Classifications

• • G—PHYSICS
  • G08—SIGNALLING
  • G08G—TRAFFIC CONTROL SYSTEMS
  • G08G1/00—Traffic control systems for road vehicles
  • G08G1/16—Anti-collision systems
  • G08G1/167—Driving aids for lane monitoring, lane changing, e.g. blind spot detection
• • 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
  • B60W30/00—Purposes 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
  • B60W30/18—Propelling the vehicle
  • B60W30/18009—Propelling the vehicle related to particular drive situations
  • B60W30/18163—Lane change; Overtaking manoeuvres
• • 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
  • B60W40/08—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 drivers or passengers
  • B60W40/09—Driving style or behaviour
• • 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
  • G08G1/0108—Measuring and analyzing of parameters relative to traffic conditions based on the source of data
  • G08G1/0112—Measuring and analyzing of parameters relative to traffic conditions based on the source of data from the vehicle, e.g. floating car data [FCD]
• • 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
  • G08G1/0125—Traffic data processing
  • G08G1/0129—Traffic data processing for creating historical data or processing based on historical data
• • 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
  • G08G1/0137—Measuring and analyzing of parameters relative to traffic conditions for specific applications
  • G08G1/0141—Measuring and analyzing of parameters relative to traffic conditions for specific applications for traffic information dissemination
• • 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
  • B60W2520/00—Input parameters relating to overall vehicle dynamics
  • B60W2520/10—Longitudinal speed
• • 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
  • B60W2540/00—Input parameters relating to occupants
  • B60W2540/043—Identity of occupants
• • 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
  • B60W2540/00—Input parameters relating to occupants
  • B60W2540/21—Voice
• • 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
  • B60W2540/00—Input parameters relating to occupants
  • B60W2540/22—Psychological state; Stress level or workload
• • 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
  • B60W2540/00—Input parameters relating to occupants
  • B60W2540/221—Physiology, e.g. weight, heartbeat, health or special needs
• • 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
  • B60W2540/00—Input parameters relating to occupants
  • B60W2540/30—Driving style
• • 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
  • B60W2554/802—Longitudinal 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
  • B60W2555/20—Ambient conditions, e.g. wind or rain

Definitions

• the invention relates to methods for operating a driver assistance device of a motor vehicle.
• sensor data of a surrounding area of the motor vehicle are detected.
• At least one target vehicle is located in the surrounding area.
• the target vehicle is located in front of the motor vehicle and on the same lane as the motor vehicle.
• the invention relates to a driver assistance device having an evaluation unit which is designed to execute a corresponding method.
• the invention also relates to a motor vehicle with a corresponding driver assistance device.
• Methods for operating a driver assistance device of a motor vehicle are known from the prior art. For example, it is known that a future overtaking path of a motor vehicle is calculated based on environmental sensor data or map data, and the overtaking path or a hint based thereon is output to assist a driver in an overtaking operation.
• a driver assistance device of a motor vehicle is operated. The following steps are carried out:
• Motor vehicle in which at least one target vehicle is located, which is, in particular in the direction of travel of the motor vehicle, in front of the motor vehicle and which is located in the same lane as the motor vehicle;
• Reading in driver-class-specific overtaking information which describes a passing habit of the driver based on the driver class assigned to the overtaking information
• the invention is based on the finding that the over ⁇ holvorgang by the driver class specific overtaking information, ie by taking into account the driver's individual driving style, can be performed safely.
• the passing information for the different driver classes is available in different ways.
• Overtaking information describes the overtaking habit of the driver or his driving habit in overtaking maneuvers.
• the driver is in turn assigned to the driver class.
• the driver class can For example, include only the driver or multiple drivers and thus serve as a generalization of the overtaking information for overtaking habits of multiple drivers.
• the overtaking information can be present as a, in particular unitless, number vector or one, in particular unitless, number matrix with one or more dimensions.
• the overtaking information is then compared with the overtaking information associated with the reference control signals. In particular, the reference control signal with the most similar overtaking information is selected.
• This preference for the operation of the motor vehicle can be represented numerically on the basis of properties described below. These properties, which can also be represented numerically, since they are characterized by parameters or values of states of components of the motor vehicle, are taken as the basis for the machine learning to generate the overtaking information.
• the overtaking information can be present in particular as a classifier for selecting the control signal.
• the overtaking information of the reference control signals provides various classes to which the driver is assigned on the basis of the comparison.
• each class provides a different control signal.
• one class provides the override control signal with maximum possible acceleration
• another class provides, for example, the control signal which only passes with half maximum possible acceleration. It detects the surrounding area of the motor vehicle and the sensor data is provided.
• the surrounding area can be detected for example with a variety of environmental sensors of the motor vehicle, for example a camera, a radar sensor, a lidar sensor, a laser scanner, an ultrasonic sensor or a combination of these sensors.
• the target vehicle In front of the motor vehicle is the target vehicle.
• the motor vehicle thus approaches the target vehicle.
• the motor vehicle is on the same lane as the goal ⁇ vehicle, so the vehicle has to overtake the target vehicle to drive past a speed higher than the target vehicle to the target vehicle.
• the surrounding area is detected.
• the driver By the driver of the motor vehicle the driver be ⁇ write feature is detected.
• the driver can feature game, in ⁇ by an inner space of the motor vehicle camera, an RFID chip (RFID - Radio Frequency Identification) generating key in the vehicle or recorded a fingerprint reader.
• RFID chip RFID - Radio Frequency Identification
• the driver feature may thus be a feature that preferably uniquely identifies the driver.
• the driver attribute assigns the driver to the predetermined driver class. This is done in particular on the basis of a mapping of the driver characteristic to the driver class.
• the Fah ⁇ rerstal alone can have only the driver and does not necessarily contain multiple drivers.
• the assigned driver class is assigned the overtaking information.
• the overtaking information describes the Sprintholge ⁇ habits of the driver or the overtaking habits of the driver class, which is assigned to the driver.
• the overhauls ⁇ la are preferably of driver class driver class different. Thus, with respect to exhibit an overtaking maneuver on ⁇ example riskier driver a driver class, while another driver class having less risky drivers.
• the overtaking information is read, for example, from a memory of the motor vehicle or a vehicle-external device.
• the control signal of the driver assistance device is selected.
• Rate ⁇ be indicated, for example, a risk or risk International ⁇ probability for the overtaking operation is determined. Evaluating, however, can also mean that it is determined whether the overtaking process can be carried out safely or whether the overtaking process can not be carried out safely.
• This review may, for example, methods of data analysis, movement of such data ⁇ processing, data transformation, data modeling as well as with methods of machine learning and artificial intelligence, such as neural networks, SVM methods (SVM - support vector machine), deep learning, ANN (ANN - k-nearest-neighbor), regression or the like.
• the control signal is output by the driver assistance device.
• a visual and / or acoustic and / or haptic indication can be triggered by the control signal.
• an at least semi-autonomous or piloted performed overtaking process of the motor vehicle can be triggered.
• At least semi-autonomously ⁇ that the steering of the motor vehicle and / or the acceleration of the motor vehicle automatically means in this context that is, substantially without the influence of a human driver, is carried out.
• the overtaking information is generated based on training data with at least one of the following characterizing the driver.
• the training data thus have at least one of the properties.
• the property can be present, for example, as a travel speed relative to the regulation speed, as a safety distance when reclosing after the overtaking process has taken place, or as an exiting distance to the target vehicle before the overtaking process has taken place.
• the property can also be present in particular by an off ⁇ shear angle of the vehicle at the beginning of overtaking.
• the Ausscherwinkel is described with which angle to the lane longitudinal axis, the motor vehicle performs the lateral movement with respect to the lane at the beginning of the overtaking process.
• the regulation speed can be present, for example, as a directional speed or as a legal speed limit.
• the overtaking information is then generated, for example, by machine learning with the training data.
• the overtaking information is then assigned to a driver class or the driver class is created by the respective overtaking information.
• the steering- signal is then selected depending on the habits of the driver.
• control signal can also be selected according to whether the driver would like to have a large safety margin during re-scissors after a successful overtaking or a smaller safety distance to the target vehicle after a successful overtaking ⁇ process. So whether the safety distance exceeds or falls below a distance limit.
• the distance may be in the unit m, for example. So many drivers would like with a large safety margin, so play as Reeve at ⁇ several car lengths ahead of the target vehicle again and some other drivers, for example, it is sufficient even if the safety distance is the length of a vehicle.
• the driver is then assigned, for example, a first class of overtaking habits and in the second case a second class.
• a first reference control signal is preferably present, and in the second class, a second reference control signal, which differs from the first reference control signal, is preferably present.
• the Ausscherentfernung to the target vehicle can be present as the property.
• the Ausscherentfernung to the target vehicle indicates how far ahead of the target vehicle, the motor vehicle leaves the lane side or with an at least partially lateral movement.
• the acceleration profile can also serve as a property.
• the acceleration profile describes with which acceleration values the motor vehicle is operated in the different phases of the overtaking process.
• the motor vehicle can be accelerated more sharply at the beginning of the overtaking process than at the end of the acceleration process, or vice versa, or else the motor vehicle is only in the In the middle of the overtaking process, it accelerates sharply, while at the beginning of the overtaking process and at the end of the overtaking process it is only accelerated to a lesser extent than in the middle phase.
• the property may also be the acceleration behavior.
• the acceleration behavior can describe, for example, how the driver accelerates the motor vehicle.
• the accelerator pedal can be pushed completely through or only partially pushed through. This corresponds to different degrees of utilization of the power reserve.
• a shift to a lower gear prior to or during the overtaking process for example, be assigned to a first class, while maintaining the gear before or during the over ⁇ holvorgangs a second class is assigned.
• the Sprint ⁇ linformation is generated depending on a current weather condition and / or a current time and / or a current day of the week.
• the overtaking information can also be generated depending on a current traffic density and / or a current road category.
• the current weather condition for example, it can be specified whether it is raining, whether it is snowing, what outside temperatures are present, and then again how the overtaking habit of the driver is dependent on the weather condition. For example, there are drivers who are on wet roads or ice-covered ones Reward less frequently than they would on dry and / or unfrozen lanes.
• the overtaking information can be generated or trained, for example, depending on the current time. For example, there are drivers who drive in the morning on the way to work and / or in the evening on the way home from work more risky and thus overtake more often than is the case for example during working hours. Also, for example, the reverse case may occur, so that is overtaken more often during working hours, because then important dates must be adhered to. It can also be provided that the overtaking information is generated depending on the current day of the week. For example, the driver may overtake you more often on weekdays than on holidays or on public holidays
• the traffic volume can be used as the property for generating the passing information. For example, there are drivers who overtake only with low traffic, since the overtaking situation is then usually clearer. But it can also be the other way round, so that it is attempted to get to the destination more quickly, especially when traffic is high. Even with the street category read the property, the overtaking information can be generated.
• Road category classifies the road within a road network in terms of road construction load, design standard or usage constraint. For example, a driver overhauls federal roads differently than he overtakes on county or municipal roads.
• a current operating parameter of the motor vehicle is detected and an overtaking intention of the driver is recognized on the basis of the operating parameter and the overtaking information and the control signal is dependent is selected from the recognized overtaking intent.
• the Be ⁇ operating parameters can match, for example, with the properties of the training data.
• a specific distance behavior or a specific acceleration behavior can be detected by the operating parameter, which has a similarity with the overtaking information.
• the operating parameter can be compared with the overtaking information and then classified as similar or dissimilar, for example, based on the comparison. If the operating parameter with the overtaking information is estimated to be similar, then it can then be used, for example, to trace back to the presence of the driver's intention to overtake.
• the control signal to the driver that the overtaking at a certain distance without risk would be possible or that the present overtaking option should be used, as in the next travel section no overtaking is available.
• a currently physio logically ⁇ recognizable indicator of the driver is detected and the flag is compared with a plurality of reference indicator, wherein a overtake the driver is detected based on the comparison.
• the physiologically recognizable mark becomes a body of the driver
• a degree of nervousness and / or an eye movement and / or a volume of a noise emitted by the driver and / or a semantic meaning of a noise emitted by the driver is detected as the currently physiologically recognizable indicator.
• the degree of nervousness may for example be determined based on welding ⁇ formation on one end of the driver or welding surfaces on the palms of the driver.
• the sweat formation can be detected, for example, with sensors of the motor vehicle, for example a camera.
• the given by the driver from ⁇ noise can for example be a statement or a blow to an object in the vehicle.
• the noise is a driver's statement, for example, the semantic meaning of the phrase can be detected by a speech recognition program. For example, if the driver is shouting or cursing, this may be taken as an indication of overtaking intent.
• the control signal in turn can be selected depending on the intention to overtake. For example, a warning can be issued to the driver if the intention to overtake is recognized, but overtaking, taking into account his overtaking habit, is not possible. In turn, the motor vehicle can be operated more safely.
• an individual overtaking path is determined for the overtaking procedure with respect to the target vehicle. So it may be, for example, that is known by the overtaking information that the driver usually reconditioned leisurely while not exploiting the power reserves of the motor vehicle. For example, the driver can then be given no indication of an imminent overtaking opportunity if the exploitation of the power reserve in this case is a prerequisite for the success of the overtaking process is. A driver who exploits the power reserves of the motor vehicle and is fundamentally more willing to take risks, the overtaking proposal can be issued in this case.
• the physiologically recognizable indicator recognizes that the driver is in a state which is either particularly relaxed and therefore more likely to assume a slow overtaking maneuver or the driver is particularly tense and excited, which is why Driver no overtaking proposal is issued because the driver is a deliberate procedure just not dared.
• the motor vehicle can thus in turn be operated safely.
• the Sprint ⁇ linformation is adjusted depending on a current vehicle guidance of the driver, in particular by an online training, during operation of the motor vehicle.
• the driver's current vehicle guidance in particular including the operating parameters of the motor vehicle, is detected and the overtaking information is adjusted.
• the current vehicle guidance of the driver can then also be described, for example, with the properties of the training data, which are used to generate the overtaking information.
• Overtaking information the overtaking information can be kept up to date and accurate. For example, the overhaul habit of a driver may change over time. For example, the driver can get older and therefore more relaxed or less willing to take risks in certain overtaking situations.
• the method can also be carried out with a few initial training data, since the
• Online training describes a method of machine learning in which a classifier is adjusted during operational operation.
• the classifier is thus trained with training examples, which it has previously assigned to the classes themselves.
• initial training data are usually assigned to the classes manually, ie by people.
• the overtaking information adapted during operation of the motor vehicle is transmitted to at least one device external of the motor vehicle.
• the vehicle-external device can be formed in ⁇ example, as an external server or multiple external server.
• the adapted overtaking information can thus be transmitted, for example, into a so-called computer cloud, that is, into a distributed computer network.
• the device can be stationary, distributed or centrally available.
• a model underlying the overtaking information can be adapted by the adapted overtaking information. This is also advantageous as data backup of the overtaking information.
• the overtaking information can be deleted, for example, from a memory of the motor vehicle or even not exist in a new car and be restored by the backup of overtaking information.
• a Matterholönkeits fundamental or warning ⁇ indicative for the overtaking the motor vehicle of the Target vehicle is output by the driver assistance device.
• a visual and / or acoustic and / or haptic indication to the driver of the motor vehicle are issued that the overtaking is possible.
• the driver of the motor vehicle for example, visually and / or acoustically and / or haptically, issued that the overtaking process, for example, just for a certain distance or a certain period of time is not feasible or can only be carried out in the manner that the Overtaking the
• the overtaking ⁇ process of the motor vehicle with respect to the target vehicle based on the control signal is performed at least semi-autonomously.
• semiautonomous overtaking for example, a steering intervention and / or an acceleration or braking intervention can take place.
• the driver of the motor vehicle is therefore moved so well when at least se ⁇ miautonomen overtaking the vehicle that its Kochholge stickheit is considered.
• the overtaking process can also be performed fully autonomously, so that the driver is present only as a spectator or monitor in the vehicle. Also in this case, the driver will feel more comfortable when the motor vehicle behaves when overtaking as he would do it during manual operation of the motor vehicle itself.
• the invention also relates to a driver assistance device having an evaluation unit.
• the driver assistance device or the Driver assistance system is designed to carry out a erfindungsge ⁇ zeßes method.
• the evaluation unit may be playing formed at ⁇ as a unit with a processor.
• the evaluation unit may have, for example, an internal or external memory.
• the driver assistance device may be formed for example as overtaking or Studentsholvorschlagsassistent or overtaking ⁇ warning assistant.
• the invention further relates to a motor vehicle with a driver assistance device according to the invention.
• the motor vehicle is designed in particular as a passenger car.
• the motor vehicle can be operated at least semi-autonomously.
• Advantageous embodiments of the method according to the invention are to be regarded as advantageous embodiments of the driver assistance device and of the motor vehicle.
• the representational components of the driver assistance device and of the motor vehicle are each designed to carry out the respective method steps.
• FIG. 1 is a schematic plan view of an exemplary embodiment of a force ⁇ vehicle according to the invention with a driver assistance device and a target vehicle on a lane.
• Figure 2 is a schematic plan view illustration of the motor vehicle on the lane ⁇ with a passing distance.
• FIG. 3 shows a schematic representation of a driver sitting in the motor vehicle in side view
• FIG. 4 shows a flow chart of an exemplary embodiment of a method according to the invention for operating the driver assistance device.
• the motor vehicle 1 shows an exemplary embodiment of a motor vehicle 1 according to the invention with a driver assistance device 2.
• the motor vehicle 1 is located on a roadway 3.
• the roadway 3 has a traffic lane 4 and a further traffic lane 5.
• the motor vehicle 1 is located in the traffic lane 4.
• a target vehicle 6 In the direction of travel in front of the motor vehicle 1 is in the lane 4, a target vehicle 6.
• the target vehicle 6 may, for example, move, but it may also be stationary.
• the roadway 3 may, for example, be part of a federal road, motorway or any other road where overtaking of the destination vehicle 6 by the motor vehicle 1 is possible is.
• the lane 4 and the other lane 5 are separated according to the embodiment by a lane marker 7.
• the pavement marking 7 is formed in particular as a guideline from ⁇ .
• the lane 4 is bounded laterally by the lane marking 7 and an edge marking 8. The edge marking
• the motor vehicle 1 has an environmental sensor 9.
• the environmental sensor 9 can be designed, for example, as a camera, radar sensor, ultrasound sensor, lidar sensor or laser scanner.
• the motor vehicle 1 may, for example, also have a plurality of environmental sensors 9. Through the environmental sensor
• the surrounding area 11 surrounds the motor vehicle 1 at least partially.
• the target vehicle 6 is at least partially contained.
• the sensor data 10 can also be provided, for example, by a sensor data fusion of various environmental sensors 9 of the motor vehicle 1.
• the driver feature 12 may be provided, for example, by identifying the driver 13 in a variety of ways (described below) or by recognizing that it is the driver 13. Recognizing he can follow ⁇ example, based on a characteristic driving style.
• the driver 13 is a Fah ⁇ rertre 14 is assigned based on the driver feature 12th
• the assignment can, for example, take place such that the driver is assigned to the driver class 14 in which the driver feature 12 has the greatest similarity with a feature of the respective driver class 14.
• the driver 13 is classified in such a way that he is now classified as, for example, a risk-taking driver or a comfortable driver or a safety-conscious driver or a slower driver or a faster driver.
• the driver class 14 can play multiple drivers are assigned to 13 at ⁇ , that is, so that the driver class 14 is a generalization for multiple drivers. 13 But it may also be that the driver class 14 is provided individually for the driver 13 and therefore each of the driver classes 14 has only one of the drivers 13.
• Based on the driver class 14 is now a driver class specific overtaking information 15 read. In particular, each driver class has different overtaking information 15.
• the overtaking information 15 describes a passing habit 16 of the driver 13.
• the about ⁇ holinformation 16 is especially read by the driver assistance ⁇ tenzvorraum second
• the read-in is performed by an evaluation unit 17 of the driver assistance device 2.
• the evaluation unit 17 has, for example, a processor and a computer-readable memory.
• a control signal 18 of the driver assistance device 2 is now selected.
• the control signal 18 is selected from a plurality of reference control signals 19.
• the majority of Referenzsteu ⁇ ersignale 19 may for example be stored in the driver assistance device. 2
• the control signal 18 is a Overtaking 20 of the motor vehicle 1 with respect to the target ⁇ vehicle 6 evaluated. It is thus evaluated, for example on the basis of the overtaking information 15 and the sensor data 10, whether an overtaking intention of the driver 13 exists and / or whether overtaking is currently possible or in the future.
• the assessment is thus made not only on the basis of the sensor data 10 but also on the basis of the driver class specific overtaking information 15.
• the evaluation of the overtaking process 20 is carried out only on the basis of the sensor data 10.
• the overtaking information 15 is important because each driver has different overtaking habits 16. For example, one driver is more willing to take risks than another driver and one driver makes full use of the power reserves of the motor vehicle 1 while another driver only partially exploits the power reserves.
• the control signal 18 is finally output by the driver assistance ⁇ tenzvorraum. 2
• the control signal 18 is in particular a Kochholticiankeitshunt 21 or a
• Warning 22 is issued.
• the driver 13 is signaled, for example, that the overtaking 20 is possible with his Matterholge baseheit 16.
• the driver 13 is signaled, for example, that the overtaking process 20 with its habit 16 is impossible.
• the motor vehicle 1 By the control signal 18, the motor vehicle 1, the over ⁇ holvorgang 20 but in addition or alternatively to the output of Matterholvenezs disruptes 21 or the warning 22nd at least semiautonomously perform.
• the overtaking information 15 is generated in particular on the basis of training ⁇ ning data, which have a driver 13 characterizing properties.
• the property may be, for example, a travel speed 23 of the motor vehicle 1 relative to a regulation speed 24.
• the Vorschriftsgeschwin ⁇ speed 24 is indicated, for example, on a track arranged next to the road sign 3 25th
• the property can be present with respect to the target vehicle 27 6 as acceleration profile, taking advantage of the power reserve of the motor vehicle 1, Be ⁇ admirungs , gear shift performance, braking performance, distance 26 with respect to behavior of the target vehicle 6, Auffahr effets .
• the property can also be present, for example, as a lateral distance 28 between the road markings and the motor vehicle 1.
• the property can also be present, for example, by a lateral travel profile 29.
• the lateral distance 28 is recorded and taken into account over time.
• the lateral driving profile 29 can be detected, for example, when the driver 13 at certain intervals again and again close to the lane marker 7 hinaide or the lane marker 7 briefly passes over to look past the target vehicle 6.
• an operating parameter 30 of the motor vehicle 1 is detected.
• the operating parameter 30 currently includes while driving the Motor vehicle 1 detected properties of Matterholinforma ⁇ tion.
• an overtaking intent 31 of the driver 13 can be detected.
• the similarity between one of the operating parameters 30 and a property of the overtaking information 15 is more similar than a similarity threshold, it can be assumed that the driver 13 is following the overtaking intention 31 and wants to overtake the target vehicle 6 with the motor vehicle 1.
• FIG. 1 shows the motor vehicle 1 analogous to FIG. 1 on the roadway 3 behind the target vehicle 6.
• the motor vehicle 1 has a vehicle length 32 and travels at a distance 33 behind the target vehicle 6.
• the driver 13 13 individually selected distance 33 to the target vehicle 6 is also a property of the training data, which flows into the overtaking information 15.
• an overtaking 34 is determined.
• the overtaking 34 is the path or the supplementraj ektorie, which would drive the motor vehicle 1, if it depends on the overtaking 16 of the driver 13.
• the overtaking 34 is thus planned as the driver 13 would overtake the target vehicle 6 when he controls the motor vehicle 1 itself, but it is also planned how pleasant the driver 13 finds the overtaking process 20, if the motor vehicle 1 is operated at least semi-autonomously , So it may for example be that for one driver class 14 of overtaking 20 is possible, while for another driver class 14 of the overtaking process 20 is not possible if the Overtaking 20 should follow the Matterholge goheit 16.
• the overtaking path 34 provides further characteristics of the training data for the overtaking information 15.
• a Ausscherwinkel 35 is provided as the property, which describes, for example, the angle between the road mark 7 and overtaking 34 at the point at which the overtaking 34 intersects the pavement marking 7. Furthermore, a property by a Ausscherentfernung 36 of the motor vehicle 1 to the target vehicle 6 at the beginning of
• the Ausschernentfernung 26 is according to the embodiment of FIG. 2 equal to the distance 33.
• a safety distance 37 is provided when reclosing after the overtaking procedure 20 has been completed.
• the safety distance 37 is the distance which, after a necessary shearing distance 38, is voluntarily complied with, as it were, when shoveling in order to give the driver a feeling of security behind a moving target vehicle driver or to avoid an accident if the target vehicle 6 accelerates unexpectedly.
• the shearing time required by the motor vehicle 1 can also be specified as the property for the overtaking information 15 as the property.
• the overtaking path 34 depends in particular on the individual acceleration profile of the driver 13, the readiness of the driver 13 to utilize power reserves of the motor vehicle 1, and the selected driving speed 23 with respect to the regulation speed 24.
• a current time a current Weekday, traffic density or street category.
• a degree of brightness in the surrounding area 11 of the motor vehicle 1 it is also possible to detect a degree of brightness in the surrounding area 11 of the motor vehicle 1 and to incorporate this as a characteristic of the overtaking information into the training of the overtaking information. For example, a driver can drive in risk-free driving during daylight hours when driving at dusk or at night.
• Fig. 3 shows the motor vehicle 1 in a partial side view.
• the driver sits 13 on a seat 41.
• the motor vehicle 1 according to the embodiment, a steering wheel 42, a ados ⁇ unit 43, an interior camera 44 and a speaker 45 on.
• the Matterhol folkkeits folk 21 and / or the warning 22 may be issued visually and / or haptically and / or acoustically in the interior 40 of the motor vehicle 1.
• the output can be made by vibrating the seat 41, vibrating the steering wheel 42, displaying by the display unit 43, or audibly outputting through the speaker 45.
• the output of Kochholvenezkeits marses 21 as well as the Warn ⁇ notice is more than a reproduction of information and includes the application of electrical voltage to the seat 41 and an actuator of the seat 41 and / or to the steering wheel 42 and an actuator of the steering wheel 42 to Purpose of the vibration and / or the application of electrical voltage to the display unit 43 for the purpose of activating pixels or light-emitting diodes.
• a currently physiologically recognizable characteristic 46 of the driver 13 is detected.
• the mark 46 may be predetermined, for example as nervousness, Au ⁇ genschul, screaming, sweating or other characteristic actions.
• the physiologically recognizable Flag 46 is a behavior regarding the body of the driver 13.
• the license plate 46 can also be trained as the Kochho ⁇ linformation 15. Based on a comparison of the number plate 46 with a plurality of reference numbers, the overtaking intention 31 of the driver 13 is recognized.
• the indicator 46 can be detected, for example, with the interior camera 44, a microphone 47 or a pulse or respiratory frequency meter, not shown, or a transpiration meter, also not shown.
• the license plate 46 is in particular a degree of nervousness 47 of the driver 13 and / or an eye movement 48 of the driver 13 and / or a noise 49 emitted by the driver 13.
• a semantic significance of the noise 49 output by the driver 13 may also be detected ,
• a statement of the driver 13 with a voice recognition program of the driver assistance device 2 can be recognized as a dirty word.
• the degree of nervousness 50 can be detected, for example, based on the respiratory rate.
• the detection of the driver feature 12 can be done for example by the interior camera 44, the microphone 47, a seat adjustment of the seat 41, a radio receiver or a fingerprint reader.
• the driver feature 12 may also be the driving style of the driver 13 itself or a characteristic Be ⁇ actuation of an operating element of the motor vehicle 1, in particular a turn signal helix, an accelerator pedal or a brake pedal.
• a step Sl the sensor data 10 and the operating parameters 30 are detected. Generally speaking, so it will be Vehicle data, vehicle signals, sensor data and, for example, also receive backend data from a vehicle external server.
• the overtaking information 15 is trained or learned or analyzed.
• the overflow information 15 can then be provided.
• the overtaking information 15 can be transmitted, for example, from the motor vehicle 1 to a vehicle-external device 51.
• the overtaking information is transmitted to the motor vehicle ⁇ external device 51 when it is adjusted during operation of the motor vehicle 1 based on currently detected properties, for example, in the operating parameters 30 properties. From the motor vehicle external device 51, the overtaking information 15 can then be sent, for example, to other motor vehicles.
• the vehicle-external device 51 is thus present, for example, as a computer cloud, which may for example be stationary, distributed or centrally present.
• step S4 the individual overtaking path 34 is calculated.
• step S5 the overtaking intention 31 is recognized.
• the training of the overtaking information 15 preferably takes place with methods of machine learning and artificial intelligence, in particular with neural networks, SVM methods (SVM - support vector machine), deep learning, KNN (KNN-k-nearest-neighbor) or regression.
• the training can take place, for example, within the motor vehicle 1 or else in the vehicle-external device 51.
• the weight of the motor vehicle 1, the motorization of the motor vehicle 1, the physical resistances of the motor vehicle 1 are taken into account as further input variables for calculating the overtaking travel 34.
• the overtaking information 15 can be stored, for example, in a portable vehicle key or as already mentioned in the vehicle-external device 51, so that the driver 13 can take the overtaking information when changing a vehicle, for example when changing to a rental vehicle.
• the Studentsholinformation 15 is also such abstracted by the driver class 14, that a transfer to under ⁇ Kunststofferie vehicles, for example with different equipment, in particular engine types, is possible.
• the adapted overtaking information 15 from a plurality of motor vehicles, so to speak as swarm data is transmitted to the vehicle-external device 51 in order to be distributed from there to other motor vehicles. For example, a future first-time learning of other drivers can be accelerated.

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• 2017
  • 2017-05-15 DE DE102017208159.7A patent/DE102017208159B4/de active Active
• 2018
  • 2018-04-27 EP EP18721753.4A patent/EP3625788A1/de not_active Withdrawn
  • 2018-04-27 CN CN201880047245.6A patent/CN110869993B/zh active Active
  • 2018-04-27 JP JP2019563206A patent/JP7014818B2/ja active Active
  • 2018-04-27 US US16/612,567 patent/US11305776B2/en active Active
  • 2018-04-27 KR KR1020197036891A patent/KR102286674B1/ko active IP Right Grant
  • 2018-04-27 WO PCT/EP2018/060970 patent/WO2018210555A1/de unknown

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