Method and assistance device for supporting the lateral guidance of a motor vehicle and motor vehicle

Abstract

Method (14) for assisting the guidance of a motor vehicle (6) along a section of road (1), wherein automatically - Swarm data (15) for the track section (1) are recorded and a drivable path (13) leading through the track section (1) is determined from it, - during the operation of the motor vehicle (6) by means of an environmental sensor (8) of the motor vehicle (6) a course of a lane (2) traveled by the motor vehicle (6) in the section of the route (1) is detected, - if the course of the drivable path (13) and the detected course of the lane (2) differ from each other, the curvature of the track segment (1) ahead in the direction of travel of the motor vehicle (6) is determined and compared with a predetermined curvature threshold value, and - it is decided that the driving of the motor vehicle (6) - along the drivable path (13) if the preceding curvature is greater than the curvature threshold, and - along the detected lane (2) if the determined preceding curvature is less than the curvature threshold.

Description

[0001] The present invention relates to a method and an assistance device for supporting the guidance, in particular the lateral guidance, of a motor vehicle. The invention further relates to a correspondingly equipped motor vehicle.

[0002] Supporting vehicle control, for example through assisted, semi-automated, or highly automated driving, can be helpful in many ways and is currently a key development goal. However, this presents numerous challenges. For instance, lane markings are not always present or detectable by the vehicle's own environmental sensors, the detection range of such sensors may be limited, and fleet or swarm data may not always be up-to-date or reliable, among other issues.

[0003] As one approach, US patent 2022 / 0009526A1 describes a control unit for an autonomous vehicle. This unit is configured to determine the path of an ego lane, in which the vehicle travels, based on the trajectories of numerous other vehicles in the vicinity. Furthermore, the control unit is configured to operate an automated function for longitudinal and / or lateral guidance of the vehicle, depending on the determined path of the ego lane.

[0004] A method for lateral vehicle guidance is described, for example, in DE 10 2019 213 185 A1. In this method, environmental data of a vehicle traveling along a route is recorded, and stored environmental data is retrieved from a plurality of other vehicles not currently traveling on the route while traveling along it. The stored environmental data is then validated against the recorded environmental data, and lateral vehicle guidance is performed based on this validated environmental data.

[0005] DE 10 2019 217 428 A1 describes a method for operating a driver assistance system for the assisted or automated execution of a driving maneuver. The system checks, before or during the execution of the driving maneuver, whether it should be authorized or aborted, based on swarm data.

[0006] German patent DE 10 2010 021 591 A1 describes a method for controlling a fully automated driver assistance system of a motor vehicle. This method uses ego data and environmental data, determined by a sensor and / or vehicle system and describing the current operating state of the motor vehicle, to check whether a fault condition exists that is excluded with regard to the function of the driver assistance system. If a fault condition exists, an action plan is executed, which includes a driving intervention to bring the motor vehicle to a safe state and contains a sequence of control commands for vehicle systems.

[0007] Some details regarding the fundamentals of driver assistance systems are described in Hakuli, Stephan, et al. Handbook of Driver Assistance Systems: Fundamentals, Components and Systems for Active Safety and Comfort. Springer, 2015, pages 863-864. There, for example, relationships between curve radius, maximum lateral acceleration, vehicle speed, and the required maximum range of an environmental sensor are explained.

[0008] An approach to improving the lateral guidance of vehicles is described in DE 10 2019 213 185 A1. This approach involves collecting environmental data from a vehicle while it travels along a route. It also retrieves stored environmental data collected by a number of other vehicles, not currently traveling on the route, while they were traveling along it. The stored environmental data is then validated against the collected environmental data. Finally, the vehicle's lateral guidance is performed based on this validated environmental data.

[0009] US patent 2016 / 0325753A1 describes a system comprising an image capture device for recording a multitude of images of the surrounding area of ​​a user vehicle, a data interface, and a processor. The processor is configured to receive the multitude of images via the data interface. Furthermore, the processor is configured to calculate a road profile along at least one predicted path of the user vehicle. This at least one predicted path is generated based on image data.

[0010] US patent 2012 / 0245817A1 discloses a driver assistance system for providing driver and vehicle feedback control signals. The system includes a map database with navigation characteristics, a GPS unit for receiving vehicle position data, and a vehicle sensor unit for generating vehicle data. A map mapping module is also provided, which receives the position data, navigation characteristics, and vehicle data and outputs the vehicle's position relative to a road. Furthermore, a path tree module is provided, which, based on this position, generates a path tree comprising a set of possible forward paths for the vehicle, starting from its current path. A prediction module is configured to receive the path tree and determine the most probable future path for the vehicle.

[0011] The object of the present invention is to enable a particularly robust guidance system, especially a transverse guidance system, for a motor vehicle.

[0012] This problem is solved according to the invention by the subject matter of the independent claims. Possible embodiments and further developments of the present invention are disclosed in the dependent claims, in the description, and in the figures.

[0013] The method according to the invention serves, and can therefore be used, to support the guidance or operation of a motor vehicle along a respective section of road, in particular for at least partially automated lateral or longitudinal guidance of the motor vehicle. For this purpose, the corresponding motor vehicle can, in particular, have environmental sensors for recording environmental data and a communication module for receiving swarm data and be configured to carry out the method according to the invention. In one step of the method according to the invention, the swarm data for the respective section of road is recorded, and a drivable path leading through the respective section of road is determined from this data; this path is also referred to as the swarm path. The swarm data can, for example, be recorded by means of the corresponding communication device of the motor vehicle.The swarm data can, for example, include or specify one or more trajectories or paths driven by at least one vehicle in the swarm, or a corresponding averaged or smoothed trajectory or path. The swarm data can also include swarm environment data, which, for example, specifies or reproduces at least one longitudinal marker of the respective route segment, its position or course, or a lane or roadway profile. This can include, for example, camera or image data, or corresponding path, position, and / or feature data generated or derived from it. This can potentially result in a reduced data volume for the swarm data compared to complete sensor data acquired by sensors on the vehicles in the swarm.The swarm data can therefore be recorded by at least one swarm vehicle using its own sensors. This at least one swarm vehicle can be a third-party vehicle, particularly as part of a swarm, i.e., a fleet of swarm vehicles. Likewise, the vehicle itself can act as a swarm vehicle, for example, if the respective swarm data was recorded by the vehicle or its environmental sensors during a previous traverse of the respective route section.

[0014] The swarm data can be transmitted directly from a swarm vehicle to the vehicle via an X2Car, mobile network or WLAN data connection, or the like, or retrieved by the vehicle from an external server facility.

[0015] The drivable path determined from the swarm data can be a path or trajectory that was actually traveled by at least one swarm vehicle during a single pass through the respective section of the route. Alternatively, the drivable path can be synthesized, i.e., formed from a superposition or combination of several swarm vehicle trajectories, for example, by piecemeal assembly, averaging, or similar methods.

[0016] In a further step of the inventive process, during the operation of the motor vehicle, i.e., in particular while the motor vehicle is driving along or towards the respective section of the road, the course of a lane driven on or to be driven on along the section of the road is detected or determined by means of environmental sensors of the motor vehicle, particularly in real time, i.e., live. For this purpose, the environmental sensors can be, for example, a camera, a radar device, a lidar device, an ultrasound device and / or the like.

[0017] To detect or determine the lane's course, at least one longitudinal marking of the respective section and / or a road or lane edge or a road or lane boundary can be detected or recognized. For this purpose, predefined image or data processing, automatic object or feature recognition, or similar methods can be applied to environmental data acquired by means of environmental sensors.

[0018] In a further step of the inventive process, the course of the drivable path determined from the swarm data can be compared with the course of the lane or the corresponding longitudinal marking or roadway / lane boundary detected by the vehicle's environmental sensors. Here, a comparison or plausibility check of the detected lane course can be performed using the swarm data, or conversely, a plausibility check of the drivable path determined from the swarm data can be performed using the detected lane course. If the course of the drivable path determined from the swarm data and the course of the lane detected by the vehicle's environmental sensors differ from each other, the curvature or... ahead in the direction of travel of the vehicle is...The corresponding radius of the respective track segment is determined and compared with a predefined curvature threshold, also referred to here as the first curvature threshold. This can be done, for example, if the curves determined from the two different data sources mentioned—i.e., the swarm data and the environmental data recorded live by the vehicle's own sensors—differ from each other by more than a predefined tolerance, or if the corresponding plausibility check fails.

[0019] Here, the preceding curvature can be determined, for example, as the curvature of the next curve to be negotiated or of the next section of the route to be negotiated.

[0020] In a further step of the inventive process, a decision is then made, based on a comparison of the approaching curvature with the predetermined first curvature threshold, as to which of the two differing data sources should be used to guide the vehicle or provide the corresponding support. The system automatically decides that the vehicle should be guided along the drivable path determined from the swarm data if the approaching curvature is greater than the predetermined first curvature threshold. Conversely, if the determined approaching curvature or the corresponding radius is less than the predetermined first curvature threshold, the system automatically decides that the vehicle should be guided along the lane detected by the vehicle's environmental sensors.In the latter case, the vehicle is guided based on the live-recorded environmental data. The vehicle can then, for example, follow the corresponding detected longitudinal marking or lane boundary.

[0021] In other words, if there is a discrepancy or contradiction between the swarm data and the live recorded environmental data, the system determines, based on the preceding curvature, which of these two data sources should be used for vehicle guidance, in particular lateral guidance, of the motor vehicle along at least part of the respective section of the route.

[0022] This enables assisted or at least semi-automated guidance, particularly lateral guidance, of the vehicle even in situations where differing or conflicting data sources are present. In contrast, a conventional lateral guidance system, assuming it can even consider both data sources, would typically determine that the two data sources differ and therefore terminate the guidance or lateral guidance of the vehicle or not offer it for the respective section of the route. Thus, the present invention enables more robust guidance or guidance support of the vehicle and increases the availability of this functionality compared to conventional solutions.

[0023] The fact that the vehicle's guidance is based on swarm data when the predefined first curvature threshold is exceeded can advantageously enable further advance planning or anticipation. This is because, with a sufficiently large upcoming curvature, the respective section of the route may more readily fall outside the detection range of the vehicle's own environmental sensors, whereas the swarm data can provide complete data for the respective section of the route, regardless of its shape and the detection range of the environmental sensors.

[0024] The fact that the vehicle's guidance is based on environmental data when the upcoming curvature is less than the predefined first curvature threshold can offer the advantage of particularly safe and reliable vehicle guidance. This is because the full detection range of the environmental sensors can then typically be utilized, and current conditions or circumstances on the respective road section can be taken into account, which, depending on the recency of the swarm data, may not yet be included or considered in it.

[0025] In this context, swarm data can be understood as data collected by multiple vehicles in a swarm—for example, more than two, more than ten, or more than 100—over a period of several days—for example, at least two, at least ten, at least 30, or at least 365 days—or over multiple journeys along the respective route segment. These vehicles in the swarm can operate independently of one another but can nevertheless be collectively referred to as a fleet or swarm. The environmental data individually collected by the vehicles in the fleet or swarm can be aggregated and thus collectively referred to as swarm data and stored accordingly, for example, in a central database or server system.A common characteristic of the swarm vehicles can be that they have traveled the same section of the route, which can be determined using location information from the environmental data. Therefore, environmental data can be available for numerous journeys along the route within the swarm data. The swarm vehicles can determine environmental data based on shared, predefined environmental properties. The swarm data can be stored externally, for example, on a server system such as a backend, a cloud server, a data center, or similar.

[0026] In one possible embodiment of the present invention, the inventive method is used only in an emergency situation for the lateral guidance of the motor vehicle, in which the motor vehicle is brought to a standstill automatically or is intended to be brought to a standstill automatically. Such an emergency situation can occur, for example, if a driver of the motor vehicle can no longer control it manually or can no longer monitor the at least partially automated operation of the motor vehicle. In other words, the inventive method can thus be carried out here by an emergency stopping assistance system of the motor vehicle.

[0027] Outside of such emergency situations, a conventional longitudinal and / or lateral guidance assistance system, such as the "Travel Assist" system, can be used to steer or support the steering of the vehicle. In such emergency situations, the improved robustness offered by the present invention can be particularly important, as otherwise a complete loss of control of the vehicle may occur, or, for example, a potentially more dangerous emergency braking maneuver might be necessary. Thus, the proposed embodiment of the present invention can significantly improve the safety of vehicle operation.

[0028] In another possible embodiment of the present invention, after determining the deviation between the course of the drivable path derived from the swarm data and the course of the lane detected by the vehicle's environmental sensors, the swarm data is only considered further if the deviation is smaller than a predefined threshold. Otherwise, the swarm data is discarded and thus remains unused for supporting vehicle guidance. In the latter case, i.e., if the deviation is greater than the threshold, vehicle guidance can be based on the live-acquired environmental data. A deviation exceeding the predefined threshold may, for example, indicate that the swarm data was acquired under changed conditions and therefore no longer reflects the current situation on site.The proposed embodiment of the present invention can achieve improved safety because, in the case of sufficiently large deviations, the risk of the vehicle being directed into oncoming traffic or off the roadway based on swarm data can be reduced. A sufficiently large deviation might occur, for example, if a relatively long-standing but temporary change in traffic flow, such as to bypass a construction site or the like, is lifted immediately before the vehicle enters the respective section of the road. The swarm data may then have been recorded during the changed traffic flow and thus reflect this change rather than the current traffic flow.

[0029] In a further possible embodiment of the present invention, the upcoming curvature is determined using swarm data. This can enable a particularly early or particularly far-reaching determination of the upcoming curvature, since the swarm data is not limited by the limited detection range of the vehicle's environmental sensors. Thus, the upcoming curvature can be determined particularly reliably, for example, when the respective section of the road is only partially visible.

[0030] In a further possible embodiment of the present invention, if the preceding curvature is greater than the predetermined first curvature threshold, the vehicle is guided along the drivable path determined from the swarm data only if the curvature of this drivable path is less than a predetermined path curvature threshold or the corresponding radius of the drivable path is greater than a predetermined path radius threshold. The curvature of the drivable path determined from the swarm data can also be referred to as path curvature. The predetermined path curvature threshold can also be referred to as the second curvature threshold. In particular, the path curvature threshold and the first curvature threshold can be of different values.The path curvature threshold proposed here can, for example, take into account that regular traffic management and road construction typically only allow for or utilize limited curvatures or correspondingly larger radii. A correspondingly greater curvature of the determined drivable path could therefore indicate an unusual, unsafe, or unjustified trajectory choice by the swarm of vehicles, or similar issues. By considering the path curvature threshold as proposed here, the vehicle guidance or guidance system can be adjusted in such situations based on live environmental data. This can potentially lead to further improvements in safety.

[0031] In a possible further development of the present invention, the vehicle is guided along the lane path detected live by the vehicle's environmental sensors when both the preceding curvature and the path curvature are greater than the predetermined path curvature threshold. In other words, the vehicle can then, for example, follow the corresponding detected longitudinal marking or lane boundary of the road section, based on the environmental data recorded live by the vehicle itself. This thus provides a possibility for assisted or at least semi-automated vehicle guidance even in such situations. Overall, this results in particularly good robustness and safety of the method according to the invention.This can be achieved during assisted or at least partially automated driving of the motor vehicle.

[0032] In a further possible embodiment of the present invention, if the preceding curvature is greater than the predetermined first curvature threshold, the vehicle is guided along the drivable path determined from the swarm data, i.e., based on the swarm data, only if the curvature of the drivable path (i.e., the path curvature) and the curvature of the driving lane, or the curvatures or bends of the corresponding profiles, point in the same direction—at least up to a predetermined tolerance. For example, a tolerance band, tolerance range, or tolerance interval can be specified around the curvature of the driving lane profile, within which the curvature of the drivable path must lie to allow the vehicle to be guided along the drivable path, i.e., based on the swarm data.This allows the vehicle to be guided along the drivable path determined from the swarm data, even with sufficiently small curvatures, if the directions or signs of the curvatures differ, but the absolute magnitudes or values ​​of the curvatures are similar, i.e., differ from each other by, for example, the specified tolerance or half the width of the corresponding tolerance band or tolerance range. Guiding the vehicle along the drivable path determined from the swarm data can enable particularly far-sighted planning, and the embodiment of the present invention allows this guidance to be carried out with particular safety.Thus, if the curves or trajectories point in the same direction according to the live recorded environmental data and the swarm data, i.e., have the same sign, it can be assumed with a particularly high degree of certainty or probability that guiding the motor vehicle along the drivable path determined from the swarm data will not lead to the motor vehicle being led into oncoming traffic or leaving the roadway.

[0033] In a further possible embodiment of the present invention, if the preceding curvature is greater than the predetermined first curvature threshold, the vehicle is guided along the drivable path determined from the swarm data only if this drivable path—at least apparently—does not lead into an adjacent lane, particularly one intended for oncoming traffic or the opposite direction of travel. For this purpose, at least one corresponding predetermined criterion can be evaluated. Such a criterion could, for example, be the agreement of the directions or signs of the curvatures, as described elsewhere, or the corresponding similarity between the curvatures of the drivable path and the lane's course determined from the live-recorded environmental data.Similarly, for example, a corresponding evaluation of map data and / or environmental data can be carried out to determine whether the drivable path actually, or at least likely or apparently, leads into a corresponding other lane. For this purpose, adjacent lanes, lane markings, road or carriageway boundaries, traffic signs, and / or the like can be detected and taken into account. Likewise, it can be assumed, for example, that the drivable path leads into an adjacent lane if the curvature of the drivable path reaches or exceeds a corresponding threshold, for example, the path curvature threshold mentioned elsewhere. The embodiment of the present invention proposed here can achieve further improved safety and robustness.

[0034] Another aspect of the present invention is an assistance device or assistance system for a motor vehicle. The assistance device according to the invention comprises a processor, for example a microchip, microprocessor, microcontroller, or the like, and a computer-readable data storage device coupled thereto. The assistance device according to the invention is configured for the execution, in particular automatically, of the method according to the invention. For this purpose, for example, a corresponding operating or computer program, which encodes or implements the process steps, sequences, or measures described in connection with the method according to the invention, can be stored in the data storage device. This operating or computer program can be executed by the processor to effect or initiate the execution of the corresponding method.The assistance device according to the invention can, for example, be designed as an emergency stop assistant for the motor vehicle. The assistance device according to the invention can, for example, be designed as a control unit with an input and output interface, which can be connected, for example, directly or via the vehicle's electrical system to the vehicle's environmental sensors and / or at least one other control unit and / or a vehicle control system for driving the motor vehicle and / or the like. Likewise, the assistance device according to the invention can, for example, comprise the environmental sensors completely or partially.

[0035] Another aspect of the present invention is a motor vehicle comprising environmental sensors for recording environmental data that characterize the course of a road segment ahead of the motor vehicle in the direction of travel, a communication device for acquiring swarm data, a lateral guidance device for at least partially automated lateral guidance of the motor vehicle, and an assistance device according to the invention. In other words, the motor vehicle according to the invention can be configured for the execution of the method according to the invention, particularly automatically. Some or all of the aforementioned devices can be combined or integrated with one another. The motor vehicle according to the invention can, in particular, be the motor vehicle mentioned in connection with the method according to the invention and / or in connection with the assistance device according to the invention, or correspond to it.

[0036] Further features of the invention may become apparent from the following description of the figures and from the drawings. The features and combinations of features mentioned above in the description, as well as the features and combinations of features shown below in the description of the figures and / or in the figures themselves, can be used not only in the combinations specified, but also in other combinations or individually, without departing from the scope of the invention.

[0037] The drawing shows in: Fig. 1. A schematic overview to illustrate a procedure for vehicle control; and Fig. 2. An exemplary schematic flowchart for the procedure.

[0038] Fig. Figure 1 shows a schematic overview of a traffic situation on a road 1 with one lane 2 and an adjacent lane 3. Lane 2 is marked or delimited on its outer edge by a longitudinal marking 4, while the adjacent lane 3 is also marked or delimited only on its outer edge by a second longitudinal marking 5. In the example shown here, there is no lane or road marking in the middle of road 1, i.e., where lane 2 and the adjacent lane 3 meet or merge into each other.

[0039] In this case, a motor vehicle 6 is traveling in lane 2. The adjacent lane 3 may be intended for the opposite direction of travel. Furthermore, an external server 7 is indicated here, from which the motor vehicle 6 can retrieve swarm or fleet data regarding road 1 or the currently traveled section of road.

[0040] Conventional emergency braking assistance systems often only offer lateral guidance of a vehicle if lane markings or boundaries on both sides are detected by means of the vehicle's own sensors, which is not the case in the present situation.

[0041] The motor vehicle 6 is equipped with an environmental sensor 8 for environmental perception, which may in particular be or include a camera. The environmental sensor 8 is also schematically indicated here, at least partially, and in particular not to scale, within the detection or recording areas 9. Furthermore, the motor vehicle 6 is equipped with an assistance device 10, which, schematically indicated here, includes a processor 11 and a data storage device 12 in order to execute the procedure described below.

[0042] The motor vehicle 6 or the assistance device 10 can be equipped to guide, in particular to guide the motor vehicle 6 laterally, even in such situations, i.e. based on only one recognizable or detectable lane or roadway boundary on one side, here for example on the basis of the longitudinal marking 4.

[0043] The assistance system 10 is also configured to take swarm or fleet data from the server system 7 into account. This allows the guidance, in particular the lateral guidance, of the vehicle 6 to be based on at least one of two data sources or data bases: on the one hand, the swarm or fleet data, and on the other hand, data recorded live during the current journey of the vehicle 6 by means of the environmental sensors 8, which are also referred to here as environmental data 16 (see Fig. 2) can occur. Situations may then arise in which these two data sources differ or contradict each other. For example, the swarm or fleet data may indicate a swarm path 13, shown schematically here, which represents a drivable path. There may then be discrepancies between this path and the course of lane 2 or longitudinal marking 4 determined by the vehicle 6 itself. For example, the swarm path 13 may continue straight ahead, while the longitudinal marking 4 may turn right. Similarly, the longitudinal marking 4 may continue straight ahead, while the swarm path 13 may, for example, turn left around an obstacle in lane 2 that may no longer be present at the current time.

[0044] Fig.Figure 2 shows an exemplary schematic flowchart 14 to enable robust behavior of the assistance system 10 even in such situations. In process step S1, the swarm data 15 and environmental data 16 are acquired. The swarm data 15 can, for example, be retrieved from the server 7. The environmental data 16, on the other hand, can be recorded in real time, i.e., live, using the environmental sensors 8. A comparison or plausibility check of the swarm data 15 and the environmental data 16 is then performed. If a deviation is found between the course of the swarm path 13 and the course of the lane 2 or the longitudinal marking 4 determined based on the environmental data 16, the process continues in process step S2. Otherwise, the guidance of the vehicle 6 can, for example, be based on both, then matching, data sources.

[0045] In process step S2, a curvature ahead in the direction of travel of the motor vehicle 6 is determined, for example, the curvature or radius of the next curve or the next section of road 1 to be traveled, or the like. This ahead curvature is then compared with a predetermined first curvature threshold value. If the ahead curvature is smaller than the first curvature threshold value, the process jumps to process step S3.

[0046] In process step S3, it is decided that the vehicle 6, on the current section of the route, should be guided, for example, through the next curve or the next section of road to be traversed (for which the preceding curvature has been determined), based on the environmental data 16, i.e., along or relative to the longitudinal marker 4. Depending on the configuration of the assistance device 10, a corresponding signal can be sent to a lateral guidance device, or the lateral guidance can be carried out accordingly. Similarly, the lateral guidance based on the environmental data 16 can be set or reserved for an emergency stop assistant of the vehicle 6 in the event of an emergency situation occurring on the current section of the route.

[0047] If, however, the comparison in process step S2 reveals that the preceding curvature is greater than the predefined first curvature threshold, the process jumps to process step S4 instead. In process step S4, it is checked whether the path curvature, i.e., the curvature of the swarm path 13 determined from or specified by the swarm data 15, is greater or less than a predefined second curvature threshold. Furthermore, in process step S4, the path curvature and the curvature of the lane 2, determined based on the environmental data 16, are compared. It is checked whether these curvatures are equal within a predefined tolerance and / or have the same sign, i.e., point in the same direction. If at least one of these two conditions is not met, the process jumps to process step S3.If both conditions are met, the procedure jumps to a process step S5.

[0048] In or according to procedure step S5, it is decided that the lateral guidance of the motor vehicle 6 should be based on the swarm data 15, i.e., for example, along the swarm path 13. Likewise, this lateral guidance can be initiated, carried out, or – for example, for an emergency situation that may occur on the current section of the route – set or scheduled here.

[0049] The first curvature threshold and / or the second curvature threshold can be specified or defined as fixed values ​​or, for example, as a characteristic map. In such a characteristic map, the respective curvature threshold can be stored or defined, i.e., predefined, depending on the current speed of the vehicle 6. This can be useful because, with increasing speed of the vehicle 6, less reaction time is available for measures to guide the vehicle 6 based on features or obstacles detected by the environmental sensors 8, due to the limited detection range of the environmental sensors 8 and the limited data processing speed.Accordingly, for example at higher speeds, an adjustment of one or both of the specified curvature thresholds may be provided such that the guidance of the motor vehicle 6 based on the swarm data 15 is increasingly preferred.

[0050] Overall, the examples described show how a method for optimizing lateral guidance, in particular an emergency stop assistant, of a vehicle can be optimized using swarm or fleet data in order to achieve improved robustness even in situations with conflicting different data sources. Reference symbol list 1 Street 2 lanes 3 adjacent lanes 4 Longitudinal marking 5 second longitudinal marking 6 Motor vehicle 7 Server setup 8 Environmental sensors 9 Recording area 10 Assistance facilities 11 processor 12 Data storage devices 13 Swarm path 14. Schedule 15 swarm data 16 Environmental data S1 - S5 Procedure steps

Claims

[1] Method (14) for assisting the guidance of a motor vehicle (6) along a section of road (1), wherein automatically - Swarm data (15) for the track section (1) are recorded and a drivable path (13) leading through the track section (1) is determined from it, - during the operation of the motor vehicle (6) by means of an environmental sensor (8) of the motor vehicle (6) a course of a lane (2) traveled by the motor vehicle (6) in the section of the route (1) is detected, - if the course of the drivable path (13) and the detected course of the lane (2) differ from each other, the curvature of the track segment (1) ahead in the direction of travel of the motor vehicle (6) is determined and compared with a predetermined curvature threshold value, and - it is decided that the driving of the motor vehicle (6) - along the drivable path (13) if the preceding curvature is greater than the curvature threshold, and - along the detected lane (2) if the determined preceding curvature is less than the curvature threshold. [2] Method (14) according to claim 1, characterized by , that the procedure (14) is only used in an emergency situation for the lateral guidance of the motor vehicle (6) in which the motor vehicle (6) is to be brought to a standstill automatically. [3] Method (14) according to any one of the preceding claims, characterized by , that the swarm data (15) are only taken into account further after the deviation has been determined if the deviation is smaller than a specified limit deviation and are otherwise discarded. [4] Method (14) according to any one of the preceding claims, characterized by , that the preceding curvature is determined using the swarm data (15). [5] Method (14) according to any one of the preceding claims, characterized by , that in the case that the preceding curvature is greater than the specified curvature threshold, the guidance of the motor vehicle (6) will only take place along the drivable path (13) if the curvature of the drivable path (13) is less than a specified path curvature threshold. [6] Method (14) according to claim 5, characterized by , that otherwise the motor vehicle (6) will be guided along the detected lane (2). [7] Method (14) according to any one of the preceding claims, characterized by , that the guidance of the motor vehicle (6) will only take place along the drivable path (13) if the curvature of the drivable path (13) and the curvature of the driving lane point in the same direction. [8] Method (14) according to any one of the preceding claims, characterized by, that the driving of the motor vehicle (6) takes place along the drivable path (13) only if this does not appear to lead into an adjacent lane (3), in particular one intended for oncoming traffic. [9] Assistance device (10) for a motor vehicle (6), comprising a processor device (11) and a computer-readable data storage device (12) coupled thereto, wherein the assistance device (10) is configured to perform a method (14) according to one of the preceding claims. [10] Motor vehicle (6) comprising an environmental sensor system (8) for recording environmental data (16) that characterizes the course of a section of road (1) preceding the motor vehicle (6) in the direction of travel, a communication device (10) for recording swarm data (15), a lateral guidance device (10) for at least partially automated lateral guidance of the motor vehicle (6) and an assistance device (10) according to claim 9.

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