an amplitude / time signal corresponding for example the steering angle of a vehicle versus time is recorded . this recording is made for example for tire tests purposes where specific maneuvers are to be identified . an analysis of this signal is carrying out in order to proceed to patterns recognition . in order to create discrete driving events , a phase detection algorithm is applied to the signal as a first step . a phase is a period of time delimited by the extrema of both the signal f ( t ) and its first derivative df ( t )/ dt , as illustrated in fig1 . the signal is cut or split at phase changes ; these phase changes being either extrema values of the signal or extrema values of the first derivatives . in other words , this means that the signal is cut at all maximum and minimum points as well as at all inflection points . since these two criteria correspond to changes in concavity of the signal , it can be expected that each phase of signal comprises a single concavity . for a better understanding , this operation can be compared to the splitting of handwritings into letters . it shall be mentioned here that the use of the extrema of both the signal and of its first derivative is purely exemplary . indeed , depending on the morphology of the measured signal , other criteria can be considered . for example , it might be appropriate to use as criterion only the extrema of the signal without considerations of the first derivative . in such a case , the phases would be in principle rougher and longer what might be precise enough for recognizing simple or obvious patterns . as another example of alternative , it might be appropriate to split the signal into phases based on the second derivative , resulting in a more precise splitting . as a second step , the different phases of the signal have to be classified in to order to be analyzed later on . this operation is made on the basis of one or more parameter of the phase . the phase signal illustrated in fig1 shows a single and constant concavity . based on the above mentioned criteria ( max ( f ( f )) and max ( df ( t )/ dt )), it can be deducted that any phase of signal detected on that basis will be comprised within a rectangle delimited by the tangential to the signal at the point max ( f ( f )) and a parallel thereto passing at the point max ( df ( t )/ dt ). the rectangle is also delimited by a perpendicular to the tangential and passing at the point max ( df ( t )/ dt ) and a parallel thereto passing at the point max ( f ( f )). as is illustrated in fig2 , the phase of signal can be characterized by the length of the two sides of the rectangle , i . e . dl the length or lapse of time of the phase , and dh the amplitude or height of the phase signal . although the two parameters dl and dh are representative and discriminatory parameters of the signal in the phase , the signal can still take various shapes while said parameters remaining the same . for example , an imaginary phase passing at the same extreme points max ( df ( t )/ dt ) and max ( f ( f )), but with an opposite concavity would still be characterized by the same values of the parameters dl and dh as the phase of fig1 and 2 . for such an imaginary phase the extreme points max ( df ( t )/ dt ) and max ( f ( f )) would be reversed but this phase would still meet the criteria of the phase as described above and be characterized by the values of dl and dh . since the concavity of the phase is considered to be important for the pattern recognition , a further parameter representative of the shape of the signal within the rectangle has been defined as the area s covered by the signal in the phase . this area is illustrated as a dashed area in fig2 and is calculated as follows : the three parameters dl , dh and s are found to be representative of the signal and with a low correlation level . more generally speaking , the parameters characterizing the signal should be chosen in such a way that : they closely represent geometric features of the signal and they can be assessed visually . they are independent of signal offsets and from signal sign . applied to a signal corresponding to a steering angle of a vehicle ; this means that parameters are tolerant to constant road curvatures ( independent of signal offset ) and can be used to describe both left and right maneuvers . the three parameters described here above fulfill indeed these criteria . more particularly , the area factor s has the ability to be noise tolerant in that any noise of higher frequency will not have any noticeable influence on this factor . the reconstruction of a signal affected by such noise , based on dh , dl and s by means of a polynomial function will provide a noise free reconstructed signal . the different phases identified can be classified by means of the above mentioned parameters . depending on the nature of the signal to be treated , some phase classes are more frequent than others , so that it can be efficient and appropriate to neglect some types of phase and to allocate a class only to the phases corresponding to frequent and / or representative classes . this can be much dependent on the nature of the signal to be treated . one option for simplifying the classification and reducing the number of classes is to consider a sequence of two consecutive phases according to the definition made above . the analysis is then based on the type of convexity of the current phase and its predecessor . it is therefore an analytical classification based on geometrical features . the goal is to distinguish different parts of a wave , regardless of the sign of the derivative . this permits to identify parts of steering maneuver for both left and right directions . four types of convexity for each phase give 16 possible combinations for a 2 - phase sequence . as the sign of the derivative is not taken into account , 8 cases remain . these cases are represented in fig3 . a statistical analysis of the phases illustrated in fig3 on the basis of a signal corresponding to the steering angel of a vehicle has yielded the percentage indicated in the column named “ class , p ”. these percentages correspond to probabilities p of presence of the different classes in a master signal . the first class (# 1 ) with a cap shape is found to occur at 32 . 98 %, i . e . at a high rate . this shape of signal corresponds indeed to a natural movement of inversion of the rotation direction of the steering wheel . the fourth class (# 4 ) with a saddle shape corresponds to a movement of continuous rotation of the steering wheel with a slight slow - down section ( centered at f ( t )= 0 ). this corresponds to a natural common movement of the steering wheel and is found to occur at a rate of 12 . 02 %. the seventh class (# 7 ) with a slope shape is similar in shape to the fourth one whereas the rotation speed is approximately constant at the center portion of the signal whereas the rotation speed is slower at the beginning and the end of the signal . this corresponds also to a natural movement of the steering wheel while driving the vehicle and shows therefore a high rate of 44 . 15 %. the classes # 2 , 3 , 6 and 8 show an inflexion point ( at f ( t )= 0 ) with no derivative . the class # 5 shows also a point ( at f ( t )= 0 ) with no derivative . physically , these classes correspond to movements of the steering wheel which do not correspond to common movements of maneuvers . this explains the very low frequency of these shapes of signal . among the identified 8 classes for 2 - phase sequences , only three classes will be considered , i . e . classes # 1 ( cap ), 4 ( saddle ) and 7 ( slope ) in view of their recurrences . in practice , only these three classes can be found in a continuous signal after filtering . the other classes correspond generally to smaller phases and are rejected . a more precise classification can be done when considering more parameters . in the case of the signal of the steering angle of a vehicle , the parameter space of the different phases is convex , meaning that it does not show any self - organization of the classes . in order to have classification results which can be exploited , a training - based approach or calibration has been used . several runs were chosen for training purposes and maneuvers in these runs were recognized manually with the help of experts . phases of these maneuvers have then been manually attributed to classes , resulting in the definition of regions in the parameter space defining the different classes of the classification scheme . such a region is illustrated in fig4 showing a parameter space with two coordinate axes , one for the function log ( h · l ) and one for the function log ( h / l ). the different points illustrated by circles and crosses correspond to different phases in this specific parameter space . the points represented by a circle correspond to phases belonging to the same class , this class being represented by the ellipse . the points represented by a cross correspond to phase of a different class or at least not belonging to the class in question . the parameters log ( h · l ) and log ( h / l ) have been chosen for they show a very low level of correlation . the introduction of one or more parameters would of course increase the accuracy of classification . when selecting parameters , it is generally appropriate to consider parameters with a low level of correlation with each other in order to increase their discriminatory effect . the boundaries of the regions of the different classes can be initially roughly set by means of lines as illustrated in fig4 . indeed , the region or area where so - called “ good ” points for a manually identified class can be delimited by vertical , horizontal and oblique lines in the parameter space . fig4 illustrates four “ good ” points and two “ bad ” points . the four “ good ” points are delimited by four line , where two of them are parallel to the first coordinate axis log ( h · l ) and the two others are parallel to the second coordinate axis log ( h / l ). it can be observed that the rectangle area delimited by these lines includes a “ bad ” point ( in the upper left corner ). four additional parallel oblique lines are also present . two of them are parallel and correspond to a function log ( l ) and the two others are also parallel and correspond to the function log ( h ). it can be observed that the region delimited by these lines and by the rectangle now excludes the “ bad ” point , i . e . thereby increasing the accuracy of the definition of the class . it is indeed important not to include “ bad ” points in the definition of class in order to avoid a false classification which might lead to false pattern detection . it can be appropriate to consider improving the boundaries of the classes by means of further training sessions . indeed , this can be done in a partly automated manner in that some parameters of the lines discussed here above are adapted in an iterative way to take into consideration further points manually identified during a further training session . the mathematical functions of the log ( l ) line and its parallel have log ( h · l ) as variable so that they differ only in by a parameter value ( log ( h · l )= log ( h )+ log ( l ), meaning that log ( l )= log ( h · l )− log ( h )). the parameter values of these lines can therefore be adapted iteratively during such further training sessions . the third major step of the method is the pattern recognition , more precisely expressed , the sequence detection . indeed a pattern is a sequence of phases which have to be detected by the algorithm . it resembles to a simple text search , where a longer sequence of letters ( text ) is searched for occurrences of a shorter sequence ( word ). the recognition performance of the algorithm can be assessed and improved by training sessions . as some maneuvers can be recognized manually in a training session , the points corresponding to their phases form some shapes in the parameter space , and regions are defined to approximate these shapes . this is illustrated in fig5 . the points illustrated with circles and crosses in fig5 correspond to phases identified . the boundary region represented by an oval form or ellipse corresponds to a class which corresponds to the manually recognized maneuver . points corresponding to phases which belong to the pattern are called “ good ” points and are represented by a circle . the points corresponding to phases which do not belong to the pattern are called “ bad ” points and are represented by a cross . the “ good ” points which are outside the ellipse are called “ outsider ” points and are represented in dashed line . the “ bad ” points which are inside the ellipse are called “ intruder ” points and are represented also in dashed line . in practice , this means that the detection of a maneuver or pattern is based on a reduced number of phase shapes as the maneuver normally includes . in others words and in reference to the comparison with the detection of handwriting , this means that a word or a letter would be detected by too narrow definitions of the shape of the different letters . the narrow definitions will exclude some “ good ” letters but will exclude most of the wrong ones in order to minimize the risk of false detection . for each point or phase , two probabilities are defined with respect to the number of points in each category ( good , bad , intruder and outsider ): where p true p is the correct detection probability for a given phase ; p false is the false detection probability for a given phase ; n good is the number of manually recognized phases located within the ellipse ; n bad is the number of phases found not to be present in the manually recognized maneuver and outside the ellipse ; n outsider is the number of manually recognized phases outside the ellipse ; and n intruder is the number of phases found not to be present in the manually recognized maneuver and inside the ellipse . p true can be considered independent of the percentage of manually recognized maneuvers . if n intruder is big enough , we can also consider p false also to be independent of manual recognition . but technically , p false is dependent on the manual recognition because more “ good ” points mean fewer “ intruder ” points . for any given maneuver m which is composed of n phases p 1 , . . . , p n , we have p true ( m )= p true ( p 1 )× . . . + p true ( p 1 ). again , the same formula is not true for p false , as false phase detection cannot be considered independent or perfectly random . but at least we can say that p false ( p 1 )× . . . × p false ( p n )& lt ; p false ( m )& lt ; min ( p false ( p 1 ), . . . , p false ( p n )). the general flowchart of the method is illustrated in fig6 which shows the succession of the three major steps preceded by a prior calibration or training step . as mentioned before , training sessions can be carried out afterwards in order to assess the performance of recognition and to improve the parameters settings of classification of the phases . while the invention has been described with respect to a limited number of embodiments or examples , these should not be construed as limitations on the scope of the invention , but rather as exemplifications of some of the preferred embodiments . other possible variations , modifications , and applications are also within the scope of the invention . accordingly , the scope of the invention should not be limited by what has thus far been described , but by the appended claims and their equivalents .