SYSTEMS AND METHODS FOR ROAD ROAD ROUGHNESS MEASUREMENT

Abstract

A vehicle equipped with numerous road noise reduction sensors collects vibration data while driving along a road. The vehicle checks whether a set of operating conditions is met. If the set of operating conditions is met, the vehicle determines the portion of the vibration data acquired during the time frame in which the set of operating conditions was fulfilled. The vehicle can then use this portion of the vibration data to extract feature data for one or more features. The vehicle then uses the feature data associated with the one or more features to determine a roughness indicator for the road it is currently driving on. This road roughness indicator measurement can be used to trigger further analysis of the vibration data to determine other vehicle parameters.

Description

AREA

[0001] The present disclosure relates to the field of real-time road roughness measurement by a vehicle. GENERAL STATE OF THE ART

[0002] Conventional techniques for measuring road roughness, such as the International Roughness Index (IRI), have several problems. The IRI is based on the response of a standard-sized car to the roughness of the road surface. However, real cars vary in size and can differ from the ideal car used in the IRI definition. As a result, recordings made in cars of different sizes can lead to estimates that differ somewhat from the IRI. Furthermore, roughness measurement methods have not been stable over time. Measurements taken today with road measuring instruments cannot be compared to those taken a few years ago. Roughness measurements were not portable. Road measuring instrument measurements taken by one system are rarely reproducible by another.Recent studies have shown inconsistencies in IRI results, including biases, random errors, and inconsistencies between devices.

[0003] These challenges suggest that while the IRI is a useful tool, it may not be sufficient to accurately describe road roughness in all scenarios. Some experts propose supplementing the IRI with additional numerical properties, such as the power-law exponent, which describes how the effect of roughness changes as the size of the vehicle changes. BRIEF OVERVIEW

[0004] This disclosure describes systems and methods for the real-time measurement of road roughness. The determination of the road condition can then be used as a trigger condition for collecting and / or analyzing other vehicle data, which can be used in various other determinations.

[0005] In some cases, a procedure is provided that is carried out by a vehicle capable of determining a measure of the roughness of the road it is driving on in real time.

[0006] The procedure involves the vehicle determining that it is in motion on a road. During this motion, the vehicle can acquire vibration data associated with it using a variety of vehicle sensors. The procedure can further involve the vehicle determining operational data associated with it and, based on this operational data, determining that a set of operating conditions is met. The procedure can also involve the vehicle determining one or more characteristics using the vibration data and based on the fulfillment of the set of operating conditions. Subsequently, the procedure can involve the vehicle determining a roughness indicator for the road based on the one or more characteristics.

[0007] In another scenario, a vehicle is provided that includes multiple accelerometers or road noise suppression sensors mounted at various locations on the vehicle. The vehicle also includes one or more processors that work in conjunction with the sensors to determine that the vehicle is in motion on a road. The vehicle can also acquire vibration data associated with it using the sensors. The one or more sensors can further determine operational data associated with the vehicle and, based on this operational data, determine that a set of operating conditions is met. Based on the fulfillment of the set of operating conditions, the one or more processors use the vibration data to determine one or more features associated with the vibration data.Additionally, one or more processors can determine a roughness indicator for the road based on one or more features.

[0008] In yet another scenario, a vehicle is provided that can determine a measure of road roughness in real time. First, the vehicle determines that it is currently moving on a road. The vehicle then receives vehicle-specific vibration data from a variety of sensors coupled to it. After receiving the vibration data, the vehicle determines that a set of operating conditions associated with the vehicle is met for an initial period. Next, the vehicle determines a first segment of the vibration data associated with this initial period and, using this segment, further determines one or more features associated with it. Finally, the vehicle determines a roughness indicator for the road based on these one or more features.

[0009] These and other benefits of the present revelation are provided in detail in this document. BRIEF DESCRIPTION OF THE DRAWINGS

[0010] The detailed description is set forth with reference to the accompanying drawings. The use of the same reference numerals may indicate similar or identical elements. Different embodiments may use different elements and / or components than those illustrated in the drawings, and some elements and / or components may not be present in different embodiments. The elements and / or components in the figures are not necessarily drawn to scale. Throughout this disclosure, singular and plural expressions may be used interchangeably depending on the context. Fig. Figure 1 illustrates a block diagram of a vehicle according to an embodiment of the present disclosure. Fig. Figure 2 illustrates a section of a vehicle incorporating a vibration detection sensor, according to an embodiment of the present disclosure. Fig. Figure 3 illustrates a block diagram for a road roughness measurement system according to an embodiment of the present disclosure. Fig. 4A and Fig. Figure 4B illustrates vibration data for two different types of roads according to one embodiment of the present disclosure. Fig. Figures 5A-5D illustrate feature data determined from the vibration data for the four wheels of a vehicle, according to an embodiment of the present disclosure. Fig. Figure 6 illustrates a flowchart for determining road roughness according to an embodiment of the present disclosure. Fig. Figure 7 illustrates a block diagram of a server according to an embodiment of the present disclosure. DETAILED DESCRIPTION

[0011] The disclosure is described in more detail below with reference to the accompanying drawings, which show exemplary embodiments of the disclosure, and is not intended to be restrictive.

[0012] Fig. Figure 1 illustrates a block diagram of a vehicle 100 in which the embodiment of the present disclosure can be implemented. The vehicle 100 can include a plurality of units, including, among others, a vehicle computer 108, a vehicle control unit (VCU) 110, and an infotainment unit 138. The VCU 110 can include a plurality of electronic control units (ECUs) 114 arranged in communication with the vehicle computer 108.

[0013] In some embodiments, a user device, such as a mobile phone, a laptop computer, or the like, may be configured to connect to the vehicle computer 108, which can communicate via one or more wireless connections, and / or can communicate using near field communication (NFC) protocols, Bluetooth ® -Protocols, Wi-Fi, Ultra Wideband (UWB) and other possible data connection and sharing techniques directly connect to the vehicle 100.

[0014] According to the disclosure, the vehicle computer 108 can be installed at any location in the vehicle 100. The vehicle computer 108 can be or include an electronic vehicle control unit comprising one or more processors 102, one or more memory units 104, and one or more transceivers 106.

[0015] The processor(s) 102 can be arranged in communication with one or more storage devices, which are arranged in communication with the respective computing systems (e.g., the memory 104 and / or one or more external databases located in Fig. (not shown in Figure 1). The processor(s) 102 can / can use the memory 104 to store programs in code and / or data for performing operations according to the disclosure. The memory 104 can be a persistent, computer-readable storage medium or persistent, computer-readable memory in which program code for controlling vehicles is stored. The memory 104 can include any or a combination of volatile memory elements (e.g., dynamic random access memory (DRAM), synchronous dynamic random access memory (SDRAM), etc.) and any or more non-volatile memory elements (e.g., erasable programmable read-only memory (EPROM), flash memory, electronically erasable programmable read-only memory (EEPROM), programmable read-only memory (PROM), etc.).In some embodiments, the memory 104 may include a module 145 that can implement the various embodiments of the present disclosure. The module 145 may contain instructions that can be executed by the processor 102 to realize the various embodiments of the present disclosure.

[0016] The vehicle computer 108 may also include a transceiver 106. The transceiver 106 may be configured to receive information / inputs from one or more external devices or systems, such as a user device 108, an external server, and / or the like. Furthermore, the transceiver 106 may transmit notifications, requests, signals, etc., to the external devices or systems. Additionally, the transceiver 106 may be configured to receive information / inputs from vehicle components, such as the vehicle sensor system 132, one or more ECUs 114, and / or the like. Furthermore, the transceiver 106 may transmit signals (e.g., command signals) or notifications to the vehicle components, such as the BCM 120, the infotainment system 138, and / or the like.

[0017] In some embodiments, the VCU 110 can share a power bus with the vehicle computer 108 and can be configured and / or programmed to coordinate data between vehicle systems, connected servers, and / or the like. The VCU 110 can include or communicate with any combination of the ECUs 114, such as a Body Control Module (BCM) 120, an Engine Control Module (ECM) 122, a Transmission Control Module (TCM) 124, a Telematics Control Unit (TCU) 126, a Driver Assistance Technologies (DAT) 128, etc. The VCU 110 can also include and / or communicate with a Vehicle Perception System (VPS) 130, which has connectivity with and / or controls one or more vehicle sensor systems 132.The vehicle sensor system 132 may include one or more vehicle sensors, including, but not limited to, a radio detection and ranging sensor (RADAR or “radar”) configured to detect and locate objects inside and outside the vehicle 100 using radio waves, seat belt buckle sensors, seat area sensors, a light detection and ranging sensor (“LIDAR”), door sensors, proximity sensors, temperature sensors, wheel sensors, one or more ambient weather or temperature sensors, vehicle interior and exterior cameras, steering wheel sensors, road noise cancellation (RNC) sensors, etc. The sensors that are part of the vehicle sensor system 132 may be coupled to the vehicle 100 at one or more locations and in one or more ways.For example, the various sensors of the vehicle sensor system 132 can be integrated into the various subsystems of the vehicle 100, such as mirrors, roof, underbody components, etc., or attached to the vehicle 100 using a suitable mounting mechanism. In some embodiments, the various sensors of the vehicle sensor system 132 can be located on the front, rear, sides, top, bottom, and underside of the vehicle 100. The position of a sensor can depend on its function. For example, a sensor that monitors the area under the vehicle can be connected to a floor surface of the vehicle 100, while a sensor that can monitor an area on both sides of the vehicle 100 can be mounted on or integrated into the doors of the vehicle 100.The vehicle sensor system 132 may also include one or more road noise sensors, such as accelerometers, which are coupled to various mechanical components and / or systems of the vehicle 100. A person skilled in the art will recognize that the sensors may be coupled to the vehicles in different ways and at positions other than those mentioned above.

[0018] In some embodiments, the VCU 110 can control operational aspects of the vehicle and implement one or more sets of instructions received from the server 106, the user device 108, or from one or more sets of instructions stored in the memory 104.

[0019] The TCU 126 can be configured and / or programmed to provide vehicle connectivity with wireless computing systems inside and outside the vehicle 100, and can include a navigation (NAV) receiver 134 for receiving and processing a GPS signal, a BLE ® -Module (BLEM) 136, a Wi-Fi transceiver, a UWB transceiver and / or other wireless transceivers (in Fig. 1 not shown) include those for wireless communication (which includes mobile communication) between the vehicle 100 and other systems (e.g. a vehicle key fob (in Fig. (1 not shown), one or more servers, a user device, etc.), computers, and modules. The TCU 126 can communicate with the ECUs 114 via a bus. In some aspects, the TCU 126 can be configured to determine a real-time vehicle geolocation, e.g., via the NAV receiver 134.

[0020] The ECUs 114 can control aspects of vehicle operation and communication using inputs from human drivers, inputs from the vehicle computer 108 and / or via wireless signal inputs received via the wireless connection(s) from other connected devices, such as, among others, the server 106.

[0021] The BCM 120 generally integrates sensors, vehicle performance indicators, and variable throttles assigned to the vehicle systems. It can also include processor-based power distribution circuits capable of controlling functions associated with the vehicle body, such as lights, windows, security, camera(s), audio system(s), speakers, windshield wipers, door locks and access control, various comfort controls, etc. The BCM 120 can also function as a gateway for bus and network interfaces to communicate with remote ECUs (in Fig. (1 not shown) to interact.

[0022] The DAT 128 controller can provide Level 1 to Level 3 automated driving and driver assistance functionality, which may include, for example, active parking assistance, reverse parking assistance, and / or adaptive cruise control, among other features. The DAT 128 controller can also provide aspects of user and environmental input that can be used for user authentication.

[0023] In some embodiments, the vehicle computer 108 can connect to an infotainment system 138 (or a human-machine interface (HMI) of the vehicle). The infotainment system 138 can include a touchscreen interface and can incorporate speech recognition features and biometric identification capabilities that can identify users based on facial recognition, voice recognition, fingerprint identification, or other biological identification methods. Furthermore, the infotainment system 138 can be configured to receive user instructions via the touchscreen interface and / or to output or display notifications, navigation maps, etc., on the touchscreen interface.

[0024] Certain computing modules may be omitted from the computer system architecture of the vehicle computer 108 and / or the VCU 110. It goes without saying that the in Fig. The computing environment shown in Figure 1 is an example of a possible implementation according to the present disclosure and should therefore not be considered as limiting or exclusive.

[0025] In some embodiments, the vehicle 100 may include an autonomous driving system 140. The vehicle 100 may be manually controlled or configured to operate using the autonomous driving system 140, in a fully autonomous (e.g., driverless) mode (e.g., Autonomy Level 5), or in one or more partial autonomy modes, which may include driver assistance technology. Examples of partial autonomy modes (or driver assistance modes) are widely known in the field as Autonomy Levels 1 to 4. For example, a Level 1 autonomy vehicle may include a single automated driver assistance function, such as steering or acceleration assistance. Adaptive cruise control is one such example of a Level 1 autonomy system that includes aspects of both acceleration and steering.

[0026] Level 2 autonomy in vehicles can provide driver assistance technologies, such as partial automation of steering and acceleration functionality, with the automated system(s) being / being monitored by a human driver who performs non-automated operations, such as braking and other controls. In some embodiments with Level 2 and higher autonomy features, a primary user can control the vehicle while inside the vehicle or, in some embodiments, at a location remote from the vehicle but within a control zone extending up to several meters away while the vehicle is operating remotely.

[0027] Level 3 autonomy in a vehicle can provide conditional automation and control of driving characteristics. For example, a Level 3 vehicle autonomy might include "surveillance" capabilities, where the autonomous vehicle (AV) can make informed decisions independently of a driver, such as accelerating past a slow-moving vehicle, while the driver remains ready to resume control of the vehicle at any time should the system be unable to perform the task.

[0028] Level 4 autonomous vehicles (AVs) can operate independently of a human driver, but still include controls for human override. Level 4 automation can also allow a self-driving mode to intervene in response to a predefined conditional trigger, such as a hazard in the road or a system event.

[0029] Level 5 autonomous vehicles may include fully autonomous vehicle systems that do not require human input for operation and may not include controls for human driving.

[0030] In addition to the components mentioned above, the Vehicle 100 may have numerous mechanical systems and subsystems. A chassis or frame may form the backbone of the Vehicle 100, supporting the body and other components. The Vehicle 100 may include an engine that converts fuel into mechanical power, thus propelling the vehicle forward. The engine includes various components, such as the engine block, pistons, valves, and spark plugs. The Vehicle 100 also includes a transmission system. The transmission system transfers the engine's power to the wheels. It includes, among other components, the clutch, gearbox, driveshaft, and differentials. The transmission adjusts the power output to the vehicle's speed and load. The Vehicle 100 may also include a suspension system.The suspension system absorbs shocks and maintains contact between the tires and the road, providing a smooth ride. It includes components such as springs, shock absorbers, and linkages. The Vehicle 100 also includes a braking system that allows the driver to slow down or stop the Vehicle 100. It includes components such as brake pedals, master cylinders, brake lines, and brake pads or shoes. The Vehicle 100 also includes a steering system that allows the driver to steer the car. The steering system includes components such as the steering wheel, steering column, rack and pinion, and tie rods. The Vehicle 100 also includes an exhaust system that removes and filters the exhaust gases produced by the engine. It includes, among other things, the exhaust manifold, catalytic converter, muffler, and tailpipe. The Vehicle 100 also includes a cooling system that prevents the engine from overheating.It includes components such as the radiator, water pump, thermostat, and coolant. The vehicle also includes a cooling system that stores fuel and supplies it to the engine. This system includes the fuel tank, fuel pump, fuel filter, and fuel injectors. The vehicle's electrical system provides power to the car's electrical components. It includes the battery, alternator, starter motor, and wiring. The heating, ventilation, and air conditioning (HVAC) system regulates the temperature inside the vehicle. It includes the heater core, blower motor, and air conditioning compressor. When all these mechanical components work together, they ensure that the vehicle functions smoothly and satisfactorily.

[0031] During operation, the vehicle is exposed to road conditions and may experience vibrations based on road quality. These vibrations affect the service life and lifespan of several vehicle components, particularly the mechanical and electrical components located on the underside of the vehicle, commonly referred to as "underbody components and systems." Accurate real-time measurement of road roughness is advantageous for understanding the road's influence and the resulting vibrations on these components. Furthermore, real-time roughness measurement can also help determine when to collect other types of vehicle data for various vehicle-related analyses. Thus, in one embodiment, the road roughness measurement can serve as a decision point to determine whether other vehicle-related measurements are necessary.

[0032] Fig. Figure 2 illustrates an exemplary setup for how vibration data can be acquired to determine road roughness according to an embodiment of the present disclosure. Fig. Figure 2 illustrates a wheel 202 of a vehicle, e.g., of vehicle 100. Fig. 1. A control arm 204 is attached to the wheel 202, and a sensor 206, such as a road noise suppression sensor, can be attached to the control arm 204. This setup can be repeated for all four wheels of the vehicle. This results in four sensors 206, each attached to its respective control arm. It should be noted that additional sensors 206 can also be placed at other locations on the underside of the vehicle to collect vibration data. In one embodiment, each sensor 206 can measure acceleration along three directions, x, y, and z, effectively generating three data channels. Together, the four sensors can output twelve (12) data channels. Subsequent processing may or may not use all of this data output by the four sensors. In one embodiment, the sensors 206 can communicate their data on the Automotive Audio Bus (A2B).The A2B is well known in the field and an explanation of this technology is omitted here for the sake of brevity.

[0033] Fig. Figure 3 illustrates a block diagram of a system 300 for measuring road roughness according to an embodiment of the present disclosure. The system 300 can be implemented entirely, e.g., in the vehicle 100, or can be implemented partly in the vehicle and partly within an external server, e.g., the server 700. Fig. 7. System 300 includes a data collection unit 302. The data collection unit can include one or more road noise suppression sensors located at multiple points throughout the vehicle. In one embodiment, four road noise suppression sensors can be present, each outputting three data channels. In addition to the vibration data collected by the road noise suppression sensors, the data collection unit can also receive data about the vehicle's operating conditions from the vehicle's controller area network (CAN bus). The data received via the CAN bus can include, among other things, the tire pressure of each tire, the tire temperature, the ambient temperature, the vehicle speed, the acceleration and braking torque, the steering angle, the gross train weight, etc.

[0034] To ensure the reliability of road roughness measurement data, it is advantageous to collect and analyze vibration data from road noise sensors under conditions that provide optimal results. The vibration data captured by the road noise sensors can be influenced by one or more vehicle operating conditions. For example, vibration data collected at high speeds, such as above 70 mph, may not provide a true measure of road roughness, as the vehicle is generally subject to greater vibrations at this speed, regardless of road conditions. Similarly, other vehicle operating conditions, such as steering angle, tire pressure, etc., can also affect the road noise sensor data.Therefore, it can be advantageous to collect road noise sensor data when certain vehicle operating conditions fall within a specific range. For example, it may be most beneficial to collect road noise sensor data when the vehicle is operating at speeds between 45 and 55 mph, or when the steering angle is between 20 and 25 degrees on each side, or when the ambient temperature is between 60 and 80 °F, etc. Operating conditions and their respective ranges can vary for different vehicles, different geographic areas, and so on. By adapting the set of operating conditions to specific vehicles and / or geographic areas, it is possible to obtain an accurate measurement of road roughness that is more representative of actual road conditions than the conventional method of measuring road roughness used by a standard vehicle model.In summary, this series of operating conditions can be referred to as the "trigger condition".

[0035] System 300 further includes a trigger / operating condition verification unit 304. The trigger condition verification unit 304 receives the current operating condition data from the vehicle's CAN bus and the vibration data from one or more road noise suppression sensors. Based on the predetermined set of operating conditions for this specific vehicle, the trigger condition verification unit 304 can determine whether the vehicle is currently fulfilling the set of operating conditions. In one embodiment, the trigger condition verification unit can be implemented, for example, in the VCU 110 of vehicle 100. In another embodiment, the trigger condition verification unit 304 can be implemented in an external server. The set of operating conditions can be fulfilled for several periods of time during a specific journey of the vehicle.A "drive" can be defined as the time between the ignition being switched on and the ignition being switched off in the vehicle. For example, during a drive, the set of operating conditions may be met three times, e.g., for periods of 10 seconds, 20 seconds, and 1 minute. The trigger condition check unit 304 can determine how often the set of operating conditions is met during a drive. Based on this determination, the vibration data collected during these time intervals / durations can be used for subsequent processing to determine the road roughness measurement.

[0036] During the periods in which the set of operating conditions is met, multiple samples of data collected by the road noise sensors may be available. In some embodiments, a mean, average, or median, etc., of the collected vibration data can be calculated for each period and used for subsequent processing. The trigger condition verification unit 304 determines the periods during which the set of operating conditions is met and can output a notification to the data collection unit 302. The data collection unit 302 can then send the vibration data collected during these periods, in which the set of operating conditions is met, to the road roughness measurement unit 306.The road roughness measurement unit can then analyze the vibration data collected by the road noise sensors and output a road roughness measurement, which can be a single value or a set of values. In another embodiment, the data collection unit 302 can send all vibration data collected by the road noise suppression sensors, and the trigger condition verification unit 304 can extract only the vibration data collected during the periods in which the set of operating conditions is met for further processing. The road roughness measurement unit 306 can determine feature data from the vibration data and generate a rule-based model for classifying the road roughness.The rule-based model can be a binary model in which the road can be classified as "rough" if a certain feature or features are above a certain threshold, and the road can be classified as "smooth" if the certain feature or features are below the respective threshold.

[0037] Fig. 4A and Fig. Figure 4B illustrates the vibration data for two different roads according to one embodiment of the present disclosure. Fig. Figure 4A illustrates vibration data collected by the road noise suppression sensors when the vehicle is driven on road “A”. Vibration signature 402 illustrates the vibration data collected over time. During this time period, the set of operating conditions is satisfied during two time periods 404 and 406. As explained above, the vibration data collected during the two time periods 404 and 406 can be used for road roughness measurement. The amplitude of vibration signature 402 illustrates the vertical acceleration measured by the road noise suppression sensors. In some embodiments, a window of vibration data can be collected every 100 milliseconds, although this setting can be programmable. Fig. Figure 4B illustrates vibration signature data 408 for road "B". The vibration signature data is collected while the vehicle is driven on road B. As illustrated, while the vehicle is driven, the set of operating conditions for the vehicle is met during two time periods 410 and 412. Accordingly, the vibration data collected during these two time periods 410 and 412 are used for further analysis.

[0038] Fig. Figures 5A-5D illustrate feature data that are determined from the vibration data. This feature data is then used to determine, according to one embodiment of the present disclosure, whether a road is rough or smooth. Each of the Fig. Figures 5A-5D illustrate the feature data determined from the vibration data collected by a single road noise reduction sensor. For example, illustrates Fig. 5A Feature data determined from vibration data collected by a first road noise sensor coupled to the right front wheel of a vehicle, Fig. Figure 5B illustrates feature data determined from vibration data collected by a second road noise sensor coupled to the right rear wheel of the vehicle. Fig. Figure 5C illustrates feature data determined from vibration data collected by a third road noise sensor coupled to the vehicle's left rear wheel, and Fig. Figure 5D illustrates feature data determined from vibration data collected by a fourth road noise sensor coupled to the vehicle's left front wheel. In this embodiment, the vertical or z-direction acceleration data are used to determine road roughness. The feature determined from the vibration data, as shown in Fig. Described in Figures 5A-5D, the mean absolute deviation (MAD) of the acceleration data in the z-direction is the value of the vibration data. In one embodiment, the vibration data are divided into equal time windows, e.g., 10 seconds, and the MAD value of the conditioned vibration data is determined for each of the 10-second windows. The diagrams in Fig. Figures 5A-5D illustrate the MAD values ​​for each of these 10-second windows of vibration data. As shown in Fig. As illustrated in Figure 5A, the MAD values ​​represented by signature 502 are for a rough road, while the MAD values ​​represented by signature 504 are for a smooth road. As can be seen, the MAD values ​​502 for rough roads are considerably higher than the MAD values ​​504 for smooth roads. An appropriate threshold can be chosen to determine whether the road is rough or smooth. For example, in Fig. 5A the threshold value is chosen to be 0.15. This threshold value provides a good distinction between a smooth road and a rough road, so that the respective MAD values, represented by signatures 502 and 504, can be used to classify a given road as smooth or rough.

[0039] In some embodiments, a mean of the MAD values ​​+ / - 3 standard deviations can be used to generate the rule-based model. In real time, the vibration data from the road noise sensors, as described above, can be collected and analyzed to determine feature data. This feature data can then be compared with the rule-based model generated for this vehicle to determine whether the road can be classified as smooth or rough. The classification of a road as smooth or rough can be used as a trigger point for collecting and / or analyzing other data collected for the vehicle for other purposes, such as determining vehicle suitability, road quality assessment, durability and service life estimation, noise and vibration benchmarking, environmental impact, road design and analysis, ride comfort assessment, and the like. Fig. Figures 5A-5D illustrate the MAD values ​​of acceleration in the z-direction; this is merely an example. Several other characteristics can be determined from the vibration data and used to measure or determine road roughness. These characteristics may be as follows.

[0040] Time domain features: mean, standard deviation, variance, root mean square (RMS), skewness, kurtosis, peak, vertex factor, peak-to-peak, median, min, max, range, mean absolute deviation (MAD), momentum factor, form factor, distance factor, or RMS of the derivative.

[0041] Frequency domain characteristics: spectral center, spectral bandwidth, spectral flatness, spectral rolloff, frequency center, RMS frequency, frequency, variance and spectral kurtosis.

[0042] Intrinsic modus functions (IMFs) time domain features: mean, standard, variance, RMS, skewness, kurtosis, peak, crest factor, peak-to-peak, median, min, max, range, MAD, momentum factor, form factor, distance factor, or RMS of the derivative.

[0043] IMF frequency domain characteristics: spectral center, spectral bandwidth, spectral flatness, spectral rolloff, frequency center, RMS frequency, frequency, variance and spectral kurtosis.

[0044] One approach to durability analysis of a vehicle's underbody components uses a simulation environment that mimics various load factors and conditions, and their resulting effects on component dynamics. The accuracy of this durability analysis depends on factors that influence the gap between reality and the simulation environment. One example of such a factor is the force / load exerted by the road on the underbody components, which is a variable of the road roughness condition. Therefore, accurately characterizing the road roughness condition and analyzing the force / load exerted on the vehicle are crucial to minimizing the gap between the simulation environment and real-world conditions.Vibration data collected by sensors, together with information about vehicle operating conditions, can provide ground truth for the simulations required for durability analysis. Additionally, embodiments of this disclosure provide clarification of the effects of road-induced loads on underbody components. Reducing the gap in the simulation environment achieved by outputs of the proposed method leads to more accurate modeling of the influence of road roughness on vehicle underbody components and improved durability analysis. This improvement is important for analyzing wear patterns and / or service life under various road conditions.Furthermore, accurate measurement of road roughness under varying road surfaces and conditions, such as potholes, bumps, gravel, and varying levels of roughness, improves the predictive accuracy of durability models. This predictive accuracy helps in designing products that meet or exceed performance expectations and safety standards.

[0045] Furthermore, improved simulation accuracy and durability analysis provide a selection of materials and components that balance performance, cost, and longevity. An accurate simulation environment also enables the exploration of various materials and design configurations to identify the best options for meeting durability requirements without excessive engineering or cost. Improved simulation accuracy is also beneficial for life cycle analysis and sustainability enhancement. It allows for the design of products that are easier to maintain, repair, or recycle, thus contributing to the goals of more sustainable development.

[0046] Fig. Figure 6 illustrates a flowchart for a process 600 for determining road roughness according to an embodiment of the present disclosure. The process 600 can be fully implemented, e.g., in the vehicle 100 made of Fig. 1. In another embodiment, process 600 can be implemented jointly in the vehicle and an external server. In step 602, the vehicle acquires data about its current operating conditions, such as speed, acceleration, etc., as explained above. In step 604, vibration data is collected by one or more accelerometers or road noise suppression sensors of the vehicle. In some embodiments, steps 602 and 604 can be performed concurrently. In step 606, the operating condition data is analyzed to determine whether the set of operating conditions is met. If it is determined in step 608 that the set of operating conditions is not met, process 600 returns to step 606, and the vehicle can continue to analyze the most recent operating condition data.If, in step 608, it is determined that the operating conditions for the vehicle are met, the vibration data associated with the duration during which the operating conditions are met are used for further analysis. In step 610, the vibration data are downsampled to compress the data and make it more efficient for storage and further analysis.

[0047] In step 612, noise removal is performed on the downsampled data to eliminate outliers and other noise. In step 614, the data undergoes bandpass filtering to extract the appropriate vibration data for further analysis. In step 616, the data can be transformed into the frequency domain, for example, using fast Fourier transform techniques. After step 616, the data is in a format from which feature data extraction can be performed. In step 618, data relating to one or more features are determined from the vibration data. The list of possible features is mentioned above. Once the data for the desired features have been determined from the vibration data, a dimensionality reduction process is performed on the determined feature data in step 620.This process is performed to reduce the amount of feature data that needs to be processed. For example, once the feature data is determined at step 618, an assessment is made to identify which feature data are highly correlated. Then, from the highly correlated feature data, data relating to one or more features can be ignored for further processing. This helps to reduce redundancy in the feature data. After step 620, the dimensionally reduced vibration data are then analyzed to determine the road roughness specification at step 622, as described above. Fig. 5A-5D explained.

[0048] Accurate measurement of road roughness is advantageous in determining various aspects of a vehicle. Some of these applications are mentioned below. It should be understood that the applications mentioned below are only an example of how road roughness measurement can be used to improve vehicle design and performance.

[0049] Durability analysis and lifetime prediction: The evaluation of road roughness using vibration data collected by sensors provides an accurate reconstruction of the stress profile for vehicle components and an improvement in lifetime prediction.

[0050] Structural condition monitoring and stress / fatigue analysis: Accurate estimation of road roughness using vibration data along with identified CAN signals available in a vehicle improves the accuracy of condition monitoring analysis.

[0051] Adaptive suspension control: Accurate assessment of road roughness can be used for real-time adjustment of suspension design variables.

[0052] Improvement of a navigation system: Measuring road roughness and accurately profiling road conditions provides improvements to the navigation system.

[0053] Improvement of handling and chassis stability: Chassis stability and handling capability can be improved by using accurate quantification of road roughness.

[0054] Safety: Accurate mapping of road roughness provides an improvement in vehicle safety regardless of road surface conditions.

[0055] Calibration and improvement of advanced driver assistance systems: The accurate identification and quantification of road roughness are important for the calibration, triggering and control of advanced driver assistance systems.

[0056] Tire durability and dynamic analysis: The precise identification and quantification of road roughness enables the investigation of the effects of different types of road roughness on tire wear patterns, material fatigue, and the likelihood of tire failure. This can help in the design of more durable tires suitable for specific driving environments.

[0057] Road quality assessment: Accurate road roughness measurement enables improved creation of road quality profiles and assessments.

[0058] Energy optimization and improvement of regenerative braking efficiency: Quantifying road roughness and measuring the acceleration applied to the control arm can be directly applied to the efficiency of the regenerative braking system.

[0059] Driving pattern analysis: Precise quantification of road roughness provides an evaluation of driving behavior and determines the percentage of time a vehicle was driven on a rough or smooth road. Analyzing driving patterns leads to the development of driver assistance systems that help mitigate the effects of rough roads.

[0060] Noise and vibration benchmarking: Vibration data collected by sensors and precise measurements of road roughness are used to evaluate a vehicle's behavior in terms of noise, vibration, and harshness (NVH).

[0061] Road design and analysis: Accurate mapping of road roughness provides useful information that can be used for road design and analysis.

[0062] Ride comfort assessment: The accurate measurement of vibration data from sensors with minimized inference and directly from the road can be used for ride comfort assessment.

[0063] Prioritizing maintenance efforts: Accurate mapping of road roughness and identification of affected regions provide insights into maintenance actions that are assigned to the vehicle.

[0064] Collection and analysis of historical data: Recording historical data collected from road roughness measurements can be used directly for event analysis and correlation with historical events.

[0065] Fig.Figure 7 shows a block diagram of an exemplary control server 700 on which one or more arbitrary techniques (e.g., methods) according to one or more exemplary embodiments of the present disclosure can be performed. In other embodiments, the server 700 can be operated as a standalone device or connected (e.g., networked) to other servers. In a networked deployment, the server 700 can operate in the capacity of a server machine, a client machine, or both in server-client network environments. In one example, the server 700 can function as a peer server in peer-to-peer (P2P) (or other distributed) network environments.Server 700 can be a personal computer (PC), a tablet PC, a set-top box (STB), a personal digital assistant (PDA), a mobile phone, a smart key fob, a wearable computing device, a web device, a network router, a switch or bridge, or any machine capable of executing instructions (sequentially or otherwise) specifying actions to be performed by this server, such as a base station. Furthermore, although only a single server is illustrated, the term "server" can also be understood to include any collection of servers that, individually or collectively, execute a set (or multiple sets) of instructions to perform one or more of any of the methodologies discussed in this document, such as cloud computing, software as a service (SaaS), or other computer cluster configurations.

[0066] Examples, as described in this document, may include or operate with logic or a set of components, modules, or mechanisms. Modules are tangible units (e.g., hardware) capable of performing specified operations during operation. A module includes hardware. In one example, the hardware may be specifically configured to perform a particular operation (e.g., hardwired). In another example, the hardware may include configurable execution units (e.g., transistors, circuits, etc.) and a computer-readable medium containing instructions, the instructions configuring the execution units to perform a specific task when operated. This configuration may occur under the guidance of the execution units or a loading mechanism.Accordingly, the execution units are communicatively coupled to the computer-readable medium when the device is operated. In this example, the execution units can be an element of more than one module. For instance, during operation, the execution units can be configured by a first set of instructions to execute a first module at one time, and reconfigured by a second set of instructions to execute a second module at a second time.

[0067] The server (e.g., the computer system) 700 can include a hardware processor 702 (e.g., a central processing unit (CPU), a graphics processing unit (GPU), a hardware processor core, or any combination thereof), main memory 704, and static memory 706, some or all of which can communicate with each other via a coupling (e.g., a bus) 708. The server 700 can further include a graphics display device 710, an alphanumeric input device 712 (e.g., a keyboard), and a user interface navigation device (UI navigation device) 714 (e.g., a mouse). In an example, the graphics display device 710, the alphanumeric input device 712, and the UI navigation device 714 can be a touchscreen display. The server 700 can additionally include a storage device (i.e., a memory card).The server 700 includes a drive unit 716, a network interface device / transmitter 720 coupled to antenna(s), and one or more sensors 728, such as a global positioning system (GPS) sensor, a compass, an accelerometer, or another sensor. The server 700 may include an output controller 734, such as a serial (e.g., Universal Serial Bus (USB)), parallel, or other wired or wireless (e.g., infrared (IR)) near field communication (NFC) connection, etc., for communicating with or controlling one or more peripheral devices (e.g., a printer, a card reader, etc.).

[0068] The storage device 716 can include a machine-readable medium 722 on which one or more sets of data structures or instructions 724 (e.g., software) are stored, embodying or utilizing one or more of any of the techniques or functions described herein. The instructions 724 may also reside, wholly or at least partially, within the main memory 704, within the static memory 706, or within the hardware processor 702 during their execution by the server 700. In an example, any one or any combination of the hardware processor 702, the main memory 704, the static memory 706, or the storage device 716 can constitute machine-readable media.

[0069] Although machine-readable medium 722 is illustrated as a single medium, the term "machine-readable medium" can include a single medium or multiple media (e.g., a centralized or distributed database and / or associated caches and servers) configured to store the one or more instructions 724.

[0070] Various embodiments can be implemented wholly or partially in software and / or firmware. This software and / or firmware can take the form of instructions contained in or on a non-transferable, computer-readable storage medium. These instructions can then be read and executed by one or more processors to enable the execution of the operations described herein. The instructions can be in any suitable form, such as, but not limited to, source code, compiled code, interpreted code, executable code, static code, dynamic code, and the like.Such a computer-readable medium can include any tangible non-transient medium for storing information in a form readable by one or more computers, such as, but not limited to, read-only memory (ROM); random access memory (RAM); magnetic disk storage media; optical storage media; flash memory, etc.

[0071] The term “machine-readable medium” can include any medium capable of storing, encoding, or carrying instructions for execution by the Server 700, and which causes the Server 700 to perform one or more of any of the techniques of this disclosure, or which is capable of storing, encoding, or carrying data structures used by or associated with such instructions. Non-restrictive examples of machine-readable media can include semiconductor memory and optical and magnetic media. In one example, a machine-readable medium with mass includes a machine-readable medium with a plurality of particles that has a rest mass. Specific examples of machine-readable media with mass can include non-volatile memory, such as semiconductor memory devices (e.g.,electrically programmable read-only memory (EPROM) or electrically erasable programmable read-only memory (EEPROM) and flash memory devices; magnetic disks, such as internal hard disks and removable disks; magneto-optical disks; and CD-ROM and DVD-ROM disks.

[0072] The instructions 724 can also be transmitted or received via a communication network 726 using a transmission medium via the network interface device / transmitter 720 using any of a number of transmission protocols (e.g. Frame Relay, Internet Protocol (IP), Transmission Control Protocol (TCP), User Datagram Protocol (UDP), Hypertext Transfer Protocol (HTTP), etc.). Examples of communication networks include local area networks (LAN), wide area networks (WAN), packet data networks (e.g., the Internet), mobile phone networks (e.g., cellular networks), analog telephone networks (plain old telephone networks - POTS networks), wireless data networks (e.g., IEEE 802.11 standard family, known as Wi-Fi®, IEEE 802.16 standard family, known as WiMax®), IEEE 802.15.4 standard family, and peer-to-peer (P2P) networks.In one example, the network interface device / transmitter 720 can include one or more physical jacks (e.g., Ethernet, coaxial, or telephone jacks) or one or more antennas for connecting to the communication network 726. In another example, the network interface device / transmitter 720 can include a plurality of antennas for wireless communication using at least one single-input multiple-output (SIMO), multiple-input multiple-output (MIMO), or multiple-input single-output (MISO) technology.The term "transmission medium" shall be understood to include any intangible medium capable of storing, encoding, or carrying instructions for execution by the Server 700, and including digital or analog communication signals or other intangible media to facilitate communication of such software. The operations and processes described and illustrated above may be performed or carried out in any suitable order in various implementations. Additionally, in certain implementations, at least some of the operations may be performed in parallel. Furthermore, in certain implementations, fewer or more of the operations than described may be performed.

[0073] It should be noted that the vehicle implements and / or performs operations as described herein in accordance with the user manual and safety guidelines. Additionally, any action taken by the vehicle owner based on recommendations or notifications provided by the vehicle should comply with all regulations specific to the vehicle's location and operation (e.g., federal, state, country, city, etc.). Recommendations or notifications provided by the vehicle should be treated as suggestions and followed only in accordance with any regulations specific to the vehicle's location and operation.The preceding disclosure refers to the accompanying drawings, which form part thereof and illustrate specific implementations in which the present disclosure can be practically implemented. It is understood that other implementations may be used and structural modifications made without deviating from the scope of the present disclosure. References in the description to "an embodiment," "an exemplary embodiment," etc., indicate that the described embodiment may include a specific feature, structure, or property, but not every embodiment necessarily includes that specific feature, structure, or property. Furthermore, such formulations do not necessarily refer to the same embodiment.Furthermore, if a feature, structure or property is described in connection with an embodiment, the person skilled in the art will recognize such a feature, structure or property in connection with other embodiments, whether this is expressly described or not.

[0074] Furthermore, the functions described in this document may be performed in one or more hardware, software, firmware, digital components, or analog components. For example, one or more application-specific integrated circuits (ASICs) may be programmed to execute one or more of the systems and procedures described in this document. Certain terms used throughout this description and in the claims refer to specific system components. It is obvious to those skilled in the art that the components may be designated by other names. This document does not distinguish between components that differ in name but not in function.

[0075] It is also understood that the word "example," as used in this document, is not intended to be exclusive or restrictive. In particular, the word "example," as used in this document, indicates one of several examples, and it is understood that no undue emphasis or preference is placed on the specific example described.

[0076] A computer-readable medium (also called a processor-readable medium) comprises any non-volatile (e.g., physical) medium involved in providing data (e.g., instructions) that can be read by a computer (e.g., by a computer's processor). Such a medium can take many forms, including, without limitation, both non-volatile and volatile media. Computing devices can contain computer-executable instructions, the instructions being executable by one or more computing devices, such as those listed above, and being stored on a computer-readable medium.

[0077] With regard to the processes, systems, procedures, heuristics, etc., described in this document, it is understood that although the steps of such processes, etc., have been described as occurring according to a specific, ordered sequence, such processes could be implemented in practice, with the described steps being carried out in a sequence that differs from the sequence described in this document. Furthermore, it is understood that certain steps could be carried out simultaneously, that other steps could be added, or that certain steps described in this document could be omitted. In other words, the descriptions of processes in this document serve the purpose of illustrating various embodiments and should in no way be interpreted as limiting the patent claims.

[0078] Accordingly, it is understood that the foregoing description is intended to be illustrative and not limiting. Many other embodiments and applications beyond the examples provided will become apparent from reading the preceding description. The scope should not be determined by reference to the foregoing description, but instead by reference to the attached claims, together with the full scope of equivalents to which these claims entitle. It is expected and intended that there will be future developments in the technologies discussed in this document and that the disclosed systems and methods will be incorporated into such future embodiments. Overall, it is understood that the application may be modified and varied.

[0079] All terms used in the patent claims shall have their general meaning as known to a person skilled in the art in the field of the technologies described in this document, unless expressly stated otherwise herein. In particular, the use of singular articles such as "a", "an", "the", "the", etc., shall be understood to refer to one or more of the elements indicated, unless a patent claim expressly limits this to the contrary.Phrases expressing conditional relationships, such as "may," "could," "might," or "might possibly," are generally intended to convey that certain embodiments might include certain features, elements, and / or steps, whereas other embodiments might not include them, unless specifically stated otherwise or the context makes it clear. Thus, such conditional phrases generally do not imply that features, elements, and / or steps are required in any way for one or more embodiments.

[0080] In one aspect of the invention, the set of operating conditions is satisfied for one or more time periods, and the determination of one or more features involves analyzing vibration data acquired during one or more time periods.

[0081] In one aspect of the invention, the one or more features include a mean absolute deviation value of an acceleration measurement in the z-direction for each of the one or more time durations.

[0082] In one aspect of the invention, the operating data includes one or more of the following: a vehicle speed, a vehicle acceleration, a steering angle associated with the vehicle, a tire pressure associated with the vehicle, an ambient temperature of an environment in which the vehicle is operated, a total train weight of the vehicle, or a braking torque associated with the vehicle.

[0083] In one aspect of the invention, the method includes: classifying that the road is smooth, based on the road roughness indicator; and analyzing the vibration data based on the determination that the road is smooth, in order to determine one or more additional parameters for the vehicle.

[0084] In one aspect of the invention, one or more additional parameters are assigned to one or more of the following: vehicle safety, road quality assessment, durability and service life estimation, noise and vibration benchmarking, environmental impact, road design and analysis, or ride comfort assessment.

Claims

[1] Vehicle, comprising: one or more processors; one or more memories that are coupled to one or more processors; a communication interface that is coupled to one or more processors; and a large number of sensors coupled to one or more processors, wherein the one or more memory stores instructions which, when executed by the one or more processors, cause the one or more processors to do the following: Determine that the vehicle is in motion on a road; Acquiring vibration data associated with the vehicle using a variety of the vehicle's sensors; Determining operational data assigned to the vehicle; Determine, based on operational data, that a set of operating conditions is met; Determine, using the vibration data and based on the fact that the set of operating conditions is met, one or more characteristics; and Determine, based on one or more characteristics, a roughness indicator of the road. [2] Vehicle according to claim 1, wherein the plurality of sensors includes road noise suppression sensors. [3] Vehicle according to claim 1, wherein the vibration data includes acceleration data measured along one or more axes. [4] Vehicle according to claim 1, wherein the set of operating conditions is satisfied for one or more time periods and wherein, to determine the one or more features, the one or more processors are further configured to analyze only vibration data acquired during the one or more time periods. [5] Vehicle according to claim 4, wherein one or more features include a mean absolute deviation value of an acceleration measurement in the z-direction for each of the one or more time durations. [6] Vehicle according to claim 1, wherein the set of operating conditions includes one or more of the following: a vehicle speed, a vehicle acceleration, a steering angle associated with the vehicle, a tire pressure associated with the vehicle, an ambient temperature of an environment in which the vehicle is operated, a total train weight of the vehicle, or a braking torque associated with the vehicle. [7] Vehicle according to claim 1, wherein the one or more processors are further configured to: Classify, based on the road roughness indicator, that the road is smooth; and Analyzing the vibration data based on the determination that the road is smooth, in order to determine one or more additional parameters for the vehicle. [8] Vehicle, comprising: one or more processors; and a large number of sensors coupled to one or more processors; where one or more processors are configured to do the following: Determine that the vehicle is currently in motion on a road; Received from the multitude of sensors, from vibration data assigned to the vehicle; Determine for an initial period of time that a set of operating conditions assigned to the vehicle is fulfilled; Determining a first section of the vibration data that is assigned to the first period; Determine one or more characteristics associated with the first section of the vibration data using the first section of the vibration data; and Determine, based on one or more characteristics, a roughness indicator of the road. [9] Vehicle according to claim 8, wherein the one or more processors are further configured to: Determine for a second period of time that the first of the operating conditions assigned to the vehicle is met; Determining a second section of the vibration data, which is assigned to the second period; and Determining one or more characteristics further using the second section of the vibration data. [10] Vehicle according to claim 8, wherein one or more features include: mean, standard deviation, variance, root mean square (RMS), skewness, kurtosis, peak, vertex factor, peak-to-peak, median, min, max, range, mean absolute deviation (MAD), impulse factor, form factor, distance factor, RMS of derivative, spectral centroid, spectral bandwidth, spectral flatness, spectral rolloff, frequency center, RMS frequency, frequency, variance or spectral kurtosis. [11] Vehicle according to claim 8, wherein the plurality of sensors includes 3-axis road noise suppression sensors and the plurality of sensors is mounted on an underbody section of the vehicle. [12] Vehicle according to claim 8, wherein the set of operating conditions includes one or more of the following: a vehicle speed, a vehicle acceleration, a steering angle associated with the vehicle, a tire pressure associated with the vehicle, an ambient temperature of an environment in which the vehicle is operated, a total towing weight of the vehicle, or a braking torque associated with the vehicle. [13] Procedures, including: To determine, by means of a vehicle, that the vehicle is in motion on a road; Acquisition, by the vehicle, of vibration data associated with the vehicle, using a variety of the vehicle's sensors; Determined by the vehicle, by operational data assigned to the vehicle; Determine, by the vehicle, based on the operating data, that a set of operating conditions is met; Determine, by the vehicle, using the vibration data and based on the fact that the set of operating conditions is met, one or more characteristics; and Determine, by the vehicle and based on one or more characteristics, a roughness indicator of the road. [14] Method according to claim 13, wherein the plurality of sensors includes road noise suppression sensors. [15] Method according to claim 13, wherein the vibration data include acceleration data measured along one or more axes.

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