EP1947623B1 - Procédé et dispositif destinés à la classification dynamique d'objets et/ou de situations de trafic - Google Patents

Procédé et dispositif destinés à la classification dynamique d'objets et/ou de situations de trafic Download PDF

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Publication number
EP1947623B1
EP1947623B1 EP20070024268 EP07024268A EP1947623B1 EP 1947623 B1 EP1947623 B1 EP 1947623B1 EP 20070024268 EP20070024268 EP 20070024268 EP 07024268 A EP07024268 A EP 07024268A EP 1947623 B1 EP1947623 B1 EP 1947623B1
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Prior art keywords
classifier
initial conditions
classification
basis
selection
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Ceased
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EP20070024268
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German (de)
English (en)
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EP1947623A1 (fr
Inventor
Helmut Dr. Riedel
Frank Meier
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Audi AG
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Audi AG
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    • G—PHYSICS
    • G08—SIGNALLING
    • G08G—TRAFFIC CONTROL SYSTEMS
    • G08G1/00—Traffic control systems for road vehicles
    • G08G1/01—Detecting movement of traffic to be counted or controlled
    • G08G1/0104—Measuring and analyzing of parameters relative to traffic conditions
    • G—PHYSICS
    • G08—SIGNALLING
    • G08G—TRAFFIC CONTROL SYSTEMS
    • G08G1/00—Traffic control systems for road vehicles
    • G08G1/16—Anti-collision systems
    • G08G1/161—Decentralised systems, e.g. inter-vehicle communication

Definitions

  • the invention relates to a method and a device for the dynamic classification of objects and / or traffic situations.
  • the invention relates to a method for classifying objects and / or traffic situations, wherein for the classification of at least one object or a traffic situation, a first classifier and at least a second classifier are provided, wherein the first classifier and the at least second classifier are different or differently trained classifiers are.
  • the invention further relates to a device for object and / or situation classification comprising an assistance system having a first classifier, wherein the assistance system has at least one second classifier different from the first classifier and / or one differently trained compared to the first classifier.
  • driver assistance or safety systems The assistance of the driver of a motor vehicle by means of technical means has become increasingly important in the recent past. Depending on the intended use, such technical aids are also referred to as driver assistance or safety systems.
  • driver assistance or safety systems are the group of so-called predictive driver assistance or safety systems.
  • objects and / or traffic situations are to be detected and classified by technical aids, in particular by cameras or other sensors, in order to be able to make the driver aware of possible dangerous situations at an early stage or initiate countermeasures.
  • countermeasures are, for example, the triggering of belt tensioner systems and interventions in brake or steering.
  • classifiers For the classification of objects and / or traffic situations, different classification methods are used, for example based on decision trees, neural networks or support vector machines. Classification software modules created on the basis of such classification methods are referred to as classifiers.
  • Classifiers can be optimized using training data to increase the number of objects and / or traffic situations to be detected, as well as the success rate.
  • the disadvantage is that training the classifiers is not only costly, but also there is a risk that classifiers are "over-trained". Indeed, over-training of a classifier will result in this classifier having high performance with respect to the trained patterns, whereas performance in classifying non-trained patterns will be significantly reduced.
  • a generalized classifier has high performance with respect to non-trained patterns, but in special cases does not achieve the performance of a specialized classifier. Therefore, classifiers can not be interpreted as having a high classification performance in nearly all applications, similar to the human brain.
  • the DE 103 36 638 A1 describes a generic method and a generic device for classifying objects by means of an environment sensor.
  • the surroundings sensor system records object data which comprise a shape, dimensions and a speed of the respective objects.
  • object data are fed to different classifiers.
  • a first classifier is here designed to recognize a pedestrian on the basis of the object data made available to him, while a second classifier is designed to recognize a car.
  • the WO 2005/052883 A1 describes a method for determining a driving situation, for example stop and go traffic, city traffic or highway traffic.
  • a driving speed as well as longitudinal and lateral accelerations of the motor vehicle, an engaged gear and steering wheel movements are detected as input data and evaluated by means of a trained neural network as a classifier. It stores the input data for which Driving situation typical data compared and classified due to a similarity of the data the driving situation.
  • the WO 2005/064566 A1 describes a method in which for a road type, such as a highway, main road, side road or the like, an expected minimum speed is determined. If this minimum speed is fallen below, the traffic situation is classified as a "traffic jam".
  • typical minimum speeds can be modified by taking into account boundary conditions such as weather and road guidance.
  • the road type is determined by means of a navigation system.
  • the post-published DE 10 2005 043 471 A1 describes a method for driving a driver assistance system.
  • different observers are used to observe spatially different sections. For example, a first observer can be used in dense traffic and a second observer in quiet traffic. The observers then track vehicles recognized as objects on the respective sections of track.
  • the object of the invention is to provide a method and a device for classifying objects and / or traffic situations with increased performance.
  • a first classifier and at least one second classifier are made available for classifying objects and / or traffic situations for classifying at least one object or a traffic situation, wherein the first classifier and the at least second classifier are different or differently trained classifiers ,
  • at least one boundary condition is determined in a first step.
  • a selection of at least one classifier to be used from the classifiers provided is carried out, adapted to the determined boundary conditions.
  • the at least one selected classifier is used to classify objects and / or traffic situations. boundary conditions may be in this context all detectable parameters in the environment of the execution of the method.
  • boundary conditions may be, for example, information on the outside temperature, the position of the motor vehicle, the light and / or road conditions, individual vehicle parameters, etc.
  • the boundary conditions are determined periodically so that a classifier selection can be made dynamically adapted to the boundary conditions.
  • a periodic determination of the boundary conditions has the advantage, in particular if there are only small time intervals between the individual determinations, that changes in the boundary conditions are detected promptly and can be taken into account directly in the classifier selection. If, for example, a vehicle drives into a tunnel on a sunny day, the lighting conditions change within a very short time, while the other boundary conditions remain essentially constant. If the boundary conditions are determined periodically, this change is immediately recognized within a cycle and taken into account in such a way that - if a classifier designed especially for darkness or better suited for darkness is available - this classifier is selected directly.
  • the determination of the boundary conditions is carried out with the aid of auxiliary means arranged in a motor vehicle.
  • auxiliary means arranged in a motor vehicle.
  • Most modern motor vehicles already have, in their basic configuration, a multiplicity of aids which are suitable for providing useful information in relation to a classifier selection. Such tools can be used with almost no additional technical and financial overhead to determine constraints.
  • a first example of such an aid is a possibly existing state and environment sensor system, in particular an electronic stability program (ESP), a camera, a radar system, the information of a Global Positioning System (GPS) etc.
  • ESP electronic stability program
  • GPS Global Positioning System
  • a state and environment sensor system for example current position (coordinates) of a vehicle and thus the country are determined in which a vehicle is located. Furthermore, the speed and direction of movement and the current traction of the vehicle can be determined.
  • Such information may be used to the extent that any existing country-specific classifiers are selected based on country-specific markings, left- or right-hand traffic, a country-specific arrangement of traffic signs (eg traffic signs arranged predominantly on the right-hand side of the road), country-specific traffic signs, etc. are.
  • aids arranged in a motor vehicle are telematics and weather services as well as date and / or time information, wherein the term telematics services also includes the use of GPS and digitized maps. With such aids particular features with respect to the weather or in relation to the current day and / or season can be considered.
  • a classifier selection takes place on the basis of a correlation table, wherein it is determined in the correlation table under which boundary conditions which classifier has the highest performance. This can be done, for example, by establishing a quality measure for the performance in advance and by using tests for each combination of boundary conditions to determine a classifier which has the highest performance under the given boundary conditions.
  • An example of a usable measure of merit is the hit rate of the classifiers, i. the probability that a classifier performs a correct classification under given boundary conditions.
  • the assignment between the detected boundary conditions and the most powerful classifier does not necessarily have to be done by means of a correlation table. Alternative assignment models can also be used.
  • a classification of an object or a traffic situation takes place on the basis of two or more classifiers, wherein the selected classifiers are used sequentially.
  • the sequential use of classifiers for example, the advantages of a generalized classifier can be combined with the advantages of specialized classifiers by performing a rough classification by means of a first classifier and then refining the classification with the aid of a second, downstream classifier.
  • the classifier selection can either be made strictly deterministic or based on a higher-level classification method, such as with the aid of a decision tree or a neural network. In this case, according to a first alternative, it can be specified whether the classifier selection should be made by a superordinate classification method or is performed according to a second alternative depending on the determined boundary conditions.
  • the second alternative is particularly suitable when it is to be feared that individual boundary conditions may not be clearly determined by the system or can be ascertainable. If, for example, due to contradictory information from two devices, reliable information about the weather situation is not available, a weather situation can be determined on the basis of a neural network, which seems obvious on the basis of the other available information.
  • a classifier selection should be based on a decision tree or on another deterministic method.
  • the determined boundary conditions are checked and / or processed before the selection of a classifier to be used.
  • a control unit may be provided for this purpose which partially or completely checks information about determined boundary conditions and, in particular in the case of contradictory or incomplete information, plausibility of the boundary conditions.
  • a plausibility check also "blurred" methods, such as. a classification using neural networks.
  • the invention is also reflected in an inventive device for object and / or situation classification comprising an assistance system with a first classifier and a data input for boundary conditions, wherein the assistance system at least one different from the first classifier and / or one compared to the first classifier differently trained second classifier, wherein a Klassifikatoraushuisko is provided, which is adapted to make on the basis of detected via the data input boundary conditions adapted to the detected boundary conditions selecting at least one classifier from the provided classifiers and wherein the at least one selected classifier is usable to perform an object and / or situation classification.
  • Fig. 1 shows a system 100 comprising a first assistance system 120, a second assistance system 140, further assistance systems (in Fig. 1 indicated by four points) as well as an mth assistance system 160.
  • Each of the assistance systems 120, 140, 160 is a device according to the invention for object and / or situation classification.
  • the invention is explained in more detail below with reference to the second assistance system 140, wherein the second assistance system 140 is used for traffic sign recognition and is part of a motor vehicle (not shown).
  • the second assistance system 140 has a data input for constraints 142, a Klassifikatoraushuissen 144 and a first classifier (K 21) 146, a second classifier (K 22) 148, a third classifier (K 23) 150 and a fourth classifier (K 24) 152 on.
  • the first classifier 146 is a generalized classifier
  • the second classifier 148 is a classifier specialized in daytime traffic in Germany
  • the third classifier is a classifier specialized in night traffic in Germany
  • the fourth classifier is a specialized classifier for the classifier Traffic outside Germany.
  • the classifier selection unit 144 has an interrogation unit (not shown) via which the second assistance system 140 can permanently query data of the GPS system present in the vehicle as well as date and time information.
  • the second assistance system 140 If the second assistance system 140 is activated, an interrogation of the GPS about the position of the vehicle and a query about the current date and the current time are carried out with the aid of the interrogation unit.
  • the boundary conditions thus queried are transmitted via the data input 142 to the classifier selection unit 144, which compares the determined boundary conditions with a correlation table.
  • the correlation table contains for each complete constellation of boundary conditions, i. for each combination of location of the vehicle, date and time an assignment of a particularly suitable for this combination classifier.
  • the classifier selection unit 144 decides based on this correlation table which classifier is used for the classification of the traffic sign.
  • the classifier selection unit 144 determines that the first generalized classifier 146 is used in a first step to provide a first coarse classification and using a neural network to determine which position of the vehicle is most likely. On the basis of this first classification, a second classification with the classifier is then made in a second step, wherein the classifier is selected, which is the most powerful due to the information available after the first classification.
  • the remaining assistance systems 120, 160 are constructed analogously, with the number of classifiers integrated into the respective assistance systems being variable depending on the desired power, as indicated by the dotted lines between K 12 and K 1 n or K m1 and K mn , What is common to the assistance systems, however, is that the first and the m-th assistance systems 120, 160 also have a data input 122 or 162, the data transmission to all assistance systems 120, 140, 160, in particular if they are arranged in spatial proximity to one another a common data line 180, in particular a CAN bus can be made.
  • FIG. 2 System 200 shown comprises a first assistance system 220, a second assistance system 240, further assistance systems (in Fig. 2 indicated by four points) as well as an m-th assistance system 260 and is analogous to that in Fig. 1 shown system 100 constructed. For the same elements are therefore in the in Fig. 2 shown second assistance system 200 by 100 increased reference numerals used.
  • This in Fig. 2 System 200 shown differs from that in Fig. 1 shown system in that the system 200 additionally includes a control unit 290.
  • assistance systems 220, 240, 260 are dispensed generalized classifiers, if for each constellation of boundary conditions specific classifiers are available and it is ensured that missing by the control unit missing boundary conditions with high probability properly.

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  • Physics & Mathematics (AREA)
  • General Physics & Mathematics (AREA)
  • Chemical & Material Sciences (AREA)
  • Analytical Chemistry (AREA)
  • Traffic Control Systems (AREA)

Claims (19)

  1. Procédé pour la classification d'objets et/ou de situations de trafic, sachant que
    pour la classification d'au moins un objet et/ou situation de trafic, un premier classificateur (146, 246) et au moins un deuxième classificateur (148 ; 248) sont mis à disposition,
    le premier classificateur (146 ; 246) et cet au moins deuxième classificateur (148 ; 248) étant des classificateurs différents ou entraînés différemment,
    caractérisé par les étapes suivantes :
    a) détermination, au moins une fois, de conditions marginales
    b) sélection adaptée aux conditions marginales déterminées d'au moins un classificateur parmi les classificateurs (146 ; 246 ; 148 ; 248) mis à disposition, et
    c) utilisation de cet au moins un classificateur sélectionné pour la classification d'objets et/ou de situations de trafic.
  2. Procédé selon la revendication 1,
    caractérisé en ce que
    les conditions marginales sont déterminées périodiquement et qu'il s'en suit un choix de classificateur adapté dynamiquement aux conditions marginales déterminées périodiquement.
  3. Procédé selon la revendication 1 ou 2,
    caractérisé en ce que
    la détermination des conditions marginales est réalisée à l'aide de moyens auxiliaires disposés dans un véhicule automobile.
  4. Procédé selon la revendication 3,
    caractérisé en ce que
    l'on utilise, en guise de moyens auxiliaires, un système de capteurs d'état et/ou d'environnement d'un véhicule automobile.
  5. Procédé selon la revendication 3 ou 4,
    caractérisé en ce que
    l'on utilise des services de télématique et/ou météorologiques en guise de moyens auxiliaires.
  6. Procédé selon l'une quelconque des revendications 3 à 5, caractérisé en ce que l'on utilise, en guise de moyens auxiliaires, des informations de date et/ou d'heure de montre.
  7. Procédé selon l'une quelconque des revendications 1 à 6,
    caractérisé en ce
    qu'un choix de classificateur s'effectue sur la base d'une table de corrélation, la table de corrélation déterminant celui des classificateurs qui est le plus puissant en fonction des conditions marginales.
  8. Procédé selon l'une quelconque des revendications 1 à 7,
    caractérisé en ce
    qu'une classification d'un objet et/ou d'une situation de trafic s'effectue sur la base d'un ou de plusieurs classificateurs, les classificateurs sélectionnés étant utilisés de manière séquentielle.
  9. Procédé selon l'une quelconque des revendications 1 à 8,
    caractérisé en ce que
    le choix du classificateur s'effectue sur la base d'un procédé de classification prioritaire, tel un arbre de décision, et/ou sur la base d'un réseau neuronal et/ou sur la base d'une Support Vector Machine et/ou d'un autre système à base de régulation d'un genre différent.
  10. Procédé selon la revendication 9,
    caractérisé en ce que
    indépendamment du fait que le choix du classificateur repose sur un arbre de décision et/ou sur un réseau neuronal, la sélection s'effectue en fonction des conditions marginales déterminées.
  11. Procédé selon l'une quelconque des revendications 1 à 10,
    caractérisé en ce que
    les conditions marginales déterminées sont vérifiées et/ou préparées avant le choix d'un classificateur à utiliser.
  12. Procédé selon la revendication 11,
    caractérisé en ce que
    pour la préparation, on utilise un procédé à incertitude, en particulier une classification à l'aide de réseaux neuronaux.
  13. Dispositif pour la classification d'objets et/ou de situations de trafic comportant un système d'aide (120 ; 140 ; 160 ; 220 ; 240 ; 260) avec un premier classificateur (146),
    le système d'aide (120 ; 140 ; 160 ; 220 ; 240 ; 260) présentant au moins un deuxième classificateur (148 ; 248) se distinguant du premier classificateur (146 ; 246) et/ou entraîné différemment par rapport au premier classificateur,
    caractérisé en ce que
    l'on prévoit une entrée de données (122 ; 142 ; 162 ; 222 ; 242 ; 262) pour des conditions marginales et une unité de sélection de classificateur (124 ; 144 ; 164 ; 224 ; 244 ; 264), l'unité de sélection de classificateur (124 ; 144 ; 164 ; 224 ; 244 ; 264) étant réalisée de manière à, sur la base de conditions marginales saisies par l'intermédiaire de l'entrée de données (122 ; 142 ; 162 ; 222 ; 242 ; 262), procéder à une sélection adaptée aux conditions marginales saisies d'au moins un classificateur parmi les classificateurs mis à disposition (146 ; 246 ; 148 ; 248), cet au moins un classificateur sélectionné pouvant être utilisé pour réaliser une classification d'objets et/ou de situations de trafic.
  14. Dispositif selon la revendication 13,
    caractérisé en ce que
    l'unité de sélection de classificateur (124 ; 144 ; 164 ; 224 ; 244 ; 264) est réalisée de manière à vérifier périodiquement l'entrée de données (122 ; 142 ; 162 ; 222 ; 242 ; 262) en ce qui concerne une modification des conditions marginales saisies.
  15. Dispositif selon la revendication 13 ou 14,
    caractérisé en ce que
    l'unité de sélection de classificateur (124 ; 144 ; 164 ; 224 ; 244 ; 264) présente une unité d'interrogation, laquelle est réalisée de manière à accéder de manière autonome à des moyens auxiliaires pour la saisie de conditions marginales d'un véhicule automobile et/ou aux conditions marginales saisies par les moyens auxiliaires d'un véhicule automobile.
  16. Dispositif selon l'une quelconque des revendications 13 à 15,
    caractérisé en ce que
    l'unité de sélection de classificateur (124 ; 144 ; 164 ; 224 ; 244 ; 264) présente une mémoire pour une table de corrélation ainsi qu'un module de comparaison.
  17. Dispositif selon l'une quelconque des revendications 13 à 16,
    caractérisé en ce que
    l'unité de sélection de classificateur (124 ; 144 ; 164 ; 224 ; 244 ; 264) permet l'implémentation d'un arbre de décision et/ou d'un réseau neuronal et/ou d'une Support Vector Machine et/ou d'un autre système à base de régulation d'un genre différent.
  18. Dispositif selon l'une quelconque des revendications 13 à 17,
    caractérisé en ce que
    l'unité de sélection de classificateur (124 ; 144 ; 164 ; 224 ; 244 ; 264) présente une unité de commande pour vérifier et/ou préparer des conditions marginales saisies.
  19. Système pour la classification d'objets et/ou de situations de trafic, comportant plusieurs dispositifs selon l'une quelconque des revendications 13 à 18.
EP20070024268 2007-01-17 2007-12-14 Procédé et dispositif destinés à la classification dynamique d'objets et/ou de situations de trafic Ceased EP1947623B1 (fr)

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DE200710002562 DE102007002562A1 (de) 2007-01-17 2007-01-17 Verfahren und Vorrichtung zur dynamischen Klassifikation von Objekten und/oder Verkehrssituationen

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EP1947623B1 true EP1947623B1 (fr) 2009-11-11

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DE102013110867A1 (de) * 2013-10-01 2015-04-02 Scania Cv Ab Vorrichtung für ein Fahrzeug
US9754049B2 (en) 2014-09-30 2017-09-05 International Business Machines Corporation Characterizing success pathways in networked graphs

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US8653482B2 (en) 2006-02-21 2014-02-18 Goji Limited RF controlled freezing
DE102007034505A1 (de) * 2007-07-24 2009-01-29 Hella Kgaa Hueck & Co. Verfahren und Vorrichtung zur Verkehrszeichenerkennung
DE102008043761B4 (de) * 2008-11-14 2017-04-27 Robert Bosch Gmbh Verfahren und Steuergerät zum Anpassen eines Fahrzeugassistenzsystems
DE102009057553A1 (de) 2009-12-09 2011-06-16 Conti Temic Microelectronic Gmbh Verfahren zur Unterstützung des Fahrers eines straßengebundenen Fahrzeugs bei der Fahrzeugführung
JP5819420B2 (ja) 2010-06-15 2015-11-24 コンティ テミック マイクロエレクトロニック ゲゼルシャフト ミットベシュレンクテル ハフツングＣｏｎｔｉ Ｔｅｍｉｃ ｍｉｃｒｏｅｌｅｃｔｒｏｎｉｃ ＧｍｂＨ 自動車の交通標識認識システムと車線認識システムとを融合する方法
DE102010025351A1 (de) * 2010-06-28 2011-12-29 Audi Ag Verfahren und Vorrichtung zum Unterstützen eines Fahrzeugführers
DE102012213485A1 (de) 2012-07-31 2014-02-06 Robert Bosch Gmbh Verfahren und Vorrichtung zum Überprüfen einer in einem Fahrempfehlungsspeicher gespeicherten Fahrempfehlungsinformation für Fahrzeuge sowie Verfahren und Vorrichtung zum Bereitstellen einer Fahrempfehlungsnachricht
DE102013219909A1 (de) 2013-10-01 2015-04-02 Conti Temic Microelectronic Gmbh Verfahren und Vorrichtung zur Erkennung von Verkehrszeichen
DE102017215868A1 (de) * 2017-09-08 2019-03-14 Robert Bosch Gmbh Verfahren und Vorrichtung zum Erstellen einer Karte
DE102018205248B4 (de) * 2018-04-09 2024-08-22 Bayerische Motoren Werke Aktiengesellschaft Fusionssystem zur Fusion von Umfeldinformation für ein Kraftfahrzeug
DE102019218590B4 (de) * 2019-11-29 2025-02-13 Volkswagen Aktiengesellschaft Verfahren und Vorrichtung zur Objekterkennung

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DE10336638A1 (de) * 2003-07-25 2005-02-10 Robert Bosch Gmbh Vorrichtung zur Klassifizierung wengistens eines Objekts in einem Fahrzeugumfeld
DE10354322B4 (de) * 2003-11-20 2022-06-09 Bayerische Motoren Werke Aktiengesellschaft Verfahren und System zur Ermittlung der Fahrsituation
WO2005064564A1 (fr) * 2003-12-19 2005-07-14 Bayerische Motoren Werke Aktiengesellschaft Determination du niveau de vitesse attendu
DE102005043471A1 (de) * 2005-09-13 2007-03-15 Daimlerchrysler Ag Verfahren zur Ansteuerung eines verkehrsadaptiven Assistenzsystems, Abstandsregeltempomat und Tempomat

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DE102013110867A1 (de) * 2013-10-01 2015-04-02 Scania Cv Ab Vorrichtung für ein Fahrzeug
US9754049B2 (en) 2014-09-30 2017-09-05 International Business Machines Corporation Characterizing success pathways in networked graphs

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DE102007002562A1 (de) 2008-07-24
DE502007001957D1 (de) 2009-12-24

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