DE102016220717A1 - Determining a lane and lateral control for a vehicle - Google Patents

Abstract

The invention relates to capturing image data (6) with a camera (2) of a vehicle (10) and merging this image data (6) with route data (1) to determine fused data (7) and dependent on the fused data (7) to determine a lane for the vehicle (10). The route data (1) comprise information about a route on which the vehicle (10) travels. By means of the fused data, a curvature (ĸ) of the route for determining the traffic lane can be taken into account, which can advantageously be used for a lateral control or a lane keeping assistance function of the vehicle (10), for example by determining a steering angle (δ).

Description

The present invention relates, on the one hand, to the determination of a traffic lane of a vehicle and, on the other hand, to the determination of a steering angle for the transverse control of a vehicle.

The WO 2011/131165 A1 discloses the determination of the road course for a motor vehicle. Sensor data from an environmental sensor system, which can represent both a camera and a digital map with GPS, are provided for an evaluation unit. A further processing of the data obtained by the environmental sensor system is not described.

Based on this prior art, the present invention has the object to process sensor data before they are used for the determination of a lane or for the lateral control of a vehicle.

According to the invention, this object is achieved by a method for a vehicle according to claim 1 and by a device according to claim 8. The dependent claims define preferred and advantageous embodiments of the present invention.

• • Erfassen von Bilddaten mit Hilfe einer Kamera des Fahrzeugs.
• • Fusionieren dieser Bilddaten mit Streckendaten, welche Informationen über eine Fahrstrecke umfassen, auf welcher das Fahrzeug fährt oder steht, um dann durch diese Fusion fusionierte Daten zu erstellen. Die Streckendaten umfassen dabei insbesondere Krümmungsinformationen und/oder Höhendaten der Fahrstrecke und können auf Navigationsdaten und hinterlegten Straßenkarten basieren. Die Streckendaten ermöglichen eine Vorausschau auf die vor dem Fahrzeug befindliche Straßengeometrie. Die Krümmungsinformationen umfassen insbesondere Informationen über eine Krümmung oder ein Krümmungsmaß beispielsweise einer Mittellinie der Fahrstrecke. Beispielsweise kann der Streckenverlauf der Fahrstrecke anhand von Klothoiden eindeutig beschrieben werden, wobei die Streckendaten zu jedem Ortpunkt dieser Klothoiden eine Krümmungsinformation bereitstellen. Die Höhendaten umfassen beispielsweise eine Angabe über die Höhe bestimmter Stellen (z.B. der Mittellinie oder der Ortspunkte der Klothoiden) der Fahrstrecke. Die fusionierten Daten beschreiben insbesondere Eigenschaften der Fahrstrecke, wie beispielsweise Ausmaße und einen Verlauf der Fahrstrecke, so dass aus den fusionierten Daten eine Krümmung oder ein Krümmungsverlauf der Fahrstrecke oder einer Fahrspur des Fahrzeugs innerhalb der Fahrstrecke abgeleitet werden kann. Die fusionierten Daten stützen sich dabei in der Nähe des Fahrzeugs mehr auf die Bilddaten, während sie sich in größerer Entfernung vom Fahrzeug mehr auf die Streckendaten stützen. Mit anderen Worten stützen sich die fusionierten Daten umso mehr auf die Streckendaten, je weiter entfernt von dem Fahrzeug sich diejenige Stelle der Fahrstrecke befindet, deren Eigenschaften die fusionierten Daten beschreiben.
• • Bestimmen der Fahrspur abhängig von den fusionierten Daten. Die Fahrspur entspricht dabei insbesondere quasi einem Fahrschlauch, welcher in der Breite dieselben Ausmaße wie das Fahrzeug aufweist und denjenigen Weg beschreibt, den das Fahrzeug auf der Fahrstrecke voraussichtlich bzw. laut Plan zurücklegen wird. Es ist aber auch möglich, dass die Fahrspur quasi einer Fahrlinie entspricht, wie es bei einem Einspurmodell der Fall ist.

In the context of the present invention, a method for a vehicle is provided which comprises the following steps:

• • Capture image data using a camera of the vehicle.
• • Fusion of this image data with route data, which includes information about a route on which the vehicle is traveling or standing, and then to create data fused by this merger. The route data in particular include curvature information and / or altitude data of the route and can be based on navigation data and stored road maps. The route data enables a preview of the road geometry in front of the vehicle. The curvature information in particular includes information about a curvature or a curvature, for example, a center line of the route. For example, the route of the route can be clearly described on the basis of clothoids, the route data providing curvature information for each location point of these clothoids. The height data include, for example, an indication of the height of specific locations (eg the center line or the location points of the clothoids) of the route. The merged data describe in particular properties of the route, such as dimensions and a course of the route, so that from the merged data, a curvature or a curvature of the route or a lane of the vehicle can be derived within the route. The fused data rely more on the image data near the vehicle, while relying more on the distance data at a greater distance from the vehicle. In other words, the farther from the vehicle the location of the route whose properties describe the fused data is all the more based on the fused data.
• • Determine the lane depending on the fused data. The lane corresponds in particular quasi a driving tube, which has the same dimensions in width as the vehicle and describes the path that the vehicle is expected to cover on the route or according to plan. But it is also possible that the lane is more or less equivalent to a driving line, as is the case with a single-track model.

The fusion of the image data and route data for determining the traffic lane represent an advantageous further processing of sensor data (i.e., the image data) so that the above object is achieved. By determining the lane on the basis of the fused data, the accuracy of the image data in a near area in front of the vehicle is more or less combined with the accuracy of the route data in a more distant area in front of the vehicle. This combines the benefits of camera accuracy with predictive path data forecasting. The lane determined according to the invention can be used on the one hand for automatic lateral control, but also for a lane departure warning. Here, the automatic lateral control of a vehicle is understood that the driver does not intervene in the steering and the steering does not need to monitor. This means that the automatic lateral control steers the vehicle autonomously. On the other hand, the driver has to monitor a lane-keeping assistant, whereby the driver can intervene in the steering. That is, the lane departure warning assists the driver in steering the vehicle and only steers the vehicle alone when the driver is not engaged.

According to one embodiment of the invention, determining the lane includes determining a curvature or curvature of the route from the fused data. In addition, in this embodiment, condition data of the vehicle is measured. Based on the curvature and the measured condition data, a steering angle of the Vehicle determined. In this case, a steering angle is to be understood as meaning, in particular, the mean wheel steering angle of the steered axle of the vehicle in the force-free state.

This embodiment can be used, for example, for an automatic lateral control of the vehicle.

• • Einen Schwimmwinkel des Fahrzeugs. Dabei wird unter dem Schwimmwinkel der Winkel zwischen der Fahrzeuglängsachse und dem Geschwindigkeitsvektor des Fahrzeugs im Fahrzeugschwerpunkt verstanden.
• • Eine Giergeschwindigkeit des Fahrzeugs. Die Giergeschwindigkeit des Fahrzeugs bezeichnet die Winkelgeschwindigkeit der Drehung des Fahrzeugs um seine Hochachse. Dabei bezeichnet die Hochachse oder Gierachse die vertikale Achse des Fahrzeugs oder genauer des fahrzeugfesten Koordinatensystems.
• • Eine Querabweichung des Fahrzeugs. Die Querabweichung bezeichnet den Abstand des Lots des Mittelpunkts des Fahrzeugs auf die Fahrspur senkrecht zur Mittellinie der Fahrspur.
• • Einen Gierwinkelfehler des Fahrzeugs. Dabei entspricht der Gierwinkelfehler der Differenz zwischen dem Fahrzeuggierwinkel und dem Markierungsgierwinkel. Das heißt, der Gierwinkelfehler entspricht dem Winkel der Schiefstellung zwischen der Fahrzeuglängsachse und der Fahrbahnmarkierung.
• • Eine Längsgeschwindigkeit des Fahrzeugs. Die Längsgeschwindigkeit des Fahrzeugs entspricht einer Geschwindigkeit des Fahrzeugs, mit welcher sich das Fahrzeug in der Fahrrichtung des Fahrzeugs bewegt.

The state data may include at least one of the following values of the vehicle:

• • A float angle of the vehicle. Here, the angle between the vehicle longitudinal axis and the speed vector of the vehicle in the vehicle's center of gravity is understood by the float angle.
• • A yaw rate of the vehicle. The yaw rate of the vehicle denotes the angular velocity of the rotation of the vehicle about its vertical axis. In this case, the vertical axis or yaw axis denotes the vertical axis of the vehicle or more precisely the vehicle-fixed coordinate system.
• • A transverse deviation of the vehicle. The lateral deviation refers to the distance of the center of the vehicle to the lane perpendicular to the center line of the lane.
• • A yaw angle error of the vehicle. Here, the yaw rate error corresponds to the difference between the vehicle yaw angle and the yaw angle. That is, the yaw rate error corresponds to the angle of inclination between the vehicle longitudinal axis and the lane mark.
• • A longitudinal speed of the vehicle. The longitudinal speed of the vehicle corresponds to a speed of the vehicle with which the vehicle is moving in the direction of travel of the vehicle.

According to the invention, an actual steering angle of a steering of the vehicle can be adjusted according to the steering angle.

The steering angle can be regarded as a desired steering angle, for example, a steering control of the vehicle strives to follow the actual angle of the steering of the vehicle as closely as possible this target steering angle. In other words, according to the invention, the steering of the vehicle with respect to the steering angle is set to the steering angle determined according to the invention.

In particular, the state data are measured in each case for a current one of several time steps. For the future of these time steps, the state data are determined as a function of a model of lateral dynamics of the vehicle, starting from the state data measured for the current time step and the curvature determined according to the invention. In other words, the state variables in discrete time steps are predicted via the prediction horizon, for example by means of a controller. If this prediction horizon includes, for example, 50 time steps, this results in a look-ahead of 2 s for a cycle time of 40 ms. That According to the invention, the state variables for the next 50 time steps or 2 s are calculated. Of course, the number of time steps, the cycle time and thus the forecast can be set arbitrarily. The state variables or state data are measured in each current time step and serve as the starting point of the prediction.

By determining the future state data of the vehicle, the determination of the steering angle can advantageously be performed better than if the future state data of the vehicle is not known.

According to a further embodiment of the invention, the steering angle is determined as a manipulated variable by means of a controller based on a target function depending on the state data and the curvature. The curvature is regarded as a disturbance variable for the regulator. The disturbance or curvature is thereby provided for all time steps (i.e., for the current and the future, up to the prediction horizon), in particular as a vector. Based on the curvature, this predictive vector forms the future course of the route or the part of the route to be traveled by the vehicle in the future. In other words, the disturbance variable provided is the curvature profile of the route known from the prediction horizon of the controller, which is obtained from the fused data.

By using the curvature, which is also known for future time steps, as the disturbance variable, according to the invention it is possible to react even before the occurrence of a transverse deviation from the center of the traffic lane. Thus, for example, while a classical PID controller initially requires a control deviation (in the case of a lane departure warning device a transverse deviation from the center of the traffic lane) in order to generate a response, the model-predictive controller according to the invention can provide a compensating manipulated variable (ie a steering angle) are applied. Since, according to the invention, the foresight of the camera (i.e., the image data) is widened, as it were, by means of the predictive path data, the quality of the control is improved.

The model-predictive controller realizes a model-based predictive control, which is a model of the dynamic behavior of the controlled system used for control during operation. This model allows prediction of system behavior and can thus be used to adjust the manipulated variable even before a control deviation occurs.

In this case, by means of the target function, a profile of the steering angle over the time steps can be determined such that on the one hand a change in the steering angle over time is minimized and, on the other hand, a transverse deviation of the vehicle and a yaw angle error of the vehicle are minimized.

The control according to the invention is therefore based on the optimization of the target function. The target function takes into account, on the one hand, a predicted center deviation or transverse deviation of the vehicle and, on the other hand, the predicted yaw angle error, which can be calculated on the basis of the specific manipulated variable profile or steering angle curve on the basis of the model. In this case, the steering angle profile determined by means of the target function is weighted such that the output manipulated variables, i. the temporal changes of the steering angle are as small as possible. The aim of the regulation is therefore that the transverse deviation and the yaw angle error with the least possible control use, i. with as small as possible changes in the steering angle, are led to zero.

In particular, the controller works with a one-track model to determine the state data in the future time steps as a function of the currently measured state data and the curvature.

• • Der Gesamtschwerpunkt des Fahrzeugs liegt auf der Fahrbahnhöhe, so dass auch bei einer schnellen Kurvenfahrt keine Radlastunterschiede und keine Wankbewegung entstehen.
• • Die Radaufstandspunkte werden achsweise zusammengeführt, so dass das Fahrzeug nur noch auf einer Spur bzw. Linie fährt.
• • Eine Änderung der Fahrgeschwindigkeit des Fahrzeugs wird quasistationär behandelt, so dass keine Reifenumfangskräfte auftreten.
• • Eine Gierbewegung und Schwimmbewegung sind die einzigen Freiheitsgrade.
• • Hub- und Nickbewegungen treten nicht auf.
• • Eine auftretende Querbeschleunigung ist kleiner als 0,4 g.
• • Infolge eines Schräglaufwinkels treten weder ein Nachlauf noch Rückstellmomente auf.

The single-track model corresponds to the already 1940 Riekert and dr. Schunk featured single track model. This one-track model works with the following simplifications and assumptions:

• • The overall center of gravity of the vehicle lies on the road surface height, so that even with a fast cornering no wheel load differences and no rolling motion arise.
• • The wheel contact points are brought together on an axle basis, so that the vehicle only travels on one lane or line.
• • A change in the driving speed of the vehicle is treated quasistationär, so that no tire circumferential forces occur.
• • Yawing and swimming are the only degrees of freedom.
• • Lifting and pitching movements do not occur.
• • An occurring lateral acceleration is less than 0.4 g.
• • As a result of a slip angle, neither caster nor restoring moments occur.

In the context of the present invention, a device for a vehicle is also provided, wherein the device comprises a camera for capturing image data and control means. The control means are configured to fuse the image data with route data comprising information about a route on which the vehicle is moving in order to generate fused data. In addition, the control means are configured to determine a lane of the vehicle depending on the fused data.

The advantages of the device according to the invention essentially correspond to the advantages of the method according to the invention, which are carried out in advance in detail, so that a repetition is dispensed with here.

In particular, the device comprises sensor means for measuring or detecting status data of the vehicle.

Finally, in the context of the present invention, a vehicle is provided, which comprises a device according to the invention.

The present invention is particularly suitable for use in motor vehicles. Of course, the present invention is not limited to this preferred application, since the present invention is also applicable to aircraft.

In the present invention, image data and predictive route data are merged, resulting in an extension of the route preview in comparison with the prior art. As a result, findings of the routing can be determined in the future, which in turn allows a better adaptation of the control strategy for a lane departure warning or for the determination of the steering angle. According to the present invention, an on-line model predictive controller may be used to calculate the manipulated variable (steering angle) taking into account the obtained predictive information (i.e., the merged data).

For example, in a lane departure warning system, the present invention provides an adaptive track center guide that assists the driver as quietly as possible and approximately continuously. Just winding road sections can be traversed in comparison to the prior art with a significant gain in comfort according to the invention, the forward-looking human Driving behavior are clearly better represented, as by a classic PID controller. Thus, the present invention provides an immediate comfort and confidence gain when using the device according to the invention.

• In 1 ist ein erfindungsgemäßes Reglersystem dargestellt.
• In 2 ist der Flussplan eines erfindungsgemäßen Verfahrens dargestellt.
• In 3 ist ein erfindungsgemäßes Fahrzeug mit einer erfindungsgemäßen Vorrichtung schematisch dargestellt.

In the following, the present invention will be described with reference to preferred embodiments of the invention with reference to the figures.

• In 1 an inventive control system is shown.
• In 2 the flow chart of a method according to the invention is shown.
• In 3 a vehicle according to the invention is shown schematically with a device according to the invention.

At the in 1 The control system according to the invention shown are those of a camera 2 captured image data 6 with predictive route data, which are stored for example in a navigation system, to fused data 7 fused 15. From these merged data 7 will the curvature ÿ the route or the lane on which the vehicle is traveling determined. This curvature ÿ is doing as a disturbance 14 considered.

The model predictive control 12 be next to the disturbance variable 14 state variables 13 , which is also a longitudinal speed v _x of the vehicle may be supplied. The state variables 13 are thereby measured or detected by sensor means of the vehicle periodically for the current time step and then for future time steps in particular depending on the measured state variables 13 and the curvature ÿ determined by a single-track model. Depending on the disturbance variable 14 or the curvature ÿ and depending on the state variables 13 determines the scheme 12 a desired steering angle δ , The regulation according to the invention 12 is anxious, the changes in the manipulated variable or the steering angle δ keep as low as possible. The goal of the scheme 12 is there, the transverse deviation e _y and the yaw angle error e _ψ minimize as much as possible. The target steering angle δ becomes a steering 11 supplied to the vehicle, which the actual steering angle as soon as possible to the target steering angle δ established.

In addition to the illustrated scheme 12 can also be used according to the invention, a regulation, which instead of the desired steering angle δ outputs a difference to the actual steering angle. In this often used control, the actual steering angle of the control must be supplied as an input variable.

In 2 the flow chart of a method according to the invention is shown.

In the first step S1 are from a camera 2 image data 6 detected. This image data 6 in step S2 with predictive track data 1 to merged data 7 merged. In step S3, state data becomes 13 of the vehicle, while in step S4 the curvature ÿ the route based on the merged data 7 is determined. Finally, in step S5, the steering angle δ depending on the curvature ÿ and the state data 13 certainly.

In 3 schematically is a vehicle according to the invention 10 with a device according to the invention 20 shown. The device according to the invention 20 includes tax credits 3 a camera 2 , Sensor means 4 and a steering 11 , The control means 3 create from the camera 2 captured image data 6 and predictive route data 1 merged data 7 , Depending on this merged data 7 and the sensor means 4 acquired status data 13 determine the control means 3 a steering angle, which of the steering 11 is supplied.

LIST OF REFERENCE NUMBERS

1

predictive route data

2

camera

3

control means

4

sensor means

6

image data

7

merged data

10

vehicle

11

steering

12

model predictive control

13

state variables

14

disturbance

15

merger

20

contraption

δ

steering angle

β

float angle

ψ

yaw rate

e _y

lateral deviation

e _ψ

Yaw angle error

ÿ

curvature

v _x

longitudinal speed

QUOTES INCLUDE IN THE DESCRIPTION

This list of the documents listed by the applicant has been generated automatically and is included solely for the better information of the reader. The list is not part of the German patent or utility model application. The DPMA assumes no liability for any errors or omissions.

Cited patent literature

• WO 2011/131165 A1 [0002]

Claims (10)

A method for a vehicle (10), the method comprising: Acquiring image data (6) by means of a camera (2) of the vehicle (10), Merging the image data (6) with route data (1) comprising information about a route on which the vehicle (10) travels to determine fused data (7), and Determining a lane for the vehicle (10) depending on the fused data (7). Method according to Claim 1 characterized in that the determining of the traffic lane comprises determining a curvature (ĸ) of the travel route depending on the merged data (7), the method further comprising: measuring state data (13) of the vehicle (10), and determining a steering angle (δ) of the vehicle (10) depending on the curvature (ĸ) and the state data (13). Method according to Claim 2 , characterized in that an actual steering angle of a steering (11) of the vehicle (10) according to the steering angle (δ) is set. Method according to Claim 2 or 3 characterized in that the state data (13) are respectively measured for a current one of several time steps, that the state data (13) for future of the time steps depending on a model of lateral dynamics of the vehicle (10) starting from the state data (13) of the current Time step and the curvature (ĸ) are determined. Method according to one of Claims 2 - 4 , characterized in that the steering angle (δ) is determined as a manipulated variable by means of a controller (12) based on a target function depending on the state data (13) and the curvature (ĸ), and that the curvature (ĸ) is a disturbance variable for the controller ( 12). Method according to Claim 4 and 5 , characterized in that by means of the objective function a profile of the steering angle (δ) over the time steps is determined such that a temporal change of the steering angle (δ) is minimized and that a transverse deviation (e _y ) of the vehicle (10) and a yaw angle error ( e _ψ ) of the vehicle (10) is minimized. Method according to Claim 5 or 6 , characterized in that the controller (12) operates with a one-track model to determine the state data (13) in the future time steps, starting from the currently measured state data (13) and the curvature (ĸ). Device for a vehicle (10), the device (20) comprising: a camera (2) for acquiring image data (6), and Control means (3) for merging the image data (6) with route data (1) comprising information about a route on which the vehicle (10) travels to determine fused data (7) and determining a lane for the vehicle (10). Device after Claim 8 , characterized in that the device (20) comprises sensor means (4) for measuring state data (13) of the vehicle (10). Device after Claim 8 or 9 , characterized in that the device (20) for carrying out the method according to one of Claims 1 - 7 is designed.

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