DE102024200645A1 - Controlling an autonomous or semi-autonomous vehicle using a digital map with a behavioral data layer - Google Patents

Abstract

The present invention relates to a method, a computer program with instructions, and a device for controlling an autonomous or semi-autonomous vehicle. In a first step, sensor data or decisions of a motion planner are acquired (10). The acquired data are used to determine the current driving behavior of the vehicle (11). The current driving behavior is compared with an expected driving behavior (12) stored in a behavior data layer of a digital map. If a deviation between the current driving behavior and the expected driving behavior is detected (13), it can first be determined (14) whether the detected deviation should be used to update the digital map, whether a teleoperator should be involved, or whether a maneuver should be initiated to bring the vehicle into a safe state. Alternatively or additionally, a notification and optionally sensor data can be sent to a backend (15). In either case, the current driving behavior is adjusted (16). The invention also relates to an autonomous or semi-autonomous vehicle, a digital map and a method, a computer program with instructions and a device for updating such a digital map.

Description

The present invention relates to a method, a computer program with instructions, and a device for controlling an autonomous or semi-autonomous vehicle. The invention also relates to an autonomous or semi-autonomous vehicle that uses a method or device according to the invention, as well as to a digital map for use in such a method. The invention further relates to a method, a computer program with instructions, and a device for updating such a digital map.

• • Level 0: „Nur Fahrer“, der Fahrer fährt selbst, lenkt, beschleunigt, bremst usw.
• • Level 1: Bestimmte Assistenzsysteme helfen bei der Fahrzeugbedienung, darunter ein Geschwindigkeitsregelungssystem wie ACC (engl. Automatic Cruise Control).
• • Level 2: Teilweise Automatisierung. Dabei werden automatisches Einparken, Spurführung, allgemeine Längsführung, Beschleunigung, Verzögerung usw. von den Assistenzsystemen übernommen, einschließlich der Kollisionsvermeidung.
• • Level 3: Hohe Automatisierung. Der Fahrer muss das System nicht ständig überwachen. Das Fahrzeug führt selbständig Funktionen wie das Auslösen des Blinkers, den Spurwechsel und die Spurführung aus. Der Fahrer kann sich anderen Dingen zuwenden, muss aber auf Anforderung innerhalb einer Vorwarnzeit die Kontrolle übernehmen.
• • Level 4: Vollständige Automatisierung. Die Führung des Fahrzeugs wird permanent vom System übernommen. Ist das System nicht mehr in der Lage, die Aufgaben zu bewältigen, kann der Fahrer aufgefordert werden, die Steuerung zu übernehmen.
• • Level 5: Kein Fahrer erforderlich. Abgesehen vom Setzen des Ziels und Starten des Systems ist kein menschliches Eingreifen erforderlich.

Automated driving, also known as autonomous driving, automated driving, or guided driving, is the movement of vehicles, mobile robots, and driverless transport systems that are largely autonomous. There are different degrees of automated driving. In Europe, various transport ministries, such as the Federal Highway Research Institute in Germany, have defined the following levels of automation:

• • Level 0: “Driver only”, the driver drives, steers, accelerates, brakes, etc.
• • Level 1: Certain assistance systems help with vehicle operation, including a cruise control system such as ACC (Automatic Cruise Control).
• • Level 2: Partial automation. Automatic parking, lane guidance, general longitudinal guidance, acceleration, deceleration, etc., including collision avoidance, are handled by the assistance systems.
• • Level 3: High automation. The driver does not need to constantly monitor the system. The vehicle performs functions such as activating the turn signal, changing lanes, and maintaining lane guidance independently. The driver can attend to other tasks but must assume control upon request within a certain warning period.
• • Level 4: Full automation. The system permanently assumes control of the vehicle. If the system is no longer able to handle the tasks, the driver can be asked to take over.
• • Level 5: No driver required. Aside from setting the destination and starting the system, no human intervention is required.

A slightly different definition of steps is known from the Society of Automotive Engineers (SAE). In this context, reference is made to the SAE J3016 standard. Such definitions can be used alternatively to the definitions given above.

Autonomous or semi-autonomous vehicles must exhibit driving behavior that is consistent with traffic regulations and swarm behavior. A fundamental assumption is that the driving behavior of autonomous or semi-autonomous vehicles exhibits a certain correlation with the respective environment. For example, during a tight 90° right turn, the vehicle's trajectory will most likely deviate from the ideal centerline of the roadway to the right, as is the case with most manually controlled vehicles. This implies that the behavior of autonomous or semi-autonomous vehicles at the same geographical location is similar with respect to certain environmental characteristics, such as the road layout or the behavior of other road users.

• • Physische Datenschicht: Eine Menge physischer Elemente der realen Welt, die die stationäre Umgebung darstellen, z.B. Straßenmarkierungen und Verkehrsschilder.
• • Relationale Datenschicht: Eine Menge semantischer Elemente, die sich aus physischen Elementen zusammensetzen, z.B. Fahrspuren, die aus Fahrbahnmarkierungen gebildet sind, oder Baustellen, die aus temporären Fahrbahnmarkierungen, temporären Verkehrsschildern und temporären Verkehrsampeln gebildet sind.
• • Topologische Datenschicht: Eine Menge von Verknüpfungen zwischen semantischen Elementen, z.B. Vorgänger, Nachfolger und Nachbarn von Fahrspuren.
• • Metaschicht: Zusätzliche abstrakte Informationen für physische oder semantische Elemente, z.B. Konnektivität/Kommunikation, Zeitlichkeit, beispielsweise bei Baustellen.

Autonomous or semi-autonomous vehicles typically derive environmental features from high-resolution map data. High-resolution maps can, in particular, display four different layers:

• • Physical data layer: A set of physical real-world elements that represent the stationary environment, e.g. road markings and traffic signs.
• • Relational data layer: A set of semantic elements composed of physical elements, e.g., lanes formed from road markings, or construction sites formed from temporary road markings, temporary traffic signs, and temporary traffic lights.
• • Topological data layer: A set of links between semantic elements, e.g. predecessors, successors and neighbors of lanes.
• • Meta layer: Additional abstract information for physical or semantic elements, e.g. connectivity/communication, temporality, for example at construction sites.

In this context, US 2018 / 0 004 225 A1 A method for operating a vehicle. In the method, a number of sub-maps from a collection of sub-maps are retrieved from a storage medium. Each sub-map represents an area of a road network for a geographical region. Each sub-map is provided with an identifier, several data layers, each data layer containing a set of features of the area of the road network, the This partial map, and a connection data set to connect the partial map to another partial map that represents an adjacent area to the road network area of this partial map. The vehicle is controlled autonomously using the retrieved partial maps.

In principle, it can be assumed that the driving behavior of autonomous or semi-autonomous vehicles is generally based on the traffic rules encoded in the high-resolution maps, e.g., with regard to lane routing, direction of travel, speed limits, right of way, etc.

• • Die hochauflösenden Karten sind veraltet. Z.B. kann es vorkommen, dass Veränderungen der Umgebung wie beispielsweise Baustellen nicht in der Karte kodiert sind oder dass Fehler aufgrund einer unvollkommenen Kartenerstellung auftreten.
• • Es treten Wahrnehmungsprobleme auf, z.B. durch eine fehlerhafte Wahrnehmung, durch Verdeckungen oder durch Grenzen der Leistungsfähigkeit der verwendeten Algorithmen.
• • Besondere Ereignisse, wie beispielsweise die Bildung einer Rettungsgasse aufgrund eines Rettungseinsatzes.

Violations by autonomous or semi-autonomous vehicles of the traffic rules encoded in the high-resolution maps can have several causes:

• • The high-resolution maps are outdated. For example, environmental changes such as construction sites may not be encoded in the map, or errors may occur due to incomplete map creation.
• • Perception problems occur, e.g. due to faulty perception, occlusions or limitations of the performance of the algorithms used.
• • Special events, such as the formation of an emergency lane due to a rescue operation.

The causes mentioned above each lead to an anomaly in the driving behavior of the autonomous or semi-autonomous vehicle, which in the worst case scenario endangers the safety of all road users. Autonomous or semi-autonomous vehicles therefore require a strategy to detect their own deviant driving behavior and thus avoid accidents.

It is an object of the invention to provide improved solutions for controlling an autonomous or semi-autonomous vehicle.

This object is achieved by a method having the features of claim 1 or 8, by a device having the features of claim 5 or 9, by an autonomous or semi-autonomous vehicle according to claim 6, by a digital map according to claim 7, and by a computer program with instructions according to claim 10. Preferred embodiments of the invention are the subject of the dependent claims.

• - Vergleichen eines aktuellen Fahrverhaltens des Fahrzeugs mit einem erwarteten Fahrverhalten, das in einer Verhaltensdatenschicht einer digitalen Karte gespeichert ist;
• - Feststellen einer Abweichung zwischen dem aktuellen Fahrverhalten und dem erwarteten Fahrverhalten; und
• - Anpassen des aktuellen Fahrverhaltens.

According to a first aspect of the invention, a method for controlling an autonomous or semi-autonomous vehicle comprises the steps:

• - Comparing a current driving behavior of the vehicle with an expected driving behavior stored in a behavior data layer of a digital map;
• - Detecting a deviation between the current driving behavior and the expected driving behavior; and
• - Adjusting the current driving behavior.

• - Vergleichen eines aktuellen Fahrverhaltens des Fahrzeugs mit einem erwarteten Fahrverhalten, das in einer Verhaltensdatenschicht einer digitalen Karte gespeichert ist;
• - Feststellen einer Abweichung zwischen dem aktuellen Fahrverhalten und dem erwarteten Fahrverhalten; und
• - Anpassen des aktuellen Fahrverhaltens.

According to a further aspect of the invention, a computer program contains instructions which, when executed by a computer, cause the computer to perform the following steps for controlling an autonomous or semi-autonomous vehicle:

• - Comparing a current driving behavior of the vehicle with an expected driving behavior stored in a behavior data layer of a digital map;
• - Detecting a deviation between the current driving behavior and the expected driving behavior; and
• - Adjusting the current driving behavior.

The term computer is to be understood broadly. In particular, it also includes control units, embedded systems and other processor-based

Data processing devices. The execution of the aforementioned steps can be performed directly by the computer or can consist of the computer controlling a component intended to execute a step.

The computer program may, for example, be made available for electronic retrieval or stored on a computer-readable storage medium.

• - ein Vergleichsmodul zum Vergleichen eines aktuellen Fahrverhaltens des Fahrzeugs mit einem erwarteten Fahrverhalten, das in einer Verhaltensdatenschicht einer digitalen Karte gespeichert ist, und zum Feststellen einer Abweichung zwischen dem aktuellen Fahrverhalten und dem erwarteten Fahrverhalten; und
• - ein Steuerungsmodul zum Anpassen des aktuellen Fahrverhaltens.

According to a further aspect of the invention, a device for controlling an autonomous or semi-autonomous vehicle comprises:

• - a comparison module for comparing a current driving behavior of the vehicle with an expected driving behavior stored in a behavior data layer of a digital map and for detecting a deviation between the current driving behavior and the expected driving behavior; and
• - a control module to adapt the current driving behavior.

The inventive solution uses a digital map that, in addition to a relational data layer and a topological data layer, has a behavioral data layer. In the behavioral data layer, an expected Driving behavior is stored. This indicates the actual behavior of manually controlled vehicles on the roads with respect to specific road sections from the relational data layer and the topological data layer of the map. During the journey, the current driving behavior is compared with the expected driving behavior, i.e. with the relevant information from the behavior data layer. If a deviation is detected, this may be due to the map deviating from the real situation, a perception problem having occurred, or a special event having occurred. In this case, the vehicle adapts its driving behavior, for example, by reducing its speed. This significantly reduces the risk of an accident.

According to one aspect of the invention, the current driving behavior of the vehicle is an actual or planned driving behavior that is determined based on sensor data or decisions of a motion planner. The current driving behavior can be determined, on the one hand, by evaluating data from the vehicle's sensors, e.g., data from environmental sensors or data from sensors for recording vehicle-internal variables, e.g., acceleration or steering angle sensors. In this case, the actual driving behavior is determined. Alternatively or additionally, decisions of a motion planner can also be considered, i.e., the planned driving behavior is determined. This has the advantage that a check for deviations can already be carried out before a planned driving maneuver is executed.

According to one aspect of the invention, if a deviation is detected between the current driving behavior and the expected driving behavior, a notification and optionally sensor data are sent to a backend. For this purpose, a mobile phone connection can be used, for example. The notifications can, in particular, also contain information about the current driving behavior. The backend can use the notification and the optionally sent sensor data to update the data in the behavior data layer or to influence the vehicle.

According to one aspect of the invention, if a deviation is detected between the current driving behavior and the expected driving behavior, it is determined whether the detected deviation should be used to update the digital map, whether a teleoperator should be involved, or whether a maneuver to bring the vehicle into a safe state should be initiated. These decisions can be made both in the backend and by means of a suitable device in the vehicle, e.g., using an artificial intelligence algorithm. If the decision to involve a teleoperator or to initiate a maneuver to bring the vehicle into a safe state is made in the backend, corresponding information or instructions are sent to the vehicle. In addition, an updated map can be sent to the vehicle or other vehicles if necessary.

Advantageously, an autonomous or semi-autonomous vehicle comprises a device according to the invention or is configured to carry out a method according to the invention. The vehicle can be any type of vehicle, e.g., a passenger car, a bus, a motorcycle, or a commercial vehicle, in particular a truck or an agricultural machine. Such a vehicle is characterized by the fact that the risk of accidents resulting from anomalies in driving behavior is reduced.

• • durchschnittliche Fahrgeschwindigkeit
• • durchschnittliche Beschleunigung / Verzögerung
• • Lenkwinkel / Kurvenschneidverhalten / Abweichung von der Mittellinie
• • Anhalten, z.B. Fahrzeuge halten an einer bestimmten Stelle aufgrund eines Hindernisses an
• • Fahrspurwechsel trotz Verbotes durch durchgezogene Linie oder Beschilderung, d.h. unter Berücksichtigung von Regeln, die in der topologischen Datenschicht für die befahrbaren Vorgänger- und Nachfolgespuren codiert sind
• • Wahrnehmungsgenauigkeit, z.B. um Aussagen über Lichtverhältnisse zu erhalten
• • Signalisierung, z.B. Blinken und Hupen
• • Steuergerätestatus, z.B. Diagnosedaten
• • dynamische Umgebung, z B. Anzahl der Fahrzeuge in der Umgebung

A digital map suitable for implementing a solution according to the invention preferably has at least one relational data layer, one topological data layer, and one behavioral data layer, wherein an expected driving behavior is stored in the behavioral data layer. The expected driving behavior can comprise a variety of information, e.g.:

• • average driving speed
• • average acceleration / deceleration
• • Steering angle / cornering behavior / deviation from the center line
• • Stopping, e.g. vehicles stop at a certain point due to an obstacle
• • Lane change despite prohibition by solid line or signage, ie taking into account rules coded in the topological data layer for the passable preceding and following lanes
• • Perception accuracy, e.g. to obtain information about lighting conditions
• • Signaling, e.g. flashing and honking
• • Control unit status, e.g. diagnostic data
• • dynamic environment, e.g. number of vehicles in the area

• - Empfangen von Benachrichtigungen und optional Sensordaten von einer Vielzahl von autonomen oder teilautonomen Fahrzeugen zu Abweichungen zwischen einem aktuellen Fahrverhalten des jeweiligen Fahrzeugs und einem erwarteten Fahrverhalten;
• - Auswerten der empfangenen Benachrichtigungen und Sensordaten; und
• - Aktualisieren eines in einer Verhaltensdatenschicht der digitalen Karte gespeicherten erwarteten Fahrverhaltens auf Basis eines Ergebnisses der Auswertung.

According to a further aspect of the invention, a method for updating a digital map comprises the steps of:

• - Receive notifications and optionally sensor data from a variety of autonomous or semi-autonomous vehicles to deviations between the current driving behavior of the respective vehicle and the expected driving behavior;
• - Evaluate the received notifications and sensor data; and
• - Updating an expected driving behavior stored in a behavioral data layer of the digital map based on a result of the evaluation.

According to a further aspect of the invention, a computer program includes instructions which, when executed by a computer, cause the computer to perform the following steps to update a digital map:

• - Receiving notifications and optionally sensor data from a variety of autonomous or semi-autonomous vehicles regarding deviations between the current driving behavior of the respective vehicle and an expected driving behavior;
• - Evaluate the received notifications and sensor data; and
• - Updating an expected driving behavior stored in a behavioral data layer of the digital map based on a result of the evaluation.

The term computer is to be understood broadly. In particular, it also includes workstations, distributed systems and other processor-based

Data processing devices. The execution of the aforementioned steps can be performed directly by the computer or can consist of the computer controlling a component intended to execute a step.

The computer program may, for example, be made available for electronic retrieval or stored on a computer-readable storage medium.

• - ein Empfangsmodul zum Empfangen von Benachrichtigungen und optional Sensordaten von einer Vielzahl von autonomen oder teilautonomen Fahrzeugen zu Abweichungen zwischen einem aktuellen Fahrverhalten des jeweiligen Fahrzeugs und einem erwarteten Fahrverhalten;
• - ein Auswertemodul zum Auswerten der empfangenen Benachrichtigungen und Sensordaten; und
• - ein Aktualisierungsmodul zum Aktualisieren eines in einer Verhaltensdatenschicht der digitalen Karte gespeicherten erwarteten Fahrverhaltens auf Basis eines Ergebnisses der Auswertung.

According to a further aspect of the invention, a device for updating a digital map comprises:

• - a receiving module for receiving notifications and optionally sensor data from a plurality of autonomous or semi-autonomous vehicles regarding deviations between the current driving behavior of the respective vehicle and an expected driving behavior;
• - an evaluation module for evaluating the received notifications and sensor data; and
• - an update module for updating an expected driving behavior stored in a behavior data layer of the digital map based on a result of the evaluation.

In the solution according to the invention, the vehicles of a fleet send a notification and optionally sensor data to a backend, e.g., via a mobile phone connection, in the event of a detected deviation in driving behavior. The notifications can, in particular, also contain information on the current driving behavior. The received information on the anomalous behavior is collected and processed in the backend. This makes it possible to determine whether a deviation is a problem with a single vehicle or not. This makes it possible to distinguish map deviations from other anomalies. Since multiple vehicles in the same geographical area will report anomalous behavior, the expected driving behavior can also be adjusted based on the reported deviations, i.e., the information in the behavioral data layer can be adapted by learning from the fleet. The correspondingly processed information is integrated into the behavioral data layer in the backend. At least the updated behavioral data layer can then be sent from the backend to the vehicles in the fleet, e.g., via a mobile phone connection.

To generate the behavioral data layer, the vehicles in a fleet can continuously send the driving behavior information described above to a backend, e.g., via a cellular connection. This information is accumulated and processed in the backend, for example, by extracting trajectories or creating speed profiles. Both autonomous or semi-autonomous vehicles and manually controlled vehicles can be used to collect the data to generate statistics on nominal behavior.

• 1 zeigt schematisch ein Verfahren zum Steuern eines autonomen oder teilautonomen Fahrzeugs;
• 2 zeigt eine erste Ausführungsform einer Vorrichtung zum Steuern eines autonomen oder teilautonomen Fahrzeugs;
• 3 zeigt eine zweite Ausführungsform einer Vorrichtung zum Steuern eines autonomen oder teilautonomen Fahrzeugs;
• 4 stellt schematisch ein Fahrzeug dar, in dem eine erfindungsgemäße Lösung realisiert ist;
• 5 zeigt schematisch ein Verfahren zum Aktualisieren einer digitalen Karte;
• 6 zeigt eine erste Ausführungsform einer Vorrichtung zum Aktualisieren einer digitalen Karte;
• 7 zeigt eine zweite Ausführungsform einer Vorrichtung zum Aktualisieren einer digitalen Karte;
• 8 veranschaulicht ein erwartetes Fahrverhalten am Beispiel einer Kurvenfahrt; und
• 9 stellt schematisch eine zur Umsetzung einer erfindungsgemäßen Lösung geeignete digitale Karte dar.

Further features of the present invention will become apparent from the following description and the appended claims taken in conjunction with the figures.

• 1 shows schematically a method for controlling an autonomous or semi-autonomous vehicle;
• 2 shows a first embodiment of a device for controlling an autonomous or semi-autonomous vehicle;
• 3 shows a second embodiment of a device for controlling an autonomous or semi-autonomous vehicle;
• 4 schematically represents a vehicle in which a solution according to the invention is implemented;
• 5 shows schematically a method for updating a digital map;
• 6 shows a first embodiment of a device for updating a digital map;
• 7 shows a second embodiment of a device for updating a digital map;
• 8 illustrates an expected driving behavior using the example of cornering; and
• 9 schematically represents a digital map suitable for implementing a solution according to the invention.

To better understand the principles of the present invention, embodiments of the invention are explained in more detail below with reference to the figures. It is understood that the invention is not limited to these embodiments and that the described features may also be combined or modified without departing from the scope of the invention as defined in the appended claims.

1 shows a schematic of a method for controlling an autonomous or semi-autonomous vehicle. In a first step, sensor data or decisions from a motion planner are recorded 10. The current driving behavior of the vehicle is determined 11 from the recorded data. The current driving behavior is compared with an expected driving behavior 12 stored in a behavior data layer of a digital map. If a deviation between the current driving behavior and the expected driving behavior is detected 13, a notification and optionally sensor data can be sent to a backend 15. The notification can, in particular, also contain information about the current driving behavior. Alternatively or additionally, the next steps can first be determined 14, e.g., whether the detected deviation should be used to update the digital map, whether a teleoperator should be involved, or whether a maneuver should be initiated to bring the vehicle into a safe state. In each case, the current driving behavior is adjusted 16.

2 shows a simplified schematic representation of a first embodiment of a device 20 for controlling an autonomous or semi-autonomous vehicle. The device 20 has an input 21 for receiving sensor data SD from a sensor system 41 or decisions E from a movement planner 42. A processing module 22 is configured to determine a current driving behavior F _a of the vehicle from this data. A comparison module 23 is configured to compare the current driving behavior F _a with an expected driving behavior F _e , which is stored in a behavior data layer of a digital map, and to determine any deviation A. A control module 24 is configured to adapt the current driving behavior F _a in the event of such a deviation A. For this purpose, the control module 24 can output a corresponding control signal S via an output 27 of the device 20. The comparison module 23 or the control module 24 can also be configured to send a notification B and optionally sensor data SD to a backend 80 via the output 27. Notification B can, in particular, also contain information about the current driving behavior. Alternatively or additionally, in addition to adjusting the current driving behavior _Fa, the next steps can first be determined, e.g., whether the detected deviation A should be used to update the digital map, whether a teleoperator should be involved, or whether a maneuver should be initiated to bring the vehicle into a safe state.

The processing module 22, the comparison module 23, and the control module 24 can be controlled by a control module 25. Settings of the processing module 22, the comparison module 23, the control module 24, or the control module 25 can be changed via a user interface 28. The data generated in the device 20 can be stored in a memory 26 of the device 20 if necessary, for example, for later evaluation or for use by the components of the device 20. The processing module 22, the comparison module 23, the control module 24, and the control module 25 can be implemented as dedicated hardware, for example, as integrated circuits. Of course, they can also be partially or completely combined or implemented as software running on a suitable processor, for example, a CPU or a GPU. The input 21 and the output 27 can be implemented as separate interfaces or as a combined bidirectional interface.

3 shows a simplified schematic representation of a second embodiment of a device 30 for controlling an autonomous or semi-autonomous vehicle. The device 30 has a processor 32 and a memory 31. For example, the device 30 is a computer, a control unit, or an embedded system. Instructions are stored in the memory 31 which the device 30 executes by the processor 32, the steps are carried out according to one of the described methods. The instructions stored in the memory 31 thus embody a program executable by the processor 32, which implements the method according to the invention. The device 30 has an input 33 for acquiring sensor data or decisions of a motion planner. Data generated by the processor 32 is provided via an output 34. In addition, data can be stored in the memory 31. The input 33 and the output 34 can be combined to form a bidirectional interface.

The processor 32 may include one or more processor units, such as microprocessors, digital signal processors, or combinations thereof.

The memories 26, 31 of the described embodiments can have both volatile and non-volatile memory areas and can comprise a wide variety of storage devices and storage media, for example hard disks, optical storage media or semiconductor memories.

4 schematically illustrates an autonomous or semi-autonomous vehicle 40 in which a solution according to the invention is implemented. The vehicle 40 has a sensor system 41 for detecting environmental information, such as cameras, radar sensors, lidar sensors or ultrasonic sensors. A movement planner 42 is configured to plan a driving behavior of the vehicle 40. The movement planner 42 can be implemented, for example, in an assistance system or a high-performance computer of the vehicle 40. A device 20 according to the invention is configured to compare the current driving behavior of the vehicle 40, i.e. the planned or actual driving behavior, with an expected driving behavior. The expected driving behavior is stored in a behavior data layer of a digital map K, which is stored in a memory 44 of the vehicle 40. By means of a data transmission unit 43, a connection to a backend 80 can be established, e.g., for transmitting sensor data SD or notifications B regarding deviations in driving behavior, for retrieving updated software for the components of the vehicle 40, or for retrieving an updated map K. The data exchange between the various components of the vehicle 40 takes place via a network 45.

5 schematically shows a method for updating a digital map. In the method, notifications and optionally sensor data are received from a plurality of autonomous or semi-autonomous vehicles regarding deviations between the current driving behavior of the respective vehicle and an expected driving behavior 50. The notifications can, in particular, also contain information about the current driving behavior. The received notifications and sensor data are evaluated 51. Based on the result of the evaluation, the expected driving behavior stored in a behavior data layer of the digital map is then updated 52. At least the updated behavior data layer, but alternatively also the entire updated digital map, can then be sent to the vehicles 53.

6 shows a simplified schematic representation of a first embodiment of a device 60 for updating a digital map K. The device 60 has an input 61, via which a receiving module 62 receives notifications B and optionally sensor data SD from a plurality of autonomous or semi-autonomous vehicles 40 regarding deviations between a current driving behavior of the respective vehicle 40 and an expected driving behavior. The notifications B can in particular also contain information about the current driving behavior. An evaluation module 63 is configured to evaluate the received notifications B and sensor data SD. An updating module 64 is configured to update an expected driving behavior stored in a behavior data layer _SV of the digital map K based on a result of the evaluation. The updated behavior data layer _SV or the entire updated map K can be output via an output 67 of the device 60 for further use.

The receiving module 62, the evaluation module 63, and the update module 64 can be controlled by a control module 65. Settings of the receiving module 62, the evaluation module 63, the update module 64, or the control module 65 can be changed via a user interface 68. The data generated in the device 60 can be stored in a memory 66 of the device 60 if necessary, for example, for later evaluation or for use by the components of the device 60. The receiving module 62, the evaluation module 63, the update module 64, and the control module 65 can be implemented as dedicated hardware, for example, as integrated circuits. Of course, they can also be partially or completely combined or implemented as software running on a suitable processor, for example, a CPU or a GPU. The input 61 and the output 67 can be implemented as separate interfaces or as a combined bidirectional interface.

7 shows a simplified schematic representation of a second embodiment of a device 70 for updating a digital map. The device 70 has a processor 72 and a memory 71. For example, the device 70 is a computer, a workstation, or a distributed system. Instructions are stored in the memory 71 which, when executed by the processor 72, cause the device 70 to carry out the steps according to one of the described methods. The instructions stored in the memory 71 thus embody a program executable by the processor 72 which implements the method according to the invention. The device 70 has an input 73 for receiving sensor data or notifications of deviations in driving behavior. Data generated by the processor 72 are provided via an output 74. In addition, data can be stored in the memory 71. The input 73 and the output 74 can be combined to form a bidirectional interface.

The processor 72 may include one or more processor units, such as microprocessors, digital signal processors, or combinations thereof.

The memories 66, 71 of the described embodiments can have both volatile and non-volatile memory areas and can comprise a wide variety of storage devices and storage media, for example hard disks, optical storage media or semiconductor memories.

• • durchschnittliche Fahrgeschwindigkeit
• • durchschnittliche Beschleunigung / Verzögerung
• • Lenkwinkel / Kurvenschneidverhalten / Abweichung von der Mittellinie
• • Anhalten
• • Fahrspurwechsel trotz Verbotes durch durchgezogene Linie oder Beschilderung
• • Wahrnehmungsgenauigkeit
• • Signalisierung
• • Steuergerätestatus
• • dynamische Umgebung

8 illustrates in a simplified manner expected driving behavior using the example of cornering. It shows a road ST with an autonomous or semi-autonomous vehicle 40 on it. The road ST has a curve. The dashed line represents the center line M of the lane. Manually controlled vehicles will generally deviate from this center line M, with the trajectories driven usually lying within the area delimited by the dashed-dotted lines. This area represents the expected driving behavior. If, for example, vehicle 40 cuts the curve and follows the dotted line, there is a deviation between the current driving behavior and the expected driving behavior. In practical implementation, the expected driving behavior can include a variety of information, e.g.:

• • average driving speed
• • average acceleration / deceleration
• • Steering angle / cornering behavior / deviation from the center line
• • Stop
• • Changing lanes despite prohibition by solid line or signage
• • Perceptual accuracy
• • Signaling
• • Control unit status
• • dynamic environment

The information stored in the map can be specified as individual values or value ranges. When assessing a deviation from expected driving behavior, the vehicle can consider 40 tolerance ranges. These can be adjustable if necessary.

9 schematically represents a digital map K suitable for implementing a solution according to the invention. The map K comprises several data layers in which different information is stored. A physical data layer S _P comprises a set of physical elements of the real world that represent the stationary environment. Examples of such physical elements are road markings and traffic signs. A relational data layer S _R comprises a set of semantic elements composed of physical elements. Examples of such semantic elements are lanes formed from road markings, or construction sites formed from temporary road markings, temporary traffic signs, and temporary traffic lights. A topological data layer S _T comprises a set of links between semantic elements. Examples of such links are predecessors, successors, and neighbors of lanes. A behavioral data layer S _V comprises an expected driving behavior, i.e., an expected target behavior. This specifies the actual behavior of vehicles on the roads with respect to specific road sections from the relational data layer S _R and the topological data layer S _T of the map K.

List of reference symbols

10

Collecting sensor data or decisions

11

Determining current driving behavior

12

Comparing current driving behavior with expected driving behavior

13

Detecting a deviation

14

Determine the next steps

15

Sending a notification, optionally sending sensor data

16

Adjusting current driving behavior

20

device

21

Entrance

22

Processing module

23

Comparison module

24

Control module

25

Control module

26

memory

27

Exit

28

User interface

30

device

31

memory

32

processor

33

Entrance

34

Exit

40

vehicle

41

Sensor technology

42

Movement planner

43

Data transmission unit

44

memory

45

network

50

Receive notifications and optionally sensor data

51

Evaluate the received notifications and sensor data

52

Updating an expected driving behavior

53

Sending the updated behavioral data layer

60

device

61

Entrance

62

Receiver module

63

Evaluation module

64

Update module

65

Control module

66

memory

67

Exit

68

User interface

70

device

71

memory

72

processor

73

Entrance

74

Exit

80

backend

A

deviation

B

notification

E

Decision

fa

Current driving behavior

Fe

Expected driving behavior

K

Digital map

M

center line

S

control signal

SP

Physical data layer

SR

Relational data layer

ST

Topological data layer

SV

Behavioral data layer

SD

Sensor data

ST

Street

QUOTES CONTAINED IN THE DESCRIPTION

This list of documents submitted by the applicant was generated automatically and is included solely for the convenience of the reader. This 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 2018 / 0 004 225 A1 [0006]

Claims (10)

Method for controlling an autonomous or semi-autonomous vehicle (40), comprising the steps of: - comparing (12) a current driving behavior (F _a ) of the vehicle (40) with an expected driving behavior (F _e ) stored in a behavior data layer (S _V ) of a digital map (K); - determining (13) a deviation (A) between the current driving behavior (F _a ) and the expected driving behavior (F _e ); and - adapting (16) the current driving behavior (F _a ). Procedure according to Claim 1 , wherein the current driving behavior (F _a ) of the vehicle (40) is an actual or planned driving behavior which is determined (11) on the basis of sensor data (SD) or decisions (E) of a movement planner (42). Procedure according to Claim 1 or 2 , wherein in the event of a detected deviation (A) between the current driving behavior (F _a ) and the expected driving behavior (F _e ), a notification (B) and optionally sensor data (SD) are sent (15) to a backend (80). Method according to one of the preceding claims, wherein in the case of a detected deviation (A) between the current driving behavior (F _a ) and the expected driving behavior (F _e ), it is determined (14) whether the detected deviation (A) should be used to update the digital map (K), whether a teleoperator should be involved or whether a maneuver to transfer the vehicle (40) into a safe state should be initiated. Device (20) for controlling an autonomous or semi-autonomous vehicle (40), comprising: - a comparison module (23) for comparing (12) a current driving behavior (F _a ) of the vehicle (40) with an expected driving behavior (F _e ) stored in a behavior data layer (S _V ) of a digital map (K), and for determining (13) a deviation (A) between the current driving behavior (F _a ) and the expected driving behavior (F _e ); and - a control module (24) for adapting (16) the current driving behavior (F _a ). Autonomous or semi-autonomous vehicle (40), wherein the vehicle (40) comprises a device (20) according to Claim 5 has or is arranged to implement a method according to one of the Claims 1 until 4 to control the vehicle (40). Digital card (K) for use in a method according to one of the Claims 1 until 4 , wherein the digital map (K) has at least one relational data layer (S _R ), one topological data layer (S _T ) and one behavioral data layer (S _V ), wherein an expected driving behavior (F _e ) is stored in the behavioral data layer (S _V ). Method for updating a digital map (K), comprising the steps of: - receiving (50) notifications (B) and optionally sensor data (SD) from a plurality of autonomous or semi-autonomous vehicles (40) relating to deviations (A) between a current driving behavior (F _a ) of the respective vehicle (40) and an expected driving behavior (F _e ); - evaluating (51) the received notifications (B) and sensor data (SD); and - updating (52) an expected driving behavior (F _e ) stored in a behavior data layer (S _V ) of the digital map (K) on the basis of a result of the evaluation. Device (60) for updating a digital map (K), comprising: - a receiving module (62) for receiving (50) notifications (B) and optionally sensor data (SD) from a plurality of autonomous or semi-autonomous vehicles (40) relating to deviations (A) between a current driving behavior (F _a ) of the respective vehicle (40) and an expected driving behavior (F _e ); - an evaluation module (63) for evaluating (51) the received notifications (B) and sensor data (SD); and - an updating module (64) for updating (52) an expected driving behavior (F _e ) stored in a behavior data layer (S _V ) of the digital map (K) on the basis of a result of the evaluation. A computer program comprising instructions which, when executed by a computer, cause the computer to carry out the steps of a method according to any one of the Claims 1 until 4 or 8 initiate.