NETWORKED VEHICLE-BASED ROAD SURFACE QUALITY ASSESSMENT

Abstract

A system (100) comprising a computer (110) containing a processor and memory, the memory containing instructions such that the processor is programmed for: receiving sensor data including at least one of the following: wheel speed measurements, suspension displacement measurements, or tire leak detection data from a vehicle; estimating a road roughness measurement based on a deviation of the wheel speed from the average wheel speed; and generating temporary spatial map data indicating a location and roughness metric of a road segment based on the road roughness measurement, wherein the road roughness measurement is a function of the sum of the absolute deviations of the speeds of the non-driven wheels from the average wheel speed.

Description

INTRODUCTIONThe present disclosure relates to determining road surface quality based on vehicle data received from multiple vehicles.The characteristics of a road surface being traveled by a vehicle may affect the operation of the vehicle in various ways. Thus, a vehicle may be operated in a particular manner when crossing a roadway having particular features, while it may be operated differently for a roadway having other features. Thus, a vehicle traveling a roadway with one or more pinholes may travel at a lower speed than would be the case with a smooth roadway surface.DE 10 2021 209 131 A1 relates to a method for determining and characterizing unevenness of the road surface of a road surface. For this purpose, sensor data are generated by at least one wheel rotational speed sensor and / or at least one acceleration sensor of a motor vehicle travelling on the roadway. The unevenness of the road surface is determined and characterized by a computing device using the generated sensor data.WO 2018 / 235 239 A1 describes a method which judges whether the vehicle has passed a braking threshold on the basis of the driving state of a vehicle. From the result of the judgment as to whether the vehicle has passed over the braking threshold, the position of the vehicle, and the travel history including the travel state of the vehicle, position information of the braking threshold and vehicle speed information when passing over the braking threshold are obtained and identified as speed limit information.US 10 821 968 B2 describes a travel control method that may include performing road surface matching capability control in which, when an uneven road surface of a road on which a vehicle travels is recognized by a controller, wheel torque control of the vehicle is performed.DE 10 2021 110 962 A1 describes adjusting operating parameters on the basis of identified roadway irregularities. Methods and systems are provided for improving vehicle speed measurements. A vehicle may detect the impact of a road irregularity on its front and rear wheels, and may calculate an actual vehicle speed based on its wheel base and a time elapsed between the two impacts.US 2015 / 0 291 177 A1 describes an apparatus for detecting braking thresholds and may include a sensor for measuring driving data of a vehicle during forward travel of the vehicle and a controller for monitoring the generation of external disturbances at front wheels and rear wheels and for determining the presence of a braking threshold, wherein the controller determines that the braking threshold is present when |L - S|K when an external disturbance is generated in a rear wheel, wherein L is the wheelbase of the vehicle and S is the moving distance of the vehicle after an external disturbance generated in a front wheel, and K is a preset threshold.CN 1 14 506 326 A relates to preview of road unevenness based on the travel history. A vehicle includes a sensor configured to detect a first road feature, an adjustable suspension system, and a processing device. The processing device is configured to determine a location of the vehicle using a driving history map and apply a suspension setting to the suspension system based on the first road feature. The suspension setting is associated with a second road feature determined by the driving history map and located elsewhere than the first road feature.DESCRIPTIONThe object of the invention is to eliminate the above-mentioned disadvantages. This object is achieved by the subject matter according to claim 1.A system includes a computer having a processor and a memory. The memory includes instructions such that the processor is programmed to receive sensor data including wheel speed measurements from a vehicle, estimate a measurement of road roughness based on a deviation of a wheel speed with respect to an average wheel speed, and generate temporal-spatial map data indicative of a location and roughness level metric of a road segment based on the measurement of road roughness.The road roughness measurement is a function of the sum of the absolute deviations of the non-driven wheel speeds from the average wheel speed.In other features, the measurement of road roughness is calculated according to where RRM comprises the measurement of road roughness, abs represents an absolute value, x represents a deviation of the wheel speed of a non-driven wheel from the average wheel speed, and i represents one of the non-driven wheels.In other cases, the sensor data also includes data about the suspension travel and the tire pressure.In other cases, the mark comprises a hue indicative of the severity of the roughness metric.In other features, the processor is also programmed to classify a bump based on the measurement of road roughness.The processor is also programmed to be able to estimate when a roadway surface will exceed a predefined International Roughness Index (IRI) value.In other features, the processor is also programmed to estimate when the road surface will exceed the predefined International Roughness Index (IRI) value by applying a regression function to match a cumulative distribution function for the road roughness metric to a corresponding International Roughness Index (IRI) value.In other features, the regression function includes a linear regression function.In other features, the regression function includes a nonlinear regression function.A method includes receiving, at a processor, sensor data including wheel speed measurements from a vehicle, estimating a measurement of road roughness based on a deviation of a wheel speed with respect to an average wheel speed, and generating temporal-spatial map data indicative of a location and roughness level metric of a road section based on the measurement of road roughness.In other features, the measurement of road roughness is a function of the sum of the absolute deviations of the non-driven wheel speeds from the average wheel speed.In other features, the measurement of road roughness is calculated according to where RRM comprises the measurement of road roughness, abs represents an absolute value, x represents a deviation of the wheel speed of a non-driven wheel from the average wheel speed, and i represents one of the non-driven wheels.In other cases, the sensor data also includes data about the suspension travel and the tire pressure.In other cases, the mark comprises a hue indicative of the severity of the roughness metric.In other features, the method includes classifying a knock hole based on the measurement of road roughness.The further features of the method include estimating when a portion of the roadway surface will exceed a predefined International Roughness Index (IRI) value.In other features, the method includes estimating when the road surface will exceed the predefined value of the International Roughness Index (IRI) by applying a regression function to fit a cumulative distribution function for the road roughness metric to a corresponding value of the International Roughness Index (IRI).In other features, the regression function includes a linear regression function.In other features, the regression function includes a nonlinear regression function.Further areas of applicability will become apparent from the description herein. It should be understood that the specification and specific examples are intended to be illustrative only and not to limit the scope of the present disclosure.BRIEF DESCRIPTION OF THE FIGURESThe figures described herein are for illustrative purposes only and are not intended to limit the scope of the present disclosure in any way. FIG. 1 is a block diagram of an example system for estimating the severity of a road obstacle on a roadway; FIG. 2 is a block diagram of an example server within the system; FIG. 3 shows an example environment with a vehicle traveling a roadway with an obstacle; FIG. 4 is an example of a temporal-spatial map with multiple markers indicating road obstacles; FIG. 5 is a flow chart showing an example method for estimating severity of a roadway obstacle and / or roadway deformation; and FIG. 6 is a flowchart showing an example method of predicting road degradations based on road roughness (RRM) measurement.DETAILED DESCRIPTIONThe following description is merely exemplary in nature and is not intended to limit the present disclosure, application, or use.FIG. 1 is a block diagram of an example vehicle system 100. The system 100 includes a vehicle 105 that is a land vehicle such as a car, truck, etc. The vehicle 105 includes a computer 110, vehicle sensors 115, actuators 120 for actuating various vehicle components 125, and a vehicle communication module 130. Via a network 135, the communication module 130 allows the computer 110 to communicate with a server 145.The computer 110 includes a processor and a memory. The memory includes one or more forms of computer readable media and stores instructions executable by the computer 110 to perform various operations, including those disclosed herein.The computer 110 may operate a vehicle 105 in an autonomous, a semi-autonomous, or a non-autonomous (manual) mode. For purposes of this disclosure, an autonomous mode is defined as a mode in which vehicle 105 propulsion, braking, and steering are controlled by the computer 110; in a semi-autonomous mode, the computer 110 controls one or two of the vehicle 105 propulsion, braking, and steering ranges; in a non-autonomous mode, a human operator controls each of the vehicle 105 propulsion, braking, and steering ranges.The computer 110 may be programmed to control the brakes of the vehicle 105, propulsion (e.g., control of acceleration of the vehicle by control of one or more engines, electric motors, hybrid motors, etc.), steering, air conditioning, interior and / or exterior lighting, etc., and determine whether and when the computer 110, unlike a human operator, is to control these operations. In addition, the computer 110 may be programmed to determine whether and when a human operator should control these operations.The computer 110 may include, e.g., via the communication module 130 of the vehicle 105, as described further below, or be communicatively coupled to, more than one processor, e.g., in electronic control units (ECUs) or the like included in the vehicle 105 to monitor and / or control various vehicle components 125, e.g., a powertrain control unit, a brake control unit, a steering control unit, etc. Further, the computer 110 may communicate via the communication module 130 of the vehicle 105 with a navigation system using the global position system (GPS). For example, the computer 110 may request and receive location data of the vehicle 105. The location data may be in a known form, e.g., geo-coordinates (latitude and longitude coordinates).The computer 110 is generally configured for communication with the communication module 130 of the vehicle 105, and also with an internal wired and / or wireless network of the vehicle 105, e.g., a bus or the like in the vehicle 105, such as a controller area network (CAN) or the like, and / or other wired and / or wireless mechanisms.Via the vehicle 105 communication network, the computer 110 may send messages to various devices in the vehicle 105 and / or receive messages from the various devices, e.g., vehicle sensors 115, actuators 120, vehicle components 125, a human machine interface (HMI), etc. In cases where the computer 110 actually comprises a plurality of devices, the vehicle 105 communication network may be used for communication between the devices represented as the computer 110 in this disclosure. Additionally, as mentioned below, various controllers and / or vehicle sensors 115 may provide data to the computer 110.The vehicle sensors 115 may include a variety of devices known for providing data to the computer 110. For example, the vehicle sensors 115 may include one or more light detection and ranging (lidar) sensors 115, etc., disposed on the top of the vehicle 105, behind the windshield of the vehicle 105, around the vehicle 105, etc., and providing relative positions, sizes, and shapes of objects and / or conditions in the environment of the vehicle 105. As another example, one or more radar sensors 115 attached to the vehicle 105 bumper may provide data to determine the speed of objects (possibly including second vehicles 106), etc., relative to the location of the vehicle 105. The vehicle sensors 115 may further include camera sensors 115, e.g., for the front view, the side view, the rear view, etc., that provide images from a field of view inside and / or outside of the vehicle 105.Vehicle sensors 115 may also include wheel speed sensors that measure vehicle wheel 105 speed parameters, tire pressure monitoring sensors that measure a pressure in a corresponding vehicle tire 105, and / or suspension sensors that measure the displacement of the vehicle suspension 105 relative to a predetermined fixed point. As discussed in greater detail below, the computer 110 may selectively transmit vehicle data including vehicle speed, tire pressure, and / or deflection of the vehicle suspension 105 to the server 145 to characterize the condition of a roadway.The vehicle actuators 120 are implemented by circuits, chips, motors, or other electronic and / or mechanical components that can actuate various vehicle subsystems in accordance with suitable control signals, as is known. The actuators 120 may be used to control components 125, including braking, acceleration, and steering of a vehicle 105.In the context of the present disclosure, a vehicle component 125 is one or more hardware components that can perform a mechanical or electromechanical function or operation, such as moving the vehicle 105, braking or stopping the vehicle 105, steering the vehicle 105, etc. Non-limiting examples of components 125 include a propulsion component (e.g., including an engine and / or an electric motor, etc.), a transmission component, a steering component (e.g., which may include one or more steering wheels, a steering rack, etc.), a braking component (as described below), a parking assist component, an adaptive cruise control component, an adaptive steering component, a movable seat, etc.Moreover, the computer 110 may be configured to communicate with devices external to the vehicle 105 via a vehicle-to-vehicle communication module or interface 130, e.g., via a vehicle-to-vehicle (V2V) or vehicle-to-infrastructure (V2X) wireless communication with another vehicle and (typically, via the network 135) with a remote server 145. The module 130 could include one or more mechanisms by which the computer 110 can communicate, including any desired combination of wireless (e.g., cellular, wireless, satellite-based, microwave, and radio frequency) communication mechanisms and any desired network topology (or topologies when multiple communication mechanisms are used). Exemplary communications capabilities provided via module 130 include cellular, Bluetooth® IEEE 802.11, dedicated short range communications (DSRC), and / or wide area networks (WAN), including the Internet, providing data communication services.The network 135 may be comprised of one or more of a variety of wired or wireless communication mechanisms, including any desired combination of wired (e.g., cable and fiber) and / or wireless (e.g., cellular, wireless, satellite, microwave, and radio frequency) communication mechanisms and any desired network topology (or topologies when multiple communication mechanisms are used). Example communication networks include wireless communication networks (e.g., using Bluetooth, Bluetooth Low Energy (BLE), IEEE 802.11, vehicle-to-vehicle (V2V) such as dedicated short-range communications (DSRC), etc.), local area networks (LAN), and / or wide area networks (WAN), including the Internet, providing data communication services.A computer 110 may receive and analyze data from sensors 115 substantially continuously, periodically, and / or upon instruction of a server 145, etc. Furthermore, object classification or identification methods may be used, e.g., in a computer 110, based on data from the lidar sensor 115, the camera sensor 115, etc., to identify an object type, e.g., a vehicle, a person, a stone, a club, a bicycle, a motorcycle, etc., as well as physical characteristics of objects.FIG. 2 is a block diagram of an example server 145. The server 145 includes a computer 235 and a communication module 240. The computer 235 includes a processor and a memory. The memory includes one or more forms of computer readable media and stores instructions executable by the computer 235 to perform various operations, including those disclosed herein. The communication module 240 allows the computer 235 to communicate with other devices, such as the vehicle 105.As described in more detail below, the vehicle computer 110 and / or the server 145 generate road surface metrics based on sensor data collected from one or more vehicles 105. For example, the server 145 may communicate with multiple vehicles, such as a fleet of vehicles 105. Based on the received sensor data, the server 145 determines the road surface metrics, which may include, but are not necessarily limited to, road degradation such as road roughness and / or road surface quality, location of the shot holes, and / or severity of the shot holes. In some implementations, the server 145 may apply temporal-spatial analysis techniques to the received sensor data to generate road surface metrics over a defined period of time. For example, the server 145 may generate road surface metrics representing the degradation of the road surface over a certain period of time, e.g., a week, a month, a year, etc.FIG. 3 shows an example environment 300 in which the vehicle 105 is travelling along a roadway 305. The roadway 305 may include a roadway obstacle 310, such as a keyhole, and one or more surface deformations 315 that represent surface degradation. The sensors 115 may generate data that is selectively transmitted to the server 145, and the server 145 may use the data to generate the road surface metrics.FIG. 4 shows an example map 400 having one or more lanes 405 and one or more markings 410 indicating the lane surface metrics. The markers 410 may be positioned over a portion of the lanes 405 to represent a relative position of the lane surface metric corresponding to the position within the map 400. The severity of road degradation may correspond to a hue of the markings 410. For example, a marker 410 having a relatively dark hue may be defined as a portion of the roadway that has a greater, i.e., heavier, road degradation than a marker 410 having a relatively light hue. Road damage may include, among other things, the presence of one or more roadway obstacles 310, such as pinholes and / or frost fractures.In some implementations, the map 400 may be updated by the server 145 in near real-time or at predetermined periodic time intervals. Once the map 400 is updated, the server 145 may communicate data representing the updated map 400 to one or more locations, such as government locations, enterprises, or the like. The government entities may include local community responsible for maintenance of the roadway, such as a local traffic office. Companies may be insurance companies that provide insurance protection for vehicles, vehicle manufacturers, or the like. In some cases, the server may send data representing the updated map 400 to the vehicle 105 via the network 135.In some cases, based on the configuration of the vehicle 105, one or more vehicle components 125 may be actuated based on data in the map 400. For example, the computer 110 may transmit control signals to the vehicle actuators 120 to cause the vehicle 105 to change a vehicle direction, a vehicle speed, or the like, based on the presence or absence, i.e., recently repaired, of a roadway obstacle 310 and / or surface deformations 315. In some examples, the computer 110 may cause the map 400 to be displayed via a display in the vehicle 105, such as the HMI.The server 145 is configured to receive sensor data from multiple vehicles and estimate the road surface metrics. In an example implementation, the computer 110 may send selected sensor data to the server 145, as described in detail herein.Within the scope of the present disclosure, the computer 110 transmits sensor data representing wheel speed measurements to the server 145. The computer 110 may also transmit vehicle type data, e.g., data indicative of the vehicle type, to the server 145. Based on the sensor data, the server 145 may determine speed deviations representing one or more road surface metrics including a measurement of road roughness (RRM). In an example implementation, the measurement of road roughness comprises a function of the sum of the absolute deviations of the non-driven wheel speeds from an average wheel speed as defined in Equation 1: wherein RRM comprises the measurement of road roughness, abs represents the absolute value, x represents the deviation of the wheel speed of a non-driven wheel from an average wheel speed, e.g., the average non-driven wheel speed, and i represents one of the non-driven wheels. In various implementations, the computer 235 normalizes the RRM according to the vehicle type, as the wheel measurements for different vehicle types may be different. For example, the computer 235 may access a look-up table that relates a vehicle type to a normalization factor. The normalization factor may be applied to the RRM to mitigate distortion caused by the different vehicle types.The RRM can be a measure of the roughness of a roadway section. As discussed further below, the server 145 uses the calculated RRMs to generate temporal-spatial map data that indicates the roughness severity metric. The metric for the roughness level may be defined as a quantitative value indicating how damaged a portion of the roadway surface is.The vehicle 105 may also include sensors 115 and / or components 125 including a continuous damping control (CDC) chassis and suspension technology or a magnetic chassis control chassis and suspension technology. Sensors 115 associated with chassis and suspension technology may provide data representing a distance a suspension, i.e., suspension displacement, of the vehicle 105 has travelled. For example, suspension of the vehicle 105 may experience greater changes in distance traveled when the vehicle 105 is traversing a roadway with greater surface damage as compared to a roadway with less surface damage.The vehicle 105 may also include sensors 115 capable of generating data indicative of tire pressure. The data generated by the tire pressure sensors 115 may be transmitted to the server 145 such that the server 145 may determine whether one or more tires of the vehicle 105 have a liquid loss, e.g., air, that may indicate a tire leak. The computer 110 may also provide location data with the tire pressure data so that the server 145 may determine potential locations where the tire leak occurred.During operation, one or more vehicles 105 may transmit sensor data to the server 145 via the network 135. The sensor data can include, among other things, wheel speed measurement data, data about the wheel suspension distance, and / or tire pressure data. The server 145 may use the received sensor data to estimate the road surface metrics as described herein.FIG. 5 is a flow diagram of an example process 500 for detecting and / or determining severity of a roadway obstacle 310 and / or roadway deformation 315. The blocks of the process 500 may be executed by the computer 235 of the server 145. The process 500 begins at block 505, where sensor data is received. For example, the computer 235 may receive sensor data from one or more vehicles 105 via the network 135. The vehicle data may include wheel speed measurement data, suspension distance data, and / or tire pressure data. As mentioned above, the server 145 may receive vehicle data from multiple vehicles, e.g., from a fleet of vehicles. In some cases, the computer 235 and / or the computer 110 may anonymousize the data to remove data that could be used to identify the vehicle 105. For example, the computer 110 may remove all vehicle identification data prior to transmitting the sensor data.In block 510, the computer 235 normalizes the vehicle data based on the vehicle speed and / or the vehicle type. For example, the server 145 may include a look-up table that relates vehicle speed and / or vehicle type to a normalization factor. The normalization factor may be determined by suitable empirical methods. In block 515, the computer 235 filters the normalized sensor data based on vehicle direction parameters. For example, the computer 235 may further filter the normalized sensor data to select normalized vehicle data having the same vehicle direction parameters, e.g., vehicles traveling in the same direction. In block 520, the computer 235 may apply suitable clustering techniques to group the normalized sensor data. Suitable clustering techniques may be, but are not necessarily limited to, centroid-based clustering, distribution-based clustering, density-based clustering, or the like.In block 525, the computer 235 determines metrics for the roadway surface based on the normalized sensor data. For example, the computer 235 may access a look-up table that relates sensor data to road surface metrics. The metrics for the roadway surface may be related to the sensor data based on empirical analysis. In some cases, a neural network, such as a deep neural network, may be used to associate sensor data with road surface metrics.In block 530, the computer 235 generates and / or updates a temporal-spatial map, such as the temporal-spatial map 400 shown in FIG. 4. In an example implementation, the computer 235 retrieves map data corresponding to the location data of the vehicle 105. Using the retrieved map data, the computer 235 assigns road surface metrics, such as an RRM-based roughness level metric, to one or more road segments such that one or more markings are included in the temporal-spatial map 400. The computer 235 may further match the road surface metrics to locations of wells and / or railroad tracks so that one or more markings are not included. For example, the determined positions of the road surface metrics can be matched to known positions of shafts and / or railroad tracks. By cross-referencing the locations of road surface metrics to known locations of wells and / or railroad tracks, the computer 235 may not incorporate markers indicative of the road surface metrics, as wells and / or railroad tracks do not indicate road degradation. In some cases, the computer 235 retrieves the hoistway and / or track data via a suitable application programming interface (API).In block 535, the computer 235 may classify the road surface metric as a keyhole and / or a keyhole severity. The computer 235 classifies the impact hole and / or the severity of the impact hole based on the roughness severity metric. In an example implementation, the computer 235 may access a look-up table that relates the metric for the roughness severity to the drumstick severity. The severity of the knock hole may be input to the look-up table based on empirical analysis of various types of knock holes corresponding to the sensor data. The computer 235 may classify the roadway obstacle as a tapped hole even if the location of the roadway obstacle does not match the location of a track and / or a shaft. The location of the impact hole may be determined by the computer 235 based on location data that matches the vehicle data indicating the severity of the impact hole. Various marks displayed in the map may indicate a severity of the estimated road roughness score. For example, markers with a first hue may be considered more severe than markers with a second hue. The server 145 may communicate the updated temporal-spatial map to one or more vehicles 105 after the computer 235 updates the temporal-spatial map data.In block 540, the computer 235 determines whether additional vehicle data has been received. If no additional data has been received, the process 500 returns to block 540. Otherwise, the process 500 returns to block 505.FIG. 6 is a flow diagram of an example process 600 for predicting road degradations based on a measurement of road roughness (RRM). The blocks of the process 600 may be executed by a computer 235. The process 600 begins at block 605, where sensor data is received. For example, the computer 235 may receive sensor data from one or more vehicles 105 via the network 135.In block 610, the computer 235 calculates a probability mass function (PMF) using the roadway measurement (RRM) obtained from multiple vehicles 105. The measurement of road roughness may be normalized prior to calculation depending on the type of vehicle. The probability mass function may be calculated for a particular road segment over a defined period of time, e.g., one (1) day, two (2) days, one (1) week, one (1) month, etc. The road segment may be defined as a predefined portion of the roadway, e.g., one hundred (100) feet, one hundred (100) meters, two hundred (200) feet, two hundred (200) meters, etc. In some examples, the road segment may be defined based on road segments determined by a state office, e.g., a traffic agency. In other examples, the road segments may be specified by the operator of the server 145.In block 615, the computer 235 accesses a Probability Density Function (PDF) of the International Roughness Index (IRI) values. The computer 235 may obtain the PDF of the IRI based on the RRM value. In block 620, the computer 235 may truncate the RRM PMF and the IRI PDF. For example, the computer 235 cuts the lower and upper ends, e.g., values below ten percent (10%) and values above ninety percent (90%) of the RRM PMF to remove data that could be potential outliers.In block 620, the computer 235 generates the RRM PMF and the IRI PDF with the clipped data. In block 625, the computer 235 generates a cumulative distribution function (CDF) for RRM using the RRM PMF and generates a cumulative distribution function (CDF) for IRI using the IRI PDF.In block 630, the computer 235 matches the RRM CDF with the IRI CDF. For example, the computer 235 may use a regression function, such as a linear regression function or a nonlinear regression function, to fit the RRM CDF to the IRI CDF. In block 635, the computer 235 estimates when the road surface metric will exceed a road surface roughness metric. For example, the computer 235 may estimate when the adjusted road surface metric defined by the RRM will exceed a predefined IRI road value, e.g., as an indicator of when the road surface should be replaced or repaired. In block 640, the estimated road surface metric is transmitted to one or more locations. For example, the estimated road surface metric can be transmitted to a specific state location which is responsible for the relevant road section. Process 600 is thus ended.The description of the present disclosure is merely exemplary, and variations that do not depart from the gist of the present disclosure are intended to fall within the scope of the present disclosure. Such variations are not to be regarded as a departure from the spirit and scope of the present disclosure.In general, the computer systems and / or devices described may use any number of computer operating systems, including, but by no means limited to, versions and / or varieties of the Microsoft Automotive® operating system, the Microsoft Windows® operating system, the Unix operating system (e.g., the Solaris® operating system sold by Oracle Corporation of Redwood Shores, California), the AIX UNIX operating system sold by International Business Machines of Armonk, New York, the Linux operating system, the Mac OSX and iOS operating systems sold by Apple Inc. of Cupertino, California, the BlackBerry OS sold by Blackberry, Ltd. of Waterloo, Canada, the Android operating system developed by Google, Inc. and the Open Handset Alliance, or the QNX® CAR platform for infotainment offered by QNX software systems. Examples of computing devices include an onboard vehicle computer, a computer workstation, a server, a desktop, notebook, laptop, or handheld computer, or other computer system and / or device.Computers and computing devices generally include computer-executable instructions, where the instructions may be executed by one or more computing devices such as those mentioned above. Computer-executable instructions may be compiled or interpreted from computer programs created using a variety of programming languages and / or technologies, including, without limitation, and either alone or in combination, Java™ C, C++, Matlab, Simulink, Stateflow, Visual Basic, Java Script, Perl, HTML, etc. Some of these applications may be compiled and executed on a virtual machine, such as the Java Virtual Machine, the Dalvik Virtual Machine, or the like. Generally, a processor (e.g., a microprocessor) receives instructions, e.g., from a memory, a computer readable medium, etc., and executes these instructions, thereby executing one or more processes, including one or more of the processes described herein. Such instructions and other data may be stored and transmitted using a variety of computer readable media. A file in a computing device is generally a collection of data stored on a computer readable medium such as a storage medium, random access memory, etc.The memory may comprise a computer readable medium (also referred to as a processor readable medium) including any non-transitory (e.g., tangible) medium that participates in providing data (e.g., instructions) that may be read by a computer (e.g., by a processor of a computer). Such a medium may take many forms, including, but not limited to, non-volatile media and volatile media. Non-volatile media includes, for example, optical or magnetic hard disks and other persistent storage. The volatile media includes, for example, dynamic random access memories (DRAMs) which typically comprise main memory. Such commands may be transmitted over one or more transmission media, including coaxial cables, copper wires, and fiber optic cables, including the wires forming a system bus connected to a processor of a controller. Common forms of computer-readable media include, for example, a floppy disk, a flexible disk, hard disk, magnetic tape, any other magnetic medium, a CD-ROM, a DVD, any other optical medium, punch cards, paper tape, any other physical medium with patterns of holes, a RAM, a PROM, an EPROM, a FLASH EEPROM, any other memory chip or cartridge, or any other medium from which a computer can read.Databases, data stores, or other data stores described herein may include various types of mechanisms for storing, accessing, and retrieving various types of data, including a hierarchical database, a series of files in a file system, an application database in a proprietary format, a relational database management system (RDBMS), etc. Each of these data stores is typically included in a computing device that uses a computer operating system such as one of those mentioned above, and access is via a network in one or more of a variety of ways. A file system may be accessed from a computer operating system and may include files stored in various formats. An RDBMS generally employs the Structured Query Language (SQL) in addition to a language for creating, storing, editing, and executing stored procedures, such as the PL / SQL language mentioned above.In some examples, system elements may be implemented as computer readable instructions (e.g., software) on one or more computing devices (e.g., servers, personal computers, etc.) stored on associated computer readable media (e.g., floppy disks, memories, etc.). A computer program product may include such instructions stored on computer readable media for performing the functions described herein.In this application, including the definitions below, the term "module" or the term "controller" may be replaced by the term "circuit". The term "module" may refer to, be part of, or include: an application specific integrated circuit (ASIC); a digital, analog, or mixed analog / digital discrete circuit; a digital, analog, or mixed analog / digital integrated circuit; a combinational logic circuit; a field programmable gate array (FPGA); a (shared, dedicated, or grouped) processor circuit that executes code; a (shared, dedicated, or grouped) memory circuit that stores code executed by the processor circuit; other suitable hardware components that provide the described functionality; or a combination of some or all of the above, for example, in a system-on-chip.The module may include one or more interface circuits. In some examples, the interface circuits may include wired or wireless interfaces connected to a local area network (LAN), the Internet, a wide area network (WAN), or combinations thereof. The functionality of any module of the present disclosure may be distributed among multiple modules connected via interface circuits. For example, multiple modules may allow load balancing. In another example, a server module (also known as a remote or cloud module) may perform some functions on behalf of a client module.With respect to the media, processes, systems, methods, heuristics, etc. described herein, it should be understood that, although the steps of such processes, etc. have been described as occurring in a particular ordered sequence, such processes may be performed with the described steps performed in a different order than that described herein. Further, certain steps may be performed simultaneously, other steps may be added, or certain steps described herein may be omitted. In other words, the descriptions of methods herein are provided to illustrate certain embodiments and should not be construed as limiting the claims.Accordingly, the above description is intended for purposes of illustration only and not limitation. Many embodiments and applications beyond the examples will be apparent to those skilled in the art upon reading the above description. The scope of the invention should be determined, not with reference to the above description, but should instead be determined with reference to the appended claims, along with the full scope of equivalents to which these claims are entitled. It is anticipated and intended that future developments will occur in the fields discussed herein, and that the disclosed systems and methods will be incorporated into such future embodiments. In summary, it is to be understood that the invention may be modified and varied and is limited only by the following claims.All terms used in the claims have their plain and ordinary meanings as understood by those skilled in the art unless expressly stated otherwise. In particular, the use of singular articles such as "a," "the," "said," etc. should be understood to refer to one or more of the stated elements unless a claim includes an explicit limitation to the contrary.

Claims

A system (100) comprising a computer (110) including a processor and a memory, the memory including instructions, such that the processor is programmed to: receive sensor data including at least one of wheel speed measurements, suspension displacement measurements, or tire leak detection data from a vehicle; estimate a measurement of road roughness based on a deviation of the wheel speed from the average wheel speed; and generate temporary spatial map data indicative of a location and roughness degree metric of a road section based on the measurement of the road roughness, wherein the measurement of the road roughness is a function of the sum of the absolute deviations of the speeds of the non-driven wheels from the average wheel speed.The system (100) of claim 1, wherein the measurement of road roughness is calculated based on at least one of a suspension displacement sensor and a wheel speed sensor.The system (100) of claim 2, wherein the processor is further programmed to improve an estimation of the roughness degree metric by applying a regression function to adapt a cumulative distribution function for the wheel speed sensor-based measurement of road roughness to a cumulative distribution function for the corresponding suspension displacement sensor-based measurement of road roughness that can then be used to improve the estimates of roughness degree metrics of other similar vehicles having only wheel speed sensor-based measurements of road roughness.The system (100) of claim 1, wherein data about uneven roads is represented as a map plane, wherein color and color intensity values represented within the map plane indicate a severity of the roughness severity metric.The system (100) of claim 1, wherein the processor is further programmed to classify a pothole or a ground wave based on the measurement of road roughness.The system (100) of claim 5, wherein the processor is further programmed to detect a road safety hazard if a bump or a ground wave has been rated higher than a predetermined calibration threshold and a tire leak rate is higher than a predetermined calibration threshold.The system (100) of claim 1, wherein the processor is further programmed to be able to estimate when a roadway surface will exceed a predefined value of the International Roughness Index (IRI).The system (100) of claim 7, wherein the processor is further programmed to estimate an International Roughness Index (IRI) value by applying a regression function to fit a cumulative distribution function for the road roughness metric to a cumulative distribution function for the corresponding International Roughness Index (IRI) value.The system (100) of claim 1, wherein the processor is further configured to predict a future condition of a roadway and plan road repairs for the roadway using a time course of appropriate measurements of roadway roughness and at least one other factor.

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