Apparatus and method for automatic detection of road conditions for vehicle applications

Abstract

Embodiments of the invention relate to apparatuses and methods for automatic detection of road conditions for vehicle applications. A road condition detection apparatus coupled to a wheel of a vehicle is provided with an electrostatic charge variation sensor to provide a charge variation signal indicative of electrostatic charge variations associated with rotation of the wheel, and a processing unit coupled to the electrostatic charge variation sensor to receive the charge variation signal and further to receive a rotational speed signal indicative of rotational speed of the wheel. In particular, the processing unit jointly processes the rotational speed signal and the charge variation signal to detect a road condition of a wet road condition and a dry road condition.

Description

Technical Field

[0001] This solution relates to an apparatus and method for automatic road condition detection in vehicle applications, particularly for autonomous or automated driving. Background Technology

[0002] It is known that road vehicles are typically equipped with multiple sensors to improve their safety and to enable autonomous or automatic driving.

[0003] In this regard, monitoring and detecting road conditions, as well as the subsequent forces generated on vehicle tires / wheels, are crucial for ensuring safety and reliability.

[0004] Specifically, road condition detection (e.g., distinguishing between dry and wet roads) can enable intervention modes that significantly improve important vehicle systems such as steering or braking systems in active suspension. Furthermore, the possibility of signaling and warning the driver regarding road conditions can significantly improve driving safety.

[0005] Typically, two methods are proposed for monitoring road conditions:

[0006] The first method provides identification of road friction conditions indirectly by estimating the response of vehicle dynamic parameters based on the effect.

[0007] The second approach uses various sensors suitable for this purpose, based on cause, to provide cause detection in a direct way before road conditions significantly affect vehicle behavior, namely, the actual changes in road conditions.

[0008] For example, regarding the second method, J. Häkli et al.'s "Road surface condition detection using 24 GHz automotive radar technology," 14th International Radar Symposium (IRS), 2013, pp. 702-707, discloses a measurement system using 24 GHz radar technology to study the backscattering characteristics of different asphalt surfaces (such as dry, wet, or icy surfaces).

[0009] I. Abdić et al.'s "Detecting road surface wetness from audio: A deep learning approach," 23rd International Conference on Pattern Recognition (ICPR), 2016, pp. 3458-3463, discloses a method for detecting road surface wetness based on processing sound signals generated by the interaction between the same surface and a tire.

[0010] E. Šabanovič et al.'s "Identification of Road-Surface Type Using Deep Neural Networks for Friction Coefficient Estimation," Sensors 2020, 20, No. 3, 612, discloses a solution for road surface type detection based on image acquisition and processing of the acquired images using a DNN (deep neural network).

[0011] However, the applicant has found that the currently used solutions have some limitations and drawbacks, generally concerning their applicability to various operating conditions, their reliability and robustness to external and unwanted factors, and the lifespan and reliability of the sensors used. Summary of the Invention

[0012] This solution aims to overcome the shortcomings of known methods and provide an improved solution for detecting road conditions.

[0013] According to this solution, an apparatus and method for detecting road conditions are provided. The road condition detection apparatus is coupled to a vehicle wheel and includes an electrostatic charge change sensor configured to provide a charge change signal indicating a change in electrostatic charge associated with the rotation of the wheel, and a processing unit coupled to the electrostatic charge change sensor and configured to receive the charge change signal, and further configured to receive a rotational speed signal indicating the rotational speed of the wheel. The processing unit is configured to jointly process the rotational speed signal and the charge change signal to detect road conditions between wet and dry conditions in response to the amplitude of the charge change signal and the rotational speed signal. Attached Figure Description

[0014] To better understand this disclosure, embodiments thereof will now be described by way of non-limiting example with reference to the accompanying drawings, in which:

[0015] Figure 1This is a schematic diagram of a road condition detection device coupled to the wheels of a vehicle.

[0016] Figure 2A and Figure 2B The time-domain and frequency-domain trends of the charge change signal are shown respectively under the first road condition of dry road;

[0017] Figure 3A and Figure 3B The time-domain and frequency-domain trends of the charge change signal are shown respectively under the second road condition of wet road;

[0018] Figure 4 This is a schematic block diagram of a detection device according to an embodiment of this solution;

[0019] Figure 5 Show Figure 4 Possible circuit embodiments for an electrostatic charge change sensor in a detection device;

[0020] Figure 6 According to an embodiment of this solution, Figure 4 The operation flowchart of the testing equipment;

[0021] Figure 7 The graphs showing the correlation between the electrostatic charge change signal, the wheel rotation speed signal, and the first and second road conditions are presented; and

[0022] Figure 8 It can be used Figure 4 A schematic block diagram of the vehicle system for the detection equipment. Detailed Implementation

[0023] like Figure 1 As shown, according to one aspect of this solution, road condition detection for vehicle applications is provided by detection device 1, which is designed to be integrally coupled to the wheels 2 of vehicle 3 and is based on the use of electrostatic charge change sensors.

[0024] The detection device 1 may be coupled to the rim of the wheel 2, near the tire, or coupled to the tire, or integrated into the rim or tire.

[0025] As will be discussed in detail below, the detection device 1 is configured to process, in combination or in conjunction with, the charge change signal provided by the electrostatic charge change sensor and the wheel rotation speed information to identify road conditions, particularly to distinguish between dry and wet road conditions.

[0026] As is well known, electric charge is a fundamental component of nature. When components are in direct contact or at a certain distance, the charge of an electrostatically charged body can easily transfer to another object. When charge transfers between two electrostatically insulating objects, the object with more electrons becomes negatively charged, while the object with fewer electrons becomes positively charged, thus creating static charge. The displacement of charge has different properties depending on whether the object is a conductor or a dielectric. In a conductor, electrons are distributed throughout the material and move freely under the influence of an external electric field. In a dielectric, there are no freely moving electrons except for electric dipoles, which have random orientations in space (thus resulting in zero net charge). Electric dipoles can be oriented or deformed by an external electric field, thus creating an ordered charge distribution and therefore a bias voltage. Charge can be mobile, depending on material properties and other environmental factors.

[0027] In this solution, the electrostatic charge change sensor of the detection device 1 is configured to detect changes in the electric field (and thus the electrostatic potential) by electrostatic induction, which occur over time due to local electrostatic charge changes caused by the rotation of the wheel 2 of the vehicle 3 (coupled with the same sensor).

[0028] Specifically, when the part of wheel 2 coupled with the charge sensor comes into contact with the road surface (especially asphalt) during its rotation, electrostatic charge is generated (due to the triboelectric effect), and then a significant change occurs in the charge change signal provided by the charge change sensor.

[0029] in this regard, Figure 2A The charge change signal S is shown. Q time domain S Q The trend in (t) indicates the change in static charge sensed by the charge change sensor at different rotational speeds (represented as RPS, rotations per second, or revolutions per minute) of the wheel 2 coupled to the detection device 1.

[0030] Specifically, it is evident that, corresponding to the rotation of wheel 2 (occurring within a shorter time period, i.e., at a higher angular frequency and greater rotational speed), the charge change signal S is generated due to the aforementioned electrostatic charge generation effect caused by the contact between the tire and the road. Q Peak values ​​are generated in the middle.

[0031] Figure 2B The same charge change signal S is shown. Q frequency domain S Q The corresponding trend in (f) is considered again for different rotational speeds of wheel 2.

[0032] In the charge change signal S QIn the spectrum, there are various harmonics of the wheel rotation frequency; specifically, the frequency of the main harmonic or fundamental harmonic has a value corresponding to the reciprocal of the rotation period of wheel 2, that is, its rotational speed (in RPS, revolutions per second), and has a significantly larger amplitude; there are more harmonics at multiples of the main harmonic frequency.

[0033] The applicant discovered that the aforementioned charge change signal S Q The amplitude is strongly influenced by road conditions (and thus indicates road conditions). In particular, when there is water on the road surface (the so-called "water film"), the amplitude is significantly reduced due to the fact that the electrostatic charge caused by the triboelectric effect (i.e., due to tire friction on the road) is significantly reduced (this effect is largely offset by wet road conditions).

[0034] in this regard, Figure 3A and Figure 3B and Figure 2A and Figure 2B The trend shown (in relation to the dry road condition "dry") is similarly illustrated in wet road conditions ("wet"), at different rotational speeds of wheel 2 (the two rotational values ​​correspond to the above). Figure 2A and Figure 2B The values ​​shown), charge change signal S Q The trend in the time domain and the trend in the frequency domain.

[0035] From these Figure 3A and Figure 3B And the preceding Figure 2A and Figure 2B In the comparative inspection, the charge change signal S Q The amplitude is significantly reduced, specifically the amplitude of the fundamental harmonic of the signal in the frequency domain, and therefore this amplitude is closely related to the asphalt condition: specifically, the maximum amplitude corresponds to dry roads, and the minimum amplitude (i.e. significantly smaller relative to dry road conditions) corresponds to wet roads (with a film or water layer on them).

[0036] In detail, the applicant discovered that the charge change signal S considered in the time domain Q The amplitude decreases by an average of approximately 100 times; and the charge change signal S considered in the frequency domain... Q Correspondingly, the average reduction is approximately 5000 times.

[0037] Therefore, one aspect of this solution provides a charge change signal S. Q The combined or combined processing of the signals, especially considering the frequency domain (where the aforementioned amplitude shows a more significant reduction), and the combined or combined processing of the signals indicating the rotational speed of wheel 2, are used to identify the same charge change signal S. Q The basic (and most prominent) harmonics.

[0038] As will be emphasized, according to one aspect of this solution, the rotational speed change signal is also used to suppress the detection of road conditions at reduced rotational speeds of wheel 2, because the applicant has found that at reduced speeds, the aforementioned static electricity generated in relation to the rotation of wheel 2 may be insignificant and therefore cannot provide a reliable indication.

[0039] Figure 4 A detection device 1 according to an embodiment of the present solution is schematically shown. The detection device includes a speed sensor 12 configured to provide a speed indication of a wheel 2, and the same detection device 1 is designed to be coupled to the wheel 2.

[0040] Specifically, in a possible embodiment, the aforementioned speed sensor 12 is a semiconductor gyroscope manufactured using MEMS (Micro-Electro-Mechanical Systems) technology, which is of a known type and not described in detail herein, and is configured to provide a speed (or angular velocity) signal S expressed in degrees per second. V ;

[0041] Electrostatic charge change sensor 14 is configured to provide a charge change signal S indicating a change in electrostatic charge. Q ;as well as

[0042] Processing unit 16 is coupled to speed sensor 12 and electrostatic charge change sensor 14 to receive speed signal S V and charge change signal S Q And is configured to jointly process the aforementioned speed signal S V and charge change signal S Q It detects road conditions and outputs information about the detected road conditions.

[0043] In a manner not shown here, this road condition information may be transmitted, for example, wirelessly to the management and control unit of the vehicle 3, which has the detection device 1 included therein, so as to perform and activate certain actions in response to the road condition detection (e.g., activating appropriate adjustments to one or more systems of the vehicle 3, such as the braking system of the suspension or steering).

[0044] More specifically, the aforementioned processing unit 16 includes, for example, a microcontroller or an MLC (Machine Learning Core) processor located in an ASIC (Application Specific Integrated Circuit) electronic circuit, which is coupled to the speed sensor 12 and the electrostatic charge change sensor 14 for processing the corresponding speed signal S. V and charge change signal S QThe aforementioned speed sensor 12, electrostatic charge change sensor 14, and processing unit 16 can be integrated into the same package (or chip), which is equipped with appropriate components for external electrical connections.

[0045] In a possible implementation, the aforementioned chip can be coupled to a printed circuit board (not shown here), and a wireless communication module (also not shown) can be coupled to the same printed circuit board (e.g., coupled to the management and control unit of the aforementioned vehicle 3) for remote transmission of road condition information provided by the detection device 1.

[0046] Figure 5 An exemplary and non-limiting embodiment of an electrostatic charge change sensor 14 is shown, which includes at least one input electrode IN or detection electrode made of a metallic material, possibly covered with a dielectric material layer, and is designed to be arranged toward or near the operating environment associated with the wheel 2 for detecting local charge changes (and the resulting field and potential changes) due to the rotation of the same wheel 2 and contact with the road.

[0047] In the exemplary solution shown, the detection electrode IN forms part of the differential input 19 of the instrumentation amplifier 22 and is coupled to the corresponding first input terminal 19a.

[0048] Input capacitor C I and input resistor R I The first input terminal 19a and the second input terminal 19b of the differential input 19 are connected in parallel with each other.

[0049] During operation, the input capacitor C I Input voltage V at both ends d It changes due to charge redistribution in the external environment. In the transient state (whose duration is determined by the capacitor C)... I and resistor R I The constant R defined between the parallel connections I ·C I After (given), the input voltage V d Returns its steady-state value (the so-called "steady state").

[0050] The instrumentation amplifier 22 is basically composed of two operational amplifiers OP1 and OP2, having non-inverting input terminals connected to the first and second input terminals 19a and 19b respectively, and a gain resistor R. G2 The inverting terminals are connected to each other.

[0051] The bias stage (buffer) OP3 biases the instrumentation amplifier 22 to the common-mode voltage V through a resistor R1 coupled to the second input terminal 19b. CM .

[0052] The output terminals of operational amplifiers OP1 and OP2 are connected to corresponding gain resistors R. G1 Connect to the corresponding inverting input terminal; there is an output voltage V between the same output terminals. d '.

[0053] As can be clearly seen from the circuit inspection, the gain Ad of instrumentation amplifier 22 is equal to (1 + 2·R1 / R2); therefore, the aforementioned output voltage V d 'Equals: V' d ·(1+2·R1 / R2).

[0054] The components of instrumentation amplifier 22 are selected to give the same instrumentation amplifier 22 reduced noise and high impedance (e.g., 10 Hz) in its passband (e.g., included between 0 and 500 Hz). 9 (On the order of ohms).

[0055] The above output voltage V d The signal is input to an analog-to-digital converter (ADC) 24, which outputs the aforementioned charge change signal S to the processing unit 16. Q The charge change signal S Q It can be, for example, a (16 or 24-bit) high-resolution digital stream.

[0056] Depending on the embodiment, the instrumentation amplifier 22 of the analog-to-digital converter 24, which has suitable characteristics (e.g., differential input, high input impedance, high resolution, dynamic range optimized for the measurement, low noise), can be omitted. In this case, the input voltage V... d It is directly provided as input to the analog-to-digital converter 24.

[0057] The charge change signal S can be represented in a manner not shown. Q The first input is provided to the multiplexer block, which can also receive the aforementioned speed signal S at at least one other input. V (And may receive other detection signals at other inputs). In this case, the output of the multiplexer block is coupled to the input of the processing unit 16, providing the aforementioned charge change signal and speed signal S. Q S V (and possibly other detection signals) for processing by the same processing unit 16.

[0058] Figure 6 The flowchart illustrates in more detail, in a possible embodiment of this solution, the charge change and rotation speed signals S implemented by the processing unit 16. Q S V The combined processing operation.

[0059] As will now be described, this joint processing provides two processing channels that are executed in parallel and sequentially in time, with the first channel dedicated to the charge change signal S. Q The second channel is dedicated to the speed signal S. V .

[0060] Specifically, the processing unit 16 is initially configured to acquire a charge change signal in the time domain at block 30; specifically, it acquires corresponding samples at subsequent time points (e.g., these samples are stored in an acquisition buffer that is progressively fed and updated by the same samples).

[0061] As shown in box 32, the above charge change signal can be normalized, for example by removing the corresponding base value, the so-called "baseline" (e.g. due to environmental electrostatic charge or uncompensated offset of the sensor).

[0062] The resulting signal is then fed to a frequency transform, box 33, for example, by a known FFT (Fast Fourier Transform) operation prior to a windowing operation (e.g., via a Hann window), to obtain a charge change signal in the frequency domain as shown in box 34.

[0063] Then, in box 35, an operation is performed to identify peaks in the frequency domain of the charge change signal to obtain a series of N identified peaks [Pk1, ..., Pk] as shown in box 36. N Each peak occurs at the associated frequency.

[0064] In processing the above charge change signal S Q At the same time, like a Figure 6 As shown, the speed signal S provided by the speed sensor 12 is processed in the second processing channel. V This signal will be continuous in time under any circumstances.

[0065] In the illustrated embodiment, the rotational speed sensor 12 is a gyroscope with three detection axes, which provides three angular velocity components Gx, Gy, and Gz along the three detection axes.

[0066] In detail, the angular velocity components Gx, Gy, and Gz are collected in box 38, and their modulus G (expressed in degrees per second) is obtained in box 39.

[0067] As a function of this modulus, as shown in box 40, the rotational speed of wheel 2 is obtained in terms of revolutions per second (RPS) (for example, the above modulus is divided by 360 to obtain the rotational speed value RPS).

[0068] According to one aspect of this solution, the rotational speed value RPS is then compared with the minimum speed threshold RPS in box 41. TH(For example, compared to the speed of vehicle 3, which is equal to 4-5 km / h) in such a way that the RPS of the aforementioned engine speed does not exceed the minimum speed threshold RPS. TH In such cases, the detection algorithm will not continue.

[0069] On the other hand, if the above threshold condition is verified (i.e., the rotational speed value RPS is higher than the minimum speed threshold RPS) TH Then the algorithm continues to box 42, where the distance (absolute value) in the frequency domain is the fundamental frequency determined between each previously detected peak ([Pk1, ..., PkN]) and the frequency corresponding to the aforementioned rotational speed RPS (as mentioned before, the value of which actually corresponds to the charge change signal); the aforementioned distance is denoted by ([d1, ..., dN]).

[0070] In the next box 43, then select the peak with the smallest distance d relative to the RPS value. m The peak value, as shown in box 44, is represented by Pk. m express.

[0071] Then, in box 45, the minimum distance d mentioned above is... m With minimum distance threshold d TH Compare them.

[0072] At the minimum distance d m Less than the minimum distance threshold d TH In the case of box 46, at the selected peak amplitude Pk mentioned above m and peak amplitude threshold Pk TH (The values ​​can be set, for example, during the design process, or adjusted or selected by the operator to suit various use cases) to perform further comparisons.

[0073] If the selected peak amplitude Pk m Greater than the peak amplitude threshold Pk TH If the algorithm determines that there is a dry road condition "dry", as shown in box 47.

[0074] Otherwise, as shown in box 48, at the minimum distance d m Not less than the minimum distance threshold d TH (At box 45 above), or the selected peak amplitude P km Not greater than the peak amplitude threshold Pk th In the case of (box 46 above), as shown in box 48, the algorithm determines the presence of the wet road condition "wet".

[0075] It should be noted that the aforementioned wet road conditions are characterized by the presence of liquid, particularly a water film or layer, on the surface in contact with the wheel 2, which is absent in dry road conditions.

[0076] As the same Figure 6 As shown, based on the collected rotational speed and charge change signals S V S Q The new samples are processed over time.

[0077] In one possible implementation (not shown), a time count for the duration of a specific condition can also be provided so that the detected condition is only verified if the condition lasts for more than a minimum time: for example, for asphalt to be identified as dry, the system can continuously provide a detection of the “dry” condition for at least five seconds (or other appropriate time intervals).

[0078] Therefore, the algorithm implemented by the processing unit 16 essentially provides a means for parallel acquisition of the speed signal S. V (Advantageously provided by the speed sensor 12, in this case a three-axis gyroscope, so as to directly correspond to the speed of the wheel 2 or revolutions per second, and thus to the charge change signal S) Q The fundamental frequency of the pulse occurrence and the same charge change signal S provided by the charge change sensor 14. Q .

[0079] Based on the charge change signal S Q It performs a search for peaks in the spectrum to identify the location and amplitude of all detected peaks.

[0080] If the above speed value RPS is greater than the minimum speed threshold RPS TH That is, if the vehicle is moving at a speed greater than the minimum effective speed, the algorithm searches the spectrum for the peak that is closest to the fundamental frequency, which corresponds to the same value RPS.

[0081] If it is identified that has an absolute value not greater than the minimum distance threshold d TH The distance to the peak value was measured, and it was also verified that the amplitude of this peak value was greater than the peak amplitude threshold PK. TH If the condition is positive, then the road is classified as "dry"; otherwise, the road is classified as "wet".

[0082] Regarding the content just discussed Figure 7 The charge change signal S is shown. Q and speed signal S V The trend of the rotational speed of wheel 2 of vehicle 3 changing over time is referenced to the experimental test performed by the applicant.

[0083] Charge change signal S Q The amplitude is dimensionlessly expressed as LSB ("least significant bit"), which is the smallest digital value output from the analog-to-digital converter, and its value is related to the voltage detected at the aforementioned input electrode IN (refer again to the previously referenced). Figure 5 The content under discussion is proportional. For example, 1 LSB might correspond to a value contained between a few nV and tens of µV. The scaling constant (or sensitivity) depends on the amplifier gain, the resolution of the analog-to-digital converter, and possible digital processing (e.g., oversampling, decimation, etc.). LSB representation is common in the art and ignores quantization in physical units because its purpose is usually to detect corresponding changes relative to a steady-state or baseline state.

[0084] Specifically, Figure 7 The time interval is indicated, wherein the algorithm described above, executed by the processing unit 16, determines the presence of a "dry road" (dry) or "wet road" (wet) condition.

[0085] Figure 8 A vehicle system is schematically shown, wherein a vehicle 3 (e.g., a motor vehicle) includes at least one previously described detection device 1, which is suitably coupled to at least one corresponding wheel 2.

[0086] The vehicle 3 also includes a main controller 52 (microcontroller, microprocessor or similar digital processing unit) which is coupled to the processing unit 16 of the detection device 1 to receive information related to the detected road conditions.

[0087] In the previously described embodiments, the main controller 52 (e.g., from the processing unit 16 of the detection device 1) receives information about "dry road" or "wet road" conditions to activate or deactivate certain adjustments of the same vehicle 3, such as the corresponding braking systems of the steering or suspension, as previously described.

[0088] The advantages that this solution allows for are clear from the preceding description.

[0089] In any case, it needs to be emphasized that testing equipment 1:

[0090] Normal operation at all speeds of vehicle 3 (within the speed measurement range of the speed sensor (e.g., gyroscope);

[0091] It exhibits high robustness to external and unwanted factors;

[0092] The sensors used (charge change sensor 14 and speed sensor 12) have high durability and reliability;

[0093] Ensure accurate and reliable classification of road conditions (distinguishing between dry and wet road conditions), allowing for the elimination or significant reduction of false detections (false alarms) in any situation.

[0094] The detection device 1 also features optimized energy consumption (very low consumption associated with the aforementioned speed sensor 12 and charge change sensor 14 and the corresponding electronics) and reduced space footprint (specifically, the possibility of integrating detection, speed and charge change technologies into a single package).

[0095] In this regard, as previously stated, it is particularly advantageous to use a MEMS gyroscope as the aforementioned speed sensor 12 according to the following expression (in this case, actually, the charge change signal S) Q The fundamental harmonic frequency is equal to the reciprocal of the rotation period of wheel 2, that is, equal to the RPS value calculated directly from the output of the same gyroscope).

[0096] RPS = DPS / 360

[0097] DPS (degrees per second) corresponds to the modulus of the angular velocity vector (considering three detection axes).

[0098] Finally, variations and modifications may be applied to this solution without departing from the scope defined by the claims.

[0099] For example, in a manner not shown, multiple of the aforementioned charge change sensors 14 may be used, which may be coupled to different wheels 2 of the vehicle 3 and / or to each of these wheels 2 in a certain number of times, in order to further improve the reliability and accuracy of road condition determination (e.g., by averaging or processing the indications provided by the various sensors in an appropriate manner).

[0100] Similarly, the system can provide the use of multiple speed sensors 12, also for example, coupled to each wheel 2 of the vehicle 3.

[0101] The aforementioned speed sensor 12 can be a different sensor, such as a single-axis or dual-axis gyroscope, or in any case, a different sensor configured to provide speed information of the wheel 2.

[0102] Furthermore, after properly calibrating the detection device 1, taking into account factors such as the tread type and material of wheel 2, wear condition, and road surface type, variations of this solution can also distinguish between different and other conditions, except for the two extreme conditions of "dry" (no large amount of liquid) and "wet" (large amount of liquid).

[0103] It should be emphasized that this solution can be advantageously applied to any road vehicle, especially motor vehicles, but also to motor vehicles or other types of vehicles (bicycles, mopeds, etc.) equipped with wheels that come into contact with the road (in materials suitable for the generation of said electrostatic charge, such as rubber).

[0104] Furthermore, it should be emphasized again that the detection device 1 can be manufactured in a single chip containing the charge change sensor 14 (and its electronic circuitry), the speed sensor 12, and the processing unit 16; alternatively, the charge change signal S can be processed in the main controller 52 of the vehicle 3, which contains the detection device 1. Q and speed signal S V (The aforementioned processing unit 16 is implemented in this case).

[0105] Furthermore, it should be emphasized that the input electrode IN (or detection electrode) of the electrostatic charge change sensor 14 can also be a simple conductive probe; a metal plane or path in a printed circuit board; or any conductive element that can operate as a detection electrode, which is arranged to face the operating environment related to the rotation of the wheel 2 of the vehicle 3 to detect charge changes.

[0106] A road condition detection device (1), configured to be coupled to a wheel (2) of a vehicle (3), and can be summarized as including an electrostatic charge change sensor (14), configured to provide a charge change signal (S) indicating an electrostatic charge change associated with the rotation of the wheel (2). Q ); and processing unit (16), coupled to electrostatic charge change sensor (14) to receive charge change signal (S Q ), and is also configured to receive a rotational speed signal (S) indicating the rotational speed of the wheel (2). V ), wherein the processing unit (16) is configured to jointly process the rotational speed signal (S) V ) and charge change signal (S Q ( ), to detect road conditions.

[0107] The processing unit (16) can be configured to determine one of the following road conditions: dry road conditions and wet road conditions.

[0108] The device may also include a speed sensor (12) configured to provide the speed signal (S). V ).

[0109] The rotational speed sensor (12) may be a MEMS triaxial gyroscope; and the processing unit (16) may be configured to determine the rotational speed of the wheel (2) based on the modulus of the angular velocity signal provided by the gyroscope.

[0110] The charge change sensor (14) may include at least one detection electrode (IN) configured to detect the change in electrostatic charge; a high-impedance instrumentation amplifier (22) having an input coupled to the detection electrode (IN); and an analog-to-digital converter (24) coupled to the output of the instrumentation amplifier (22) to provide the charge change signal (S). Q ).

[0111] The processing unit (16), the speed sensor (12), and the electrostatic charge change sensor (14) can be integrated into the same chip.

[0112] The processing unit (16) can be configured to: in the charge change signal (S Q The peak amplitude of the fundamental frequency at the rotational speed of the wheel (2) is determined from the trend of the frequency domain; and the peak amplitude is compared with the threshold amplitude (Pk) TH The two conditions are compared to determine whether the road is dry or wet.

[0113] The processing unit (16) can be configured to: determine the frequency distance between the fundamental frequency and the frequency associated with the rotational speed of the wheel (2); and compare the distance with a threshold distance (d). TH The two conditions are compared to determine one of the road conditions: dry road conditions and wet road conditions.

[0114] The processing unit (16) can be configured to operate when the distance is less than a threshold distance (d). TH And the peak amplitude is greater than the threshold amplitude (Pk) TH Determine the dry road condition under the condition that the distance is not less than the threshold distance (d). TH ), or the peak amplitude is not greater than the threshold amplitude (PK). TH Determine the condition of wet roads in the case of [missing information].

[0115] The processing unit (16) can be configured to: perform a transformation in the frequency domain to obtain a charge change signal in the frequency domain; perform a peak identification operation in the frequency domain of the charge change signal to obtain a series of identified peaks, each identified peak located at an associated frequency; determine the frequency distance between each of the detected peaks and the frequency corresponding to the rotational speed of the wheel (2); and select the frequency with the minimum distance (d) relative to the frequency corresponding to the rotational speed of the wheel (2). m The peak value of the selected peak value is determined as the peak amplitude of the fundamental frequency.

[0116] The processing unit (16) can be configured to: compare the rotational speed with a minimum speed threshold (RPS). TH The comparison is performed only when the rotational speed value is higher than the minimum speed threshold (RPS). TH Only under certain conditions will the road condition be further detected.

[0117] A vehicle system may be summarized as including a detection device (1) and a main controller (52) coupled to the detection device (1) for receiving information about road conditions and for performing and / or activating certain actions based on said road conditions.

[0118] A method for detecting road conditions for a vehicle (3) having at least one wheel (2) can be summarized as including generating a charge change signal (S) indicating a change in electrostatic charge associated with the rotation of the wheel (2) by means of an electrostatic charge change sensor (14). Q ); and receive the rotational speed signal (S) through the processing unit (16) coupled to the electrostatic charge change sensor (14). V ) and the charge change signal (S) indicating the rotational speed of the wheel (2). Q ), and also includes jointly processing the speed signal (S) by the processing unit (16). V ) and charge change signal (S Q ( ), to detect road conditions.

[0119] The detection may include determining one of the following road conditions: dry road conditions and wet road conditions.

[0120] The processing may include: in the charge change signal (S Q In the frequency domain trend of ), determine the peak amplitude of the fundamental frequency of the wheel (2) at the said rotational speed; and compare the peak amplitude with the threshold amplitude (Pk). TH The two conditions are compared to determine whether the road is dry or wet.

[0121] The process may include: determining the frequency distance between the fundamental frequency and the frequency associated with the rotational speed of the wheel (2); and comparing the distance with a threshold distance (d). TH The conditions are compared to determine whether the road conditions are dry or wet.

[0122] The method may include: when the distance is less than the threshold distance (d) TH And the peak amplitude is greater than the threshold amplitude (Pk) TH Determine the dry road condition under the condition that the distance is not less than the threshold distance (d). TH Or the peak amplitude is not greater than the threshold amplitude (PK). TH Determine the condition of wet roads in the case of [missing information].

[0123] The process may include: performing a transformation in the frequency domain to obtain a charge change signal in the frequency domain; performing a peak identification operation in the frequency domain of the charge change signal to obtain a series of identified peaks, each identified peak located at an associated frequency; determining the distance between the frequency of each detected peak and the frequency corresponding to the rotational speed of the wheel (2); selecting the frequency with the smallest distance (d) relative to the rotational speed value. m The peak value of the selected peak value is determined as the peak amplitude of the fundamental frequency.

[0124] The method may include: comparing the rotational speed value with a minimum speed threshold (RPS). TH The comparison is performed only when the stated rotational speed value is above the minimum speed threshold (RPS). TH Only if the road conditions are not properly monitored will the road condition be monitored.

[0125] The method may include performing and / or activating certain actions in the vehicle (3) based on the detection of the road conditions.

[0126] The various embodiments described above can be combined to provide other embodiments. If it is necessary to use concepts from various patents, applications, and publications to provide other embodiments, various aspects of the embodiments can be modified.

[0127] These and other modifications can be made to the embodiments based on the detailed description above. Generally, the terminology used in the following claims should not be construed as limiting the claims to the specific embodiments disclosed in the specification and claims, but should be interpreted to include all possible embodiments and all equivalents to which these claims are given. Therefore, the claims are not limited to this disclosure.

Claims

1. A road condition detection device configured to be coupled to a wheel of a vehicle, the road condition detection device comprising: An electrostatic charge change sensor is configured to provide a charge change signal indicating a change in electrostatic charge associated with the rotation of the wheel; as well as The processing unit is coupled to the electrostatic charge change sensor and configured to receive the charge change signal, and also configured to receive a rotational speed signal indicating the rotational speed of the wheel. The processing unit is configured to jointly process the rotational speed signal and the charge change signal to detect road conditions between wet and dry conditions in response to the amplitude of the charge change signal and the rotational speed signal. The processing unit is configured to: determine, in the frequency domain trend of the charge change signal, the peak amplitude of the fundamental frequency at the rotational speed of the wheel; and compare the peak amplitude with a threshold amplitude to determine a road condition, either dry or wet. The processing unit is configured to: determine the frequency distance between the fundamental frequency and the frequency associated with the rotational speed of the wheel; and compare the distance with a threshold distance to determine one of the road conditions, namely, the dry road condition and the wet road condition.

2. The road condition detection device according to claim 1, comprising: An analog-to-digital converter is located between the electrostatic charge change sensor and the processing unit, the electrostatic charge change sensor including a first operational amplifier and a second operational amplifier coupled to the input of the analog-to-digital converter.

3. The road condition detection device according to claim 1 further includes a speed sensor configured to provide the speed signal.

4. The road condition detection device according to claim 3, wherein the rotational speed sensor is a MEMS three-axis gyroscope; and wherein the processing unit is configured to determine the rotational speed of the wheel based on the modulus of the angular velocity signal provided by the gyroscope.

5. The road condition detection device according to claim 3, wherein the charge change sensor comprises: At least one detection electrode is configured to detect the change in electrostatic charge; A high-impedance instrumentation amplifier having an input coupled to the detection electrode; An analog-to-digital converter is coupled to the output of the instrumentation amplifier to provide the charge change signal.

6. The road condition detection device according to claim 3, comprising a chip, the chip comprising the processing unit, the speed sensor and the electrostatic charge change sensor.

7. The road condition detection device of claim 1, wherein the processing unit is configured to determine the dry road condition when the distance is less than the threshold distance and the peak amplitude is greater than the threshold amplitude; and to determine the wet road condition when the distance is not less than the threshold distance or the peak amplitude is not greater than the threshold amplitude.

8. The road condition detection device according to claim 1, wherein the processing unit is configured to: perform a transformation in the frequency domain to obtain the charge change signal in the frequency domain; perform a peak identification operation in the frequency domain of the charge change signal to obtain a series of identified peaks, each identified peak located at an associated frequency; determine the frequency distance between each of the detected peaks and a frequency corresponding to the rotational speed of the wheel; select the peak with the smallest distance relative to the frequency corresponding to the rotational speed of the wheel, and determine the amplitude of the selected peak as the peak amplitude of the fundamental frequency.

9. The road condition detection device of claim 1, wherein the processing unit is configured to: compare the rotational speed with a minimum speed threshold; and only continue detecting the road condition if the rotational speed value is higher than the minimum speed threshold.

10. A vehicle system comprising: Road condition detection device according to any one of the preceding claims; and The main controller is coupled to the processing unit and is configured to receive information about road conditions and take action in response to the road conditions.

11. The system of claim 10, wherein the detection device includes a rotation sensor coupled to the wheel and the processing unit, the processing unit being configured to evaluate a rotational speed value using a minimum speed threshold and to continue detecting the road conditions in response to a rotational speed value exceeding the minimum speed threshold.

12. A method for detecting road conditions for a vehicle having at least one wheel, comprising: A charge change signal is generated by an electrostatic charge change sensor, the charge change signal indicating the electrostatic charge change associated with the rotation of the wheel; as well as The processing unit coupled to the electrostatic charge change sensor receives the charge change signal and the rotation speed signal indicating the rotation speed of the wheel; The processing unit detects road conditions by jointly processing the rotational speed signal and the charge change signal. The peak amplitude of the fundamental frequency at the stated rotational speed of the wheel is determined from the frequency domain trend of the charge change signal; and the peak amplitude is compared with a threshold amplitude to determine one of dry road conditions and wet road conditions. Determine the frequency distance between the fundamental frequency and the frequency associated with the rotational speed of the wheel; and compare the distance with a threshold distance to determine one of the road conditions, namely, dry road conditions and wet road conditions.

13. The method of claim 12, comprising: The dry road condition is determined when the distance is less than the threshold distance and the peak amplitude is greater than the threshold amplitude; and the wet road condition is determined when the distance is not less than the threshold distance or the peak amplitude is not greater than the threshold amplitude.

14. The method of claim 12, wherein the processing comprises: A transformation is performed in the frequency domain to obtain the charge change signal in the frequency domain; A peak identification operation is performed in the frequency domain of the charge change signal to obtain a series of identified peaks, each identified peak located at an associated frequency; the frequency distance between each of the detected peaks and the frequency corresponding to the rotational speed of the wheel is determined; the peak with the smallest distance relative to the rotational speed value is selected, and the amplitude of the selected peak is determined as the peak amplitude of the fundamental frequency.

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