WO2024121172A1

WO2024121172A1 - Method for detecting a centre line of a traffic lane and for controlling a motor vehicle

Info

Publication number: WO2024121172A1
Application number: PCT/EP2023/084385
Authority: WO
Inventors: Federico CAMARDA; Chrysanthi PAPAMICHAIL
Original assignee: Ampere S.A.S.
Priority date: 2022-12-05
Filing date: 2023-12-05
Publication date: 2024-06-13
Also published as: FR3142730A1

Abstract

The invention relates to a method for controlling a motor vehicle (10) driving on a road comprising at least one traffic lane (1), the method comprising steps, implemented in a loop in consecutive time steps by an electronic and/or computer unit, of: a) attempting to acquire information characterising two edge lines (ID, IG) bounding the traffic lane; b) if the information has been acquired, characterising at least one centre line of the traffic lane according to this information; and c) determining a control instruction for a steering actuator of the motor vehicle, according to the centre line. According to the invention, if, during the implementation of step a) in a current time step, the information characterising at least one of the two edge lines could not be acquired, then, in step b), the centre line is characterised according to the information acquired in a preceding time step.

Description

Title of the invention: Method for detecting a central line of a traffic lane and controlling a motor vehicle

Technical field of the invention

[0001] The present invention generally relates to piloting aids.

[0002] It applies more particularly to cars and other motorized vehicles traveling on roads, but also applies to other fields such as robotics.

[0003] The invention relates to a method for controlling a motor vehicle traveling on a road comprising at least one traffic lane, this method comprising steps, implemented in a loop at successive time steps by an electrical unit. electronics and/or IT: a) attempt to acquire information characterizing two edge lines delimiting said traffic lane, b) if the information has been acquired, characterization of at least one central line of said lane circulation as a function of said information, and c) determining a control instruction for a steering actuator of the motor vehicle, as a function of said central line.

[0004] The invention also relates to a motor vehicle adapted to implement such a method.

State of the art

[0005] In order to secure motor vehicles, they are currently equipped with driver assistance systems or even highly automated driving systems.

[0006] These typically involve lane center keeping systems (better known by the acronym LCA for “Lane Centering Assist”), adaptive cruise control systems, etc.

[0007] Many of these systems need, to operate, to know the position of the central line of the traffic lane taken by the vehicle, or even the positions of the central lines of other traffic lanes (for example the one towards which the vehicle steers when changing lanes).

[0008] Currently, it is known to use a sensor, such as a camera, which embeds image processing means in order to determine the positions of the edge lines delimiting each traffic lane of the road taken by the vehicle. A calculator can then, on the basis of these positions, determine the position of the center line of each traffic lane.

[0009] The disadvantage is that in the event of failure to detect at least one of the two lines bordering a traffic lane, the computer will no longer be able to characterize the central line.

[0010] In this eventuality, the most common solution consists of deactivating the aforementioned driving assistance systems in order to prevent them from being sources of accidents. We understand that this solution, however, proves uncomfortable for the driver, since the latter is then forced to take control of the vehicle and then manually reactivate, as soon as this is possible again, the driving assistance systems.

[0011] Another solution consists of determining the position of the center line taking into account the GPS position of the vehicle and an up-to-date map of the locations, so as not to deactivate the driving assistance systems. Unfortunately, this solution turns out to be too imprecise to allow these systems to be implemented as securely as desired.

Presentation of the invention

[0012] In order to remedy the aforementioned drawback of the state of the art, the present invention proposes, in the event of non-detection of at least one of the two lines bordering the traffic lane, to characterize the central line taking into account the information relating to the edge lines which had been obtained earlier.

[0013] More particularly, according to the invention we propose a method as defined in the introduction, in which if, during the implementation of step a) at a current time step, the information characterizing the at least one of the two edge lines could not be acquired, in step b), said central line is characterized according to the information acquired at a previous time step.

[0014] Thus, thanks to the invention, the position of the central line remains available even in the event of temporary non-detection of one or both edge lines delimiting the traffic lane. Therefore, the functions which rely on this position (LCA, LKA function, etc.) can remain activated longer, so that the driver will not have to reactivate them as soon as, for one reason or another, the edge lines could not be detected. If the situation persists, this solution also allows for a more gentle deactivation of these functions, which will notably allow the driver to be warned in advance in order to give him time to absorb his environment before resuming control of driving the vehicle. vehicle.

[0015] Another advantage of the proposed solution is that it does not involve any additional costs since it relies on data already available for the standard implementation of the detection of the edge lines of a traffic lane. It also does not rely on any third-party data requiring connectivity with other vehicles or road infrastructures. Other advantageous and non-limiting characteristics of the process according to the invention, taken individually or in all technically possible combinations, are as follows:

- if, in step a), the information characterizing the two edge lines has been acquired, step b) provides sub-steps of:

E acquisition of data relating to the posture and/or movement of the motor vehicle on the road,

Q creation of at least two candidate central lines located between the two edge lines, during which said electronic and/or computer unit calculates coefficients characterizing said at least two candidate central lines as a function of the information acquired in step a ), and o selection, as central line, of one of the candidate central lines based on the acquired data;

- if, during the implementation of step a) at a current time step, the information characterizing only one of the two edge lines has been acquired, namely the edge line delimiting a right or respectively left side of the traffic lane, in step b), the center line is deduced from the candidate central line which was created at the previous time step and which is the one located furthest to the right or respectively furthest to the left of the traffic lane. traffic ;

- if, during the implementation of step a) at a current time step, no information characterizing the edge lines was acquired and if the information characterizing the two edge lines could be acquired at the previous time step, one of the candidate central lines created at the previous time step is selected and the central line is deduced from the selected candidate central line;

- it is planned to calculate, for each of the candidate central lines created at the previous time step, a probability of existence based on at least covariance matrices associated with the candidate central lines, then the selected candidate central line is the one which has the greater probability of existence;

- if, during the implementation of step a) at a current time step, no information characterizing the edge lines has been acquired and if, at the previous time step, the information characterizing the at least one of the two edge lines could not be acquired, the candidate central line selected at the previous time step is selected at the current time step;

- the coefficients characterizing at least part of the candidate central lines are calculated based solely on the information characterizing only one of the two edge lines and a width of the traffic lane;

- if, during the implementation of step a) at the current time step, the information characterizing at least one of the two edge lines could not be acquired, step b), after the central line has been characterized, sub-steps are provided for: » acquisition of data relating to the movement of the motor vehicle between the previous time step and the current time step,

H modification of the central line by performing a reference change calculation based on the acquired data;

- during the modification sub-step, a part of the central line is deleted, said part being that having been passed by the motor vehicle at the current time step.

[0017] The invention also proposes a motor vehicle comprising means for acquiring information characterizing two edge lines of a traffic lane of a road taken by the motor vehicle and at least one actuator for controlling the motor vehicle, as well as a computer adapted to implement a method such as aforementioned.

[0018] Of course, the different characteristics, variants and embodiments of the invention can be associated with each other in various combinations to the extent that they are not incompatible or exclusive of each other. Detailed description of the invention

The description which follows with reference to the appended drawings, given as non-limiting examples, will make it clear what the invention consists of and how it can be carried out.

[0020] In the attached drawings:

[0021] [Fig.l] is a schematic view of a motor vehicle adapted to implement a method according to the present invention;

[0022] [Fig.2] is a schematic top view of the motor vehicle of [Fig.l], on which central and edge lines of a traffic lane appear;

[0023] [Fig.3] is a block diagram illustrating the different stages of a process according to the present invention;

[0024] [Fig.4] is a schematic top view of the motor vehicle of [Fig.l], shown in two successive positions along the travel lane of [Fig.2].

[0025] In [Fig.l], there is shown an automobile vehicle 10 adapted to implement the invention.

[0026] This is a car. Alternatively, it could be another type of vehicle (truck, motorcycle, etc.).

[0027] Here, this vehicle 10 conventionally comprises a passenger compartment in which there are in particular a seat for the driver 20 of the vehicle, a dashboard with a display screen, and a steering wheel 12.

[0028] This vehicle 10 comprises a powertrain, a braking system and a steering system allowing the vehicle to turn (not visible in the figure). Conventionally, the steering system includes an electronically controllable power steering actuator, the powertrain includes an electronically controllable motor control actuator, and the braking system includes an electronically controllable brake actuator.

[0029] The vehicle 10 also comprises an electronic and/or computer processing unit (hereinafter called computer 11) comprising at least one microprocessor or microcontroller, at least one memory and input and output interfaces.

[0030] Thanks to its input interfaces, the computer 11 is adapted to receive different input data, which comes from sensors or third-party computers.

[0031] Among these sensors, at least one sensor is provided making it possible to detect the edges of the road, for example:

- a front camera, and/or

- a RADAR or LIDAR remote detector.

[0032] Thanks to its output interfaces, the computer 11 is adapted to control the display screen, the power steering actuator, F engine control actuator, and F braking actuator.

[0033] Thus, the computer 11 is adapted to implement driving assistance functions and/or automated driving functions (in which the vehicle can move in traffic autonomously, without intervention from the driver).

[0034] Thanks to its memory, the computer 11 stores a computer application, made up of computer programs comprising instructions whose execution by the computer allows the implementation of the method described below.

[0035] In [Fig.2], the motor vehicle 10 is shown seen from above, while it is taking a lane 1 of a road.

[0036] In the example considered here for illustrative purposes, this road is of the “motorway” type.

Only one of the traffic lanes 1 of this highway is represented here, namely that taken by the motor vehicle 10.

[0037] In this figure, we observe that the traffic lane 1 is delimited by two lane edge lines, namely left edge lines IG and right edge lines 1D (here, the terms right and left are used when considering the direction advance of the vehicle). In this particular example, traffic lane 1 widens since the vehicle is at a motorway exit.

[0038] Of course, the process described below will apply to any other traffic situation.

[0039] The left edge lines IG and right edge 1D can be formed by road markings affixed to the road (continuous white line, broken line, etc.), by safety barriers, by raised shoulders or by any other distinctive element. [0040] In this [Fig.2], there is also shown a marker (X, Y) attached to the motor vehicle 10, which will be the one considered for the calculations developed below. This mark includes an abscissa which extends along the longitudinal axis of the vehicle, towards the front, and an ordinate which extends towards the left of the vehicle. Its origin is located for example at the level of the center of gravity of the motor vehicle 10 or at the level of the center of its rear axle.

[0041] In [Fig.3], the method that the computer 11 is adapted to implement is illustrated so as to characterize the central line of the traffic lane 1.

[0042] It will be noted that this central line can be defined as a virtual line which is substantially positioned in the center of the traffic lane 1, between the right edge lines 1D and left IG, and which will be taken into account to control the vehicle automobile in an automated manner.

[0043] As will become clear after reading this presentation, this central line will not necessarily be central in the geometric sense of the term. Rather, it will be a line close to this geometric center line.

Here, the traffic lane 1 considered will be the lane on which the vehicle 10 is traveling. Of course, it could be another lane, in particular an adjacent lane towards which the vehicle would like to move (for example when changing lanes).

[0045] The method according to the invention comprises several successive steps, all of these steps being repeated in a loop at regular intervals so as to regularly update the shape and position of this central line when the vehicle moves along the road.

[0046] In other words, the method makes it possible to characterize the central line in a loop, with a regular time step of a few milliseconds, 40 ms for example.

[0047] The detailed steps of this process are as follows.

[0048] In this example, we can consider that the process is implemented at a time t, and that it was already implemented at previous times (t-1, t-2, etc.).

[0049] During the first step E0, the computer 11 attempts to acquire information characterizing the two left edge lines IG and right 1D.

[0050] This information can be provided directly by a sensor or be pre-processed.

[0051] Preferably, only the information measured by the vehicle sensors is used here to detect these left edge lines IG and right edge lines 1D. For example, no information received from road infrastructures or third-party vehicles is used.

[0052] Here, each left IG and right 1D edge line is modeled in analytical form.

[0053] By way of example, it could be envisaged that these lines be modeled under a polynomial form. However, here, they are modeled in the form of a spiral, this form being preferred because it provides better results (for an identical number of coefficients taken into account).

[0054] Such a spiral is defined as having a curvature c which depends on its length, which can be written:

[0055] [Math.l] ()

[0056] In this equation:

- this is therefore the equation of the spiral,

- 1 is the curvilinear abscissa of the arc of the spiral, which varies between 0 included and L included,

- L is the length of the arc of the spiral,

- c ₀ is the curvature at the origin of the arc, and

- This is the rate of variation of curvature of the arc.

[0057] In other words, a spiral can be defined by six coefficients which are:

- x ₀ , the abscissa of the point of origin of the spiral in the coordinate system (X, Y),

- y ₀ the ordinate of this point of origin,

- W _o which is the heading angle of the spiral at the origin of the arc, - L,

-c ₀ , and

- This.

[0058] In the following, each spiral will be defined by the set C of these six coefficients, which can be noted as follows: C (x ₀ , y ₀ , Wo, c ₀ , c _b L) .

The spiral characterizing the shape of the left edge line IG will then be defined by the set C _g (x _Og , y _Og , c _Og , Ci _g , L _g ) while the spiral characterizing the shape of the line right edge 1D will be defined by the set Cd (x _Od , y _Od , T^d, c _Od , c _id , L _d ).

[0060] For information, the heading angle along the spiral can be written as follows:

[0061] [Math.2] ^

[0062] The Cartesian coordinates (x, y) of a point located along the spiral can be written as follows:

[0063] [Math.3]

[0064] During this first step E0, it can be envisaged that the computer 11 receives the two sets C _g , C _d from one of the sensors on board the vehicle (for example from the camera if the latter is equipped with a processor adapted to carry out the operations necessary for determining the desired coefficients).

[0065] Alternatively, the computer 11 could receive the data recorded by several of the vehicle's sensors, merge this data, and deduce the desired coefficients.

[0066] Still as a variant, the calculator could receive coefficients defining the two right edge lines 1D and left IG in polynomial forms (for example in the form of coefficients of polynomials of order 3). In this variant, as shown in dotted lines in [Fig.3], step E0 will be followed by a step E2 during which the calculator must transform these coefficients so as to find, by calculation, the values of the six coefficients sought. Such a calculation is well known to those skilled in the art and will therefore not be provided here.

[0067] During step E0, in addition to the aforementioned coefficients, the calculator 11 receives the two variance-covariance matrices (hereinafter called covariance matrices) associated respectively with the coefficients of the two right edge lines 1D and left IG . Each covariance matrix makes it possible to take into account the uncertainty in the detection of each track edge line by the sensor(s).

[0068] These matrices are for example provided by the manufacturer of the sensors used. Alternatively, they can be determined during a sensor test campaign and recorded in the computer memory. The coefficients of these matrices are preferably invariable.

[0069] It will be noted here that if several sensors are used to determine the coefficients characterizing an edge line, their uncertainties will be associated with each other in order to obtain the covariance matrix of a detected line.

[0070] Finally, during step E0, the computer 11 acquires data relating to the posture and/or the kinematics of the motor vehicle 10 in its lane 1. These are kinematic data which are preferably obtained using only the sensors on board the motor vehicle 10.

[0071] Here, these data include values of:

- speed V of the motor vehicle relative to the ground, expressed in m/s,

- longitudinal acceleration a _x of the motor vehicle along the X axis, expressed in m/s 2

- lateral acceleration a _y of the motor vehicle along the Y axis, expressed in m/s ² ,

- yaw speed of the motor vehicle, expressed in rad/s,

- yaw acceleration of the motor vehicle, expressed in rad/s ² ,

- angle of the front wheel of the motor vehicle, expressed in rad,

- angle of the rear wheel of the motor vehicle, expressed in rad, and - instantaneous curvature c ₀ , _ego of the trajectory of the motor vehicle (this curvature being defined here by the inverse of the radius of curvature, expressed in nr ¹ ).

The second step E4 consists, for the calculator 11, of determining the number N of edge line(s) which could be characterized in the form of a spiral(s).

[0073] Here, we will consider that this number N can be equal to 0 or 1 or at least 2.

[0074] If this number N is at least equal to 2, which means that the right edge line 1D and the left edge line 1 G have both a priori been detected, the process continues in a step E6.

[0075] If this number N is equal to 1, which means that only the right edge line 1D or the left edge line IG has been detected, the process continues in a step E12.

[0076] Finally, if this number N is equal to 0, which means that none of the right edge lines 1D and left edge lines IG could be detected, the process continues in a step E14.

[0077] Let us first consider the case in which the two edge lines have been detected, which will in practice be the most frequent case.

[0078] Then, during a step E6, the calculator 11 orders the edge lines by their ordinate (i.e. from the rightmost to the leftmost).

[0079] These edge lines are associated in pair(s) delimiting the traffic lanes, taking into account their ordinates and their lengths, as described for example in document FR3106918.

[0080] Then, during a step E10, the computer continues the process by constructing several candidate central lines (see [Fig.2]).

[0081] The idea here consists of constructing at least two candidate central lines, so as to then be able to select the best one taking into account the conditions under which the motor vehicle 10 is traveling on its track.

[0082] Here, three candidate central lines are constructed. Alternatively, this number could be smaller (equal to two) or larger.

[0083] Each of these lines is modeled by a spiral.

One of these candidate central lines (called the left candidate line 2G) is created based solely on the left edge line 1G and the width of the lane. In other words, this left candidate line 2G is defined by a set of six coefficients whose values are deduced from the values of the coefficients of the spiral representative of the left line 1 G and from the value of the width of the way.

Another of these candidate central lines (called the 2D straight candidate line) is created based solely on the 1D straight lane edge line and the width of the lane.

[0086] Finally, the third of these central candidate lines (called intermediate candidate line 2DG) is created on the basis of both the straight line 1D and the left line IG.

[0087] Alternatively, these three lines could be modeled differently. Typically, the three lines could all be calculated based on the shapes of the two track edge lines.

[0088] In the embodiment considered here, the left candidate line 2G is the result of a translation operation of the spiral defining the left edge line IG, over half the width of the track and towards the center of this one. In the same way, the 2D straight candidate line is the result of a translation operation of the spiral defining the 1D straight edge line, over half the width of the track and towards the center of it.

[0089] Thus we can write that:

- the left candidate line 2G is defined by the set of coefficients C _gg (x _Og , (y _Og +yoa)/2, W _Og , Cog, Cig, Lg), and that

- the 2D straight candidate line is defined by the set of coefficients C _dd (x _Od , (y _Og +yod)/2, ^od> Co _d , CM, L _d ).

[0090] The intermediate candidate line 2DG could for its part be obtained in various ways. Here, a barycentric type operation is used. Thus, we can consider that this line is defined by the set of coefficients C _dg ((x _Og -i-Xo _d )/2, (y og+yod)/2, (W _Og + W _0d )/2, (c _0g +c _0d )/2, (ci _g + Ci _d )/2, (L _g +L _d )/2).

[0091] These three candidate lines being defined, the calculator 11 calculates the co-variance matrix of each candidate line. Each covariance matrix is calculated here as a function of the spiral coefficients associated with this candidate line and as a function of the covariance matrix(es) associated with the right 1D and left edge lines IG (depending on whether one or both lines have considered to define the candidate line in question).

[0092] In the example illustrated in [Fig.2] where the motor vehicle 10 is at a motorway exit, the left candidate line 2G seems the most appropriate if the vehicle remains on the highway while the straight 2D candidate line seems the most appropriate if the vehicle exits the highway.

[0093] At this stage, the calculator therefore seeks to select the candidate line most appropriate to the situation.

[0094] For this, a preliminary filtering operation could possibly be implemented in order to set aside the absurd candidate line(s) (for example a candidate line which would come too close to one of the track edge lines).

[0095] Then, the calculator calculates a parameter which will make it possible to select the best candidate line, taking into account the data relating to the motor vehicle 10.

[0096] This parameter is preferably a statistical distance. This is more precisely a Mahalanobis distance d _M .

[0097] The method used then consists, for the calculator 11, of calculating the distance of Mahalanobis d _M between each candidate line and the motor vehicle 10 (at the current time, at the level of the motor vehicle 10).

[0098] To do this, the calculator performs the following calculation:

[0100] In this equation, u is a first vector relating to the candidate line considered and Su is its covariance matrix. In the same way, v is a second vector relating to the motor vehicle 10 and Sv is its covariance matrix.

[0101] Preferably, the vectors include the same number of coefficients, between two and six. Here, only two coefficients will be used since it was observed that the results remained very reliable with only two coefficients.

[0102] One of the coefficients of each vector is a heading angle (such an angle in fact giving a good indication of the driver's intention), while the other coefficient is a curvature (the heading angle does not not sufficient to indicate this intention if the road and the trajectory of the vehicle are curved).

[0103] Here, the first vector u is therefore defined by:

[0104] u= [^o, c ₀ ] ^T

[0105] The second vector v is defined by:

[0106] v= [0, c ₀ , _ego ] ^T.

[0107] 0 is the heading angle of the motor vehicle 10 and is equal either to the steering angle of the front wheels, or to the difference between the steering angle of the front wheels and the steering angle of the rear wheels if the latter is non-zero.

[0108] It will be noted that the first vector u will present different values from one candidate line to another.

[0109] It will be noted here that the covariance matrix Su will be deduced from the co-variance matrix attached to the candidate line considered.

[0110] The covariance matrix Sv will be read (its coefficients are stored in the computer memory and depend on the type of sensor and the method of determining the uncertainty of the sensor(s) used - if several sensors are used in combination). This covariance matrix is therefore determined in a manner analogous to those associated with the two track edge lines. So, here again, if several sensors are used in combination, it is possible to associate the uncertainties linked to each sensor together.

[0111] During this operation, the Mahalanobis distance is then calculated for each of the candidate lines, at the current time t.

[0112] The candidate line for which the Mahalanobis distance thus calculated is the lowest is then selected. [0113] In the example illustrated in [Fig.2], this is the straight 2D candidate line, which appears preferable insofar as the vehicle is traveling in the direction of the motorway exit.

[0114] At the end of this step E8, the selected right 2D, intermediate 2DG or left 2G candidate line and its set of coefficients C _gg , C _dg , C _dd are stored in the memory of the calculator 11 during a step E10.

[0115] In practice, during these steps E10, the sets of coefficients characterizing all the candidate lines and the edge lines are stored in the memory of the calculator, as well as the data relating to the posture and/or the kinematics of the motor vehicle 10 in its lane 1. They are thus available at a future time, if necessary.

[0116] We can now consider the case where one, or even both, edge line(s) of traffic lane 1 was or could not be detected.

[0117] This loss of detection can occur for example if, taking into account the sampling frequency of the camera, no new line has been calculated since the last sampling instant t-1. It can also happen in the event of a problem with the sensor (here the camera) or with the CAN network allowing the sensor to be connected to the computer. It can also happen if one or the other of the edge lines is not visible, for example because it is hidden by a truck that vehicle 10 would overtake.

[0118] To begin with, we can consider the case where exactly one of the two right edge lines 1D or left IG has been detected.

[0119] In this eventuality, during a step E12, the computer determines whether the track edge line which has been detected is the left edge line IG or the right edge line 1D.

[0120] If it is the straight edge line 1D, the calculator 11 finds in its memory the set of coefficients characterizing the candidate right central line 2D determined at the previous sampling instant t-1 , and it selects it as the set of coefficients characterizing the new central line.

[0121] Conversely, if it is the left edge line IG, the calculator 11 finds in its memory the set of coefficients characterizing the left central candidate line 2G determined at the previous sampling instant t- 1, and he selects it as the set of coefficients characterizing the new central line.

[0122] We can now consider the case where none of the two right edge lines 1D and left IG could be detected since the previous sampling instant t-1.

[0123] Then, in step E14, the calculator proceeds in one of the following two ways.

[0124] The first case is that where, at the previous sampling time t-1, the two track edge lines could have been detected.

[0125] In this case, the calculator finds in its memory the sets of coefficients characterizing the right and left central candidate lines 2D, 2G determined at the previous sampling instant t-1.

[0126] It then selects one or the other of these lines to characterize the new central line. It could typically select the same one that was selected at the previous sampling time t-1.

[0127] However, preferably, it selects the one which has the best probability of existence Pe. This probability of existence Pe is calculated according to at least the covariance matrices of the two central right and left candidate lines 2D, 2G. It quantifies the confidence that one can have in the quality of the determination of this candidate line. For example, it is all the greater the greater the contrast between the road markings and the rest of the road.

[0128] The second case is that where, at the previous sampling time t-1, none or only one of the two right edge lines 1D and left IG could have been acquired.

[0129] In this case, the calculator finds in its memory the set of coefficients characterizing the candidate central line which was selected at the previous sampling instant t-1, and it selects it as the new central line.

[0130] Steps E10, E12 and E14 then continue in step E16.

[0131] This step El 6 consists of checking that the selected central line is indeed expressed in a suitable reference frame.

[0132] In practice, when step E16 follows step E10 (since the two edge lines had been detected), it is considered that the coefficients characterizing the central line are indeed expressed in the reference mark of the motor vehicle 10 at l current moment t, and that they are therefore usable.

[0133] On the other hand, when step E16 follows step E12 or E14 (since at least one of the two edge lines could not be detected), it is considered that the coefficients characterizing the central line are expressed in the reference frame of the motor vehicle 10 at the previous sampling time t-1, so that they must be corrected in order to express them in the reference frame of the motor vehicle 10 at the current sampling time t .

[0134] Indeed, between the previous sampling time t-1 and the current time t, the motor vehicle 10 has moved, which is illustrated in [Fig.4].

[0135] This displacement is here characterized by three data, namely a longitudinal displacement dx, a lateral displacement dy and a change of heading dW. Note that these data could be expressed in the vehicle's reference frame at the current time t. However, we will consider here that they are expressed in the marker attached to the motor vehicle at the previous sampling time t-1.

[0136] During this step E16, it is proposed here to adapt the position and orientation of the central line according to the movement of the vehicle. [0137] Expressing the central line in the form of a spiral makes it possible to simplify these calculations. We can indeed carry out this change of reference by carrying out the following calculations:

[0138]

[0139]

[0140]

[0141]

[0142]

[0143] Finally, during this step E16, it is also planned to delete the part of the selected central line which has been passed by the vehicle (its length L is therefore modified).

[0144] Thus, whatever the situation, the method makes it possible to characterize in step E16 the central line of the traffic lane, by means of a set of coefficients characterizing this line in the form of a spiral in the reference frame attached to the motor vehicle at the current time t.

[0145] Consequently, during a final step El 8, the computer 11 can transmit this set of coefficients to the vehicle's CAN network.

[0146] During this step, this set is also stored in the memory of the calculator (which is illustrated in [Fig.3] by the dotted arrow).

[0147] We thus understand that thanks to this process, the coefficients characterizing the central line (its shape and its position) remain available a few additional moments after the coefficients characterizing at least one of the two right edge lines 1D and left IG could not be acquired.

[0148] Therefore, this process makes it possible to extend the vehicle control functions for a few more moments, but not indefinitely if the right edge lines 1D and left edge lines IG remain undetectable. The idea is essentially to avoid deactivating these steering functions when the edge lines are undetectable for only a few moments.

[0149] In this regard, it is possible to use a time delay in order to limit the duration during which the central line is reconstructed.

[0150] In any case, the selected central line can then be used to control the vehicle actuators, for example the LCA function for centering the vehicle in its lane.

[0151] For this, the coefficients characterizing it can be used to determine a steering instruction for the steering wheels of the motor vehicle (to be transmitted to the power steering actuator), for example according to a method such as that described in document FR3082162 .

[0152] Alternatively or in addition, the selected central line could also be used to implement other vehicle driving assistance functions, for example the LKA functions of keeping the vehicle in its lane, emergency braking, avoidance, overtaking a third party vehicle , adaptive cruise control, collision warning. ...

[0153] Furthermore, the central line could be displayed on the display screen of the motor vehicle, superimposed on an image already displayed of this vehicle and its environment. This will allow the driver to better understand the reactions of the motor vehicle.

[0154] The present invention is in no way limited to the embodiment described and represented, but those skilled in the art will be able to make any variation conforming to the invention.

[0155] By way of example, the method could be used to determine the shape of the central line of a traffic lane adjacent to that taken by the motor vehicle.

[0156] The invention also applies to the field of robotics and to any other field in which an object must follow a particular trajectory on a road (this road being understood here in its most general sense, as a traffic zone for the robot).

Claims

[Claim 1] Method for controlling a motor vehicle (10) traveling on a road comprising at least one lane (1), the method comprising steps, implemented in a loop at time steps (t-1 , t) successive by an electronic and/or computer unit (11): a) attempt to acquire information characterizing two edge lines (ID, IG) delimiting said traffic lane (1), b) if the information have been acquired, characterizing at least one central line of said traffic lane (1) as a function of said information, and c) determining a control instruction for a steering actuator of the motor vehicle (10 ), as a function of said central line, characterized in that if, during the implementation of step a) at a current time step (t), the information characterizing at least one of the two edge lines (ID, IG) could not be acquired, in step b), said central line is characterized as a function of the information acquired at a previous time step (t-1).

[Claim 2] Control method according to claim 1, in which if, in step a), the information characterizing the two edge lines (ID, IG) has been acquired, it is provided in step b) sub-steps of:

- acquisition of data relating to the posture and/or movement of the motor vehicle (10) on the road,

- creation of at least two candidate central lines (2D, 2G, 2DG) located between the two edge lines (ID, IG), during which said electronic and/or computer unit (11) calculates coefficients characterizing said at least two candidate central lines (2D, 2G, 2DG) based on the information acquired in step a), and

- selection, as central line, of one of the candidate central lines (2D, 2G, 2DG) based on the acquired data.

[Claim 3] Control method according to claim 2, in which if, during the implementation of step a) at a current time step (t), the information characterizing only one of the two edge lines (ID, IG) have been acquired, namely the edge line delimiting a right or respectively left side of the traffic lane (1), in step b), the center line is deduced from the candidate center line (2D, 2G, 2DG) which was created at the previous time step (t-1) and which is the one located furthest right or respectively the leftmost of the traffic lane (1).

[Claim 4] Control method according to one of claims 2 and 3, in which if, during the implementation of step a) at a current time step (t), no information characterizing the edge lines (ID, IG) has not been acquired and if the information characterizing the two edge lines (1D, IG) could have been acquired at the previous time step (t-1), one of the candidate central lines (2D, 2G, 2DG) created at the previous time step (t-1) is selected and the central line is deduced from the candidate central line (2D, 2G, 2DG) selected.

[Claim 5] Control method according to claim 4, in which it is planned to calculate, for each of the candidate central lines (2D, 2G, 2DG) created at the previous time step (t-1), a probability of existence (Pe) as a function of at least covariance matrices associated with the candidate central lines (2D, 2G, 2DG), and in which the candidate central line (2D, 2G, 2DG) selected is the one which has the greatest probability of existence (Pe).

[Claim 6] Control method according to one of claims 4 and 5, in which if, during the implementation of step a) at a current time step (t), no information characterizing the edge lines (ID, IG) has not been acquired and if, at the previous time step (t-1), the information characterizing at least one of the two edge lines (ID, IG) could not be acquired, the candidate central line selected at the previous time step (t-1) is selected at the current time step (t).

[Claim 7] Control method according to one of claims 2 to 6, in which the coefficients characterizing at least part of the candidate central lines (2D, 2G) are calculated based solely on the information characterizing only one of the two lines edge (ID, IG) and a width of the traffic lane (1).

[Claim 8] Control method according to one of claims 1 to 7, in which if, during the implementation of step a) at the current time step (t), the information characterizing one at least of the two edge lines (1D, IG) could not be acquired, in step b), after the central line has been characterized, sub-steps of:

- acquisition of data relating to the movement of the motor vehicle (10) between the previous time step (t-1) and the current time step (t),

- modification of the central line by performing a reference change calculation based on the acquired data.

[Claim 9] Control method according to claim 8, in which, during the modification sub-step, a part of the central line is deleted, said part being that having been passed by the motor vehicle (10) at the step of current time (t).

[Claim 10] Motor vehicle (10) comprising means for acquiring information characterizing two edge lines (ID, IG) of a lane of a road taken by the motor vehicle (10) and at least one motor vehicle control actuator (10), characterized in that it comprises a computer (11) adapted to implement a method according to one of claims 1 to 9.