Method and apparatus for predictive control of a vehicle suspension using a combination of non-contact and contact sensors

Abstract

In one embodiment, a method of operating a suspension system in a vehicle is disclosed. The method includes obtaining a first signal from a first sensor, obtaining a second signal from a second sensor, filtering the first signal and the second signal to obtain a first filtered signal and a second filtered signal, blending the first filtered signal and the second filtered signal to obtain a blended signal, generating a command signal for at least one component of the vehicle suspension system based on the blended signal, and operating the at least one component of the vehicle suspension system.

Description

METHOD AND APPARATUS FOR PREDICTIVE CONTROL OF A VEHICLE SUSPENSION USING A COMBINATION OF NON-CONTACT AND CONTACT SENSORSCROSS-REFERENCE TO RELATED APPLICATIONS

[0001] This application claims benefit under 35 U.S.C. § 119(e) of U.S. provisional application serial number 63 / 561,873, filed March 6, 2024, the disclosure of which is incorporated herein by reference in its entirety.BACKGROUND

[0001] Road vehicles traveling along a road that exhibits road features in the direction normal to the plane of the road, typically closely aligned with the direction of gravity, may experience road input that disturbs the vehicle, leading to a reduction in occupant comfort, occupant safety, and other side effects. Suspension systems that are able to change their state or create a desired relative force between two components, such as for example the vehicle chassis and a wheel, benefit from advance knowledge of the upcoming road contour and road type, to perform optimally. It is therefore useful to obtain advance knowledge on the road features through one of several means known in the literature, including cloud-based mapping, contact sensing, or non-contact sensing.SUMMARY

[0002] According to one aspect, a method of operating a suspension system in a vehicle is disclosed. The method includes obtaining a first signal from a first sensor, obtaining a second signal from a second sensor, filtering the first signal and the second signal to obtain a first filtered signal and a second filtered signal, blending the first filtered signal and the second filtered signal to obtain a blended signal, generating a command signal for at least one component of the vehicle suspension system based on the blended signal, and operating the at least one component of the vehicle suspension system.

[0003] In some embodiments, the method includes a suspension component that includes an active suspension actuator.

[0004] In some embodiments, the method includes a suspension component that includes an air spring.

[0005] In some embodiments, the method includes a first sensor that has a higher accuracy at distances closer to the vehicle and a second sensor that has a higher accuracy at distances far away from the vehicle.

[0006] In some embodiments, the first sensor is a contact sensor. In some implementations, the contact sensor comprises a front wheel of the vehicle.

[0007] In some embodiments, wherein the second sensor is a non-contact sensor.

[0008] In some embodiments, the second sensor includes a camera.

[0009] In some embodiments, correcting the second signal based on an expected motion of a platform holding the non-contact sensor.

[0010] In some embodiments, the second sensor is a virtual sensor and the signal from the virtual sensor comprises a road profile.

[0011] According to another aspect, a vehicle is disclosed. The vehicle includes an active suspension system comprising at least one actuator, a first sensor having a first sensor signal, a second sensor having a second sensor signal, a controller configured to: filter the first sensor signal to obtain a first filtered sensor signal, filter the second sensor signal to obtain a second filtered sensor signal, blend the first filtered sensor signal and the second filtered sensor signal to obtain a blended signal, and send a command signal to the at least one actuator of the active suspension system based on the blended signal.

[0012] In some embodiments, the vehicle includes a third sensor having a third sensor signal.

[0013] In some embodiments, the active suspension system includes an air spring.

[0014] In some embodiments, wherein the first sensor has a higher accuracy at distances closer to the vehicle and the second sensor has a higher accuracy at distances far away from the vehicle.

[0015] In some embodiments, the first sensor is a contact sensor. In some implementations, the contact sensor comprises a front wheel of the vehicle.

[0016] In some embodiments, the second sensor is a non-contact sensor. In some implementations, the second sensor includes a camera.

[0017] In some embodiments, the controller is further configured to correct the second signal based on an expected motion of a platform holding the non-contact sensor.

[0018] In some embodiments, the second sensor is a virtual sensor and the signal from the virtual sensor comprises a road profile.BRIEF DESCRIPTION OF DRAWINGS

[0019] The accompanying drawings are not intended to be drawn to scale. In the drawings, each identical or nearly identical component that is illustrated in various figures may be represented by a like numeral. For purposes of clarity, not every component may be labeled in every drawing. In the drawings:

[0020] Fig 1 illustrates an ego vehicle travelling along a road surface with various anomalies and discontinuities,

[0021] Fig. 2 illustrates the use of information about a single point ahead of the vehicle,

[0022] Fig. 3 illustrates the use of information about two points ahead of the vehicle,

[0023] Fig. 4 illustrates the use of information about a road profile ahead of the vehicle,

[0024] Fig. 5 illustrates a block diagram for processing multiple points, and

[0025] Fig. 6 illustrates the use of information about a combined profile ahead of the car.DETAILED DESCRIPTION

[0026] The present disclosure relates to methods for optimally utilizing advance knowledge of road input when using a combination of sensors and cloud information.

[0027] A vehicle (henceforth the “ego vehicle”) having at least one suspension or chassis system that has at least one modifiable state travelling along a roadway may take advantage of the information about the road ahead of the vehicle received from a sensor, by changing the setting of the at least one suspension or chassis system in a way that benefits the occupants or the vehicle.

[0028] As used herein, the term “sensors” refers to three different categories, and any of the categories may be used in the systems described herein. Virtual sensors include all methods for obtaining information about the road ahead of the vehicle based on map data previously collected by the same or other vehicle(s), or by a vehicle driving near the ego vehicle. An example of a virtual sensor may be a method comprising a high-definition map, a localization system such as a GPS, terrain-based localization, or other localization system, that associates the location of the vehicle with position on the map, and an algorithm that projects the position and upcoming pathof the vehicle along the map. A virtual sensor by itself may not use any forward-looking sensor information to estimate the upcoming road, but it may be used in conjunction with other sensors listed here. Virtual sensing may include sensors placed on vehicles other than the ego vehicle, or on the ego vehicle during a previous traversal of a road segment, that allow the formation of a map of the underlying road and creating of a virtual sensor that may be used during a subsequent traversal of the road segment. For example, a vehicle ahead of the ego vehicle may sense road information and relay it to the ego vehicle, and the ego vehicle only needs to be able to locate itself relative to this information (e.g. by sensing the distance to the vehicle ahead of it, or through map-matching of properties along the road, or through GPS sensing) to obtain a preview information about the upcoming road surface.

[0029] The second category of sensors may be defined as contact sensors, where the sensor is in contact with the road surface through an intervening physical element such as a tire, and that use motion sensing or force sensing on the contact element or a connected element to estimate the underlying road surface properties or characteristics.

[0030] The third category of sensors includes non-contact sensors, including vision-based systems e.g., light-sensitive diodes or cameras; infrared sensors; LiDAR sensors; radar sensors; or other sensors that are able to measure properties of the road surface without directly contacting the surface.

[0031] Suspension or chassis systems may include, but are not limited to, systems that can adjust relative position or force between elements of the chassis such as wheels, tires, suspension links, vehicle chassis, vehicle mechanical components, seats, steering wheel, or other elements. Systems may include active suspensions, active roll systems, semi-active suspension elements, seat actuators, steering systems for front and / or rear wheels, engine or powertrain mounting systems, and drive systems. Included in this category may also be systems that adjust sound (e.g., speaker volume), lighting (e.g., ambient lighting), temperature (e.g., cabin temperature), or air flow, in a part of the interior of the vehicle or in a vehicle component. For example, adjustable headlights may benefit from knowing road surface contour ahead of the car, and sound systems may adjust volume of sound being played in the cabin based on upcoming road type or roughness.

[0032] Benefits to the occupant of a vehicle may include, but are not limited to, benefits to occupant safety or comfort, such as increasing the stability of the vehicle over a certain patch ofroad, or reducing the vibrations transmitted to an occupant; maintaining excursion of components within their range of motion to avoid end-of travel behavior, for example on a suspension actuator or a steering rack or actuator; reducing the peak force required by an actuator, for example by a suspension actuator; reducing power consumption of a component or the overall vehicle; and other benefits.

[0033] Information about the upcoming road content may include a vertical road profile of at least a stretch of road in front of a wheel and along the direction of travel of the wheel; it may but does not necessarily include a roughness, characterization, or surface type of the road; it may but does not necessarily include road camber, road slope, or road convexity, or concavity, it may but does not necessarily include road surface friction of grip; it may but does not necessarily also include other derived properties of a road surface that may be relevant to one or more systems in the vehicle. It should be understood that other road properties that may benefit a system in the vehicle are contemplated and the invention is not limited in this regard.

[0034] Information about an upcoming section of a road may also include a road to be traversed in the future by the ego vehicle; however, it should be noted that the road the ego vehicle will traverse in a near future, such as for example within the next minute or the next several seconds is typically more relevant. The latter may affect the dynamics of the ego vehicle more immediately than a road to be traversed after a longer period. However, it should also be understood that even information about a road to be traversed after a longer period of time may be useful.

[0035] A processor may be used to process the information and decide on the course of action. Such a processor may be on board the ego vehicle or may be in a remote location, such as another vehicle, or the cloud. The processor may use data from one or more sensors, and it may also use other relevant information such as the current speed of the vehicle, the desired state of the vehicle, and other setpoints such as a user-selectable mode or input from the navigation system, or any other appropriate input. Using the information thus gathered, and an understanding of a desired benefit, such as for example a cost function relating road content to vehicle occupant comfort, the processor may make a decision whether and in what way to modify the state of a suspension or chassis system. For example, a non-contact sensor may be used to collect information about the upcoming vertical deviation or anomaly of the road surface, and a processor may be used to determine the optimal actuator force of an active suspensionactuator within the constraints of maximum force and maximum suspension travel, that is expected to create the smallest vertical body acceleration at the occupant’s location.

[0036] Fig. 1 illustrates an ego vehicle 101 driving along a roadway 102. The ego vehicle 101 in this example has a non-contact sensor 103 mounted on it, which provides information about a road surface ahead of the vehicle at a point 104 and / or a point 105.

[0037] Fig. 2 shows a process used for creating a force command 205 based at least partially on data coming from a non-contact sensor, e.g., the non-contact sensor 103 of Fig.1. In this example, a data stream 201, representing a road profile ahead of an ego vehicle, e.g., ego vehicle 101 of Fig 1, for example collected at a fixed distance ahead of the ego vehicle at the point 104, is fed to a processor at step 202. Using other relevant information from input 203, such as vehicle speed or steering angle, a block 204 can create an optimal output, such as a force command 205 for an actuator system.

[0038] An example of this process 200 may be implemented using the front wheel on each side of the ego vehicle 101 as a contact sensor to estimate an input or disturbance that a rear wheel of the ego vehicle 101 is about to encounter. In this way, the rear wheel behavior or response to the input or disturbance may be improved if a suspension or chassis actuator is present. Another example of the process 200 may include using data from input 203 a forwardfacing non- contact sensor, such as one or more cameras, to estimate the road ahead of the ego vehicle 101. In this scenario, a filtering step may be performed within step 202 to estimate the road surface characteristics about to be encountered by the ego vehicle 101. For example, a road profile may be estimated by using sensor information and processing that includes an understanding of the tire behavior. For example, a model may estimate stiffness and / or damping of the tire and may be used to extrapolate a road profile or road conditions that the tire experienced to cause the observed tire behavior. Information such as a contact patch size and flexibility of a tire may be used to estimate how a given road will affect a tire.

[0039] Another filtering step may be performed within step 202 to remove content from the sensor data from input 203 that is not accurate or not useful. For example, a filtering step may be performed to remove a low frequency portion of the sensor data and / or a high frequency portion of the sensor data. In some embodiments, sensors may not be accurate at some frequencies, e.g., at low frequencies due to drift. Alternatively or additionally, high frequency road inputs or disturbances may affect the tire but not substantially affect the vehicle body, and a suspension orchassis system may not be able to respond to signals above a certain frequency. A disturbance may be from a non-road source, e.g., a wheel imbalance (non-roundness of a tire or a wheel), wind, etc. Due to the fact that the information about the road that precedes the ego vehicle may impact the ego vehicle at a future time, an acausal filtering step may be performed here to optimally maintain phase and delay of the command signal.

[0040] For example, a stereo camera may be used to estimate vertical road profile elevation anomalies or discontinuities ahead of each wheel of the vehicle, at for example a fixed distance. A filtering step may include removing or mitigating frequencies with periods that are longer than the amount of predictive information available. For example, if the vehicle is travelling at 10 m / s and the sensor information provides data about the road 10 m ahead of the vehicle, then signal below approximately 1 Hz will not be useful as predictive information because the cycle time of this signal is similar to the preview time of the road information at that speed. A second filtering step may be performed, separately or together with the first step, to remove or mitigate signal above 10 Hz. In a typical vehicle, sensor content near the resonant frequency of the tire and wheel assembly, which typically may fall approximately in the range between 10-15 Hz, may be inaccurate. Finally, a force command for an active suspension may be calculated to reduce vehicle occupant motion by using the filtered road signal as an input into a model of the transmissibility of road content to occupant motion and force content to occupant motion. Using an optimization technique and known constraints about force and suspension travel, an optimal solution may then be determined and a force command applied to the actuators in the ego vehicle.

[0041] Fig. 3 illustrates a method where two different sensing locations from one or more sensors are combined. The road information 301 from a first sensor, and the road information 302 from a second sensor, may be processed in parallel through filtering steps 303 and 304, respectively, and combined in a processing step 305 that also takes in and accounts for additional sensor input 306. An appropriate force command may then be generated at block 307.

[0042] In some embodiments, two distinct outputs from a single non-contact sensor, such as shown in Fig. 1 at points 104 and 105 can be used. A typical non-contact sensor will have accurate information near an ego vehicle, but less accurate information at a further distance from the ego vehicle. For example, a LiDAR sensor may have a range of up to 500 m, but an accuracy of ±1 m at that distance, The same LiDAR sensor may also be able to sense road content at 5 mahead of the vehicle with an accuracy that may be closer to plus or minus a few millimeters. At the same time, it is advantageous to obtain information about the upcoming road with more advance notice, such that systems in the vehicle can be prepared to react in time. For example, an air suspension system may require approximately 10 seconds to raise a vehicle, but at a vehicle speed oflO m / s, a sensor reading a road height at a distance of 5 m in front of the vehicle only provides 0.5s of advance notice to the air suspension system. To optimize overall system performance, multiple outputs of the same sensor may be used, where the long-distance signal is processed in a manner that may affect low frequency, low accuracy commands, while the short distance output may be used to affect the higher frequency, higher accuracy commands. For example, if profile 301 is obtained from a point a long distance ahead of the vehicle (e.g., approximately more than 15 m and in some instances more than 100 or 500 m), and profile 302 is obtained from a point near the vehicle (e.g., approximately within 10-15 m ahead of the vehicle), then the filtering steps 303 and 304 may include complementary filters that pass the low frequency content of signal 301 and the high frequency content of signal 302, such that the two can be added in step 305 to form a single profile that is accurate at high frequency but has content even at low frequency.

[0043] Another example of a process for combining sensor information for improving accuracy may include using two different sensors, where one sensor has a longer preview and the other sensor may have a shorter preview but may more accurate. A preferred embodiment of this process may include using a virtual sensor to predict the road ahead of the ego vehicle up to a very long distance and correcting the signal with either a non-contact sensor looking ahead of the ego vehicle or a contact sensor on the front wheel of the vehicle, or both.

[0044] Fig. 4 shows an ego vehicle 401 driving on a roadway 402. The ego vehicle has a non- contact sensor 403 that may provide road information for the upcoming road. The road information may be in a scanning format, meaning that the road information, at repeated intervals, provides a string of values at points ahead of the vehicle as shown as a set of example values 404. More values will continue to accumulate as the ego vehicle 401 moves forward (from left to right in the figure). Eventually past values 405 for the road content may be available after the ego vehicle 401 has passed a location on the roadway 402 corresponding to the past values 405.

[0045] Fig. 5 illustrates a process that may be followed in this instance. Values for multiplepoints ahead of the ego vehicle are received by the ego vehicle at step 501 from e.g., a sensor. The values may be validated and filtered at this step and collected. Once enough values have been collected to create a profile of a desired length, the new values may be averaged at step 502 into the existing profile and blended to achieve the most accurate output. This blending step 502 may include adding new information ahead of the existing information, for example the values at the leading edge of the sensor information. The blending step 502 may include averaging the values in areas where they overlap with previous values. The averaging may include performing weighted averaging based on information (e.g., sensor properties) related to the accuracy of each signal at varying distances from the vehicle. For example, a higher weight may be given to values obtained from a sensor that is more accurate the closer it is reading to the vehicle. Examples of sensors that are typically more accurate the closer they are sensing to the vehicle include non-contact sensors such as LiDAR, cameras (stereo and individual), radar, etc. If the sensor information creates a grid with a resolution that is finer than what is required by the subsequent processing steps, the averaging may also be discretized at fixed intervals.

[0046] In an example, a sensor output at step 501 may have a resolution of 1 m in the direction of travel and the ego vehicle may be travelling at 5 m / s. If the sensor information is uploaded at 10 Hz, or 10 times per second, then the new sensor information may be for points along the roadway where each successive point is 0.5 m from the previous point (5 m / s divided by 10 s). These new points may thus fall in between existing points and provide a more detailed map of the road as the ego vehicle moves along. However, in some instances, only 2 m resolution may be required for the processing that follows. For example, if the suspension system is an air spring, the air spring’s ability to move rapidly is limited and 2 m resolution may be sufficient for operation of the air spring system. In such a case, the existing and previous data may be blended into a profile with 2 m spacing that may be interpolated each time new information is available.

[0047] After repeating steps 501 and 502 one or more times, both to create a profile that is long enough for the desired processing, and to achieve a resolution that is fine enough for the desired processing, a full combined profile may be generated at step 503. As used herein the term “profile” refers to a collection of road information at various points that may be ahead and / or behind the vehicle along its path of travel. The information may be road characteristics, friction, height, slope, camber, specific identified features such as potholes, speedbumps, rail crossings, frost heaves, or any other appropriate properties or characteristics of the road surface to betraversed. The information may be collected from the portion of the road surface ahead of the vehicle that is within the range of the sensor, or closer if the subsequent processing step does not require it. It may also include content under and behind the vehicle as required. For example, when one sensor is a virtual sensor, the length of road surface ahead of the vehicle, for which road surface information may be available, may be limited only by the amount of information that may have been collected in the course of previous trips by the same and / or other vehicles. However, in such cases only a subset of this information may be used. On the other hand, if one sensor is a camera, then all of the information available may be used.

[0048] At step 504, the signal may be filtered and a motion plan created. The filtering steps of step 504 may include causal and acausal filtering. In the case of acausal filtering, it may be beneficial to have information about the road the vehicle traversed as part of the profile in order to reduce filter transients. Creating a motion plan may include calculating a strategy to optimize a cost function by using a suspension or chassis system’s settings. For example, this function may be a force command for an active suspension system that minimizes occupant discomfort by reducing the expected vertical acceleration of the occupant, or it may be a pressure command for an air spring to raise a vehicle ahead of a large obstacle, to avoid reaching travel limits on the suspension of the ego vehicle and thus increasing comfort and safety. The force command, pressure command, or other command may be output at step 505.

[0049] Fig. 6 illustrates an exemplary embodiment of the method of Fig. 5, using a combination of sensors. In this example, multiple sensors are shown but typically only some of the shown sensors are used together as they provide somewhat redundant information. Turning to Fig. 6, vehicle 601 is travelling along roadway 602. The vehicle has a forward-facing noncontact sensor 603 and a rear-facing non-contact sensor 604. Information from a virtual sensor may provide a profile 605 that starts far ahead of the vehicle 601 and may overlap the remaining sensor signals all the way to, under, and behind the vehicle 601. Profile 606 from the forwardfacing sensor 603 may cover a stretch of roadway ahead of the vehicle 601; profile 607 from a contact sensing method at a front wheel of the vehicle 601 may cover some range of roadway under the vehicle 601; profile 608 from a contact sensor at a rear wheel of the vehicle 601 may cover some distance behind the vehicle, and profile 609 from the rear-facing non-contact sensor 604 may cover a portion of the road- way at a further distance behind the vehicle 601. Each of these sensors may have certain properties, e.g., range, accuracy, precision, and reliability, thatmay make them better suited for a particular aspect of the process for creating a command for the suspension system (e.g., a force command, a pressure command, etc.). The virtual sensor may be used for long-range prediction, but, for example, due to its nature, may not account for recent changes in the roadway, e.g., that may be a result of road repair, degradation, and / or weather conditions. The front-facing non-contact sensor 603 may provide some amount of advance notice, though not enough for some types of actuation systems such as an air spring, but may also be used to validate a virtual sensor’s information. A front wheel contact sensor may be highly accurate but only has a short advance information for the rear wheels. A rear wheel contact sensor and a rear-facing non-contact sensor may serve to validate the information from the other sensors and / or to calibrate the other sensors, for example using a Kalman filter or a correction signal. In some embodiments, a signal from the rear wheel contact sensor may also be the most accurate signal to use to create a map of the roadway.

[0050] Contact sensors may be better suited at vehicle speeds where significant motion is imparted into the vehicle or the chassis component the sensor relies on, while non-contact sensors may sometimes be less accurate when significant vehicle motion is present. Blending contact and non-contact sensing methods may be useful to compensate for the shortcomings of each. A predictive or expected motion of the platform holding the non-contact sensor, possibly in addition to a sensed motion of this platform, may be used to correct the signal measured by the non-contact sensor. This correction may alleviate expected sensor discrepancies, for example due to low frequency drift of the motion sensors that may be used to correct the sensor’s signal, and may be sufficient by itself to remove the need for a motion sensor.

[0051] At the same time, contact sensors are generally not good at understanding road profile information when the vehicle speed is very low, for example below 5 mph, or below 10 mph, due to the limited motion imparted by the road to the vehicle and its components. In this range, it may be beneficial to use a non-contact sensor as an additional complementary signal, or by itself, to extract the desired road profile information.

[0052] Thus a combination of sensors may be desirable, but any combination of the types of sensors described herein may be used, as well as any one single sensor by itself as this disclosure is not so limited.

[0053] The above-described embodiments of the technology described herein can be implemented in any of numerous ways. For example, the embodiments may be implementedusing hardware, software or a combination thereof. When implemented in software, the software code can be executed on any suitable processor or collection of processors, whether provided in a single computer or distributed among multiple computers. Such processors may be implemented as integrated circuits, with one or more processors in an integrated circuit component, including commercially available integrated circuit components known in the art by names such as CPU chips, GPU chips, microprocessor, microcontroller, or co-processor. Alternatively, a processor may be implemented in custom circuitry, such as an ASIC, or semicustom circuitry resulting from configuring a programmable logic device. As yet a further alternative, a processor may be a portion of a larger circuit or semiconductor device, whether commercially available, semicustom or custom. As a specific example, some commercially available microprocessors have multiple cores such that one or a subset of those cores may constitute a processor. Though, a processor may be implemented using circuitry in any suitable format.

[0054] Further, it should be appreciated that a computer may be embodied in any of a number of forms, such as a rack-mounted computer, a desktop computer, a laptop computer, or a tablet computer. Additionally, a computer may be embedded in a device not generally regarded as a computer but with suitable processing capabilities, including a Personal Digital Assistant (PDA), a smart phone or any other suitable portable or fixed electronic device.

[0055] Also, a computer may have one or more input and output devices. These devices can be used, among other things, to present a user interface. Examples of output devices that can be used to provide a user interface include printers or display screens for visual presentation of output and speakers or other sound generating devices for audible presentation of output.

[0056] Examples of input devices that can be used for a user interface include keyboards, and pointing devices, such as mice, touch pads, and digitizing tablets. As another example, a computer may receive input information through speech recognition or in other audible format.

[0057] Such computers may be interconnected by one or more networks in any suitable form, including as a local area network or a wide area network, such as an enterprise network or the Internet. Such networks may be based on any suitable technology and may operate according to any suitable protocol and may include wireless networks, wired networks or fiber optic networks.

[0058] Also, the various methods or processes outlined herein may be coded as software that is executable on one or more processors that employ any one of a variety of operatingsystems or platforms. Additionally, such software may be written using any of a number of suitable programming languages and / or programming or scripting tools, and also may be compiled as executable machine language code or intermediate code that is executed on a framework or virtual machine.

[0059] In this respect, the embodiments described herein may be embodied as a computer readable storage medium (or multiple computer readable media) (e.g., a computer memory, one or more floppy discs, compact discs (CD), optical discs, digital video disks (DVD), magnetic tapes, flash memories, circuit configurations in Field Programmable Gate Arrays or other semiconductor devices, or other tangible computer storage medium) encoded with one or more programs that, when executed on one or more computers or other processors, perform methods that implement the various embodiments discussed above. As is apparent from the foregoing examples, a computer readable storage medium may retain information for a sufficient time to provide computer-executable instructions in a non-transitory form. Such a computer readable storage medium or media can be transportable, such that the program or programs stored thereon can be loaded onto one or more different computers or other processors to implement various aspects of the present disclosure as discussed above. As used herein, the term "computer- readable storage medium" encompasses only a non-transitory computer-readable medium that can be considered to be a manufacture (i.e., article of manufacture) or a machine. Alternatively, or additionally, the disclosure may be embodied as a computer readable medium other than a computer-readable storage medium, such as a propagating signal.

[0060] The terms “program” or “software” are used herein in a generic sense to refer to any type of computer code or set of computer-executable instructions that can be employed to program a computer or other processor to implement various aspects of the present disclosure as discussed above. Additionally, it should be appreciated that according to one aspect of this embodiment, one or more computer programs that when executed perform methods of the present disclosure need not reside on a single computer or processor but may be distributed in a modular fashion amongst a number of different computers or processors to implement various aspects of the present disclosure.

[0061] Computer-executable instructions may be in many forms, such as program modules, executed by one or more computers or other devices. Generally, program modules include routines, programs, objects, components, data structures, etc. that perform particular tasks orimplement particular abstract data types. Typically, the functionality of the program modules may be combined or distributed as desired in various embodiments.

[0062] Also, data structures may be stored in computer-readable media in any suitable form. For simplicity of illustration, data structures may be shown to have fields that are related through location in the data structure. Such relationships may likewise be achieved by assigning storage for the fields with locations in a computer-readable medium that conveys relationship between the fields. However, any suitable mechanism may be used to establish a relationship between information in fields of a data structure, including through the use of pointers, tags or other mechanisms that establish relationship between data elements.

[0063] Various aspects of the present disclosure may be used alone, in combination, or in a variety of arrangements not specifically discussed in the embodiments described in the foregoing and is therefore not limited in its application to the details and arrangement of components set forth in the foregoing description or illustrated in the drawings. For example, aspects described in one embodiment may be combined in any manner with aspects described in other embodiments.

[0064] Also, the embodiments described herein may be embodied as a method, of which an example has been provided. The acts performed as part of the method may be ordered in any suitable way. Accordingly, embodiments may be constructed in which acts are performed in an order different than illustrated, which may include performing some acts simultaneously, even though shown as sequential acts in illustrative embodiments.

[0065] Further, some actions are described as taken by a “user.” It should be appreciated that a “user” need not be a single individual, and that in some embodiments, actions attributable to a “user” may be performed by a team of individuals and / or an individual in combination with computer-assisted tools or other mechanisms.

Claims

CLAIMS1. A method of operating a suspension system in a vehicle, the method comprising: obtaining a first signal from a first sensor; obtaining a second signal from a second sensor; filtering the first signal and the second signal to obtain a first filtered signal and a second filtered signal; blending the first filtered signal and the second filtered signal to obtain a blended signal; generating a command signal for at least one component of the vehicle suspension system based on the blended signal; and operating the at least one component of the vehicle suspension system.

2. The method of claim 1, wherein the suspension component includes an active suspension actuator.

3. The method of any of claims 1-2, wherein the suspension component includes an air spring.

4. The method of any of claims 1-3, wherein the first sensor has a higher accuracy at distances closer to the vehicle and the second sensor has a higher accuracy at distances far away from the vehicle.

5. The method of claim 4, wherein the first sensor is a contact sensor.

6. The method of claim 4, wherein the contact sensor comprises a front wheel of the vehicle.

7. The method of any of claims 1-6, wherein the second sensor is a non-contact sensor.

8. The method of any of claims 1-7, wherein the second sensor includes a camera.

9. The method of any of claims 1-8, further comprising correcting the second signal based on an expected motion of a platform holding the non-contact sensor.

10. The method of any of any of claims 1-7, wherein the second sensor is a virtual sensor and the signal from the virtual sensor comprises a road profile.

11. A vehicle comprising: an active suspension system comprising at least one actuator; a first sensor having a first sensor signal; a second sensor having a second sensor signal; a controller configured to: filter the first sensor signal to obtain a first filtered sensor signal, filter the second sensor signal to obtain a second filtered sensor signal, blend the first filtered sensor signal and the second filtered sensor signal to obtain a blended signal, and send a command signal to the at least one actuator of the active suspension system based on the blended signal.

12. The vehicle of claim 11, further comprising a third sensor having a third sensor signal.

13. The vehicle of any of claims 11-12, wherein the active suspension system includes an air spring.

14. The vehicle of any of claims 11-13, wherein the first sensor has a higher accuracy at distances closer to the vehicle and the second sensor has a higher accuracy at distances far away from the vehicle.

15. The vehicle of claim 14, wherein the first sensor is a contact sensor.

16. The vehicle of claim 14, wherein the contact sensor comprises a front wheel of the vehicle.

17. The vehicle of any of claims 11-16, wherein the second sensor is a non-contact sensor.

18. The vehicle of any of claims 11-17, wherein the second sensor includes a camera.

19. The vehicle of any of claims 11-18, wherein the controller is further configured to correct the second signal based on an expected motion of a platform holding the noncontact sensor.

20. The vehicle of any of claims 11-17, wherein the second sensor is a virtual sensor and the signal from the virtual sensor comprises a road profile.

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