Patent Description:
Road pavements must be designed in such a way as to ensure a rolling surface that is substantially regular and with little deformation in order to meet safety and comfort requirements for the motor vehicles that drive on them. As is known, in fact, the impact of a wheel of a motor vehicle against any obstacle on the road pavement (such as, for example, a sidewalk, a pothole or a speed ramp) may cause damage to the wheel pneumatic tyre, in particular to the carcass (i.e., the casing) of the pneumatic tyre.

More specifically, a protuberance that is externally visible upon the side of a pneumatic tyre typically indicates that some cords have been broken within the carcass due to an impact since driving over objects such as curbs, speed ramps and potholes may cause individual cords to break.

If a damaged pneumatic tyre (e.g. a pneumatic tyre with some damaged cords) is not detected promptly and, therefore, is not promptly repaired or replaced, in continuing to drive with said damaged pneumatic tyre there is a risk of completely breaking/destroying the carcass of the pneumatic tyre and also of damaging the wheel rim and/or the suspension (e.g. in the case of further impacts to the damaged pneumatic tyre against other obstacles).

Until now, systems were periodically implemented in order to monitor the level of regularity of individual roads, mainly for the purpose of planning maintenance work. Typically, said monitoring systems are based upon a calculation of the International Roughness Index (IRI) which represents the international index for the irregularity of road pavements.

However, in recent years, within the automotive sector, there has been a strong need for technologies for detecting road surface conditions that are able to automatically and continuously detect the presence of potential obstacles (such as sidewalks, potholes or speed ramps) and that are able to promptly report them to the drivers of such motor vehicles.

Some methods for the recognition of the irregularities of a road pavement are described, for example, in:.

The aim of the present invention is thus to provide a method for the recognition of the irregularities of a road pavement that is free from the disadvantages of the state of the art and that is, in particular, easy and inexpensive to implement.

A further aim of the present invention is thus to provide a system for the recognition of the irregularities of a road pavement that is free from the disadvantages of the state of the art and that is, in particular, easy and inexpensive to make.

According to the present invention a method and a system are provided for the recognition of the irregularities of a road pavement according to that which is determined within the attached claims.

The present invention will now be described with reference to the attached drawings, which show an exemplary, non-limiting embodiment, wherein:.

The applicant has experimentally verified that the normalized wheel speed (i.e., the ratio between an acquired/measured wheel speed and the corresponding motor vehicle speed) is correlated to the wheel driving over or impacting an irregularity within the road pavement. In the following section, the term irregularity refers to any obstacle potentially present upon the road pavement (such as, for example, sidewalks, potholes, curbs, speed ramps, etc.).

Based upon the results of the tests performed, the applicant has designed and developed an innovative technology to detect the irregularities of the road pavement described in the following section and including a preliminary step and an actual irregularity detection step.

More specifically, the preliminary step for the optimization of this technology involves the execution of tests that envisage pneumatic tyres driving over or impacting different types of irregularities and at different speeds of the motor vehicle. The preliminary testing step is also conducted with different types of pneumatic tyres with specific characteristics (in terms of pressure, size and stiffness) and with different types of vehicles with specific characteristics (for example, in terms of shock absorber stiffness).

In particular, at least three test campaigns were followed in order to study the response in relation to:.

<FIG> schematically illustrates, by means of a block diagram, the functional architecture of a system <NUM> for the recognition of irregularities of a road pavement.

In particular, the system <NUM> for the recognition of irregularities of the road pavement includes an acquisition device <NUM> that is installed on board a motor vehicle equipped with two or more wheels, each equipped with a pneumatic tyre, and is coupled to a vehicle bus <NUM> (e.g. based upon a standard Controller Area Network (CAN) bus) of said motor vehicle.

According to a preferred variant, the acquisition device <NUM> is fixed/bound to the chassis of the motor vehicle. In particular, the acquisition device <NUM> is connected to the chassis of the motor vehicle in such a way that the acquisition device <NUM> is subjected to the same vibrations to which the chassis of the motor vehicle is subjected.

Preferably, the acquisition device <NUM> is placed near an OBD connector of the motor vehicle.

The system <NUM> for the recognition of irregularities of a road pavement also comprises a processing device <NUM> that is connected, in wired or wireless mode, to the acquisition device <NUM>.

The acquisition device <NUM> is configured to acquire from the vehicle bus <NUM> signals that are indicative of the speed of the motor vehicle and the speed of a wheel of said motor vehicle (speed signals that are, for convenience, expressed in kilometers or miles per hour). Furthermore, the acquisition device <NUM> is configured to provide at the output measurements that are indicative of the speeds of the motor vehicle and the speed of the wheel thereof.

The acquisition device <NUM> is also configured to acquire from the vehicle bus <NUM> signals linked to the driving of the motor vehicle. In particular, the acquisition device <NUM> is configured to acquire from the vehicle bus <NUM> signals such as vertical acceleration, yaw rate, pitch and roll (by means of a gyroscope), the steering angle of the vehicle and information relating to the position of the vehicle (by means of a GPS signal).

According to a first embodiment, the processing device <NUM> is configured to receive from the acquisition device <NUM> those measurements that are indicative of the speeds of the motor vehicle and the speed of the wheel of said motor vehicle. In addition, the processing device <NUM> is configured to receive from the acquisition device <NUM> also those measurements that are indicative of the steering angle of the vehicle and information related to the position of the vehicle (by means of a GPS signal).

In more detail, the acquisition of the signal relating to the wheel speed is performed with a sampling frequency of at least <NUM>. Preferably, the acquisition of the signal relating to the wheel speed is performed with a sampling frequency of <NUM>.

The processing device <NUM> is intended for the analysis of that measurement which is indicative of the steering angle of the wheel of said motor vehicle, wherefore transformations that alter the distribution of said measurement are used. In particular, the processing device <NUM> performs a FFT (Fast Fourier Transform) of that measurement which is indicative of the steering angle of the wheel of said motor vehicle over a reference section of the road pavement of variable length. The reference section of the road pavement has a variable and/or adjustable length; the reference section of the road pavement has a length of between <NUM> and <NUM> linear meters, preferably between <NUM> and <NUM> linear meters.

Said analysis by means of the FFT makes it possible to identify the frequency content of that measurement which is indicative of the steering angle of the wheel; moreover, said analysis makes it possible to highlight a minimum threshold that varies according to the driving style of the driver of the motor vehicle within the reference section.

The processing device <NUM> is thus configured such as to perform filtering of that measurement which is indicative of the speed of the wheel of the motor vehicle. The filtering of that measurement which is indicative of the speed of the wheel of the motor vehicle is also performed over the reference section of the road pavement.

The filtering is at least of the high-pass type; preferably, the filtering is of the band-pass type. The minimum threshold determined during the previous section by the analysis of that measurement which is indicative of the steering angle of the wheel is used within the high-pass filter; in this way it is possible to analyze only that part of the signal containing information relating to the irregularities of the road pavement and not to the driving style of the driver of the vehicle.

The processing device <NUM> is then configured to calculate, on the basis of those measurements that are indicative of the speeds of the motor vehicle and of the speeds of the wheel thereof, a normalized wheel speed that is indicative of a ratio (preferably a percentage ratio) of the wheel speed in relation to the speed of the motor vehicle.

Alternatively, the processing device <NUM> is configured to calculate, on the basis of those measurements that are indicative of the speeds of the motor vehicle and of the speeds of the wheel thereof, a normalized wheel speed that is indicative of a ratio (preferably, a percentage ratio) of the wheel speed in relation to the motor vehicle speed and to subsequently perform the filtering of the normalized wheel speed over the reference section of the road pavement.

The processing device <NUM> is thus configured to calculate the standard deviation of said normalized wheel speed over the reference section of the road pavement.

The preliminary step described above involves determining on the basis of the results of the tests performed one or more predefined models in order to associate the standard deviation of the normalized wheel speed over the reference section with the presence of irregularities of the road pavement. In essence, the preliminary test step includes, in succession, a sub-step in which tests are performed by having pneumatic tyres drive over and/or impact different irregularities at different speeds of the motor vehicle; a sub-step in which, during the tests performed, the wheel speeds and the speeds of the motor vehicle are acquired and the normalized wheel speeds are calculated in relation to those tests performed by means of the ratio between the wheel speeds and the respective speeds of the motor vehicle; and a sub-step for the construction of at least one model for associating the standard deviation of the normalized speeds with the irregularities on the road pavement. Preferably, the preliminary test step involves the construction of a number of models depending upon the type of pneumatic tyre and the type of motor vehicle.

The standard deviation of said normalized wheel speed is thus compared to the predefined models developed during the preliminary test step and is used to recognize the presence of irregularities of the road pavement. The irregularity that has just been recognized can be located by means of information related to the position of the vehicle (by means of a GPS signal).

According to a further embodiment, the processing device <NUM> is configured to receive from the acquisition device <NUM> those measurements that are indicative of vertical acceleration (along the z axis). Furthermore, the processing device <NUM> is configured to receive from the acquisition device <NUM> also those measurements that are indicative of the steering angle and information related to the position of the vehicle (by means of a GPS signal). The acquisition device <NUM> is also configured to acquire from the vehicle bus <NUM> and to transmit to the processing device <NUM> signals related to the driving of the motor vehicle. In particular, the acquisition device <NUM> is configured to acquire from the vehicle bus <NUM> signals such as yaw rate, pitch and roll (by means of a gyroscope).

In more detail, the acquisition of the signal related to vertical acceleration is performed with a sampling frequency of at least <NUM>.

The processing device <NUM> is thus configured to initially perform filtering of that measurement which is indicative of vertical acceleration. The filtering of that measurement which is indicative of vertical acceleration is performed over a reference section of the road pavement of variable length. The reference section of the road pavement has a variable and/or adjustable length; the reference section of the road pavement has a length of between <NUM> and <NUM> linear meters, preferably between <NUM> and <NUM> linear meters.

The filtering is preferably of the high-pass type; the minimum threshold of the high-pass filter is preferably less than or equal to <NUM>.

Once the high-pass filtering has been performed, the processing device <NUM> is intended to analyze that measurement which is indicative of vertical acceleration by means of a transformation that alters the distribution of said measure. In particular, the processing device <NUM> performs a FFT (Fast Fourier Transform) of that measurement which is indicative of vertical acceleration over the reference section.

Said analysis by means of the FFT makes it possible to identify the frequency content of that measurement which is indicative of vertical acceleration over the reference section.

The processing device <NUM> is thus configured to calculate the standard deviation of that measurement which is indicative of vertical acceleration over the reference section. In particular, the processing device <NUM> is configured to calculate the standard deviation of that measurement which is indicative of vertical acceleration over the reference section and at relevant frequencies. Preferably, the relevant frequencies include a first range of vibration frequencies of the motor vehicle suspension system; preferably the first frequency range is between <NUM> and <NUM>. Preferably, the relevant frequencies also include a second range of vibration frequencies of the motor vehicle chassis.

It has been experimentally verified that a minimum threshold for the high-pass filter of less than or equal to <NUM> and a range of relevant frequencies that corresponds to the vibration frequencies of the motor vehicle suspension system (between <NUM> and <NUM>) makes it possible to obtain reliable results without exceeding the computational burden of the processing device <NUM>. Similarly, it has been experimentally verified that the best results, in terms of the reliability of the results, are obtained when the reference portion of the road surface has a length of between <NUM> and <NUM> linear meters, preferably between <NUM> and <NUM> linear meters.

The preliminary step described above involves determining on the basis of the results of the tests performed one or more predefined models in order to associate the standard deviation of that measurement which is indicative of vertical acceleration over the reference section and at relevant frequencies with the presence and size of irregularities of the road pavement.

In essence, the preliminary test step includes, in succession, a sub-step in which to perform the tests by having pneumatic tyres drive over and/or impact different irregularities at different speeds of the motor vehicle; a sub-step in which to acquire the vertical acceleration during the tests performed; and a sub-step of construction of at least one model for associating the standard deviation of the vertical acceleration with the presence and size of the irregularities of the road pavement.

Preferably, the preliminary test step involves the construction of a number of models depending upon the type of pneumatic tyre and the type of motor vehicle.

The standard deviation of said measurement that is indicative of the vertical acceleration over the reference section is thus compared to the predefined models developed during the preliminary test step and is used to recognize the presence of irregularities of the road pavement. The irregularity that has just been recognized can be located by means of information related to the position of the vehicle (by means of a GPS signal).

The first and second embodiments described in the previous section can be used alternatively in order to recognize the presence of irregularities of the road pavement. The first and second embodiments described in the previous section can be used at the same time and in parallel in order to recognize, with a higher degree of precision and more reliably, the presence of irregularities of the road pavement.

<FIG> schematically illustrates a first variant of the system <NUM>* for the recognition of irregularities of the road pavement wherein the processing device <NUM> is implemented/carried out by means of a cloud-type computing system <NUM>* that is remotely wirelessly connected to the acquisition device <NUM> (e.g. by means of one or more mobile communication technologies, such as GSM, GPRS, EDGE, HSPA, UMTS, LTE, LTE Advanced and/or 5th generation (or even beyond) wireless communication systems).

Claim 1:
A method for the recognition of the irregularities of a road pavement comprising:
(A) a preliminary test step including in turn
- a sub-step wherein tests are performed in having pneumatic tyres drive over and/or impact different irregularities at different speeds of the motor vehicle;
- a sub-step wherein during the tests the vertical acceleration is acquired; and
- a sub-step for the construction of at least one first model for associating the standard deviation of the vertical acceleration in relation to the tests performed with the irregularities on the road pavement; and
(B) an actual recognition step including in turn
- a sub-step wherein the vertical acceleration is acquired;
- a sub-step wherein high-pass filtering of the vertical acceleration is implemented; wherein a minimum filtering threshold of the high-pass filter is preferably less than or equal to <NUM>;
- a sub-step wherein the high-pass-filtered vertical acceleration is processed by means of a Fast Fourier Transform;
- a sub-step including calculating the standard deviation of the Fast-Fourier-Transform-processed vertical acceleration at relevant frequencies; and
- recognizing the presence and the dimensions of the irregularities on the road pavement on the basis of a comparison between said first model and the standard deviation calculated at the relevant frequencies;
characterized in that:
- the sub-step of high-pass filtering is performed on a reference section of the road pavement of variable length having a length of between <NUM> and <NUM> linear meters, preferably between <NUM> and <NUM> linear meters; and
- the relevant frequencies comprise a first range of frequencies of vibration of a suspension system of the motor vehicle, said first range of vibration frequencies being preferably between <NUM> and <NUM>.