Method and apparatus for predicting 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

[0001] Cross-references to related applications

[0002] This application claims the benefit of U.S. Provisional Application No. 63 / 561,873, filed March 6, 2024, pursuant to 35 USC § 119(e), the disclosure of which is incorporated herein by reference in its entirety. Background Technology

[0003] Vehicles traveling along roads that exhibit road characteristics in a direction perpendicular to the road plane (typically closely aligned with the direction of gravity) may experience road inputs that disrupt the vehicle, leading to reduced occupant comfort, occupant safety, and other side effects. Suspension systems capable of altering their state or generating desired relative forces between two components (e.g., vehicle chassis and wheels) benefit from prior knowledge of the upcoming road profile and road type for optimal performance. Therefore, obtaining prior knowledge of road characteristics through one of the several methods known in the literature (including cloud-based mapping, contact sensing, or non-contact sensing) is useful. Summary of the Invention

[0004] According to one aspect, a method for 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, mixing the first filtered signal and the second filtered signal to obtain a mixed signal, generating a command signal for at least one component of the vehicle suspension system based on the mixed signal, and operating at least one component of the vehicle suspension system.

[0005] In some embodiments, the method includes a suspension component that includes an active suspension actuator.

[0006] In some embodiments, the method includes a suspension component, which includes an air spring.

[0007] In some implementations, the method includes a first sensor with higher accuracy at a distance closer to the vehicle and a second sensor with higher accuracy at a distance farther from the vehicle.

[0008] In some implementations, the first sensor is a contact sensor. In some implementations, the contact sensor includes the front wheels of the vehicle.

[0009] In some implementations, the second sensor is a non-contact sensor.

[0010] In some implementations, the second sensor includes a camera device.

[0011] In some implementations, the second signal is corrected based on the expected movement of the platform holding the non-contact sensor.

[0012] In some implementations, the second sensor is a virtual sensor, and the signal from the virtual sensor includes the road contour.

[0013] According to another aspect, a vehicle is disclosed. The vehicle includes: an active suspension system including at least one actuator; a first sensor having a first sensor signal; a second sensor having a second sensor signal; and 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, mix the first filtered sensor signal and the second filtered sensor signal to obtain a mixed signal, and send a command signal to at least one actuator of the active suspension system based on the mixed signal.

[0014] In some implementations, the vehicle includes a third sensor with a third sensor signal.

[0015] In some implementations, the active suspension system includes air springs.

[0016] In some implementations, the first sensor has higher accuracy at a distance closer to the vehicle, and the second sensor has higher accuracy at a distance farther from the vehicle.

[0017] In some implementations, the first sensor is a contact sensor. In some implementations, the contact sensor includes the front wheels of the vehicle.

[0018] In some implementations, the second sensor is a non-contact sensor. In some implementations, the second sensor includes a camera device.

[0019] In some implementations, the controller is also configured to correct the second signal based on the expected movement of the platform holding the non-contact sensor.

[0020] In some implementations, the second sensor is a virtual sensor, and the signal from the virtual sensor includes the road contour. Attached Figure Description

[0021] The accompanying drawings are not intended to be drawn to scale. In the drawings, each identical or nearly identical component illustrated in various figures may be indicated by similar reference numerals. For clarity, not every component may be labeled in every figure. In the drawings:

[0022] Figure 1 The image shows an autonomous vehicle traveling along a road surface with various anomalies and discontinuities.

[0023] Figure 2 This demonstrates the use of information about a single point in front of the vehicle.

[0024] Figure 3 This demonstrates the use of information about two points in front of the vehicle.

[0025] Figure 4 This demonstrates the use of information about the road outline in front of the vehicle.

[0026] Figure 5 A block diagram for processing multiple points is shown, and

[0027] Figure 6 The use of information about the combined outline of the front of the car is shown. Detailed Implementation

[0028] This disclosure relates to methods for optimally utilizing prior knowledge of road inputs when using a combination of sensor and cloud information.

[0029] A vehicle (hereinafter referred to as "self-vehicle") having at least one suspension or chassis system including at least one modifiable state and traveling along a road can utilize information received from sensors about the road ahead of the vehicle by changing the settings of at least one suspension or chassis system in a manner beneficial to the occupants or the vehicle.

[0030] As used herein, the term "sensor" refers to three distinct categories, any of which can be used in the systems described herein. Virtual sensors encompass all methods for obtaining information about the road ahead of a vehicle based on map data previously collected by the same or other vehicles or by vehicles traveling near the vehicle. Examples of virtual sensors may include methods that include high-resolution maps, positioning systems (e.g., GPS, terrain-based positioning, or other positioning systems) that correlate the vehicle's location with its position on the map, and algorithms that project the vehicle's position and the upcoming path along the map. Virtual sensors themselves may not use any forward-looking sensor information to estimate the upcoming road, but they can be used in conjunction with other sensors listed herein. Virtual sensing may include sensors placed on vehicles other than the vehicle itself or placed on the vehicle during previous crossings of the road segment, allowing for the formation of a map of the underlying road and the creation of virtual sensors that can be used during subsequent crossings of the road segment. For example, a vehicle in front of the ego vehicle can sense road information and relay that information to the ego vehicle, and the ego vehicle only needs to be able to locate itself relative to that information (e.g., by sensing the distance to the vehicle in front of it, or by map matching along the properties of the road, or by GPS sensing) to obtain preview information about the upcoming road surface.

[0031] The second category of sensors can be defined as contact sensors, in which the sensor contacts the road surface through an intermediate physical element such as a tire, and uses motion sensing or force sensing on the contact element or connected element to estimate the properties or characteristics of the underlying road surface.

[0032] The third category of sensors includes non-contact sensors, including vision-based systems such as photodiodes or cameras; infrared sensors; LiDAR sensors; radar sensors; or other sensors that can measure the properties of a road surface without direct contact with it.

[0033] Suspension or chassis systems can include, but are not limited to, systems that can adjust the relative position or forces between chassis components (such as wheels, tires, suspension links, vehicle chassis, vehicle mechanical components, seats, steering wheels, or other components). Systems can include active suspension, active roll systems, semi-active suspension elements, seat actuators, steering systems for the front and / or rear wheels, engine or powertrain mounting systems, and drive systems. This category may also include systems that regulate sound (e.g., speaker volume), lighting (e.g., ambient lighting), temperature (e.g., cabin temperature), or airflow within a portion of the vehicle's interior or vehicle components. For example, adjustable headlights can benefit from knowing the longitudinal profile of the road surface ahead of the vehicle, and sound systems can adjust the volume of sounds played inside the cabin based on the upcoming road type or roughness.

[0034] Benefits to vehicle occupants may include, but are not limited to, benefits to occupant safety or comfort, such as increasing vehicle stability on a particular road surface or reducing vibrations transmitted to occupants; for example, maintaining the offset of components within their range of motion in suspension actuators or steering racks or actuators to avoid end-of-stroke behavior; reducing the peak force required by actuators (e.g., suspension actuators); reducing power consumption of components or the vehicle as a whole; and other benefits.

[0035] Information regarding the upcoming road conditions may include a vertical road profile of at least one segment of the road in front of the wheels and along the direction of travel; it may include, but does not necessarily include, road roughness, character, or surface type; it may include, but does not necessarily include, road camber, road slope, or road convexity or concavity; it may include, but does not necessarily include, road surface friction forces that provide grip; and it may include, but does not necessarily include, other derived properties of the 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 systems in the vehicle are also contemplated, and the invention is not limited in this respect.

[0036] Information about upcoming road segments may also include roads that your vehicle will be crossing in the future; however, it should be noted that roads that your vehicle will be crossing in the near future (e.g., in the next minute or seconds) are generally more relevant. These latter roads may have a more direct impact on your vehicle's dynamics than roads that will be crossed more recently. However, it should also be understood that information about roads that will be crossed more recently can also be useful.

[0037] A processor can be used to process information and determine actions. Such a processor can be located on the vehicle itself or in a remote location such as another vehicle or in the cloud. The processor can use data from one or more sensors, and can also use other relevant information such as the vehicle's current speed, desired vehicle state, and other setpoints or any other appropriate inputs, such as user-selectable modes or input from a navigation system. Using the information thus collected and an understanding of desired benefits, such as a cost function relating road conditions to vehicle occupant comfort, the processor can decide whether and how to modify the state of the suspension or chassis system. For example, non-contact sensors can be used to collect information about impending vertical deviations or anomalies in the road surface, and the processor can be used to determine the optimal actuator force of the active suspension actuators within constraints of maximum force and maximum suspension travel, which is expected to produce minimal vertical body acceleration at the occupant's position.

[0038] Figure 1 A self-driving vehicle 101 is shown traveling along road 102. In this example, the self-driving vehicle 101 has a non-contact sensor 103 mounted thereon, which provides information about the road surface in front of the vehicle at points 104 and / or 105.

[0039] Figure 2 It is shown that the method is based at least in part on non-contact sensors (e.g., Figure 1 The process of creating a force command 205 using data from a non-contact sensor 103. In this example, at step 202, it represents, for example, data collected at a fixed distance in front of the vehicle at point 104 (e.g., data from the vehicle itself). Figure 1 The data stream 201 of the road profile in front of the vehicle 101 is fed to the processor. Using other relevant information from the input 203, such as vehicle speed or steering angle, the block 204 can create an optimal output, such as a force command 205 for the actuator system.

[0040] An example of process 200 can be implemented using the front wheels on each side of the self-vehicle 101 as contact sensors to estimate the inputs or disturbances that the rear wheels of the self-vehicle 101 will encounter. In this way, if suspension or chassis actuators are present, rear wheel behavior or response to inputs or disturbances can be improved. Another example of process 200 may include estimating the road ahead of the self-vehicle 101 using data from input 203 from forward-facing non-contact sensors (e.g., one or more camera devices). In this case, a filtering step can be performed within step 202 to estimate the road surface characteristics that the self-vehicle 101 will encounter. For example, the road profile can be estimated by using sensor information and processing that includes an understanding of tire behavior. For example, a model can estimate the stiffness and / or damping of the tires and can be used to infer the road profile or road conditions that the tires experience to cause the observed tire behavior. Information such as tire contact surface dimensions and flexibility can be used to estimate how a given road will affect the tires.

[0041] Another filtering step can be performed within step 202 to remove inaccurate or useless content from the sensor data from input 203. For example, a filtering step can be performed to remove low-frequency and / or high-frequency components of the sensor data. In some implementations, the sensor may be inaccurate at certain frequencies (e.g., at low frequencies due to drift). Alternatively or additionally, high-frequency road inputs or interference may affect the tires but not substantially the vehicle body, and the suspension or chassis system may not respond to signals above a certain frequency. Interference can originate from non-road sources, such as wheel imbalance (tire or wheel non-roundness), wind, etc. Due to the fact that information about the road ahead of the vehicle may affect the vehicle at future times, a non-causal filtering step can be performed here to optimally preserve the phase and delay of the command signal.

[0042] For example, a stereo camera can be used to estimate vertical road profile elevation anomalies or discontinuities at, for example, a fixed distance in front of each wheel of a vehicle. The filtering step can include removing or mitigating frequencies with longer time intervals than the amount of predictable information available. For example, if a vehicle is traveling at 10 m / s and sensor information provides data about the road 10 m ahead of the vehicle, signals below approximately 1 Hz will not be usable as predictive information because the period of that signal is similar to the preview time of road information at that speed. A second filtering step can be performed alone or in conjunction with the first step to remove or mitigate signals above 10 Hz. In typical vehicles, sensor content close to the resonant frequencies of the tire and wheel assemblies may be inaccurate, which typically fall roughly between 10 Hz and 15 Hz. Finally, force commands for the active suspension can be calculated to reduce vehicle occupant motion by using the filtered road signal as input to models of the transmission rate from road content to occupant motion and the transmission rate from force content to occupant motion. Using optimization techniques and known constraints regarding force and suspension travel, the optimal solution can then be determined and force commands can be applied to the actuators in the vehicle.

[0043] Figure 3 A method for combining two different sensing locations from one or more sensors is illustrated. Road information 301 from a first sensor and road information 302 from a second sensor can be processed in parallel through filtering steps 303 and 304, respectively, and combined in a processing step 305, which also receives and considers additional sensor input 306. An appropriate force command can then be generated at block 307.

[0044] In some implementations, two different outputs from a single non-contact sensor can be used, for example in Figure 1As shown at points 104 and 105. Typical non-contact sensors will have accurate information near the vehicle but less accurate information at greater distances. For example, a LiDAR sensor can have a range of up to 500m, but its accuracy at that distance is ±1 meter. The same LiDAR sensor can also be able to sense road content 5m in front of the vehicle with an accuracy closer to plus or minus a few millimeters. It is also advantageous to obtain information about the approaching road with earlier notification, allowing the vehicle's systems to be prepared to react promptly. For example, an air suspension system might require approximately 10 seconds to lift the vehicle, but at a vehicle speed of 10 m / s, a sensor reading the road height 5m in front of the vehicle only provides the air suspension system with 0.5 seconds of advance notification. To optimize overall system performance, multiple outputs of the same sensor can be used, where long-range signals are processed in a manner that might affect low-frequency, low-accuracy commands, while short-range outputs can be used to affect higher-frequency, higher-accuracy commands. For example, if profile 301 is obtained from a point a long distance in front of the vehicle (e.g., approximately greater than 15 m and in some cases greater than 100 m or 500 m), and profile 302 is obtained from a point near the vehicle (e.g., approximately within 10 m to 15 m in front of the vehicle), then filtering steps 303 and 304 may include complementary filters that allow the low-frequency content of signal 301 and the high-frequency content of signal 302 to pass through, such that the low-frequency content of signal 301 and the high-frequency content of signal 302 can be added together in step 305 to form a single profile that is accurate at high frequencies but also has content even at low frequencies.

[0045] Another example of a process for combining sensor information to improve accuracy may include using two different sensors, one with a longer preview and the other with a shorter preview but greater accuracy. A preferred implementation of this process may include using a virtual sensor to predict the road ahead of the vehicle up to a very long distance, and using a non-contact sensor looking ahead of the vehicle or a contact sensor on the front wheels of the vehicle, or both, to correct the signal.

[0046] Figure 4 A self-driving vehicle 401 is shown traveling on road 402. The self-driving vehicle has a non-contact sensor 403 that can provide road information for the upcoming road. The road information can be in a scan format, meaning that the road information provides a series of values ​​at repeated intervals at points ahead of the vehicle, as shown as an example set of values ​​404. As the self-driving vehicle 401 moves forward (from left to right in the figure), more values ​​will continue to accumulate. Finally, after the self-driving vehicle 401 has passed a position on road 402 corresponding to a past value 405, a past value 405 of the road content can be obtained.

[0047] Figure 5 The process that can be followed in this scenario is illustrated. At step 501, the autonomous vehicle receives values ​​from multiple points in front of it, for example, from sensors. These values ​​can be verified, filtered, and collected at this step. Once enough values ​​have been collected to create a profile of the desired length, new values ​​can be averaged into the existing profile and blended at step 502 to achieve the most accurate output. This blending step 502 may include adding new information, such as values ​​at the leading edge of sensor information, in front of existing information. Blending step 502 may include averaging the values ​​in areas where they overlap with previous values. Averaging may include performing a weighted average based on information related to the accuracy of each signal at different distances from the vehicle (e.g., sensor properties). For example, values ​​obtained from sensors can be given higher weights, becoming more accurate the closer the sensor is to the vehicle for reading. Examples of sensors include non-contact sensors such as LiDAR, camera devices (stereo cameras and single cameras), radar, etc., which are generally more accurate the closer they are to the vehicle for sensing. If the sensor information creates a grid with a finer resolution than required by subsequent processing steps, the averaging can also be discretized at fixed intervals.

[0048] In the example, the sensor output at step 501 can have a resolution of 1 m in the direction of travel, and the autonomous vehicle can travel at 5 m / s. If the sensor information is uploaded at 10 Hz or 10 times per second, the new sensor information can be targeted at points along the road, where each consecutive point is 0.5 m from the previous point (5 m / s divided by 10 s). Therefore, these new points can fall between existing points and provide a more detailed map of the road as the autonomous vehicle travels along it. However, in some cases, subsequent processing may only require a resolution of 2 m. For example, if the suspension system is an air spring, the ability of the air spring to move quickly is limited, and a resolution of 2 m may be sufficient for the operation of the air spring system. In this case, existing and previous data can be blended into a profile with a 2 m spacing, which can be interpolated each time new information becomes available.

[0049] After repeating steps 501 and 502 one or more times, at step 503, a fully combined profile can be generated, creating a profile long enough for the desired processing and achieving a resolution sufficiently fine for the desired processing. As used herein, the term "profile" refers to a collection of road information at various points in front of and / or behind the vehicle along its path of travel. This information may include road characteristics, friction, height, slope, camber angle, specifically identified features such as potholes, speed bumps, railway crossings, frost heave, or any other suitable property or characteristic of the road surface to be traversed. Information may be collected from a portion of the road surface in front of the vehicle that is within the sensor's range, or from a closer portion if not required by subsequent processing steps. The information may also include content below and behind the vehicle, as needed. For example, when a sensor is a virtual sensor, the length of the road surface in front of the vehicle from which road surface information can be obtained may be limited only by the amount of information that can be collected by the same and / or other vehicles during previous journeys. However, in this case, only a subset of the information may be used. On the other hand, if a sensor is a camera device, all available information can be used.

[0050] At step 504, the signal can be filtered and motion planning can be created. The filtering step in step 504 can include causal filtering and non-causal filtering. In the case of non-causal filtering, incorporating information about the road the vehicle is traveling on as part of the profile may be beneficial to reduce filter transients. Creating motion planning can include calculating a strategy for optimizing a cost function using settings of the suspension or chassis system. For example, this function could be a force command for an active suspension system that minimizes occupant discomfort by reducing the expected vertical acceleration, or a pressure command for an air spring to lift the vehicle in front of a large obstacle to avoid reaching the vehicle's suspension limits, thereby increasing comfort and safety. Force commands, pressure commands, or other commands can be output at step 505.

[0051] Figure 6 This demonstrates the use of a combination of sensors. Figure 5 An exemplary implementation of the method. In this example, multiple sensors are shown, but typically only some of the sensors shown are used together because they provide some redundant information. Go to... Figure 6Vehicle 601 travels along road 602. The vehicle has a forward-facing non-contact sensor 603 and a rearward-facing non-contact sensor 604. Information from the virtual sensors can provide a profile 605 starting far in front of vehicle 601 and extending to the vehicle, below, and behind, where the remaining sensor signals overlap. Profile 606 from the forward-facing sensor 603 can cover the road segment in front of vehicle 601; profile 607 from a contact sensing method at the front wheels of vehicle 601 can cover a range of road below vehicle 601; profile 608 from a contact sensor at the rear wheels of vehicle 601 can cover a distance behind the vehicle; and profile 609 from the rearward-facing non-contact sensor 604 can cover a portion of the road at an additional distance behind vehicle 601. Each of these sensors can possess certain properties, such as range, accuracy, precision, and reliability, that can make the sensor more suitable for specific aspects of the process of creating commands for the suspension system (e.g., force commands, pressure commands, etc.). Virtual sensors can be used for long-range predictions; however, due to their nature, they may not take into account recent changes in the road, such as road maintenance, deterioration, and / or weather conditions. A forward-facing non-contact sensor 603 can provide a certain amount of advance notice, which, while insufficient for some types of actuation systems such as air springs, can also be used to validate the information from the virtual sensors. Front wheel contact sensors can be highly accurate but only provide short advance information for the rear wheels. Rear wheel contact sensors and rearward-facing non-contact sensors can be used to validate information from other sensors and / or calibrate other sensors, for example, using Kalman filters or correction signals. In some implementations, the signal from the rear wheel contact sensor can also be the most accurate signal for creating a map of the road.

[0052] Contact sensors may be better suited for vehicle speeds where significant motion is imparted to the vehicle or chassis components upon which the sensor relies; while non-contact sensors may sometimes be less accurate when significant vehicle motion is present. Hybrid contact and non-contact sensing methods can be useful for compensating for their respective shortcomings. The predicted or anticipated motion of the platform from which the non-contact sensor is located (potentially in addition to the sensed motion of the platform) can be used to correct the signal measured by the non-contact sensor. This correction can mitigate anticipated sensor variance, such as the low-frequency drift of the motion sensor due to the signal that can be used to correct the sensor, and may itself be sufficient to eliminate the need for a motion sensor.

[0053] At the same time, due to the limited motion of vehicles and their components imposed by the road, contact sensors are generally not good at interpreting road contour information when vehicle speeds are very low (e.g., below 5 mph or below 10 mph). In this range, it may be beneficial to use non-contact sensors as an additional complementary signal, or to use non-contact sensors alone to extract the desired road contour information.

[0054] Therefore, combinations of sensors may be desirable, but any combination of the sensor types described herein, as well as any single sensor on its own, can be used, as this disclosure is not limited thereto.

[0055] The embodiments of the technology described herein can be implemented in any of a number of ways. For example, the embodiments can be implemented using hardware, software, or a combination thereof. When implemented in software, the software code can execute on any suitable processor or set of processors, whether located in a single computer or distributed across multiple computers. Such a processor can be implemented as an integrated circuit having one or more processors in an integrated circuit component, including commercially available integrated circuit components known in the art as having names such as CPU chips, GPU chips, microprocessors, microcontrollers, or coprocessors. Alternatively, the processor can be implemented in a custom circuit system such as an ASIC or in a semi-custom circuit system resulting from configuring programmable logic devices. As yet another alternative, the processor can be part of a larger circuit or semiconductor device, whether commercially available, semi-custom, or custom. As a specific example, some commercially available microprocessors have multiple cores, such that one or a subset of these cores can constitute a processor. However, the processor can be implemented using a circuit system of any suitable format.

[0056] Furthermore, it should be understood that a computer can be embodied in any of a variety of forms, such as a rack-mounted computer, a desktop computer, a laptop computer, or a tablet computer. Additionally, a computer can be embedded in devices that are not typically considered computers but have suitable processing capabilities, including personal digital assistants (PDAs), smartphones, or any other suitable portable or fixed electronic devices.

[0057] In addition, a computer may have one or more input devices and output devices. Among other things, these devices may be used to present a user interface. Examples of output devices that may be used to provide a user interface include a printer or display screen for visual presentation of output, and a speaker or other sound generating device for audible presentation of output.

[0058] Examples of input devices that can be used for user interfaces include keyboards and pointing devices such as mice, touchpads, and digitizers. As another example, computers can receive input information via speech recognition or in other audible formats.

[0059] Such computers can be interconnected through one or more networks of any suitable form, including local area networks (LANs) or wide area networks (WANs), such as enterprise networks or the Internet. These networks can be based on any suitable technology and can operate according to any suitable protocol, and can include wireless networks, wired networks, or fiber optic networks.

[0060] Furthermore, the various methods or processes outlined herein can be encoded as software that can execute on one or more processors employing any of a variety of operating systems or platforms. Moreover, such software can be written using any of several suitable programming languages ​​and / or programming or scripting tools, and can also be compiled into executable machine language code or intermediate code that executes on a framework or virtual machine.

[0061] In this regard, the embodiments described herein can be implemented as computer-readable storage media (or multiple computer-readable media) encoding one or more programs (e.g., computer memory, one or more floppy disks, compact discs (CDs), optical discs, digital video discs (DVDs), magnetic tape, flash memory, circuit configurations in field-programmable gate arrays or other semiconductor devices, or other tangible computer storage media), which, when executed on one or more computers or other processors, perform methods implementing the various embodiments discussed above. It is evident from the foregoing examples that a computer-readable storage medium can retain information for a sufficient time to provide computer-executable instructions in a non-transitory form. One or more such computer-readable storage media can be transportable, such that one or more programs stored thereon can be loaded onto one or more different computers or other processors to implement various aspects of this disclosure as discussed above. As used herein, the term "computer-readable storage medium" includes only non-transitory computer-readable media that can be considered an article of manufacture (i.e., an article of production) or a machine. Alternatively or additionally, this disclosure can be embodied in computer-readable media other than computer-readable storage media, such as propagating signals.

[0062] In this document, the terms "program" or "software" are used in a general sense to refer to any type of computer code or set of computer-executable instructions that can be used to program a computer or other processor to implement the various aspects of this disclosure as discussed above. Furthermore, it should be understood that, according to one aspect of this implementation, one or more computer programs that perform the methods of this disclosure when executed do not need to reside on a single computer or processor, but can be distributed in a modular manner across multiple different computers or processors to implement the various aspects of this disclosure.

[0063] Computer-executable instructions can take many forms, such as program modules executed by one or more computers or other devices. Typically, program modules include routines, programs, objects, components, data structures, etc., that perform specific tasks or implement specific abstract data types. Generally, in various implementations, the functionality of program modules can be combined or distributed as desired.

[0064] Furthermore, data structures can be stored in any suitable form on a computer-readable medium. For simplicity, a data structure can be represented as having fields related to locations within the data structure. Such relationships can also be achieved by allocating storage of the located fields in a computer-readable medium that conveys the relationships between the fields. However, any suitable mechanism can be used to establish relationships between the information in the fields of a data structure, including by using pointers, labels, or other mechanisms that establish relationships between data elements.

[0065] The various aspects of this disclosure can be used individually, in combination, or in various arrangements not specifically discussed in the embodiments described above, and therefore are not limited in their application to the details and arrangements of the components set forth in the foregoing description or shown in the accompanying drawings. For example, aspects described in one embodiment can be combined in any way with aspects described in other embodiments.

[0066] Furthermore, the embodiments described herein can be embodied as methods for which examples have been provided. Actions performed as part of a method can be ordered in any suitable manner. Therefore, embodiments can be constructed that perform actions in an order different from the order shown, which could include performing some actions simultaneously, even if they are shown as sequential actions in the illustrative embodiments.

[0067] Furthermore, some actions are described as being performed by a “user.” It should be understood that a “user” does not necessarily have to be a single individual, and in some implementations, actions attributed to a “user” may be performed by a group of individuals and / or individuals in combination with computer-aided tools or other mechanisms.

Claims

1. A method for operating a suspension system in a vehicle, the method comprising: The first signal is obtained from the first sensor; A second signal is obtained from the second sensor; The first signal and the second signal are filtered to obtain a first filtered signal and a second filtered signal; The first filtered signal and the second filtered signal are mixed to obtain a mixed signal; Based on the mixed signal, a command signal is generated for at least one component of the vehicle suspension system; as well as Operate at least one component of the vehicle suspension system.

2. The method according to claim 1, wherein, The suspension components include active suspension actuators.

3. The method according to any one of claims 1 to 2, wherein, The suspension components include air springs.

4. The method according to any one of claims 1 to 3, wherein, The first sensor has higher accuracy at a distance closer to the vehicle, and the second sensor has higher accuracy at a distance farther from the vehicle.

5. The method according to claim 4, wherein, The first sensor is a contact sensor.

6. The method according to claim 4, wherein, The contact sensor includes the front wheel of the vehicle.

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

8. The method according to any one of claims 1 to 7, wherein, The second sensor includes a camera device.

9. The method according to any one of claims 1 to 8, further comprising: The second signal is corrected based on the expected movement of the platform holding the non-contact sensor.

10. The method according to any one of claims 1 to 7, wherein, The second sensor is a virtual sensor, and the signals from the virtual sensor include road contours.

11. A vehicle comprising: An active suspension system, the active suspension system including at least one actuator; A first sensor, the first sensor having a first sensor signal; A second sensor, the second sensor having a second sensor signal; The controller is configured to: The first sensor signal is filtered to obtain a first filtered sensor signal. The second sensor signal is filtered to obtain a second filtered sensor signal. The first filtered sensor signal and the second filtered sensor signal are mixed to obtain a mixed signal, and Based on the mixed signal, a command signal is sent to at least one actuator of the active suspension system.

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

13. The vehicle according to any one of claims 11 to 12, wherein, The active suspension system includes air springs.

14. The vehicle according to any one of claims 11 to 13, wherein, The first sensor has higher accuracy at a distance closer to the vehicle, and the second sensor has higher accuracy at a distance farther from the vehicle.

15. The vehicle according to claim 14, wherein, The first sensor is a contact sensor.

16. The vehicle according to claim 14, wherein, The contact sensor includes the front wheel of the vehicle.

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

18. The vehicle according to any one of claims 11 to 17, wherein, The second sensor includes a camera device.

19. The vehicle according to any one of claims 11 to 18, wherein, The controller is also configured to correct the second signal based on the expected movement of the platform holding the non-contact sensor.

20. The vehicle according to any one of claims 11 to 17, wherein, The second sensor is a virtual sensor, and the signals from the virtual sensor include road contours.

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