DE102009035072A1 - Motor vehicle collision warning system has a transceiver working with a transponder at the object in the path, e.g. a pedestrian, to determine its position and predict its future position - Google Patents

Abstract

The motor vehicle (300) has a system to predict the position and/or movement of an object (210) in the path, e.g. a pedestrian. The actual position of the object is detected by a transceiver (121) and a transponder in a unit (110) at the object. The movement data gathered from the sensor assembly at the object allow its position and predicted movement to be assessed in relation to the vehicle.

Description

The The present invention relates to a method for prediction the position or the area of residence and / or the movement an object or vulnerable road user, for example a pedestrian, relative to a vehicle, wherein the current relative position of the object to the vehicle for prediction is used, which by locating a object side arranged transmitter / receiver unit determined by a vehicle-side transceiver unit becomes.

The generic methods are used, forthcoming Collisions of a vehicle with other vulnerable road users like pedestrians, cyclists or even animals like To predict dogs. So are the drivers of collision vehicles if necessary, the road users before an impending collision be warned in time or according to the collision risks or heavy countermeasures such as full braking or deployment of airbags for pedestrians early be initiated. This should primarily collision accidents be avoided with vulnerable road users and second Line the accident severity in collisions are reduced.

Further The present invention relates to device subunits for Performing the method according to the invention.

method or devices for the prediction of the position and / or Movement of an object or a pedestrian relative to a vehicle using imaging sensors have been around for some Known years ago.

to Avoiding accidents with pedestrians, Cyclists or animals in traffic become vehicles equipped with imaging sensors such as cameras, which the road permanently monitored in vehicle direction. For example, a Pedestrian captured by the camera, facing the vehicle approaches dangerously, the driver is warned or autonomous braking maneuvers initiated.

disadvantage Such procedure is that the road users, by the parked cars, trees or light poles are obscured, due to interrupted line of sight to the vehicle from the camera can not be recorded. This leads to, that the camera-based pedestrian protection system in imminent collision with concealed road users not or react too late and so not reliable Offers protection.

A solution without the above disadvantage offers a in the Technology Conference: "Workshop Driver Assistance Systems 2008" in April 2008 presented procedure: "Pedestrian protection by means of cooperative sensors" , According to this method, the road users are detected like pedestrians by means of cooperative sensors. Accordingly, the road users carry an active or passive RFID transponder whose relative position to a vehicle is determined by a transponder detection device arranged in the vehicle. The transponder detection device receives signals from the transponders located in the vicinity of the vehicle and evaluates these signals. Signal transit time measurement determines the distance from the vehicle to each transponder or the pedestrian carrying this transponder. With the aid of the multiple antenna system of the transponder detection device, the azimuth angle of the transponder or pedestrian with respect to the vehicle's longitudinal axis is also determined. As a result, the current position of each individual transponder or pedestrian carrying this transponder can be determined relative to the vehicle, even when the line of sight is interrupted, simply, clearly and reliably.

at Prediction of a future position or movement the vulnerable road user encounters that above method with cooperative sensors, however, at its limits. To take a position of the pedestrian to a future Predict time, several must succeed each other the following, current positions are recorded. From this plurality of current and past position values can then be a future Position are derived by means of time-consuming calculation steps. This causes an imminent collision with pedestrians in some cases not predicted in time can be.

One Another known method for the prediction of the position and / or movement of the pedestrian with imaging Sensor technology by evaluating the change in behavior such as changing the viewing angle of the pedestrian also proved due to the too large amount of time in image processing and too many uncertain influencing factors as hard to realize and also unreliable.

The The object of the present invention is to provide a method which has none of the above-mentioned disadvantages and thus in the sense maximum effectiveness and minimum false triggering rate optimal solution for pedestrian protection.

This object is achieved by a method having the characterizing features according to the patent claim 1. In accordance with the invention, movement-related data of the vulnerable objects participating in road traffic, such as pedestrians, are determined on the basis of the sensors arranged on the object, eg. a motion sensor, detected and transmitted from the object-side transceiver unit to the vehicle-side transceiver unit. From this movement data, the future positions of the object are then predicted at one or more points in time.

The "movement related Data "in this invention, in addition to the information about the dynamic or kinematic properties of the object such as z. As the speed, acceleration, braking and / or rotation, Advantageously, and depending on the version, the information about the posture and the orientation of the object such. B. the diffraction, tilt and / or orientation of the object to Direction.

Of the Term "object" sums up in this document all non-motorized or weak motorized, "vulnerable" road users, namely all road users except motor vehicles with protective body together. The term "vulnerable" comes therefore, that the un-motorized or poorly motorized road users like pedestrians or motorcyclists due the lack of protective body in collisions with motor vehicles particular risk of injury. Count among the objects thus all pedestrians, especially children, cyclists, Wheelchair users, small cyclists, motorcyclists and even animals like z. B. dogs and bicycles. Motorcycles, wheelchairs, Roller skates, etc.

to simple illustration of the invention are all vulnerable below Road users by pedestrians or cyclists designated. However, that does not mean that the present invention limited only to pedestrians or cyclists. For the sake of simplicity, the vulnerable road users are grouped together also the object genant.

The Invention is based on the idea that pedestrians, especially children (including animals) as road users in comparison to Vehicles have relatively high maneuverability and accordingly their future position or movement very much difficult to predict.

A method of assigning each pedestrian a static, mostly circular lounge area as a possible future location and based on this to determine collision risks with the vehicles, assuming that the pedestrian can move in all directions at a fixed speed, resulted from too large potential lounge areas pedestrians often give false collision warnings and even endanger traffic (cf. 4 ).

This is physically justified by the fact that the essential information for the prediction of the future Position of a pedestrian in the change the speed of the pedestrian or his Stirring rate stuck.

Around the future position of the pedestrian more accurately to predict, the idea arose, in addition to consideration the current position of the pedestrian also the current state of motion of the pedestrian with included. The current state of motion becomes the first Line with the help of a pedestrian Motion sensor determined. The thus determined motion data give, for example. Information about whether the pedestrian stops will or will continue.

advantageously, Other motion data such as the acceleration, the rotation rate of the Pedestrian, the orientation of the pedestrian to Direction of the compass with the help of the pedestrian arranged sensors determined. These data provide further information to the state of motion of the pedestrian. In connection with the current position data can be so the potential future position of the pedestrian with predict significantly higher accuracy.

Preferably, the accelerations of the pedestrian are measured both in its longitudinal axis, transverse axis and in its vertical axis and used for prediction. From the measured values of the longitudinal, transverse or vertical acceleration a _x , a _y and a _{z of} the pedestrian, two- or three-dimensional acceleration vector a → = (a _x , a _y ) ^T or a → = (a _x , a _y , a _z ) ^T formed.

gemessen und in Form eines zwei- oder dreidimensionalen Drehratenvektors ω → = (α . , β . )^T, ω → = (β . , γ . )^T oder ω → = (α . , β . , γ . )^T zur Prädiktion herangezogen. Je nach Ausführung wird auch nur die Drehbewegung um die Vertikalachse γ gemessen und zur Prädiktion herangezogen.Accordingly, from the rotational movements of the pedestrian about the vertical axis γ. or diffractions or inclination of the pedestrian about the transverse axis β. if necessary, also the lateral extensions or inclination of the pedestrian about the longitudinal axis

measured and in the form of a two- or three-dimensional yaw rate vector ω → = (α., β.) ^T , ω → = (β., γ.) ^T or ω → = (α., β., γ.) ^T for prediction used. Depending on the design, only the rotational movement about the vertical axis γ is measured and used for prediction.

From the movement data can also predict the future state of motion of the pedestrian. Thus, for example, from a current running speed of 1 m / s, an actual acceleration of 1 m / s ² , a yaw rate of 0 ° / s from a pedestrian and its orientation north, one can predict with a high probability that he is in one second with a running speed of 2 m / s and acceleration of 1 m / s ² and a rate of rotation of 0 ° / s continues to move north. Combine the current Be motion data with the current position data, it can be predicted that the pedestrian is in a second with a high probability at 1.5 m north of the current position.

In Road traffic is not just pedestrians considered as a potential collision risk with vehicles that are exposed to serious injury risks. Also cyclists, Wheelchair users, roller skaters or pets become like dogs in collisions with motor vehicles also as potential accident victims seen with serious injury risks. These different vulnerable road users have different physical or physiological properties and are thus different from each other in terms of their physical or physical movement characteristics to look at separately. Belong to the pedestrians including children, adults, the elderly, who are also in terms of their physical or physical physical properties differ.

• – die Art des Objekts,
• – das physiologische bzw. physikalische Bewegungsvermögen des Objekts, darunter
• – das Gewicht,
• – die Höhe,
• – die maximale Geschwindigkeit,
• – die maximale Beschleunigung,
• – die maximale Drehrate,
• – das Bewegungsmuster des Objekts, nämlich typische Bewegungsfolge eines Verkehrsteilnehmers, bspw. Beugen des Oberkörpers vor dem Loslaufen bei einem Kind.

Advantageously, therefore, a so-called object model is used for prediction according to the invention. As an "object model" is here a parameter set to be understood, which is assigned to each traffic participant (namely, any vulnerable road user) and contains the following physical or physiological characteristics of the traffic participant in the form of parameters. These properties include

• - the type of object,
• - The physiological or physical mobility of the object, including
• - the weight,
• - the height,
• - the maximum speed,
• - the maximum acceleration,
• The maximum rate of rotation,
• - The movement pattern of the object, namely typical sequence of movements of a road user, eg. Bending of the upper body before starting in a child.

The Type of object gives information about which one Road user, namely, whether the object an adult, a child, an old person with a walker or a child with roller skates, a bicycle, a wheelchair or a pet like, for example, a dog.

Out The type of object can be the maximum radius of the object Range of motion of the object, the maximum achievable speed, Roughly determine acceleration or rate of rotation, etc. So is, for example, in Regarding the same observation period, the radius of the range of motion a pedestrian with walker much smaller as that of a child with roller skates.

There however, the values of the maximum movement radius, the maximum speed etc. even in the same object group from object to object strong can be different, more parameters are in Form of physiological or physical mobility of the object is added to the parameter set of the object model. These Parameters include weight, body height, the maximum speed, the maximum acceleration and / or the maximum rate of rotation of the object. Depending on your needs can also added other parameters such as body width become. Taking into account the legal regulation can provide more information such as chronic diseases, the reaction or mobility of the pedestrian affect, also in the form of parameters to the object model to be added. These parameters are more accurate than the roughly estimated parameter values from the type of object and thus for the estimation of the physiological or physical mobility more meaningful. On the basis of these parameters can be, for example, an overweight (thus less mobile) child from a normal weight differ.

additionally the movement ability of the object becomes the movement pattern of the object added to the object model. The movement patterns, namely the typical sequence of movements of a road user, give information about which movement changes follow the currently detected movement changes. prevents For example, a child his upper body (it changes the value of the yaw rate about the transverse axis β. ), that's how it works predict with a high probability that the child is the same will start running.

The Parameters for the object model can be used at startup the object-side device subunit are manually adjusted. Is the object-side device subunit, for example, integrated in a mobile phone, so these parameters can be easily accessed via the mobile phone keypad enter. If the object-side device subunit is, for example, in integrated into a satchel, it is integrated by means of an integrated Interface connected to a computer. About keyboard input on the computer, the above parameters can then be set become. Changes the value of one or the other parameter like For example, the body weight of the pedestrian Over time, the corresponding parameter is updated manually.

From the parameters of the object model and the currently measured movement data (depending on the model also from the temporally immediate movement data) of the object are advantageously leading indicators for the prediction of the future position and / or movement of the Ob determined. Based on the current position of the object, the future position of the object can be predicted using the leading indicators. Leading indicators are understood here to be characteristic changes in the posture or in the state of movement of the object, which indicate a change in the movement or direction of movement.

The Features of an early indicator thus obtained leave one safe prediction of the movement of the road user already in the early prediction phase, while For example, an image-based prediction method according to Prior art even a significant change in behavior of the Could not identify the road user.

each Position and / or movement of a road user to a given Future time will ideally be associated with one Position or movement probability predicts. The future position is preferably in the form of a Stay area with an associated position or Probability of residence predicts.

Preferably the inventive method using at least two device subunits performed, which are arranged separately in the vehicle and on the object. Both Device subunits each have a transceiver unit up and communicate by using wireless data communication between these two transceiver units to each other and exchange data. The vehicle-mounted transceiver unit locates the object-side transceiver unit by means of electromagnetic wave. Preferably, the object-side transceiver unit an active or passive RFID transponder and the vehicle side a transponder detection device with multiple antenna system. The object-side device subunit has a motion sensor, an acceleration sensor for measuring the translational acceleration of the object, a rotation rate sensor for measuring the rotational movement of Object around its vertical axis and / or a magnetic compass sensor for detecting the orientation of the object to the direction of the compass.

Advantageously, by the above-mentioned sensors at the location of the transponder all six kinetically relevant parameters, namely the longitudinal, lateral or vertical acceleration a _x , a _y and a _z and the rotation about the vertical axis γ. to the transverse axis β. or about the longitudinal axis α. , the object movement and the orientation of the object to the direction of the compass. For simplified execution, only a part of the kinetic parameters can be used.

The Transponder detection device in the vehicle determines the in the Vehicle environment located transponder and determines the distance and Azimutwickel each transponder to the vehicle, preferably in relation to the vehicle's longitudinal axis. The radial distances the transponder to the transponder detection device or to the vehicle are measured by the signal propagation time measurement. The azimuth angle be using the multiple antenna system of the transponder detection device determined.

Out the distances and azimuth angles determined by the vehicle side Device subunit the current position of each individual transponder and thus every single transponder carrier, ie object relative to the vehicle. Further, the on-vehicle device subunit transmits Wake-up signals to the tracked transponder or object-side device subunits and stimulates these, by means of the various, arranged on the object Sensors motion data such as the speed, acceleration, Rate of rotation and / or orientation of the object to the direction of the sky measure and the measured data together with the object model parameters as well Identification code to the vehicle-mounted device subunit to send. The on-vehicle device subunit authenticates the transponders by checking the identification codes and determines from the received data leading indicators for the Objects. Alternatively, the leading indicators can also determined on the object by the object-side device subunit and then sent to the on-vehicle device subunit become.

The Vehicle-side device subunit then predicts from the leading indicators and the current positions of the Objects as well as the vehicle dynamics data of the future vehicles Positions of the objects relative to the vehicle to given future Times. The driving dynamics data of the vehicle can for example, the wheel speed, the steering wheel angle, the brake pressure, etc. be and preferably derived from the electrical stability system become.

Advantageously, the vehicle-mounted device subunit calculates a driving tube for the vehicle from the vehicle dynamics data. For each given point in time, the device subunit in the travel tube calculates an area as the location area of the vehicle at the given point in time and compares it with the predicted position or the predicted location area of the object at said time. If the two areas of residence of the vehicle and of the object overlap, an imminent collision is considered to be detected and accordingly measures are taken to avoid the collision. Are the two Aufenthaltsberei Close to each other, an increased risk of collision is predicted.

In a little critical case, the driver is facing a possible Collision by means of an optical, acoustic or haptic signal warned. If the driver does not react or the situation becomes more critical, For example, a full braking can be initiated. In unavoidable Collision will be measures to reduce the consequences of accidents causes. It may, for example, the hood in the direction of possibly bouncing object raised in advance or, should airbags for pedestrian protection bspw. be installed in the vehicle front area, airbags are deployed. The movement data or the parameters of the Object model, such as the type, the speed, the body height, the weight of the object for the adaptive pedestrian protection measure be used, which in the course of the invention Prediction of the object-side transmitting / receiving unit has been transmitted to the vehicle-side transmitting / receiving unit are. So the hood can be the weight or the height of the object can be raised or adjusted adaptively the airbags for pedestrian protection also adapted adaptive be unfolded. This will minimize the consequences of accidents and thus achieving optimal pedestrian protection.

thanks the determination of the movement data directly at the road user can be the movement-related leading indicators even with interrupted line of sight between the vehicle and the road user and used for prediction become. By detecting the leading indicators directly on the road user can characteristic movement changes reliably and over a longer period and without temporal Delay to be detected. The so-called leading indicators are much more reliable than the indirect image-based and on the vehicle identified leading indicators. This allows the inventive method a much earlier and reliable detection of impending collisions with vulnerable road users as the image-based method, and thus offers an optimal solution for pedestrian protection in the sense of maximum effectiveness and minimum false triggering rate.

The Object-side device subunits can be used in cell phones, Clothing, especially safety vests, shoes, bags, accessories such as Handbags, belts, watches, spectacles, bicycles, Bicycle helmets, cycle computers, scooters, roller skates, pushchairs, Satchels, wheelchairs, walking aids, walking sticks, Pet Collars etc are integrated.

The Components of the object-side device subunit, such as Transmitter / receiver unit, the acceleration, yaw rate, magnetic compass sensors, as well as the energy source like batteries can be in a closed Housing to be installed. Alternatively, the Components also separated from each other in the same carrier arranged like a garment or bicycle become.

The Power supply for the object-side device subunit including transponder and sensors can be installed with interchangeable Button batteries or by solar cells are ensured. alternative can be the kinetic generated by the motion of the wearer Energy can be used to power. Is the object-side Device subunit in already existing portable electronic devices (eg mobile phone or iPod), the device subunit can with the current of those in these electronic devices already integrated batteries or batteries are supplied.

In the use of the object model mentioned above, in particular the parameters Concerning the nature of the object, however, there is a danger that the object is incorrectly verified. To give an example a father the satchel of his child with an integrated object-side Device subunit, so the father can not be considered an adult Pedestrians with a lower maneuverability, but falsely as a child with a higher one Maneuverability verified. Or a pedestrian, his bicycle with a built-in object-side device subunit pushes is falsely called a cyclist with high verified potential speed. Furthermore you can Misuse cases (cases of abuse) occur, wherein For example, the object-side device subunits of the carriers For example, children simply throw them on the street. aid The data of the object-bound sensors can be checked whether it is the movement of the object-side device subunit, a realistic movement of a road user, especially a pedestrian. Thereby can such misuse cases or incorrect verifications excluded by the use of object-bound sensors and False triggering in the vehicle can be avoided.

The use of object-bound sensors can thus additionally serve to verify the object. The sensors carried along by the object and the measurement data available with them can be used to verify a running movement that is characteristic, for example, of pedestrians or animals. Thus, the sensor data in the above examples can be used to check the object type determined by the parameters of the object model. If the sensors provide measurement data that is typical for a walking movement of an adult pedestrian, the pedestrian with the satchel of his child, despite the parameters of the object model, becomes the object integrated in the satchel side device subunit not verified as a child, but as an adult pedestrian. Likewise, the cyclist pushing his bike is properly verified based on the sensor measurement data as a pedestrian. If the motion data of a typical throwing curve indicating the discarding of the transponder is correct, followed by movement data indicating that the transponder is motionless, it is highly likely that the transponder will be lost or misused. As a result, misuse cases or incorrect object verification can be largely excluded.

in the The invention will be described below with reference to the exemplary embodiments explained in more detail with the aid of figures. For a simplified description of the invention, the inventive Device subunits and the items shown in the figures like vehicle, pedestrian, street etc. shown schematically and simplified. Show,

1 a road image in plan view, illustrating in general how an impending collision between a vehicle and a pedestrian is predicted by means of the method according to the invention,

2 a detailed representation of the vehicle-mounted device subunit for carrying out the method according to the invention,

3 a detailed representation of the object-side device subunit for performing the method according to the invention,

4A . 4B in two examples, the inventive prediction of the position or movement of a pedestrian relative to a vehicle,

5 an example of the prediction of the position or movement of the pedestrian without the use of movement-related leading indicators,

6A . 6B in another two examples, the inventive prediction of the position or movement of a cyclist or a pedestrian relative to a vehicle, and

7 . 8th in each case a detailed example, the inventive prediction of the future position of a cyclist based on the current movement data and in conjunction with associated probabilities of residence.

The 1 shows a section of road 500 with a passing vehicle 300 that with an inventive vehicle-mounted device subunit 120 equipped, and a pedestrian 210 , which is an inventive object-side device subunit (transponder with integrated sensors 113 . 114 . 115 . 116 ) 110 carries with itself.

The pedestrian 210 is currently running between two consecutively parked vehicles 411 . 412 in the street. Through the parked vehicle 411 is the line of sight 800 between the approaching vehicle 300 and the pedestrian 210 interrupted. The raceways of the vehicle 300 and the pedestrian 210 Cross in the middle of the street 600 and there is thus a risk of collision between these two road users 210 . 300 ,

The vehicle-side device part unit 120 for carrying out the method according to the invention comprises according to 2 a computing / control unit 122 and a transmitting / receiving unit 121 with a transmitting antenna 1212 and a receiving unit 1213 consisting of a multiple antenna system with multiple antennas 1214 is trained on. The transmitting / receiving unit 121 sends over the transmitting antenna 1212 Interrogation signals in the vehicle environment. At first, interrogation signals are sent to all potential transponders in the area 110 Posted 901 , The transponders 110 that are in the environment of the vehicle 300 are stimulated and send by the interrogation signals 110 in turn, response signals to the vehicle-side transmitting / receiving unit 121 , These response signals preferably contain the identification number of the transponder 110 which causes the transponders 110 can be clearly distinguished from each other and the parameters of the object model of the object 210 . 220 that the transponder 110 carries with it, and the sensor data from different in the transponder 110 integrated sensors 113 , ..., 116 were measured. The response signals are from the antennas 1214 of the multiple antenna system 1213 receive 902 ,

The signal processing unit 1211 then calculates between the transmission based on the signal propagation time 901 and receiving 902 the signal and the signal velocity the radial distance of each individual transponder 110 to the vehicle 300 , By comparing the signal levels of several antennas 1214 of the multiple antenna system 1213 repeatedly received response signal 902 every single transponder 110 determines the signal processing unit 1211 then the azimuth angle of each transponder 110 to the vehicle longitudinal axis. In the signal processing unit 1211 depending on the design filtering, modulation, demodulation, analog / digital or digital / analog conversion and coding / decoding, etc. take place to generate the interrogation signals or from the response signals from the transponders 110 transmitted identification number, object model parameters and sensor data to win. Depending on the off leadership is in this signal processing unit 1211 a first selection of the transponder 110 performed according to their distance and azimuth angle to the vehicle 300 in the risk area. As a result, especially the transponder 110 or the road users 210 . 220 excluded from the further collision risk assessment, eg behind the vehicle 300 reside or in an area where there are no collisions between these transponders 110 and the vehicle 300 possible are. Preferably takes place in the signal processing unit 1211 also the authentication of the transponder 110 on the basis of the transponders 110 transmitted identification number instead.

The computing / control unit 122 contains a data acquisition unit 1221 , a unit for data fusion and tracking 1222 , a risk calculation unit 1223 and a decision-making unit 1224 to trigger the corresponding warning signals when detecting a collision hazard.

The data acquisition unit 1221 decomposes the received data packets from those in the signal processing unit 1211 already preselected transponders 110 in corresponding sensor data and object model data and passes this data into the data fusion and tracking unit 1222 further. The data fusion and tracking unit 1222 then determines from the parameters of the object model and the sensor data the type of the transponder carrier (namely the road user) 210 . 220 and extrapolated based on this data and based on the current positions of the transponder 110 relative to the vehicle 300 the potential residence area of the road user 210 . 220 at given future times. In this case, preferably first leading indicators are determined. Preferably, the predicted location areas are provided with associated probabilities.

It also determines the data fusion and tracking unit 1222 on the basis of the components of the electronic stability system 310 transmitted vehicle dynamics data of the vehicle 300 the driving hose of the vehicle 300 or the occupied areas of the vehicle 300 at the same future times.

Optional determines the data fusion and tracking unit 1222 from the sensor data of the imaging sensor on the vehicle 300 like vehicle front camera 331 The current position data of the detected road users 210 . 220 and compares these data with those using the multiple antenna system 1213 determined position data. This increases system reliability even more.

In the risk assessment unit 1223 become the in the data fusion and tracking unit 1222 determined potential areas of residence of the vehicle 300 and the road user 210 . 220 compared to each other. Overlap the area of residence of the vehicle 300 with a residence area of a road user 210 . 220 at a future time, an imminent danger of collision is predicted. Furthermore, risk assessments of possible collisions taking into account the probabilities of residence of the predicted residential areas of the road users 210 . 220 . 300 carried out. At the same time, the vehicle dynamics data such as speed, yaw rate or weight of the vehicle are optional as well 300 used. On the basis of these data, it is estimated or estimated how likely a collision will be and how serious the collision or the injury of the road user involved in the collision 210 . 220 can be. For estimating the collision severity or the potential injury, the parameters of the object model and the sensor data of the object-side sensors also become 113 . 114 . 115 . 116 used.

The result of the risk assessment is in the form of, for example, collision risk values, accident severity values or injury risk values to the triggering decision unit 1224 forwarded. The trigger decision unit 1224 then selects the calculated collision risk values, accident severity values or injury risk values in accordance with the appropriate countermeasure to avoid the collision or to reduce the consequences of an accident. Among the measures include, for example, acoustic, visual or haptic warning signals for the driver of the vehicle 300 Adjusting the vehicle speed by interrupting the fuel supply, autonomous braking maneuver, adjusting the parameters for lifting the hood or unfolding the airbags for pedestrian protection, etc ..

Optionally generates the triggering decision unit 1224 a warning signal for the road user 210 . 200 , This warning signal can, for example, acoustic horn or flashing lights on the vehicle 300 be. Advantageously, but this warning signal on the transmitting antenna 1212 to the transponder 110 the affected road user 210 . 220 Posted 901 , Receives the transponder 110 such a warning signal from the vehicle 300 so does the transponder 100 by emitting, for example, an audible warning tone or by vibration the road user 210 . 220 from a dangerously approaching vehicle 300 or a risk of collision with the vehicle 300 attentively.

The object-side device subunit 110 according to 3 a transmitting / receiving unit 111 that with the vehicle-side send / receive unit 121 communicates via electromagnetic wave and exchanges data 903 . 904 and in the form of a transponder, a control unit 112 , one movement each 113 , Acceleration 114 , Rotation rate 115 and magnetic compass sensor 116 on. Furthermore, the device subunit 110 a memory 117 and a synchronization unit 118 for synchronizing the data communication.

The send / receive unit 111 has an antenna 1111 , an antenna switch 1112 known type for switching between the transmission and reception mode or transmission / reception frequencies of the antenna 1111 , a signal receiver 1113 and a signal transmitter 1114 , Preferably, the transmitting / receiving unit 111 Directional couplers and circulators for the separation of transmit and receive signals.

By means of the sensors, in particular inertial sensors 113 . 114 . 115 . 116 detects the object-side device subunit 110 the current movement data of the transponder carrier 210 ,

Depending on the design and quality requirements, the acceleration sensor detects 114 or the rotation rate sensor 115 the accelerations or the rotational movements of the road user 210 . 220 three, two, or one-dimensional.

By three-dimensional measurement of the acceleration can be primarily the orientation of the acceleration sensor 114 or should the sensors 113 . 114 . 115 . 116 and the control unit 112 as well as the transmitting / receiving unit 111 be installed in one and the same housing, then the orientation of the whole object-side device subunit 110 be recorded. The detection of the orientation of the acceleration sensor 114 or the device 110 is so important because of the acceleration sensor 114 or the device 110 by the carrier or road user 210 . 220 is constantly placed in different positions and so for the same acceleration movement of the road user 210 . 220 provides different measured values.

To give an example, the acceleration sensor becomes 114 thus the object-side device subunit 110 integrated in a mobile phone that is the pedestrian 210 leads with it, so the sensor can 114 eg when calling from the pedestrian 210 held in an upright position and after telephoning, for example, be brought in a bag in a horizontal position. The sensor delivers in these different positions 114 even when the pedestrian is steadily accelerating 210 different acceleration values.

This is how the acceleration sensor delivers 114 in the upright position, a first three-dimensional acceleration value a → ₁ = (a _x1 , a _y1 , a _z1 ) ^T. After telephoning is the pedestrian 210 his phone horizontally in his pocket. Now, the acceleration _sensor provides another three-dimensional acceleration _value a → ₂ = (a _x2 , a _y2 , a _z2 ) ^T.

Although the pedestrian 210 has uniformly accelerated over the entire time, the measured values of the two three-dimensional acceleration vectors - when viewed in pairs relative to the respective sensitivity or measuring axes - deviate strongly from one another. Thus the two measured values a _x1 and a _x2 for x-axis, a _y1 and a ₂ for y-axis, a _z1 , and a _z2 for z-axis are _different .

This is due to the fact that the orientation of the acceleration sensor 114 or the sensitivity axes of the acceleration sensor 114 changed over time. Therefore, it is necessary to align the sensors 113 . 114 . 115 . 116 always update.

The orientation of the sensors esp. The inertial sensors 113 . 114 . 115 . 116 is advantageously based on the acceleration sensor 114 For example, in a resting state of the pedestrian 210 is determined, forming a vector sum a → from three measured vector components a _x , a _y , a _z . If the magnitude of the vector sum a → approximately the value of the gravitational acceleration of gravity 9.81 m / s ² , then the direction of the vector sum a → shows the direction of the gravitational force. In combination with the measured values of the magnetic compass sensor 116 From the direction of earth gravity, the orientation of the acceleration sensor can be determined 114 determine.

The rotational movement of the pedestrian 210 will also be recorded three, two or one dimensional depending on the design and quality requirements. The rotational movement about the vertical axis γ. of the pedestrian 210 Like the lateral acceleration a _y gives information about whether the pedestrian 210 his direction changes. The rotational movement of the pedestrian 210 around its transverse axis β. namely, the inclination of the pedestrian 210 is, for example, an indicator that the pedestrian will immediately start running.

The object-side device subunit 110 is powered by an integrated energy store (power source) 119 For example, one or more of kinetic (movement) energy of the pedestrian 210 automatically rechargeable battery cells is powered. Thus, the device subunit 110 in the 3 as an active transponder with several integrated sensors 113 , ..., 116 to be viewed as.

The object-side device subunit 110 is preferably equipped with energy-saving mode. For example, the device subunit 110 put in a power-saving idle state as long as the unit 110 no signal from the outside receives and the motion or acceleration sensor 113 . 114 no movement or acceleration of the pedestrian 210 registered.

Receives the send / receive unit 111 Signal from the outside 904 , so it sends a wake-up signal 1191 to the power source 119 and sensors 113 , ..., 116 and more units 117 . 118 , Alternatively, the device subunit 110 from the motion or acceleration sensor 113 . 114 "to be woken up. Registers the motion or acceleration sensor 113 . 114 Movement or acceleration of the pedestrian 210 , so he sends wake-up signal 1192 . 1193 to the power source 119 and more units 111 . 112 . 115 , ... 118 ,

The object-side device subunit 110 also has a memory 117 in, in the identification code of the device subunit 110 and parameters of the object model for the transponder carrier 210 . 220 are stored. The parameters of the object model can during the operation of the transponder 110 be updated manually. For this purpose, the device subunit 110 optionally an interface with which the device subunit 110 For example, with a computer can exchange data.

The object-side device subunit 110 Depending on the version, it is optional with a synchronization unit 118 fitted. The synchronization unit 118 serves to protect the time to synchronize the signal transmission between the vehicle-side and the object-side device subunits 110 . 120 to provide in time-division multiplexing. Alternatively, the data communication may also be accomplished in code division multiplexing or frequency division multiplexing. In this case, the vehicle-side and object-side device subunits 110 . 120 corresponding components for performing code division multiplexing or frequency division multiplexing.

The 4A . 4B each show an example of how an imminent collision or an uncritical situation can be clearly predicted by means of the method according to the invention. The two 4A . 4B show two similar street pictures like the 1 , In the street 500 a vehicle approaches 300 in Westeast direction. Between two parked vehicles 411 . 412 a pedestrian is walking 210 , where the pedestrian 210 in the 4A North or direction to the road 500 in the street 500 runs in and the pedestrian 210 in the 4B South or back to the road 500 from the street 500 away runs. We assume that the two pedestrians 210 in both 4A . 4B moved at the same speed v0 and acceleration a0. The orientation of the pedestrian 210 in the 4A pointing to the north and the pedestrian 210 in the 4B to the south. These motion data such as speed v0, acceleration a0 and orientation to the direction of the compass are from the sensors 113 , ..., 116 the device subunit 110 that the pedestrian 210 carries, measured and to the vehicle-mounted device subunit 120 transfer. The device subunit calculates from these data 120 Early indicators of the state of motion of the pedestrian 210 , Based on the current position of the pedestrian 210 determines the device subunit 210 from the leading indicators and the vehicle dynamics data 300 the future positions or areas of residence of the vehicle 300 and the pedestrian 210 at one or more predetermined times.

Thus, at a time t1, a position or a residence area respectively 302 for the vehicle 300 and a location 212 for the northbound pedestrian 210 predicted that partially overlap 601 (please refer 4A ). The overlap area 601 indicates an impending collision of the vehicle 300 with the pedestrian 210 out. Consequently, the computing / control unit sends 122 the vehicle-side device subunit 120 a control signal to the pedestrian protection system 320 and this causes it to take appropriate action.

Unlike the example in the 4A lie the predicted positions or areas of residence 302 . 214 of the vehicle 300 and the pedestrian 210 when in the 4B shown separated example. Although the pedestrian 210 dangerously close to the road 500 or to the driving tube 301 of the vehicle 300 there is no risk of collision in this case, since the pedestrian 210 clearly from the street 500 or from the driving tube 301 and thus also from the predicted area of residence 302 of the vehicle 300 moved away. The established leading indicators such as rate of rotation also do not indicate a intention of the pedestrian 210 to turn around and into the street 500 inside run.

From the examples in the 4A . 4B the advantages of the method according to the invention are clearly visible. By using the movement data from the pedestrian 210 , with this movement data directly on the pedestrian 210 can be captured, the future position of the pedestrian 210 Predict at a given time with a high accuracy.

An inaccurate and overly predicted up enthaltsbereich 216 of the pedestrian 210 with the assumption that the pedestrian 210 Moving in all directions at the same speed or acceleration, often leads to a false prediction of an imminent collision, as in the 5 is illustrated.

Although the pedestrian 210 away from the street 500 moved away and obviously not with the vehicle 300 will collide, due to the lack of information on the state of motion - eg. The direction of movement 213 - of the pedestrian 300 falsely an overlap 602 the future living areas 302 . 216 of the vehicle 300 and the pedestrian 210 determined. Based on this, a false collision is predicted and unnecessary even dangerous pedestrian protection measures are initiated to avoid an alleged collision.

The 4B . 5 show the differences between a motion-related prediction and a prediction without using the motion data of the road user 210 and thus the advantages of the prediction according to the invention clearly.

The 6A . 6B show further examples of the prediction according to the invention. In both 6A . 6B is a section of an east-west main street 500 with a south in this main street 500 merging side street 510 displayed. In the main street 500 a vehicle approaches 300 from the west with the direction east. From the side street 510 a cyclist drives 220 ( 6A ) or runs a pedestrian 210 ( 6B ) from the south to the north in the main street 500 into it. The line of sight of the vehicle 300 to the cyclist 220 or pedestrian 210 is through the trees 420 interrupted.

The cyclist 220 has higher speed than the pedestrian 210 , Consequently, the predicted area of residence according to the invention 222 of the cyclist 220 much larger than the living area 218 of the pedestrian 210 , The predicted area of residence 222 of the cyclist 220 is partly in the same area predicted for the same time 302 of the vehicle 300 , This indicates an impending collision of the cyclist 220 with the vehicle 300 out. Corresponding measures to avoid collision must be initiated. The predicted area of residence 218 of the pedestrian 210 By contrast, it is far away from the common area 302 of the vehicle 300 , This means that there is no risk of collision between the vehicle 300 and the pedestrian 210 consists. The vehicle 300 may therefore pass unchecked.

The 7 . 8th show the prediction according to the invention in detail. According to the 7 become the movement data of the current state of motion of the cyclist 220 such as speed, acceleration, yaw rate, etc. for predicting the future position of the cyclist 220 used. Thus, for example, becomes a possible residence area 2221 of the cyclist 220 at a given time t1 when a current speed of v1 = 2 m / s, acceleration of a1 = 0 m / s ² and rate of rotation of ω1 = 0 ° / s predicted. At the same speed of v1, a higher acceleration of a2 = 2 m / s ² and the same rotation rate of ω1 becomes a comparatively larger residence area 2222 predicted. At a higher velocity of v2 = 4 m / s, an acceleration of a1 = 0 m / s ² and yaw rate of ω1 becomes an even larger residence area 2223 predicted.

The 8th shows the predicted areas of residence 222A . 222B . 222C . 222D . 222E . 222F of the cyclist 220 with the associated probability of residence at a given point in time t1 for current movement data: v1 = 2 m / s, a2 = 2 m / s ² , ω1 = 0 ° / s. For prediction, the parameters of the object model, for example, the weight of the cyclist 220 used together with the bike. Accordingly, the cyclist is 220 at the time t1 with, for example, a probability of 80% in the range 222C , each with a probability of 5% in the range 222B or 222D , With a residual probability of 10%, the cyclist could be in one of the areas 222A . 222E . 222F or in the area outside the predicted area of residence 222 lie. The area 222C Therefore, it is predicted with such a high probability of stay, because the parameter of the object model esp. With respect to the weight of the cyclist 220 indicates a moment of inertia, which prevents a rapid change of direction or abrupt braking of the bicycle.

For even more accurate prediction of the position of the cyclist 220 can get the environmental information around the cyclist 220 be used. The residence probabilities for the predicted lounge areas may then be altered according to the surrounding information. Is, for example, in the area 222D a tree, so may the probability of residence for this area 222D lower to zero, since it is assumed that the cyclist 220 do not drive against a tree.

The environment information includes, for example, road surface information, infrastructures in the vehicle environment, weather, etc. This information is taken from the on-vehicle device subunit 120 for example, the geographical navigation data or report the weather stations.

LIST OF REFERENCE NUMBERS

110

object-sided Device subunit, transponder with integrated sensors 113, 114, 115, 116

111

Transmit / receive unit the object-side device sub-unit 110

1111

Transmitting / receiving antenna

1112

antenna Combiner

1113

signal receiver Rx

1114

signal transmitter Tx

112

Computing / control unit the object-side device sub-unit 110

1121

identification examiner

1122

signal modulation and demodulation unit

1123

data mixer

113

motion sensor

114

accelerometer

115

Yaw rate sensor

116

Magnetic compass sensor

117

Storage

118

synchronization unit

119

power source

1191

wake-up from the transmitting / receiving unit 111

1192

wake-up from the motion sensor 113

1193

wake-up from the acceleration sensor 114

120

Onboard Device component unit

121

Transmit / receive unit the on-vehicle device subunit 120

1211

Signal processing unit the on-vehicle transmitting / receiving unit 121

1212

transmitting antenna the on-vehicle transmitting / receiving unit 121

1213

Multiple antenna system the on-vehicle transmitting / receiving unit 121

1214

receiving antenna the on-vehicle transmitting / receiving unit 121

122

Computing / control unit the on-vehicle device subunit 120

1221

Data recovery unit

1222

data fusion and tracking unit

1223

Risk Assessment Unit

1224

Deployment decision unit

210

pedestrian

211 213, 215

direction of the pedestrian 210

212 214

According to the predicted residence area of the pedestrian 210 to a given future time t1

216

Without Consideration of the current state of motion predicated Residence area of the pedestrian 210 to the time t1

218

According to the predicted residence area of the pedestrian 210 to a given future time t2

220

cyclist

221

direction of travel of the cyclist 220

222

According to the predicted residence area of the cyclist 220 at the time t2

2221

According to the predicted residence area of the cyclist 220 at the time t2 at a bicycle speed of v1 (v1 = 2 m / s), cycling acceleration of a1 (a1 = 0 m / s2)

2222

According to the predicted residence area of the cyclist 220 at the time t2 at a bicycle speed of v1, cycling acceleration of a2 (a2 = 2 m / s2)

2223

According to the predicted residence area of the cyclist 220 at the time t2 at a bicycle speed of v2 (v2 = 4 m / s), cycling acceleration of a1 (a1 = 0 m / s2)

222A, ..., 222F

Predicted according to the invention Common areas of the cyclist 220 with associated probabilities

300

vehicle

301

direction of travel of the vehicle 300

302

lounge area of the vehicle 300 at a given time t1 at a constant Cruising speed

310

electronic stability system

320

Pedestrian safety control system

331

Fahrzeugfronkamera

332

Image processing unit

411 412

parked vehicles

420

trees

500

Street for vehicle traffic

501

road marking

510

Side street, which opens into the street 100

600 601, 602,

predicated Collision area between the vehicle 300

603

and the pedestrian 210 or cyclist 220

800

By a parked vehicle 411 interrupted line of sight between the vehicle 300 and the pedestrian 210

901

signal to the transponder

902

signal from the transponder

903

signal to the vehicle

904

signal from the vehicle

QUOTES INCLUDE IN THE DESCRIPTION

This list The documents listed by the applicant have been automated generated and is solely for better information recorded by the reader. The list is not part of the German Patent or utility model application. The DPMA takes over no liability for any errors or omissions.

Cited non-patent literature

• - Technology Conference: "Workshop Driver Assistance Systems 2008" in April 2008 presented procedure: "Pedestrian protection by means of cooperative sensors" [0007]

Claims (21)

Method for predicting the position and / or movement of an object ( 210 . 220 ), for example a pedestrian, relative to a vehicle ( 300 ), - where the current position of the object ( 210 . 220 ) relative to the vehicle ( 300 ) for predicting the future position and / or movement of the object ( 210 . 220 ) relative to the vehicle ( 300 ) is used at one or more predefined future times, the current position being determined by locating an object-side transceiver unit ( 111 ) by a vehicle-side transceiver unit ( 121 ), wherein the location of the current position based on a wireless data communication between the object-side transceiver unit ( 111 ) and the vehicle-mounted transceiver unit ( 121 ), characterized in that - movement-related data of the object ( 210 . 220 ) of at least one object-side sensor ( 113 . 114 . 115 . 116 ) and from the object-side transceiver unit ( 111 ) to the vehicle-mounted transceiver unit ( 121 ), and - the future position and / or movement of the object ( 210 . 220 ) relative to the vehicle ( 300 ) are predicted using this motion data. Method according to claim 1, wherein the movement-related data for verifying the object ( 210 . 220 ) are used. Method according to claim 1 or 2, wherein an object model for the prediction of the position and / or movement of the object ( 210 . 220 ), wherein the object model has at least one of the following parameters, - type of object ( 210 . 220 ), such. An adult person, a child, a bicycle, a wheelchair, a walker, a roller skate, a football, an animal such as a dog, physiological or physical mobility of the object ( 210 . 220 ), including the weight, body height, maximum speed, maximum acceleration and / or maximum spin rate of the object ( 210 . 220 ), - movement pattern of the object ( 210 . 220 ), namely the typical motion sequence of the object ( 210 . 220 ). Method according to one of the preceding claims, wherein from the movement data of the object ( 210 . 220 ) and / or the parameters of the object model of the object ( 210 . 220 ) Early indicators for the prediction of the position and / or movement of the object ( 210 . 220 ) and using the leading indicators to determine the position and / or movement of the object ( 210 . 220 ) are predicted. Method according to one of the preceding claims, wherein the position and / or movement of the object ( 210 . 220 ) at each predefined future time with an associated position or movement probability. Method according to one of the preceding claims, wherein the orientation of the object ( 210 . 220 ) to the direction of the sky for prediction of the future position and / or movement of the object ( 210 . 220 ) relative to the vehicle ( 300 ) is used. Method according to one of the preceding claims, wherein the direction of movement of the object ( 210 . 220 ) relative to the vehicle ( 300 ) for predicting the future position and / or movement of the object ( 210 . 220 ) relative to the vehicle ( 300 ) is used. Method according to one of the preceding claims, wherein the rate of rotation of the object ( 210 . 220 ) around the vertical axis of the object ( 210 . 220 ) for predicting the future position and / or movement of the object ( 210 . 220 ) relative to the vehicle ( 300 ) is used. Method according to one of the preceding claims, wherein the inclination of the object ( 210 . 220 ) about the longitudinal and / or transverse axis of the object ( 210 . 220 ) for predicting the future position and / or movement of the object ( 210 . 220 ) relative to the vehicle ( 300 ) is used. Method according to one of the preceding claims, wherein the translational acceleration of the object ( 210 . 220 ) for predicting the future position and / or movement of the object ( 210 . 220 ) relative to the vehicle ( 300 ) is used. Method according to claim 10, wherein the translational acceleration of the object ( 210 . 220 ) with respect to the longitudinal, transverse and / or vertical axis of the object ( 210 . 220 ) are measured in two or three dimensions and for the prediction of the future position and / or movement of the object ( 210 . 220 ) relative to the vehicle ( 300 ) is used. Method according to one of the preceding claims, wherein information about the road condition, infrastructure and / or weather conditions in the environment of the vehicle ( 300 ) for predicting the future position and / or movement of the object ( 210 . 220 ) relative to the vehicle ( 300 ) is used. Device subunit ( 110 ) for performing a method according to any one of claims 1 to 12, which is arranged on the object side, said object-side device subunit ( 110 ) the ob Project-side transceiver unit ( 111 ) having. Device subunit ( 110 ) according to claim 13, wherein the object-side transceiver unit ( 111 ) has a transponder. Device subunit ( 110 ) according to claim 13 or 14, wherein said object-side device subunit ( 110 ) an acceleration sensor ( 114 ) for detecting the translational acceleration of the object ( 210 . 220 ) having. Device subunit ( 110 ) according to claim 15, wherein the acceleration sensor ( 114 ) the translational acceleration of the object ( 210 . 220 ) about the longitudinal, transverse and / or vertical axis of the object ( 210 . 220 ) recorded in one, two or three dimensions. Device subunit ( 110 ) according to one of claims 13 to 16, wherein said object-side device subunit ( 110 ) a magnetic compass sensor ( 116 ) for detecting the orientation of the object ( 210 . 220 ) to the direction of the compass. Device subunit ( 110 ) according to one of claims 13 to 17, wherein said object-side device subunit ( 110 ) a rotation rate sensor ( 115 ) for detecting the rotational movement of the object ( 210 . 220 ), wherein the rotation rate sensor ( 115 ) the rotational movement of the object ( 210 . 220 ) to the vertical, transverse and / or longitudinal axis one, two or three-dimensional detected. Device subunit ( 110 ) according to one of claims 13 to 18, wherein said object-side device subunit ( 110 ) has a memory for storing parameters of the object model, wherein these parameters can be changed manually via an interface. Device subunit ( 120 ) for performing a method according to one of claims 1 to 12, which is arranged on the vehicle side, said vehicle-side device subunit ( 120 ) the on-board transceiver unit ( 121 ), this vehicle-mounted transceiver unit ( 121 ) the object-side transceiver unit ( 111 ) by means of wireless data communication and by measuring the signal transit time between the vehicle-side and object-side transceiver units ( 121 . 111 ) the radial distance from the vehicle ( 300 ) to the object ( 210 . 220 ) detected. Device subunit according to claim 20, wherein the vehicle-side transceiver unit ( 121 ) has a multiple antenna system and on the basis of the object-side transceiver unit ( 111 ) and transmitted by the multiple antenna system ( 121 ) signals receive the azimuth angle of the object ( 210 . 200 ) with respect to the longitudinal axis of the vehicle ( 300 ).

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