Patent Description:
A current intelligent suspension system is usually equipped with a semi-active suspension or an active suspension. This imposes a high requirement on an environmental perception capability of a vehicle. The vehicle is required to be able to accurately learn of a road condition in real time and transmit the road condition to a suspension control unit, so that the suspension control unit adjusts a suspension control strategy in real time based on road condition information. In view of this, the vehicle is usually provided with a plurality of or as many as possible high-precision environmental perception sensors, for example, a laser radar sensor and an optical camera, both of which undoubtedly increase hardware costs of the vehicle. In addition, a signal processing process for the high-precision environmental perception sensor needs to occupy a large number of computing resources of the vehicle. In particular, the optical camera needs to perceive unevenness of a ground based on an image recognition algorithm for a high computational load, to ensure computational accuracy.

Cited document (<CIT>) discloses an improved method for controlling a suspension apparatus for a vehicle which is capable of enhancing a boarding-on feeling and a running stability by increasing a boarding-on feeling and the road surface contact force of wheels based on the size of a road surface input and a control logic which is variable in accordance with a frequency. The method includes the steps of obtaining a displacement value by passing an acceleration value measured by a vehicle vertical acceleration sensor through an integration unit having the following Equation (<NUM>); and computing a predetermined road surface signal by using the acceleration and displacement value as shown in Equation (<NUM>).

Cited document (<CIT>) discloses that the road surface determining section <NUM> includes a high-pass filter (HPF) <NUM>, a band-stop filter (BPF) <NUM>, an absolute value calculating section <NUM>, a low-pass filter (LPF) <NUM>, and a coefficient determining section <NUM>. As illustrated in FIG. <NUM>, the wheel speed signal is inputted to the high-pass filter <NUM>, and the low-pass filter <NUM> is provided in a stage subsequent to the high-pass filter <NUM>. The vehicle can be configured to include a vertical G sensor for detecting an acceleration in a vertical direction of the vehicle. Then, the road surface determining section <NUM> can be configured to determine the road surface condition with reference to the vertical G signal indicative of the acceleration in the vertical direction.

Different aspects of the invention aim to provide an improved road condition determining device, a road condition determining method, and a computer-readable storage medium for executing such a method. According to the road condition determining device or method, a road condition for traveling of a vehicle can be accurately perceived with few computing resources of the vehicle being occupied.

In addition, the invention also aims to solve or alleviate other technical problems existing in the prior art.

The invention is defined by the independent claim(s). Preferable embodiments are defined by the dependent claims.

In the road condition determining method according to the invention, the road condition coefficient can be obtained based on a sensor signal with a low computational load, which can reduce software and hardware costs of the vehicle to some extent. In addition, both a high-frequency characteristic and a low-frequency characteristic of each wheel center vertical acceleration signal are kept, so that this case can be close to a real traveling condition of the vehicle.

The invention is illustrated in more detail below with reference to the accompanying drawings, in which.

The following specific embodiments and the accompanying drawings are merely exemplary descriptions of the technical solutions of the invention, and should not be construed as the entirety of the invention or construed as limiting the technical solution of the invention.

Orientation terms, such as up, down, left, right, front, rear, front side, back side, top, and bottom, which are or may be mentioned in this description, are defined with respect to the structures shown in the accompanying drawing, and are relative concepts, and therefore may correspondingly vary depending on different positions and different usage states. Therefore, these or other orientation terms should not be construed as restrictive terms as well.

<FIG> show a road condition determining device for a vehicle according to an implementation and main steps of a road condition determining method executable by the road condition determining device respectively. The road condition determining device <NUM> includes an obtaining module <NUM>, a preprocessing module <NUM>, a high-pass filter <NUM>, a low-pass filter <NUM>, and a determining module <NUM>. The preprocessing module <NUM> is located at a post-stage of the obtaining module <NUM>, and is configured to preprocess a signal obtained by the obtaining module <NUM> for road condition determining. The low-pass filter <NUM> and the high-pass filter <NUM> are connected in parallel between the preprocessing module <NUM> and the determining module <NUM>, so as to retain a high-frequency characteristic and a low-frequency characteristic of the signal respectively, which is closer to a real traveling environment of the vehicle than a series-connection manner. For example, a wheel center vertical acceleration signal on a gravel road has a high-frequency characteristic more significant than that on a gentle large-camber uneven road surface. The determining module <NUM> is located at a post-stage of the two filters, and is configured to determine a road condition coefficient based on a signal obtained after high-pass filtering and a signal obtained after low-pass filtering. The road condition coefficient may represent toughness of a road condition for traveling of the vehicle. A relationship (for example, positive correlation or negative correlation) between the road condition coefficient and the toughness of the road condition may be set based on a requirement or a suspension control logic.

Correspondingly, the road condition determining method according to the invention can be implemented by such a road condition determining device as follows.

First, a wheel center vertical acceleration signal of each wheel and an initial determining signal capable of characterizing a fluctuation of the wheel center vertical acceleration signal are obtained, which is step S100.

Then, high-pass filtering is performed by the high-pass filter on the initial determining signal by using a preset high-pass filtering factor, and low-pass filtering is performed by the low-pass filter on the initial determining signal by using a preset low-pass filtering factor, which is step S200.

Finally, a road condition coefficient is determined based on an initial determining signal obtained after high-pass filtering and an initial determining signal obtained after low-pass filtering, which is step S300.

Herein, road condition determining is performed based on an original device of the vehicle or a detected sensor signal instead of an additional high-precision environmental perception sensor, so that hardware costs of the vehicle can be reduced to some extent. In addition, compared with a complex image processing algorithm in the prior art, the road condition determining device and the road condition determining method are based on the wheel center vertical acceleration signal, and includes a computing process that needs only a low computational load, so that computing resources of the vehicle can be released.

The obtaining module <NUM> is configured to obtain the wheel center vertical acceleration signal of each wheel of the vehicle, that is, an acceleration signal of a wheel center in a vertical direction. Unevenness of a road surface on which the vehicle travels can directly reflect a bouncing motion of an unsprung mass. A wheel center vertical acceleration of the unsprung mass, that is, the "wheel", can be and is suitable for being used as a reference for determining the road condition for traveling of the vehicle. The obtaining module <NUM> can be communicatively connected to a vehicle data bus, so as to read and select, from the vehicle data bus, a related signal associated with the road condition. Alternatively, the obtaining module can be directly communicatively connected to a vehicle sensor, and compute the wheel center vertical acceleration signal based on a received related signal. Alternatively, on the vehicle with a wheel center vertical acceleration sensor, the obtaining module <NUM> can be directly communicatively connected to the wheel center vertical acceleration sensor, and invoke the detected wheel center vertical acceleration signal of each wheel.

Optionally, the related signal associated with the road condition can relate to the wheel vertical acceleration signal, a vehicle body vertical acceleration signal, a suspension compression displacement signal, a shock absorber stroke signal, and an inertial measurement unit signal. Correspondingly, combinations of the following sensors can be involved: a vertical acceleration sensor at a sprung vehicle body (for example, three vertical acceleration sensors are disposed, so as to obtain a vehicle body attitude parameter, for example, a vehicle body roll feature or a vehicle body pitch feature), a shock absorber stroke sensor at an unsprung shock absorber (for example, each shock absorber is provided with such a shock absorber stroke sensor), an inertial measurement unit sensor (for example, one) at the sprung vehicle body and the shock absorber stroke sensor at the unsprung shock absorber (for example, each shock absorber is provided with such a shock absorber stroke sensor), a vertical acceleration sensor (for example, three) at the sprung vehicle body, and a vertical acceleration sensor at a wheel center of an unsprung front wheel (for example, located at a front left wheel and a front right wheel respectively). It should be noted herein that selection or a combination manner of the related signal and selection or a combination manner of the involved sensors may be designed according to an existing vehicle configuration solution.

The preprocessing module <NUM> is configured to process the received wheel center vertical acceleration signal into the initial determining signal capable of characterizing the fluctuation of the wheel center vertical acceleration signal. Optionally, the initial determining signal represents a deviation value of each wheel center vertical acceleration at each moment or at each sampling point relative to an average wheel center vertical acceleration within a preset time period. The deviation value reflects a fluctuation of each wheel center vertical acceleration, and thus reflects a change of an uneven part of the road surface on which the vehicle travels. Herein, in one aspect, the preset time period can relate to historical sampled data. In this case, an arithmetic average of wheel center vertical accelerations at a plurality of sampling points within a continuously accumulated historical road length or historical traveling time period is obtained as an average wheel center vertical acceleration, and a difference between the average wheel center vertical acceleration and each wheel center vertical acceleration signal that is acquired in real time is computed to obtain the deviation value. Herein, a vehicle motion status signal can also be used, for example, a Boolean signal ("<NUM>" represents that the vehicle is still, and "<NUM>" represents that the vehicle is moving). A change of the Boolean signal indicates that a sampling point based on which the arithmetic average is computed is reset. In this case, it should be noted that a length of the preset time period needs to be set and adjusted based on required computational accuracy. In another aspect, the preset time period can alternatively relate to sampled data in a road condition determining process that is being performed. In this case, a difference between an arithmetic average of sampled data in a part of a sampling cycle or in an entire sampling cycle and the wheel center vertical acceleration at each moment can be computed to obtain the deviation value.

More specifically, before the initial determining signal is separately input into the high-pass filter <NUM> and the low-pass filter <NUM>, the initial determining signal needs to be pre-filtered, so as to filter the signal and improve the computational accuracy. Based on this, the preprocessing module <NUM> further includes an additional low-pass filter (not shown) configured to perform low-pass filtering processing on the initial determining signal by using an initial filtering factor, particularly a preset initial filtering factor.

In general, the preprocessing module <NUM> performs the following process on the wheel center vertical acceleration signal of each wheel computed based on the related signal or directly read from the sensor: obtaining a deviation value of each wheel center vertical acceleration signal at each moment relative to an average wheel center vertical acceleration signal within the preset time period (which is sub-step S110); and performing low-pass filtering on the deviation value by using the preset initial filtering factor, to generate the initial determining signal (which is sub-step S120).

Optionally, the initial filtering factor of the additional low-pass filter, the high-pass filtering factor of the high-pass filter <NUM>, and the low-pass filtering factor of the low-pass filter <NUM> can be preset and stored in corresponding computing units, or can be set and adjusted based on a vehicle status signal. The concept "filtering factor" is a critical frequency for filtering processing, and may also be referred to as a cut-off frequency. For example, during high-pass filtering, a part of the initial determining signal lower than the high-pass filtering factor is removed. Specifically, the road condition determining device according to the invention is further provided with a filtering factor determining module <NUM> capable of computing the foregoing three filtering factors based on the vehicle status signal, for example, a vehicle speed signal and a vehicle start/stop signal. The vehicle start/stop signal may be used for correcting the vehicle speed signal. A vehicle speed is closely related to bouncing and the wheel center vertical acceleration of the wheel. For example, on a same uneven road surface, the wheel at a high vehicle speed bounces more violently than that at a low vehicle speed. Therefore, determining of at least one of the foregoing three filtering factors based on the vehicle speed, particularly determining of the foregoing three filtering factors based on the vehicle speed, can be facilitated.

More specifically, the initial filtering factor FrqD, the high-pass filtering factor HFrqD, and the low-pass filtering factor LFrqD can be determined according to the following formula (<NUM>):
<MAT>.

According to the invention, the determining module <NUM> includes an analysis submodule <NUM> and a coefficient determining submodule <NUM>. The analysis submodule <NUM> is configured to obtain, through Fourier analysis, for example, through fast Fourier transform, an amplitude and a frequency of the initial determining signal obtained after high-pass filtering and an amplitude and a frequency of the initial determining signal obtained after low-pass filtering respectively. Specifically, the analysis submodule <NUM> obtains, through fast Fourier transform, a first amplitude and a first frequency of the initial determining signal obtained by the high-pass filter by performing high-pass filtering, and obtains, through fast Fourier transform, a second amplitude and a second frequency of the initial determining signal obtained by the low-pass filter by performing low-pass filtering. The coefficient determining submodule <NUM> is configured to obtain the road condition coefficient based on the amplitudes and the frequencies. The analysis submodule <NUM> computes, for the wheel center vertical acceleration of each wheel, an amplitude and a frequency of the wheel center vertical acceleration. Then, the coefficient determining submodule <NUM> determines a lateral road condition coefficient on each side of the vehicle based on the amplitude and the frequency, so as to obtain an overall road condition coefficient of the vehicle.

More specifically, step S300 performed by the determining module <NUM> includes the following sub-steps.

S310: Through Fourier analysis, obtain the first amplitude and the first frequency of the initial determining signal obtained after high-pass filtering, and obtain the second amplitude and the second frequency of the initial determining signal obtained after low-pass filtering.

S320: Seek for a first amplitude, a first frequency, a second amplitude, and a second frequency of each same-side wheel on a same side of the vehicle, and determine a lateral road condition coefficient on each side of the vehicle based on the first amplitude, the first frequency, the second amplitude, and the second frequency.

S330: Compute a weighted average of the lateral road condition coefficient on each side of the vehicle as the road condition coefficient.

Herein, sub-step S310 is performed in the analysis submodule <NUM>, and sub-steps S320 and S330 are performed in the coefficient determining submodule <NUM>. In sub-steps S320 and S330, the amplitude and the frequency of each wheel on the same side may be integrated, through secondary computation (for example, addition of a user-defined function and weighted averaging), into the lateral road condition coefficient capable of reflecting a road condition on this side, and then all lateral road condition coefficients are integrated, through secondary computation (for example, addition of a user-defined function and weighted averaging), into the overall road condition coefficient that can be used for a suspension control system. Herein, a four-wheel vehicle may include a left side and a right side, and wheels can be divided into left-side wheels (including a front left wheel and a rear left wheel) and right-side wheels (including a front right wheel and a rear right wheel). This will be described in more detail below.

More specifically, obtaining of each lateral road condition coefficient can be implemented through the following sub-steps.

S321: Obtain an amplitude average of the first amplitude and the second amplitude of each same-side wheel, and obtain a frequency average of the first frequency and the second frequency of each same-side wheel.

S322: Compute a weighted average of the amplitude average of each same-side wheel as a weighted amplitude, and compute a weighted average of the frequency average of each same-side wheel as a weighted frequency.

S323: Compute a root mean square of a product of the weighted amplitude and the weighted frequency as the lateral road condition coefficient.

It should be noted herein that the road condition determining device, the road condition determining method, and a computer-readable storage medium according to the invention can be applied to different types of vehicles, where differences rely on different hardware configurations, including different numbers of axles, different numbers of wheels, and the like. For ease of understanding, a specific implementation of the road condition determining method is described based on the four-wheel vehicle in detail below with reference to <FIG> and <FIG>.

First, the obtaining module <NUM> of the road condition determining device reads the following signals from the vehicle data bus: vehicle body vertical acceleration signals over the four wheels, denoted as TMAZ* (including TMAZFL, TMAZFR, TMAZRL, and TMAZRR), and four suspension compression signals on a suspension, denoted as WhlZ* (including WhlZFL, WhlZFR, WhlZRL, and WhlZRR). Second-order derivative computation is performed on the suspension compression signal WhlZ*, and a difference between the suspension compression signal and the vehicle body vertical acceleration signal TMAZ* is computed to obtain a wheel center vertical acceleration signal of each wheel, denoted as RoadStSig* (including RoadStSigFL, RoadStSigFR, RoadStSigRL, and RoadStSigRR). It should be noted that a suffix "FL" in a sign used herein or hereinafter represents the front left wheel of the vehicle, "FR" represents the front right wheel of the vehicle, "RL" represents the rear left wheel of the vehicle, and "RR" represents the rear right wheel of the vehicle.

Then, the preprocessing module <NUM> of the road condition determining device computes a difference between the wheel center vertical acceleration signal RoadStSig* of each wheel and an average wheel center vertical acceleration signal RoadStSig*', so as to obtain a deviation value Δ RoadStSig*. Then, the additional low-pass filter performs low-pass filtering on ΔRoadStSig* by using the initial filtering factor FrqD, so as to obtain an initial determining signal RoadStRaw* (including RoadStRawFL, RoadStRawFR, RoadStRawRL, and RoadStRawRR).

Then, a related amplitude and frequency are obtained the initial determining signal RoadStRaw* with reference to <FIG>. In one aspect, the high-pass filter <NUM> performs high-pass filtering on the initial determining signal RoadStRaw* by using the high-pass filtering factor HFrqD, then obtains, through fast Fourier transform (FFT for short), a first amplitude RoadStMagHP* (including RoadStMagHPFL, RoadStMagHPFR, RoadStMagHPRL, and RoadStMagHPRR) after high-pass filtering, and obtains, through fast Fourier transform, peak detection, and peak cycle computation, a first frequency RoadStFreqHP* (including RoadStFreqHPFL, RoadStFreqHPFR, RoadStFreqHPRL, and RoadStFreqHPRR) after high-pass filtering. In another aspect, the low-pass filter <NUM> simultaneously performs low-pass filtering on the initial determining signal RoadStRaw* by using the low-pass filtering factor LFrqD, then obtains, through fast Fourier transform, a second amplitude RoadStMagLP* (including RoadStMagLPFL, RoadStMagLPFR, RoadStMagLPRL, and RoadStMagLPRR) after low-pass filtering, and obtains, through fast Fourier transform, peak detection, and peak cycle computation, a second frequency RoadStFreqLP* (including RoadStFreqLPFL, RoadStFreqLPFR, RoadStFreqLPRL, and RoadStFreqLPRR) after low-pass filtering. In this process, the foregoing computation needs to be separately performed on initial determining signals RoadStRawFL, RoadStRawFR, RoadStRawRL, and RoadStRawRR of the wheels.

Then, the coefficient determining submodule <NUM> of the road condition determining device computes a road condition coefficient the received RoadStMagHP*, RoadStFreqHP*, RoadStMagLP*, and RoadStFreqLP* with reference to <FIG>. First, the foregoing parameters are divided. To be specific, the foregoing parameters of the front left wheel and the rear left wheel are divided into a first group, and the foregoing parameters of the front right wheel and the rear right wheel are divided into a second group. Based on this, lateral road condition coefficients on the left side of the vehicle and the right side of the vehicle are computed respectively. Obtaining of the lateral road condition coefficient on the left side is now used as an example for description.

Weighted averaging is performed on a mathematical average of the first amplitude RoadStMagHPFL and the second amplitude RoadStMagLPFL of the front left wheel and a mathematical average of the first amplitude RoadStMagHPRL and the second amplitude RoadStMagLPRL of the rear left wheel, to obtain a weighted amplitude of the left side of the vehicle. In addition, weighted averaging is performed on a mathematical average of the first frequency RoadStFreqHPFL and the second frequency RoadStFreqLPFL of the front left wheel and a mathematical average of the first frequency RoadStFreqHPRL and the second frequency RoadStFreqLPRL of the rear left wheel, to obtain a weighted frequency of the left side of the vehicle. Root mean square (RMS) effective value computation is performed on a product of the weighted amplitude and the weighted frequency. RMS effective value computation is performed particularly within a preset sampling time period (for example, when there are <NUM> sampling points). The lateral road condition coefficient RoadStL on the left side of the vehicle is obtained based on an obtained root mean square. In addition, a lateral road condition coefficient RoadStR on the right side of the vehicle is obtained in the same manner.

Finally, weighted average computation is performed on the two lateral road condition coefficients RoadStL and RoadStR, to obtain the road condition coefficient RoadSt* that can be output to the suspension control system of the vehicle. Herein, respective weight coefficients of RoadStL and RoadStR can be preset based on a test, or can be adaptively adjusted based on a running mode of the vehicle, for example, turning. In addition, the two lateral road condition coefficients RoadStL and RoadStR may alternatively be directly transmitted to the suspension control system, which is particularly advantageous in a case in which the vehicle turns or road conditions on the left and right sides are greatly different.

In conclusion, in the road condition determining method according to the invention, the road condition coefficient is obtained based on a sensor signal with a low computational load, which can reduce software and hardware costs of the vehicle to some extent. In addition, both a high-frequency characteristic and a low-frequency characteristic of each wheel center vertical acceleration signal are kept, so that this case can be close to a real traveling condition of the vehicle. In an implementation of the invention, a critical frequency in each filtering process is set and adjusted based on the vehicle speed, so that interference caused by the vehicle speed to road condition determining can be eliminated. The weighted average of the lateral road condition coefficients on the left side of the vehicle and the right side of the vehicle is used as the road condition coefficient, so that there is a probability of matching a running status of the vehicle, and a targeted reference can be provided for the suspension control system of the vehicle.

Claim 1:
A road condition determining device (<NUM>), comprising:
an obtaining module(<NUM>) configured to obtain a wheel center vertical acceleration signal of each wheel of a vehicle;
a preprocessing module(<NUM>) disposed at a post-stage of the obtaining module and configured to preprocess each of the wheel center vertical acceleration signal to obtain an initial determining signal capable of characterizing a fluctuation of the wheel center vertical acceleration signal;
a high-pass filter(<NUM>) disposed at a post-stage of the preprocessing module and configured to perform high-pass filtering on the initial determining signal by using a high-pass filtering factor;
a low-pass filter(<NUM>) disposed at a post-stage of the preprocessing module in parallel with the high-pass filter and configured to perform low-pass filtering on the initial determining signal by using a low-pass filtering factor; and
a determining module(<NUM>) configured to determine a road condition coefficient based on an initial determining signal obtained after low-pass filtering and an initial determining signal obtained after high-pass filtering;
the determining module(<NUM>) comprises an analysis submodule(<NUM>) and a coefficient determining submodule(<NUM>), wherein the analysis submodule(<NUM>) is configured to obtain, through Fourier analysis, a first amplitude and a first frequency of the initial determining signal obtained after high-pass filtering and a second amplitude and a second frequency of the initial determining signal obtained after low-pass filtering; and the coefficient determining submodule(<NUM>) is configured to determine the road condition coefficient based on the first amplitude, the first frequency, the second amplitude, and the second frequency.