those shown in Fig. 7-16 can be distinguished.

[0082] Although at least one exemplary embodiment has been presented in the preceding detailed description, it is understood that a large number of variations exist. It is also understood that the exemplary embodiment or embodiments are merely examples and are not intended to limit the scope, applicability, or configuration of the description in any way. Rather, the preceding detailed description will provide the person skilled in the art with a suitable plan for implementing the exemplary embodiment or embodiments. It is understood that various modifications to the function and arrangement of elements can be made without deviating from the scope of the description as set out in the appended claims and their legal equivalents.

Claims

[1] Procedure, encompassing: Received, via one or more wheel speed sensors of a vehicle, from wheel speed sensor data regarding the speed of the vehicle's wheels; Determine, via a vehicle processor, a variability of the wheel speed sensor data; and Determine, via the processor, a measure of the roughness of a path on which the vehicle operates, based on the variability. [2] Method according to claim 1, further comprising: Taking a vehicle control action according to instructions provided by the processor, based on the degree of roughness. [3] Method according to claim 2, wherein the step of taking the vehicle control action comprises controlling the braking of the vehicle via a braking system of the vehicle based on the degree of roughness according to instructions provided by the processor. [4] Method according to claim 1, wherein the degree of roughness is determined by the processor based on a moving average of the variability of the wheel speed sensor data. [5] Method according to claim 4, wherein the degree of roughness is also determined by the processor based on a moving variance of the wheel speed sensor data. [6] Method according to claim 5, wherein the degree of roughness is also determined by the processor based on curve detection and rejection of the wheel speed sensor data. [7] Method according to claim 5, wherein: The moving average is calculated by the processor according to the following equation: X n = X n-1 + (X n - X n-1 ) / n, where “X n “ represents the wheel speed at a current time, X n represents a current moving average at the current time, X n-1represents a previous moving average and "n" represents a number of samples within a mean window; and The moving variance of a mean value of the wheel speed sensor data is calculated by the processor according to the following equation: σ 2 n = (σ 2 n-1 + [(X n - X n-1 ) - σ 2 n-1 ] / n, where (σ 2 n represents a current variance, σ 2 n-1 represents a previous variance and X n represents a current wheel noise scan. [8] Method according to claim 7, wherein the calculation of the moving average is implemented by the processor using the Welford algorithm. [9] The method of claim 2, further comprising: The processor categorizes the roughness measurement for the vehicle into one of the following categories: (i) smooth if the variability is less than a first predetermined threshold; (ii) low if the variability is greater than the first predetermined threshold and less than a second predetermined threshold; (iii) mean if the variability is greater than the second predetermined threshold and less than a third predetermined threshold; (iv) high if the variability is greater than the third predetermined threshold and less than a fourth predetermined threshold; and (v) extreme if the variability is greater than the fourth predetermined threshold; and wherein the vehicle control action includes setting a braking threshold value for a braking system of the vehicle according to instructions provided to the braking system by the processor, based on the classification. [10] System, encompassing: one or more wheel speed sensors of a vehicle, configured to receive wheel speed sensor data relating to the speed of the vehicle's wheels; and a processor coupled with one or more wheel speed sensors, the processor being configured to enable at least the following: Determining the variability of the wheel speed sensor data; and determining a measure of the roughness of a path on which the vehicle is operated, based on the variability.

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