Method for detecting the center line of a traffic lane

Abstract

The invention relates to a method for detecting the center line of a lane (1) on a road used by a motor vehicle (10), comprising the steps of: - acquiring information characterizing two lane edge lines (1D, 1G), - acquiring data relating to the position and / or movement of the motor vehicle on the road, - creating at least two candidate center lines (2D, 2G, 2DG) located between the two lane edge lines, - selecting one of the candidate center lines based on the acquired data, and - deducing the center line of the lane based on the selected candidate center line. Figure for the abstract: Fig. 2

Description

Title of the invention: Method for detecting a center line of a traffic lane Technical field of the invention

[0001] The present invention relates generally to driving aids for motor vehicles.

[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 detecting a center line of a lane belonging to a road which is being used by a motor vehicle.

[0004] It also relates to a motor vehicle adapted to implement such a detection process. State of the art

[0005] In order to improve the safety of motor vehicles, they are currently being equipped with driver assistance systems or even highly automated driving systems.

[0006] These are typically lane keeping assist systems (better known by the English acronym LKA for "Lane Keeping Assist"), adaptive cruise control systems...

[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 lanes).

[0008] Currently, it is known to use a sensor, such as a camera, which incorporates image processing means in order to determine the position of this central track line.

[0009] It is also known to use several sensors, such as a camera coupled with a RADAR remote detector, and then to process together the data from these different sensors, for example by data fusion, in order to deduce the position of the center line of the track.

[0010] These different solutions make it possible to approximate the position of the center line.

[0011] Unfortunately, they do not give complete satisfaction since it sometimes happens that the central line thus identified does not allow for a comfortable driving of the vehicle and which offers the desired safety guarantees. Presentation of the invention

[0012] In order to remedy the aforementioned drawback of the prior art, the present invention proposes to construct several central lines that can potentially be used, and then to select the one that is most suitable for the traffic conditions of the motor vehicle.

[0013] More specifically, the invention proposes a detection method comprising the following steps: - acquisition of information characterizing two track edge lines (those that delimit the traffic lane whose center line we want to know), - Acquisition of data relating to the posture and / or movement of the motor vehicle on the road, and in particular on the traffic lane, - creation of at least two candidate center lines located between the two track edge lines, during which an electronic and / or computer unit calculates coefficients characterizing said at least two candidate center lines based on the information acquired, - selection of one of the candidate center lines based on the acquired data, and - deduction of the center line of the track based on the selected candidate center line.

[0014] Thus, thanks to the invention, several candidate center lines are calculated, and the selected one depends on the instantaneous behavior of the motor vehicle (for example, its position on the road and / or its velocity vector and / or its acceleration vector and / or its heading angle and / or its yaw rate and / or its yaw acceleration). In this way, the method provides center line detection that adapts to the behavior of the motor vehicle, ensuring good driving comfort and safe driving.

[0015] Indeed, the result of the invention, when used to steer the motor vehicle automatically, allows the vehicle to follow more consistent trajectories given its position and speed. In other words, the vehicle's trajectory is smoother and more reliable.

[0016] It should be noted that two identical vehicles (therefore equipped with the same detection system) successively using the same traffic lane will not necessarily detect the same center line if their positions and / or dynamic characteristics are not the same.

[0017] One advantage of the invention is that it does not generate any additional cost on the vehicle in which it is integrated, since the sensors or equipment used are already conventionally embedded in mid-range vehicles.

[0018] It should also be noted that this solution does not require any connectivity with other vehicles or infrastructures, which makes this solution inexpensive and robust.

[0019] Other advantageous and non-limiting features of the detection method In accordance with the invention, taken individually or in all technically possible combinations, are as follows: - the coefficients characterizing at least one of the candidate central lines are calculated based solely on the information characterizing one of the two track edge lines and the track width; - the coefficients characterizing at least one of the candidate central lines are calculated based on the information characterizing the two trackside lines; - exactly two central candidate lines are created (to reduce the computational load); - exactly three central candidate lines are created (which offers a greater variety in the choice, without overloading the calculations too much); - more than three candidate center lines are created (to ensure better accuracy); - a step is planned to estimate data relating to the posture and / or movement that the motor vehicle will present at a future time, then another step to select one of the candidate center lines based on the estimated data, the center line of the lane then being deduced based on the two candidate center lines selected (the one initially selected in the selection step and then the one selected during said other selection step); - if the same candidate centerline has been selected twice, at the deduction stage, it is deduced that the centerline of the track is the selected candidate centerline; - if the two selected candidate center lines are different, at the deduction stage, it is planned to construct the center line of the track according to the coefficients characterizing the two selected candidate center lines; - at each selection stage, it is planned to calculate a distance between the motor vehicle and each candidate centerline, and to select the candidate centerline for which the calculated distance is the smallest; - the calculated distance between the motor vehicle and each candidate centerline is a statistical distance, for example a Mahalanobis distance, which depends on a covariance matrix associated with said candidate centerline.

[0020] The invention also proposes a motor vehicle comprising means for acquiring information characterizing two lane edge lines of a road used by the motor vehicle and data relating to the posture and / or movement of the motor vehicle on the road, and a computer adapted to implement a detection method as described above.

[0021] Of course, the different features, variants and embodiments of the invention can be combined with each other in various ways insofar as they are not incompatible or mutually exclusive. Detailed description of the invention

[0022] The following description with regard to the attached drawings, given by way of non-limiting examples, will make it clear what the invention consists of and how it can be carried out.

[0023] On the attached drawings:

[0024] [Fig.1] is a schematic view of a motor vehicle adapted to implement a method according to the present invention;

[0025] [Fig.2] is a schematic top view of the motor vehicle of [Fig.1], on which central and edge lines of traffic lane appear;

[0026] [Fig.3] is a block diagram illustrating the different steps of a process according to the present invention.

[0027] In [Fig.1], a motor vehicle 10 adapted to implement the invention is shown.

[0028] This refers to a car. Alternatively, it could be another type of vehicle (truck, motorcycle...).

[0029] 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.

[0030] This vehicle 10 comprises a powertrain, a braking system, and a steering system for turning the vehicle (not shown in the figure). Typically, the steering system comprises an electronically controlled power steering actuator, the powertrain comprises an electronically controlled engine control actuator, and the braking system comprises an electronically controlled brake actuator.

[0031] The vehicle 10 also includes an electronic and / or computer processing unit (hereinafter referred to as computer 11) comprising at least one microprocessor or microcontroller, at least one memory and input and output interfaces.

[0032] Thanks to its input interfaces, the computer 11 is adapted to receive different input data, which come from third-party sensors or computers.

[0033] Among these sensors, the following are provided for example: - a device such as a front-facing camera, enabling the position of the motor vehicle 10 to be determined in relation to its lane of travel, and - a device such as a RADAR remote detector and / or a LIDAR remote detector, enabling the detection of the edges of the road.

[0034] Thanks to its output interfaces, the computer 11 is suitable for controlling the display screen, the power steering actuator, the engine control actuator, and the brake actuator.

[0035] Thus, the computer 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).

[0036] Thanks to its memory, the computer 11 stores a computer application, consisting of computer programs including instructions whose execution by the computer allows the implementation of the process described below.

[0037] In [Fig.2], the motor vehicle 10 is shown from above, as it travels along a traffic lane 1 of a road.

[0038] 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.

[0039] In this figure, it can be seen that the traffic lane 1 is delimited by two lane edge lines, namely left line IG and right line 1D. In this particular example, the traffic lane 1 widens since the vehicle is at a motorway exit.

[0040] Of course, the process described below will apply to any other traffic situation.

[0041] In this [Fig. 2], a coordinate system (X, Y) attached to the motor vehicle 10 is also shown; this will be the one considered for the calculations described below. This coordinate system has an abscissa extending forward along the longitudinal axis of the vehicle, and an ordinate extending to the left of the vehicle. Its origin is located, for example, at the center of gravity of the motor vehicle 10.

[0042] The objective is to determine the position and shape of the centerline of the traffic lane 1.

[0043] Figure 3 illustrates the process that the calculator 11 is adapted to implement in order to characterize this central line.

[0044] It should be noted that this central line can be defined as a line which is substantially positioned in the center of the traffic lane 1, between the right line 1D and left line IG, and which will be taken into account to pilot the motor vehicle in an automated manner.

[0045] As will become clear from reading this explanation, 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 central line.

[0046] Here, the traffic lane 1 considered will be the lane on which the vehicle 10 is travelling. Of course, it could be another lane, in particular an adjacent lane towards which the vehicle wishes to move (for example during a lane change).

[0047] The method for detecting the center line comprises several successive steps, all of these steps are repeated in a loop at regular intervals so as to regularly update the shape and position of this centerline as the vehicle moves along the road.

[0048] In summary, the method according to the invention comprises steps consisting of acquiring information on the geometry of the traffic lane 1 and on the attitude of the motor vehicle 10, then creating different "candidate center lines" according to the information on the lane, and finally selecting one of these candidate center lines according to the attitude of the motor vehicle 10.

[0049] In other words, as will be described in detail in steps E1 to E5 which follow, the idea is to create candidate center lines according to the characteristics of the traffic lane 1 used, and then to select the center line best suited to the current behavior (at time t0) of the motor vehicle.

[0050] It could be envisaged that the candidate central line thus selected forms the central line of the traffic lane 1 to be considered for piloting the motor vehicle in an automated manner.

[0051] However, preferably, as will be described in detail in steps E6 to E10, an additional approach will be to predict the behavior of the motor vehicle in the short term (at a time t'>t0), and then to select the candidate center line best suited to the future behavior (at time t') of the motor vehicle. From this, it will be possible to deduce the position of the center line of lane 1 based on the candidate center lines selected at times t0 and t' (which lines may be the same or different).

[0052] The detailed steps of this process are as follows.

[0053] During the first step El, the computer 11 receives information characterizing the two track edge lines (the left line IG and the right line 1D).

[0054] These track edge lines are typically formed by the edge of the road shoulder and / or by a road marking line.

[0055] This information can be provided directly by a sensor or be pre-processed.

[0056] Preferably, only information measured by the vehicle's sensors is used here to detect these lane edge lines. No information received from road infrastructure or third-party vehicles is used, for example.

[0057] Here, each left line IG and right line 1D is modeled in analytical form.

[0058] By way of example, one could foresee that these lines be modeled in polynomial form. However, here they are modeled in clothoid form, this form being preferred because it provides better results (for the same number of coefficients taken into account).

[0059] Such a clothoid is defined as having a curvature c which depends on its length, which can be written:

[0060] [Math.l] = co +

[0061] In this equation: - This is therefore the equation of the clothoid, -1 is the curvilinear abscissa of the clothoid arc, 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 in arc curvature.

[0062] 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) coordinate system, - y0, the ordinate of this origin point, - Wq, which is the angle of incidence of the arc, -L, - c0, and - Ci.

[0063] In the following, each clothoid will be defined by the set C of these six coefficients, which can be noted in the following way: C (x0, yo, ^o, c0, cb L).

[0064] The clothoid characterizing the shape of the left line IG will then be defined by the set Cg (xOg, yOg, ^og, cOg, Cig, Lg) while the clothoid characterizing the shape of the straight line 1D will be defined by the set Cd (xOd, yod, 'Lod, cOd, Cid, Ld).

[0065] For information, the angle of incidence along the clothoid can be written as follows:

[0066] [Math.2] 'V(l) = ^,+ [ ^:(11)^ = ^ + c0.l +

[0067] The Cartesian coordinates (x, y) of a point located along the clothoid can be written as follows:

[0068] [Math.3] f 1 x(Z) - j0 + J cos (W(u)).du fz y(Z) = >'0 + J o sin

[0069] During this first step El, it is possible to consider 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 perform the operations necessary to determine the coefficients sought).

[0070] Alternatively, the computer 11 could receive the data collected by several of the vehicle's sensors, merge this data, and deduce the desired coefficients.

[0071] Alternatively, the calculator could receive coefficients defining the two lines right 1D and left IG in polynomial form (for example, as coefficients of third-order polynomials). The calculator would then have to transform these coefficients to find, by calculation, the values ​​of the six required coefficients. Such a calculation is well known to those skilled in the art and will therefore not be provided here.

[0072] During this step El, in addition to the aforementioned coefficients, the computer 11 receives the two variance-covariance matrices (hereinafter referred to as covariance matrices) associated respectively with the coefficients of the two right-hand lines 1D and left-hand lines IG. Each covariance matrix takes into account the uncertainty in the detection of each track edge line by the sensor(s).

[0073] These matrices are, for example, provided by the manufacturer of the sensors used. Alternatively, they can be determined during a sensor testing campaign and stored in the computer's memory. The coefficients of these matrices are preferably invariant.

[0074] It should be noted here that if several sensors are used to determine the coefficients characterizing a track edge line, their uncertainties will be associated together in order to obtain the covariance matrix of a detected line.

[0075] Finally, during step El, the computer 11 acquires data relating to the posture and / or kinematics of the motor vehicle 10 in its lane of travel 1. This is kinematic data which is preferentially obtained through the sensors on board the motor vehicle 10.

[0076] Here, this data includes values ​​of: - speed V of the motor vehicle relative to the ground, expressed in m / s - Longitudinal acceleration of the motor vehicle along the X-axis, expressed in m / s² - 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, - Front wheel angle of the motor vehicle, expressed in rad, - Rear wheel angle 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).

[0077] The second step E2 consists, for the computer 11, of constructing by calculation several candidate central lines.

[0078] The idea here is to construct at least two candidate center lines, so that the best one can then be selected taking into account the conditions under which the motor vehicle 10 travels on its lane.

[0079] This construction is entirely performed by the computer 11. However, as an alternative, some of the candidate center lines could be created by the camera processor. However, this solution is not preferred as it is unnecessarily costly.

[0080] Here and preferably, exactly three candidate central lines are constructed (see [Fig.2]). Alternatively, this number could be more restricted (equal to two) or greater.

[0081] Each of these lines is modeled by a clothoid.

[0082] One of these candidate center lines (called candidate left line 2G) is created solely on the basis of the left track edge line 1G and the track width. In other words, this candidate left line 2G is defined by a set of six coefficients whose values ​​are deduced from the values ​​of the coefficients of the clothoid representing the left line 1G and the value of the track width.

[0083] One of these candidate center lines (called candidate straight line 2D) is created on the basis of only the right track edge line 1D and the track width.

[0084] 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.

[0085] Alternatively, these three lines could be modeled differently. Typically, all three lines could be calculated based on the shapes of the two track edge lines.

[0086] Alternatively, at least one of the lines could not depend on either of the two track edge lines, typically when one of the sensors (for example the camera) directly provides the coefficients of a candidate center line.

[0087] In the embodiment considered here, the left candidate line 2G is the result of a translation operation of the clothoid defining the left line IG, over half the width of the track and towards its center. Similarly, the right candidate line 2D is the result of a translation operation of the clothoid defining the right line 1D, over half the width of the track and towards its center.

[0088] Thus, we can write that: - the left candidate line 2G is defined by the set of coefficients Cgg (xOg, (yog +yod) / 2, Wog, cOg, Cig, Lg). - the 2D straight candidate line is defined by the set of coefficients Cdd (xOd, (yog +yod) / 2, Wod, Cod, Ch, Ld).

[0089] The intermediate candidate line 2DG could, for its part, be obtained in various ways. Here, a barycentric type operation is employed. Thus, this line can be considered to be defined by the set of coefficients Cdg ((x0g+x0d) / 2, (y og+yod) / 2, OLogH- Wod) / 2, (cog+Cod) / 2, (cig+ cld) / 2, (Lg+Ld) / 2).

[0090] The third step E3 consists for the calculator 11 to calculate the covariance matrix of each candidate row.

[0091] This calculation is carried out according to the way in which each of these candidate lines was developed.

[0092] More specifically, the covariance matrix of each candidate row is calculated as a function of the coefficients of the clothoid associated with that candidate row and as a function of the covariance matrix(s) associated with the right 1D and left IG rows (depending on whether one or both rows were considered to define the candidate row in question).

[0093] Various uncertainty transfer algorithms could be implemented to perform this calculation. Since such an uncertainty transfer algorithm is well known, it will not be described in detail here. It can only be stated that the calculation of the covariance matrix can be based on the following formulas:

[0094] [Math.4] Var(AX) = A * Var(x) * AT

[0095] [Math.5] Var (X + Y) = Var(X) + cov(X, Y) + ccMT, X) + Var(Y)

[0096] In these two equations, "Var" denotes the covariance matrix and "cov" denotes the cross-covariance matrix. X and Y are the vectors of the variables in the detected rows, then of size 6x1. A is a constant matrix of size 6x6 in this case.

[0097] It will be noted that assuming that the lines are detected and estimated independently, the cross covariance matrices cov(X,Y) and cov(Y,X) are zero.

[0098] 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 exits the motorway.

[0099] At this stage, the present invention therefore proposes to choose the candidate line most appropriate to the situation.

[0100] For this purpose, 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 get too close to one of the track edge lines).

[0101] In any case, during a fourth step E4, the computer calculates a parameter which will allow selection of the best candidate line, taking into account the data relating to motor vehicle 10.

[0102] This parameter is preferably a statistical distance. More precisely, it is a Mahalanobis dM distance.

[0103] The method used then consists, for the computer 11, in calculating the Mahalanobis distance dM between each candidate line and the motor vehicle 10 (at the current time t0, at the level of the motor vehicle 10).

[0104] To do this, the calculator performs the following calculation:

[0105] [Math.6] — ^(u - v) T. ( Su + Sv ) .(il - v)

[0106] In this equation, u is a first vector relating to the candidate line under consideration and Su is its covariance matrix. Similarly, v is a second vector relating to the motor vehicle 10 and Sv is its covariance matrix.

[0107] Preferably, the vectors have the same number of coefficients, between two and six.

[0108] Thus, it could be predicted that the first vector u has six coefficients corresponding to the six coefficients of the clothoid.

[0109] However, a smaller number of coefficients will be used so that the calculation does not require excessive computing power. Here, only two coefficients will be used since it has been observed that the results remain very reliable with only two coefficients.

[0110] One of the coefficients of each vector is a heading angle (such an angle giving in fact 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 vehicle's trajectory are curved).

[0111] Here, the first vector u is therefore defined by:

[0112] u=[W0,c0]T

[0113] The second vector v is defined by:

[0114] v=[0,Co,ego]T.

[0115] 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 not zero.

[0116] It should be noted that the first vector u will have different values ​​from one candidate line to another.

[0117] It will be noted here that the covariance matrix Su will be deduced from the covariance matrix attached to the candidate row considered.

[0118] The covariance matrix Sv will be read (its coefficients are stored in The computer's memory and the method used to determine the uncertainty of the sensor(s) used (if multiple sensors are used in combination) depend on the type of sensor and the method employed. This covariance matrix is ​​therefore determined in a manner analogous to those associated with the two trackside lines. Thus, here again, if multiple sensors are used in combination, it is possible to associate the uncertainties related to each sensor.

[0119] During this fourth step E4, the Mahalanobis distance is then calculated for each of the candidate lines, at time t0.

[0120] The fifth step E5 then consists of selecting the candidate line for which the Mahalanobis distance thus calculated is the smallest.

[0121] In the example illustrated in [Fig.2], this is the straight candidate line 2D, which appears preferable since the vehicle is traveling towards the motorway exit.

[0122] As previously stated, the process could end here.

[0123] However, this process is continued here in order to obtain even more reliable results, particularly when the driver of the vehicle suddenly changes behavior (for example, ultimately staying on the highway and not getting off it).

[0124] To summarize, the plan is to estimate where the vehicle will be at a future time t' and what its dynamic characteristics will be, and then to repeat part of the aforementioned steps in order to select one of the candidate lines (another one or the same one as the one selected in step E5).

[0125] The instant t' is separated from the instant to by a duration between 0.5 and 2 seconds, for example equal to 1 or 1.3 seconds.

[0126] These different subsequent steps can be described in more detail.

[0127] During a sixth step E6, the computer 11 uses a predictive model of the vehicle's movement in order to determine the kinematic data that the vehicle will present at time t'.

[0128] This modeling simplifies calculations. Its purpose is to calculate the coefficients of the second vector v at time t' as well as those of the covariance matrix Sv.

[0129] The chosen modeling may be more or less simple and precise.

[0130] By way of example, it is possible to use a constant curvature and zero acceleration evolution model, according to which the predicted trajectory of the vehicle is deduced from the velocity vector of this vehicle, considering that the magnitude of this velocity vector will not evolve in the next few seconds and that the direction of this velocity vector will continue to evolve at a constant yaw rate in the next few seconds.

[0131] Alternatively, it is possible to use a curvature and acceleration evolution model constants, according to which the predicted trajectory of the vehicle is deduced from the velocity vector and the acceleration vector of that vehicle.

[0132] Other variants would of course be conceivable.

[0133] Thanks to this modeling, the curvilinear abscissa 1 indicating the position of the motor vehicle at time t' is calculated for each candidate line.

[0134] The first vector u can thus be calculated at time t'. Its covariance matrix Su at time t' can also be calculated using a well-known algorithm for transferring uncertainty along a line.

[0135] It can only be specified with regard to this algorithm that at curvilinear abscissa 1, the covariance matrix can be deduced from the following equations:

[0136] [Math.7] Z (Z) = B*S(0)*BT

[0137] In this equation, B is a matrix defined as follows:

[0138] [Math.8] B = R 0 L0 1.

[0139] In this matrix B, J is the Jacobian of the function fl which can be defined as follows:

[0140] [Math.9] SW = [x(l) y(l) psi(l) c(l) cl ]T = [ / / (x0 yO psiO cO clfclf

[0141] Note that the first vector u can also be recalculated taking into account the equation of the clothoid of the candidate line considered, as well as its covariance matrix Su.

[0142] During a sixth step E7, knowing the vectors u and v at time f and the covariance matrices Su and Sv at that same time t', the Mahalanobis distances at that time t' are calculated and the candidate line presenting the minimum distance is selected and then stored in memory (as in steps E4 and E5).

[0143] At step E8, the candidate line selected at time t0 (step E5) is compared by the computer 11 with the candidate line selected at time t' (step E7).

[0144] If it is the same candidate line, then this candidate line is definitively chosen as the central line to be considered (step E9).

[0145] Otherwise (for example because the driver starts to turn his steering wheel to the right and does not ultimately leave the motorway), a hybrid line is constructed and then definitively chosen as the central line to be considered (step E10).

[0146] This hybrid line can be constructed in various ways.

[0147] By way of example, the results of the modeling used in step E6 make it possible to determine the position that the motor vehicle should probably occupy at time t' and the yaw angle that it should exhibit.

[0148] Knowing the current position of this vehicle and its yaw angle, it is possible to construct a continuous and continuously differentiable line passing through these two positions. This line can, for example, be modeled as a clothoid or in another form (typically polynomial).

[0149] It is then definitively chosen as the central line to be considered.

[0150] The center line to be considered from step E9 or E10 can then be used to control the vehicle's actuators so that the vehicle remains centered in its lane without the driver having to intervene. It can also be used to implement other vehicle driver assistance functions, for example emergency braking, avoidance, overtaking, adaptive cruise control, collision warning, etc.

[0151] In addition, the center line may be displayed on the display screen of the motor vehicle, superimposed on an image already displayed of that vehicle and its surroundings.

[0152] The present invention is in no way limited to the embodiment described and represented, but a person skilled in the art will be able to make any variation in accordance with the invention.

[0153] By way of example, the method could be used to determine the shape of the centre line of a traffic lane adjacent to that used by the motor vehicle.

[0154] 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 area for the robot).

Claims

Demands

1. A method for detecting a centerline of a lane (1) of a road used by a motor vehicle (10), comprising the steps of: - acquiring information characterizing two lane edge lines (1D, IG), - acquiring data relating to the posture and / or movement of the motor vehicle (10) on the road, - creating at least two candidate centerlines (2D, 2G, 2DG) located between the two lane edge lines (1D, IG), during which an electronic and / or computer unit (11) calculates coefficients characterizing said at least two candidate centerlines (2D, 2G, 2DG) as a function of the information acquired, - selecting one of the candidate centerlines (2D, 2G, 2DG) as a function of the data acquired, and - deducing the centerline of the lane (1) as a function of the selected candidate centerline (2D, 2G, 2DG).

2. A detection method according to the preceding claim, wherein the coefficients characterizing at least one of the candidate center lines (2D, 2G) are calculated based solely on the information characterizing one of the two track edge lines (1D, IG) and the track width (1).

3. A detection method according to any one of the preceding claims, wherein the coefficients characterizing at least one of the candidate center lines (2DG) are calculated as a function of the information characterizing the two track edge lines (1D, IG).

4. A detection method according to any one of the preceding claims, wherein exactly three candidate center lines (2D, 2G, 2DG) are created.

5. A detection method according to any one of the preceding claims, wherein: - a step is provided for estimating data relating to the posture and / or movement that the motor vehicle (10) will exhibit at a future time, - a further step is provided for selecting one of the candidate center lines (2D, 2G, 2DG) based on the estimated data, and - the center line (1) of the lane is deduced also based on the candidate center line (2D, 2G, 2DG) selected in said further step selection.

6. A detection method according to the preceding claim, wherein, if the same candidate centerline (2D, 2G, 2DG) has been selected twice at said selection step and then at said other selection step, at the deduction step, it is deduced that the centerline of lane (1) is the selected candidate centerline (2D, 2G, 2DG).

7. A detection method according to one of the two preceding claims, wherein, if the two candidate centerlines (2D, 2G, 2DG) selected at said selection step and then at said other selection step are different, at the deduction step, it is provided to construct the centerline (1) of the track as a function of the coefficients characterizing the two candidate centerlines (2D, 2G, 2DG) selected.

8. A detection method according to any one of the preceding claims, wherein, at each selection step, it is provided to calculate a STATISTIC distance between the motor vehicle (10) and each candidate centerline (2D, 2G, 2DG), and to select the candidate centerline (2D, 2G, 2DG) for which the calculated distance is the smallest.

9. A detection method according to the preceding claim, wherein the calculated distance between the motor vehicle (10) and each candidate centerline (2D, 2G, 2DG) is a statistical distance, for example a Mahalanobis distance, which depends on a covariance matrix associated with said candidate centerline (2D, 2G, 2DG).

10. Motor vehicle (10) comprising means for acquiring information characterizing two lane edge lines (1D, IG) of a road used by the motor vehicle (10) and data relating to the posture and / or movement of the motor vehicle (10) on the road, characterized in that it comprises a computer (11) adapted to implement a detection method according to one of the preceding claims.

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