WO2019120709A1 - Procédé et unité de commande servant à commander une fonction d'un véhicule se déplaçant au moins en partie de manière automatisée - Google Patents

Procédé et unité de commande servant à commander une fonction d'un véhicule se déplaçant au moins en partie de manière automatisée Download PDF

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Publication number
WO2019120709A1
WO2019120709A1 PCT/EP2018/079423 EP2018079423W WO2019120709A1 WO 2019120709 A1 WO2019120709 A1 WO 2019120709A1 EP 2018079423 W EP2018079423 W EP 2018079423W WO 2019120709 A1 WO2019120709 A1 WO 2019120709A1
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WO
WIPO (PCT)
Prior art keywords
vehicle
unstructured
control unit
traffic
function
Prior art date
Application number
PCT/EP2018/079423
Other languages
German (de)
English (en)
Inventor
Stefan Erschen
Felix Klanner
Thomas Helmer
Horst KLÖDEN
Christopher Bach
Original Assignee
Bayerische Motoren Werke Aktiengesellschaft
Priority date (The priority date 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 date listed.)
Filing date
Publication date
Application filed by Bayerische Motoren Werke Aktiengesellschaft filed Critical Bayerische Motoren Werke Aktiengesellschaft
Publication of WO2019120709A1 publication Critical patent/WO2019120709A1/fr

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Classifications

    • G—PHYSICS
    • G08—SIGNALLING
    • G08G—TRAFFIC CONTROL SYSTEMS
    • G08G1/00—Traffic control systems for road vehicles
    • G08G1/16—Anti-collision systems
    • G08G1/166—Anti-collision systems for active traffic, e.g. moving vehicles, pedestrians, bikes
    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B60—VEHICLES IN GENERAL
    • B60W—CONJOINT 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
    • B60W40/00—Estimation 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
    • B60W40/02—Estimation 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 ambient conditions
    • B60W40/04—Traffic conditions
    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B60—VEHICLES IN GENERAL
    • B60W—CONJOINT 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
    • B60W50/00—Details 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
    • B60W50/08—Interaction between the driver and the control system
    • B60W50/082—Selecting or switching between different modes of propelling
    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B60—VEHICLES IN GENERAL
    • B60W—CONJOINT 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
    • B60W50/00—Details 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
    • B60W50/08—Interaction between the driver and the control system
    • B60W50/14—Means for informing the driver, warning the driver or prompting a driver intervention
    • G—PHYSICS
    • G08—SIGNALLING
    • G08G—TRAFFIC CONTROL SYSTEMS
    • G08G1/00—Traffic control systems for road vehicles
    • G08G1/09—Arrangements for giving variable traffic instructions
    • G08G1/0962—Arrangements for giving variable traffic instructions having an indicator mounted inside the vehicle, e.g. giving voice messages
    • G08G1/09626—Arrangements for giving variable traffic instructions having an indicator mounted inside the vehicle, e.g. giving voice messages where the origin of the information is within the own vehicle, e.g. a local storage device, digital map
    • G—PHYSICS
    • G08—SIGNALLING
    • G08G—TRAFFIC CONTROL SYSTEMS
    • G08G1/00—Traffic control systems for road vehicles
    • G08G1/09—Arrangements for giving variable traffic instructions
    • G08G1/0962—Arrangements for giving variable traffic instructions having an indicator mounted inside the vehicle, e.g. giving voice messages
    • G08G1/0967—Systems involving transmission of highway information, e.g. weather, speed limits
    • G08G1/096708—Systems involving transmission of highway information, e.g. weather, speed limits where the received information might be used to generate an automatic action on the vehicle control
    • G08G1/096725—Systems involving transmission of highway information, e.g. weather, speed limits where the received information might be used to generate an automatic action on the vehicle control where the received information generates an automatic action on the vehicle control
    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B60—VEHICLES IN GENERAL
    • B60W—CONJOINT 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
    • B60W2554/00—Input parameters relating to objects
    • B60W2554/80—Spatial relation or speed relative to objects
    • B60W2554/801—Lateral distance
    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B60—VEHICLES IN GENERAL
    • B60W—CONJOINT 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
    • B60W2555/00—Input parameters relating to exterior conditions, not covered by groups B60W2552/00, B60W2554/00
    • B60W2555/60—Traffic rules, e.g. speed limits or right of way
    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B60—VEHICLES IN GENERAL
    • B60W—CONJOINT 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
    • B60W2720/00—Output or target parameters relating to overall vehicle dynamics
    • B60W2720/10—Longitudinal speed

Definitions

  • the invention relates to a method and a control unit for controlling a function, in particular an output function and / or a guidance function, of a vehicle which is at least partially automated.
  • Automated vehicles typically rely on detailed digital map information and sensor data from environmental sensors to create a model of the environment of the vehicle and to determine and implement a driving strategy based thereon.
  • independent claim dependent patent claim without the features of the independent claim or only in combination with a subset of the features of the independent claim may form an independent and independent of the combination of all features of the independent claim invention, the subject of an independent claim, a divisional application or an N be made can. This applies equally to technical teachings described in the specification, which may form an independent invention of the features of the independent claims.
  • automated driving in the context of this document can be understood as driving with automated catches or transverse guidance or autonomous driving with automated catches and transverse guidance.
  • the automated driving may be, for example, a time-prolonged driving, such as on the highway, or a time-limited driving in the context of parking or maneuvering.
  • automated driving includes automated driving with any one
  • Degree of automation Exemplary automation s are an assisted, semi-automated, highly automated or fully automated driving. These degrees of automation were defined by the Federal Highway Research Institute (BASt) (see BASt publication “Forschung kompakt", issue 11/2012).
  • assisted driving the driver performs the catching or transverse guidance permanently, while the system assumes the other function within certain limits.
  • semi-automated driving TAF
  • the system takes over the crab and lateral guidance for a certain period of time and / or in specific situations, whereby the driver must permanently monitor the system as in assisted driving.
  • highly automated driving HAF
  • the system takes over the catch and lateral guidance for a certain period of time, without the driver having to permanently monitor the system; However, the driver must be in a position to be in charge of taking over the vehicle.
  • VAF fully automated driving
  • VAF the system can automatically handle driving in all situations for a specific application; no driver is required for this application.
  • SAE Fevel 1 to 4 of SAE J3016 (SAE - Society of Automotive Engineering).
  • HAF Highly Automated Driving
  • SAE level 5 is still the highest Automation level not included in the definition of BASt.
  • SAE level 5 is driverless driving, which allows the system to automatically handle all situations like a human driver throughout the journey; a driver is generally no longer required.
  • a control unit for an at least partially automated vehicle (in particular a road vehicle) is described.
  • the vehicle may preferably be set up to drive at least highly automated.
  • the control unit may be configured to determine sensor data regarding an environment of the vehicle.
  • the sensor data can be detected by means of one or more environment sensors of the vehicle.
  • Exemplary sensor data include a radar sensor, an image sensor, an ultrasonic sensor, a Fidar sensor, etc.
  • the sensor data may be repeatedly and, in particular, periodically acquired and provided for a sequence of times, for example, at a sampling rate of IHz, 10Hz or more.
  • the control unit can thus be set up to determine a temporal sequence of sensor data for a corresponding sequence of times.
  • control unit may be configured to determine on the basis of the sensor data (in particular on the basis of the temporal sequence of sensor data) whether there is an unstructured traffic situation or not.
  • an unstructured traffic situation may be characterized by one or more properties and / or caused by one or more causes.
  • the control unit may be configured to determine on the basis of the sensor data (in particular on the basis of the temporal sequence of sensor data) whether there is a traffic situation in the surroundings of the vehicle which has the one or more properties of an unstructured traffic situation, and / or Whether there are one or more possible causes of an unstructured traffic situation in the vicinity of the vehicle.
  • a particularly reliable feature for the detection of an unstructured traffic situation is that, in the case of an unstructured traffic situation, at least one traffic rule valid in the surroundings of the vehicle in accordance with a highway code is provided by at least one other traffic participant, in particular by a plurality of other traffic participants. is not followed.
  • the control unit can thus be set up on the basis of (sequence of)
  • Machine-learned classification methods can be used to detect an unstructured traffic situation.
  • the control unit may be configured to determine at least one value of a feature vector having a plurality of features based on the (sequence of) sensor data.
  • Particularly reliable features for detecting an unstructured traffic situation are: the driving speed of the vehicle; a distance measure with respect to a distance of the vehicle to one or more others
  • Driving participants in the environment of the vehicle within at least one defined lane for example, the one described in this document
  • the control unit may be further configured based on the value of
  • the classifier can be set up to identify at least one subspace for the existence of an unstructured traffic situation within a total of possible values of the feature vector.
  • Classifier may preferably have a decision tree (DT) and / or a random Forest (RF) classifier include. By using a machine-learned classifier, it can be reliably determined whether or not there is an unstructured traffic situation.
  • the control unit may be further configured to control a function of the vehicle depending on whether there is an unstructured traffic situation or not.
  • the function may include an output function for outputting information relating to the unstructured traffic situation to a user of the vehicle and / or an output function for sending information relating to the unstructured traffic situation to a receiver outside the vehicle.
  • a central processing unit may be aware of the presence of an unstructured traffic situation
  • the function may include a guidance function for at least partially automated driving of the vehicle.
  • the mode of operation (in particular the driving strategy) of the at least partially automated vehicle may depend on whether there is an unstructured traffic situation or not.
  • the control unit may be configured to determine a cause from a plurality of possible causes for the unstructured traffic situation on the basis of the (temporal sequence of) sensor data. This can be done, for example, by using a classifier. For this purpose, the subspace for the presence of an unstructured traffic situation in a plurality of
  • Subspaces may be subdivided for the corresponding plurality of possible causes.
  • the function of the vehicle can then be controlled as a function of the determined cause for the unstructured traffic situation.
  • a user and / or a central processing unit can be informed about the cause of the unstructured traffic situation.
  • the ascertained cause can be taken into account when driving the at least partially automated vehicle. So can the
  • Reliability and comfort of an at least partially automated moving vehicle can be further increased.
  • control unit can be set up to determine a traffic rule which is effective within the unstructured traffic situation on the basis of the (temporal sequence of) sensor data.
  • the effective traffic rule may deviate from a traffic regulation in force under the Highway Code.
  • the effective traffic rule can be based on a
  • Pattern recognition method for detecting at least one pattern of the sensor data are determined.
  • the effective traffic rule can indicate how the one or more traffic participants in the vicinity of the vehicle behave differently from the one or more traffic regulations in force in accordance with the Highway Code.
  • the function of the vehicle can then be controlled or operated as a function of the determined, effectively valid traffic rule.
  • the vehicle may continue to be at least partially, in particular at least highly automated, taking into account the effective traffic rule.
  • a takeover request can be issued to a user of the vehicle to guide the vehicle at least partially manually via a user interface of the vehicle.
  • information relating to the determined, effective traffic rule can be output to the user via the user interface of the vehicle. The user can then consider the effective traffic rule when manually guiding the vehicle.
  • the comfort for a driver of a vehicle can be increased within an unstructured traffic situation.
  • a method for controlling or operating a function of an at least partially automated vehicle includes determining sensor data relating to an environment of the vehicle. In addition, the method includes determining, based on the sensor data, whether an unstructured traffic situation exists or not. Further, the method includes controlling a function of the vehicle depending on whether there is an unstructured traffic situation or not.
  • a road vehicle in particular a passenger car or a truck or a bus
  • the control unit described in this document comprises the control unit described in this document.
  • SW software program
  • the SW program can be set up to run on a processor (eg a control unit of a vehicle) and thereby carry out the method described in this document.
  • a processor eg a control unit of a vehicle
  • the storage medium may include an SW program that is set up to be executed on a processor, and thereby perform the same in this
  • FIG. 1 shows an exemplary unstructured traffic situation
  • FIG. 2 shows a block diagram with exemplary components of a vehicle
  • FIG. 3 shows a flow chart of an exemplary method for controlling an at least partially automated vehicle.
  • Automated moving vehicle in complex traffic situations. 1 shows an exemplary unstructured traffic situation 100, in which it is provided by a traffic rule stipulated in the prevailing road traffic regulations that the vehicles 112, 113 travel a first traffic lane 101 into a second traffic lane 102 and from there left onto a third traffic lane 103 turn off.
  • the Traffic rule is given in the example shown by a traffic sign 104 and by lane markers.
  • the vehicles 112, 113 do not thread from the first lane 101 into the second lane 102, but remain on an improvised lane 103 adjacent to the second lane 102 to turn onto the third lane 105. This results in the problem for a vehicle 110 in the second lane 102 that when turning to the third lane 105 not only vehicles 111 on the third lane 105 but also vehicles 112, 113 must be considered on the improvised lane 103.
  • the unstructured traffic situation 100 illustrated by way of example in FIG. 1 is typically associated with a relatively high level of stress for a human driver, but can usually be mastered by the driver.
  • an at least partially automated vehicle 110 usually does not cope with such a traffic situation 100, since such a traffic situation 100 requires an adapted driving strategy.
  • FIG. 2 shows a block diagram of a vehicle 110 that is configured to drive at least partially automated.
  • the vehicle 110 may be capable of at least highly automated driving.
  • the vehicle 110 includes one or more environmental sensors 201 configured to detect sensor data relating to an environment of the vehicle 110.
  • exemplary environmental sensors 201 configured to detect sensor data relating to an environment of the vehicle 110.
  • Environment sensors 201 are a radar sensor, an image camera, a
  • the vehicle 110 further includes a storage unit 202 on which digital map information related to the
  • Road network in the environment of the vehicle 110 may be stored. Further, the vehicle 110 includes a control unit 200 that is configured to Vehicle 110 based on the sensor data and / or based on the digital
  • control unit 200 may determine control data for one or more longitudinal and / or lateral guide actuators 203 of the vehicle 110 (e.g., a steering, a drive motor, and / or a braking device of the vehicle)
  • longitudinal and / or lateral guide actuators 203 of the vehicle 110 e.g., a steering, a drive motor, and / or a braking device of the vehicle
  • the control unit 200 may be configured to determine that an unstructured traffic situation 100 exists on the basis of the sensor data and / or on the basis of the digital map information.
  • An unstructured traffic situation 100 can have one or more properties:
  • a relatively high level of attentiveness is required by a user of the vehicle 110 and / or a relatively high level of stress is caused to a user.
  • Traffic is primarily geared to free and less open spaces
  • An unstructured traffic situation 100 may be caused by one or more causes:
  • the control unit 200 may be configured to determine feature values for one or more features on the basis of the sensor data and / or on the basis of the digital map information.
  • the one or more features can be considered
  • exemplary features for detecting an unstructured traffic situation 100 are:
  • the driving speed of the ego vehicle 110 possibly a sliding one
  • a mean distance can be determined based on the following formula:
  • di is the distance of the vehicle 110 to another
  • Travel participants incl. The direction of movement, stopped and / or parked objects or Vlos participants are distinguished; furthermore, the moving average and / or the standard deviation of the total number can be considered.
  • Traffic participant 112, 113 are weighted.
  • An exemplary measure is
  • TTC Time to collision with another party
  • the control unit 200 may be configured to repeatedly and / or periodically determine a value of a feature vector having one or more of the above-mentioned. Features includes. It can thus be a temporal sequence of the values of the
  • control unit 200 may be configured to determine, based on one or more values of the feature vector and on the basis of a classifier, whether an unstructured
  • the classifier may be arranged to divide the space from possible values of the feature vector into subspaces for different classes. Possible classes are:
  • the existence of an unstructured traffic situation 100 The class for the presence of an unstructured traffic situation 100 may possibly be subdivided into several subclasses, in particular depending on the cause of the unstructured traffic situation 100. Exemplary subclasses are:
  • the classifier may have been learned during a learning phase. For this purpose, training data with a variety of
  • a training data set comprises a value of the feature vector and a corresponding class.
  • the classes may have been assigned manually if necessary.
  • the classifier may include a neural network, a support vector machine, a decision tree (DT) and / or a random forest (RF) classifier.
  • the control unit 200 may thus be set up to determine whether an unstructured traffic situation 100 exists or not. Furthermore, the control unit 200 may be configured to assign an unstructured traffic situation 100 to a subclass and / or determine the cause of the unstructured traffic situation 100.
  • the effective traffic rule may differ from the one or more traffic regulations in force in accordance with the Highway Code.
  • the effective traffic rule can then be taken into account in the control of the (at least partially automated) ego vehicle 110.
  • an at least partially automated ego vehicle 110 may use the determined effective traffic rule to continue to maintain the ego vehicle 110 at least partially automated within the unstructured traffic situation 100.
  • An effective traffic rule can be determined on the basis of the sensor data and / or the digital map information. For this purpose can
  • Pattern recognition method can be used to determine patterns for the behavior of the vehicle participants 112 based on the time sequence of the sensor data.
  • Network models and / or deep leaming methods may be used to determine effective traffic rules within an unstructured traffic situation 100 based on the sensor data.
  • the identified cause of the unstructured traffic situation can be 100, the location of the unstructured traffic situation 100, the traffic density, the traffic speed, the Time of day, the day of the week and / or the weather are taken into account to determine the effective traffic rule with increased accuracy.
  • the vehicle 110 may be configured to communicate information regarding a detected unstructured via a communication unit of the vehicle 110
  • the information relating to the unstructured traffic situation 100 may then be e.g. be taken into account in the determination of a driving route, e.g. in order to be able to offer a user a route that is as stress-free as possible (in which the location of the unstructured traffic situation 100 is bypassed).
  • FIG. 3 shows a flowchart of an exemplary method 300 for
  • Control or for operating a function of an at least partially automated moving vehicle 110 The vehicle 110 may in particular be configured to drive at least highly automated.
  • the function may include an output and / or a guidance function of the vehicle 110.
  • the method 300 may be performed by a control unit 200 of the vehicle 110.
  • the method 300 includes determining 301 sensor data with respect to an environment of the vehicle 110.
  • a temporal sequence of sensor data for a corresponding sequence of times can be determined.
  • sensor data with a time frequency of 1 Hz, 10 Hz or more can be detected and provided for further processing.
  • the (temporal) behavior of one or more other vehicle participants 112, 113 in the environment of the vehicle 110 can be determined.
  • method 300 includes determining 302 based on
  • an unstructured traffic situation 100 may be present if one or more traffic participants 112, 113 in the vicinity of the vehicle 110 do not comply with the one or more traffic regulations for the surroundings of the vehicle 110 that are valid in accordance with a highway code.
  • An unstructured traffic situation 100 may be present if one or more traffic participants 112, 113 in the vicinity of the vehicle 110 do not comply with the one or more traffic regulations for the surroundings of the vehicle 110 that are valid in accordance with a highway code.
  • Income situation 100 may be based on this property and / or the o.g.
  • Income situation 100 may be based on a machine-learned classifier.
  • the method 300 further comprises controlling 303 a function of the vehicle 110 depending on whether an unstructured
  • Guiding function of the vehicle 110 (for at least partially automated driving of the vehicle 110) are controlled or operated on the basis of the traffic regulations in force, if it has been determined that there is no unstructured traffic situation 100. On the other hand, at least one may be unstructured within a detected one
  • the control of an at least partially automated vehicle 110 can be improved.
  • an at least partially automated driving of the vehicle 110 can be made possible.
  • one or more effective traffic rules can be determined and used to control the vehicle 110.
  • a transfer to a driver of the vehicle 110 may occur, wherein the driver may be notified of the one or more effective traffic rules in use (eg via a user interface of the vehicle 110).
  • information regarding the existence of an unstructured Traffic situation 100 and / or transmitted in relation to a determined effective effective traffic rule to a central unit to take this information in the context of the control of another vehicle to take into account (eg for planning a route).

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  • Engineering & Computer Science (AREA)
  • Automation & Control Theory (AREA)
  • Physics & Mathematics (AREA)
  • Transportation (AREA)
  • Mechanical Engineering (AREA)
  • General Physics & Mathematics (AREA)
  • Human Computer Interaction (AREA)
  • Mathematical Physics (AREA)
  • Life Sciences & Earth Sciences (AREA)
  • Atmospheric Sciences (AREA)
  • Traffic Control Systems (AREA)

Abstract

L'invention concerne une unité de commande (200) pour un véhicule (110) se déplaçant au moins en partie de manière automatisée. L'unité de commande (200) est mise au point pour déterminer des données de capteur par rapport à un champ environnant du véhicule (110). L'unité de commande (200) est mise au point en outre pour définir sur la base des données de capteur si une situation de trafic non structurée (100) est de mise ou non. Par ailleurs, l'unité de commande (200) est configurée pour commander une fonction du véhicule (110) selon qu'une situation de trafic (100) non structurée est de mise ou non.
PCT/EP2018/079423 2017-12-21 2018-10-26 Procédé et unité de commande servant à commander une fonction d'un véhicule se déplaçant au moins en partie de manière automatisée WO2019120709A1 (fr)

Applications Claiming Priority (2)

Application Number Priority Date Filing Date Title
DE102017223621.3 2017-12-21
DE102017223621.3A DE102017223621A1 (de) 2017-12-21 2017-12-21 Verfahren und Steuereinheit zur Steuerung einer Funktion eines zumindest teilweise automatisiert fahrenden Fahrzeugs

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WO2019120709A1 true WO2019120709A1 (fr) 2019-06-27

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Cited By (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN111137229A (zh) * 2019-12-27 2020-05-12 深圳市九洲电器有限公司 一种汽车智能驾驶方法和系统、机顶盒

Families Citing this family (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
DE102020113611A1 (de) 2020-05-20 2021-11-25 Bayerische Motoren Werke Aktiengesellschaft Verfahren und Sicherheitssystem zum Absichern einer automatisierten Fahrzeugfunktion und Kraftfahrzeug
DE102021127704A1 (de) 2021-10-25 2023-04-27 Bayerische Motoren Werke Aktiengesellschaft Verfahren und System zur Vorhersage eines Fahrverhaltens von Fahrzeugen

Citations (5)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
DE102006040537A1 (de) * 2006-08-30 2008-03-13 Deutsches Zentrum für Luft- und Raumfahrt e.V. Fahrzeugassistenzsystem
DE202014006923U1 (de) * 2014-08-27 2015-11-30 GM Global Technology Operations LLC (n. d. Ges. d. Staates Delaware) Fahrassistenzsystem, Computerprogrammprodukt sowie Kraftfahrzeug
DE102014111023A1 (de) * 2014-08-04 2016-02-04 Fraunhofer-Gesellschaft zur Förderung der angewandten Forschung e.V. Verfahren und Vorrichtung zum Steuern eines automatisierten Fahrzeuges
DE102016108812A1 (de) * 2015-06-04 2016-12-08 Toyota Jidosha Kabushiki Kaisha Umstellen von Betriebsmodi eines Kraftfahrzeugs
US20170113689A1 (en) * 2015-10-27 2017-04-27 International Business Machines Corporation Controlling Driving Modes of Self-Driving Vehicles

Family Cites Families (3)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
JP4623145B2 (ja) * 2008-06-16 2011-02-02 トヨタ自動車株式会社 運転支援装置
DE102014203983A1 (de) * 2014-03-05 2015-09-10 Bayerische Motoren Werke Aktiengesellschaft Verfahren und Vorrichtung zum Anzeigen eines Verbotes und/oder Gebotes in einer Umgebung mit einem hochautomatisiert fahrenden Kraftfahrzeug und einem nicht hochautomatisierten Verkehrsteilnehmer
DE102015201555A1 (de) * 2015-01-29 2016-08-04 Robert Bosch Gmbh Verfahren und Vorrichtung zum Betreiben eines Fahrzeugs

Patent Citations (5)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
DE102006040537A1 (de) * 2006-08-30 2008-03-13 Deutsches Zentrum für Luft- und Raumfahrt e.V. Fahrzeugassistenzsystem
DE102014111023A1 (de) * 2014-08-04 2016-02-04 Fraunhofer-Gesellschaft zur Förderung der angewandten Forschung e.V. Verfahren und Vorrichtung zum Steuern eines automatisierten Fahrzeuges
DE202014006923U1 (de) * 2014-08-27 2015-11-30 GM Global Technology Operations LLC (n. d. Ges. d. Staates Delaware) Fahrassistenzsystem, Computerprogrammprodukt sowie Kraftfahrzeug
DE102016108812A1 (de) * 2015-06-04 2016-12-08 Toyota Jidosha Kabushiki Kaisha Umstellen von Betriebsmodi eines Kraftfahrzeugs
US20170113689A1 (en) * 2015-10-27 2017-04-27 International Business Machines Corporation Controlling Driving Modes of Self-Driving Vehicles

Non-Patent Citations (2)

* Cited by examiner, † Cited by third party
Title
MEHDIYEV ET AL.: "Determination of Rule Patterns in Complex Event Processing Using Machine Learning Techniques", PROCEDIA COMPUTER SCIENCE, vol. 61, 2015, pages 395 - 401
WEI ZHAN ET AL: "A non-conservatively defensive strategy for urban autonomous driving", 2016 IEEE 19TH INTERNATIONAL CONFERENCE ON INTELLIGENT TRANSPORTATION SYSTEMS (ITSC), 1 November 2016 (2016-11-01), pages 459 - 464, XP055547130, ISBN: 978-1-5090-1889-5, DOI: 10.1109/ITSC.2016.7795595 *

Cited By (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN111137229A (zh) * 2019-12-27 2020-05-12 深圳市九洲电器有限公司 一种汽车智能驾驶方法和系统、机顶盒
CN111137229B (zh) * 2019-12-27 2021-08-27 深圳市九洲电器有限公司 一种汽车智能驾驶方法和系统、机顶盒

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