DE102024125278A1 - Automatic or semi-automatic braking control of a vehicle - Google Patents

Abstract

For the at least partially automatic braking control of a vehicle (1), object data is generated or received by means of a data processing system (3) of the vehicle (1). This data includes an object class (9) of an object (6) in the vehicle's (1) environment. Environmental data (10) is generated or received by means of the data processing system (3). This environmental data includes statistical traffic accident data concerning the environment and/or environmental conditions. Depending on the object class (9) and the environmental data (10), control data concerning a braking intensity for a desired braking maneuver of the vehicle (1) is determined by means of the data processing system (3). Depending on the control data, at least one control signal for at least one brake actuator of the vehicle (1) is generated.

Description

The present invention relates to a method for at least partially automatic brake control of a vehicle, a data processing system for carrying out such a method, an electronic vehicle guidance system comprising such a data processing system, and corresponding computer program products.

When a vehicle is driven fully or partially automatically, particularly at Level 3 or Level 4, it is necessary, among other things, to automatically or partially automatically control the vehicle's braking system to prevent critical collisions with objects in its vicinity. Especially in the context of driver assistance systems, such systems are also referred to as emergency braking assistants or similar.

For example, such systems or functions are used to control the braking system, enabling the vehicle to be brought to a complete stop, especially at relatively low speeds. This is achieved, for instance, by initiating hard braking, also known as emergency braking or full braking, when a corresponding object is detected. This is unpleasant for the vehicle's passengers and can lead to increased wear and tear on the braking system, tires, and other components. Furthermore, such emergency braking can also cause an accident with another road user, particularly one following behind.

It is an object of the present invention to avoid emergency braking in the at least partially automatic brake control of a vehicle.

This problem is solved by the respective subject matter of the independent claims. Advantageous further developments and preferred embodiments are the subject matter of the dependent claims.

The invention is based on the idea of individually adapting the braking intensity used for a desired braking maneuver to the current situation by taking into account, for example, the type of object and, for example, environmental conditions and/or statistical traffic accident data for the environment in order to determine the braking intensity.

According to one aspect of the invention, a method for at least partially automatic brake control of a vehicle is described. Object data is generated or received, which includes an object class of an object in the vehicle's environment, in particular in the vehicle's current external environment. For example, environmental data is generated or received, which includes statistical traffic accident data concerning the environment and/or environmental conditions. Depending on the object class and, for example, the environmental data, in particular the statistical traffic accident data and/or the environmental conditions, control data concerning a braking intensity for a desired braking maneuver of the vehicle are determined. Depending on the control data, at least one control signal is generated for at least one brake actuator of the vehicle, in particular for carrying out the desired braking maneuver by means of the at least one brake actuator.

The vehicle can be, in particular, a motor vehicle, for example a car, a passenger car, a truck, a motorcycle or a commercial vehicle.

Unless otherwise specified, all steps of the procedure can be performed by a data processing system comprising at least one data processing device, in particular by a data processing system of the vehicle. Specifically, the at least one data processing device is configured or adapted to perform the steps of the computer-implemented procedure. For this purpose, the at least one data processing device may, for example, store a computer program containing instructions which, when executed by the at least one data processing device, cause it to perform the corresponding steps of the procedure. The terms "data processing system" and "at least one data processing device" may be used interchangeably.

All data processing devices of the at least one data processing device can be part of the vehicle. However, it is also possible that all data processing devices of the at least one data processing device are part of an external computing system outside the vehicle, for example, a backend server or a cloud computing system. It is also possible that the at least one data processing device is both at least one vehicle data processing device of the vehicle and at least one external data processing device of the external computing system. The vehicle data processing device comprises, for example, one or more electronic control units (ECUs), one or more zone control units (ZCUs), one or more domain control units (DCUs), and one or more sensor systems of the vehicle.

In the event that the at least one data processing device comprises two or more data processing devices, certain steps performed by the at least one data processing device can be understood, for example, as different data processing devices performing different steps or different parts of a step. In particular, it is not necessary for each data processing device to perform the steps completely. In other words, the execution of the steps can be distributed among the two or more data processing devices.

If all steps of the method are performed by the data processing system, the method can be described as a computer-implemented method. Each embodiment of a computer-implemented method according to the invention yields a corresponding embodiment of the method that is not purely computer-implemented, in that it includes, for example, corresponding steps for detecting the environment or the object, for example, by means of an environmental sensor system of the vehicle, and/or for performing the braking maneuver by means of the at least one brake actuator.

An environmental sensor system can be understood, for example, as a sensor system capable of generating sensor data or sensor signals that map, represent, or reproduce the environment of the environmental sensor system. In particular, the ability to detect electromagnetic or other signals from the environment is not sufficient to classify a sensor system as an environmental sensor system. For example, cameras, radar systems, lidar systems, or ultrasonic sensor systems can be considered environmental sensor systems.

In the present disclosure, a data processing device can be understood, for example, as a device with processing circuits for processing data. A data processing device can thus perform arithmetic operations to process data. Indexed access to a data structure, for example, a lookup table (LUT) or a database, can also be considered an arithmetic operation.

A data processing device may, in particular, comprise one or more computers, one or more microcontrollers, and/or one or more integrated circuits, for example, one or more application-specific integrated circuits (ASICs), one or more field-programmable gate arrays (FPGAs), and/or one or more systems-on-a-chip (SoCs). A data processing device may also comprise one or more processors, for example, one or more microprocessors, one or more central processing units (CPUs), one or more graphics processing units (GPUs), and/or one or more signal processors, in particular one or more digital signal processors (DSPs). The data processing device may also comprise a physical or virtual cluster of computers or other devices of the aforementioned type. A data processing device may also comprise one or more hardware and/or software interfaces, for example, for receiving and/or providing data.

A data processing device may also include one or more storage devices. A storage device may be implemented as volatile memory, such as dynamic random access memory (DRAM) or static random access memory (SRAM), or as non-volatile data storage, such as read-only memory (ROM), programmable read-only memory (PROM), erasable programmable read-only memory (EPROM), electrically erasable programmable read-only memory (EEPROM), flash memory or flash EEPROM, ferroelectric random access memory (FRAM), magnetoresistive random access memory (MRAM), or phase-change random access memory (PCRAM).

The data processing system can, in particular, be part of an electronic vehicle control system. An electronic vehicle control system can be understood as an electronic system designed to control a vehicle fully automatically or autonomously, especially without requiring any intervention by a driver. The vehicle automatically performs all necessary functions, such as steering, braking, and/or acceleration maneuvers, monitoring and recording road traffic, and reacting accordingly. Specifically, the electronic vehicle control system can implement a fully automatic or fully autonomous driving mode of the motor vehicle according to Level 5 of the SAE J3016 classification. An electronic vehicle control system can also include a driver assistance system. An advanced driver assistance system (ADAS) is understood to be a system that supports the driver during partially automated or semi-autonomous driving. Specifically, the electronic vehicle guidance system can implement a partially automated or semi-autonomous driving mode according to levels 1 to 4 of the SAE J3016 classification. Here and in the following, "SAE J3016" refers to the corresponding standard in its April 2021 version.

At least partially automated vehicle control can therefore include driving the vehicle in accordance with a fully automated or fully autonomous driving mode of Level 5 according to SAE J3016. At least partially automated vehicle control can also include driving the vehicle in accordance with a partially automated or semi-autonomous driving mode according to Levels 1 to 4 of SAE J3016.

Partially automatic brake control means, in particular, either fully automatic or partially automatic brake control. With fully automatic brake control, no action by the driver, such as pressing the brake pedal, is required. With partially automatic brake control, the effect of an action by the driver, such as pressing the brake pedal, can be automatically supplemented, increased, or decreased.

The object data can be generated, for example, based on sensor data from at least one of the vehicle's environmental sensor systems, which represent or map the surroundings. The object class is, in particular, a result of an object recognition algorithm that is performed, or has been performed, based on the sensor data. An object recognition algorithm can be understood, for example, as a computer algorithm capable of identifying and locating one or more objects within a provided input data set, such as the sensor data. This can be achieved, for example, by defining corresponding boundary boxes or regions of interest (ROIs) and, in particular, by assigning a corresponding object class to each of the boundary boxes. The object classes can be selected from a predefined set of object classes. The assignment of an object class to a boundary box can be understood as providing a corresponding confidence value or probability that the object identified within the boundary box belongs to the corresponding object class. For example, the algorithm can provide such a confidence value or probability for each of the object classes for a given boundary box. The assignment of the object class can, for example, involve selecting or providing the object class with the highest confidence value or the highest probability.

The object recognition algorithm can be performed prior to the method according to the invention, for example by the at least one environmental sensor system and/or a further data processing system. To perform the method according to the invention, the data processing system can then receive the object class from the at least one environmental sensor system and/or the further data processing system. Alternatively, the data processing system can perform the object recognition algorithm wholly or partially based on the sensor data.

The environmental data, or a portion thereof, such as environmental conditions, can also be the result of the object detection algorithm and/or one or more other environmental perception algorithms, also known as perception algorithms or computer vision algorithms. For example, object detection algorithms, object tracking algorithms, image classification algorithms, semantic segmentation algorithms, depth estimation algorithms, optical flow estimation algorithms, and so on can be considered perception algorithms.

The perception algorithms can also be performed prior to the method according to the invention, for example by the at least one environmental sensor system and/or the further data processing system. To perform the method according to the invention, the data processing system can then receive the environmental data or the corresponding part of the environmental data from the at least one environmental sensor system and/or the further data processing system. Alternatively, the data processing system can perform the perception algorithm(s) wholly or partially based on the sensor data.

The data processing system can receive environmental data, or a further part thereof, in particular statistical traffic accident data, but also environmental conditions or parts thereof, from a data storage device or database, or read from a digital map. The data storage device, database, or digital map can be located in the vehicle or external to the vehicle, for example in a server system or cloud computer system, and so on.

The data processing system can receive environmental conditions, or parts thereof, from other vehicle sensors or determine them based on sensor data provided by these other sensors. This includes, for example, outside temperature, the presence or absence of rain or other precipitation, solar radiation, and so on.

The statistical traffic accident data indicates, in particular, how many traffic accidents were recorded in the current environment within a past period, or contains, for example, a corresponding risk indicator relating to the number of traffic accidents in the current environment within the past period, or relating to a probability of a traffic accident in the current environment based on the traffic accidents recorded in the past period. In some versions, the statistical traffic accident data may also be provided for a current time and/or day of the week and/or season or other current period.

Environmental conditions can include, for example, weather conditions such as the outside temperature, the presence or absence of rain or other precipitation, solar radiation, and so on. Environmental conditions can also include the type of environment, such as whether it is an urban or rural setting, a tunnel, a bridge, and so forth.

The control data relating to the braking intensity for the intended braking maneuver may include one or more values, for example, a braking pressure applied by the at least one brake actuator, a braking force applied by the at least one brake actuator, or a deceleration of the vehicle achieved or intended by the at least one brake actuator, and so on.

The control data may include, in particular, a maximum value of the braking intensity, especially the brake pressure, braking force or deceleration, and/or a profile of the braking intensity, especially the brake pressure, braking force or deceleration, and so on.

The at least one control signal is then generated in such a way that, upon corresponding transmission to the at least one brake actuator, it causes the at least one brake actuator to initiate or carry out the braking maneuver in accordance with the control data.

By generating the control data based on the object class and environmental data, particularly statistical traffic accident data and/or environmental conditions, the control data, and thus the braking intensity for the braking maneuver, can be individually adapted to the specific situation. In particular, this ensures that full braking is only performed if the object, according to the object class, is a critical object—for example, a person or an object that could severely damage the vehicle in a collision, or where a collision would pose a high risk to the vehicle occupants—and/or if the risk of an accident is particularly high according to statistical traffic accident data and/or environmental conditions. For less critical objects, such as bushes or other soft vegetation, the braking maneuver can be performed with lower braking intensity or a smoother braking intensity curve. While this increases the risk of a collision, it is acceptable due to the minor consequences of such a collision. Similarly, intermediate stages are possible in various implementations.

The object class of the object is, in particular, one of a multitude of predefined object classes. This multitude of predefined object classes may, for example, include one or more classes indicating that the object is a human being, such as an object class "human," an object class "child," an object class "pedestrian," an object class "cyclist," and so on. The multitude of predefined object classes may, for example, include one or more classes indicating that the object is a living object, such as the aforementioned object classes for humans, an object class "living object," an object class "animal," an object class "small animal," an object class "medium-sized animal," an object class "large animal," and so on. Specifically, here and in the following, only humans and animals are understood as living objects, whereas plants are considered non-living objects. The multitude of predefined object classes may, for example, include one or more classes... These include object classes that indicate the object is a non-living object, for example, an object class "non-living object", an object class "soft non-living object", an object class "hard non-living object", an object class "small non-living object", an object class "medium-sized non-living object", an object class "large non-living object", an object class "tree", an object class "bush", an object class "road edge", an object class "guardrail", an object class "curb", an object class "car", an object class "truck", and so on. The multitude of predefined object classes may, for example, include one or more object classes that indicate the object is a static object. The multitude of predefined object classes may, for example, include one or more object classes that indicate the object is a dynamic object, i.e., a moving or movable object.

According to at least one embodiment, the control data includes a maximum braking intensity for the desired braking maneuver. In particular, the maximum braking intensity is determined depending on the object class and, for example, the environmental data, and the at least one control signal is determined depending on the maximum braking intensity.

Since the maximum braking intensity has a significant impact on the execution and effectiveness of the braking maneuver, as well as on the comfort of the vehicle occupants and the safety of following road users, such designs are particularly advantageous. The greater the maximum braking intensity, the harder the braking maneuver.

For example, the maximum braking intensity can be determined such that it is greater for objects of the object class "human" than for objects of the object class "animal" and/or than for objects of the object class "small animal" and/or than for objects of the object class "non-living object" and/or than for objects of the object class "soft non-living object," and so on. For example, the maximum braking intensity can be determined such that it is greater for objects of the object class "large animal" than for objects of the object class "small animal" and/or than for objects of the object class "soft non-living object," and so on. For example, the maximum braking intensity can be determined such that it is greater for objects of the object class "hard non-living object" than for objects of the object class "soft non-living object," and so on. This list is neither exhaustive nor binding, but merely illustrates how, in principle, different maximum braking intensities can be selected or determined for different types of objects in corresponding embodiments. Furthermore, it was assumed that the same environmental data was present in the different cases.

According to at least one embodiment, the control data includes a profile, in particular a temporal profile, of the braking intensity during the intended braking maneuver. Specifically, the profile of the braking intensity is determined depending on the object class and, for example, the environmental data, and the at least one control signal is determined depending on the profile of the braking intensity.

Since the braking intensity profile has a significant impact on the execution and effectiveness of the braking maneuver, as well as on the comfort of the vehicle occupants and the safety of following road users, such designs are particularly advantageous. Different braking intensity profiles can vary, in particular, in how much the braking intensity changes within a period corresponding to a portion of the braking maneuver—for example, through an average rate of change of the braking intensity during the entire braking maneuver or during a portion of it. The higher the average rate of change, the more severe the braking maneuver. The rate of change of the braking intensity can be understood, in particular, as the first derivative of the braking intensity over time. The examples above for maximum braking intensity depending on the object classes can also be applied analogously to the braking intensity profile with regard to the corresponding severity of the braking maneuver.

According to at least one embodiment, the control data includes the maximum rate of change of the braking intensity during the intended braking maneuver. In particular, the maximum rate of change of the braking intensity is determined depending on the object class and, for example, the environmental data, and the at least one control signal is determined depending on the maximum rate of change of the braking intensity.

Since the maximum rate of change of braking intensity has a significant impact on the execution and effectiveness of the braking maneuver, as well as on the comfort of the vehicle occupants and the safety of following road users, such designs are particularly advantageous. The greater the maximum rate of change of braking intensity, the harder the braking action. An example is the braking maneuver. The maximum rate of change of the braking intensity can be understood, in particular, as the maximum of the first time derivative of the braking intensity. The examples above for the maximum braking intensity depending on the object classes can also be applied analogously to the maximum rate of change of the braking intensity with regard to the corresponding severity of the braking maneuver.

According to at least one embodiment, current motion parameters of the vehicle and the object are determined or received, particularly by means of the data processing system. Depending on the object class and/or the environmental data, especially the statistical traffic accident data and/or the environmental conditions, a target distance between the vehicle and the object, achieved through the intended braking maneuver, is determined. The control data is determined based on the target distance and the current motion parameters.

In such embodiments, the vehicle is braked, in particular until it comes to a standstill, according to the braking maneuver. The target distance then corresponds to the distance of the vehicle to the object once the vehicle has been braked to a standstill.

The motion parameters include, in particular, corresponding motion parameters of the vehicle in the reference coordinate system of the environment and corresponding motion parameters of the object in the reference coordinate system of the environment. Alternatively or additionally, the motion parameters include relative motion parameters of the object with respect to the vehicle. These motion parameters can include, in particular, the velocity of the vehicle and the velocity of the object in the reference coordinate system of the environment and/or the relative velocity of the object with respect to the vehicle. The motion parameters can also include the direction of movement of the vehicle and the direction of movement of the object in the reference coordinate system of the environment and/or the direction of movement of the object relative to the vehicle.

The motion parameters, in particular the motion parameters of the vehicle, can be determined based on corresponding sensor data from suitable vehicle sensors, for example, IMUs (inertial measurement units), speed sensors, wheel speed sensors, acceleration sensors, yaw rate sensors, and so on, and/or based on the time course of sensor data from the at least one environmental sensor system. The motion parameters, in particular the motion parameters of the object, can be determined, for example, based on the sensor data from the at least one environmental sensor system and/or the time course of this sensor data.

The motion parameters indicate, in particular, whether the object and the vehicle are moving towards or away from each other, and if so, how fast this is happening. Accordingly, by considering the motion parameters when determining the control data, the risk of a collision in a specific situation can be taken into account in more detail. The higher the collision risk, the more severe the braking maneuver can be, and conversely, the less severe the braking maneuver can be the lower the collision risk according to the motion parameters. Furthermore, the target distance can be set smaller the lower the collision risk according to the motion parameters.

According to at least one embodiment, the object data includes the object's position, in particular its relative position to the vehicle, and the braking intensity is determined based on the object's position. Specifically, the target distance is determined based on the object's position.

The object's position influences, firstly, the collision risk, with the risk increasing the closer the object is to the vehicle, and secondly, even under otherwise identical conditions, it affects at least one control signal. This makes it possible to avoid emergency braking in certain situations.

According to at least one embodiment, the target distance is a first target distance if the object class is a predefined first object class indicating that the object is a human being, and the target distance is a second target distance if the object class is a predefined second object class indicating that the object is a non-living object, in particular a soft non-living object. The second target distance is smaller than the first target distance.

This applies particularly when all other conditions regarding the vehicle and the object are identical, and when environmental data is identical. Consequently, a harder braking maneuver is performed when the object is a person than when it is an inanimate object, which can be justified, especially since a collision with a person may be considered more serious. as a collision with a non-living object, in particular a soft non-living object.

For example, in some embodiments, the second target distance can also be zero, and/or the target distance can be zero if the object class corresponds to a predefined third object class. The third object class can, for example, correspond to the object class "soft non-living object," the object class "bush," or any other object class indicating that the object is one that, in the event of a collision with the vehicle, is expected to cause no or only minor damage to the vehicle. This makes it possible to avoid emergency braking in other situations.

According to at least one embodiment, the environmental data, for example the statistical traffic accident data, are read out, in particular by the data processing system, at least in part from a digital map of the environment, which is stored in particular in the vehicle or in a data storage device provided externally to the vehicle.

According to at least one embodiment, the environmental data, for example the statistical traffic accident data, are obtained at least partly from an external computer system, for example through wireless communication, in particular by the data processing system.

According to at least one embodiment, the environmental data, for example the statistical traffic accident data, are obtained at least in part from another vehicle in the vicinity, for example by wireless communication, in particular by C2C technology (car-to-car), in particular by the data processing system.

According to at least one embodiment, environmental sensor data of the surroundings, representing the environment, are generated by means of an environmental sensor system of the vehicle. The object data, for example the object class, are generated, in particular by means of the data processing system, at least partially based on the environmental sensor data, and/or the environmental data, for example the environmental conditions, are generated or determined, at least partially, based on the environmental sensor data, in particular by means of the data processing system.

Generating the object data and/or the environment data can, for example, involve applying an object recognition algorithm to the environment sensor data or to data dependent on the environment sensor data.

According to at least one embodiment, the braking maneuver is initiated, and in particular initiated and carried out, by controlling the at least one brake actuator by the at least one control signal. In particular, the vehicle is braked to a standstill by the braking maneuver.

In other words, the at least one control signal, in particular through the data processing system, is transmitted to the at least one brake actuator, and the at least one brake actuator exerts a braking force or brake pressure depending on the at least one control signal in order to decelerate the vehicle according to the braking maneuver or according to the control data.

For use cases or application situations that may arise in a method according to the invention and that are not explicitly described herein, it may be provided that, according to the method, an error message and/or a request for user feedback is issued and/or a default setting and/or a predetermined initial state is set.

According to another aspect of the invention, a data processing system is provided which is adapted to carry out a method according to the invention, in particular a computer-implemented method according to the invention.

According to another aspect of the invention, an electronic vehicle guidance system comprising a data processing system according to the invention is specified.

According to at least one embodiment of the electronic vehicle guidance system, the system includes an environmental sensor system for the vehicle, configured to generate environmental sensor data representing the surroundings. The data processing system is configured to generate object data, at least partially, based on the environmental sensor data and/or to generate environmental data, at least partially, based on the environmental sensor data.

According to at least one embodiment of the electronic vehicle guidance system, the electronic vehicle guidance system includes at least one brake actuator for the vehicle. The at least one brake actuator is configured to control the vehicle depending on the to at least slow down a control signal, in particular to initiate and carry out the braking maneuver.

Further embodiments of the electronic vehicle guidance system according to the invention follow directly from the various configurations of the method according to the invention, and vice versa. In particular, individual features and corresponding explanations as well as advantages relating to the various configurations of the method according to the invention can be transferred analogously to corresponding configurations of the electronic vehicle guidance system according to the invention. In particular, the electronic vehicle guidance system according to the invention is configured or programmed to carry out a method according to the invention. In particular, the electronic vehicle guidance system according to the invention carries out the method according to the invention.

According to another aspect of the invention, a computer program with instructions is specified. When the instructions are executed by a data processing system, the instructions cause the data processing system to carry out a computer-implemented method according to the invention.

The instructions can be provided, for example, as program code. This program code can be provided, for example, as binary code or assembly language, and/or as source code in a programming language such as C, and/or as a program script, such as Python.

According to a further aspect of the invention, a further computer program with additional commands is specified. When the additional commands are executed by an electronic vehicle guidance system according to the invention, in particular by the data processing system of the electronic vehicle guidance system, the additional commands cause the electronic vehicle guidance system to carry out a method according to the invention.

The additional instructions can be provided as program code. This program code can be, for example, binary code or assembly language, and/or source code in a programming language such as C, and/or a program script such as Python.

According to another aspect of the invention, a computer-readable storage medium is specified which stores a computer program according to the invention and/or a further computer program according to the invention.

The computer program, the further computer program, and the computer-readable storage medium are each computer program products with the commands or the further commands, respectively.

Further features of the invention are evident from the claims, the figures, and the description of the figures. The features and combinations of features mentioned above in the description, as well as those mentioned below in the description of the figures and/or illustrated in the figures, may be encompassed by the invention not only in the combinations specified, but also in other combinations. In particular, embodiments and combinations of features that do not include all the features of an originally formulated claim may also be encompassed by the invention. Furthermore, embodiments and combinations of features that go beyond or deviate from the combinations of features mentioned in the claims may also include the invention.

The invention is explained in more detail below with reference to specific exemplary embodiments and corresponding schematic drawings. Identical or functionally equivalent elements in the drawings may be provided with the same reference numerals. The description of identical or functionally equivalent elements is not necessarily repeated with respect to the different figures.

• 1 eine schematische Darstellung eines Fahrzeugs mit einer beispielhaften Ausführungsform eines erfindungsgemäßen elektronischen Fahrzeugführungssystems; und
• 2 ein schematisches Ablaufdiagramm einer beispielhaften Ausführungsform eines erfindungsgemäßen Verfahren zur wenigstens teilweise automatischen Bremssteuerung eines Fahrzeugs.

The figures show

• 1 a schematic representation of a vehicle with an exemplary embodiment of an electronic vehicle guidance system according to the invention; and
• 2 a schematic flowchart of an exemplary embodiment of a method according to the invention for at least partially automatic brake control of a vehicle.

In 1 Figure 1 schematically depicts a vehicle 1, in particular a motor vehicle, which has an exemplary embodiment of an electronic vehicle guidance system 2 according to the invention. The electronic vehicle guidance system 2 includes a data processing system 3, which is adapted to carry out a computer-implemented method according to the invention for at least partially automatic brake control of the vehicle 1. The vehicle 1, for example the electronic vehicle guidance system 2, has one or more environmental sensor systems 4, 5, for example a camera 4 and/or a Lidar system 5 and/or an ultrasonic sensor system and/or a radar system. The vehicle 1, for example the electronic vehicle guidance system 2, has at least one brake actuator which, when controlled accordingly by the data processing system 3, which may in particular also include one or more control units, can decelerate the vehicle 1 at least partially automatically in accordance with a braking maneuver, in particular to a standstill.

2 Figure 1 shows a schematic flowchart of an exemplary embodiment of a method according to the invention for at least partially automatic brake control of the vehicle 1. The method can be implemented, for example, by means of an electronic vehicle guidance system 2, as described above. 1 It is described, and will be carried out.

Step 210 is optional, meaning it is only included in some embodiments of the method according to the invention. Step 220 is also optional, meaning it is only included in some embodiments of the method according to the invention. Step 250 is also optional, meaning it is only included in some embodiments of the method according to the invention.

In step 210, environmental sensor data 7 of the vehicle 1's surroundings are generated using at least one environmental sensor system 4, 5 of the vehicle 1. This data represents the surroundings. Object data, which includes an object class 9 of an object 6 in the vehicle 1's surroundings, are generated at least partially based on the environmental sensor data 7, for example by applying an object recognition algorithm to the environmental sensor data 7, for example by the at least one environmental sensor system 4, 5 or the data processing system 3.

Furthermore, environmental data 10 are generated, at least in part, based on the environmental sensor data 7, for example, by the at least one environmental sensor system 4, 5 and/or the data processing system 3. Alternatively or additionally, in step 220, map data 8 from a digital map of the environment are read, which contain at least some of the environmental data 10. The environmental data 10 includes statistical traffic accident data concerning the environment and/or environmental conditions.

In step 230, depending on the object class 9 and the environmental data 10, control data relating to a braking intensity for a desired braking maneuver of the vehicle 1 are determined. The control data can include, in particular, a maximum value of the braking intensity, especially the brake pressure, braking force, or deceleration, and/or a profile of the braking intensity, especially the brake pressure, braking force, or deceleration, and/or an average rate of change of the braking intensity and/or a maximum rate of change of the braking intensity.

In step 240, depending on the control data, at least one control signal is generated for at least one brake actuator of vehicle 1.

In step 250, at least one control signal can be transmitted to at least one brake actuator, which can then initiate and carry out the braking maneuver based on at least one control signal.

In various embodiments, the safety of people and/or animals can be increased. For example, if the electronic vehicle guidance system 2 recognizes the object 6 as a person or animal, a different braking strategy can be applied, such as higher braking pressure, a greater target distance when braking to a standstill, and so on, than for other objects, such as bushes, in order to increase safety.

In various embodiments, the highest possible braking force can be applied for the most critical objects 6, for example, people, and the braking maneuver can be calculated with a maximum target distance. For other objects, the softness of the braking reaction can be increased or the severity of the braking reaction reduced, depending on their prioritization. For some object classes, even a soft impact can be permitted, in which no damage or only minor damage to the vehicle 1 is expected.

In various designs, emergency braking can be avoided as much as possible, whereas, especially in critical situations, for example at night, in heavy rain, heavy traffic, and so on, accidents involving a person or animal can still be reliably avoided.

Claims (15)

Method for at least partially automatic brake control of a vehicle (1), wherein - object data is generated or received which includes an object class (9) of an object (6) in an environment of the vehicle (1); - environment data (10) is generated or received will be, which include statistical traffic accident data concerning the environment and/or environmental conditions of the environment; - depending on the object class (9) and the environmental data (10) control data concerning a braking intensity for a desired braking maneuver of the vehicle (1) will be determined; and - depending on the control data, at least one control signal for at least one brake actuator of the vehicle (1) will be generated. Procedure according to Claim 1 , where the control data includes a maximum braking intensity for the intended braking maneuver. Method according to one of the preceding claims, wherein the control data includes a progression of the braking intensity during the desired braking maneuver. Method according to one of the preceding claims, wherein the control data includes a maximum rate of change of the braking intensity during the desired braking maneuver. A method according to any of the preceding claims, wherein - current motion parameters of the vehicle (1) and the object (6) are determined or received; - a target distance between the vehicle (1) and the object (6) is determined depending on the object class (9) and/or the environmental data, as determined by the intended braking maneuver; and - the control data are determined depending on the target distance and the current motion parameters. Procedure according to Claim 5 , where - the target distance is a first target distance if the object class (9) of the object (6) is a predefined first object class (9) indicating that the object (6) is a human; and - the target distance is a second target distance if the object class (9) of the object (6) is a predefined second object class (9) indicating that the object (6) is a non-living object (6), where the second target distance is smaller than the first target distance. procedure according to one of the preceding Claim 5 , where the target distance is zero if the object class (9) corresponds to a predefined third object class (9). Method according to one of the preceding claims, wherein the object data includes a position of the object (6) and the braking intensity is determined depending on the position. Method according to any of the preceding claims, wherein - a direction of movement of the object (6) is determined and the braking intensity is determined depending on the direction of movement; and/or - a speed of movement of the object (6) is determined and the braking intensity is determined depending on the position. Method according to one of the preceding claims, wherein the environmental data (10) - are read at least partially from a digital map of the environment; and/or - are obtained at least partially from an external computer system; and/or - are obtained at least partially from another vehicle (1) in the environment. Method according to one of the preceding claims, wherein - environmental sensor data (7) of the environment are generated by means of an environment sensor system (4, 5) of the vehicle (1), which represent the environment; and - the object data are generated at least partly based on the environment sensor data (7) and/or the environment data (10) are generated at least partly based on the environment sensor data (7). Method according to one of the preceding claims, wherein the braking maneuver is initiated by controlling the at least one brake actuator by the at least one control signal. data processing system (3) adapted to perform a procedure according to one of the Claims 1 until 10 to carry out. Electronic vehicle guidance system (2) comprising a data processing system (3) according to Claim 13 as well as - an environment sensor system (4, 5) for the vehicle (1) which is configured to generate environment sensor data (7) of the surroundings, which represent the surroundings, wherein the data processing system (3) is configured to generate the object data at least partly based on the environment sensor data (7) and/or to generate the environment data (10) at least partly based on the environment sensor data (7); and/or - the at least one brake actuator for the vehicle (1) which is configured to brake the vehicle (1) depending on the at least one control signal. computer program product comprising instructions which, when executed by a data processing system (3), will execute the data processing system (3) induce proceedings under one of the Claims 1 until 10 to perform; and/or - further commands which, when executed by an electronic vehicle guidance system (2), Claim 14 cause the electronic vehicle guidance system (2) to initiate a procedure according to one of the Claims 11 or 12 to carry out.