DE102016005580A1 - Method and system for predicting a driving behavior of a vehicle - Google Patents

Method and system for predicting a driving behavior of a vehicle

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Publication number
DE102016005580A1
DE102016005580A1 DE102016005580.4A DE102016005580A DE102016005580A1 DE 102016005580 A1 DE102016005580 A1 DE 102016005580A1 DE 102016005580 A DE102016005580 A DE 102016005580A DE 102016005580 A1 DE102016005580 A1 DE 102016005580A1
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DE
Germany
Prior art keywords
vehicle
driving behavior
class
classes
vehicles
Prior art date
Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
Pending
Application number
DE102016005580.4A
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German (de)
Inventor
Marcus Kühne
Current Assignee (The listed assignees may be inaccurate. Google has not performed a legal analysis and makes no representation or warranty as to the accuracy of the list.)
Audi AG
Original Assignee
Audi AG
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Filing date
Publication date
Application filed by Audi AG filed Critical Audi AG
Priority to DE102016005580.4A priority Critical patent/DE102016005580A1/en
Publication of DE102016005580A1 publication Critical patent/DE102016005580A1/en
Application status is Pending legal-status Critical

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Classifications

    • BPERFORMING OPERATIONS; TRANSPORTING
    • B60VEHICLES IN GENERAL
    • B60WCONJOINT CONTROL OF VEHICLE SUB-UNITS OF DIFFERENT TYPE OR DIFFERENT FUNCTION; CONTROL SYSTEMS SPECIALLY ADAPTED FOR HYBRID VEHICLES; ROAD VEHICLE DRIVE CONTROL SYSTEMS FOR PURPOSES NOT RELATED TO THE CONTROL OF A PARTICULAR SUB-UNIT
    • B60W40/00Estimation or calculation of non-directly measurable driving parameters for road vehicle drive control systems not related to the control of a particular sub unit, e.g. by using mathematical models
    • B60W40/08Estimation or calculation of non-directly measurable driving parameters for road vehicle drive control systems not related to the control of a particular sub unit, e.g. by using mathematical models related to drivers or passengers
    • B60W40/09Driving style or behaviour
    • BPERFORMING OPERATIONS; TRANSPORTING
    • B60VEHICLES IN GENERAL
    • B60WCONJOINT CONTROL OF VEHICLE SUB-UNITS OF DIFFERENT TYPE OR DIFFERENT FUNCTION; CONTROL SYSTEMS SPECIALLY ADAPTED FOR HYBRID VEHICLES; ROAD VEHICLE DRIVE CONTROL SYSTEMS FOR PURPOSES NOT RELATED TO THE CONTROL OF A PARTICULAR SUB-UNIT
    • B60W50/00Details of control systems for road vehicle drive control not related to the control of a particular sub-unit, e.g. process diagnostic or vehicle driver interfaces
    • B60W50/0097Predicting future conditions
    • BPERFORMING OPERATIONS; TRANSPORTING
    • B60VEHICLES IN GENERAL
    • B60WCONJOINT CONTROL OF VEHICLE SUB-UNITS OF DIFFERENT TYPE OR DIFFERENT FUNCTION; CONTROL SYSTEMS SPECIALLY ADAPTED FOR HYBRID VEHICLES; ROAD VEHICLE DRIVE CONTROL SYSTEMS FOR PURPOSES NOT RELATED TO THE CONTROL OF A PARTICULAR SUB-UNIT
    • B60W30/00Purposes of road vehicle drive control systems not related to the control of a particular sub-unit, e.g. of systems using conjoint control of vehicle sub-units, or advanced driver assistance systems for ensuring comfort, stability and safety or drive control systems for propelling or retarding the vehicle
    • B60W30/08Active safety systems predicting or avoiding probable or impending collision or attempting to minimise its consequences
    • B60W30/095Predicting travel path or likelihood of collision
    • B60W30/0956Predicting travel path or likelihood of collision the prediction being responsive to traffic or environmental parameters

Abstract

The invention relates to a method for predicting a driving behavior of a vehicle (36), comprising the steps of: providing a data set (14) which includes respective vehicle class-specific driving behavior profiles (22, 24, 26) for different vehicle classes (16, 18, 20) a server device (12); Detecting the vehicle (36) by means of another vehicle (28); Assigning the detected vehicle (36) to one of the vehicle classes (16, 18, 20) of the provided record (14); Selecting the vehicle class specific driveability profile (22, 24, 26) associated with the associated vehicle class (16, 18, 20); Predicting the driveability of the detected vehicle (36) based on the selected driveability profile (22, 24, 26). The invention further relates to a system (10) for predicting a driving behavior of a vehicle (36).

Description

  • The invention relates to a method and system for predicting a driving behavior of a vehicle.
  • The estimation or prediction of the driving behavior of other road users is important, especially with regard to avoiding accidents. In particular, this also plays an increasingly important role in connection with efforts to let motor vehicles drive partially autonomously or completely autonomously or to control them.
  • The DE 10 2014 207 666 A1 shows a method for autonomously controlling a motor vehicle. By means of a sensor device of the motor vehicle, data relating to another vehicle are provided. The data may be, for example, a blue light emitted by the other vehicle or an acoustic signal. Based on the data, it is determined what kind of task the vehicle is in, for example, whether it is a police vehicle, an ambulance or a fire engine. A related to the task of the vehicle behavior of the vehicle is taken into account in the autonomous control of the motor vehicle.
  • The DE 10 2014 200 700 A1 shows a method for detecting a rescue gas situation. On the basis of recorded lateral distances to other vehicles, the emergency lane situation is determined, in which a rescue lane for rescue vehicles is to be formed. A congestion assistance function unit for the autonomous guidance of a motor vehicle is deactivated when the emergency lane situation is detected, and a driver of the motor vehicle is made aware of the emergency lane situation.
  • The DE 10 2011 083 677 A1 shows a method for predicting a traffic situation for a motor vehicle, in which historical driving profiles of different vehicles to predict the traffic situation are taken into account.
  • It is the object of the present invention to provide a method and a system for predicting a driving behavior of a vehicle, by means of which a particularly good predictive quality with regard to the driving behavior of the vehicle in question can be made possible.
  • This object is achieved by a method and by a system for predicting a driving behavior of a vehicle having the features of the independent patent claims. Advantageous embodiments with expedient and non-trivial developments of the invention are specified in the dependent claims.
  • In the driving behavior predicting method of the present invention, a record is provided by a server device. The data set comprises different vehicle class-specific driving behavior profiles for different vehicle classes. By means of another vehicle, the vehicle in question is detected. Subsequently, the detected vehicle is assigned to one of the vehicle classes of the provided data record. After that, the vehicle class-specific driving behavior profile associated with the assigned vehicle class is selected. Finally, the driving behavior of the detected vehicle is predicted on the basis of the selected driving behavior profile.
  • The invention is based on the finding that usually different vehicle classes, such as SUVs, trucks, buses, sports cars and the like, are driven differently. One and the same person will usually move a particularly powerful sports car in a very different way on the road than, for example, a heavy SUV. Therefore, it is provided according to the invention to provide a data set by means of a server device, which comprises different vehicle class-specific driving behavior profiles for different vehicle classes. In other words, the driving behavior profiles are to be understood as driving behavior patterns which characterize or describe how the various vehicle classes are usually moved in traffic. At least one vehicle in the vicinity of the further vehicle is then detected by means of the further vehicle and assigned to one of the vehicle classes. As soon as the detected vehicle has been assigned to one of the vehicle classes, the corresponding driving behavior profile is selected. Based on this, the future driving behavior of the detected vehicle can be predicted with particular accuracy.
  • On the basis of the assignment of a vehicle to one of the predefined vehicle classes and by a corresponding selection of the relevant driving profile, it can be predicted relatively reliably how the driver in question of the vehicle currently being registered will behave in the road, for example if he or she has to make particularly abrupt braking maneuvers, accelerations or the like tends or not. By the described selection of the appropriate driving behavior profile, a particularly high predictive quality with regard to the driving behavior of the detected vehicle can be ensured.
  • An advantageous embodiment of the invention provides that in the vehicle-class-specific driving behavior profiles probabilities for the implementation of respective driving maneuvers be deposited for predetermined traffic situations. Under driving maneuvers, for example, a braking operation, an acceleration process or the like may fall. These various maneuvers are assigned to different traffic situations, such as driving on a highway, a highway in city traffic, particularly dense traffic, at night, during the day or the like. Depending on which type of vehicle class is concerned, different probabilities for different driving maneuvers are deposited in varying degrees for the various predefined traffic situations. For the various vehicle classes, it is thus possible to provide very detailed and different driving behavior profiles with regard to their respective characteristics. As a result, a particularly good predictive quality for the driving behavior of a vehicle that has just been detected can be achieved.
  • A further advantageous embodiment of the invention provides that the detection of the vehicle and assignment to one of the vehicle classes is based on an optical detection of the vehicle and subsequent image processing and / or based on a Car2Car communication. The further vehicle which detects the other vehicle may, for example, have a camera system with a corresponding image processing device, so that the relevant vehicle can be optically recorded and subsequently evaluated and assigned to one of the vehicle classes on the basis of certain stored criteria. Alternatively or additionally, it is also possible for the vehicle to be detected to send information about its vehicle class to the other vehicle, so that based on this a very simple assignment of the detected vehicle to the appropriate vehicle class can take place. In both cases, a detection of the relevant vehicle and assignment to the appropriate vehicle class can be carried out in a relatively reliable manner.
  • According to a further advantageous embodiment of the invention, it is provided that the further vehicle is autonomously controlled by means of a driver assistance system taking into account the predicted driving behavior of the vehicle. In particular, when the other vehicle is controlled autonomously, ie without any intervention by a driver, the high predictive quality achievable by the method according to the invention or an advantageous embodiment of the method according to the invention is particularly advantageous with regard to the driving behavior of the other vehicle. Taking into account the predicted driving behavior of the detected vehicle, the driver assistance system can control the further vehicle particularly reliably autonomously.
  • A further advantageous embodiment of the invention provides that the further vehicle is part of a vehicle fleet of several vehicles, which continuously capture other vehicles and their driving behavior and transmit related data to the server device, which updates the record based on these data and the updated record the vehicles the vehicle fleet provides. In other words, it is thus preferably provided that the provided data set is not kept unchanged but is continuously updated. The more vehicles that are part of the vehicle fleet, the more data is collected continuously with regard to the traffic behavior of the vehicles participating in the traffic. As a result, in particular the vehicle-class-specific continuous adaptation of the different driving characteristics can be continuously expanded. Due to the continuous observation and analysis of the various road users or vehicles by the vehicle fleet and adaptation of the data set containing the driving behavior profiles, the predictive quality with regard to the various road users can be further refined and improved.
  • According to a further advantageous embodiment of the invention, it is provided that the vehicle class-specific driving behavior profiles of the data record are updated on the basis of the data. In particular, deviations between stored driving patterns or types of driving maneuvers and actually determined driving behavior patterns or driving maneuvers can be identified and the corresponding driving behavior profiles for the different vehicle classes continuously adapted. As a result, the predictive quality can be further increased by exploiting the data set.
  • A further advantageous embodiment of the invention provides that based on the data not yet deposited vehicle classes created in the record and created vehicle class specific driving characteristics for these vehicle classes. This is particularly advantageous when new vehicle classes appear in the traffic scene, which have characteristic of this vehicle class characteristic driving characteristics. This also contributes to continuously updating, refining and improving the data set comprising the vehicle classes with the corresponding driving behavior profiles, which has a positive effect on the achievable forecasting quality.
  • In a further advantageous embodiment of the invention, it is provided that the provided by means of the server device data set transmitted to the vehicles of the vehicle fleet and in respective vehicle-side storage units is stored, wherein in each case the vehicle side, the prediction of the driving behavior of other vehicles is based on the respective stored data set. This has the advantage that even if no communication link between the server device and the vehicles of the vehicle fleet should be established, the vehicles of the vehicle fleet can independently make predictions about the respective driving behavior of other road users. An updating of the locally stored data records is preferably always carried out as soon as the server-side record has been updated and a communication link between the server device and the respective vehicles of the vehicle fleet can be established. This ensures that the respective data sets which the vehicles of the vehicle fleet use to predict the driving behavior of other road users are always up-to-date.
  • The system for predicting a driving behavior of a vehicle according to the invention comprises a server device which is designed to provide a data record which comprises respective vehicle class-specific driving behavior profiles for different vehicle classes. The system further includes at least one other vehicle configured to capture the vehicle, associate it with one of the vehicle classes of the data set, select the vehicle class-specific driving habits associated with the associated vehicle class, and predict the driving behavior of the detected vehicle based on the selected driving behavior profile. Advantageous embodiments of the method according to the invention are to be regarded as advantageous embodiments of the system according to the invention, wherein the system has in particular means for carrying out the method steps.
  • Further advantages, features and details of the invention will become apparent from the following description of a preferred embodiment and from the drawing. The features and feature combinations mentioned above in the description as well as the features and feature combinations mentioned below in the description of the figures and / or in the figures alone can be used not only in the respectively specified combination but also in other combinations or in isolation, without the scope of To leave invention.
  • The drawing shows in:
  • 1 a schematic representation of a system for predicting a driving behavior of a vehicle, which includes a server device and at least one vehicle; and in
  • 2 a schematic representation of a traffic situation in which the in 1 vehicle behind a bus drives, which in turn drove behind a truck.
  • In the figures, identical or functionally identical elements are provided with the same reference numerals.
  • A total with 10 A system for predicting a driving behavior of a vehicle is shown in a schematic representation in FIG 1 shown. The system 10 includes a server device 12 , which is designed to be a record 14 to provide which for different vehicle classes 16 . 18 . 20 respective vehicle-class-specific driving behavior profiles 22 . 24 . 26 includes. The system 10 further includes a plurality of vehicles associated with a vehicle fleet 28 , in the present case, only one of these vehicles 28 is indicated schematically. The vehicles 28 comprise a communication device 30 for data exchange with the server device 12 , a detection device 32 for detecting other vehicles and at least partially still a driver assistance system 34 , which is designed to be the vehicles concerned 28 to control autonomously.
  • In 2 is a traffic situation in which one of the vehicles 28 of the system 10 currently located, indicated schematically. As you can see, the vehicle drives 28 behind a bus 36 which in turn is behind a truck 38 moves. The following explanations, which with reference to the vehicle 28 apply, apply equally to the vehicles not shown here 28 which are also part of the system 10 are.
  • The vehicle 28 is by means of the driver assistance system 34 just autonomously controlled and approaches the bus from behind 36 at. By means of the detection device 32 becomes the bus 36 recorded and to one of the vehicle classes 16 . 18 . 20 of the record 14 assigned. Preferably, the record is 14 , which by means of the server device 12 is provided in a local storage unit of the vehicle 28 deposited. Once the bus 36 to the appropriate vehicle class 16 . 18 . 20 has been assigned to the corresponding vehicle class 16 . 18 or 20 associated vehicle-class-specific driving behavior profile 22 . 24 . 26 selected. Subsequently, the driving behavior of the detected bus 36 based on the selected driving behavior profile 22 . 24 or 26 predicted. The autonomous control of the vehicle 28 serving driver assistance system 34 considered while the predicted or predicted driving behavior of the bus 36 ,
  • For example, it may be of interest whether the bus 36 the truck 38 will overtake and how he will do this, for example, particularly abrupt or rather less abrupt. In the respective driving profiles 22 . 24 . 26 Probabilities for the implementation of different driving maneuvers for different traffic situations are stored. Among other things, for example, for the in 2 shown schematically traffic situation or driving situation the probability of performing a certain maneuver, such as an overtaking maneuver, be deposited exactly for the vehicle class bus. Should the vehicles 28 . 36 . 38 For example, if you are on a highway, it is relatively likely that the bus 36 the truck 38 will someday overtake. Would the vehicle 28 instead of the bus 36 Follow a sports car, which is behind the truck 38 Afterwards, it is very likely that the sports car will move out much faster to the truck 38 to overtake. By doing that the vehicle 28 capable of doing the bus 36 and also to record all other types of vehicles and road users and appropriate vehicle classes 16 . 18 or 20 To assign a particularly good prediction can be made with regard to the driving behavior of the vehicle in question. As already mentioned, the system includes 10 a variety of vehicles 28 which are part of a vehicle fleet. The vehicles 28 of the vehicle fleet continuously capture other vehicles 36 . 38 and their driving behavior and transmit related data to the server device 12 which uses this received data to record 14 continuously updated and the vehicles 28 the vehicle fleet of the system 10 in turn provides.
  • By a continuous observation of the traffic, in particular other road users and vehicles, the vehicle-class-specific driving behavior profiles 22 . 24 . 26 for the different vehicle classes 16 . 18 . 20 always refined and improved. In addition, it is also possible that not yet deposited vehicle classes in the record 14 created and for these newly created vehicle classes also vehicle class specific driving characteristics are created. For example, those already in the record 14 stored driving behavior profiles 22 . 24 . 26 used and modified accordingly.
  • Through the system 10 Thus, a particularly intelligent and self-learning system for predicting driving behavior of a wide variety of vehicles is provided, which is continuously being extended, updated and improved. This allows a particularly high predictive quality with regard to the driving behavior of a wide variety of vehicles.
  • QUOTES INCLUDE IN THE DESCRIPTION
  • This list of the documents listed by the applicant has been generated automatically and is included solely for the better information of the reader. The list is not part of the German patent or utility model application. The DPMA assumes no liability for any errors or omissions.
  • Cited patent literature
    • DE 102014207666 A1 [0003]
    • DE 102014200700 A1 [0004]
    • DE 102011083677 A1 [0005]

Claims (9)

  1. Method for predicting a driving behavior of a vehicle ( 36 ), with the steps: - Providing a data record ( 14 ), which is suitable for different vehicle classes ( 16 . 18 . 20 ) respective vehicle-class-specific driving behavior profiles ( 22 . 24 . 26 ) by means of a server device ( 12 ); - Detecting the vehicle ( 36 ) by means of another vehicle ( 28 ); - Assignment of the detected vehicle ( 36 ) to one of the vehicle classes ( 16 . 18 . 20 ) of the provided data record ( 14 ); - Selecting the assigned vehicle class ( 16 . 18 . 20 ) associated vehicle class specific driveability profile ( 22 . 24 . 26 ); - predicting the driving behavior of the detected vehicle ( 36 ) based on the selected driving behavior profile ( 22 . 24 . 26 ).
  2. Method according to Claim 1, characterized in that in the vehicle-class-specific driving behavior profiles ( 22 . 24 . 26 ) Probabilities for the execution of respective driving maneuvers for given traffic situations are deposited.
  3. Method according to claim 1 or 2, characterized in that the detection of the vehicle ( 36 ) and assigning to one of the vehicle classes ( 16 . 18 . 20 ) based on an optical detection of the vehicle ( 36 ) and subsequent image processing and / or based on a Car2Car communication.
  4. Method according to one of the preceding claims, characterized in that the further vehicle ( 28 ) by means of a driver assistance system ( 34 ) taking into account the predicted driving behavior of the vehicle ( 36 ) is autonomously controlled.
  5. Method according to one of the preceding claims, characterized in that the further vehicle ( 28 ) Is part of a vehicle fleet of several vehicles, which continuously other vehicles ( 36 . 38 ) and record their driving behavior and related data to the server device ( 12 ), which uses this data to transfer the data record ( 14 ) and the updated data set ( 14 ) the vehicles ( 28 ) of the vehicle fleet.
  6. Method according to Claim 5, characterized in that, based on the data, the vehicle-class-specific driving behavior profiles ( 22 . 24 . 26 ) of the data record ( 14 ).
  7. A method according to claim 5 or 6, characterized in that based on the data so far not yet stored vehicle classes in the data set ( 14 ) and vehicle class-specific driving behavior profiles are created for these vehicle classes.
  8. Method according to one of claims 5 to 7, characterized in that by means of the server device ( 12 ) provided dataset ( 14 ) to the vehicles ( 28 ) of the vehicle fleet and stored in respective vehicle-mounted storage units, wherein on the vehicle side, the prediction of the driving behavior of other vehicles ( 36 . 38 ) based on the respective stored data record ( 14 ) he follows.
  9. System ( 10 ) for predicting a driving behavior of a vehicle ( 36 ), comprising: - a server device ( 12 ), which is designed to store a data set ( 14 ), which is suitable for different vehicle classes ( 16 . 18 . 20 ) respective vehicle-class-specific driving behavior profiles ( 22 . 24 . 26 ); - at least one other vehicle ( 28 ), which is adapted to the vehicle ( 36 ), one of the vehicle classes ( 16 . 18 . 20 ) of the data record ( 14 ) assigned to the assigned vehicle class ( 16 . 18 . 20 ) vehicle class-specific driving behavior profile ( 22 . 24 . 26 ) and the driving behavior of the vehicle ( 36 ) based on the selected driving behavior profile ( 22 . 24 . 26 ) to predict.
DE102016005580.4A 2016-05-06 2016-05-06 Method and system for predicting a driving behavior of a vehicle Pending DE102016005580A1 (en)

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DE102016005580.4A DE102016005580A1 (en) 2016-05-06 2016-05-06 Method and system for predicting a driving behavior of a vehicle

Applications Claiming Priority (1)

Application Number Priority Date Filing Date Title
DE102016005580.4A DE102016005580A1 (en) 2016-05-06 2016-05-06 Method and system for predicting a driving behavior of a vehicle

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Cited By (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
DE102017208594A1 (en) * 2017-05-22 2018-11-22 Bayerische Motoren Werke Aktiengesellschaft Prediction of spatial information of motor vehicles

Citations (4)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
DE102011083677A1 (en) 2011-09-29 2013-04-04 Bayerische Motoren Werke Aktiengesellschaft Method for predicting traffic conditions for e.g. electric car, involves determining future information for traffic conditions of vehicle based on current state of vehicle and historical data
US8948955B2 (en) * 2010-10-05 2015-02-03 Google Inc. System and method for predicting behaviors of detected objects
DE102014200700A1 (en) 2014-01-16 2015-07-30 Bayerische Motoren Werke Aktiengesellschaft Method and system for detecting an emergency lane situation
DE102014207666A1 (en) 2014-04-23 2015-10-29 Bayerische Motoren Werke Aktiengesellschaft Autonomous driving in a dangerous situation

Patent Citations (4)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US8948955B2 (en) * 2010-10-05 2015-02-03 Google Inc. System and method for predicting behaviors of detected objects
DE102011083677A1 (en) 2011-09-29 2013-04-04 Bayerische Motoren Werke Aktiengesellschaft Method for predicting traffic conditions for e.g. electric car, involves determining future information for traffic conditions of vehicle based on current state of vehicle and historical data
DE102014200700A1 (en) 2014-01-16 2015-07-30 Bayerische Motoren Werke Aktiengesellschaft Method and system for detecting an emergency lane situation
DE102014207666A1 (en) 2014-04-23 2015-10-29 Bayerische Motoren Werke Aktiengesellschaft Autonomous driving in a dangerous situation

Cited By (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
DE102017208594A1 (en) * 2017-05-22 2018-11-22 Bayerische Motoren Werke Aktiengesellschaft Prediction of spatial information of motor vehicles

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