System and method for road roughness measurement

Abstract

The present disclosure provides a system and method for road roughness measurement. A vehicle having a plurality of road noise cancellation sensors captures vibration data as it travels along a road. The vehicle checks whether a set of operating conditions of the vehicle are satisfied. If the set of operating conditions is satisfied, the vehicle determines a portion of the vibration data captured during a time range in which the set of operating conditions is satisfied. The vehicle may then use the portion of the vibration data to extract feature data of the one or more features. The vehicle then uses feature data associated with the one or more features to determine a roughness indicator for the road on which the vehicle is currently traveling. The road roughness indicator measurements may be used to trigger further analysis of the vibration data to determine other parameters of the vehicle.

Description

Technical Field

[0001] This disclosure relates to the field of real-time road roughness measurement performed by vehicles. Background Technology

[0002] Traditional road roughness measurement techniques, such as the International Roughness Index (IRI), have several problems. The IRI is based on the response of a standard-sized vehicle to road surface roughness. However, actual vehicle sizes vary and may differ from the ideal vehicle used in the IRI definition. Therefore, recordings from vehicles of different sizes can lead to estimates that differ slightly from the IRI. Furthermore, roughness measurement methods are not stable over time. Measurements made with road gauges today cannot be reliably compared to measurements made several years ago. Roughness measurements are not transferable. Road gauge measurements taken by one system are rarely reproducible by another. Recent studies have shown inconsistencies in IRI results, including bias, 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 researchers have suggested supplementing the IRI with additional numerical properties, such as a power-law exponent that describes how the effect of roughness changes when we change the size of the vehicle. Summary of the Invention

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

[0005] In some cases, a method performed by a vehicle is provided that can determine, in real time, a measure of the roughness of the road on which the vehicle is currently traveling. The method includes the vehicle determining that it is moving on the road. During this movement, the vehicle can use multiple sensors to capture vibration data associated with the vehicle. The method may also include the vehicle determining operational data associated with the vehicle, and based on the operational data, the vehicle determining that a set of operating conditions is met. The method may further include the vehicle using the vibration data and determining one or more characteristics based on meeting the set of operating conditions. Thereafter, the method may include the vehicle determining a roughness index of the road based on the one or more characteristics.

[0006] In another scenario, a vehicle is provided that includes multiple accelerometers or road noise cancellation sensors attached to various locations on the vehicle. The vehicle also includes one or more processors that work in conjunction with the sensors to determine the vehicle's movement on a road. The vehicle may also use the sensors to capture vibration data associated with the vehicle. The one or more processors may also determine operational data associated with the vehicle and, based on the operational data, determine a set of operating conditions that are met. Based on the met set of operating conditions, the one or more processors use the vibration data to determine one or more characteristics associated with the vibration data. Additionally, the one or more processors may determine a roughness index of the road based on the one or more characteristics.

[0007] In 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. Then, the vehicle receives vibration data associated with it from multiple sensors connected to it. After receiving the vibration data, the vehicle determines that a set of operating conditions associated with it are met within a first duration. Subsequently, the vehicle determines a first portion of the vibration data associated with the first duration, and using this first portion, the vehicle also determines one or more features associated with it. Finally, the vehicle determines a roughness index of the road based on these one or more features.

[0008] These and other advantages of this disclosure are provided in detail herein. Attached Figure Description

[0009] Specific embodiments are illustrated with reference to the accompanying drawings. The same reference numerals may be used to indicate similar or identical items. Various embodiments may utilize elements and / or components other than those shown in the drawings, and some elements and / or components may not be present in various embodiments. Elements and / or components in the drawings are not necessarily drawn to scale. Throughout this disclosure, singular and plural terms can be used interchangeably depending on the context.

[0010] Figure 1 A block diagram of a vehicle according to an embodiment of the present disclosure is shown.

[0011] Figure 2 A portion of a vehicle including a vibration detection sensor is shown according to an embodiment of the present disclosure.

[0012] Figure 3 A block diagram of a system for measuring road roughness according to an embodiment of the present disclosure is shown.

[0013] Figure 4A and Figure 4B Vibration data for two different types of roads according to embodiments of this disclosure are shown.

[0014] Figures 5A to 5D Feature data determined based on vibration data of the four wheels of a vehicle according to an embodiment of the present disclosure is shown.

[0015] Figure 6 A flowchart for determining road roughness according to an embodiment of the present disclosure is shown.

[0016] Figure 7 A block diagram of a server according to an embodiment of the present disclosure is shown. Detailed Implementation

[0017] The present disclosure will be described more fully below with reference to the accompanying drawings, which illustrate exemplary embodiments of the present disclosure and are not intended to be limiting.

[0018] Figure 1 A block diagram of a vehicle 100 in which embodiments of the present disclosure may be implemented is shown. The vehicle 100 may include multiple units, including but not limited to an automotive computer 108, a vehicle control unit (VCU) 110, and an infotainment unit 138. The VCU 110 may include multiple electronic control units (ECUs) 114 configured to communicate with the automotive computer 108.

[0019] In some embodiments, a user device (such as a mobile phone, laptop computer, etc.) may be configured to connect to the vehicle computer 108, the user device may communicate via one or more wireless connections, and / or may communicate via the Near Field Communication (NFC) protocol. Protocols, Wi-Fi, Ultra-Wideband (UWB), and other possible data connectivity and sharing technologies can be used to directly connect to vehicle 100.

[0020] According to this disclosure, the automotive computer 108 can be installed anywhere in the vehicle 100. The automotive computer 108 may be or include an electronic vehicle controller having one or more processors 102, one or more memories 104, and one or more transceivers 106.

[0021] Processor 102 may be configured to communicate with one or more memory devices (e.g., memory 104 and / or memory 105) configured to communicate with a corresponding computing system. Figure 1The processor 102 may communicate with one or more external databases (not shown in the diagram). The processor 102 may utilize the memory 104 to store programs and / or data in code form to perform operations according to this disclosure. The memory 104 may be a non-transitory computer-readable storage medium or memory storing vehicle control program code. The memory 104 may include any or a combination of volatile memory elements (e.g., dynamic random access memory (DRAM), synchronous dynamic random access memory (SDRAM), etc.) and may include any one or more non-volatile memory elements (e.g., erasable programmable read-only memory (EPROM), flash memory, electrically erasable programmable read-only memory (EEPROM), programmable read-only memory (PROM), etc.). In some embodiments, the memory 104 may include modules 145 that may implement various embodiments of this disclosure. Modules 145 may include instructions that can be executed by the processor 102 to implement various embodiments of this disclosure.

[0022] The vehicle computer 108 may also include a transceiver 106. The transceiver 106 may be configured to receive information / input from one or more external devices or systems (e.g., user device 108, external server, etc.). Furthermore, the transceiver 106 may transmit notifications, requests, signals, etc., to external devices or systems. Additionally, the transceiver 106 may be configured to receive information / input from vehicle components (such as vehicle sensing system 132, one or more ECUs 114, etc.). Furthermore, the transceiver 106 may transmit signals (e.g., command signals) or notifications to vehicle components such as the BCM 120, infotainment system 138, etc.

[0023] In some embodiments, VCU 110 may share a power bus with vehicle computer 108 and may be configured and / or programmed to coordinate data between vehicle systems, connected servers, etc. VCU 110 may include or communicate with any combination of ECUs 114, such as, for example, Body Control Module (BCM) 120, Engine Control Module (ECM) 122, Transmission Control Module (TCM) 124, Telematics Control Unit (TCU) 126, Driver Assist Technology (DAT) Controller 128, etc. VCU 110 may also include and / or communicate with a Vehicle Perception System (VPS) 130, which connects to and / or controls one or more vehicle sensing systems 132. The vehicle sensing system 132 may include one or more vehicle sensors, including but not limited to radio detection and ranging (LiDAR or “radar”) sensors configured to use radio waves to detect and locate objects inside and outside the vehicle 100, seating area latch sensors, seating area sensors, light detection and ranging (“LiDAR”) sensors, 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. Sensors as part of the vehicle sensing system 132 may be coupled to the vehicle 100 at one or more locations in one or more ways. For example, various sensors of the vehicle sensing system 132 may be integrated into various subsystems of the vehicle 100 (such as doors, mirrors, roof, underbody components, etc.) or attached to the vehicle 100 using suitable mounting mechanisms. In some embodiments, various sensors of the vehicle sensing system 132 may be located at the front, rear, sides, top, bottom, and underside of the vehicle 100. The location of the sensors may depend on their function. For example, sensors monitoring the area beneath the vehicle can be attached to the underside of vehicle 100, while sensors monitoring areas on either side of vehicle 100 can be mounted or integrated into the doors of vehicle 100. Vehicle sensing system 132 may also include one or more road noise sensors, such as accelerometers attached to various mechanical components and / or systems of vehicle 100. Those skilled in the art will recognize that sensors can be attached to the vehicle in various different ways and locations besides those mentioned above.

[0024] In some embodiments, VCU 110 can control vehicle operation aspects and implement one or more instruction sets received from server 106, user device 108, or from one or more instruction sets stored in memory 104.

[0025] TCU 126 can be configured and / or programmed to provide vehicle connectivity to wireless computing systems on and outside the vehicle 100, and may include a navigation (NAV) receiver 134 for receiving and processing GPS signals. Module (BLEM) 136, Wi-Fi transceiver, UWB transceiver and / or may be configured for use in vehicle 100 with other systems (e.g., vehicle key fob). Figure 1 Other wireless transceivers (not shown in the image), external servers, user devices, etc., and wireless communication (including cellular communication) between computers and modules. Figure 1 (Not shown in the image). TCU 126 can communicate with ECU 114 via a bus. In some respects, TCU 126 can be configured to determine the real-time vehicle geolocation, for example, via NAV receiver 134.

[0026] The ECU 114 can control various aspects of vehicle operation and communication using inputs from the human driver, inputs from the vehicle computer 108, and / or wireless signal inputs received from other connected devices (such as server 106, etc.) via a wireless connection.

[0027] The BCM 120 typically integrates sensors, vehicle performance indicators, and variable reactors associated with vehicle systems. It may also include processor-based power distribution circuitry that controls functions associated with the vehicle body, such as lights, windows, safety devices, one or more cameras, one or more audio systems, speakers, wipers, door locks and entry controls, various comfort controls, etc. The BCM 120 can also operate as a gateway for bus and network interfaces to communicate with remote ECUs ( Figure 1 (Not shown in the image) Interaction.

[0028] The DAT controller 128 provides Level 1 to Level 3 automated driving and driver assistance functions, which may include, for example, active parking assist, vehicle reversing assist, and / or adaptive cruise control. The DAT controller 128 also provides various aspects of user and environmental inputs that can be used for user authentication.

[0029] In some embodiments, the vehicle computer 108 may be connected to the infotainment system 138 (or the vehicle human-machine interface (HMI)). The infotainment system 138 may include a touchscreen interface portion and may include voice recognition features, and the ability to identify a user's biometrics based on facial recognition, voice recognition, fingerprint recognition, or other biometric identification methods. In other aspects, the infotainment system 138 may also be configured to receive user commands via the touchscreen interface portion and / or output or display notifications, navigation maps, etc., on the touchscreen interface portion.

[0030] The computing system architecture of the automotive computer 108 and / or VCU 110 may omit certain computing modules. This should be easily understood. Figure 1 The computing environment depicted herein is an example of possible implementations according to this disclosure and should therefore not be considered limiting or exclusive.

[0031] In some embodiments, vehicle 100 may include an autonomous driving system 140. Vehicle 100 may be manually driven or configured to operate using the autonomous driving system 140 in a fully autonomous (e.g., driverless) mode (e.g., Level 5 autonomy) or in one or more partially autonomous modes that may include driver assistance technologies. Examples of partially autonomous (or driver assistance) modes are broadly understood in the art to be Level 1 to Level 4 autonomy. For example, a vehicle with Level 1 autonomy may include a single automated driver assistance feature, such as steering or acceleration assistance. Adaptive cruise control is such an example of a Level 1 autonomous system, encompassing both acceleration and steering aspects.

[0032] Level 2 autonomy in a vehicle can provide driver assistance technologies, such as partial automation of steering and acceleration functions, where the automated system is supervised by a human driver performing non-automated operations (such as braking and other controls). In some embodiments, with Level 2 and higher levels of autonomy, the primary user can control the vehicle when the user is inside the vehicle, or in some example embodiments, when the vehicle is being remotely operated, from a location far from the vehicle but within a control area extending several meters from the vehicle.

[0033] Level 3 autonomy in vehicles can provide conditional automation and control of driving characteristics. For example, Level 3 vehicle autonomy may include “environmental awareness” capabilities, where the autonomous vehicle (AV) can make informed decisions independently of the current driver, such as accelerating past slow-moving vehicles, while the current driver remains ready to regain control of the vehicle if the system is unable to perform its task.

[0034] Level 4 AV can operate independently of a human driver, but may still include human controls for overdrive operations. Level 4 automation also enables the autonomous driving mode to intervene in response to predefined conditions, such as road hazards or system events.

[0035] Level 5 AV may include fully autonomous vehicle systems that do not require human input to operate, and may not include human-operated driving controls.

[0036] In addition to the components mentioned above, vehicle 100 may also have numerous mechanical systems and subsystems. A chassis or frame may form the backbone of vehicle 100 and support the body and other components of vehicle 100. Vehicle 100 may include an engine that converts fuel into mechanical power to propel the vehicle forward. The engine includes various components such as the engine block, pistons, valves, and spark plugs. Vehicle 100 also includes a transmission system. The transmission system transmits power from the engine to the wheels. It includes a clutch, gearbox, drive shaft, differential, and other components. The transmission adjusts power output to suit the vehicle's speed and load. Vehicle 100 may also include a suspension system. The suspension system absorbs shocks and maintains contact between the tires and the road, thus providing a smooth ride. It includes components such as springs, shock absorbers, and linkages. Vehicle 100 also includes a braking system that allows the driver to slow down or stop vehicle 100. It includes components such as the brake pedal, master cylinder, brake lines, and brake pads or brake shoes. Vehicle 100 also includes a steering system that allows the driver to guide the vehicle. The steering system includes components such as the steering wheel, steering column, rack and pinion, and tie rods. Vehicle 100 also includes an exhaust system that removes and filters exhaust gases produced by the engine. This includes an exhaust manifold, catalytic converter, muffler, and exhaust tailpipe, among other components. Vehicle 100 also includes a cooling system to prevent the engine from overheating. This includes components such as the radiator, water pump, thermostat, and coolant. Vehicle 100 also includes a cooling system that stores fuel and supplies fuel to the engine. This includes a fuel tank, fuel pump, fuel filter, and fuel injectors. The electrical system of vehicle 100 powers the vehicle's electrical components. This includes a battery, alternator, starter motor, and wiring. The heating, ventilation, and air conditioning (HVAC) system regulates the temperature inside vehicle 100. This includes heater cores, blower motors, and air conditioning compressors. All these working mechanical components ensure smooth and satisfactory operation of the vehicle.

[0037] During operation, vehicle 100 is affected by road conditions and may experience vibrations based on road quality. These vibrations affect the operation and lifespan of several vehicle components, particularly the mechanical and electrical components located under the vehicle, commonly referred to as "underbody components and systems." Real-time and accurate measurement of road roughness is beneficial for understanding the impact of the road and the resulting vibrations on these components. Furthermore, real-time roughness measurement can help determine when to collect other types of vehicle data for various types of vehicle-related analyses. Thus, in one embodiment, road roughness measurement can serve as a decision point that determines whether further vehicle-related measurements should be performed.

[0038] Figure 2 An exemplary setup is shown that vibration data can be captured to determine road roughness according to embodiments of the present disclosure. Figure 2 The vehicle is shown (e.g., Figure 1 The vehicle 100 has wheels 202. A control arm 204 is attached to the wheel 202, and sensors 206 (such as road noise cancellation sensors) can be attached to the control arm 204. This setup can be repeated for all four wheels of the vehicle. This will result in four sensors 206, each attached to its corresponding control arm. It should be noted that additional sensors 206 can also be placed at other locations under the vehicle to collect vibration data. In one embodiment, each sensor 206 can measure acceleration along three directions x, y, and z, thus effectively generating three-channel data. Combined, the four sensors can output twelve (12) channels of data. Subsequent processing may or may not use all of this data output by the four sensors. In one embodiment, the sensors 206 can transmit their data on an automotive audio bus (A2B). A2B is well known in the art, and for the sake of brevity, an explanation of the technology is omitted here.

[0039] Figure 3 A block diagram of a system 300 for measuring road roughness according to an embodiment of the present disclosure is shown. System 300 may be fully implemented, for example, in vehicle 100, or may be partially in the vehicle and partially on an external server (e.g., Figure 7 The system is implemented within a server 700. System 300 includes a data collection unit 302. The data collection unit may include one or more road noise cancellation sensors located at multiple locations throughout the vehicle. In one embodiment, four road noise cancellation sensors may be present, each outputting three channels of data. In addition to vibration data collected by the road noise cancellation sensors, the data collection unit may also receive data about vehicle operating conditions from the vehicle's controller area network (CAN) bus. Data received via the CAN bus may include, but is not limited to, tire pressure, tire temperature, ambient temperature, vehicle speed, acceleration and braking torque, steering angle, and total vehicle weight.

[0040] To ensure that road roughness measurements are meaningful, it is beneficial to collect and analyze vibration data from road noise sensors under conditions that provide optimal results. Vibration data collected by road noise sensors can be affected 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 because vehicles typically experience greater vibrations at that speed regardless of road conditions. Similarly, other vehicle operating conditions (such as steering angle, tire pressure, etc.) can also affect road noise sensor data. Therefore, collecting road noise sensor data may be beneficial when certain vehicle operating conditions fall within a certain range. For example, collecting road noise sensor data may be most beneficial when the vehicle is operating in the speed range of 45 mph to 55 mph, or when the steering angle is between 20 and 25 degrees on either side, or when the ambient temperature is between 60°F and 80°F. Operating conditions and their corresponding ranges can vary for different vehicles, different geographical locations, etc. By customizing the aforementioned set of operating conditions for specific vehicles and / or geographical locations, accurate measurements of road roughness can be obtained, which more realistically represent actual road conditions than the traditional method of measuring road roughness using standard vehicle models. We can collectively refer to these sets of operating conditions as "trigger conditions".

[0041] System 300 also includes a trigger / operation condition checking unit 304. The trigger condition checking unit 304 receives current operating condition data from the vehicle's CAN bus and vibration data from one or more road noise cancellation sensors. Based on a predetermined set of operating conditions for the specific vehicle, the trigger condition checking unit 304 can determine whether the vehicle currently meets the set of operating conditions. In one embodiment, the trigger condition checking unit can be implemented, for example, in the VCU 110 of vehicle 100. In another embodiment, the trigger condition checking unit 304 can be implemented in an external server. The set of operating conditions can be met during several durations during a specific journey of the vehicle. A "journey" can be defined as the time between the vehicle's ignition being "on" and "off". For example, the set of operating conditions can be met three times during a journey, for example, time periods of 10 seconds, 20 seconds, and 1 minute. The trigger condition checking unit 304 can determine the number of times the set of operating conditions is met during the journey. Based on this determination, vibration data collected during those time intervals / durations can be used for subsequent processing to determine road roughness measurements.

[0042] During the duration of the set of operating conditions, multiple data samples may be collected by the road noise sensor. In some embodiments, for each duration, the mean, average, or median of the collected vibration data can be calculated and used for subsequent processing. The trigger condition checking unit 304 determines the duration of the set of operating conditions and can output an indication of this to the data collection unit 302. The data collection unit 302 can then send the vibration data collected during those durations when 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 sensor and output a road roughness measurement value, which may be a single value or a set of values. In another embodiment, the data collection unit 302 can send all vibration data collected from the road noise cancellation sensor, and the trigger condition checking unit 304 can extract only the vibration data collected during those durations when the set of operating conditions is met for further processing. The road roughness measurement unit 306 can determine feature data based on the vibration data and generate a rule-based model for classifying road roughness. Rule-based models can be binary models, where a road can be classified as “rough” if one or more specific features are above a corresponding threshold, and as “flat” if one or more specific features are below a corresponding threshold.

[0043] Figure 4A and Figure 4B Vibration data collected for two different roads according to embodiments of this disclosure are shown. Figure 4A Vibration data collected from a road noise cancellation sensor is shown when a vehicle is traveling on road 'A'. Vibration characteristic spectrum 402 shows the vibration data collected over time. During this duration, the set of operating conditions is met during two durations 404 and 406. As explained above, the vibration data collected during the two durations 404 and 406 can be used for road roughness measurements. The amplitude of vibration characteristic spectrum 402 illustrates the vertical acceleration measured by the road noise cancellation sensor. In some embodiments, a window for collecting vibration data every 100 milliseconds may be used, but this setting can be programmable. Figure 4B Vibration characteristic spectrum data 408 for road “B” is shown. This vibration characteristic spectrum data was collected as the vehicle traveled on road B. As shown, the vehicle's operating conditions were met during two durations, 410 and 412, while it was traveling. Therefore, the vibration data collected during these two durations, 410 and 412, were used for further analysis.

[0044] Figures 5A to 5DCharacteristic data determined based on vibration data is shown. Then, according to embodiments of this disclosure, this characteristic data is used to determine whether a road is rugged or smooth. Figures 5A to 5D Each of these shows characteristic data determined based on vibration data collected from a single road noise cancellation sensor. For example, Figure 5A The diagram shows characteristic data determined based on vibration data collected from a first road noise sensor connected to the right front wheel of the vehicle. Figure 5B The diagram shows characteristic data determined based on vibration data collected from a second road noise sensor attached to the right rear wheel of the vehicle. Figure 5C The diagram shows characteristic data determined based on vibration data collected from a third road noise sensor attached to the left rear wheel of the vehicle. Figure 5D The illustration shows characteristic data determined based on vibration data collected from a fourth road noise sensor attached to the left front wheel of the vehicle. In this embodiment, vertical or z-axis acceleration data is used to determine road roughness. Figures 5A to 5D As shown, the characteristic determined based on the vibration data is the mean absolute deviation (MAD) value of the acceleration data in the z-direction. In one embodiment, the vibration data is divided into equal duration windows, such as 10 seconds, and the MAD value of the adjusted vibration data is determined for each of the 10-second windows. Figures 5A to 5D The MAD value for each of these 10-second vibration data points is shown. Figure 5A As shown, the MAD value represented by feature spectrum 502 indicates the MAD value of a rugged road, while the MAD value represented by feature spectrum 504 indicates the MAD value of a flat road. It can be seen that the MAD value 502 for rugged roads is significantly higher than the MAD value 504 for flat roads. An appropriate threshold can be selected to determine whether a road is rugged or flat. For example, in... Figure 5A In this context, the threshold can be chosen to be 0.15. This threshold will provide good separation between flat and rugged roads, allowing the corresponding MAD values ​​represented by feature spectra 502 and 504 to be used to classify a particular road as flat or rugged.

[0045] In some embodiments, the mean of the MAD value plus or minus 3 times the standard deviation can be used to generate a rule-based model. As explained above, vibration data from road noise sensors can be collected and analyzed in real time to determine characteristic data, and this characteristic data can be compared with a rule-based model generated for the vehicle to determine whether the road can be classified as smooth or rugged. Classifying the road as smooth or rugged can serve as a trigger point for collecting and / or analyzing other data collected for the vehicle for other purposes, such as determining vehicle health status, road quality assessment, durability and lifespan estimation, noise and vibration benchmarking, environmental impact, road design and analysis, driving comfort assessment, etc. Although Figures 5A to 5DThe MAD value for acceleration in the z-direction is shown, but this is merely an example; several other characteristics exist that can be determined from vibration data and used for road roughness measurement or determination. These characteristics may be as follows.

[0046] Time-domain characteristics: mean, standard deviation, variance, root mean square (RMS), skewness, kurtosis, peak value, crest factor, peak-to-peak value, median, minimum, maximum, range, mean absolute deviation (MAD), impulse factor, shape factor, gap factor, or RMS of derivative.

[0047] Frequency domain characteristics: spectral centroid, spectral bandwidth, spectral flatness, spectral roll-off, frequency center, RMS frequency, frequency, variance, and spectral kurtosis.

[0048] Intrinsic mode function (IMF) time-domain characteristics: mean, standard deviation, variance, RMS, skewness, kurtosis, peak value, crest factor, peak-to-peak value, median, minimum, maximum, range, MAD, impulse factor, shape factor, gap factor, or RMS of derivative.

[0049] IMF frequency domain characteristics: spectral centroid, spectral bandwidth, spectral flatness, spectral roll-off, frequency center, RMS frequency, frequency, variance, and spectral kurtosis.

[0050] One method for durability analysis of vehicle body underbody components involves using a simulation environment that provides the means to model different load factors and conditions and their subsequent effects on the component's dynamics. The accuracy of the durability analysis depends on factors influencing the difference between the real-world and simulated environments. One example of such factors is the force / load applied to the vehicle body underbody components from the road, which is a variable related to road roughness conditions. Therefore, accurately labeling road roughness conditions and analyzing the forces / loads applied to the vehicle is crucial for minimizing the gap between the simulated environment and real-world conditions. Vibration data collected from sensors, along with information about vehicle operating conditions, can provide ground-based data for the simulations required for durability analysis. Furthermore, embodiments of this disclosure provide an illustration of the effects of road-applied loads on the vehicle body underbody components. Reducing the gap in the simulation environment achieved by the output of the proposed method leads to accurate modeling of the effects of road roughness on vehicle body underbody components and improves durability analysis. This enhancement is important for analyses related to wear patterns and / or lifespan under various road conditions. Furthermore, accurately measuring road roughness under different road surfaces and conditions (such as potholes, bumps, gravel, and varying levels of road roughness) improves the predictive accuracy of durability models. This predictive accuracy helps in designing products that meet or exceed performance expectations and safety standards.

[0051] Furthermore, enhanced simulation accuracy and durability analysis provide a selection of materials and components that balance performance, cost, and lifespan. Additionally, an accurate simulation environment allows for the exploration of various materials and design configurations to identify the optimal options for meeting durability requirements without over-engineering or excessive cost. Moreover, enhanced simulation accuracy contributes to life cycle analysis and improved sustainability. It allows for the design of products that are easier to maintain, repair, or recycle, thus contributing to the achievement of sustainability goals.

[0052] Figure 6 A flowchart of a process 600 for determining road roughness according to an embodiment of the present disclosure is shown. Process 600 may, for example, be performed in... Figure 1 The process 600 is fully implemented in vehicle 100. In another embodiment, process 600 can be implemented jointly in the vehicle and an external server. At step 602, the vehicle collects data about its current operating conditions (such as speed, acceleration, etc.), as explained above. At step 604, vibration data is collected by one or more accelerometers or road noise cancellation sensors of the vehicle. In some embodiments, steps 602 and 604 can be performed simultaneously. At step 606, the operating condition data is analyzed to determine whether the set of operating conditions is met. If it is determined at 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 latest operating condition data. If it is determined at step 608 that the vehicle's operating conditions are met, the vibration data associated with the duration of the met operating conditions is used for further analysis. At step 610, the vibration data is downsampled to compress the data and make it more efficient for storage and further analysis.

[0053] At step 612, noise removal is performed on the downsampled data to remove any outliers and other noisy data. At step 614, the data is bandpass filtered to extract appropriate vibration data for further analysis. At step 616, the data can be transformed to the frequency domain, for example, using a Fast Fourier Transform (FFT) technique. After step 616, the data is in a format from which feature data extraction can be performed. At step 618, data associated with one or more features are determined based on the vibration data. A list of potential features is mentioned above. Once the data for the desired features are determined from the vibration data, a dimensionality reduction process is performed on the determined feature data at 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, it is determined which feature data are highly correlated with each other. Then, among the highly correlated feature data, data associated with one or more features can be ignored for further processing. This helps reduce redundancy in the feature data. After step 620, the dimensionality-reduced vibration data is then analyzed at step 622 to determine road roughness indicators, as mentioned above regarding 5A to Figure 5D The explanation given.

[0054] Accurate measurement of road roughness is beneficial for determining various aspects of a vehicle. Some of these applications are mentioned below. It should be understood that the applications mentioned below are merely examples of how road roughness measurements can be used to improve vehicle design and performance.

[0055] Durability analysis and life prediction: Road roughness assessment using vibration data collected from sensors provides an accurate reconstruction of the load spectrum of vehicle components and enhanced life prediction.

[0056] Structural health monitoring and stress / fatigue analysis: Accurate estimation of road roughness using vibration data and identifiable CAN signals available in the vehicle improves the accuracy of health monitoring and analysis.

[0057] Adaptive suspension control: Accurate assessment of road roughness can be used for real-time adjustment of suspension design variables.

[0058] Navigation system enhancements: The measurement of road roughness and accurate analysis of road conditions provide enhancements to the navigation system.

[0059] Enhanced handling and chassis stability: Accurate quantification of road roughness can be used to improve chassis stability and handling.

[0060] Safety: Accurate mapping of road roughness provides enhanced vehicle safety regardless of road conditions.

[0061] Advanced Driver Assistance System Calibration and Enhancement: Accurate labeling and quantification of road roughness are important for the calibration, triggering, and control of advanced driver assistance systems.

[0062] Tire durability and dynamic analysis: Accurate labeling and quantification of road roughness allows for the study of the impact of different types of road roughness on tire wear patterns, material fatigue, and tire failure probability. This can help design more durable tires suitable for specific driving environments.

[0063] Road quality assessment: Accurate road roughness measurement can improve road quality analysis and assessment.

[0064] Energy optimization and improved regenerative braking efficiency: Measurements of road roughness and acceleration applied to the control arm can be directly applied to the efficiency of the regenerative braking system.

[0065] Driving pattern assessment: Accurate quantification of road roughness provides an assessment of driving behavior and determines the percentage of time a vehicle is used on rough or smooth roads. Analysis of driving patterns leads to the development of driver assistance systems that help mitigate the effects of rough roads.

[0066] Noise and Vibration Benchmarking: Accurate measurements of vibration data collected from sensors and road roughness are used to evaluate a vehicle’s noise, vibration, and harshness (NVH) performance.

[0067] Road design and analysis: Accurate mapping of road roughness provides useful information that can be used for road design and analysis.

[0068] Driving comfort assessment: Accurate measurements of vibration data from sensors and vibration data directly from the road with minimal interference can be used for driving comfort assessment.

[0069] Prioritization of maintenance work: Accurate mapping of road roughness and identification of affected areas provide insights into maintenance actions associated with vehicles.

[0070] Historical data collection and analysis: Recording historical data collected from road roughness measurements can be directly used for event analysis and correlated with historical events.

[0071] Figure 7 A block diagram depicts an exemplary control server 700, purportedly an exemplary embodiment of the present disclosure, on which one or more technologies (e.g., methods) may be performed. In other embodiments, server 700 may operate as a standalone device or may be connected to other servers (e.g., networked). In a networked deployment, server 700 may operate as a server machine, a client machine, or both in a server-client network environment. In the example, server 700 may act as a peer-to-peer (P2P) (or other distributed) network environment. Server 700 may be a personal computer (PC), tablet PC, set-top box (STB), personal digital assistant (PDA), mobile phone, smart keychain, wearable computing device, network device, network router, switch, or bridge, or any machine capable of executing instructions (continuously or otherwise) specifying actions to be taken by the server (e.g., a base station). Furthermore, while only a single server is mentioned, the term "server" should also be considered as any collection of servers that individually or jointly execute a set (or sets) of instructions for performing any one or more of the methodologies discussed herein, such as those configured for cloud computing, Software as a Service (SaaS), or other computer clusters.

[0072] The examples described herein may include logic or components, modules, or mechanisms, or may operate on logic or components, modules, or mechanisms. A module is a tangible entity (e.g., hardware) capable of performing a specified operation during operation. A module includes hardware. In the examples, the hardware may be specifically configured to perform a specific operation (e.g., hardwired). In another example, the hardware may include a configurable execution unit (e.g., a transistor, circuit, etc.) and a computer-readable medium containing instructions that configure the execution unit to perform a specific task when in operation. The configuration may occur under the guidance of the execution unit or loading mechanism. Thus, when the device is operating, the execution unit is communicatively coupled to the computer-readable medium. In this example, the execution unit may be a member of more than one module. For example, under operation, the execution unit may be configured at one point in time to implement a first module via a first set of instructions, and at a second point in time to reconfigure the execution unit to implement a second module via a second set of instructions.

[0073] Server (e.g., computer system) 700 may 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 may communicate with each other via interconnect (e.g., bus) 708. Server 700 may also include a graphics display device 710, an alphanumeric input device 712 (e.g., a keyboard), and a user interface (UI) navigation device 714 (e.g., a mouse). In this example, the graphics display device 710, the alphanumeric input device 712, and the UI navigation device 714 may be a touchscreen display. Server 700 may additionally include a storage device (i.e., a drive unit) 716, a network interface device / transceiver 720 coupled to an antenna, and one or more sensors 728, such as a global positioning system (GPS) sensor, a compass, an accelerometer, or other sensors. Server 700 may include output controller 734, such as serial (e.g., Universal Serial Bus (USB)), parallel, or other wired or wireless (e.g., infrared (IR), near field communication (NFC), etc.) connections, to communicate with or control one or more peripheral devices (e.g., printers, card readers, etc.).

[0074] Storage device 716 may include machine-readable medium 722 on which one or more sets of data structures or instructions 724 (e.g., software) embodying or being utilized by any or more of the techniques or functions described herein are stored. Instructions 724 may also reside wholly or at least partially within main memory 704, static memory 706, or hardware processor 702 during execution of the instructions by server 700. In this example, one or any combination of hardware processor 702, main memory 704, static memory 706, or storage device 716 may constitute the machine-readable medium.

[0075] Although the machine-readable medium 722 is shown 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 one or more instructions 724.

[0076] Various embodiments may be implemented wholly or partially in software and / or firmware. This software and / or firmware may take the form of instructions contained in or on a non-transitory computer-readable storage medium. Those instructions may then be read and executed by one or more processors to perform the operations described herein. The instructions may be in any suitable form, such as, but not limited to, source code, compiled code, interpreted code, executable code, static code, dynamic code, etc. Such computer-readable medium may include any tangible non-transitory 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); disk storage media; optical storage media; flash memory, etc.

[0077] The term "machine-readable medium" can include any medium having the following properties: capable of storing, encoding, or transporting instructions executable by server 700; and causing server 700 to perform any or more of the technologies disclosed herein; or capable of storing, encoding, or transporting data structures used by or associated with such instructions. Examples of non-limiting machine-readable media can include solid-state memory as well as optical and magnetic media. In examples, high-capacity machine-readable media includes machine-readable media having a plurality of particles having rest masses. Specific examples of large-scale machine-readable media 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.

[0078] The network interface device / transceiver 720 can further transmit or receive instructions 724 on the communication network 726 using a transmission medium via 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.). Exemplary communication networks may include local area networks (LANs), wide area networks (WANs), packet data networks (e.g., the Internet), mobile phone networks (e.g., cellular networks), conventional telephone (POTS) networks, and wireless data networks (e.g., networks referred to as…). The Institute of Electrical and Electronics Engineers (IEEE) 802.11 series of standards, known as The IEEE 802.16 series of standards, the IEEE 802.15.4 series of standards, and peer-to-peer (P2P) networks are examples. In the example, the network interface device / transceiver 720 may include one or more physical sockets (e.g., Ethernet sockets, coaxial sockets, or telephone sockets) or one or more antennas to connect to the communication network 726. In the example, the network interface device / transceiver 720 may include multiple antennas to communicate wirelessly using at least one of the following: Single-Input Multiple-Output (SIMO) technology, Multiple-Input Multiple-Output (MIMO) technology, or Multiple-Input Single-Output (MISO) technology. The term "transmission medium" should be considered to include any intangible medium capable of storing, encoding, or transmitting instructions for execution by the server 700 and comprising digital or analog communication signals, or other intangible media used to facilitate communication of such software. The operations and processes described and shown above may be implemented or performed in any suitable order as needed in various embodiments. Additionally, in some embodiments, at least a portion of the operations may be performed in parallel. Furthermore, in some embodiments, fewer or more operations than those described may be performed.

[0079] It should be noted that the vehicle implements and / or performs the operations described herein in accordance with the owner's manual and safety guidelines. Additionally, any action taken by the vehicle owner based on recommendations or notices provided by the vehicle should comply with all rules specific to the vehicle's location and operation (e.g., federal, state, national, city, etc.). Recommendations or notices provided by the vehicle should be considered as advice and followed only in accordance with any rules specific to the vehicle's location and operation. In the foregoing disclosure, reference has been made to the accompanying drawings, which form a part of the foregoing disclosure, illustrating specific embodiments in which the present disclosure may be practiced. It should be understood that other embodiments may be utilized and structural changes may be made without departing from the scope of the present disclosure. References to "an embodiment," "embodiment," "example embodiment," etc., in this specification indicate that the described embodiment may include a particular feature, structure, or characteristic, but each embodiment may not necessarily include said particular feature, structure, or characteristic. Furthermore, such phrases do not necessarily refer to the same embodiment. Moreover, when features, structures, or characteristics are described in connection with embodiments, those skilled in the art will recognize such features, structures, or characteristics in conjunction with other embodiments, whether explicitly described or not.

[0080] Furthermore, where appropriate, the functions described herein may be performed by one or more of the following: hardware, software, firmware, digital components, or analog components. For example, one or more application-specific integrated circuits (ASICs) may be programmed to perform one or more of the systems and programs described herein. Certain terms are used throughout the specification and claims to refer to specific system components. As those skilled in the art will appreciate, components may be referred to by different names. This document is not intended to distinguish between components with different names but identical functions.

[0081] It should also be understood that the term "example" as used herein is intended to be non-exclusive and non-restrictive in nature. More specifically, the term "example" as used herein refers to one of several examples, and it should be understood that there is no undue emphasis or preference on the particular example described.

[0082] Computer-readable media (also known as processor-readable media) include any non-transitory (e.g., tangible) medium that contributes to providing data (e.g., instructions) that can be read by a computer (e.g., by the computer's processor). Such media can take many forms, including but not limited to non-volatile and volatile media. Computing devices may include computer-executable instructions, which can be executed by one or more computing devices (such as those listed above) and stored on a computer-readable medium.

[0083] Regarding the processes, systems, methods, heuristics, etc., described herein, it should be understood that although the steps of such processes, etc., are described as occurring in a certain ordered order, such processes can be practiced by performing the described steps in a different order than that described herein. It should also be understood that some steps may be performed simultaneously, other steps may be added, or some steps described herein may be omitted. In other words, the description of processes herein is provided for the purpose of illustrating various embodiments and should in no way be construed as limiting the claims.

[0084] Therefore, it should be understood that the above description is intended to be illustrative rather than restrictive. Many embodiments and applications beyond the examples provided will become apparent upon reading the above description. The scope should not be determined by reference to the above description, but rather by reference to the appended claims and the full scope of their equivalents. It is anticipated and expected that the techniques discussed herein will evolve in the future, and the disclosed systems and methods will be incorporated into such future embodiments. In conclusion, it should be understood that modifications and changes are possible with this application.

[0085] Unless explicitly indicated otherwise herein, all terms used in the claims are intended to be given their ordinary meaning as understood by one skilled in the art as described herein. Specifically, unless the claims explicitly limit the recitation to the contrary, the use of singular articles such as “a,” “the,” or “the” should be interpreted as one or more of the elements indicated by the recitation. Unless otherwise specifically stated or otherwise understood in the context of use, conditional language such as, in particular, “can,” “may,” “may,” or “may” is generally intended to express that some embodiments may include certain features, elements, and / or steps, while other embodiments may not include certain features, elements, and / or steps. Therefore, such conditional language is generally not intended to imply that one or more embodiments require each feature, element, and / or step in any way.

[0086] In one aspect of the invention, the set of operating conditions is satisfied over one or more durations, and wherein determining the one or more characteristics includes analyzing vibration data captured during the one or more durations.

[0087] In one aspect of the invention, the one or more features include an average absolute deviation of z-direction acceleration measurements for each of the one or more durations.

[0088] In one aspect of the invention, the operating data includes one or more of the following: vehicle speed, vehicle acceleration, steering angle associated with the vehicle, tire pressure associated with the vehicle, ambient temperature of the environment in which the vehicle is operating, total train weight of the vehicle, or braking torque associated with the vehicle.

[0089] In one aspect of the invention, the method includes: classifying the road as flat based on the road roughness index; and analyzing the vibration data based on the determination that the road is flat to determine one or more additional parameters of the vehicle.

[0090] In one aspect of the invention, the one or more additional parameters are associated with one or more of the following: vehicle safety, road quality assessment, durability and life estimation, noise and vibration benchmarking, environmental impact, road design and analysis, or driving comfort assessment.

Claims

1. A vehicle comprising: One or more processors; One or more memories, the one or more memories being coupled to the one or more processors; A communication interface, wherein the communication interface is connected to the one or more processors; as well as Multiple sensors, wherein the multiple sensors are connected to the one or more processors, The one or more memory storage instructions, when executed by the one or more processors, cause the one or more processors to: Determine whether the vehicle is moving on the road; Multiple sensors of the vehicle are used to capture vibration data associated with the vehicle; Determine the operational data associated with the vehicle; Based on the operational data, a set of operating conditions is determined; Use the vibration data and determine one or more features based on satisfying the set of operating conditions; as well as The roughness index of the road is determined based on one or more of the aforementioned features.

2. The vehicle of claim 1, wherein the plurality of sensors includes a road noise cancellation sensor.

3. The vehicle of claim 1, wherein the vibration data includes acceleration data measured along one or more axes.

4. The vehicle of claim 1, wherein the set of operating conditions is satisfied for one or more durations, and wherein, in order to determine the one or more characteristics, the one or more processors are further configured to analyze only vibration data captured during the one or more durations.

5. The vehicle of claim 4, wherein the one or more features include an average absolute deviation of z-direction acceleration measurements for each of the one or more durations.

6. The vehicle of claim 1, wherein the set of operating conditions includes one or more of the following: vehicle speed, vehicle acceleration, steering angle associated with the vehicle, tire pressure associated with the vehicle, ambient temperature of the environment in which the vehicle is operating, total train weight of the vehicle, or braking torque associated with the vehicle.

7. The vehicle of claim 1, wherein the one or more processors are further configured to: Based on the road roughness index, the road is classified as flat; and Based on the determination that the road is flat, the vibration data is analyzed to determine one or more additional parameters of the vehicle.

8. A vehicle comprising: One or more processors; as well as Multiple sensors, wherein the multiple sensors are connected to the one or more processors, The one or more processors mentioned above are configured to: Determine whether the vehicle is currently moving on the road; Vibration data associated with the vehicle are received from the plurality of sensors; Determine a set of operating conditions associated with the vehicle that are met during a first duration; Determine a first portion of the vibration data that is associated with the first duration; The first portion of the vibration data is used to determine one or more features associated with the first portion of the vibration data; as well as The roughness index of the road is determined based on one or more of the aforementioned features.

9. The vehicle of claim 8, wherein the one or more processors are further configured to: Determine that the first operating condition associated with the vehicle is met during the second duration; Determine the second portion of the vibration data associated with the second duration; and The second portion of the vibration data is then used to determine the one or more characteristics.

10. The vehicle of claim 8, wherein one or more features include: Mean, standard deviation, variance, root mean square (RMS), skewness, kurtosis, peak value, crest factor, peak-to-peak value, median, minimum, maximum, range, mean absolute deviation (MAD), impulse factor, shape factor, gap factor, RMS of derivative, spectral centroid, spectral bandwidth, spectral flatness, spectral roll-off, frequency center, RMS frequency, frequency, variance or spectral kurtosis.

11. The vehicle of claim 8, wherein the plurality of sensors includes a 3-axis road noise cancellation sensor, and the plurality of sensors are attached to the bottom portion of the vehicle body.

12. The vehicle of claim 8, wherein the set of operating conditions includes one or more of the following: vehicle speed, vehicle acceleration, steering angle associated with the vehicle, tire pressure associated with the vehicle, ambient temperature of the environment in which the vehicle is operating, total train weight of the vehicle, or braking torque associated with the vehicle.

13. A method comprising: Determining whether the vehicle is moving on the road by examining the vehicle itself; The vehicle uses multiple sensors to capture vibration data associated with the vehicle. The vehicle is used to determine the operational data associated with it; The vehicle determines a set of operating conditions based on the operational data. The vehicle uses the vibration data and determines one or more characteristics based on satisfying the set of operating conditions. as well as The roughness index of the road is determined by the vehicle based on one or more of the features.

14. The method of claim 13, wherein the plurality of sensors includes a road noise cancellation sensor.

15. The method of claim 13, wherein the vibration data includes acceleration data measured along one or more axes.

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