DE102023107575A1 - METHOD FOR OPERATING AN ADAPTIVE CRUISE CONTROL - Google Patents

Abstract

Method for operating an adaptive cruise control, computer program product, control device for a vehicle and vehicle.
Method for operating an adaptive cruise control of an ego vehicle (100) comprising the steps:
a) selecting (S1) a preceding vehicle (200) as a target vehicle (200);
b) controlling (S2) a distance between the target vehicle (200) and the ego vehicle (100);
c) receiving and classifying (S3) map data (117) relating to a road section (300) in front of the target vehicle (200);
d) determining (S4), depending on the classification of the road section (300), a travel path of the target vehicle (200) in the road section (300); and
e) deselecting or retaining (S5) the preceding vehicle (200) as the target vehicle (200) depending on step d).

Description

The present invention relates to a method for operating an adaptive cruise control, a computer program product, a control device for a vehicle and a vehicle.

When using adaptive cruise control (ACC), situations arise in which a vehicle ahead (hereinafter also referred to as the target vehicle), to which the ego vehicle regulates a distance and/or adjusts a speed depending on this, turns. When the turning maneuver is carried out, the target vehicle reduces its speed, whereupon a conventional adaptive cruise control also reduces the speed of the ego vehicle, even though the ego vehicle does not want to turn. This results in unnecessary braking for the vehicle's occupants, which is also disadvantageous in terms of energy.

US 9,463,806 B2 discloses an adaptive cruise control for an ego vehicle. Predictions are made using sensor data, which are represented by a decision signal. If the decision signal exceeds a specified threshold, an activation signal is created and transmitted, which controls the ego vehicle accordingly. The activation signal can, for example, include acceleration, braking and/or steering intervention. The adaptive cruise control also creates a decision interruption signal, depending on sensor data relating to an environment of the ego vehicle. This decision interruption signal can, for example, be a braking signal that ends or interrupts an activation signal for acceleration. The decision interruption signal can, for example, be created and transmitted when a vehicle changes into the lane in front of the ego vehicle on a highway.

Against this background, an object of the present invention is to provide an improved method for adaptive cruise control.

1. a) Auswählen eines vorausfahrenden Fahrzeugs als ein Zielfahrzeug;
2. b) Regeln eines Abstands zwischen dem Zielfahrzeug und dem Ego-Fahrzeug;
3. c) Empfangen und Klassifizieren von Kartendaten, die einen Fahrbahnabschnitt vor dem Zielfahrzeug betreffen;
4. d) Ermitteln, in Abhängigkeit der Klassifizierung des Fahrbahnabschnitts, eines Fahrwegs des Zielfahrzeugs in dem Fahrbahnabschnitt; und
5. e) Abwählen oder Beibehalten des vorausfahrenden Fahrzeugs als Zielfahrzeug in Abhängigkeit von Schritt d).

According to a first aspect, a method for operating an adaptive cruise control of an ego vehicle is provided. The method comprises the following steps:

1. a) selecting a preceding vehicle as a target vehicle;
2. b) controlling a distance between the target vehicle and the ego vehicle;
3. (c) receiving and classifying map data relating to a section of road ahead of the target vehicle;
4. d) determining, depending on the classification of the road section, a path of the target vehicle in the road section; and
5. e) Deselecting or retaining the preceding vehicle as the target vehicle depending on step d).

This method has the advantage that braking of a target vehicle is not taken over by the ego vehicle if, for example, the target vehicle turns and therefore does not remain in the lane of the ego vehicle. The turn is signaled in particular by the path of the target vehicle in the section of the road. In this way, incorrect braking of the adaptive cruise control of the ego vehicle can be avoided. This leads to a higher application safety of the adaptive cruise control and to increased driving comfort for passengers of the ego vehicle. Furthermore, it is energetically advantageous if the braking of the target vehicle is not taken over by the ego vehicle.

The vehicle is, for example, a motor vehicle, such as a passenger car or a truck.

A first vehicle ahead is selected as the target vehicle if it meets predetermined criteria of the adaptive cruise control. These include, for example, that the target vehicle is a vehicle and not another road user, such as a pedestrian.

“Selecting” the target vehicle means using the corresponding vehicle as the target vehicle to which the adaptive cruise control controls. This is done in particular by selecting or setting a value in an adaptive cruise control software.

The adaptive cruise control of the ego vehicle is designed to regulate a distance between the target vehicle and the ego vehicle. The distance is regulated in particular by adjusting the speed of the ego vehicle. The adaptive cruise control receives sensor data from one or more sensors of the ego vehicle, with the help of which, for example, the speed of the target vehicle and a distance between the target vehicle and the ego vehicle are determined. The adaptive cruise control is also designed to control an engine control device, a brake control direction and/or a steering device of the ego vehicle.

The adaptive cruise control can be activated, for example, when the ego vehicle is started. The driver has the option of switching the adaptive cruise control off and on again while driving. Alternatively, this function can be blocked for the driver and the adaptive cruise control is permanently activated while driving. The adaptive cruise control can also be set up in such a way that the driver of the ego vehicle has to activate it.

Furthermore, the driver can, for example, set the distance that the adaptive cruise control regulates from the target vehicle. However, the distance can also be set by the adaptive cruise control itself. The distance can be set, for example, depending on the speed of the target vehicle, the speed of the ego vehicle and/or road conditions. In particular, a distance can be selected so that the distance is large enough in the event of emergency braking to prevent a rear-end collision.

By controlling the engine control device, the steering device and/or the braking device of the ego vehicle and thereby inducing acceleration or braking, the adaptive cruise control regulates the distance between the ego vehicle and the target vehicle.

The map data can be stored in the vehicle memory, for example. The maps can additionally or alternatively be received via a mobile network and/or via a GPS. The received map data includes at least one road route. The maps can also include any geographical information, such as contour lines.

The adaptive cruise control then classifies the received map data. The map data relates in particular to a section of road in front of the target vehicle. The map data is assigned to different classes, for example a "critical" class and a "non-critical class". The number of classes is at least two, but the number of classes can also be three or more. The classification of the map data is a classification of the probability with which the target vehicle turns. On a straight road, the probability that the target vehicle turns is close to zero. However, if there is an intersection in the road section, there is a probability that the vehicle could turn. This option classifies the road section as "critical" because possible turning signals from the target vehicle must be taken into account here. The different classes can therefore comprise at least two classes, although these differ in terms of the probability with which the target vehicle turns within the road section.

The road section in front of the target vehicle is a fixed distance in front of the target vehicle. For example, the road section can cover an area from 0 m in front of the target vehicle to 1 km in front of the target vehicle. For example, the road section can also extend up to 5 km in front of the target vehicle. For example, the road section can cover an area from 3 m to 800 m in front of the target vehicle. The road section in front of the target vehicle can be a fixed value. For example, the road section can be stored in the adaptive cruise control.

Alternatively, the road section in front of the target vehicle can be set variably. For example, the road section can be set depending on the speed of the ego vehicle, the speed of the target vehicle and/or depending on the road conditions. Furthermore, the road section can be set by the driver of the ego vehicle.

In step d), a route for the target vehicle is determined. The route for the target vehicle is determined in particular depending on sensor data from the ego vehicle and within the classified road section. For example, a probability for a route for the target vehicle can also be determined. A probability can be assigned to each of the possible routes for the target vehicle.

If it is determined in step d) that the target vehicle's path in the road section is likely to turn, for example, the target vehicle is deselected in step e). The term "probability of turning" is to be understood as meaning that the probability that the target vehicle will turn is greater than the probability that the target vehicle will drive straight ahead.

“Deselecting” the target vehicle means that the adaptive cruise control no longer controls the corresponding vehicle or that the following is terminated. To do this, a value is set in the adaptive cruise control software. The adaptive cruise control can then select a new target vehicle to follow or to be inactive if, for example, there is no vehicle ahead.

If it is determined in step d) that the target vehicle's path in the road section is likely to continue straight ahead, the target vehicle is retained. This means that the distance between the ego vehicle and the target vehicle will continue to be regulated.

According to one embodiment, the distance is controlled using sensor data of the ego vehicle in step b).

Accordingly, the adaptive cruise control is a sensor-based adaptive cruise control. The sensor is in particular a camera, so the adaptive cruise control is in particular a camera-based adaptive cruise control. The camera for providing the camera data is a camera that is attached to the ego vehicle oriented in the direction of travel. The camera data can also be provided by several cameras that are attached to the ego vehicle oriented in the direction of travel. The one or more cameras are, for example, satellite cameras and/or centrally installed cameras that are mounted, for example, at the top of the windshield, in particular in the area of the rear-view mirror.

According to one embodiment, the map data relating to the road section in front of the target vehicle contain information about a course of a road on which the ego vehicle and the target vehicle are located.

According to one embodiment, the road section is classified in step d) depending on the course of the road.

The course of the road within the road section determines the classification of the road section. One possible classification of a road section is, for example, "critical" and "non-critical", whereby a critical road section is a road section in which there is an intersection where two or more roads meet. A non-critical road section is, for example, a road section in which the course of the road is straight, i.e. where there is no intersection or similar.

According to one embodiment, the course of the road is a road junction, a traffic light system or a roundabout.

The course of the road includes, for example, outgoing roads, intersections, curves, turning lanes or similar.

A road junction where two or more roads meet, a traffic light or a roundabout can be detected by the adaptive cruise control. In addition to the classification into "critical" and "non-critical", the adaptive cruise control can be set up to detect road courses and classify the road course accordingly. For this purpose, a road course classified as "critical" can be classified again.

According to one embodiment, the travel path in step d) is determined as a function of a turning signal of the target vehicle, a trajectory of the target vehicle, a position, an orientation, a yaw angle, a positive acceleration, a negative acceleration and/or a wheel angle of the target vehicle.

A turning signal, such as a turn signal, is used as an indication of the target vehicle's path. The target vehicle's path can also be determined based on its position, for example within a lane. Wheel angles can also provide an indication of the target vehicle's path. If the wheels are turned sharply, for example, the target vehicle turns. The same applies to the orientation and yaw angle of the target vehicle. These are also an indication of the target vehicle's path.

According to one embodiment, the turning signal of the target vehicle is determined using sensor data of the ego vehicle.

The sensors of the ego vehicle are designed to detect the turning signal, for example the indicator, of the vehicle in front.

According to one embodiment, the trajectory, position, orientation, yaw angle, positive acceleration, negative acceleration and/or wheel angle of the target vehicle is determined using sensor data of the ego vehicle and/or from data received from the target vehicle.

The sensors of the ego vehicle are designed, for example, to detect the target vehicle. Furthermore, sensor data from the target vehicle can be transmitted to the ego vehicle, for example via car-2-car communication. For this purpose, the ego vehicle has a receiver and a unit that is designed to convert the data received in this way and/or to process them in such a way that they can be further used by the ego vehicle and in particular by the adaptive cruise control.

According to one embodiment, the preceding vehicle is deselected as the target vehicle in step e) if the travel path determined in step d) includes a turn of the target vehicle.

The adaptive cruise control assumes that the ego vehicle drives straight ahead on road sections classified as "critical". Accordingly, a turning target vehicle within a "critical" road section is deselected. Accordingly, the target vehicle is deselected.

According to one embodiment, step d) is additionally carried out depending on a planned route of the ego vehicle.

The adaptive cruise control is designed to compare the target vehicle's route with a planned route of the ego vehicle. A planned route is, for example, a route that the driver of the ego vehicle has stored in a navigation system of the ego vehicle. A stored route can also be, for example, a route that the ego vehicle has learned independently from the driver's habits.

According to one embodiment, the preceding vehicle is retained as the target vehicle in step e) if the travel path determined in step d) and the planned route of the ego vehicle are sufficiently identical.

Accordingly, when the target vehicle turns, it is not deselected if the planned route of the ego vehicle also turns. This means that a distance to the target vehicle is still controlled and braking and acceleration processes of the target vehicle are adopted.

According to a second aspect, a computer program product is proposed which comprises instructions which, when the program is executed by a computer, cause the computer to carry out the method described above according to the first aspect.

A computer program product according to the second aspect, such as a computer program means, can be provided or delivered, for example, as a storage medium, such as a memory card, USB stick, CD-ROM, DVD, or in the form of a downloadable file from a server in a network. This can be done, for example, in a wireless communication network by transmitting a corresponding file with the computer program product or the computer program means.

According to a third aspect, a control device for a vehicle for operating an adaptive cruise control is provided. The control device comprises: a processor unit and a memory unit on which means for carrying out the method as described above are stored.

The control unit (for example in the form of the central vehicle control unit or electronic control unit - "ECU") is designed in particular to process the computer program product described above for operating an adaptive cruise control, for example on the processor unit of the control unit.

The respective unit can be implemented using hardware and/or software. In a hardware implementation, the respective unit can be designed as a computer or as a microprocessor, for example. In a software implementation, the respective unit can be designed as a computer program product, as a function, as a routine, as an algorithm, as part of a program code or as an executable object. Furthermore, each of the units mentioned here can also be designed as part of a higher-level control system of the vehicle, such as a central electronic control device and/or an engine control device.

According to a fourth aspect, a vehicle is provided. The vehicle comprises: one or more sensors and a control unit according to the third aspect.

The sensors of the ego vehicle can be, for example, radar sensors, LiDAR sensors, ultrasonic sensors and/or cameras (as already described above). The ego vehicle can have one sensor of one type, several sensors of one type and/or several sensors of several types. The ego vehicle advantageously has several sensors of several types. The ego vehicle in particular has a camera, which is advantageously arranged in the middle of the front of the ego vehicle and oriented in the direction of travel. The sensors are designed, for example, as part of a driver assistance system of the ego vehicle. The driver assistance system is set up to carry out the adaptive cruise control method described above.

Steps a), b), c), etc. can also occur in a different order. The presence of steps a) and c) does not require that an intermediate step b) also be present, etc. "A" does not exclude a plural.

Further possible implementations of the invention also include combinations of features or embodiments described above or below with respect to the exemplary embodiments that are not explicitly mentioned. The person skilled in the art will also add individual aspects as improvements or additions to the respective basic form of the invention.

• 1 zeigt eine schematische Draufsicht eines Fahrzeugs mit einer adaptiven Geschwindigkeitsregelung gemäß einer Ausführungsform;
• 2 zeigt eine schematische Architektur eines Computerprogrammprodukts einer adaptiven Geschwindigkeitsregelung gemäß einer Ausführungsform;
• 3 zeigt eine schematische Darstellung einer Situation, in der eine adaptive Geschwindigkeitsregelung gemäß einer Ausführungsform verwendet wird;
• 4 zeigt ein Ablaufdiagramm einer adaptiven Geschwindigkeitsregelung gemäß einer Ausführungsform;
• 5 zeigt verschiedene klassifizierte Fahrbahnabschnitte in einer Draufsicht;
• 6 zeigt eine weitere schematische Darstellung einer Situation, in der eine adaptive Geschwindigkeitsregelung gemäß einer Ausführungsform verwendet wird; und
• 7 zeigt ein weiteres Ablaufdiagramm einer adaptiven Geschwindigkeitsregelung gemäß einer Ausführungsform.

Further advantageous embodiments and aspects of the invention are the subject of the subclaims and the embodiments of the invention described below. The invention is explained in more detail below using preferred embodiments with reference to the attached figures.

• 1 shows a schematic plan view of a vehicle with adaptive cruise control according to an embodiment;
• 2 shows a schematic architecture of an adaptive cruise control computer program product according to an embodiment;
• 3 shows a schematic representation of a situation in which adaptive cruise control is used according to an embodiment;
• 4 shows a flowchart of an adaptive cruise control according to an embodiment;
• 5 shows various classified road sections in a plan view;
• 6 shows a further schematic representation of a situation in which an adaptive cruise control is used according to an embodiment; and
• 7 shows another flowchart of an adaptive cruise control according to an embodiment.

In the figures, identical or functionally equivalent elements have been given the same reference symbols unless otherwise stated.

1 shows a schematic plan view of a vehicle 100 with a control unit 103 and a sensor 102 according to an embodiment. The vehicle 100 is in the example of 1 a motor vehicle, in particular a passenger car. The sensor 102 is designed, for example, as part of a driver assistance system. An adaptive cruise control is designed, for example, as a software component of the driver assistance system. The driver assistance system serves, for example, to support a driver of the vehicle 100. Furthermore, the driver assistance system can be designed for semi-autonomous or fully autonomous operation of the vehicle 100. The driver assistance system is set up, for example, to control components of the vehicle, such as an engine control device 104, a braking device 106 and a steering device 107, so that driver support, semi-autonomous and/or fully autonomous operation can be carried out. The driver assistance system is designed, for example, to operate at higher speeds, such as those found on a country road or on a motorway. Furthermore, the driver assistance system is designed to operate at lower speeds, such as those found on inner-city streets, for example.

The sensor 102 is a camera and is, as in 1 shown, arranged in the middle of the front of the vehicle 100. The sensor 102 is connected to the control unit 103 wirelessly and/or by wire for transmitting sensor data. The vehicle 100 preferably comprises further sensors (in 1 not shown) which are designed to detect the driving state of the vehicle 100 and to detect an environment of the vehicle 100. Examples of such sensors of the vehicle 100 are a radar (radio detection and ranging) or a lidar (light detection and ranging), ultrasonic sensors, location sensors, wheel angle sensors and/or wheel speed sensors.

The control unit 103 has a processor unit and a memory unit (in 1 not shown) which are designed to carry out the method described below for operating an adaptive cruise control during operation of the vehicle 100. The control unit 103 is designed to receive sensor data 116 from the sensor 102. Data connections of the control unit 103 to vehicle components are identified by the reference numeral 105, with data connections representing lines, data lines, a vehicle bus and/or wireless data transmission.

The control unit 103 is in the exemplary representation of the vehicle 100 in 1 connected to the engine control device 104 and configured to wirelessly and/or wired transmit data to the engine control device 104. The transmitted data contain, for example, control signals which the engine control device device 104 to accelerate and/or decelerate the vehicle 100.

Furthermore, the control unit 103 is in 1 connected to the braking device 106. The control unit 103 transmits data wirelessly and/or by wire to the braking device 106 of the vehicle 100, the data containing, for example, control signals. These control signals cause the braking device 106 to brake the vehicle 100.

The control unit 103 in 1 is connected to a steering device 107 of the vehicle 100. The control unit 103 transmits data wirelessly and/or by wire to the steering device 106 of the vehicle 100, the data containing, for example, control signals. These control signals cause the steering device 107 to change a steering angle of the vehicle 100.

Furthermore, the control unit 103 has a computer program product 109, which has program code means stored on a computer-readable medium in order to be able to carry out the method described below for operating an adaptive cruise control. Additionally or alternatively, the computer program product 109, or at least parts of the computer program product 109, can be stored on the sensor 102.

2 shows a schematic architecture of a computer program product 109 for implementing an adaptive cruise control, for example, the one described with reference to 3 and 4 performs the procedures described.

The computer program product 109 receives sensor data 116 from the sensor 102 of the ego vehicle 100 via an interface of the vehicle 108. The computer program product 109 also receives map data 117, also via the interface of the vehicle 108. The object recognition unit 110 carries out an object recognition in the received sensor data 116. A selection unit 111 receives information from the object recognition unit 110 and selects a target vehicle 200 depending on the information. The selection unit 111 is set up to carry out steps S2 and S4 as described in 4 shown.

A classification unit 113 is configured to classify the received map data 117 (see step S4 in 4 ). The classification unit 113 is further configured to receive information from the object recognition unit 110. Depending on this received information, the classification unit 113 is further configured to determine a route of the target vehicle (see step S4 in 4 ).

A distance control unit 114 is set up to receive information from the classification unit 113 and from the selection unit 111. Depending on the information received, the distance control unit 114 is set up to generate information for controlling the distance between the ego vehicle and the target vehicle. This is output via the interface to the vehicle 115, for example to the control unit 103 of the ego vehicle 100.

Using a schematic representation of a situation from 3 , in which an adaptive cruise control is used according to an embodiment, and the flow chart of 4 The procedure for operating an adaptive cruise control is explained in more detail.

3 shows the vehicle 100 (hereinafter “ego vehicle”) from 1 . A preceding vehicle 200 is selected as the target vehicle (see step S1 in 4 ). In the 3 In the example shown, the first preceding vehicle 200 is a passenger car. The first preceding vehicle 200 can be any vehicle, such as a motorcycle, a truck or the like.

The adaptive cruise control of the ego vehicle 100 is configured to control a distance 201 between the target vehicle 200 and the ego vehicle 100 (see step S2 in 4 ). By means of the sensor 102, sensor data 116 are recorded and transmitted to the control unit 103, with the aid of which the distance 201 and the speed and/or the acceleration of the preceding vehicle 200 are determined. The control unit 103, more precisely the computer program product 109, is further configured to regulate the distance 201 between the target vehicle 200 and the ego vehicle 100.

For this purpose, the control unit 103 transmits data containing control signals to the engine control device 104, the braking device 106 and/or the steering device 107. These devices then control the corresponding vehicle parts so that the distance 201 is regulated.

More specifically, controlling the distance 201 means that the ego vehicle 100 accelerates when the distance 201 is greater than a specified distance. Accordingly, the control unit 103 transmits data to the engine control device 104 containing control signals so that the engine control device 104 controls the engine of the ego vehicle 100 to accelerate the ego vehicle 100. If the distance 201 is smaller than a specified distance, the control unit 103 transmits data to the engine control device 104, to the steering device 107 and/or to the braking device 106, which data contain control signals so that the ego vehicle 100 has a negative acceleration.

If the ego vehicle 100 accelerates, the control unit 103 ensures that a permissible maximum speed is not exceeded. The permissible maximum speed can be derived, for example, from GPS data from the driver assistance system, with the permissible maximum speed of a section of road being indicated on a stored map. Furthermore, the permissible maximum speed can also be determined by sensors that are set up to recognize traffic signs.

Furthermore, the driver of the ego vehicle 100 can, for example, enter a maximum speed that he does not want to exceed via an input interface. The control unit 103 is also designed to adapt the data that is transmitted to the engine control device 104 so that the engine control device 104 does not accelerate the ego vehicle 100 to a speed greater than the maximum speed entered by the driver.

The distance 201 between the target vehicle 200 and the ego vehicle 100 is, for example, a distance that is determined by the adaptive cruise control as a function of the speed of the target vehicle 200. Accordingly, the distance 201 is set as a function of the speed of the target vehicle 200. The distance 201 is therefore set dynamically and is not a fixed value. The distance 201 can also be determined as a function of the road conditions that are determined by the sensors 109. For example, a distance 201 is selected to be greater when the road is determined to be wet than when the road is determined to be dry.

The distance 201 is, for example, a distance that is set by the driver of the ego vehicle 100 before step S2. The distance 201 is transmitted, for example, by the driver to the control unit 103 via an input interface. Accordingly, the adaptive cruise control is set up to control the distance 201 that is set by the driver of the ego vehicle 100. Furthermore, the adaptive cruise control can be set up to only implement inputs from the driver of the ego vehicle 100 that are greater than a safety distance. The safety distance is determined by the adaptive cruise control depending on the speed of the ego vehicle 100. The safety distance can be selected such that the ego vehicle 100 does not cause a rear-end collision in the event of an emergency braking of a vehicle 200, 300 driving ahead, but the ego vehicle 100 comes to a stop in time.

In step S3 (see 4 ), the adaptive cruise control receives and classifies map data 117 relating to a road section 300 in front of the target vehicle. The map data 117 contains at least information about a road course, depending on which the road section 300 is classified. The 3 The road section 300 shown is an example of a road section 300 classified as "critical". The road section 300 shown shows a road intersection where four roads from four directions meet. Since the road section 300 has been classified as "critical", the adaptive cruise control continues with step S4 (see 4 ). If the road section 300 is classified as “not critical”, for example because the road course in the road section 300 is straight, the adaptive cruise control jumps back to step S2 and continues to regulate a distance 201 between the ego vehicle and the target vehicle.

In step S4, the travel path of the target vehicle 200 in the classified road section 300 is determined. In 3 the target vehicle 200 sets a turning signal 202 in the form of a turn signal. The ego vehicle 100 recognizes the turning signal 202 of the target vehicle 200 using the sensors. The path of the target vehicle 200 is therefore turning according to the turning signal. According to the schematic sequence in 3 the method then continues with step S5. If no turning signal of the target vehicle 200 had been determined, the ego vehicle 100 would continue to regulate a distance to the target vehicle 200 (see step S2 in 4 ).

According to the turning path determined in step S4, the target vehicle is deselected in step S5. Accordingly, for example, a braking process that the preceding vehicle 200 performs before turning is not taken over by the ego vehicle 100. If the preceding vehicle 200 has left the lane of the ego vehicle 100 and the ego vehicle 100 crosses the intersection, it encounters 3 to a second preceding vehicle 400, which is again selected as the target vehicle according to step S1 if it meets the corresponding criteria of the adaptive cruise control.

5 shows a schematic overview of road sections 300 classified as “critical”. The arrow in each road section 300 indicates a possible direction of travel of the ego vehicle 100. 5 a) shows a schematic of a crossing, as it is also shown in 3 where roads meet from four directions. 5 b) shows a classic T junction. 5 c) shows a roundabout and the target vehicle is only deselected once it is inside the roundabout. 5 d) shows another T-junction, but this is now driven through by the ego vehicle 100 from the perspective so that one lane is continuous. The adaptive cruise control can be set up to recognize such road courses and to classify road sections classified as "critical" again according to their exact shape.

6 shows a similar situation as 3 Here too, the adaptive cruise control regulates a distance 201 between the ego vehicle 100 and the target vehicle 200. The schematic flow diagram in 7 shows the steps that take place within the adaptive cruise control. Steps S11 to S14 in 7 run analogously to steps S1 to S4 4 However, there is a difference in 6 The target vehicle 200 turns although no turning signal 202 can be recognized.

However, the ego vehicle 100 is able to detect a turning maneuver even if no turning signal 202, in the sense of a turn signal, is used by the target vehicle 200. The sensor 102 of the ego vehicle 100 detects the target vehicle 200 and, for example, based on a position, an orientation, a yaw angle, a positive acceleration, a negative acceleration and/or a wheel angle, a turning maneuver can be determined. Furthermore, a trajectory 203 of the target vehicle 200 can be determined based on this information. This is representative of the path of the target vehicle 200.

If a planned route is stored in a navigation device of the ego vehicle 100, the adaptive cruise control checks in step S15 whether the determined trajectory 203 of the target vehicle 200 matches the stored planned route. If the stored planned route matches the determined trajectory 203 of the target vehicle 200 sufficiently, the target vehicle 200 is retained. The system therefore jumps back to step S12 and continues to regulate a distance between the ego vehicle 100 and the target vehicle 200. The target vehicle 200 in 6 turns, and the stored route also turns. Accordingly, the adaptive cruise control is set up to continue to regulate the distance between the ego vehicle 100 and the target vehicle 200. As a result, the ego vehicle 100 has a target vehicle 200 available during the entire turning process, which means that the turning process can also be carried out semi- or fully autonomously.

If the stored planned route of the ego vehicle 100 does not match the determined trajectory 203 of the target vehicle 200, the target vehicle 200 is deselected in step S16. This is also analogous to the 3 and 4 illustrated embodiment.

Although the present invention has been described using exemplary embodiments, it can be modified in many ways.

REFERENCE SYMBOL LIST

100

(ego) vehicle

102

sensor

103

Control unit

104

engine control unit

105

data connection

106

braking system

107

steering system

108

vehicle interface

109

computer program product

110

object recognition unit

111

selection unit

113

classification unit

114

distance control unit

115

interface to the vehicle

116

sensor data

117

map data

200

preceding vehicle or target vehicle

201

Distance (between the ego vehicle and the vehicle in front)

202

turn signal

203

trajectory (of the vehicle ahead)

300

road section

400

second vehicle ahead

S1 - S5

procedural steps

S11 - S16

procedural steps

QUOTES INCLUDED IN THE DESCRIPTION

This list of documents listed by the applicant was 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

• US 9463806 B2 [0003]

Claims (14)

Method for operating an adaptive cruise control of an ego vehicle (100) with the steps: a) selecting (S1) a preceding vehicle (200) as a target vehicle (200); b) regulating (S2) a distance (201) between the target vehicle (200) and the ego vehicle (100); c) receiving and classifying (S3) map data (117) relating to a road section (300) in front of the target vehicle (200); d) determining (S4), depending on the classification of the road section (300), a travel path of the target vehicle (200) in the road section (300); and e) deselecting or retaining (S5) the preceding vehicle (200) as a target vehicle (200) depending on step d). procedure according to Claim 1 , wherein the distance in step b) is controlled using sensor data (116) of the ego vehicle (100). procedure according to Claim 1 or 2 , wherein the map data (117) relating to the road section (300) in front of the target vehicle (200) contain information about a course of a road on which the ego vehicle (100) and the target vehicle (200) are located. Method according to one of the preceding claims, wherein the roadway section (300) is classified in step d) depending on the course of the road. procedure according to claim 3 , where the course of the road includes a crossroads, traffic lights or a roundabout. Method according to one of the preceding claims, wherein the travel path in step d) is determined as a function of a turning signal (202) of the target vehicle (200), a trajectory (203) of the target vehicle (200), a position, an orientation, a yaw angle, a positive acceleration, a negative acceleration and/or a wheel angle of the target vehicle (200). procedure according to claim 6 , wherein the turning signal (202) of the target vehicle (200) is determined using sensor data (116) of the ego vehicle (100). procedure according to claim 6 , wherein the trajectory (203), the position, the orientation, the yaw angle, the positive acceleration, the negative acceleration and/or the wheel angle of the target vehicle (200) is determined using sensor data (116) of the ego vehicle (100) and/or from data received from the target vehicle (200). Method according to one of the preceding claims, wherein the preceding vehicle (200) is deselected as the target vehicle (200) in step e) if the travel path determined in step d) includes a turn of the target vehicle (200). Method according to one of the preceding claims, wherein step d) is additionally carried out depending on a planned route of the ego vehicle (100). procedure according to claim 10 , wherein the preceding vehicle (200) is retained as the target vehicle (200) in step e) if the travel path determined in step d) and the planned route of the ego vehicle (100) are identical. Computer program product (109) comprising instructions which, when the program is executed by a computer, cause the computer to carry out the method according to one of the Claims 1 - 11 to execute. Control device (103) for a vehicle (100) for operating an adaptive cruise control, comprising: a processor unit; and a memory unit on which means for carrying out the method according to one of the Claims 1 - 11 are stored. Vehicle (100), comprising: one or more sensors (102); and a control unit (103) according to claim 13 .

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