EP3014598B1 - Procédé de traitement de données de mesure d'un véhicule pour l'établissement du commencement d'une recherche d'une place de stationnement - Google Patents

Procédé de traitement de données de mesure d'un véhicule pour l'établissement du commencement d'une recherche d'une place de stationnement Download PDF

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EP3014598B1
EP3014598B1 EP14731561.8A EP14731561A EP3014598B1 EP 3014598 B1 EP3014598 B1 EP 3014598B1 EP 14731561 A EP14731561 A EP 14731561A EP 3014598 B1 EP3014598 B1 EP 3014598B1
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feature
search
information
vectors
vector
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EP3014598A1 (fr
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Heidrun Belzner
Peter PEDRON
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Bayerische Motoren Werke AG
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Bayerische Motoren Werke AG
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    • G—PHYSICS
    • G08—SIGNALLING
    • G08G—TRAFFIC CONTROL SYSTEMS
    • G08G1/00—Traffic control systems for road vehicles
    • G08G1/14—Traffic control systems for road vehicles indicating individual free spaces in parking areas
    • G08G1/145—Traffic control systems for road vehicles indicating individual free spaces in parking areas where the indication depends on the parking areas
    • G08G1/147—Traffic control systems for road vehicles indicating individual free spaces in parking areas where the indication depends on the parking areas where the parking area is within an open public zone, e.g. city centre

Definitions

  • the invention relates to a method for processing measurement data of a vehicle for determining the beginning of a parking space search.
  • Parking information on free parking spaces are used, for example, by parking guidance systems and / or navigation devices for navigating a parking space-seeking vehicle.
  • Modern inner-city systems work according to a simple principle. If the number of parking spaces and the inflow and outflow of the vehicles are known, this can easily determine the availability of free parking. Signposting the access roads and dynamically updating the parking information allows vehicles to navigate to free parking. In principle, this results in restrictions in that the parking areas must be clearly defined and the entry and exit of the vehicles must always be precisely controlled. For this purpose, structural measures, such as barriers or other access control systems are required.
  • parking-exploring vehicles such as public transport vehicles, such as regular buses or taxis, which have at least one sensor for parking space detection.
  • the sensors can be based on optical and / or non-optical sensors.
  • a knowledge database with historical data is generated from ascertained information about available, free parking spaces.
  • the historical data includes statistical data on free parking spaces for given streets and / or given times or periods. From the historical data and current information, which are determined at a given time for one or more selected streets of vehicles in circulation, a probability distribution of expected free parking spaces for the selected street or streets is determined.
  • the probability distribution represents parking information on free parking spaces in the selected street or streets. The accuracy of the probability distribution depends inter alia on the knowledge of a so-called parking rate ⁇ p .
  • the Einparkrate calculated according to the formula ⁇ p (t) (1-P n) ⁇ (t), where ⁇ (t) represents a request rate, which for a parking segment, ie, a region considered in which a parking operation is desired, indicates the number of requests for one parking space per time (ie time unit). P n indicates the probability of a free parking space.
  • the invention provides a method for processing measurement data of a vehicle for determining the beginning of a search for a parking space.
  • the method described below may be onboard, i. in the vehicle looking for the parking space or offboard, i. be performed by a central computer to which the driving data are transmitted.
  • the proposed method offers the possibility of making the calculations online, i. in real-time while driving, or offline, i. to carry out downstream after the trip.
  • a number of travel data vectors are acquired, wherein each travel data vector comprises information about a speed, position data and the time of the detection of the speed and the position data.
  • the detection of the number of travel data vectors takes place in a predetermined time interval (also referred to below as the sampling rate) in the seconds range, e.g. every second or every five or ten seconds.
  • the trip data vectors thus follow a fixed chronological order.
  • the position data may be represented by GPS (Global Positioning System) data.
  • the position data can be determined by a GPS module of the vehicle.
  • the speed can optionally be determined by the speed sensor of the vehicle or from the position data and detection times of two consecutive measurements.
  • a next step the determination of a feature vector at each time of the detection of a trip data vector, wherein the information of the current and temporally past travel data vectors are processed, wherein the feature vector comprises as feature components at least one speed information and a path information.
  • the course of the journey of the vehicle is taken into account.
  • the values of the features are recalculated for each newly acquired trip data vector and combined in a feature vector. To each (Measurement or detection) time is thus calculated a feature vector, with current and previous trip data vectors are used.
  • each of the feature vectors is assigned to one of two traffic categories.
  • the first traffic category designates the destination traffic, wherein the driver does not search for a parking space
  • the second traffic category designates the parking search traffic, wherein the driver searches for a parking space.
  • a probability is calculated which indicates with which probability the feature vector is to be assigned to the first or the second traffic category.
  • the generated feature vectors are considered individually and classified with respect to two traffic classes, namely a destination traffic represented by the first traffic category and a parking search traffic represented by the second traffic category.
  • there is a probability for each feature vector which indicates with which probability a feature vector belongs to the parking search traffic and to the destination traffic.
  • a segmentation takes place over the time profile of the determined traffic categories of the feature vectors, a subdivision of the journey from the start to the last detection of a travel data vector corresponding to the particular traffic categories of the feature vectors into two segments and the transition from one segment to the other segment the beginning of Parking search represents.
  • the task of the segmentation is to determine, based on the analysis of the temporal course of the classification of feature vectors, the fall data data vector which marks the start of the parking space search.
  • the result of the segmentation is a subdivision of the journey into two segments, according to the traffic categories, which forms the basis for calculating the desired information on the intensity and location of the parking search traffic.
  • the beginning of a search for a parking space can be used to calculate the probability of an available parking space in the area more accurately.
  • the method described in the introduction DE 10 2012 201 472.1 be used by the applicant.
  • the knowledge of the beginning of a parking space search can also be used by city planners to estimate parking situations in individual streets or neighborhoods.
  • travel data vectors may be disregarded in determining the beginning of the parking space search if the information about the speed of the trip data vector is greater than a first threshold or less than a second threshold.
  • the first threshold may e.g. between 50 km / h and 100 km / h and is in particular 80 km / h.
  • the second threshold may e.g. between 2 km / h and 8 km / h and is in particular 4 km / h.
  • the travel data vectors within a feature window representing a predefined route are processed, wherein the feature window displays the travel data vectors from the current position or measurement to the first position or measurement on the traveled route further than the predetermined distance ago, includes.
  • the number of travel data vectors in a feature window can thus vary as a function of sampling rate and speed. For example, if the size of the feature window is 1 km, then with a higher average speed in the last kilometer, fewer trip data vectors will be included in the feature window than at a lower speed if a constant scan rate is assumed.
  • the speed information may be an arithmetic mean and / or the median of the mean speeds of the travel data vectors taken into account for the determination of a respective feature vector.
  • the route information may be a route inefficiency, which indicates how inefficient the traveled route is by the ratio of the actually traveled route with respect to the shortest route between the positions of two travel data vectors.
  • the inefficiency of the route guidance is a feature that indicates how inefficient the driver-selected, driven route is in terms of proximity to the destination. This takes into account the characteristics of the classifiers (traffic classes), since vehicles belonging to the destination traffic try to reach the desired destination As fast as possible and efficient way closer, while parking-seeking vehicles have already reached their destination and it orbit in the search for a parking lot.
  • path inefficiency for a feature vector which is the maximum for the processed amount of travel data vectors is the maximum for the processed amount of travel data vectors.
  • the feature vectors are normalized to classify each feature vector.
  • Different feature components in short: characteristics
  • feature components with a numerically higher value range do not dominate over feature components with a numerically smaller value range and in order to make the feature values more comparable
  • the features are normalized. This has the effect that both features with a large range of values and features with a small value range reflect the same range of values.
  • a z-normalization known to the person skilled in the art can be used in which the mean value and the standard deviation are determined for each feature component and the feature components are transformed therewith.
  • Principal component analysis is an unsupervised feature reduction technique. It pursues the goal of finding those principal axes in a feature space on which the feature vectors mapped on them reach a maximum variance.
  • the calculation of the probability of the classifier can then be done with the set of Bayes, which is known to those skilled in the art, e.g. from [1] or [2] is known.
  • the last positive transition of the first classifier to the second classifier is selected as the start of the parking space search, as long as the classification result of the subsequent trip data vectors for a given journey path constantly comprises the second traffic category.
  • This segmentation alternative extends the first alternative with a distance criterion. In this case, a determined journey data vector is not immediately forgotten after a negative transition, but maintained for a certain distance after the negative transition. If a further positive transition is found within this route, it is ignored and the previously determined travel data vector is retained. If no positive transition is found, the previous trip data vector marking the beginning of the parking space search is forgotten at the end of the route after the negative transition.
  • the beginning of the parking space search is determined on the basis of an integral of the course of the probability over the distance traveled.
  • the hard decision as to whether a feature vector is parking search traffic or not is utilized to determine the start of search, but also the security with which the decision was made. If, if there is no search start, a positive transition is detected with a new trip data vector, the integral of the course of the so-called a posteriori probability over the distance traveled is continuously calculated. If the result of the integral calculation is negative, the previously determined travel data vector is discarded.
  • the invention further provides a computer program product which can be stored directly in the internal memory of a digital computer or computer system, e.g. a computer of a vehicle or a central computer, and includes software code sections, with which the steps are carried out according to one of the preceding claims, when the product is running on the computer or computer system.
  • a digital computer or computer system e.g. a computer of a vehicle or a central computer
  • software code sections with which the steps are carried out according to one of the preceding claims, when the product is running on the computer or computer system.
  • the proposed method makes it possible to determine the parking search portion of a car trip in order to determine information about a searched parking space, e.g. the time that has effectively elapsed to find a parking space, or the distance traveled while searching for a parking space, or the location or area in which a parking space was searched.
  • the method makes it possible in particular to determine the beginning of the search for a parking space for a car.
  • the method can be performed by a computing unit in the vehicle (ie onboard) or by a central processing unit outside the vehicle (ie offboard).
  • the travel data vectors x i are determined, for example, by the vehicle at predetermined measurement times and processed sequentially in several steps. If the method is carried out offboard, the travel data vectors x i are preferably transmitted in real time via a communication interface to the central computer.
  • a travel data vector x i (equation 3.1) consists in each case of information on the speed v i and the GPS position p i at the time t i of the acquisition of the travel data vector.
  • the trip data vectors follow a fixed time order since t i ⁇ t i + 1 .
  • the GPS positions p i can be detected by a navigation system installed in or incorporated in the vehicle.
  • the speed is eg detected by a sensor of the vehicle and is typically available in the vehicle in a computing unit or on a data bus.
  • a driver of the vehicle makes the decision to search for a parking space only once during a journey. Occasionally, a driver may begin to search in one area and, after a certain period of time, stop the search and continue in another area. In this case, the last decision to search for a parking space is taken as a true search start. In addition, it is assumed that each trip ends in a public parking lot at the roadside.
  • each trip has exactly one true time ⁇ park , from which a parking space is sought. If a parking space is found immediately after the decision to search for a parking space, then ⁇ park ⁇ ⁇ ends .
  • a journey into two segments can be subdivided according to the type of driving type c i (so-called traffic categories or traffic classes):
  • the first segment since the start of the journey always represents the so-called "destination traffic" ZV, while the second segment represents the so-called traffic.
  • “Parksuch horr" PSV corresponds.
  • Target traffic ZV is that part of the journey in which the driver drives from a starting point ⁇ start of the journey to the area in which a parking space is sought. During the destination traffic ZV the driver does not look for a parking space.
  • the assignment of the travel data vectors to the respective traffic class is carried out by a class label c i .
  • the true time ⁇ park and the associated location of the search start of a journey can be approximated on the basis of t i_park and p i_park in x i_park become.
  • the location of the search for a parking space can either be indicated directly by the GPS positions of the search route, by map matching of the positions on roads, or indirectly by an indication of the so-called search center of gravity and average search radius of the search route.
  • the basis for calculating these values is i park .
  • the determination of i park and thus the beginning of the parking space search, is the aim of the method described in more detail below.
  • Fig. 2 shows the procedure of the method according to the invention in a flow chart.
  • non-relevant trip data vectors are sorted out in the course of an optional prefiltering (step S1).
  • feature vectors are generated on the basis of known travel data vectors (step S2) and optionally smoothed (step S3).
  • the classification (step S4) calculates for each feature vector a probability for the class affiliation to the traffic class parking search traffic.
  • the subsequent segmentation (Step S5) analyzes the Klasstechnischsvertank and determined by the determined search start driving the final class label c i.
  • the determined results are optionally plausibilized (step S6).
  • trip data vectors x i which do not play any role in the determination of the search start on the basis of defined criteria, are identified and sorted out in step S1 of the prefiltering.
  • sorting out means that the relevant trip data vector x i is not sent to the next step, the feature extraction, for further processing. is passed on. These include, for example, driving outside of city traffic and stance phases.
  • a roadside parking lot is typically sought in city traffic.
  • the upper limit for the permitted speed is 80 km / h on inner-city roads. Since at a higher speed it is no longer possible to proceed from parking search traffic, a trip data vector x i with eg v i > 80 km / h is sorted out. This limit can also be set lower or higher.
  • the recorded travel data vectors x i during stance phases contain, with the exception of the time stamp, the same information. Since information about stance phases is not relevant for any of the subsequent steps, trip data vectors x i are sorted out with, for example, v i ⁇ 4 km / h. The choice of this threshold is due to the fact that in this way parking operations are not recorded, the speed is typically between 0-4 km / h.
  • step S2 the feature extraction takes place.
  • a low average speed, frequent turning and Blockumfahrten can be used.
  • a single trip data vector with its information about the current speed and position is not sufficient, but its course must be taken into account.
  • the average speed and the path inefficiency are taken into account as feature components (also referred to below as features). By taking into account further feature components, the accuracy of the determination of the beginning of the parking space search can still be improved, the accuracy increasing only to a small extent.
  • the current and previous trip data vector are used to calculate the various characteristics.
  • the travel data vectors to be taken into account for calculating features are determined on the basis of a feature window MF i , which is shown in FIG Fig. 3 is shown in more detail determined.
  • the size I f of the feature window MF i is based on the distance covered since the majority of the constructed features analyze the course of the route guidance. If the feature window was based on the past time, the length of the route section in a feature window MF i would vary depending on the speed, as well as a minimum length of the route section would not be guaranteed. However, this is necessary in order to be able to compare the calculated characteristics in the course of the journey.
  • the feature window MF i includes the travel data vectors from the current position x i to the first position, which lies farther back than I f on the traveled distance.
  • the number of travel data vectors in a feature window can thus vary as a function of sampling rate and speed. For example, if the size of the feature window is 1 km, then at a higher average speed in the last kilometer, fewer trip data vectors are included in the feature window than at a lower one, assuming a constant scan rate.
  • Feature vectors m i can only be calculated from a distance covered by l f since the start of the journey in order to ensure comparability between the calculated feature vectors m i .
  • this value represents the mean velocity in driving phases.
  • the inefficiency of the route guidance ⁇ is a feature which indicates how inefficient the driver-selected, driven route is with respect to the approach to the destination.
  • the idea for this is derived from the characteristics of the traffic classes, since vehicles belonging to the destination traffic try to get closer to the desired destination in the quickest and most efficient way possible, whereas parking-seeking vehicles have already reached their destination and in the search for one Park the car park.
  • s d is the shortest distance between p 1 and p k , the line of air being used in the context of this description.
  • s z represents the distance traveled between p 1 and p k .
  • the traveled distance s z is approximated by the sum of all partial distances between the individual waypoints.
  • the index k indicates which waypoint in the set [ p 1 ; ...; p k ] should serve as starting position for calculating the inefficiency.
  • a value of ⁇ k ⁇ 0 suggests an efficient route guidance, while ⁇ k ⁇ 1 means an inefficient route guidance.
  • the waypoints of a feature window [ p f1 ; p f2 ; ...; p fM ].
  • this feature is intended to provide the circularity ⁇ of the route within the feature window capture.
  • the reference quantity here is the distance s m of the current position p M to the center of gravity of the waypoints p f . If s m ⁇ l f / 2, then we assume a straight-line path ( Fig. 5 ). The smaller this distance becomes, the more circular the routing becomes ( Fig. 6 ).
  • ⁇ i 1 - ⁇ p ⁇ f p f M ⁇ s m l f . ⁇ i ⁇ 0 1
  • the distance between the center of gravity and the current position is normalized by the effective size of the feature window to obtain a value between 0 and 1.
  • the normalized term is additionally subtracted from 1.
  • the total variance of the waypoints is distributed only on the axis described by the first main component ( Fig. 7 ).
  • On the axis of the second main component accounts for only a small proportion of the total variance. Describe the waypoints a completely circular routing, the proportion of the second main component of the total variance increases, so that ⁇ 1 ⁇ ⁇ 2 ( Fig. 8 ).
  • Fig. 9 shows directions of travel ⁇ i-1 and ⁇ i at the corresponding positions p i-1 and p 1 and their difference ⁇ ⁇ , i .
  • This feature calculates the inefficiency of the routing relative to the destination of the ride.
  • the destination can not be determined from the trip data, therefore this feature can not be formed until after the end of the trip (ie offline), after all trip data vectors are known.
  • the destination position assumed is the position p N of the last trip data vector, which represents the location of the found parking space.
  • step S3 the feature vectors of a journey are smoothed.
  • the purpose of smoothing is to combine feature vectors on a particular link to a smooth feature vector. In this way, not individual waypoints are processed, but sections.
  • the generation of smoothed feature vectors is done by combining several feature vectors m i , which are located within a smoothing window GMF.
  • the smoothing window GMF is advanced with respect to the traveled distance and may overlap. This is in Fig. 11 shown.
  • the length of a respective smoothing window GMF is determined by l gf . It is anchored on the first feature vector m g 1 of a route section. The feature vector at the end of the section m g R is the last, with respect to the distance traveled less than 1 gf of m g 1 removed subsequent feature vector.
  • the number of feature vectors m i within a smoothing window GMF can vary analogously to the number of travel data vectors x i within a feature window MF i .
  • a new smoothing window MGF can be anchored within the current smoothing window MGF after exceeding a certain distance l gr . So that no more than two smoothing windows overlap simultaneously in order to limit the complexity of this step, the same applies l gr ⁇ l gf ⁇ 2 ⁇ l gr
  • Each smoothed feature vector generated by the feature vectors in a smoothing window MGF characterizes a link segment of length l gr , which starts at the position of the first enclosing feature vector and ends at the position of the first feature vector of the next smoothing window.
  • the corresponding stretch of the last smoothed feature vector of a ride may be shorter or longer.
  • the mean velocity corresponds to the median of the mean velocity of all feature vectors, while the maximum of all other features is determined.
  • step S4 the generated feature vectors are considered individually and classified with respect to the traffic classes destination traffic Z and parking search traffic P.
  • p P
  • the feature vectors are first normalized, reduced, and then classified. For all these sub-steps, training data in the form of feature vectors is required in order to be able to learn the parameters for the individual sub-steps.
  • This method uses supervised learning methods. Therefore, the class affiliation of the individual feature vectors, in Form the true label c, be known. This is made possible by the use of test drives recorded for learning purposes, where the traffic class is known at all times.
  • the training data are in the form of an N x K matrix T , where each row represents a feature, and each column for a feature vector is shown in FIG. Fig. 11.
  • Fig. 11 shows a training matrix T.
  • the times of the matrix represent the different features, while the columns represent their occurrences in a feature vector.
  • the training data in T can be divided into two matrices T Z and T P.
  • Normalized feature values are calculated using z-normalization known to those skilled in the art.
  • the mean value ⁇ n and the standard deviation ⁇ n are determined on the basis of training data in T for each individual feature m n .
  • a column of the resultant thus contains a normalized feature vector m .
  • the background of the feature reduction is the reduction of the feature components in a feature vector with minimal information loss.
  • the number of features in m is reduced from N to 1 ⁇ D ⁇ N.
  • a vector projection ⁇ N ⁇ ⁇ D is performed.
  • the preferred feature reduction technique is Principal Component Analysis (PCA), which involves a reduction N ⁇ D.
  • PCA Principal Component Analysis
  • the PCA is an unsupervised method of feature reduction. It pursues the goal of finding those principal axes in the feature space on which the feature vectors depicted on them reach a maximum variance.
  • the eigenvectors and eigenvalues of the covariance matrix are calculated, as described eg in [3].
  • the eigenvectors w i represent axes in the feature space, while the eigenvalues ⁇ i indicate the relative proportion of the total variance of the feature vectors projected on the resulting eigenvectors.
  • w 1 corresponds to the eigenvector with the largest eigenvalue ⁇ 1
  • w N represents the eigenvector with the smallest eigenvalue ⁇ N. If the eigenvectors are known, an arbitrary 1 ⁇ D ⁇ N can now be selected, which means the dimension of the transformed features.
  • the classification assigns a probability to each (reduced) feature vector. On the basis of this probability, it is possible to make a statement about the class membership c of a feature vector.
  • c z denotes membership in the class "train traffic”
  • c p represents the affiliation to "parking traffic”.
  • m ⁇ p m ⁇
  • c ) is the class-specific density function that indicates the probability of a feature vector to belong to class c.
  • p (c) is called the a priori probability and represents the probability with which class c occurs.
  • p ( m ) indicates the probability of the occurrence of a feature vector without distinguishing between classes. It can be calculated by summing up all class-specific probabilities multiplied by the occurrence probability of the corresponding class.
  • N indicates the number of feature vectors in T
  • N Z and N P specify the number of feature vectors in the class-specific training matrices T Z and T P.
  • p c Z N Z N .
  • a posteriori probability a statement can now be made about the classification of a feature vector, since p ( c z
  • m ) 1 - p ( c P
  • m ⁇ ⁇ c P p c P
  • M x ⁇ R D
  • x p c Z
  • x 1 2
  • the position of the decision boundary is influenced by the a priori probabilities: the smaller the a priori probability of a class, the further the decision boundary shifts in the direction of the corresponding class.
  • FIGS. 12 to 14 illustrate the construction of the decision boundary using the class-specific training data in the one-dimensional feature space.
  • Those graphical elements constructed using the feature vectors in T Z are labeled 10, 12, 94, while the elements labeled 11, 13, 15 have been constructed from the feature vectors in T P.
  • the decision limit is in Fig. 14 denoted by GR.
  • the task of the segmentation is to determine, based on the analysis of the temporal course of the classification of feature vectors, that travel data vector which marks the start of the parking space search.
  • the result of the segmentation is a subdivision of the journey into two segments, according to the traffic classes, which forms the basis for calculating the desired information on the intensity and location of the parking search traffic.
  • the last trip data vector is accepted as the start of the parking space search. This ensures that a value> 0 for parking search distance and parking search duration can be calculated.
  • a driving data vector x_ represents a driving data vector with a negative transition of the classification result.
  • Segmentation with distance criterion extends the simple segmentation method with a distance criterion.
  • the segmentation step provides a result regarding the start of the search for a parking space.
  • the result is not necessarily true, because it is based on the result of the classification.
  • the classification is again based on a probabilistic model, which was created with the help of training data.
  • step S6 the result of the segmentation is assessed and discarded if necessary.
  • the criteria for withholding the segmentation result are, for example, an implausibly long park search route. It seems implausible after the assumed journey that almost one entire journey serves the search for a parking space. Since it may be that the search for a parking space takes longer due to possible obstacles, this criterion is measured by the distance traveled in the destination traffic and in the parking search traffic.

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Claims (16)

  1. Procédé de traitement de données de mesure d'un véhicule pour permettre de déterminer le début d'une recherche d'emplacement de stationnement comprenant des étapes consistant à :
    a) détecter un nombre (N) de vecteurs de données de déplacement (xi), chaque vecteur de données de déplacement (xi) comprenant une information concernant la vitesse (vi), des données de position (pi) et l'instant (ti) de détection de la vitesse (vi) et des données de position (pi),
    b) déterminer un vecteur caractéristique (mi) à chaque instant (ti) de détection d'un vecteur de données de déplacement (xi), les informations des vecteurs de données de déplacement actuel et précédent (xi) étant traitées, le vecteur caractéristique (mi) renfermant en tant que composantes caractéristiques au moins une information de vitesse et une information de trajet,
    c) classer chaque vecteur caractéristique (mi), à chacun des vecteurs caractéristiques (mi) étant associée une première catégorie de trafic (cz) qui représente un déplacement du véhicule ou une seconde catégorie de trafic (cρ) qui représente un trafic de recherche d'emplacement de stationnement, et, une probabilité (ρ(P|mi)) étant déterminé, cette probabilité correspondant à la probabilité avec laquelle au vecteur caractéristique doit être associée la première ou la seconde catégorie de trafic (cz, cρ),
    d) segmenter les variations dans le temps des catégories de trafic (cz, cρ) des vecteurs caractéristiques (mi) déterminées, une subdivision du déplacement entre le départ et la dernière détection d'un vecteur de données de déplacement étant effectuée en deux segments en correspondance avec les catégories de trafic (cz, cρ) déterminées des vecteurs caractéristiques (mi) et la transition d'un segment à l'autre segment représentant le début de la recherche d'emplacement de stationnement.
  2. Procédé conforme à la revendication 1,
    selon lequel des vecteurs de données de déplacement (xi) ne sont pas pris en considération lors de la détermination du début de la recherche d'un emplacement de stationnement lorsque l'information concernant la vitesse (vi) du facteur de données de déplacement (xi) est supérieure à une première valeur de seuil ou inférieure à une seconde valeur de seuil.
  3. Procédé conforme à la revendication 1 ou 2,
    selon lequel pour la détermination d'un vecteur caractéristique respectif (mi), les vecteurs de données de déplacement (xi) sont traités dans une fenêtre caractéristique (If) qui représente une distance prédéfinie, la fenêtre caractéristique (If) comprenant les vecteurs de données de déplacement (xi) entre la mesure actuelle et la première mesure qui sur la distance parcourue remonte à plus loin que la distance prédéfinie.
  4. Procédé conforme à l'une des revendications précédentes,
    selon lequel le vecteur caractéristique (mi) comprend, en tant que composantes caractéristiques, en plus de l'information de vitesse et de l'information de trajet au moins l'une des composantes caractéristiques suivantes :
    - une information concernant le caractère circulaire de la distance parcourue,
    - une information concernant un caractère circulaire PCA de la distance parcourue,
    - une information concernant un changement de direction,
    - une information concernant une inefficacité de cible.
  5. Procédé conforme à l'une des revendications précédentes,
    selon lequel l'information de vitesse est une moyenne arithmétique et/ou la valeur médiane des vitesses moyennes des vecteurs de données de déplacement (xi) pris en considération pour la détermination d'un vecteur caractéristique (mi) respectif.
  6. Procédé conforme à l'une des revendications précédentes,
    selon lequel l'information de trajet est une inefficacité de trajet qui donne la mesure de l'inefficacité de la distance parcouru par le rapport de la distance réellement parcourue compte tenue de la distance la plus courte entre les positions de deux vecteurs de données de déplacement (xi)
  7. Procédé conforme à l'une des revendications précédentes,
    selon lequel en tant qu'information de trajet on traite l'inefficacité de trajet pour un vecteur caractéristique (mi) qui est maximum pour la quantité traitée de vecteurs de données de déplacement (xi).
  8. Procédé conforme à l'une des revendications précédentes,
    selon lequel pour permettre de classer chaque vecteur caractéristique (mi), les vecteurs caractéristiques (mi) sont normalisés.
  9. Procédé conforme à la revendication 8,
    selon lequel, pour permettre de calculer les composantes caractéristiques normalisées, on utilise une normalisation z selon laquelle, pour chaque composante caractéristique on détermine la valeur moyenne et l'écart type, et on transforme les composantes caractéristiques avec ces valeurs.
  10. Procédé conforme à la revendication 9,
    selon lequel les composantes caractéristiques sont réduites par une projection vectorielle en particulier en utilisant une analyse en composantes principales.
  11. Procédé conforme à l'une des revendications précédentes,
    selon lequel le calcul de la probabilité de la classification est effectué selon la formule de Bayes.
  12. Procédé conforme à l'une des revendications précédentes,
    selon lequel le début de la recherche d'un emplacement de stationnement est défini par une transition positive de la première catégorie de trafic (cz) à la seconde catégorie de trafic (cρ), le vecteur de données de déplacement (xi) auquel est associée la seconde catégorie de trafic (cρ) représentant le début de la recherche d'un emplacement de stationnement.
  13. Procédé conforme à la revendication 12,
    selon lequel en tant que début de la recherche d'un emplacement de stationnement on sélectionne la dernière transition positive du point de vue chronologique de la première catégorie de trafic (cz) à la seconde catégorie de trafic (cρ) tant que le résultat du classement des vecteurs de données de déplacement (xi) suivants comprend constamment la seconde catégorie de trafic (cρ).
  14. Procédé conforme à la revendication 12,
    selon lequel en tant que début de la recherche d'un emplacement de stationnement on sélectionne la dernière transition positive du point de vue chronologique de la première catégorie de trafic (cz) à la seconde catégorie de trafic (cρ) tant que le résultat du classement des vecteurs de données de déplacement (xi) suivant pour une distance de déplacement (Is) prédéfinie comprend constamment la seconde catégorie de trafic (cρ).
  15. Procédé conforme à la revendication 12,
    selon lequel le début de la recherche d'un emplacement de stationnement est déterminé à partir de l'intégrale des variations dans le temps de la probabilité sur la distance parcourue.
  16. Produit-programme d'ordinateur pouvant être directement chargé dans la mémoire interne d'un ordinateur numérique et comprenant des segments de code de programme permettant de mettre en oeuvre les étapes du procédé conforme à l'une des revendications précédentes lorsque ce produit est exécuté sur un ordinateur.
EP14731561.8A 2013-06-26 2014-06-04 Procédé de traitement de données de mesure d'un véhicule pour l'établissement du commencement d'une recherche d'une place de stationnement Active EP3014598B1 (fr)

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DE102013212235.7A DE102013212235A1 (de) 2013-06-26 2013-06-26 Verfahren zum Verarbeiten von Messdaten eines Fahrzeugs zur Bestimmung des Beginns einer Parkplatzsuche
PCT/EP2014/061633 WO2014206699A1 (fr) 2013-06-26 2014-06-04 Procédé de traitement de données de mesure d'un véhicule pour l'établissement du commencement d'une recherche d'une place de stationnement

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DE102013212235A1 (de) 2014-12-31
JP2016522526A (ja) 2016-07-28
WO2014206699A1 (fr) 2014-12-31
US20160210860A1 (en) 2016-07-21
US10115309B2 (en) 2018-10-30
JP6247754B2 (ja) 2017-12-13
CN105359200A (zh) 2016-02-24
EP3014598A1 (fr) 2016-05-04

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