Method for detecting a center line of a traffic lane and controlling a motor vehicle

Abstract

The invention relates to a method for controlling a motor vehicle (10) traveling on a road comprising at least one traffic lane (1), the method comprising steps, implemented in a loop at successive time steps by an electronic and / or computer unit: a) attempting to acquire information characterizing two edge lines (1D, 1G) delimiting said traffic lane, b) if the information has been acquired, characterizing at least one central line of said traffic lane 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.According to the invention, if, during the implementation of step a) at a current time step, the information characterizing at least one of the two edge lines could not be acquired, at step b), said central line is characterized according to the information acquired at a previous time step. Figure for the abstract: Fig.2.

Description

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

[0001] The present invention relates generally 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 electronic and / or computer unit: (a) an attempt to acquire information characterizing two edge lines delimiting the said traffic lane, (b) if the information has been acquired, characterization of at least one center line of said traffic lane based on 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 suitable for implementing such a method. State of the art

[0005] In an effort to make motor vehicles safer, they are currently being equipped with driving assistance systems and even highly automated driving systems.

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

[0007] Many of these systems need, in order to function, to know the position of the center line of the traffic lane taken by the vehicle, or even the positions of the center lines of other traffic lanes (for example the one towards which the vehicle is heading when it changes lane).

[0008] Currently, it is known to use a sensor, such as a camera, which incorporates 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 computer 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 calculator 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 the source of accidents. It is understood that this solution is however 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 is to determine 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 driver assistance systems. Unfortunately, this solution proves too imprecise to allow these systems to be implemented as safely as desired. Presentation of the invention

[0012] In order to overcome 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, a method is proposed as defined in the introduction, in which if, during the implementation of step a) at a current time step, the information characterizing at least one of the two edge lines could not be acquired, at step b), said central line is characterized as a function of 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 for longer, so that the driver will not have to reactivate them as soon as, for one reason or another, the edge lines cannot be detected. If the situation persists, this solution also allows for a gentler deactivation of these functions, which will in particular allow the driver to be warned in advance in order to give him time to become familiar with his surroundings before taking control of driving the vehicle again.

[0015] Another advantage of the proposed solution is that it does not involve any additional cost 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 infrastructure.

[0016] Other advantageous and non-limiting characteristics of the method according to the invention, taken individually or in all technically possible combinations, are the following: - if, in step a), the information characterizing the two edge lines has been acquired, it is planned in step b) for sub-steps to: H acquisition of data relating to the posture and / or movement of the motor vehicle on the road, H 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 H selection, as a center line, of one of the candidate center 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, at step b), the central 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; - if, during the implementation of step a) at a current time step, no information characterizing the edge lines has been 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 greatest 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 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: H 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 center 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 which was 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 the aforementioned.

[0018] Of course, the various features, variants and embodiments of the invention may be combined with each other in various combinations to the extent that they are not incompatible or mutually exclusive. Detailed description of the invention

[0019] 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 implemented.

[0020] In the attached drawings:

[0021] [Fig-1] is a schematic view of a motor vehicle suitable for implementing a method according to the present invention;

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

[0023] [Fig.3] is a block diagram illustrating the different steps of a method 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 traffic lane of [Fig.2].

[0025] In [Fig.l], a motor vehicle 10 is shown which is suitable for implementing 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 for turning the vehicle (not visible in the figure). Typically, the steering system includes an electronically controllable power steering actuator, the powertrain includes an electronically controllable engine control actuator, and the braking system includes an electronically controllable braking actuator.

[0029] The vehicle 10 further 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 come from sensors or third-party computers.

[0031] Among these sensors, at least one sensor is provided for detecting the edges of the road, for example: - a front camera, and / or - a RADAR or LIDAR remote sensor.

[0032] Thanks to its output interfaces, the computer 11 is adapted to control the display screen, the power steering actuator, the engine control actuator, and the 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 through traffic autonomously, without intervention from the driver).

[0034] Thanks to its memory, the computer 11 stores a computer application, consisting 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 traveling along a traffic 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 motorway is represented here, namely the one used by the motor vehicle 10.

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

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

[0039] The left IG and right 1D edge lines 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], a reference point (X, Y) attached to the motor vehicle 10 has also been shown, which will be the one considered for the calculations developed below. This reference point comprises 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 process that the computer 11 is adapted to implement is illustrated work so as to characterize the center line of 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 1D and left IG edge lines, and which will be taken into account to control the motor vehicle in an automated manner.

[0043] As will become clear from reading this presentation, this central line will not necessarily be central in the geometric sense of the term. It will rather be a line close to this geometric central line.

[0044] 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 as 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 method are as follows.

[0048] In this example, we can consider that the method 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 edge lines 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 IG and right 1D edge lines. No information received from road infrastructures or third-party vehicles is used, for example.

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

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

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

[0055] [Math.l] c(l) = c0 + c^l

[0056] In this equation: - it is therefore the equation of the clothoid, -1 is the curvilinear abscissa of the arc of the clothoid, which varies between 0 inclusive and L inclusive, - L is the length of the arc of the spiral, - c0 is the curvature at the origin of the arc, and - Ci is the rate of change of curvature of the arc.

[0057] In other words, a clothoid can be defined by six coefficients which are: - x0, the abscissa of the origin point of the clothoid in the (X, Y) frame, - y0 the ordinate of this point of origin, - Wq which is the heading angle of the clothoid at the origin of the arc, -L, - c0, and - Here.

[0058] In the following, each clothoid will be defined by the set C of these six coefficients, which can be noted as follows: C (x0, y0, c0, cb L).

[0059] The clothoid characterizing the shape of the left edge line IG will then be defined by the set Cg (xOg, yOg, ^og, cOg, Cig, Lg) while the clothoid characterizing the shape of the right edge line 1D will be defined by the set Cd (xOd, yod, 'Lod, cOd, Cid ,Ld).

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

[0061] [Math.2] + = c^l + y. / 2

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

[0063] [Math.3] r / x ( l ) = Xq+J Ocos(¥ ( u ) ). of , Y ( 0 - >' o + / o sinÇP ( u ) ).du

[0064] During this first step E0, it can be envisaged that the computer 11 receives the two sets Cg, Cd 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] As a further variant, the calculator could receive coefficients defining the two right 1D and left IG edge lines 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 will have to 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 computer 11 receives the two variance-covariance matrices (hereinafter called covariance matrices) associated respectively with the coefficients of the two right 1D and left IG edge lines. 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 traffic lane 1. This is kinematic data which is preferentially 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 ax of the motor vehicle along the X axis, expressed in m / s 2 - lateral acceleration ay of the motor vehicle along the Y axis, expressed in m / s2, - yaw rate of the motor vehicle, expressed in rad / s, - yaw acceleration of the motor vehicle, expressed in rad / s2, - 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 c0,ego of the trajectory of the motor vehicle (this curvature being defined here by the inverse of the radius of curvature, expressed in m1).

[0072] The second step E4 consists, for the calculator 11, in determining the number N of edge line(s) which could have been characterized in the form of clothoid(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 1D straight edge line and the left edge line IG have both been detected a priori, the process continues in 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 method continues in a step E12.

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

[0077] Let us first consider the case in which both 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 pairs 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 calculator continues the process by constructing several candidate central lines (see [Fig.2]).

[0081] The idea here is to construct 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 travels on its track.

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

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

[0084] One of these candidate center lines (called left candidate line 2G) is created based solely on the left edge line 1G and the lane width. 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 clothoid representative of the left line 1G and the value of the lane width.

[0085] Another such candidate center line (called 2D straight candidate line) is created based solely on the 1D straight track edge line and the track width.

[0086] Finally, the third of these candidate central lines (called the 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 trackside lines.

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

[0089] Thus we can write that: - the left candidate line 2G is defined by the set of coefficients Cgg (xOg, (yOg +y0d) / 2, WOg, cOg, clg, Lg), and that - the 2D straight candidate line is defined by the set of coefficients Cdd (xOd, (yOg +yod) / 2, c0cb Clcb Ld).

[0090] The intermediate candidate line 2DG could 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 Cdg ((xog+xOd) / 2, (y og+yod) / 2, OLog+ Wod) / 2, (cog+Cod) / 2, (cig+ Cid) / 2, (Lg+Ld) / 2).

[0091] These three candidate lines being defined, the calculator 11 calculates the covariance matrix of each candidate line. Each covariance matrix is here calculated as a function of the coefficients of the clothoid associated with this candidate line and as a function of the covariance matrix(es) associated with the right 1D and left IG edge lines (depending on whether one or both lines were 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 motorway while the right candidate line 2D seems the most appropriate if the vehicle leaves the motorway.

[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 trackside 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. More precisely, it is a Mahalanobis distance dM.

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

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

[0099] [Math.4] d M = ( m - v ) T . ( Su + Sv ) [u - vj

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

[0101] Preferably, the vectors have the same number of coefficients, between two and six. Here, only two coefficients will be used since it has been observed that the results remain 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 not being 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= [Wo, c0]T

[0105] The second vector v is defined by:

[0106] v=[0,Co,ego]T.

[0107] 0 is the heading angle of the motor vehicle 10 and is equal to either the steering angle of the front wheels, or 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] Note that the first vector u will have different values from one candidate line to another.

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

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

[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 smallest is then selected.

[0113] In the example illustrated in [Fig.2], this is the 2D straight candidate line, which appears preferable since the vehicle is traveling towards 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 Cgg, Cdg, Cdd are stored in the memory of the computer 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 computer, as well as the data relating to the posture and / or the kinematics of the motor vehicle 10 in its traffic 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 lines of traffic lane 1 have not been 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 time t-1. It can also occur in the event of a problem on the sensor (here the camera) or on the CAN network allowing the sensor to be connected to the computer. It can also occur if one and / or the other of the edge lines is not visible, for example because it is hidden by a truck that the vehicle 10 would overtake.

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

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

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

[0122] We can now consider the case where neither of the two right 1D and left IG edge lines could be detected since the previous sampling time t-1.

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

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

[0125] In this case, the calculator finds in its memory the sets of coefficients characterizing the 2D, 2G right and left candidate center lines determined at the previous sampling time 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 as that which had been selected at the previous sampling instant t-1.

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

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

[0129] In this case, the computer 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 a step E16.

[0131] This step E16 consists of checking that the selected central line is correctly 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 frame of the motor vehicle 10 at the current time 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 frame of reference of the motor vehicle 10 at the previous sampling time t-1, so that it is appropriate to correct them in order to express them in the frame of reference 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. It will be noted that these data could be expressed in the frame of reference of the vehicle at the current time t. However, it will be considered here that they are expressed in the frame of reference attached to the motor vehicle at the previous sampling time t-1.

[0136] During this step E16, it is proposed to adapt the position and orientation of the central line according to the movement of the vehicle.

[0137] Expressing the center line in the form of a clothoid makes it possible to simplify these calculations. This change of reference can in fact be carried out by performing the following calculations:

[0138] (x0,t; y0,t) = [cos(dW) sm(dW); -sm(dW) cos(dW)] ((x0,ti ; y0,ti) - (dx ; dy))

[0139] W0,t = W0,tl-dW

[0140] cOjt = cOjti

[0141] c1>t = c1>tl

[0142] Lt = Ltl

[0143] Finally, during this step E16, it is also planned to delete the part of the selected central line which has been exceeded 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 clothoid in the reference frame attached to the motor vehicle at the current time t.

[0145] Consequently, during a final step E18, the computer 11 can transmit this set of coefficients on the vehicle's CAN network.

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

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

[0148] Therefore, this method makes it possible to extend the vehicle's driving functions for a few more moments, but not indefinitely if the right 1D and left IG edge lines remain undetectable. The idea is essentially to avoid deactivating these driving 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 event, the selected center 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 steered 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 additionally, the selected center line could also be used to implement other vehicle driver assistance functions, for example LKA functions for keeping the vehicle in its lane, emergency braking, avoidance, overtaking a third vehicle, adaptive cruise control, collision warning, etc.

[0153] In addition, the center line may be displayed on the display screen of the motor vehicle, superimposed on an already displayed image 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 shown, but those skilled in the art will be able to provide any variant in accordance with the invention.

[0155] For example, the method could be used to determine the shape of the center 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

Claims

1. Method for controlling a motor vehicle (10) traveling on a road comprising at least one traffic lane (1), the method comprising steps, implemented in a loop at successive time steps (t-1, t) by an electronic and / or computer unit (11): a) attempting to acquire information characterizing two edge lines (1D, IG) delimiting said traffic lane (1), b) if the information has been acquired, characterizing at least one center 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 center 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 (1D, 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), and - if, in step a), the information characterizing the two edge lines (1D, IG) has been acquired, sub-steps are provided in step b) for: H acquisition of data relating to the posture and / or movement of the motor vehicle (10) on the road, H creation of at least two candidate central lines (2D, 2G, 2DG) located between the two edge lines (1D, IG), during which said electronic and / or computer unit (11) calculates coefficients characterizing said at least two candidate central lines (2D, 2G, 2DG) as a function of the information acquired in step a), and H selection, as a central line, of one of the candidate central lines (2D, 2G, 2DG) as a function of the acquired data.

2. A driving method according to claim 1, wherein if, during the implementation of step a) at a current time step (t), the information characterizing only one of the two edge lines (1D, IG) has been acquired, namely the edge line delimiting a right or left side respectively of the traffic lane (1), in step b), the central line is deduced from the candidate central 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).

3. Control method according to one of claims 1 and 2, in which if, during the implementation of step a) at a current time step (t), no information characterizing the edge lines (1D, IG) has been acquired and if the information characterizing the two edge lines (1D, IG) could be acquired at the previous time step (t-1), one of the candidate center lines (2D, 2G, 2DG) created at the previous time step (t-1) is selected and the center line is deduced from the selected candidate center line (2D, 2G, 2DG).

4. Control method according to claim 3, in which it is provided 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).

5. Control method according to one of claims 3 and 4, in which if, during the implementation of step a) at a current time step (t), no information characterizing the edge lines (1D, IG) has been acquired and if, at the previous time step (t-1), the information characterizing at least one of the two edge lines (1D, 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).

6. Piloting method according to one of claims 1 to 5, in which the coefficients characterizing at least part of the candidate central lines (2D, 2G) are calculated as a function solely of the information characterizing only one of the two edge lines (1D, IG) and a width of the traffic lane (1).

7. A driving method according to one of claims 1 to 6, in which if, during the implementation of step a) at the current time step (t), the information characterizing at least one of the two edge lines (1D, IG) could not be acquired, in 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 (10) between the previous time step (t-1) and the current time step (t), - modification of the central line by executing a calculation of change of reference as a function of the acquired data.

8. A driving method according to claim 7, wherein, during the modification sub-step, a part of the central line is deleted, said part being that which has been passed by the motor vehicle (10) at the current time step (t).

9. Motor vehicle (10) comprising means for acquiring information characterizing two edge lines (1D, IG) of a traffic lane of a road taken by the motor vehicle (10) and at least one actuator for controlling the motor vehicle (10), characterized in that it comprises a computer (11) adapted to implement a method according to one of claims 1 to 8.

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