DE102018218003A1 - Process, computer program and measuring system for the evaluation of movement information - Google Patents

Abstract

Method for determining the own movement of a motor vehicle (1) with the aid of a location system (26) which provides a number of targets (66a, 66b), the method: a) determining which of the targets (66a, 66b) provided by the location system are static targets (66a), b) a regression function (63) is adapted to the static targets (66a), the regression function (63) the expected distribution (f) of the static targets (66a) in the field of view (51; v, ϕ ) describes the location system (26) and is dependent on one or more movement parameters of the motor vehicle (1); and c) one or more of the movement parameters of the motor vehicle (1) are determined from the adapted regression function (63).

Description

TECHNICAL AREA

The present disclosure relates to methods and computer programs and location systems for evaluating movement information, for example movement information from a radar measuring device.

TECHNICAL BACKGROUND

Location systems are known which are used for autonomous or semi-autonomous driving or for “Advanced Driver Assistance Systems” (ADAS) functions on motor vehicles. In order to achieve an environmental perception, such measuring systems scan the surroundings of the motor vehicle for objects in order to determine their relative position and relative speed in relation to the motor vehicle. For this purpose, these measuring systems send out waves that are reflected on surrounding objects and are then received again by the measuring system. These are, for example, electromagnetic waves or acoustic waves. In particular, radar or lidar measuring systems are used for this. Autonomous vehicles use radar or lidar to get the position and speed of objects such as other road users or obstacles.

Radar technology ("Radio Detection and Ranging") relates, for example, to devices, methods and systems for locating and recognizing objects based on electromagnetic waves in the radio frequency range. The radar sends an electromagnetic signal and receives echoes from objects. Using radar technology, for example, a position can be determined by evaluating transit times and a relative speed of an object can be determined taking frequency signal changes (Doppler effect) into account.

Various algorithms are running for the task of environmental perception, such as static environmental perception, dynamic object processing, alignment calculation as well as localization and mapping.

Methods are known for identifying points of a point cloud of a motion sensor (lidar / radar / camera) as static or mobile. For example, a typical approach to radar is to use the radial speed of each point and compare it to the speed of the ego vehicle. For Lidar, two point clouds can be compared on the basis of scan-to-scan matching methods in order to identify moving objects. The scan-to-scan matching can be carried out, for example, using methods known to those skilled in the art, such as ICP (“iterative closest point”) or NDT (“normal distribution transform”). Based on the determined correspondence of points, the point cloud of the second measuring frame can be adapted to the point cloud of the first measuring frame using these methods. A camera is able to recognize moving targets or pixels with optical flow, machine learning, etc.

Proceeding from this, the object of the invention is to provide a method, a computer program and a location or measurement system for evaluating movement information, which improves the accuracy in the detection of static or mobile targets. This object is achieved by the method according to claim 1 and the method according to claim 10. Further advantageous embodiments of the invention result from the subclaims and the following description of preferred exemplary embodiments of the present invention.

The exemplary embodiments show a method for determining the self-movement of a motor vehicle with the aid of a location system that provides a number of targets, the method: a) determining which of the targets provided by the location system are static targets, b) a regression function to the static targets is adjusted, the regression function describing the expected distribution of the static targets in the field of vision of the positioning system and being dependent on one or more movement parameters of the motor vehicle; and one or more of the movement parameters of the motor vehicle are determined from the adapted regression function.

The location system includes, for example, one or more radar sensors, lidar sensors, cameras, or the like. Although the exemplary embodiments described below relate to point clouds of a radar sensor, the method can alternatively also be applied to other point-based measurement sensors such as, for example, lidar, or 3D reconstruction based on camera images (monocular or stereo). The accumulation of points of a point cloud can also take into account data from several sensors such as several radars, lidars or cameras in order to obtain a denser point cloud which represents the surroundings. Accordingly, the methods described here can also be used for other types of driver assistance measurement systems, for example for lidar measurement systems. For this purpose, the measurement data are evaluated accordingly and brought into a form suitable for the evaluation. Accordingly, any measurement system of this type, in particular a radar measurement system and a lidar measurement system, can be used independently of it for a motor vehicle Arrangement and alignment on the motor vehicle can be operated with the explained methods.

The information about the targets (also called “targets” or “objects”) is provided, for example, on the basis of the measurement data from the location system. In particular, it can be the targets of a target list, which is provided by the location system. The points on the target list can be, for example, targets from a target list provided by the location system. The targets are provided, for example, in the form of measuring frames that are supplied by the location system. A target list includes, for example, the values of the radial speed, the elevation angle and / or the side angle for each target. Alternatively, the information in the target list can be converted into a radial speed, an elevation angle and / or a side angle for each target.

According to one embodiment, an approximation course of the regression function is determined in the method and the targets are classified on the basis of the approximation course as static targets or mobile targets.

The approximate course of the regression function can be determined, for example, on the basis of approximate values of the movement parameters. In particular, the approximate values of the movement parameters can be obtained on the basis of movement parameters from a previous measurement cycle. Alternatively, approximate values of the movement parameters can also be determined using other methods to determine the own movement. The approximate determination of the vehicle's own movement can be carried out, for example, using any odometry method known to the person skilled in the art. Using such odometry methods, the odometry of the vehicle, for example position (X, Y, Z) and rotational movements (rotation rates) about a vertical, transverse and longitudinal axis of the vehicle (Euler angle Phi, Theta, Psi) is determined. For this purpose, translatory speed components can be derived from the ESP or via the wheel speeds. Rotational components (yaw rates) can also be derived from the ESP (yaw rate) or all three yaw rates from an IMU (inertial measurement unit).

According to one embodiment, the approximation course is broadened by a bandwidth and the targets within the bandwidth are classified as static targets. The bandwidth can be predetermined, or can be selected depending on the accuracy of the approximation course, for example depending on the accuracy of approximation values of the movement parameters.

The movement parameters of the motor vehicle can, for example, describe a translational and / or a rotational movement of the motor vehicle or the locating system.

The regression function can be adapted to the static targets, for example, by linear regression analysis, in particular by a least square method.

Steps a) to c) are preferably carried out continuously on the basis of measurement data of successive measurement cycles.

The regression function can also be dependent on one or more extrinsic parameters. The extrinsic parameters can, for example, describe one or more tilting angles of the location system with respect to the target installation position of the location system.

The extrinsic parameters are determined, for example, by determining in a calibration phase in which the movement parameters of the motor vehicle are known which of the targets provided by the location system are static targets, the regression function is adapted to the static targets, and one or more of the extrinsic parameters of the motor vehicle can be determined from the adapted regression function.

The exemplary embodiments also show a method for calibrating a location system, which provides a number of targets, the method: a) determining which of the targets provided by the location system are static targets, b) adapting a regression function to the static targets, wherein the regression function describes the expected distribution of the static targets in the field of view of the location system and is dependent on one or more previously known movement parameters of the motor vehicle and on extrinsic parameters, and one or more of the extrinsic parameters of the motor vehicle are determined from the adapted regression function.

Because the movement parameters of the motor vehicle are already known in the calibration phase, the regression function can be adapted on the basis of the known own movement of the motor vehicle in order to determine the extrinsic parameters.

According to one embodiment, an approximation course of the regression function is determined in the calibration method and the targets are classified as static targets or mobile targets on the basis of the approximation curve of the static function.

The approximation course of the regression function can be determined in the calibration phase, for example, on the basis of approximate values of the extrinsic parameters. In particular, the approximate values of the extrinsic parameters can be obtained on the basis of extrinsic parameters from a previous iteration.

According to one embodiment, the approximation course is broadened by a bandwidth and the targets within the bandwidth are classified as static targets.

The extrinsic parameters can, for example, describe a tilting angle of the location system with respect to the target installation position of the location system.

The movement parameters of the motor vehicle can describe a translational and / or a rotational movement of the motor vehicle or the locating system.

The regression function can also be adapted to the static targets in the calibration phase using linear regression analysis.

Steps a) to c) of the calibration method are preferably carried out cyclically. Steps a) to c) can be carried out, for example, several times on the basis of measurement data from different measurement cycles. Because steps a) to c) are carried out cyclically, the determined extrinsic parameters iteratively approximate the actual values of the parameters. The extrinsic parameters of a plurality of measurement cycles can be averaged, for example by providing a weighted mean value that describes the actual values of the parameters well.

The method was developed especially for radar measuring systems that scan a multidimensional space. The field of view of such a radar measuring system spans in particular an elevation angle and a side angle. Here, tilting about a longitudinal axis, a vertical axis and a transverse axis can occur in relation to the direction of travel. The calibration method particularly advantageously determines at least the tilt in the form of a rotation about the vertical axis and about the transverse axis. Tilting in the direction of the longitudinal axis of the motor vehicle, which corresponds in particular to the direction of movement, can be neglected. An adaptation from the elevation angle to the side angle and vice versa takes place accordingly, so that a transfer of the principle explained below to the other angular direction is possible without any problems.

The exemplary embodiments also relate to a computer program, comprising instructions which, when they are executed on a processor, cause the processor to carry out methods as described here.

The exemplary embodiments also relate to a measuring system or location system comprising a processor which is designed to carry out methods as described here. The evaluation unit can be, for example, a control unit (ECU = electronic control unit or ECM = electronic control module). The evaluation unit could be a control unit for autonomous driving or a central control unit of a motor vehicle. The control unit for autonomous driving (e.g. an "autopilot") can be used in an autonomous vehicle, for example, so that it can operate in full or in part without the influence of a human driver in traffic. The processor can be, for example, a computing unit such as a central processing unit (CPU) that executes program instructions.

The evaluation unit can be located in the vehicle, or also outside or partially outside the vehicle. The pipe radar data can be transmitted from the vehicle to a server or to a cloud system, where an optimal driving position of the vehicle is determined on the basis of the transmitted pipe radar data and a planned driving maneuver and the result is transmitted back to the vehicle. Accordingly, it is provided that the central control unit or control logic can also be located entirely or partially outside the vehicle. For example, the control logic can be an algorithm that runs on a server or a cloud system.

The exemplary embodiments also disclose a motor vehicle which comprises a radar control unit and / or an evaluation unit, as described above and in the exemplary embodiments. The vehicle can be, for example, an autonomous or semi-autonomous motor vehicle such as a car, a truck or the like.

• 1 ein Blockdiagramm zeigt, das schematisch die Konfiguration eines Fahrzeugs mit einer Steuerungseinheit für autonomes (oder teilautonomes) Fahren zeigt;
• 2 ein Blockdiagramm zeigt, das eine beispielhafte Konfiguration einer Steuerungseinheit (ECU 1, 2, 3, 4 und 5 in 1) darstellt;
• 3 ein Blockdiagramm zeigt, das eine beispielhafte Konfiguration einer Radareinheit darstellt;
• 4 ein Blockdiagramm zeigt, das eine weitere beispielhafte Konfiguration einer Radareinheit darstellt;
• 5 ein Kraftfahrzeug 1 darstellt, das an dessen Fahrzeugfront ein Ortungssystem in Form einer Radareinheit aufweist;
• 6 den Vektor der Radialgeschwindigkeit ${\overrightarrow{v}}_r$
  
  eines Ziels und den gesamten Geschwindigkeitsvektor ${\overrightarrow{v}}_{tot}$
  
  des Sensors mit dem translatorischen Anteil ${\overrightarrow{v}}^e$
  
  und dem rotatorischen Anteil $\mathrm{\Omega }{\overrightarrow{p}}_s^e$
  
  schematisch darstellt;
• 7 ein Beispiel für die Klassifizierung der vom Ortungssystem erfassten Zielpunkte eines Messrahmens als statische oder mobile Zielpunkte und eine Bestimmung der Eigenbewegung des Fahrzeugs (bzw. des Ortungssystems) zeigt;
• 8a das Kraftfahrzeug mit Radareinheit zeigt, wobei das Kraftfahrzeug zusätzlich zu der geradlinigen Bewegung eine Rotationsbewegung durchführt;
• 8b ein Diagramm zeigt, bei dem gegenüber der Rechtswertachse der Seitenwinkel und gegenüber der Hochwertachse die Radialgeschwindigkeit von Zielen aufgetragen ist;
• 9 die einzelnen Schritte des Verfahrens zur Bestimmung der Eigenbewegung eines Kraftfahrzeugs als Flussdiagramm zeigt;
• 10 schematisch eine Verstellung des Sensors („Sensorrahmen“) gegenüber dem Fahrzeug-Rahmen („Ego-Rahmen“ bzw. Fahrzeugrahmen) zeigt;
• 11 die einzelnen Schritte des Kalibrierungsverfahrens zur Bestimmung der extrinsischen Parameter als Flussdiagramm zeigt; und
• 12 in einem 2D-Raum die Schätzung der extrinsischen Parameter ψ_ext , und θ_ext basierend auf der statischen Funktion zeigt.

Embodiments will now be described by way of example and with reference to the accompanying drawings, in which:

• 1 Fig. 3 is a block diagram schematically showing the configuration of a vehicle with a control unit for autonomous (or semi-autonomous) driving;
• 2nd A block diagram showing an exemplary configuration of a control unit (ECU 1 , 2nd , 3rd , 4th and 5 in 1 ) represents;
• 3rd FIG. 12 is a block diagram illustrating an exemplary configuration of a radar unit;
• 4th FIG. 12 is a block diagram illustrating another example configuration of a radar unit;
• 5 a motor vehicle 1 which has a location system in the form of a radar unit at the front of the vehicle;
• 6 the vector of radial velocity ${\overrightarrow{v}}_r$
  
  of a target and the entire speed vector ${\overrightarrow{v}}_{tOt}$
  
  of the sensor with the translatory component ${\overrightarrow{v}}^e$
  
  and the rotary part $\mathrm{\Omega }{\overrightarrow{p}}_s^e$
  
  schematically represents;
• 7 shows an example of the classification of the target points of a measuring frame recorded by the positioning system as static or mobile target points and a determination of the own movement of the vehicle (or the positioning system);
• 8a shows the motor vehicle with a radar unit, the motor vehicle performing a rotational movement in addition to the linear movement;
• 8b shows a diagram in which the side angle is plotted against the right-hand axis and the radial speed of targets is plotted against the high-value axis;
• 9 shows the individual steps of the method for determining the own movement of a motor vehicle as a flow chart;
• 10th schematically shows an adjustment of the sensor (“sensor frame”) relative to the vehicle frame (“ego frame” or vehicle frame);
• 11 shows the individual steps of the calibration method for determining the extrinsic parameters as a flow diagram; and
• 12th the estimation of extrinsic parameters in a 2D space ψ _ext , and θ _ext based on the static function shows.

1 12 shows a block diagram that schematically illustrates the configuration of a vehicle with a radar measurement system and a control unit for autonomous (or partially autonomous) driving according to an exemplary embodiment of the present invention. The autonomous vehicle is a vehicle that can operate on the road in whole or in part without the influence of a human driver. In autonomous driving, the control system of the vehicle takes over the role of the driver entirely or as far as possible. Autonomous (or semi-autonomous) vehicles can use various sensors to perceive their surroundings, determine their position and the other road users from the information obtained, and use the vehicle's control system and navigation software to control the destination and act accordingly in road traffic.

The autonomous vehicle 1 comprises several electronic components, which via a vehicle communication network 28 are interconnected. The vehicle communication network 28 For example, a standard vehicle communication network installed in the vehicle, such as a CAN bus (controller area network), a LIN bus (local interconnect network), an Ethernet-based LAN bus (local area network), a MOST bus, an LVDS -Bus or the like.

In the in 1 The example shown includes the autonomous vehicle 1 a control unit 12th (ECU 1 ) that controls a steering system. The steering system refers to the components that enable directional control of the vehicle. The autonomous vehicle 1 further comprises a control unit 14 (ECU 2nd ) that controls a braking system. The braking system refers to the components that allow the vehicle to brake. The autonomous vehicle 1 further comprises a control unit 16 (ECU 3rd ) that controls a drive train. The drive train refers to the drive components of the vehicle. The powertrain may include an engine, a transmission, a drive / propeller shaft, a differential, and an axle drive.

The autonomous vehicle 1 also includes a control unit for autonomous driving 18th (ECU 4th ). The control unit for autonomous driving 18th is designed to be the autonomous vehicle 1 to be controlled so that it can operate in whole or in part without the influence of a human driver in road traffic. The control unit for autonomous driving 18th controls one or more vehicle subsystems while the vehicle is operating in autonomous mode, namely the braking system 14 , the steering system 12th and the drive system 14 . The control unit for autonomous driving can do this 18th for example via the vehicle communication network 28 with the corresponding control units 12th , 14 and 16 communicate.

The control units 12th , 14 and 16 can also receive vehicle operating parameters from the above-mentioned vehicle subsystems, which can record these using one or more vehicle sensors. Vehicle sensors are preferably sensors that detect a state of the vehicle or a state of vehicle parts, in particular their State of motion. The sensors may include a vehicle speed sensor, a yaw rate sensor, an acceleration sensor, a steering wheel angle sensor, a vehicle load sensor, temperature sensors, pressure sensors and the like. For example, sensors can also be arranged along the brake line in order to output signals which indicate the brake fluid pressure at various points along the hydraulic brake line. Other sensors in the vicinity of the wheel can be provided which detect the wheel speed and the brake pressure which is applied to the wheel.

The vehicle sensors of the autonomous vehicle 1 further comprises a satellite navigation unit 24th (GPS unit) and one or more optical sensors 20th that are designed to capture optical information. The optical sensors 20th can be arranged inside the vehicle or outside the vehicle. For example, a camera in a front area of the vehicle 1 be installed to take pictures of an area in front of the vehicle.

The autonomous vehicle 1 also includes one or more radar units as a positioning system 26 . With the radar unit 26 it can be, for example, a continuous wave radar (CW radar) or a modulated continuous wave radar (FMCW radar). Radar data is from the radar unit 26 recorded and for example to a central control unit 22 (or alternatively to the control unit for autonomous driving, ECU 4th ) transfer.

The central control unit 22 is designed to get the radar data from the radar unit 26 to receive and process the radar data. The radar data include information such as target lists generated on the basis of range Doppler maps. The central control unit 22 can evaluate the information received or, for example, to the control unit for autonomous driving 18th transmitted further.

If an operating state for autonomous driving is activated on the controller or driver side, the control unit determines for autonomous driving 18th , on the basis of data available over a predefined route, on the basis of that from the radar unit 26 received data, based on optical sensors 20th recorded environmental data, and on the basis of vehicle operating parameters recorded by the vehicle sensors, the control unit 18th from the control units 12th , 14 and 16 are fed, parameters for the autonomous operation of the vehicle (for example, target speed, target torque, distance to the vehicle in front, distance to the edge of the road, steering process and the like).

2nd FIG. 12 is a block diagram showing an example configuration of a control unit (ECU 1 , 2nd , 3rd , 4th and 5 in 1 ) represents. The control unit can be, for example, a control unit (electronic control unit ECU or electronic control module ECM). The control unit includes a processor 210 . With the processor 210 For example, it can be a computing unit such as a central processing unit (CPU) that executes program instructions. The radar control unit further comprises a read-only memory, ROM 230 (ROM = Read-only memory) and a random access memory, RAM 220 (RAM = Random Access Memory) (e.g. dynamic RAM ("DRAM"), synchronous DRAM ("SDRAM") etc.), which serve as a program memory area and as a data memory area. The radar control unit also includes a storage drive for storing data and programs 260 , such as a hard disk drive (HDD), a flash memory drive or a non-volatile solid state drive (SSD). The control unit further includes a vehicle communication network interface 240 , via which the control unit with the vehicle communication network ( 28 in 2nd ) can communicate. Each of the units of the control unit is via a communication network 250 connected. In particular, the control unit of the 2nd as an implementation of the central control unit 22 , ECU 5 , of the 1 serve, where ROM 230 , RAM 220 and storage drive 260 as a program storage area for programs for evaluating radar data (eg the process of 11 ) and serve as a data storage area for radar data.

3rd shows a schematic block diagram of an exemplary radar architecture known from the prior art. A radar unit 26 has an RF chip 301 on. With the RF chip 301 For example, it can be a single-chip radar in which several antennas for the millimeter-wave range (here n transmit antennas) TX and m receiving antennas RX ) are integrated in a chip. Such integration is possible because the radar frequency range makes tiny antennas possible. The size of other necessary components, such as inductors, is reduced, so that the RF chip 301 in a mass production semiconductor process as used for microprocessors. The RF chip 301 generates the radar signals and receives the reflected signals. The RF chip 301 also carries out an A / D conversion of the generated data and sends it to an interface as an A / D-converted baseband signal 302 further that as an interface to a vehicle communication network 28 serves, for example, a serializer / de-serializer. The radar unit 26 is over this vehicle communication network 28 with a processor unit 303 ("CPU" = Central Processing Unit) connected. At the processor unit 303 can be, for example, the processor of the central control unit 22 ("Vehicle controller") 1 to the processor of the control unit 18th for autonomous driving 1 , or act as a central radar processing unit which is provided for the further processing of the radar data. In this example, the vehicle communication network is concerned 28 a serial connection with a high data rate, for example a data connection according to the interface standard LVDS ("Low Voltage Differential Signaling") or an Ethernet data network. The high data rate of the vehicle communication network 28 enables a direct transmission of the A / D-converted baseband signal to the processor unit 303 .

4th shows a schematic block diagram of another exemplary radar architecture known from the prior art. As in the example of the 3rd has a radar unit 26 an RF chip 301 with several antennas for example for the millimeter wave range (here n transmit antennas TX and m receiving antennas RX ), which are integrated in a chip. The RF chip 301 generates the radar signals and receives the reflected signals. The RF chip 301 also performs A / D conversion of the generated data and outputs it as an A / D converted baseband signal to a radar microprocessor 304 further. The radar microprocessor 304 pre-processes the radar data. For example, the radar microprocessor generates 304 from the radar data a target object list ("target list" for short), for example based on a range Doppler card, and outputs it via the interface 302 to the vehicle communication network 28 further. The radar unit 26 is over this vehicle communication network 28 with a processor unit 303 ("CPU" = Central Processing Unit) connected. At the processor unit 303 it can be like in the example of the 3rd a processor of the central control unit 22 ("Vehicle controller") 1 to a processor of the control unit 18th for autonomous driving 1 , or act as a central radar processing unit which is provided for the further processing of the radar data. In this example, the vehicle communication network is concerned 28 a serial connection with a lower data rate, eg a data connection according to the interface standard CAN-FD ("Low Voltage Differential Signaling"). Since not all of the A / D-converted baseband signal is transmitted, a lower data rate is sufficient for the transmission to the processor unit 303 out.

In the 5 is a motor vehicle 1 shown, a tracking system in the form of a radar unit at the front of the vehicle 26 having. The radar unit 26 covers a viewing area as an example 51 from. The viewing area can be, for example, a two-dimensional viewing area (range - azimuth). The field of view 51 is shown by two dotted lines 51a limited. The radar unit would be in an optimal target installation position 26 with its central optical axis 52 along the vehicle's longitudinal axis 53 aligned, which is shown by the dashed line and corresponds to the target axis. The radar unit 26 and their central optical axis 52 is in this example, however, due to assembly tolerances with respect to the vehicle's longitudinal axis 53 in the lateral (azimuthal) direction by one side angle ψ _ext (also called azimuth angle or yaw angle) tilted. This tilt by the side angle ψ _ext is in 5 shown exaggerated. With a three-dimensional field of view, a tilt by an elevation angle could accordingly occur in the vertical direction θ _ext (also called pitch angle) or there is a twist by a roll angle, which in 5 but is not shown. The from the measurement data of the radar unit 26 The determined relative positions and relative speeds of the targets thus deviate from the actual relative positions and relative speeds to the motor vehicle. These deviations can be corrected by knowing the deviation of the installation position of the measuring system from the target installation position. This deviation can be determined using the calibration procedure explained below. The car 1 moves during a measurement process, for example with a movement speed 54 , represented by an arrow in the 5 , straight ahead. For the further explanations, steering or rotational movements of the motor vehicle remain for the time being 1 disregarded.

In the 5 describe radar unit 26 covers a two-dimensional field of view as an example 51 (Range - Azimuth). In alternative examples, the radar unit could 26 however, also cover a three-dimensional field of view (range - azimuth - elevation) or a four-dimensional field of view (range - azimuth - elevation - Doppler speed). The field of view of such a radar measuring system can span in particular an elevation angle (“elevation angle”), a side angle, a radial speed (“Doppler speed”) and a radial distance (“Range”). An adaptation from the elevation angle to the lateral angle and vice versa takes place accordingly, so that the principles explained here are transferred into the multidimensional space (lateral angle, elevation angle, distance, intensity, radial speed, etc.) the additional angle information is easily possible.

Classify the targets and compensate for the vehicle's own movement

The measurement data of the location system (for example radar measurement system 26 of the 5 ) are processed as usual, for example in a microprocessor ( 304 in 4th ) or in a central control unit ( 303 in 3rd respectively. 210 in 2nd ) to provide a target list. This is done, for example, via two Fourier transformations, which provide a range Doppler map. The range Doppler map comprises the information about the radial distance, the radial speed and at what angle, elevation angle and / or side angle, a target occurs. The range Doppler map thus provides information for the target list.

The destinations in the destination list include static and mobile destinations. A static target is fixed to a surface or does not move in relation to the surface. In contrast, mobile targets are targets that move themselves against the ground and therefore have a relative speed that differs from the vehicle speed. Mobile targets can occur, for example, due to pedestrians, cyclists or other motor vehicles.

der Geschwindigkeitsvektor des Sensors im Ego-Rahmen (Eigengeschwindigkeit), ${\overrightarrow{p}}_s^e$

der Positionsvektor des Sensors im Ego-Rahmen und sei ω_i die Drehgeschwindigkeit im Ego-Rahmen um die i-te Achse (i=x,y,z) mit entsprechender Rotationsmartrix Ω, so kann die statische Funktion f für die Radialgeschwindikeit v_rad für den Fall eines ideal positionierten und ausgerichteten Sensors wie folgt formuliert werden. Zur Vereinfachung sei hier nur die Geschwindigkeitskomponente $v_x^e$

in x-Richtung und nur eine Rotationsbewegung ω_z um die Hochachse, wie sie bei einer Kurvenfahrt auftritt, betrachtet: $$v_r=-{\left(\frac{\overrightarrow{r}}{r}\right)}^T\left[{\overrightarrow{v}}^e+\mathrm{\Omega }{\overrightarrow{p}}_s^e\right]=f\left({\omega }_z,v_x^e,\theta ,\varphi \right)$$

mit $$\overrightarrow{r}=r\left(\begin{array}{c}\text{cos}\left(\theta \right)\text{cos}\left(\varphi \right)\\ \text{cos}\left(\theta \right)\text{sin}\left(\varphi \right)\\ \text{sin}\left(\theta \right)\end{array}\right)$$

$${\overrightarrow{p}}_s^e={\left[t_x,t_y,t_z\right]}^T$$

$${\overrightarrow{v}}^e={\left[v_x^e\mathrm{,0,0}\right]}^T$$

$${\overrightarrow{v}}_{tot}={\overrightarrow{v}}^e+\mathrm{\Omega }{\overrightarrow{p}}_s^e$$

Be ${\overrightarrow{v}}^e$

the speed vector of the sensor in the ego frame (own speed), ${\overrightarrow{p}}_s^e$

the position vector of the sensor in the ego frame and be ω _i the speed of rotation in the ego frame around the i-th axis (i = x, y, z) with a corresponding rotation matrix Ω, so the static function f for radial speed v _rad in the case of an ideally positioned and aligned sensor as follows. For the sake of simplicity, here is only the speed component $v_x^e$

in the x direction and only one rotational movement ω _z around the vertical axis, as occurs when cornering: $$v_r=-{\left(\frac{\overrightarrow{r}}{r}\right)}^T\left[{\overrightarrow{v}}^e+\mathrm{\Omega }{\overrightarrow{p}}_s^e\right]=f\left({\omega }_{\mathrm{e.g.}},v_x^e,\theta ,\varphi \right)$$

With $$\overrightarrow{r}=r\left(\begin{array}{c}\text{cos}\left(\theta \right)\text{cos}\left(\varphi \right)\\ \text{cos}\left(\theta \right)\text{sin}\left(\varphi \right)\\ \text{sin}\left(\theta \right)\end{array}\right)$$

$${\overrightarrow{p}}_s^e={\left[t_x,t_y,t_{\mathrm{e.g.}}\right]}^T$$

$${\overrightarrow{v}}^e={\left[v_x^e\mathrm{,\; 0.0}\right]}^T$$

$${\overrightarrow{v}}_{tOt}={\overrightarrow{v}}^e+\mathrm{\Omega }{\overrightarrow{p}}_s^e$$

eines Ziels ist durch den Radialabstand r, den Seitenwinkel Φ (Azimut) und den Elevationswinkel Φ eines jeweiligen Ziels gegeben. Der Vektor der Radialgeschwindigkeit ${\overrightarrow{v}}_r$

eines Ziels und der gesamte Geschwindigkeitsvektor ${\overrightarrow{v}}_{tot}$

des Sensors mit dem translatorischen Anteil ${\overrightarrow{v}}^e$

und dem rotatorischen Anteil $\mathrm{\Omega }{\overrightarrow{p}}_s^e$

sind in 6 schematisch dargestellt.The position vector $\overrightarrow{r}$

of a target is by the radial distance r , the side angle Φ (Azimuth) and the elevation angle Φ given a particular goal. The radial velocity vector ${\overrightarrow{v}}_r$

of a target and the entire speed vector ${\overrightarrow{v}}_{tOt}$

of the sensor with the translatory component ${\overrightarrow{v}}^e$

and the rotary part $\mathrm{\Omega }{\overrightarrow{p}}_s^e$

are in 6 shown schematically.

und $$\overrightarrow{r}=r\left(\begin{array}{c}\text{cos}\left(\varphi \right)\\ \text{sin}\left(\varphi \right)\\ 0\end{array}\right)$$

und die statische Funktion f vereinfacht sich zu $$v_r=-{\left(\frac{\overrightarrow{r}}{r}\right)}^T\left[{\overrightarrow{v}}^e+\mathrm{\Omega }{\overrightarrow{p}}_s^e\right]=f\left({\omega }_z,v_x^e,\varphi \right)$$

D.h. die statische Funktion f hängt im Wesentlichen von cos(ϕ) und weiteren Termen, sowie von der Ego-Geschwindigkeit $v_x^e$

und der Drehgeschwindigkeit ω_z des Ego-Fahrzeugs ab. Die Ego-Geschwindigkeit $v_x^e$

und die Drehgeschwindigkeit ω_z des Ego-Fahrzeugs können folglich mittels Regression gewonnen werden. Es ist somit im Voraus bekannt, dass die Verteilung der statischen Ziele im v_r , ϕ -Diagramm durch eine statische Funktion gegeben ist. Diese statische Funktion, hier vereinfacht als „Cosinus-Funktion“ bezeichnet (die theoretische Funktion hat weitere Terme, man spricht nur zur Vereinfachung von einem „Cosinus“), entspricht der oben hergeleiteten theoretischen Verteilung der statischen Ziele und hängt unter anderem von der aktuellen Eigenbewegung des Kraftfahrzeugs, sowie ggf. von einem oder mehreren extrinsischen Parametern ab. Bei den extrinsischen Parametern handelt es sich beispielsweise um Winkel ψ_ext , θ_ext , um welche eine zentrale optische Achse der Radareinheit (52 in 5) aufgrund von Montagetoleranzen gegenüber der Fahrzeuglängsachse (53 in 5) verkippt ist, wie dies im Abschnitt „Kompensierung von Sensorkippung durch Kalibrierung“ unten näher beschrieben ist. Solch eine Sensorverkippung wird zur Vereinfachung der Darstellung jedoch vorerst vernachlässigt (ψ_ext= θ_ext= 0).In the two-dimensional case that in 7 is shown below, assuming ω _x = ω _y = 0, the rotation matrix can be represented as follows $$\mathrm{\Omega \; =}\left[\begin{array}{ccc}0& -{\omega }_{\mathrm{e.g.}}& 0\\ {\omega }_{\mathrm{e.g.}}& 0& 0\\ 0& 0& 0\end{array}\right]$$

and $$\overrightarrow{r}=r\left(\begin{array}{c}\text{cos}\left(\varphi \right)\\ \text{sin}\left(\varphi \right)\\ 0\end{array}\right)$$

and the static function f simplifies itself $$v_r=-{\left(\frac{\overrightarrow{r}}{r}\right)}^T\left[{\overrightarrow{v}}^e+\mathrm{\Omega }{\overrightarrow{p}}_s^e\right]=f\left({\omega }_{\mathrm{e.g.}},v_x^e,\varphi \right)$$

Ie the static function f depends mainly on cos (ϕ) and other terms, as well as on the ego speed $v_x^e$

and the speed of rotation ω _z of the ego vehicle. The ego speed $v_x^e$

and the speed of rotation ω _z of the ego vehicle can therefore be obtained by means of regression. It is therefore known in advance that the distribution of the static targets in the v _r , ϕ diagram is given by a static function. This static function, here simply referred to as the “cosine function” (the theoretical function has other terms, one speaks only for simplification of a “cosine”), corresponds to that above derived theoretical distribution of the static goals and depends, among other things, on the current own movement of the motor vehicle, and possibly on one or more extrinsic parameters. The extrinsic parameters are, for example, angles ψ _ext , θ _ext around which a central optical axis of the radar unit ( 52 in 5 ) due to assembly tolerances in relation to the vehicle's longitudinal axis ( 53 in 5 ) is tilted, as described in more detail in the section "Compensation of sensor tilt through calibration" below. Such a sensor tilt is initially neglected to simplify the representation (Darstellung _ext = θ _ext = 0).

fließen in diesem Fall als Bewegungsparameter die Fahrzeuggeschwindigkeit $v_x^e$

und der Rotationsgeschwindigkeit ω_z ein und die statische Funktion ist im Wesentlichen eine Cosinus-Funktion. 7 shows an example of the classification of the target points of a measuring frame detected by the location system as static or mobile target points and a determination of the own movement of the vehicle (or location system). Be exemplary in 7 the measured values of a two-dimensional measuring system are shown, which has a radial speed v _r and a side angle Φ recorded, but no elevation angle θ delivers and also there is no sensor tilt. In the static function $v_r=f\left({\omega }_{\mathrm{e.g.}},v_x^e,\varphi \right)$

in this case the vehicle speed flows as movement parameters $v_x^e$

and the speed of rotation ω _z and the static function is essentially a cosine function.

und einer Rotationsgeschwindigkeit ω_z = 0. In der 7 sind beispielhaft Ziele 66a, 66b eines Messvorgangs dargestellt. Die Ziele 66a, 66b sind durch Kreise sowie durch Quadrate eingezeichnet, welche jeweilige Ziele repräsentieren. Das Radar-Messsystem ermittelt die Radialgeschwindigkeit v_r und den Seitenwinkel ϕ der jeweiligen Ziele 66a, 66b. Die Radialgeschwindigkeit der jeweiligen Ziele 66a hängt von dem Winkel ϕ ab, unter dem das Ziel 66a, 66b gegenüber dem Fahrzeug auftritt.In the 7 exemplary targets of a target list are shown, with the side angle compared to the legal value axis Φ and the radial velocity compared to the high-value axis v _r is applied. The radial distance is not shown in the illustration. The motor vehicle moves, for example, at its own speed during a measurement process $v_x^e=3.7m/s$

and a rotational speed ω _z = 0. In the 7 are exemplary targets 66a , 66b of a measurement process. The goals 66a , 66b are drawn by circles and squares, which represent respective targets. The radar measuring system determines the radial speed v _r and the side angle ϕ of the respective targets 66a , 66b . The radial speed of each target 66a depends on the angle ϕ at which the target 66a , 66b occurs towards the vehicle.

(Cosinus-Verlauf) und auf Grundlage von Näherungswerten der Bewegungsparameter ω_z , $v_x^e,$

z.B. Messergebnissen eines vorherigen Messzyklus, oder alternativ auch durch eine Schätzung der aktuellen Eigenbewegung des Fahrzeugs mittels anderer Sensoren, wird ein Näherungsverlauf 60 der statischen Funktion bestimmt. Um die statischen Ziele zu erkennen, wird der Näherungsverlauf 60 um eine Bandbreite verbreitert, die in der 7 durch die beiden gestrichelten Linien 61 dargestellt ist. Die Bandbreite 61 kann beispielsweise fest vorgegeben sein, oder von den Bewegungsparametern oder von anderen Parametern abhängen. Alle Ziele der Zielliste, die innerhalb der Bandbreite 61 der Cosinus-Funktion 60 liegen, werden als statische Ziele 66a klassifiziert. Diese als statische Ziele 66a klassifizierten Ziele sind in 7 durch kleine Kreise gekennzeichnet. Alle anderen Ziele der Zielliste, werden als mobile Ziele 66b klassifiziert. Diese als mobile Ziele 66b klassifizierten Ziele sind in 7 durch kleine Quadrate gekennzeichnet.Based on the theoretical prediction $v_r=f\left({\omega }_{\mathrm{e.g.}},v_x^e,\varphi \right)$

(Cosine curve) and on the basis of approximate values of the movement parameters ω _z , $v_x^e,$

For example, measurement results from a previous measurement cycle, or alternatively by an estimate of the current own movement of the vehicle by means of other sensors, an approximation curve is obtained 60 the static function. In order to recognize the static goals, the approximation course 60 broadened by a range that in the 7 through the two dashed lines 61 is shown. The bandwidth 61 can be fixed, for example, or depend on the movement parameters or on other parameters. All targets of the target list that are within the range 61 the cosine function 60 lie as static targets 66a classified. These as static goals 66a classified goals are in 7 identified by small circles. All other destinations on the destination list are considered mobile destinations 66b classified. These as mobile goals 66b classified goals are in 7 identified by small squares.

des Messsystems bzw. des Kraftfahrzeugs. Diese Bewegungsparameter ω_z , $v_x^e$

werden durch beispielsweise lineare Regressionsanalyse ermittelt. Durch die als statisch klassifizierten Ziele 66a wird dabei eine Regressionsfunktion 62 gelegt. Die Regressionsfunktion 62 ist so gewählt, dass diese die theoretisch erwartete Verteilung (z.B. Cosinus) der statischen Ziele beschreibt, wobei die regressiven Parameter ω_z, $v_x^e$

variabel sind. Diese variablen regressiven Parameter ω_z , $v_x^e$

werden beispielsweise durch eine lineare Regressionsanalyse variiert, um eine optimale Anpassung der Regressionsfunktion an die statischen Ziele 66a bereitzustellen. Dadurch werden die variablen regressiven Parameter ω_z , $v_x^e$

ermittelt. Die regressiven Parameter ω_z , $v_x^e$

sind die regressiven Bewegungsparameter (odometrische Parameter), die es zu bestimmen gilt.In the two-dimensional example of the 7 (assuming ω _x = ω _y = 0), the odometric parameters include, for example, the speed of rotation ω _z , and the speed of movement $v_x^e$

the measuring system or the motor vehicle. These movement parameters ω _z , $v_x^e$

are determined by, for example, linear regression analysis. Through the goals classified as static 66a becomes a regression function 62 placed. The regression function 62 is chosen so that it describes the theoretically expected distribution (eg cosine) of the static targets, whereby the regressive parameters ω _z , $v_x^e$

are variable. These variable regressive parameters ω _z , $v_x^e$

are varied, for example, by a linear regression analysis in order to optimally adapt the regression function to the static goals 66a to provide. This makes the variable regressive parameters ω _z , $v_x^e$

determined. The regressive parameters ω _z , $v_x^e$

are the regressive movement parameters (odometric parameters) that need to be determined.

ω_z sind in der 7 durch ein Kreuz 63 dargestellt, das dem Extremum der Regressionsfunktion 62 entspricht. Die Position des Kreuzes 63 auf der Hochwertachse entspricht der Bewegungsgeschwindigkeit $v_x^e$

des Kraftfahrzeugs und die Position des Kreuzes 63 auf der Rechtswertachse steht mit der Rotationsgeschwindigkeit ω_z in Zusammenhang, wie dies in Zusammenhang mit 10 unten näher beschrieben ist. Das Kraftfahrzeug bewegt sich im Fall der 7 beispielhaft geradlinig (w_z = 0) mit einer Geschwindigkeit von etwa $v_x^e=\mathrm{3,7}\text{m/s}\text{.}$

Die maximale Radialgeschwindigkeit eines statischen Ziels tritt dementsprechend bei einem statischen Ziel auf, welches sich auf der Sollachse (ϕ = 0) befindet.The movement parameters $v_x^e$

ω _z are in the 7 through a cross 63 shown that the extremum of the regression function 62 corresponds. The position of the cross 63 on the high-value axis corresponds to the speed of movement $v_x^e$

of the motor vehicle and the position of the cross 63 on the legal value axis stands with the rotation speed ω _z related how this is related to 10th is described in more detail below. The motor vehicle moves in the case of 7 exemplary straight (w _z = 0) at a speed of approximately $v_x^e=3.7\text{m / s}\text{.}$

The maximum radial velocity of a static target accordingly occurs with a static target that is on the target axis (ϕ = 0).

8a shows the motor vehicle 1 with radar unit 26 , the motor vehicle 1 in addition to the linear movement (cf. 5 ) a rotational movement ω _z carries out. The rotational movement ω _z takes place around a vertical axis 36 . Such a rotational movement ω _z occurs, for example, when cornering. The target axis 35 , which represents the forward direction of the vehicle, corresponds to the target position of the optical axis 37 the radar unit 26 . As in the example of the 5 points the radar unit here too 26 tilted up so that the optical axis 37 the radar unit 26 at an angle ψ _ext with respect to the target axis 35 lies. In front of the motor vehicle 1 there are static targets 32 , 32a . The relative speed of all static targets 32 , 32a regarding the motor vehicle 1 , represented by the arrows 33a , is for all static goals 32 identical. The radial velocity of the static targets 32 , represented by the arrows 33b , however, differ depending on the angle at which the particular target 32 occurs. The radial direction of static targets is based on the radar unit 26 by dashed lines 34 drawn. Through the rotational own movement ω _z of the motor vehicle 1 becomes the determined radial velocity of the static targets 32 influenced. Because of the rotational inherent movement ω _z does not have a static target on the optical axis 37 the radar unit 26 the greatest radial velocity, but a static target 32a which is at an angle to the optical axis 37 occurs.

unter einem Seitenwinkel Φ = ψ_ext + γ auf. Angenommen ψ_ext ist aus der Kalibrierung bekannt (vgl. 11 und entsprechende Beschreibung unten), so wird mittels Regression folglich ein Scheinwinkel γ bestimmt. Aus diesem Scheinwinkel γ lässt sich die rotatorische Eigenbewegung ω_z des Fahrzeugs ableiten. Berücksichtigt die statische Funktion und die Regressionsfunktion die Rotation ω_z der Eigenbewegung, so kann mittels der oben beschriebenen Prinzipien und unter Annahme, dass der extrinsische Parameter ψ_ext bekannt ist, somit auch die Rotation ω_z des Fahrzeugs ermittelt werden.In the diagram of the 8b is like the 7 the side angle ϕ with respect to the legal value axis and the radial speed with respect to the high value axis v _r applied. Due to the distance of the radar unit 26 to the axis of rotation 36 and the rotational movement ω _z and the tilt ψ _ext occurs the extreme of the static function $v_r=f\left({\omega }_{\mathrm{e.g.}},v_x^e,{\psi }_{ext},\varphi \right)$

at a side angle Φ = ψ _ext + γ. Accepted ψ _ext is known from calibration (cf. 11 and corresponding description below), a reject angle γ is consequently determined by means of regression. The rotational inherent motion can be derived from this apparent angle γ ω _z derive from the vehicle. Takes into account the static function and the regression function the rotation ω _z the eigenmovement, can be done using the principles described above and assuming that the extrinsic parameter ψ _ext the rotation is known ω _z of the vehicle can be determined.

v_r auf Grundlage von Näherungswerten, z.B. den Bewegungsparametern ω_z , $v_x^e$

aus einem vorherigen Messzyklus bestimmt. In Schritt 903 wird der Näherungsverlauf 60 der statischen Funktion $v_r=f\left({\omega }_z,v_x^e,\varphi \right)$

v_r um eine vorbestimmte Bandbreite 61 verbreitert. In Schritt 904 werden die Ziele 66a, 66b auf Grundlage des Näherungsverlaufs 60 der statischen Funktion $v_r=f\left({\omega }_z,v_x^e,\varphi \right)$

und deren Bandbreite 61 als statische Ziele 66a bzw. mobile Ziele 66b klassifiziert. In Schritt 905 wird die Regressionsfunktion 62 an die statischen Ziele 66a angepasst, womit die aktuellen Bewegungsparameter ω_z , $v_x^e$

bestimmt werden. Mit den Bewegungsparametern (odometrischen Parametern) ist somit die Eigenbewegung des Messsystems oder des Kraftfahrzeugs bekannt. Das Verfahren kann zyklisch wiederholt werden. So können die in Schritt 905 ermittelten Bewegungsparameter als Näherungswert (902 in 9) für die Ermittlung der statischen Ziele eines nachfolgenden Durchlaufs des Verfahrens genutzt werden. Das Verfahren zur Klassifizierung der Ziele als statisch bzw. mobil und zur Ermittlung der Eigenbewegung des Fahrzeugs kann somit fortlaufend durchgeführt werden, sodass die Eigenbewegung des Kraftfahrzeugs jederzeit bekannt ist.In the 9 the individual steps of the above-described method for determining the own movement of a motor vehicle are shown as a flow chart. In step 901 become the goals 66a , 66b a target list based on the measurement data of the radar measurement system. In step 902 becomes the approximation course 60 the static function $v_r=f\left({\omega }_{\mathrm{e.g.}},v_x^e,\varphi \right)$

v _r on the basis of approximate values, for example the movement parameters ω _z , $v_x^e$

determined from a previous measurement cycle. In step 903 becomes the approximation course 60 the static function $v_r=f\left({\omega }_{\mathrm{e.g.}},v_x^e,\varphi \right)$

v _r by a predetermined bandwidth 61 broadened. In step 904 become the goals 66a , 66b based on the approximation course 60 the static function $v_r=f\left({\omega }_{\mathrm{e.g.}},v_x^e,\varphi \right)$

and their range 61 as static goals 66a or mobile targets 66b classified. In step 905 becomes the regression function 62 to the static goals 66a adjusted, with which the current movement parameters ω _z , $v_x^e$

be determined. With the movement parameters (odometric parameters), the inherent movement of the measuring system or the motor vehicle is known. The process can be repeated cyclically. So you can in step 905 determined motion parameters as an approximate value ( 902 in 9 ) can be used to determine the static goals of a subsequent run of the procedure. The method for classifying the targets as static or mobile and for determining the own movement of the vehicle can thus be carried out continuously, so that the own movement of the motor vehicle is known at all times.

Compensation of sensor tilt through calibration

• Angenommen, der Sensor (s in 10) ist um Winkel ψ_ext, θ_ext verstellt (in 10 ist zur Vereinfachung nur ψ_ext gezeigt) und angenommen, die Verstellung ϕ_ext ≈ 0 (Rollwinkel), dann wird die Rotation zwischen dem versetzten Sensorrahmen und dem Fahrzeugrahmen mit den folgenden 3D-Drehmatrizen definiert: $$\begin{array}{l}{R}_s^e=R_z\left({\psi }_{ext}\right)R_y\left({\theta }_{ext}\right)R_x\left({\varphi }_{ext}=0\right)\\ R_e^s={\left(R_s^e\right)}^T\end{array}$$
  
  $$R_z\left({\psi }_{ext}\right)=\left[\begin{array}{ccc}\text{cos}\left({\psi }_{ext}\right)& -\text{sin}\left({\psi }_{ext}\right)& 0\\ \text{sin}\left({\psi }_{ext}\right)& \text{cos}\left({\psi }_{ext}\right)& 0\\ 0& 0& 1\end{array}\right]$$
  
  $$R_y\left({\theta }_{ext}\right)=\left[\begin{array}{ccc}\text{cos}\left({\theta }_{ext}\right)& 0& -\text{sin}\left({\theta }_{ext}\right)\\ 0& 1& 0\\ -\text{sin}\left({\theta }_{ext}\right)& 0& \text{cos}\left({\theta }_{ext}\right)\end{array}\right]$$
  
  $$R_x\left({\varphi }_{ext}\right)=\left[\begin{array}{ccc}1& 0& 0\\ 0& \text{cos}\left({\varphi }_{ext}\right)& -\text{sin}\left({\varphi }_{ext}\right)\\ 0& \text{sin}\left({\varphi }_{ext}\right)& \text{cos}\left({\varphi }_{ext}\right)\end{array}\right]$$
  
  Die Winkel ψ_ext, θ_ext , ϕ_ext, welche die Verstellung des Sensors bezüglich der Soll-Lage beschreiben, werden hier als extrinsische Parameter bezeichnet, die es in der Kalibrierungsphase zu bestimmen gilt.

When determining the static function f can also be an adjustment of the sensor ("sensor frame") in relation to the vehicle frame ("ego frame" or vehicle frame), as in 10th is shown schematically, taken into account and compensated by calibration:

• Suppose the sensor ( s in 10th ) is by angle ψ _ext, θ _ext adjusted (in 10th is for simplification only ψ _ext shown) and assuming the adjustment ϕ _ext ≈ 0 (roll angle), then the rotation between the offset sensor frame and the vehicle frame is defined with the following 3D turning matrices: $$\begin{array}{l}{R}_s^e=R_{\mathrm{e.g.}}\left({\psi }_{ext}\right)R_y\left({\theta }_{ext}\right)R_x\left({\varphi }_{ext}=0\right)\\ R_e^s={\left(R_s^e\right)}^T\end{array}$$
  
  $$R_{\mathrm{e.g.}}\left({\psi }_{ext}\right)=\left[\begin{array}{ccc}\text{cos}\left({\psi }_{ext}\right)& -\text{sin}\left({\psi }_{ext}\right)& 0\\ \text{sin}\left({\psi }_{ext}\right)& \text{cos}\left({\psi }_{ext}\right)& 0\\ 0& 0& 1\end{array}\right]$$
  
  $$R_y\left({\theta }_{ext}\right)=\left[\begin{array}{ccc}\text{cos}\left({\theta }_{ext}\right)& 0& -\text{sin}\left({\theta }_{ext}\right)\\ 0& 1& 0\\ -\text{sin}\left({\theta }_{ext}\right)& 0& \text{cos}\left({\theta }_{ext}\right)\end{array}\right]$$
  
  $$R_x\left({\varphi }_{ext}\right)=\left[\begin{array}{ccc}1& 0& 0\\ 0& \text{cos}\left({\varphi }_{ext}\right)& -\text{sin}\left({\varphi }_{ext}\right)\\ 0& \text{sin}\left({\varphi }_{ext}\right)& \text{cos}\left({\varphi }_{ext}\right)\end{array}\right]$$
  
  The angles ψ _ext, θ _ext , ϕ _ext , which describe the adjustment of the sensor with respect to the target position, are referred to here as extrinsic parameters which have to be determined in the calibration phase.

Diese Gleichung kann für einen Kalibrierungsalgorithmus zur Optimierung verwendet werden. Die extrinsischen Parameter, die geschätzt werden, sind ψ_ext , θ_ext . Die Bewegungsparameter $v_x^e$

und ω_z des Fahrzeugs werden bei der Kalibrierung festgelegt und als bekannt vorausgesetzt. Nach der Bestimmung der ψ_ext , θ_ext in der Kalibrierung sind ψ_ext , θ_ext bekannt und $v_x^e$

und ω_z können nach der Kalibrierung wie oben beschrieben durch Regression geschätzt werden. Wenn die extrinsischen Parameter ψ_ext , θ_ext des Radarsensors bekannt sind, kann man demnach wie in Zusammenhang mit den 7 bis 9 beschrieben das Extremum der Radialgeschwindigkeit statischer Ziele ermitteln und man erhält so die Geschwindigkeit des Kraftfahrzeugs. Neben der Eigengeschwindigkeit $v_x^e$

kann man auf entsprechende Weise auch die Rotationsbewegung ω_z des Kraftfahrzeugs unter Berücksichtigung von Verstellungen des Sensors ermitteln.The static function f can be formulated as follows, assuming ϕ _ext ≈ 0: $$v_r=-{\left(\frac{\overrightarrow{r}}{r}\right)}^T{R}_e^s\left[{\overrightarrow{v}}^e+\mathrm{\Omega }{\overrightarrow{p}}_s^e\right]=f\left({\omega }_{\mathrm{e.g.}},v_x^e,{\psi }_{ext},{\theta }_{ext},\theta ,\varphi \right)$$

This equation can be used for a calibration algorithm for optimization. The extrinsic parameters that are estimated are ψ _ext , θ _ext . The movement parameters $v_x^e$

and ω _z of the vehicle are determined during calibration and assumed to be known. After determining the ψ _ext , θ _ext are in calibration ψ _ext , θ _ext known and $v_x^e$

and ω _z can be estimated by regression after calibration as described above. If the extrinsic parameters ψ _ext , θ _ext the radar sensor are known, you can therefore as in connection with the 7 to 9 described determine the extremum of the radial speed of static targets and you get the speed of the motor vehicle. In addition to the airspeed $v_x^e$

you can also do the rotational movement in a corresponding way ω _z of the motor vehicle taking into account adjustments of the sensor.

The following describes an example of how the regression can be carried out using a least-square method.

wobei ε das angenommene Gaußsche Rauschen ist, mit dem die Messungen behaftet sind, also der angenommene statistische Messfehler, und wobei $$\left(\begin{array}{c}{v}_x\\ v_y\\ v_z\end{array}\right)=R_e^s{\overrightarrow{v}}_{tot}=R_e^s\left[{\overrightarrow{v}}^e+\mathrm{\Omega }{\overrightarrow{p}}_s^e\right]$$

wobei $\left(\begin{array}{c}{v}_x\\ v_y\\ v_z\end{array}\right)$

die Sensorgeschwindigkeit ${\overrightarrow{v}}_{tot}$

ausgedrückt im Sensorrahmen ist und damit v_x , v_y und v_z die kartesischen Komponenten der vom Radar gemessenen Ziel-Geschwindigkeit sind.For each measurement cycle, GI. 3 Express the radial velocity of a target as follows: $$v_r=-{\left(\frac{\overrightarrow{r}}{r}\right)}^T\left(\begin{array}{c}{v}_x\\ v_y\\ v_{\mathrm{e.g.}}\end{array}\right)+\epsilon$$

in which ε is the assumed Gaussian noise with which the measurements are afflicted, that is the assumed statistical measurement error, and where $$\left(\begin{array}{c}{v}_x\\ v_y\\ v_{\mathrm{e.g.}}\end{array}\right)=R_e^s{\overrightarrow{v}}_{tOt}=R_e^s\left[{\overrightarrow{v}}^e+\mathrm{\Omega }{\overrightarrow{p}}_s^e\right]$$

in which $\left(\begin{array}{c}{v}_x\\ v_y\\ v_{\mathrm{e.g.}}\end{array}\right)$

the sensor speed ${\overrightarrow{v}}_{tOt}$

is expressed in the sensor frame and thus v _x , v _y and v _z are the Cartesian components of the target speed measured by the radar.

In the following it is assumed that the assumed Gaussian noise ε has a mean value of zero and a variance σ ² .

mit $$M=\left(\begin{array}{ccc}cos{\theta }_1\text{ cos}{\varphi }_1& \text{cos}{\theta }_1\text{ sin}{\varphi }_1& \text{sin}{\theta }_1\\ ⋮& ⋮& ⋮\\ cos{\theta }_N\text{ cos}{\varphi }_N& \text{cos}{\theta }_N\text{ sin}{\varphi }_N& \text{sin}{\theta }_N\end{array}\right)$$

wobei v_r nun ein N × 1 Vektor ist.For N measurement cycles, the model can be expressed as follows $$v_r=-M\left(\begin{array}{c}{v}_x\\ v_y\\ v_{\mathrm{e.g.}}\end{array}\right)+\epsilon$$

With $$M=\left(\begin{array}{ccc}cOs{\mathrm{<figure-callout\; id="1"\; label="\theta "\; filenames="DE102018218003A1\_0091.png"\; state="\{\{state\}\}">\theta </figure-callout>}}_1\text{cos}{\varphi }_1& \text{cos}{\theta }_1\text{<figure-callout id="1" label="sin φ" filenames="DE102018218003A1\_0091.png" state="{{state}}">sin </figure-callout>}{\varphi }_{}{}_1& \text{<figure-callout id="1" label="sin θ" filenames="DE102018218003A1\_0091.png" state="{{state}}">sin </figure-callout>}{\theta }_{}{}_1\\ ⋮& ⋮& ⋮\\ cOs{\theta }_N\text{cos}{\varphi }_N& \text{cos}{\theta }_N\text{sin}{\varphi }_N& \text{sin}{\theta }_N\end{array}\right)$$

in which v _r is now an N × 1 vector.

Wird GL. 6 nach den Geschwindigkeiten aufgelöst, so erhält man die extrinsischen Parameter ψ_ext und θ_ext .As for example in CD Ghilani, Adjustment computations: spatial data analysis. John Wiley & Sons, described in 2010, is the "unbiased estimator" for speed: $$\left(\begin{array}{c}{v}_x\\ v_y\\ v_{\mathrm{e.g.}}\end{array}\right)=-{\left(M^{T}M\right)}^{-1}{M}^T{v}_r$$

If GL. 6 resolved according to the speeds, you get the extrinsic parameters ψ _ext and θ _ext .

In diesem Fall ist σ² der wahre Wert der Varianz der Radialgeschwindigkeit. Ein unbiased Estimator für diesen Parameter ist: $${\widehat{\sigma }}^2=\frac{{\widehat{\epsilon }}^T\widehat{\epsilon }}{N-3},$$

Ausgedrückt als Residuen: $$\widehat{\epsilon }=v_{rad}+M\left(\begin{array}{c}{v}_x\\ v_y\\ v_z\end{array}\right).$$

Die Kovarianzmatrix kann aus den N Messzyklen abgeschätzt werden in der Form: $$Cov\left(\left(\begin{array}{c}{v}_x\\ v_y\\ v_z\end{array}\right)\right)=\frac{{\widehat{\epsilon }}^T\widehat{\epsilon }}{N-3}{\left(M^{T}M\right)}^{-1}$$

The covariance matrix for the system of equations is: $$cOv\left(\left(\begin{array}{c}{v}_x\\ v_y\\ v_{\mathrm{e.g.}}\end{array}\right)\right)={\sigma }^{\mathrm{2nd}}{\left(M^{T}M\right)}^{-1}.$$

In this case, σ ^{2 is} the true value of the radial velocity variance. An unbiased estimator for this parameter is: $${\widehat{\sigma }}^{\mathrm{2nd}}=\frac{{\widehat{\epsilon }}^T\widehat{\epsilon }}{N-\mathrm{3rd}},$$

Expressed as residuals: $$\widehat{\epsilon }=v_{rad}+M\left(\begin{array}{c}{v}_x\\ v_y\\ v_{\mathrm{e.g.}}\end{array}\right).$$

The covariance matrix can be derived from the N Measuring cycles are estimated in the form: $$\mathrm{C.}Ov\left(\left(\begin{array}{c}{v}_x\\ v_y\\ v_{\mathrm{e.g.}}\end{array}\right)\right)=\frac{{\widehat{\epsilon }}^T\widehat{\epsilon }}{N-\mathrm{3rd}}{\left(M^{T}M\right)}^{-1}$$

Mit dem Vektor $$\overrightarrow{\mathrm{\Theta }}={\left({\psi }_{ext},{\theta }_{ext},v_e\right)}^T$$

kann gemäß der bekannten Methoden die Jacobi-Matrix aufgestellt werden: $$J=\frac{\partial \overrightarrow{v}}{\partial \overrightarrow{\mathrm{\Theta }}}=\left(\begin{array}{ccc}\frac{\partial v_x}{\partial {\psi }_{ext}}& \frac{\partial v_x}{\partial {\theta }_{ext}}& \frac{\partial v_x}{\partial v_e}\\ \frac{\partial v_y}{\partial {\psi }_{ext}}& \frac{\partial v_y}{\partial {\theta }_{ext}}& \frac{\partial v_y}{\partial v_e}\\ \frac{\partial v_z}{\partial {\psi }_{ext}}& \frac{\partial v_z}{\partial {\theta }_{ext}}& \frac{\partial v_z}{\partial v_e}\end{array}\right)$$

und man erhält in erster Ordnung die Näherung: $$Cov\left(\overrightarrow{\mathrm{\Theta }}\right)\cong J^{-1}Cov\left(\overrightarrow{v}\right)J^{-T}$$

Die Diagonalelemente dieser Kovarianzmatrix entsprechen den Kovarianzen ${\widehat{\sigma }}_{{\psi }_{ext}}^2,{\widehat{\sigma }}_{{\theta }_{ext}}^2,{\widehat{\sigma }}_{v_x^e}^2$

der extrinsischen Winkel bzw. der geschätzten Geschwindigkeit.The speed estimated in this way can thus be a function of the extrinsic angle ψ _ext , θ _ext and the known movement parameter v _e are expressed: $$\left(\begin{array}{c}{v}_x\\ v_y\\ v_{\mathrm{e.g.}}\end{array}\right)=\overrightarrow{v}\left({\psi }_{ext},{\theta }_{ext},v_e\right).$$

With the vector $$\overrightarrow{\mathrm{\Theta }}={\left({\psi }_{ext},{\theta }_{ext},v_e\right)}^T$$

the Jacobi matrix can be set up according to the known methods: $$J=\frac{\partial \overrightarrow{v}}{\partial \overrightarrow{\mathrm{\Theta }}}=\left(\begin{array}{ccc}\frac{\partial v_x}{\partial {\psi }_{ext}}& \frac{\partial v_x}{\partial {\theta }_{ext}}& \frac{\partial v_x}{\partial v_e}\\ \frac{\partial v_y}{\partial {\psi }_{ext}}& \frac{\partial v_y}{\partial {\theta }_{ext}}& \frac{\partial v_y}{\partial v_e}\\ \frac{\partial v_{\mathrm{e.g.}}}{\partial {\psi }_{ext}}& \frac{\partial v_{\mathrm{e.g.}}}{\partial {\theta }_{ext}}& \frac{\partial v_{\mathrm{e.g.}}}{\partial v_e}\end{array}\right)$$

and you get the approximation in the first order: $$\mathrm{C.}Ov\left(\overrightarrow{\mathrm{\Theta }}\right)\cong J^{-1}\mathrm{C.}Ov\left(\overrightarrow{v}\right)J^{-T}$$

The diagonal elements of this covariance matrix correspond to the covariances ${\widehat{\sigma }}_{{\psi }_{ext}}^{\mathrm{2nd}},{\widehat{\sigma }}_{{\theta }_{ext}}^{\mathrm{2nd}},{\widehat{\sigma }}_{v_x^e}^{\mathrm{2nd}}$

the extrinsic angle or the estimated speed.

and ${\widehat{\sigma }}_{{\theta }_{ext,i}}^2$

der extrinsischen Winkel aus der Fehlerfortpflanzung wie oben beschrieben für jeden Messzyklus i ermittelt wurden, kann ein Schätzwert für den extrinsischen Parameter ψ_ext als ein gewichteter Mittelwert über die mehrere Messzyklen i wie folgt berechnet werden: $${\psi }_{ext}=\frac{{\displaystyle {\sum }_i\frac{{\psi }_{ext,i}}{{\widehat{\sigma }}_{{\psi }_{ext,i}}^2}}}{{\widehat{\sigma }}_{{\psi }_{ext}}^2}$$

wobei die Varianz ${\widehat{\sigma }}_{{\psi }_{ext}}^2$

wie folgt aus den jeweiligen Kovarianzen der einzelnen Messzyklen i abgeschätzt wird: $${\widehat{\sigma }}_{{\psi }_{ext}}^2=\frac{1}{{\displaystyle {\sum }_i\frac{1}{{\widehat{\sigma }}_{{\psi }_{ext,i}}^2}}}$$

After the covariances ${\widehat{\sigma }}_{{\psi }_{ext,i}}^{\mathrm{2nd}}$

and ${\widehat{\sigma }}_{{\theta }_{ext,i}}^{\mathrm{2nd}}$

The extrinsic angle from the propagation of errors as described above for each measurement cycle i can be an estimate of the extrinsic parameter ψ _ext as a weighted average over which several measurement cycles i are calculated as follows: $${\psi }_{ext}=\frac{{\displaystyle {\sum }_i\frac{{\psi }_{ext,i}}{{\widehat{\sigma }}_{{\psi }_{ext,i}}^{\mathrm{2nd}}}}}{{\widehat{\sigma }}_{{\psi }_{ext}}^{\mathrm{2nd}}}$$

being the variance ${\widehat{\sigma }}_{{\psi }_{ext}}^{\mathrm{2nd}}$

is estimated as follows from the respective covariances of the individual measurement cycles i: $${\widehat{\sigma }}_{{\psi }_{ext}}^{\mathrm{2nd}}=\frac{1}{{\displaystyle {\sum }_i\frac{1}{{\widehat{\sigma }}_{{\psi }_{ext,i}}^{\mathrm{2nd}}}}}$$

einzelner Messzyklen über einen bestimmten Zeitraum gepuffert werden und die gemittelten Werte ψ_ext , ${\widehat{\sigma }}_{{\psi }_{ext}}^2$

gemäß Gleichungen 8 und 9 auf Basis der gepufferten Werte berechnet werden.These averages can be calculated, for example, by the values ψ _{ext, i} , ${\widehat{\sigma }}_{{\psi }_{ext,i}}^{\mathrm{2nd}}$

individual measuring cycles over a certain period of time and the averaged values ψ _ext , ${\widehat{\sigma }}_{{\psi }_{ext}}^{\mathrm{2nd}}$

can be calculated according to equations 8 and 9 based on the buffered values.

Estimates for the extrinsic parameter θ _ext and its variance can be determined in an analogous manner.

auf Grundlage der als bekannt vorausgesetzten Bewegungsparameter ω_z , $v_x^e$

und auf Grundlage von Näherungswerten für die extrinsischen Parameter ψ_ext , θ_ext bestimmt. Als Näherungswerte für die extrinsischen Parameter ψ_ext , θ_ext werden beispielsweise die extrinsischen Parameter aus einer vorherigen Iteration des Verfahrens verwendet, oder vorbestimmte oder willkürliche Initialisierungswerte für die extrinsischen Parameter für den Beginn des Verfahrens. In Schritt 1003 wird der Näherungsverlauf 60 der statischen Funktion $v_r=f\left({\omega }_z,v_x^e,{\psi }_{ext},{\theta }_{ext},\theta ,\varphi \right)$

um eine vorbestimmte Bandbreite 61 verbreitert. In Schritt 1004 werden die Ziele 66a, 66b auf Grundlage des Näherungsverlaufs 60 der statischen Funktion $v_r=f\left({\omega }_z,v_x^e,{\psi }_{ext},{\theta }_{ext},\theta ,\varphi \right)$

und deren Bandbreite 61 als statische Ziele 66a bzw. mobile Ziele 66b klassifiziert. In Schritt 905 wird die Regressionsfunktion 62 an die statischen Ziele 66a angepasst, womit korrigierte extrinsischen Parameter ψ_ext, θ_ext bestimmt werden. Das Verfahren wird zyklisch wiederholt. Insbesondere können die Schritte 1001 bis 1005 mehrfach anhand von Messdaten unterschiedlicher Messzyklen durchgeführt werden. Dadurch, dass die Schritte 1001 bis 1005 zyklisch durchgeführt werden, nähern sich die ermittelten extrinsischen Parameter iterativ den tatsächlichen Werten der Parameter an. So können die in Schritt 1005 ermittelten extrinsischen Parameter als Näherungswert (1002 in 10) als Näherungswerte für die Ermittlung der statischen Ziele eines nachfolgenden Durchlaufs des Verfahrens genutzt werden. Auf diese Weise nähern sich die jeweils in Schritt 905 ermittelten extrinsischen Parameter ψ_ext, θ_ext den tatsächlichen extrinsischen Parameter n iterativ an.In 11 The individual steps of the calibration method described above for determining the extrinsic parameters are shown as a flow chart. In step 1001 become the goals 66a , 66b the target list based on the measurement data of the radar measurement system. In step 1002 becomes the approximation course 60 the static function $v_r=f\left({\omega }_{\mathrm{e.g.}},v_x^e,{\psi }_{ext},{\theta }_{ext},\theta ,\varphi \right)$

on the basis of the movement parameters assumed to be known ω _z , $v_x^e$

and based on approximations for the extrinsic parameters ψ _ext , θ _ext certainly. As approximate values for the extrinsic parameters ψ _ext , θ _ext For example, the extrinsic parameters from a previous iteration of the method are used, or predetermined or arbitrary initialization values for the extrinsic parameters for the start of the method. In step 1003 becomes the approximation course 60 the static function $v_r=f\left({\omega }_{\mathrm{e.g.}},v_x^e,{\psi }_{ext},{\theta }_{ext},\theta ,\varphi \right)$

by a predetermined bandwidth 61 broadened. In step 1004 become the goals 66a , 66b based on the approximation course 60 the static function $v_r=f\left({\omega }_{\mathrm{e.g.}},v_x^e,{\psi }_{ext},{\theta }_{ext},\theta ,\varphi \right)$

and their range 61 as static goals 66a or mobile targets 66b classified. In step 905 becomes the regression function 62 to the static goals 66a adapted, with which corrected extrinsic parameters ψ _ext , θ _{ext are} determined. The process is repeated cyclically. In particular, the steps 1001 to 1005 be carried out several times using measurement data from different measurement cycles. By doing the steps 1001 to 1005 are carried out cyclically, the determined extrinsic parameters iteratively approach the actual values of the parameters. So you can in step 1005 determined extrinsic parameters as approximate values ( 1002 in 10th ) are used as approximate values for determining the static goals of a subsequent run of the method. In this way, they approach each other in step 905 determined extrinsic parameters ψ _ext , θ _ext iteratively the actual extrinsic parameter.

The method can be terminated after a predetermined number of iteration steps, or if it is recognized that the extrinsic parameters determined no longer differ significantly between two successive iterations. The extrinsic parameters of a plurality of measurement cycles can be averaged, for example by providing a weighted mean value which describes the actual values of the extrinsic parameters well, as was described in detail above.

In the calibration procedure of 11 not only the side angle ϕ and the radial speed v _r considered, but also the elevation angle θ . 12th shows, for example, the estimation of the extrinsic parameters in a 2D space ψ _ext , and θ _ext based on the static function and fitting a 2D plane into the points v _{rad, n} (ϕ, θ ), which describes the radial speed of the nth target depending on the respective angle (azimuth angle and elevation angle). The method can be extended to other dimensions based on the principles described. The methods can be used in particular for location systems such as radar measuring systems that scan a multidimensional space.

Reference list

1

Motor vehicle

12th

Control unit for steering system

14

Control unit for brake system

16

Control unit for drive train

18th

Control unit for autonomous driving

20th

optical sensors

22

Central control unit

24th

Satellite navigation unit

26

Radar unit

28

Vehicle communication network

32

static goals

32a

static goals

33a

Relative speed of the static targets

34

Radial direction of the static targets

35

Target axis of the radar unit

36

Vertical axis (rotation axis)

37

optical axis of the radar unit

51

Field of view of the radar unit

51a

Limiting the field of view of the radar unit

52

central optical axis 52 of the radar unit

53

Vehicle longitudinal axis

54

Speed of movement of the vehicle

60

Approximation course of the static function

61

Bandwidth of the static function

62

Regression function

63

Extreme of the regression function

66a

static goals

66b

mobile targets

210

Processor of a control unit

220

RAM of a control unit

230

ROM of a control unit

240

Vehicle communication network interface of a control unit

250

Communication network of a control unit

260

external storage drive of a control unit

301

RF chip

302

Interface to the vehicle communication system

303

Processor unit

304

Radar µC

Claims (20)

Method for determining the self-movement of a motor vehicle (1) using a location system (26) which provides a number of targets (66a, 66b), the method: a) determining which of the targets (66a, 66b) provided by the location system are static targets (66a), b) a regression function (63) is adapted to the static targets (66a), the regression function (63) representing the expected distribution (f) of the static targets (66a) in the field of view (51; v _r , ϕ) of the location system (26) describes and one or more movement parameters $\left({\omega }_{\mathrm{e.g.}},v_x^e\right)$

the motor vehicle (1) is dependent; and c) one or more of the motion parameters $\left({\omega }_{\mathrm{e.g.}},v_x^e\right)$

of the motor vehicle (1) can be determined from the adapted regression function (63). Procedure according to Claim 1 , in which an approximation course (60) of the regression function (63) is determined and the targets (66a, 66b) are classified on the basis of the approximation course (60) as static targets (66a) or mobile targets (66b). Procedure according to Claim 2 , in which the approximation course (60) of the regression function (63) on the basis of approximation values of the movement parameters $\left({\omega }_{\mathrm{e.g.}},v_x^e\right)$

is determined. Procedure according to Claim 3 , where the approximate values of the movement parameters $\left({\omega }_{\mathrm{e.g.}},v_x^e\right)$

based on motion parameters $\left({\omega }_{\mathrm{e.g.}},v_x^e\right)$

can be obtained from a previous measurement cycle. Procedure according to one of the Claims 2 , 3rd , or 4, in which the approximation course (60) is broadened by a bandwidth (61) and the targets within the bandwidth (61) are classified as static targets (66a). Method according to one of the preceding claims, wherein the movement parameters $\left({\omega }_{\mathrm{e.g.}},v_x^e\right)$

of the motor vehicle (1) a translational $\left(v_x^e\right)$

and / or describes a rotational (w _z ) movement of the motor vehicle (1) or the location system (26). Method according to one of the preceding claims, characterized in that the regression function (63) is adapted to the static targets by linear regression analysis. Method according to one of the preceding claims, characterized in that steps a) to c) are carried out continuously on the basis of measurement data of successive measurement cycles. Method according to one of the preceding claims, wherein the regression function (63) is _further dependent on one or more extrinsic parameters (ψ _ext , θ _ext ). Procedure according to Claim 9 , in which the extrinsic parameters (ψ _ext , θ _ext ) are determined by in a calibration phase in which the movement parameters $\left({\omega }_{\mathrm{e.g.}},v_x^e\right)$

of the motor vehicle (1) are known, it is determined which of the targets (66a, 66b) provided by the location system (26) are static targets (66a), the regression function (63) is adapted to the static targets (66a), and an or several of the extrinsic parameters (ψ _ext , θ _ext ) of the motor vehicle (1) are determined from the adapted regression function (63). Procedure according to Claim 9 or 10th , wherein the extrinsic parameters describe a tilt angle (ψ _ext , θ _ext ) of the location system (26) with respect to the target installation position of the location system (26). Method for calibrating a location system (26) which provides a number of targets, the method: a) determining which of the targets (66a, 66b) provided by the location system (26) are static targets (66a), b) a Regression function (63) is adapted to the static targets (66a), the regression function (63) describing the expected distribution (f) of the static targets (66a) in the field of view (51; v _r , ϕ) of the location system (26) and by one or more known movement parameters $\left({\omega }_{\mathrm{e.g.}},v_x^e\right)$

of the motor vehicle (1) and is dependent on extrinsic parameters (ψ _ext, θ _ext ), and c) one or more of the extrinsic parameters (ψ _ext, θ _ext ) of the motor vehicle (1) are determined from the adapted regression function (63). Procedure according to Claim 12 , in which an approximation course (60) of the regression function (63) is determined and the targets (66a, 66b) are classified on the basis of the approximation course (60) as static targets (66a) or mobile targets (66b). Procedure according to one of the Claims 12 or 13 , in which the approximate course (60) of the regression function (63) is determined on the basis of approximate values of the extrinsic parameters (ψ _ext, θ _ext ). Procedure according to one of the Claims 12 to 14 , whereby the approximate values of the extrinsic parameters (ψ _ext, θ _ext ) are obtained on the basis of extrinsic parameters (ψ _ext, θ _ext ) from a previous iteration. Procedure according to one of the Claims 12 to 15 , in which the approximation course (60) is widened by a bandwidth (61) and the targets within the bandwidth (61) are classified as static targets (66a). Procedure according to one of the Claims 12 to 16 , wherein the extrinsic parameters describe a tilt angle (ψ _ext , θ _ext ) of the location system (26) with respect to the target installation position of the location system (26). Procedure according to one of the Claims 12 to 17th , the movement parameters $\left({\omega }_{\mathrm{e.g.}},v_x^e\right)$

of the motor vehicle (1) a translational $\left(v_x^e\right)$

and / or describes a rotational (w _z ) movement of the motor vehicle (1) or the location system (26). Procedure according to one of the Claims 12 to 18th , wherein the regression function (63) is adapted to the static targets by linear regression analysis. Procedure according to one of the Claims 12 to 19th , steps a) to c) being carried out cyclically.

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