FR3011793A1 - DEVICE FOR ANALYZING THE CONDITION OF THE CIRCULATION PATH OF MOTOR VEHICLES - Google Patents

Abstract

A device (D) is for analyzing the condition of traffic lanes used by motor vehicles (V). This device (D) comprises i) processing means (MT) arranged for estimating, for portions of a traffic lane taken by a vehicle (V), cumulative and normalized global values VGCNjm of a magnitude representative of vibrations undergone by this vehicle (V) over a time window associated with vehicle location data (V), each based on cumulative primary values VPCjmi of this magnitude, estimated for this vehicle (V) over time windows associated with data locus dlm substantially identical, and ii) analysis means (AM) arranged to assign each of these portions a value representative of its state and function of the cumulative overall value and normalized VGCNjm estimated for it.

Description

The invention relates to the analysis of traffic lanes on which motor vehicles may circulate. Traffic routes that may be used by motor vehicles may be in very different states depending on whether they are roads, roads (in built-up areas or out of built-up areas) or highways. It is therefore useful to know their status so that drivers of motor vehicles can borrow them knowingly or that devices for navigation aids embedded in motor vehicles can determine routes according to status (s) chosen by the drivers. Today, the condition of the taxiways is generally considered as a function of their type. Thus, it is generally considered that motorways (and highways) have very smooth surfaces and therefore are all associated with the best of states, that national roads outside urban areas and roads in built-up areas are smooth (few asperities). and therefore are all associated with a good condition, that departmental roads (or countryside) are moderately worn (frequent asperities) and therefore are all associated with a medium state, and that the paths are bumpy and therefore are all associated with the worst States. This type of very crude classification only partially reflects the reality and, above all, does not make it possible to know what is the real state of all the portions of the same lane of traffic. The invention therefore aims in particular to improve the situation, through the use of an objective criterion of analysis (or characterization) based on the effective dynamic stress level of the motor vehicle which uses the traffic lanes. . It proposes for this purpose an analysis device, intended to analyze (or characterize) the state of traffic lanes Cj borrowed by motor vehicles, and comprising: - processing means arranged to estimate for PCjm portions of a traffic lane Cj, taken by a vehicle, cumulative and normalized global values VGCNjm of a magnitude representative of vibrations undergone by this vehicle over a time window associated with vehicle location data dlm, each based on cumulative primary values VPCjmi of this magnitude, estimated for this vehicle on time windows associated with location data dlm substantially identical, and - analysis means arranged to assign each of these portions PCjm a value representative of its state and function of the value overall cumulated and standardized VGCNjm estimated for it. With these estimates of cumulative values and normalized VGCNjm over time windows, one can know the level of stress of the vehicle on each PCjm portion of a taxiway Cj and thus deduce the state of each PCjm portion. The analysis device according to the invention can comprise other characteristics that can be taken separately or in combination, and in particular: its analysis means can be arranged to compare each cumulative and normalized global value VGCNjm estimated for a PCjm portion; at least two predefined thresholds, and for assigning to each of the PCjm portions a value which is representative of its state chosen from at least three different states depending on the result of the comparison; its processing means can be arranged to estimate each cumulative primary value VPCjmi (of the magnitude) on a PCjm portion as a function of primary data which are representative of acceleration measurements made in the vehicle when it traverses this PCjm portion and associated location data dlm of this vehicle; its processing means can be arranged to estimate each cumulative primary value VPCjmi as a function of primary data sums raised to the power four; its processing means can be arranged to use primary data equal to the product of an acceleration measurement by a weighting coefficient chosen; it may comprise measurement means arranged to perform the acceleration measurements; its processing means can be arranged to use a sliding time window; its processing means can be arranged to estimate each cumulated and normalized global value VGCNjm as a function of a sum, o raised to the power 0.25, of three cumulative and normalized global primary values VGCNjm; of magnitude, raised to the power of four, and estimated respectively in three directions of space perpendicular to each other; its processing means can be arranged to estimate each cumulative and normalized global primary value VGCNjm; depending on the result, raised to the power 0.25, of a product between a cumulative primary value VPCjmi raised to the power of four, and a ratio between a normalization number P and the duration of the time window; It may comprise locating means arranged to provide the location data dlm. The invention also proposes a motor vehicle suitable for running on traffic lanes and comprising an analysis device of the type described above. Other features and advantages of the invention will be apparent from the following detailed description, and the accompanying drawings, in which: FIG. 1 schematically and functionally illustrates, in a view from above, an example of a vehicle automobile equipped with an exemplary embodiment of an analysis device according to the invention, and FIG. 2 illustrates in a diagram two examples of frequency weighting curves (pf - in dB) as a function of the frequency (f - in hertz), which can be used by an analysis device according to the invention. The object of the invention is in particular to propose an analysis device D intended to analyze (or characterize) the state of traffic lanes Cj used by motor vehicles.

FIG. 1 shows schematically and functionally an exemplary embodiment of an analysis device D according to the invention, installed (not limited to) in a motor vehicle V dedicated to analyzes. It is important to note that an analysis device D, according to the invention, may not be installed in a motor vehicle V. In fact, it may be installed in a personal computer, or in any electronic equipment having means calculation and means to use software. Consequently, the analysis device D can be implemented in the form of a combination of electronic circuits (or "hardware") and software modules (or "software"), or only software modules ( especially for signal processing). Note also that when the analysis device D is not installed in a motor vehicle, it is necessary that a motor vehicle acquires at least raw raw data (which will be discussed later) and that they can be transmitted to the analysis device D. In what follows, it is considered, by way of non-limiting example, that the motor vehicle V which carries out the analysis of the traffic lanes Cj is a car. But it could be another type of motor vehicle, including a truck, a commercial vehicle or a motorcycle, and more generally any equipment that can run on the road. For example, the vehicle (automobile) V comprises an on-board communication network (or network) RC, possibly of multiplexed type, and allowing the exchange of information between on-board electronic equipment (and here in particular the analysis device D). This information may be, for example, commands (or instructions), messages, data files, parameter values, quantities or states measured or estimated, or calculation results. As illustrated, an analysis device D according to the invention comprises at least processing means MT and analysis means MA. The processing means MT are arranged to estimate for PCjm portions of a taxiway Cj, taken by the vehicle V, accumulated and normalized global values VGCNjm of a magnitude which is representative of the vibrations undergone by this vehicle V on a time window which is associated with location data dlm of the vehicle V. Each estimate is made for a portion PCjm as a function of cumulative primary values VPCjmi of the magnitude, estimated for the vehicle V on time windows which are associated with data of location lo dlm substantially identical. It will be understood that the index j denotes here a taxiway Cj, while the index m denotes one of the portions PCjm of a taxiway Cj. The location data dlm are provided by a location module ML, for example by satellites (possibly GPS type). In the nonlimiting example illustrated, this location module ML is part of the analysis device MA. But this is not obligatory. Indeed, it could be part of another equipment embedded in the vehicle V (even temporarily) and using location data for personal use. Thus, this other equipment could be a navigation aid device. What is important here is that primary data (described later) acquired in the vehicle V can be associated in real time with location data dlm which is representative of the current position of the vehicle V, and that location data dlm and the associated primary data can be processed by the analysis device D. The analysis means MA are arranged to assign to each of the PCjm portions of a taxiway Cj a value which is representative of its state and function of the cumulated and normalized global value VGCNjm estimated for it (PCjm). The estimation of cumulated and normalized global values VGCNjm over time windows advantageously makes it possible to know the level of loading of the vehicle V on each portion PCjm of a taxiway Cj and thus to deduce the state of each portion PCjm .

The accuracy of the analysis depends on the number of different states that can be associated with the portions PCjm as a function of the cumulative values and normalized VGCNjm estimated that are associated with the latter (PCjm).

For example, the analysis means MA can assign the state values after making comparisons. In this case, they are arranged to compare each cumulative and normalized global value VGCNjm estimated for a PCjm portion with at least two predefined thresholds, and to assign to each of the PCjm portions a value that is representative of its state chosen from at least three states. different depending on the result of the comparison. These three states can be for example a very good state (defined by a first state value), an average state (defined by a second state value), and a bad state (defined by a third state value) .

A number of states greater than three can be used. For example, three predefined thresholds can be used to qualify four different states: a very good state (defined by a first state value), a good state (defined by a second state value), an average state (defined by a third state value), and a bad state (defined by a fourth state value).

For example, the processing means MT can estimate each cumulative primary value VPCjmi (of the magnitude) on a portion PCjm as a function of primary data which are representative of acceleration measurements made in the vehicle V when it traverses this portion PCjm and which are associated with location data dlm of the vehicle V.

It should be noted that the acceleration measurements can have several origins. Indeed, they can for example be performed by equipment that is embedded in the vehicle V and that use them for its internal needs. This may, for example, be the computer responsible for controlling the trajectory of the vehicle V (ESP (Electronic Stability Program)). As a variant, they may for example be made by an acceleration measurement box implanted specifically in the vehicle V, preferably in the vicinity of its center of gravity, and capable of transmitting them to the analysis device D (for example via the network RC communication). In yet another variant, illustrated without limitation in FIG. 1, they may for example be made by an acceleration measurement module MM which is part of the analysis device D.

Preferably, these acceleration measurements are made in three directions of space (X, Y, Z) which are perpendicular to each other. The direction X is the longitudinal direction (parallel to the longitudinal sides of the vehicle V), the direction Y is the transverse direction (perpendicular to the longitudinal direction X), and the direction Z is the vertical direction io (perpendicular to the longitudinal directions X and transverse Y). The estimation of each cumulative primary value VPCjmi can be done in different ways. The index i denotes here a direction of space, i = x, y or z. Thus, it can be done according to sums (or integrals) of primary data raised to the power four (4). Power four is used here to give more importance to high levels. Alternatively, one could use different powers, multiples of 2, for example between 2 to 20. For example, the primary data can be untreated (or raw) acceleration measurements ai (t) performed at a time t by the measuring means MM. But, it is preferable to use primary data which are each equal to a weighted acceleration (alpe) resulting from the product of an untreated (or gross) acceleration measurement ai (t) by a weighting coefficient wi. Then we have alpe) = ai (t) * wi. This weighting is intended to provide more realistic results with respect to the taxiway device. Two examples of frequency weighting curves (pf) in dB, referenced C1 and C2, are illustrated in FIG. 2 as a function of the frequency (f) in Hz. The first curve C1 consists of coefficients w, dedicated to the accelerations a. , in the vertical direction Z, while the second curve C2 consists of coefficients wx, y dedicated to the accelerations ax in the longitudinal direction X as well as to the accelerations ay in the transverse direction Y.

The curves of coefficients w i are stored and used by the processing means MT of the analysis device D. However, it will be noted that when the analysis device D is external to the vehicle V and fed with primary data of the weighted acceleration type alpe), it is the primary data acquisition device which is installed in the vehicle V which itself determines the weighted accelerations alpe) from the raw accelerations ai (t). When the primary data are weighted accelerations api (t), each cumulative primary value VPCjmi can be given by the lo relation: 7 71 0.25 where t = 0 is the time when a time window starts, and T is the moment when this time window ends. The duration T of the time window is chosen according to the type of analysis to allow discrimination of the vehicle V stress variations directly related to the quality of the traffic lanes used. For example, it can be between 0.5 s and 10 s. The shorter the duration T, the greater the importance of the defects on a PCjm portion and the ability to detect short "obstacles" in distance on the roadway. On the other hand, the longer the duration T, the more the defects are reduced on a PCjm portion because of the averaging. It will be noted that a portion PCjm may possibly be associated with at least two time windows. It will also be noted that the processing means MT may possibly be arranged to use a sliding time window. In this case, there may be a recovery rate between the successive sliding time windows. This makes it possible to smooth the results obtained, in particular when the speed of the vehicle V varies frequently and substantially. For example, this recovery rate can range from about 10% to about 90%. The higher this rate, the more the evaluation is done on points that are close together for the distance traveled.

Note also that the magnitude VPCjmi, defined by the previous relationship, represents somehow the dose (or quantity) of vibrations undergone by the vehicle V during a time window. When possible, each cumulated and normalized global value VGCNjm is preferably estimated as a function of a sum, raised to the power 0.25 (or 1/4), of three cumulative and normalized global primary values VGCNjm; (of magnitude), raised to the power of four (4), and estimated respectively as a function of first (i = x), second (i = y) and third (i = z) primary data representative of o acceleration measurements performed in the vehicle V respectively in the three directions of the space X, Y and Z which are perpendicular to each other. Then VGCNjm = (VGCNjmx4 + VGCNjmy4 + VGCNjm74) ° 25, where VGCNjmx is the cumulative primary value of the magnitude in the longitudinal direction X, VGCNjmy is the cumulative primary value of the magnitude along the Y direction, and VGCNjm, is the cumulative primary value of the magnitude in the vertical direction. In this case, each state value associated with a PCjm portion may, for example, be considered as representative of the level of stress of the vehicle when the latter is traveling on this PCjm portion.

It should be noted that one could confine oneself to the acquisition of acceleration measurements in the only vertical direction Z. In this case VGCNjm = VGCNjmz. In this case, each state value associated with a PCjm portion may, for example, be considered as representative of the level of solicitation of the passengers of the vehicle when the latter is traveling on this PCjm portion. By way of example, the processing means MT may be arranged to estimate each cumulative and normalized global primary value VGCNjm; depending on the result, raised to the power 0.25 (or 1/4), of a product between a cumulative primary value VPCjmi raised to the power four (4), and a ratio between a normalization number P and the duration T of the time window. We then have: For example, the normalization number P can be equal to (8 * 3600 = 28800) when we want to normalize over a duration of eight hours reported in seconds (in this case T is also given in seconds). It will be noted that, in general, the normalization number P is equal to (p * 3600) when it is desired to normalize over a duration of p hours reported in seconds. It will also be noted that if the same portion PCjm of a traffic lane Cj is taken Njm times by the vehicle V (with Njm 2), the cumulated overall value and normalized VGCNjm associated with it may be equal to the average value of the cumulative global values. and normalized VGCNjm obtained for this PCjm portion. Alternatively, if the same portion PCjm of a taxiway Cj is taken Njm times by the vehicle V (with Njm 2), the cumulated primary value VPCjmi associated may be equal to the average of the accumulated primary values VPCjmi obtained for this PCjm portion in each of the relevant time windows.

Claims (10)

REVENDICATIONS1. Device (D) for analyzing the condition of traffic lanes (Cj) used by motor vehicles (V), characterized in that it comprises i) processing means (MT) arranged to estimate for portions (PCjm) of a traffic lane (Cj), taken by a vehicle (V), cumulative and normalized global values VGCNjm of a magnitude representative of vibrations undergone by this vehicle (V) on a window o temporal associated with location data dlm of said vehicle (V), each based on cumulative primary values VPCjmi of said magnitude, estimated for said vehicle (V) on time windows associated with location data dlm substantially identical, and ii) means for analysis (MA) arranged to assign to each of said portions (PCjm) a value representative of its state and a function of the accumulated and normalized global value VGCNjm estimated for it (PCjm). 2. Device according to claim 1, characterized in that said analysis means (MA) are arranged to compare each cumulative and normalized global value VGCNjm estimated for a portion (PCjm) to at least two predefined thresholds, and to attribute to each of said portions (PCjm) a value representative of its state chosen from at least three different states depending on the result of said comparison. 3. Device according to one of claims 1 and 2, characterized in that said processing means (MT) are arranged to estimate each cumulative primary value VPCjmi of said quantity over a portion (PCjm) as a function of primary data representative of acceleration measurements performed in said vehicle (V) as it traverses said portion (PCjm) and associated with location data dlm of said vehicle (V). 4. Device according to claim 3, characterized in that said processing means (MT) are arranged to estimate each cumulative primary value VPCjmi as a function of primary sums of data raised to the power four. 5. Device according to one of claims 3 and 4, characterized in that said processing means (MT) are arranged to use primary data equal to the product of an acceleration measurement by a weighting factor chosen. 6. Device according to one of claims 3 to 5, characterized in that it comprises measuring means (MM) arranged to perform said acceleration measurements. 7. Device according to one of claims 1 to 6, characterized in that said processing means (MT) are arranged to estimate each cumulative overall value and normalized VGCNjm as a function of a sum, raised to 0.25 power. , of three cumulative and standardized global primary values VGCNjm; of said magnitude, raised to the power of four, and estimated respectively in three directions of space perpendicular to each other. 8. Device according to claim 7, characterized in that said processing means (MT) are arranged to estimate each cumulative and normalized global primary value VGCNjm; according to the result, raised to the power 0.25, of a product between a cumulative primary value VPCjmi raised to the power of four, and a ratio between a normalization number P and the duration of said time window. 20 9. Device according to one of claims 1 to 8, characterized in that it comprises locating means (ML) arranged to provide said location data dlm. 10. Motor vehicle (V) adapted to circulate on taxiways (Cj), characterized in that it comprises an analysis device (D) according to one of the preceding claims.