Method for controlling an at least partially autonomous ego-motor vehicle

Abstract

In order to create a method for controlling at least a semi-autonomous ego-motor vehicle (100) with particularly positive driving behavior when turning or changing lanes of a relevant road user (20), it is proposed that the method includes the following steps: d. Determining a current shortest distance (22) between the ego-motor vehicle (100) and the relevant road user (20); e. Determining a safety measure that includes at least a probability of collision; f. Determining an ideal speed of the Ego vehicle (100) at which a given safety level can be achieved; g. Determining a speed-based control value for accelerating and / or decelerating the Ego vehicle (100) based on the ideal speed determined in step f.; h. Determining a distance-based setpoint for accelerating and / or decelerating the Ego vehicle (100) based on a hazard factor stored by means of the storage unit (13); i. Selection between the speed-based control value and the distance-based control value; and j. Controlling the braking or acceleration of the Ego vehicle (100) based on the setpoint selected in step i.

Description

[0001] The invention relates to a method for controlling an at least partially autonomous ego-motor vehicle, wherein the Ego motor vehicle has a control unit with a computing unit and a sensor unit or communication unit, the procedure includes at least the following steps: a. by means of the sensor device or the communication device, detection of a relevant road user; b. using the computing device, determining a probability value that the relevant road user will deviate from a likely route of the ego vehicle; c. using the computing device, comparing the probability value with a defined limit value.

[0002] Methods for regulating vehicle speed are known from the prior art, which regulate a desired distance when a vehicle is following a relevant road user. It is also known to determine a probability value to ascertain when a relevant road user turns, thus terminating the following control and enabling free speed regulation.

[0003] CN115257734A, for example, discloses a method for adaptive cruise control for a motor vehicle, wherein, in a first step, the status of the brake lights of a vehicle ahead is determined. In a subsequent step, the relative position and speed of the vehicle relative to the vehicle ahead are determined. Taking the determined values ​​into account, if predefined thresholds are exceeded, the vehicle is braked to a safe distance.

[0004] JP2022157639A, for example, discloses a method for a motorcycle driver assistance system, in which a necessary vehicle distance to the vehicle ahead is determined based on the prevailing road conditions. The motorcycle's distance can be adjusted by regulating the vehicle speed when a curve is detected.

[0005] WO2022157433A1, for example, discloses a method for controlling an adaptive cruise control system of a motor vehicle, wherein the motor vehicle follows a second motor vehicle through a curve. In a first step, the current curve and the maximum speed of the motor vehicle within the curve are determined. In a subsequent step, based on the time the second motor vehicle enters the curve, acceleration to a target acceleration value can be executed, depending on the vehicle distance, a minimum safety distance, and a maximum speed.

[0006] For example, DE102019129512 discloses a control module which is set up to adjust the speed of a vehicle based on the probability of a vehicle ahead turning.

[0007] The invention, in contrast to previously known methods, control devices and motor vehicles, is based on the objective of solving the following problem with the simplest and most cost-effective means possible: A procedure, a control device and a motor vehicle are to be provided. which enables predictive speed control of the vehicle during a maneuver (such as turning, changing lanes, or similar) by a road user driving ahead or alongside. This allows for responses to unexpected situations, For example, it is possible to react to a stopped vehicle ahead that is turning, without having to reduce speed drastically and unnecessarily. For example, this makes it possible to achieve a particularly human-like driving behavior and to accelerate the vehicle again at an early stage.

[0008] According to claim 1, the problem is solved by further comprising the following steps for controlling the speed of the Ego motor vehicle when the probability value exceeds the limit: d. using the computing device, determining a current, shortest distance between the ego-motor vehicle and the relevant road user; e. by means of the computing device, determining a safety measure which includes at least a probability of collision; f. using the computing device, determining an ideal speed for the Ego vehicle at which a predetermined safety level can be achieved; g. using the computing device, determining a speed-based control value for accelerating and / or decelerating the Ego vehicle based on the ideal speed determined in step f. h. by means of the computing device, determining a distance-based setpoint for accelerating and / or decelerating the Ego vehicle based on a hazard factor stored by the storage unit; i. by means of the computing device, selection between the speed-based control value and the distance-based control value; and j. by means of the control unit, controlling a braking or an acceleration of the Ego vehicle based on the setpoint selected in step i.

[0009] According to claim 14, the problem is solved by designing the control device to perform such a method.

[0010] According to claim 15, the problem is solved by the Ego motor vehicle having such a control device which is at least configured to control the drive unit.

[0011] Advantageous embodiments of the invention are characterized in the dependent claims.

[0012] According to a first embodiment of the invention, a method for controlling an at least partially autonomous ego-vehicle is proposed, wherein the ego-vehicle comprises a control unit with a computing unit and a sensor unit or communication unit, the procedure includes at least the following steps: a. by means of the sensor device or the communication device, detection of a relevant road user; b. using the computing device, determining a probability value that the relevant road user will deviate from a likely route of the ego vehicle; c. using the computing device, comparing the probability value with a defined limit value.

[0013] The procedure is characterized primarily by the fact that, if the probability value exceeds the limit, the procedure further includes the following steps to control the speed of the Ego vehicle: d. using the computing device, determining a current, shortest distance between the ego-motor vehicle and the relevant road user; e. by means of the computing device, determining a safety measure which includes at least a probability of collision; f. using the computing device, determining an ideal speed for the Ego vehicle at which a predetermined safety level can be achieved; g. using the computing device, determining a speed-based control value for accelerating and / or decelerating the Ego vehicle based on the ideal speed determined in step f.; h. by means of the computing device, determining a distance-based setpoint for accelerating and / or decelerating the Ego vehicle based on a hazard factor stored by the storage unit; i. by means of the computing device, selection between the speed-based control value and the distance-based control value; and j. by means of the control unit, controlling a braking or an acceleration of the Ego vehicle based on the setpoint selected in step i.

[0014] The relevant road user is preferably a road user traveling ahead. Alternatively, the relevant road user could be, for example, a road user traveling alongside.

[0015] Preferably, the specified safety margin achievable with the ideal speed determined in step f corresponds to the safety margin determined in step e. Thus, the ideal speed in step f is preferably determined based on the safety margin determined in step e. such that the safety margin is achieved with the ideal speed.

[0016] In an advantageous embodiment of the method, the probability value determined in step b. is a probability value for a turning and / or a lane change by the relevant road user.

[0017] In an advantageous embodiment of the method, step b comprises the following substeps: b.1 by means of a computing device, determining the maneuver intention of the relevant road user to carry out a steering maneuver, for example a turning and / or lane change; b.2 by means of the computing device, determining a follow intention of the ego-motor vehicle to follow the steering maneuver of the relevant road user; and b.3 using the computing device, determining the probability value based on the maneuver intention determined in step b.1 and the follow-up intention determined in step b.2.

[0018] In an advantageous embodiment of the method, the maneuver intention is determined as a maneuver probability and the follow-up intention as a follow-up probability.

[0019] In an advantageous embodiment of the method, the probability value in step b. is determined on the basis of at least one of the following factors: - a turning signal, preferably a light signal or a hand signal, from the relevant road user and / or the Ego motor vehicle; - a lateral acceleration of the relevant road user and / or the ego vehicle; - a longitudinal acceleration of the relevant road user and / or the ego-motor vehicle; - Navigation data of the relevant road user and / or the ego vehicle; - a communication message from the relevant road user; and - Detecting a turning opportunity or lane change opportunity, preferably based on a traffic sign, a map and / or a camera recording.

[0020] In an advantageous embodiment of the method, the probability of collision is determined based on the following sub-steps: e.1.1 Determining a turning point of the relevant road user; e.1.2 Determining at least one potential journey path of the relevant road user; e.1.3 Determining at least one potential journey path of the Ego vehicle; e.1.4 Determining a potential time course of the shortest distance based on the turning point determined in step e.1.1, the potential journey path of the relevant road user determined in step e.1.2 and the potential journey path of the ego vehicle determined in step e.1.3.

[0021] In an advantageous embodiment of the method, in step e.2 an action reserve of the Ego vehicle for a worst-case scenario is determined, and In step e. the safety measure is determined based on the action reserve and the collision probability.

[0022] In an advantageous embodiment of the method, the action reserve in step e.2 includes a residual distance if the ego vehicle and the relevant road user come to a complete stop in a worst-case scenario.

[0023] In an advantageous embodiment of the method, the action reserve for a straight-ahead journey of the relevant road user is determined and the remaining distance is a longitudinal distance.

[0024] In an advantageous embodiment of the method, the action reserve is determined on the basis of at least one of the following parameters for the relevant road user or the ego-motor vehicle: - a type of driver; - the type of vehicle; - other road users; - Road conditions; and - Weather conditions; - Current kinematic data (distance, speed, acceleration, yaw angle, yaw rate, etc.) of the ego and the relevant vehicle.

[0025] In an advantageous embodiment of the method, the action reserve includes a safety factor, so that the action reserve can be determined by means of the safety factor between a minimum distance at which a crash in a worst-case scenario can just be prevented and a maximum distance.

[0026] In an advantageous embodiment of the method, in step e. the safety measure, preferably the action reserve and / or the collision probability, is determined on the basis of map data and / or swarm data, preferably by means of artificial intelligence.

[0027] In an advantageous embodiment of the method, the hazard factor according to step h. is determined based on the collision probability and the action reserve, preferably by means of a characteristic map.

[0028] Furthermore, a control device is proposed which includes at least the following components: - a computing facility; - a storage unit; and - a sensor device and / or a communication device; wherein the control device is designed to perform a method according to an embodiment as described above.

[0029] Furthermore, an Ego-powered vehicle is proposed, which includes at least the following components: - a drive unit for providing drive torque; - a braking device for providing a braking torque; - at least one drive wheel coupled to the drive unit and the braking device in a torque-transmitting manner, by means of which a propulsion of the motor vehicle can be provided on the basis of the drive torque or the braking torque; - a control device according to an embodiment as described above, which is configured at least for controlling the drive unit according to a method according to an embodiment as described above.

[0030] Exemplary embodiments of the invention are explained in more detail below with reference to the drawing. The drawing shows: Fig. 1: a flowchart of a procedure for speed control of an Ego motor vehicle, Fig. 2: a decision diagram from a section of the procedure according to Fig. 1, Fig. 3: a schematic representation of a turning maneuver by a vehicle driving ahead, Fig. 4: another schematic representation of a turning maneuver by a relevant road user, Fig. 5: a schematic representation of a lane change by a relevant road user, and Fig. 6: a motor vehicle with a control device for carrying out the procedure according to Fig. 1.

[0031] Fig. Figure 1 shows a flowchart of a procedure for speed control of an ego-vehicle 100. The procedure serves to control the speed of an ego-vehicle 100 or the distance 22 between an ego-vehicle 100 and a relevant road user 20 when the relevant road user 20 is expected to deviate from a route 21 of the ego-vehicle 100. Such a departure from the route 21 occurs, for example, when the relevant road user 20 turns or changes lanes.

[0032] The relevant road user 20 is preferably a motor vehicle 100. However, the procedure can also be used for a bicycle, pedestrian or other relevant road user 20.

[0033] In the first phase 30 of the procedure, a corresponding situation is recognized as a turning or lane change by the relevant road user 20, and the procedure is thus initiated. For this purpose, the relevant road user 20 is recorded in step a.

[0034] For example, the motor vehicle 100 follows the relevant road user 20 in a following maneuver. For this purpose, a control device of the ego-motor vehicle 100 preferably implements a control system for the following maneuver, which is not described in detail here, in which the ego-motor vehicle 100 follows the relevant road user 20 at a safe distance 22. Such control systems are known to those skilled in the art, for example, under the term Adaptive Cruise Control (ACC). This control of the following maneuver occurs, for example, when the ego-motor vehicle 100 and the relevant road user 20 are traveling straight ahead and / or when the relevant road user 20 turns or changes lanes, during which the ego-motor vehicle 100 follows.

[0035] If the self-driving vehicle 100 follows the relevant road user 20 in a subsequent journey, the relevant road user 20 is already detected in step a. Preferably, the vehicle 100 has a sensor device 14, for example, a front camera, a LiDAR sensor, and / or a radar sensor, by means of which the relevant road user 20 can be detected. Alternatively or additionally, the self-driving vehicle 100 includes a communication device by means of which data about a relevant road user 20 can be received. Preferably, the communication device is configured for vehicle-to-vehicle communication, so that data or messages from the relevant road user 20 can be received by means of the communication device of the self-driving vehicle 100.

[0036] In a case where the ego-vehicle 100 is not following the relevant road user 20 in a controlled manner, it is still possible to carry out the procedure. For example, the relevant road user 20 is so far away from the ego-vehicle 100 that a controlled following is not possible. However, it is suspected that the relevant road user 20 is about to turn, for example, by braking particularly hard or remaining stationary for a long time on the route 21 of the ego-vehicle 100. For example, there is a high probability that the relevant road user 20 is driving around a particularly tight curve 26, has activated its turn signal for a lane change, but that lane is still occupied by other road users 20, or intends to turn left due to oncoming traffic or turn right when cyclists or pedestrians are passing.In such a case, the relevant road user 20 can still be detected in step a. by means of the sensor device 14 and / or the communication device and the procedure can be carried out.

[0037] In step b., the computing device 11 determines a probability value that the relevant road user 20 will deviate from the anticipated route 21 of the ego vehicle 100. For example, the relevant road user 20 turns or changes lanes while the ego vehicle 100 continues straight ahead.

[0038] Preferably, in a sub-step b.1, a computing device 11 is used to determine the maneuver intention of the relevant road user 20 to perform a steering maneuver, preferably turning and / or changing lanes. Preferably, the maneuver intention is a maneuver probability, i.e., a probability with which the relevant road user 20 performs a corresponding steering maneuver to turn or change lanes.

[0039] For example, the maneuver intention or maneuver probability is determined based on a turning signal, preferably a light signal or a hand signal, from the relevant road user 20; a lateral acceleration of the relevant road user 20; a longitudinal acceleration of the relevant road user 20; a communication message from the relevant road user 20 to the ego-vehicle 100, which informs the ego-vehicle 100 about the impending steering maneuver; and / or the detection of a turning opportunity or lane-changing opportunity, preferably based on a traffic sign, a map and / or a camera recording.

[0040] Furthermore, the system preferentially determines, based on these factors, which maneuver should be performed. For example, if the relevant road user (20) has activated their turn signal (i.e., a turning signal) before a junction or intersection, there is a high probability of turning. However, if the turn signal is activated on an open stretch of road, the probability of turning is low, and the probability of a turn is assigned a low value, for example, only slightly higher than for driving straight ahead. Instead, the system anticipates, for instance, stopping or pulling over to the side of the road.

[0041] Preferably, the maneuver intention is determined before the relevant road user 20 begins a lateral movement.

[0042] In substep b.2, the computing device 11 determines the intention of the ego-vehicle 100 to follow the steering maneuver of the relevant road user 20. Preferably, the intention to follow is a probability of the ego-vehicle 100 following, i.e., a probability with which the ego-vehicle 100 follows the steering maneuver of the relevant road user 20.

[0043] The intention to follow or the probability of following is determined, for example, on the basis of a turning signal, preferably a light signal, of the ego vehicle 100; a steering angle of the ego vehicle 100; a longitudinal acceleration or accelerator pedal position of the ego vehicle 100; a detection of a turning opportunity or lane change opportunity, preferably on the basis of a traffic sign, a map and / or a camera recording and / or the data, preferably of a planned route 21, of a navigation system of the ego vehicle 100.

[0044] Substeps b.1 and b.2 thus lead to a subsequent probability. The probability value determined in step b. is, for example, the reciprocal of the subsequent probability and is determined, for example, in substep b.3 using the computing device 11.

[0045] The anticipated route 21 of the Ego vehicle 100 does not refer to route 21 as a total distance set in the navigation system, but rather to a lane occupied by the Ego vehicle 100 on a preceding section of the route. Preferably, the corresponding section of the route is a maximum of one kilometer long, and more preferably, a maximum of 500 meters.

[0046] For example, a convoy journey may have been predetermined, resulting in a very low probability value, and the subsequent journey will only be aborted or interrupted if necessary due to traffic conditions. A similarly low probability value exists, for instance, if the relevant road user 20 changes lanes to bypass a convoy of trucks on the highway. A high probability value exists, for example, if the relevant road user 20 turns into a driveway, or if the route of the ego vehicle 100 involves proceeding straight ahead and the relevant road user 20 signals a turn.

[0047] The probability value is compared with a defined limit value in step c using the computing device 11. If the probability value exceeds the limit value, the steps described below for regulating the vehicle speed are carried out. These procedural steps are designed to create the most comfortable driving experience possible for the occupant of the ego vehicle 100 if the relevant road user 20 deviates from the expected route 21 of the ego vehicle 100. For example, timely braking is desirable to ensure sufficient safety or a high level of safety for the occupant if the relevant road user 20 slows down while turning or changing lanes. Furthermore, early acceleration or re-acceleration is possible.

[0048] In a second phase 31, intermediate values ​​are first calculated which are used for speed control while the preceding road user 20 leaves the route 21 of the Ego vehicle 100.

[0049] If it has been determined that the probability value exceeds a threshold, a current, shortest distance 22 between the ego-vehicle 100 and the relevant road user 20 is calculated in step d. using the computing device 11. The shortest distance 22 is the distance 22 along a direct connection between the ego-vehicle 100 and the relevant road user 20. For example, the shortest distance 22 extends from a corner of the ego-vehicle 100 to a corner of the relevant road user 20, instead of, as in previous methods, from a midpoint of the front edge of the ego-vehicle 100 to the relevant road user 20. Furthermore, the shortest distance 22 is preferably defined such that it also includes the distance 22 in the transverse direction 28, i.e., not only in the longitudinal direction 27, which is also referred to below as the longitudinal direction, of the ego-vehicle 100.

[0050] In step e., a safety measure is determined using the computing device 11, which includes at least a collision probability. Preferably, the safety measure also includes a reserve for action.

[0051] Determining the collision probability is represented here by step e.1, and determining the safety margin by step e.2. For example, the safety margin consists solely of the collision probability. In one embodiment, the safety margin and the collision probability are combined into a single safety margin, which is used in step f. to calculate the ideal speed. Alternatively, the safety margin comprises the collision probability and the safety margin as separate values, which are used, for example, separately in step f. to calculate the ideal speed or in subsequent steps.

[0052] To determine the probability of a collision, a turning point 23 of the relevant road user 20 is first determined in a sub-step e.1.1. The turning point 23 is, for example, the point at which the relevant road user 20 transitions from driving straight ahead to driving around a curve (compare Fig. 4).

[0053] In a further substep e.1.2, at least one potential journey path 24 of the relevant road user 20 is determined. Preferably, the potential journey path 24 includes a location-time profile, i.e., it indicates where the relevant road user 20 is expected to be at a given time. For example, the journey path 24 is determined based on one, preferably several, driver profiles. For example, three driver profiles are available: one for sporty turning behavior, one for average turning behavior, and one for slow turning behavior. Environmental conditions such as weather, the radius 25 of the curve 26, and other road users are also taken into account.

[0054] In substep e.1.3, at least one potential journey path of the ego-vehicle 100 is determined. For example, the journey path is a position-time profile of a straight-ahead journey, thus specifying the route with a time component. This journey path also indicates, for example, where the ego-vehicle 100 is expected to be at a specific time. For example, the journey path of the ego-vehicle 100 is determined based on one, preferably several, driver profiles. For example, three driver profiles are available: one for sporty turning behavior, one for average turning behavior, and one for slow turning behavior. Preferably, the journey path of the ego-vehicle 100 is determined based on an individual driver profile of the respective driver of the ego-vehicle 100.Environmental conditions such as weather, radius 25 of curve 26 and other road users are also taken into account.

[0055] In substep e.1.4, a potentially shortest distance 22 is determined based on the turning point 23 determined in step e.1.1, the potential travel path 24 of the relevant road user 20 determined in step e.1.2, and the potential travel path of the ego vehicle 100 determined in step e.1.3. For example, the shortest distance 22 is determined for each driver profile or combination of driver profiles for the relevant road user 20 and the ego vehicle 100 according to substeps e.1.2 and e.1.3.

[0056] The collision probability is then determined from the potentially shortest distance 22. For example, a collision probability is assigned to each shortest distance 22 via a characteristic map, or the collision probability can be determined from the potentially shortest distance 22 using an analytical equation. For example, the overall collision probability is determined by averaging the individual driver profiles, for example, according to the law of total probability. The collision probability thus provides a basis for assessing whether a critical approach will occur between the ego vehicle 100 and the relevant road user 20 during an expected driving trajectory, and therefore the ideal speed of the ego vehicle 100 should be reduced compared to the current vehicle speed, or can even be increased to avoid such a critical approach.

[0057] The action reserve according to step e.2 is determined for a worst-case scenario, i.e., not the expected journey path according to step e.1. The action reserve according to step e.2 is based on a residual distance between the ego vehicle 100 and the relevant road user 20 when the ego vehicle 100 and the relevant road user 20 come to a complete stop in a worst-case scenario. Thus, if, for example, the relevant road user 20 brakes abruptly before it has completely left the route 21 of the ego vehicle 100, for example, because an unexpected obstacle blocks the turning route, the ego vehicle 100 will also brake accordingly.Due to reaction time or differing braking distances, for example, due to previous speed differences, the brakes installed, or the vehicle weight, the distance 22 between the ego-vehicle 100 and the relevant road user 20 decreases. The shortest remaining distance, which is usually reached when both have come to a stop, is therefore determined for such a worst-case scenario. This remaining distance is calculated for a case in which the relevant road user 20 does not deviate from the ego-vehicle 100's route 21, i.e., does not turn. Preferably, the remaining distance is determined as the longitudinal distance between the ego-vehicle 100 and the relevant road user 20.

[0058] For example, the action reserve is a distance reserve. To determine the distance reserve, the remaining distance is first calculated. Then, for example, a percentage-based evaluation of the remaining distance is performed, such that remaining distances below a defined minimum distance are assigned a distance reserve value of 0% [zero percent]. Remaining distances exceeding a defined maximum distance are assigned a distance reserve value of 100% [one hundred percent]. Remaining distances between the minimum and maximum distances are assigned a value that is, for example, linearly interpolated. In this way, a safety factor can be represented, allowing the necessary action reserve to be determined by the safety factor between a minimum distance, at which a crash can just barely be prevented in a worst-case scenario, and a maximum distance.

[0059] In a third phase 32, control values ​​are determined from the intermediate values ​​which were determined in the second phase 31 in order to regulate the speed of the Ego vehicle 100.

[0060] In step f., the computing device 11 determines an ideal speed for the ego-vehicle 100 based on the safety margin determined in step e. For example, the ideal speed of the ego-vehicle 100 is set such that a critical approach between the ego-vehicle 100 and the relevant road user 20 does not occur. The calculation of the ideal speed preferably takes into account the time or distance required to adjust the speed from the current vehicle speed to the ideal speed. The ideal speed can be determined, for example, based on a driver profile, such as the driver profile used to determine the safety margin.

[0061] In step g., a speed-based control value for accelerating and / or decelerating the Ego vehicle 100 is determined by the computing unit 11, based on the safety factor determined in step f. The speed-based control value is configured, for example, to control a drive unit 15 and / or a braking device, so that the Ego vehicle 100 can be accelerated or decelerated based on the speed-based control value. For example, a maximum acceleration value and / or a maximum deceleration value is stored on the memory unit 13, which limits the speed-based control value. In this way, a high level of user comfort can be ensured and / or compliance with certain standards can be guaranteed.

[0062] In step h, a distance-based control value for accelerating and / or decelerating the ego vehicle 100 is determined by the computing unit 11 based on a hazard factor stored in the memory unit 13. The hazard factor is, for example, a risk factor or a harmlessness factor. Preferably, the hazard factor as a risk factor takes a value between 0 and -1, thus causing the ego vehicle 100 to decelerate. Preferably, the hazard factor as a harmlessness factor takes a value between 0 and 1, thus causing the ego vehicle 100 to accelerate. Preferably, the hazard factor is stored in a characteristic map on the memory unit 13 or in several characteristic maps, for example, separately as a risk factor and as a harmlessness factor.For example, the characteristic map(s) represent the hazard factor as a function of the action reserve, which is determined in step e.2. Alternatively or additionally, the hazard factor is based, for example, on the critical approach or collision probability between the ego vehicle 100 and the relevant road user 20, as determined in step e.1. Alternatively or additionally, the hazard factor is determined analytically using a mathematical model or AI.

[0063] In step i., the computing unit 11 selects between the speed-based and the distance-based control value. The control value that results in minimal acceleration and / or maximum deceleration of the Ego vehicle 100 is preferred. In other words, a minimum value selection takes place.

[0064] In a final phase 33, in step j., the Ego vehicle 100 is decelerated or accelerated by means of the control unit 10 based on the control value selected in step i. For example, a braking torque is provided by means of a braking device, such as disc brakes or drum brakes, or an electric drive motor, or a drive torque is provided by means of the drive unit 15.

[0065] For example, an early reduction in speed takes place initially to maintain the necessary action reserve, and subsequently an early acceleration again when it is clear that the necessary action reserve is maintained, for example because the relevant road user 20 has moved completely out of the route 21 of the ego vehicle 100, even if he has not yet reached a large distance 22 to the ego vehicle 100, especially in the longitudinal direction 27.

[0066] Fig. Figure 2 shows an example decision diagram from a section of the procedure according to Fig. 1. In a control block C1, for example, a follow-up control of a following journey takes place, in which the Ego vehicle 100 follows a relevant road user 20, for example, at a defined shortest distance 22.

[0067] To check whether steps d. to j. of the procedure according Fig. Before step 1 is to be carried out, condition R1 is checked to see if the maneuver probability, which is determined, for example, in step h, exceeds a predetermined maneuver limit. If the maneuver probability is less than the maneuver limit, a steering maneuver by the relevant road user 20 is unlikely and the following journey or following control continues.

[0068] If the maneuver probability exceeds the maneuver limit, condition R2 is checked to see if the probability value, which was determined in step b. according to Fig. If the probability value exceeds a limit, it is determined that the ego vehicle 100 will follow the relevant road user 20. For example, in such a case, the ego vehicle 100, following the relevant road user 20, will turn or follow the relevant road user 20 when changing lanes. Accordingly, the vehicle 100 will either remain in the following control mode or switch to a separate following control mode for the respective steering maneuver, represented here by control block C3.

[0069] If the probability value is greater than the limit, there is a high probability that the relevant road user 20 will leave the route 21 of the ego vehicle 100. The probability that the following journey will continue is therefore low. Thus, the control unit 10 switches to the one with respect to Fig. 1 described control procedure and performs steps d. to j., which are represented here by the control block C2.

[0070] Fig. Figure 3 shows a schematic representation of a turning maneuver by a relevant road user 20. The shortest distance 22 between the relevant road user 20 and the ego-motor vehicle 100 is shown, at which the regulation according to the procedure according to Fig. 1 oriented. Instead of orienting itself to the center of the rear of the relevant road user 20 and using the distance 22 in its own direction of travel or longitudinal direction 27 for regulation, in step d., as already explained, the shortest distance 22 is determined.

[0071] Therefore, if the relevant road user 20 turns, the longitudinal distance 22 27 is not ideal for the control system. This is because the relevant road user 20, when turning, moves both longitudinally 27 and laterally 28. Thus, the longitudinal movement 27 decreases until, when turning 90°, the relevant road user 20 is ultimately only moving laterally 28. From the perspective of the ego vehicle 100, the relevant road user 20 appears to be stationary longitudinally 27, leading to an unnecessarily large speed reduction under known control systems.

[0072] The proposed procedure has the effect accordingly Fig. 1, in contrast, by referring to the shortest distance 22 that extends between a part of the relevant road user 20 that is closest to the ego-vehicle 100 and a part of the ego-vehicle 100 that is closest to the relevant road user 20. Thus, for example, according to the right-hand illustration, a distance 22 from vehicle corner to vehicle corner represents the shortest distance 22. Furthermore, the shortest distance 22 extends, for example, as can also be seen in the right-hand illustration, both in the transverse direction 28 and in the longitudinal direction 27.

[0073] Thus, the shortest distance 22 after the relevant road user 20 turns increases again more quickly, and the journey of the ego-motor vehicle 100 is less delayed.

[0074] Fig. Figure 4 shows a further schematic representation of a turning maneuver by a relevant road user 20. In order to obtain a critical approximation in step e.1 of the procedure according to Fig. To determine 1, the journey path 24 of the relevant road user 20 is determined, as explained. For example, a turning point 23, from which the journey through the curve 26 of the relevant road user 20 begins, and a curve end point 29, at which the journey of the relevant road user 20 transitions back into a straight-ahead journey and has completely left the route 21 of the Ego vehicle 100, are determined.

[0075] To determine the route 24 of the relevant road user 20, swarm data and / or map data, for example from a digital navigation map, can be used. For example, such data includes a radius 25 within which the relevant road user 20 is expected to travel through curve 26, and a speed at which, for example, a specific driver profile travels through a curve with such a radius 25.

[0076] Swarm data is collected by multiple vehicles, such as a fleet, as they travel along a section of road. This includes, for example, camera footage, which automatically detects and records lane markings, lane boundaries, and other traffic signs. Swarm data thus encompasses, for example, recorded camera images and / or information about objects derived from these images. Furthermore, swarm data includes, for example, speed profiles along a route and other vehicle characteristics. The swarm data is stored on a central server, such as a cloud server or backend. For example, the swarm data can be accessed from the server using the control unit 10 of the Ego vehicle 100.This can be done as needed, for example, based on the current position and / or a set navigation route of the motor vehicle 100.

[0077] Alternatively or additionally, artificial intelligence can be used, for example to determine the subsequent journey 24 of the relevant road user from their movement 20.

[0078] Fig. Figure 5 shows a schematic representation of a lane change by a relevant road user 20. As already explained, the procedure is according to Fig. 1. This method can also be used for a lane change by the relevant road user 20. Such a lane change is shown here as an example. The method offers the advantage that, for example, the acceleration of the ego vehicle 100 can begin before the relevant road user 20 has completely turned.

[0079] Fig. Figure 6 shows a motor vehicle 100 with a control device 10 for carrying out the procedure according to Fig. 1. The control unit 10 comprises a computing unit 11 with a storage unit 13 and a processor 12. The motor vehicle 100 also has a drive unit 15 for providing drive torque. The drive unit 15 is connected, for example, by means of a transmission 16 to at least one drive wheel 17, or, as shown, two drive wheels 17, in a torque-transmitting manner. The drive wheels 17 enable the torque to be converted into propulsion of the motor vehicle 100.

[0080] At least the drive unit 15 of the motor vehicle 100 is controllable by means of the control unit 10 in order to regulate the speed of the motor vehicle 100. Additionally, for example, other components of the motor vehicle 100 are controllable by means of the control unit 10; preferably, a braking system and / or a steering system are controllable by means of the control unit 10. The motor vehicle 100 is at least a semi-autonomous motor vehicle 100, for example, a fully autonomous motor vehicle 100.

[0081] Furthermore, the motor vehicle 100 includes a sensor device 14, which may, for example, comprise a camera, a LiDAR sensor and / or a radar sensor. Preferably, the sensor device 14 is also designed to detect the distance 22 to a relevant road user 20. Reference symbol list 100 Ego vehicles 10 Control unit 11 Computing equipment 12 processor 13 storage units 14 Sensor device 15 Drive unit 16 gearboxes 17 Drive wheel 20 relevant road users 21 Route 22 shortest distance 23 Turning point 24 Route description 25 radius 26 Curve 27 Longitudinal direction 28 Transverse direction 29 Curve endpoint 30 first phase 31 second phase 32 third phase 33 last phase QUOTES INCLUDED IN THE DESCRIPTION

[0000] This list of documents cited by the applicant was automatically generated and is included solely for the reader's convenience. The list is not part of the German patent or utility model application. The DPMA accepts no liability for any errors or omissions. Cited patent literature

[0000] CN 115257734A

[0003] JP 2022157639A

[0004] WO 2022157433A1

[0005] DE 102019129512

[0006]

Claims

[1] Method for controlling an at least partially autonomous ego motor vehicle (100), wherein the motor vehicle (100) has a control device (10) with a computing device (11) and a sensor device (14) or communication device, wherein the method comprises at least the following steps: a. by means of the sensor device (14) or the communication device, detecting a relevant road user (20); b. by means of the computing device (11), determining a probability value that the relevant road user (20) leaves an expected route (21) of the ego motor vehicle (100); c. by means of the computing device (11), comparing the probability value with a specified limit value, characterized by that if the probability value exceeds the limit value, the method further comprises the following steps for controlling the speed of the ego motor vehicle (100): d. by means of the computing device (11), determining a current, shortest distance (22) between the ego motor vehicle (100) and the relevant road user (20); e. by means of the computing device (11), determining a safety measure which comprises at least a collision probability; f. by means of the computing device (11), determining an ideal speed of the ego motor vehicle (100) at which a predetermined safety level can be achieved; g. by means of the computing device (11), determining a speed-based control value for accelerating and / or braking the ego motor vehicle (100) on the basis of the ideal speed determined in step f.; h. by means of the computing device (11), determining a distance-based control value for accelerating and / or braking the ego motor vehicle (100) on the basis of a hazard factor stored by means of the storage unit (13); i. by means of the computing device (11), selecting between the speed-based control value and the distance-based control value; and j. by means of the control device (10), controlling a braking or an acceleration of the ego motor vehicle (100) on the basis of the control value selected in step i. [2] Method according to claim 1, wherein the probability value determined in step b. is a probability value for a turn and / or a lane change of the relevant road user (20). [3] The method of claim 1 or claim 2, wherein step b comprises at least the following substeps: b.1 by means of a computing device (11), determining a maneuver intention of the relevant road user (20) to carry out a steering maneuver, preferably a turn and / or lane change; b.2 by means of the computing device (11), determining a follow-up intention of the ego motor vehicle (100) to follow the steering maneuver of the relevant road user (20); and b.3 by means of the computing device (11), determining the probability value on the basis of the maneuver intention determined in step b.1 and the follow-up intention determined in step b.

2. [4] Method according to claim 3, wherein the maneuver intention is determined as a maneuver probability and the follow-up intention as a follow-up probability. [5] Method according to one of the preceding claims, wherein the probability value in step b. is determined on the basis of at least one of the following factors: - a turning signal, preferably a light signal or a hand signal, of the relevant road user (20) and / or the ego motor vehicle (100); - a lateral acceleration of the relevant road user (20) and / or the ego motor vehicle (100); - a longitudinal acceleration of the relevant road user (20) and / or the ego motor vehicle (100); - navigation data of the relevant road user (20) and / or the ego motor vehicle (100); - a communication message from the relevant road user (20); and - detection of a turning or lane change option, preferably based on a traffic sign, a map and / or a camera recording. [6] Method according to one of the preceding claims, wherein the collision probability is determined on the basis of the following sub-steps: e.1.1 Determining a turning point (23) of the relevant road user (20); e.1.2 Determining at least one potential journey route (24) of the relevant road user (20); e.1.3 Determining at least one potential route of the ego vehicle (100); e.1.4 Determining a potential temporal course of the shortest distance (22) based on the turning point (23) determined in step e.1.1, the potential journey path (24) of the relevant road user (20) determined in step e.1.2 and the potential journey path of the ego motor vehicle (100) determined in step e.1.

3. [7] Method according to one of the preceding claims, wherein in a step e.2 an action reserve of the ego motor vehicle (100) for a worst-case scenario is determined, and wherein in step e. the safety measure is determined on the basis of the action reserve and the collision probability. [8] Method according to claim 7, wherein the action reserve in step e.2 comprises a residual distance when the ego motor vehicle (100) and the relevant road user (20) come to a complete stop in the worst-case scenario. [9] Motor vehicle (100) according to claim 8, wherein the action reserve is determined for a straight-ahead travel of the relevant road user (20) and the remaining distance is a longitudinal distance. [10] Method according to one of Claim 7 to Claim 9, wherein the action reserve is determined on the basis of at least one of the following parameters for the relevant road user (20) or the ego motor vehicle (100): - a driver type; - the type of vehicle; - other road users (20); - road conditions; and - Weather conditions - current kinematic data (distance, speed, acceleration, yaw angle, yaw rate, etc.) of the ego vehicle and the relevant vehicle. [11] Method according to one of claims 7 to 10, wherein the action reserve comprises a safety factor, so that the action reserve can be determined by means of the safety factor between a minimum distance at which a crash can just be prevented in the worst-case scenario and a maximum distance. [12] Method according to one of the preceding claims, wherein in step e. the safety measure, preferably the action reserve and / or the collision probability, is determined on the basis of map data and / or swarm data, preferably by means of artificial intelligence. [13] Method according to one of the preceding claims, wherein the danger factor according to step h. is determined based on the collision probability or a critical approach and the action reserve, preferably by means of a characteristic map. [14] Control device (10) comprising at least the following components: - a computing device (11); - a storage unit (13); and - a sensor device (14) and / or a communication device, wherein the control device (10) is designed to carry out a method according to one of the preceding claims. [15] Motor vehicle (100) comprising at least the following components: - a drive unit (15) for providing a drive torque; - a braking device for providing a braking torque; - at least one drive wheel (17) coupled to the drive unit (15) and the braking device in a torque-transmitting manner, by means of which propulsion of the motor vehicle (100) can be provided on the basis of the drive torque or the braking torque; - a control device (10) according to claim 14, which is arranged at least for controlling the drive unit (15) according to a method according to one of claims 1 to 13.

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