Method for generating a lane change recommendation, lane change assistance system, and motor vehicle with a lane change assistance system

Abstract

Method for generating a lane change recommendation (24) for a motor vehicle (10) from an exit lane (12) to a destination lane (14) of a road (18), wherein the motor vehicle (10) is traveling at a speed (v E ) along the exit lane (12) of the road (18), whereby - a current self-position of the motor vehicle (10) along the exit lane (12) is determined in a digital environment map, wherein an average known exit lane speed (v) is defined in the digital environment map for the current self-position along the exit lane (12). A ) is stored, whereby - a lane change assistant (34) the own speed (v E ) of the motor vehicle (10) at the initial lane speed (v A) compares and generates the lane change recommendation (24) from the exit lane (12) to the destination lane (14) as soon as a deviation between the vehicle's own speed (v E ) and the exit lane speed (v A ) reaches a predetermined limit, and where - the exit lane speed (vA) is made plausible by comparison with at least one other known average speed of a further lane adjacent to the exit lane (12) and / or the destination lane (14).

Description

[0001] The invention relates to a method for operating a driver assistance system for a motor vehicle, more precisely for operating a lane change assistance system, wherein a lane change recommendation is generated for the motor vehicle. Furthermore, the invention relates to such a lane change assistance system, as well as a motor vehicle with a lane change assistance system.

[0002] Driver assistance systems that support a driver in executing a desired, planned, or necessary driving maneuver are well known. This includes familiar systems for assisting with lane changes. The focus of known lane change assistance systems is on monitoring the traffic surrounding the driver's vehicle (either the driver's own vehicle or an ego vehicle). This monitoring can be performed by sensors on the ego vehicle or through communication between the ego vehicle and surrounding road users. If the surrounding road users are other vehicles, known lane change assistance systems rely on car-to-car communication between the ego vehicle and these other road users.

[0003] US Regulation 2018 / 0061236 A1 describes car-to-car communication to ensure safe lane changes. This communication takes place between the road users involved in the immediate lead-up to a lane change and is intended to increase safety.

[0004] US 2020 / 0189598 A1 and US 2015 / 0194055 A1 address the observation of flowing traffic to recommend a lane change operation or maneuver. Specifically, they observe gaps appearing and / or moving in the traffic flow currently surrounding the ego vehicle.

[0005] The lane-change assistance systems described here all rely on real-time observation of the traffic situation and / or live communication with the vehicles surrounding the respective "ego-vehicle." This results in several disadvantages. Firstly, continuous monitoring of the surrounding traffic situation and / or more or less constant live communication with other road users requires increased processing capacity within the "ego-vehicle" to handle the resulting data. In other words, a considerable amount of computing power must be available within the "ego-vehicle" to process the observation results and / or the communication streams.Secondly, current methods only allow for an assessment of the likelihood of success of a traffic maneuver once the autonomous vehicle is already in a traffic situation where the maneuver will affect other road users. Without the presence of other road users who can be observed and / or with whom communication is possible, these systems simply lack the ability to assess, for example, the safety of a lane-change maneuver.

[0006] Furthermore, DE 10 2017 216 202 A1 discloses a method for predicting an optimal lane on a multi-lane road using a trained machine learning model. In this method, the current traffic situation and the vehicle's lane-specific position are acquired in real time as input data via in-vehicle and external sensors. The machine learning model processes the data in such a way that, for predefined road segments, a travel time for each lane of the multi-lane road is determined. Depending on predefined conditions for each road segment, the optimal predicted lane is then output.

[0007] Furthermore, an assistance system for a vehicle is known from DE 10 2018 202 736 A1. This assistance system is designed to determine, based on environmental data of the vehicle, whether a lane change from the currently occupied first lane to an adjacent second lane is sensible and possible. In response, the assistance system can issue a notification to the driver of the vehicle indicating that a lane change to the second lane is sensible and possible.

[0008] US patent 2013 / 0282264 A1 discloses a method in which vehicle measurement data is used to determine a historical speed profile for each lane of a multi-lane road section, where the multiple lanes have the same direction of travel.

[0009] Furthermore, DE 10 2017 005 166 A1 discloses a method for operating a vehicle in the overtaking lane of a road section comprising at least the overtaking lane and a normal lane, wherein the overtaking lane and the normal lane run in the direction of travel of the vehicle. The system monitors the area behind the vehicle for approaching vehicles, and when an approaching vehicle is detected, the differential speed between the vehicle and the other vehicle is determined. If the determined differential speed exceeds a predetermined value, the system checks whether a lane change to the normal lane is possible, taking into account other road users. If the lane change is possible, a notification is issued to the driver of the vehicle.

[0010] Furthermore, DE 10 2005 051 597 A1 discloses a vehicle operation support device which includes a vehicle operation plan determination section for determining a vehicle operation plan which includes a future sequence of selecting a planned target vehicle and a future sequence of selecting a planned target lane position for a temporal forecast horizon.

[0011] Furthermore, US patent 9,672,734 B1 discloses a method for determining lane information in a road section in order to control a first vehicle in such a way as to minimize travel time.

[0012] The purpose of the invention is to increase the safety of lane-changing maneuvers.

[0013] This problem is solved by the subject matter of the independent patent claims. Further possible embodiments of the invention are disclosed in the dependent claims, the description, and the figures.

[0014] The invention is based on the understanding that the safety of a lane-changing maneuver can be increased in particular by planning and, if necessary, carrying out the maneuver in advance and independently of other road users currently present.

[0015] The invention provides a method for generating a lane change recommendation for a motor vehicle or self-driving vehicle from an exit lane to a destination lane of a road along which the motor vehicle is moving at its own speed. The road is preferably a multi-lane road. In particular, the road can be a road with multiple lanes in each direction, such as a highway.

[0016] As the vehicle travels at its own speed along the outgoing lane of the road, its current position is determined in a digital map of the environment. This can be achieved, for example, using GPS-based and / or dead reckoning-based localization methods (GPS - Global Positioning System).

[0017] Alternatively or additionally, the current position can also be determined using environmental data. This is described in more detail below.

[0018] The aforementioned environmental data can be acquired by a sensor device. This sensor device can comprise a variety of sensors, such as camera sensors, radar sensors, and / or lidar sensors. The sensor device can be permanently installed in the vehicle or easily removable, for example, as a retrofit solution.

[0019] The environmental data that can be acquired by the sensor device can include the current distances of the vehicle to predetermined, fixed landmarks in the vehicle's vicinity. Such a landmark could be, for example, an infrastructure component like a traffic sign or traffic light, or a distinctive section of a guardrail. However, buildings and / or vegetation along the road can also serve as landmarks. The camera sensors of the sensor device can be configured to recognize the landmarks based on captured image data sets. Known image data processing algorithms can be used for this purpose. The radar sensors of the sensor device, in turn, can be configured to determine the current distance of the vehicle to such a recognized landmark. The landmarks can be recorded or stored in the digital map of the road's environment.In other words, a digital map of the environment or road map can exist for the road along which the ego-vehicle travels. This digital map can contain the local or geographical positions of landmarks.

[0020] Preferably, a localization unit can locate one or more of the detected landmarks in the digital environment map. Based on the current distances of the vehicle to the landmarks, the vehicle's current position along the exit lane of the road in the digital environment map can then be determined. In other words, the ego-vehicle can be precisely located in the digital environment map using the detected landmarks. The localization can preferably have an accuracy or resolution of less than 5 m, particularly less than 3.5 m. This allows the ego-vehicle to be identified along which lane of a multi-lane road, such as a highway, it is traveling.

[0021] The digital map of the surroundings can contain not only the positions of landmarks, but also further information about the road and / or the surrounding area.

[0022] According to the invention, the digital environment map stores an average known speed for the current position along the starting lane. This known speed can be calculated and stored in the map based on historical speed data or swarm data from other vehicles that have traveled along the starting lane in the past (swarm data analysis).

[0023] According to the invention, a lane change assistant compares the vehicle's own speed with the initial lane speed resulting from swarm data analysis. As soon as a deviation between the vehicle's own speed and the initial lane speed reaches a predetermined threshold, the lane change assistant generates a lane change recommendation from the initial lane to the target lane.

[0024] The invention provides that the initial lane speed is validated by comparison with at least one other known average speed of a further lane located adjacent to the initial lane and / or the destination lane. This offers the advantage of increased reliability of the information about the individual lanes provided by the swarm data analysis.

[0025] The invention offers the advantage that the self-driving vehicle does not rely on live communication with other road users currently surrounding it to recognize the need for a lane change. In other words, the need for a lane change can be recognized independently of the current traffic situation. This is the case, for example, when the self-driving vehicle determines, based on the described speed comparison, that its current speed does not match the average speed of its current lane. Because a lane change recommendation can be made based on the speed comparison alone, a situation can be avoided in which another road user, such as another vehicle, has to approach the self-driving vehicle to such an extent that it is forced to change lanes.In other words, it enables the avoidance of critical driving situations and advantageously increases the safety of lane changes compared to known methods.

[0026] The invention also includes embodiments that offer additional advantages.

[0027] One embodiment provides that the lane change assistant generates the lane change recommendation as soon as the vehicle's own speed is closer to a known average target lane speed than to the initial lane speed. In other words, the swarm data or swarm data analysis provides not only an initial lane speed but also an average target lane speed. Therefore, if it is detected that the vehicle's current speed is closer to the target lane speed than the initial lane speed along which the vehicle is currently traveling, the lane change recommendation is generated.For example, if the known starting lane speed is 120 km / h and the destination lane speed is 80 km / h, then, according to the embodiment described here, the lane change recommendation would be generated as soon as the vehicle's own speed falls below 100 km / h. If several possible destination lanes are available, this can advantageously indicate not only that a lane change away from the starting lane would be advisable, but also which of the possible destination lanes best matches the current vehicle speed. A clear example of this is driving on a highway. A highway can have three lanes, with the slowest speed being recorded in the right-hand lane and the fastest in the left-hand lane, according to the rules of right-hand traffic.The middle lane is typically driven at a speed that lies between the speeds typically found in the right and left lanes. For example, an average speed in the right lane might be 80 km / h, in the middle lane 100 km / h, and in the far left lane 120 km / h. If it is determined that the ego vehicle is traveling in the left lane at a speed between 80 and 90 km / h, a lane change recommendation can be generated. This recommendation would suggest not only moving from the left lane but ideally moving to the far right lane, as the vehicle's speed best matches the typical speed for the right lane.

[0028] A preferred embodiment provides that the lane change assistant generates the lane change recommendation when it detects that the target lane is clear, at least within a target area. The target area refers to the portion of the target lane where the vehicle would likely arrive after changing lanes. This advantageously increases the safety of a lane change. The fact that the target lane is clear within the target area can also be detected, for example, by the camera sensors of the sensor device.

[0029] According to a further advantageous embodiment, the lane change recommendation is generated if the target lane is assigned to at least one predetermined lane category. In other words, the lane change recommendation is only generated if the target lane is, for example, not an exit or a hard shoulder. The information regarding the lane category of the target lane can be derived from the information stored in the digital environment map in the form of swarm data.

[0030] Alternatively or additionally, the lane change assistant can be configured to generate a lane change recommendation if the distance between the ego vehicle and at least one other vehicle in the outgoing lane is less than a predetermined minimum distance. In other words, even if the vehicle's speed deviates from the average outgoing lane speed, the lane change recommendation can be generated only if, for example, the ego vehicle gets too close to another vehicle in front and / or if a vehicle approaching from behind is so close that the predetermined minimum distance is breached. This has the advantage of avoiding unnecessary lane changes.

[0031] Alternatively or additionally, the lane change assistant can be configured to generate a lane change recommendation when it detects that another vehicle is approaching the vehicle or ego vehicle from behind at a predetermined minimum speed. In other words, a condition for issuing the lane change recommendation can be that the other vehicle is approaching the ego vehicle from behind at such a speed that there is potential for a critical driving situation. This might be the case, for example, if the vehicle approaching from behind is traveling at a speed that is at least one and a half times the ego vehicle's own speed. The approaching vehicle can be detected in various ways. For example, the ego vehicle itself can use radar and / or lidar to detect that the vehicle is approaching from behind.Alternatively or additionally, the approaching vehicle can use car-to-car communication to signal to the ego vehicle that it is approaching.

[0032] One possible embodiment of the last described configuration involves the ego vehicle and / or the approaching vehicle detecting that a potential destination lane is clear. This can be determined by the vehicles' camera sensors and / or radar and / or lidar sensors. In other words, the lane change recommendation can be generated when an approaching vehicle, as described above, approaches the ego vehicle from behind at a differential speed and when a potential destination lane is simultaneously clear. In this context, the use of swarm data described above for determining the lane category of the clear destination lane is particularly advantageous. This effectively ensures that no lane change recommendation is generated for an exit ramp and / or a hard shoulder.

[0033] Alternatively or additionally, if the destination lane is clear, the approaching vehicle can issue a warning to the ego vehicle via car-to-car communication.

[0034] According to a further advantageous embodiment, the lane change assistant generates the lane change recommendation when it detects that the ego vehicle is overtaken at least once by another vehicle in the target lane. In particular, the lane change recommendation is generated if such an overtaking maneuver occurs within a predetermined time interval. This can be particularly advantageous for driving situations on a highway where the ego vehicle is traveling in the left, i.e., fastest, lane and is overtaken at least once in a right-hand lane, for example, within 10 minutes.

[0035] According to a preferred embodiment, the target lane speed is validated by comparison with at least one other known average speed of a further lane adjacent to the exit lane and / or the target lane. This offers the advantage of increased reliability of the information about the individual lanes provided by the swarm data analysis.

[0036] The invention further relates to a lane-change assistance system configured to perform a method according to the invention. The lane-change assistance system can include a sensor device configured to execute the process steps of the method performed by the sensor device. Such a sensor device can, for example, comprise one or more camera sensors and / or radar sensors and / or lidar sensors. The sensor device can further comprise a processing unit configured to process the data acquired by the respective sensors. The lane-change assistance system can also include a localization unit configured to perform the process steps that can be executed by the localization unit according to the method. In particular, the digital environment map can be stored in the localization unit.Furthermore, the lane change assistance system can include a lane change assistant that is configured to perform the inventive process steps relating to the lane change assistant. This includes, in particular, generating the lane change recommendation and / or generating a corresponding display for a vehicle occupant or driver.

[0037] Furthermore, the present invention relates to a motor vehicle with such a lane change assistance system.

[0038] The invention also includes further developments of the lane change assistance system and / or the motor vehicle according to the invention, which have features already described in connection with the further developments of the method according to the invention, and vice versa. For this reason, the corresponding further developments of the lane change assistance system and / or the motor vehicle according to the invention are not described again here.

[0039] The invention also includes combinations of the features of the described embodiments. 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.

[0040] The drawing shows in: Fig. 1 a schematic representation of an exemplary driving situation; Fig. 2 a schematic representation of another exemplary driving situation; Fig. 3 a schematic representation of a transfer of swarm data from a central server facility to a lane change assistance system; Fig. 4 A schematic representation of a method for generating a lane change recommendation according to a preferred embodiment of the invention.

[0041] The exemplary embodiments described below are preferred embodiments of the invention. In these exemplary embodiments, the described components each represent individual features of the invention, which can be considered independently of one another and each further develops the invention independently. Therefore, the disclosure is intended to include combinations of features of the embodiments other than those shown. Furthermore, the described embodiments can also be supplemented by further features of the invention already described.

[0042] Identical or functionally equivalent elements are marked with the same reference symbols in the figures.

[0043] Fig. Figure 1 shows an example driving situation. In the driving situation shown, a motor vehicle or ego-vehicle 10 is moving at a speed v. E along an exit lane 12. Along a destination lane 14 adjacent to the exit lane 12, another motor vehicle or foreign vehicle 16 is moving at a foreign vehicle speed v. F The two lanes 12 and 14 are part of a road 18, along which the exemplary driving situation takes place.

[0044] In the Fig. In the embodiment shown in Figure 1, the Ego vehicle 10 has a lane change assistance system 20, which, in conjunction with the explanations regarding Fig. 3 is described in detail. The driving situation described below includes procedural steps which are also related to Fig. The three components of the lane change assist system 20, described in more detail, are carried out. For the sake of clarity, these components are listed in the diagram described here. Fig. 1 not shown in detail. For a detailed description of the components mentioned, the reader is referred to the explanations in connection with Fig. 3 referred.

[0045] During travel along the exit lane 12, the ego vehicle 10 can record environmental data, including the current distances of the ego vehicle 10 to predetermined fixed landmarks 22 within its vicinity. These current distances are displayed in Fig. 1 exemplified by arrows between the Ego vehicle 10 and the four landmarks 22 shown here as examples.

[0046] Based on the current distances to landmarks 22, the ego-vehicle 10 can be located in a digital environment map of road 18 (not shown here). In other words, the current position of the ego-vehicle 10 along the exit lane 12 in the digital environment map can be determined based on the distances of the ego-vehicle 10 to landmarks 22.

[0047] The current self-position can also be determined in other ways, for example by GPS-based and / or dead reckoning-based methods.

[0048] In the digital environment map, an average known exit lane speed v can be used. A be stored.

[0049] The in Fig. The driving situation shown as an example can be based on the fact that the vehicle's own speed v E of the Ego vehicle 10 from the initial lane speed v Adeviates. In the event of a deviation between the actual speed v E and the exit lane speed v A If a predetermined limit is reached, a lane change recommendation 24 can be generated for the ego vehicle 10. For example, if it is detected that the vehicle's own speed v E of the Ego vehicle 10 closer to a target lane speed v Z is, as at the initial lane speed v A , a particularly urgent lane change recommendation 24 can be generated.

[0050] If the driver of the ego vehicle 10 follows the lane change recommendation 24, they will position themselves within a target area 26 (shown hatched here) of the target lane 14 behind the other vehicle 16. Subsequently, the exit lane 12 is then free for other road users (not shown here) whose speed is lower than the exit lane speed v. A corresponds.

[0051] With reference to the issues related to Fig. The components designated and described in section 1 are in Fig. Figure 2 schematically depicts another exemplary driving situation. In the driving situation shown here, the ego vehicle 10 is traveling at a speed v. E drives, in front of a foreign vehicle 16, which is traveling at a foreign vehicle speed v F driving in exit lane 12. For the driving situation depicted here, it is assumed that the speed of the other vehicle is v F the speed of the foreign vehicle 16 is at least 1.5 times greater than its own speed v E of the Ego vehicle 10. If a predetermined minimum distance 28 between the two vehicles 10, 16 is not maintained, an urgent lane change recommendation 24 can be issued.

[0052] Preferably, the lane change recommendation 24 is generated when it is detected that the target lane 14 is clear, at least within the target driving area 26. Preferably, additional or alternative car-to-car communication can take place between the other vehicle 16 and the ego vehicle 10. Preferably, it can be determined from information in the digital environment map that the target lane 14 is neither an exit nor a hard shoulder. In other words, the target lane 14 can be secured using the swarm data stored in the digital environment map.

[0053] With reference to the issues related to the Fig. 1 and Fig. The two components described and identified are described Fig. Figure 3 schematically shows a possible embodiment of a lane change assist system 20. In the embodiment shown here, the lane change assist system 20 comprises a sensor device 30, a localization unit 32, and a lane change assist device 34. The lane change assist system 20 can be configured to receive historical driving data or swarm data 36 from a large number of other vehicles 16. The swarm data 36 may have previously been transmitted by the other vehicles 16 to a central server 38, where it can be combined, for example, with the digital environment map. A communication link can be provided between the server 38 and the lane change assist system 20 to transmit the swarm data 36. The lane change assist system 20 can also be configured to transmit its own information to the server 38.

[0054] The swarm data 36 can, for example, contain information about the typical speeds traveled by other vehicles 16 in a given exit lane 12 and / or destination lane 14. In other words, the swarm data 36 can represent historical speed data for the other vehicles 16. The average speed can be resolved time-dependently in the future. The time of day can therefore be included as an additional parameter in the comparison of the different speeds. In the server facility 38, the swarm data 36 can be combined with the aforementioned digital map of the road 18. The result is a digital map containing information about the typical speeds of vehicles traveling along the roads 18 shown on the map.

[0055] The localization unit 32 of the lane change assist system 20 can, as described above, determine the respective self-position of the ego vehicle 10 based on the distances of the ego vehicle 10 to the known landmarks 22 described above. For this purpose, the localization unit 32 can use sensor data, for example camera data, from the sensor device 30. If, for example, the localization unit 32 detects that a current self-speed v E of the Ego vehicle 10 from an initial lane speed v determined from the swarm data 36 A If the lane change recommendation 24 deviates, it can be generated by the lane change assistant 34 of the lane change assistance system 20.

[0056] Fig.Figure 4 schematically illustrates a procedure for generating a lane change recommendation 24, referring to the above explanations. The starting scenario for the procedure described here can be that an ego-vehicle 10 is traveling at a speed v EA vehicle 10 travels along an exit lane 12 of a road 18. In a process step S1, a sensor device 30 can acquire environmental data, wherein the environmental data includes the current distances of the vehicle 10 to predetermined fixed landmarks 22 in the vicinity of the vehicle 10. In a process step S2, a localization unit 32 can locate the landmarks 22 in a digital map of the road 18 and, based on the current distances of the vehicle 10 to the landmarks 22, determine the current position of the vehicle 10 along the exit lane 12 in the digital map. As already described, an average known exit lane speed v can be determined in the digital map for each current position of the vehicle 10 along the exit lane 12. Abe stored. This can be calculated, for example, from swarm data 36. In a process step S3, a lane change assistant 34 can determine the vehicle's own speed v. E of motor vehicle 10 with the initial lane speed v A compare. If the comparison reveals a deviation between the actual velocity v E and the exit lane speed v A If a predetermined limit value is reached, the lane change assistant 34 can generate a lane change recommendation 24 for the vehicle 10. This occurs in a process step S4. However, if the predetermined limit value is not reached, process step S4 can be omitted and the acquisition of environmental data can continue according to process step S1.

[0057] Many drivers struggle to choose the correct lane on highways. They frequently drive in the passing lane, even though this is unnecessary and prohibited by the rule to drive on the right. When a fast vehicle approaches from behind, these errors and the resulting high speed differential can create critical situations. Car2X communication can be used to warn other road users of potentially critical situations.

[0058] The invention provides for the use of swarm data to reliably identify lanes. Furthermore, the swarm data can be used to issue an automated lane change recommendation, for example, to relieve the driver. If a fast vehicle approaches from behind, a warning cascade can be initiated. The invention helps, firstly, to comply with the rule to drive on the right (also applicable in left-hand traffic), and secondly, it serves to prevent critical situations when vehicles with a high speed differential collide.

[0059] Swarm data makes it possible to generate the highest possible confidence about the type of neighboring lane (or a lane category of a possible destination lane), among other things by comparing the swarm speed with the ego vehicle speed.

[0060] The invention follows the principle of using vehicle sensors (radar, camera, PDC, etc.) to calculate an environmental model around the vehicle. By using environmental sensors, such as those found in modern production vehicles, it is possible to create a very accurate model of the vehicle's surroundings. For example, front radars, front corner radars, rear radars, ultrasound, laser scanners, and / or cameras can detect all vehicles surrounding the ego vehicle.

[0061] Using digital maps and swarm data (REM - Road Experience Management), very precise information about the drivability of individual lanes is available. For example, a turning lane or a hard shoulder can be identified. If a free adjacent lane is detected and confirmed with swarm data, a lane change recommendation is issued.

[0062] In a typical driving scenario, a vehicle might approach an ego vehicle from behind at a significant speed differential, even though the right lane is clear. In this case, an urgent recommendation to change lanes to the right is issued. Speed ​​differentials between vehicles can be determined using radar (Car2Car offers higher accuracy and greater range). The vehicle in front receives the warning or lane change recommendation when the potential target lane is clear. This can be achieved by comparing data from front cameras, radar, lidar, etc.

[0063] Alternatively or additionally, the rear vehicle can detect that the target lane is clear (Car2Car / Car2X, Lidar etc.) and send a warning to the vehicle in front.

[0064] In this use case, car-to-car communication combined with swarm data analysis is used to increase road safety. If a vehicle approaches from behind at a high speed differential, the driver of the "ego" vehicle is warned. If the adjacent lane offers the possibility (verified by swarm data), an urgent lane change recommendation is issued. This communication can either occur via detection of the faster vehicle using a rear-view camera, rear radar, or car-to-car communication, and runs on the "slower" vehicle – or it can also be a function of the faster approaching vehicle (then via front sensors).

[0065] The use of swarm data is of considerable importance for the invention, among other things to ensure that no recommendations to change lanes are issued for exits, hard shoulders, etc.

[0066] In another exemplary driving situation, the current speed of the ego vehicle can be compared with the average speed in the lane based on swarm data. If the following conditions are met, a warning is issued to the driver of the vehicle to comply with the rule to drive on the right (or left): - The right-hand lane is clear and usable (the opposite is true in left-hand traffic) - No traffic jam - The speed of the ego vehicle is significantly below the average speed of the lane

[0067] Overall, the examples show how the invention can provide a method for automated lane change recommendations. Reference symbol list 10 Motor vehicle (Ego vehicle) 12 Exit lane 14 Destination lane 16 foreign vehicle 18th Street 20 Lane Change Assist System 22 Landmark 24 Lane Change Recommendation 26 Target area 28 Minimum distance 30 Sensor device 32 Localization unit 34 Lane Change Assist 36 swarm data 38 Server setup v A Exit lane speed v E Own speed v F Foreign driving speed v Z Target lane speed

Claims

[1] Method for generating a lane change recommendation (24) for a motor vehicle (10) from an exit lane (12) to a destination lane (14) of a road (18), wherein the motor vehicle (10) is traveling at a speed (v E ) along the exit lane (12) of the road (18), whereby - a current self-position of the motor vehicle (10) along the exit lane (12) is determined in a digital environment map, wherein an average known exit lane speed (v) is defined in the digital environment map for the current self-position along the exit lane (12). A ) is stored, whereby - a lane change assistant (34) the own speed (v E ) of the motor vehicle (10) at the initial lane speed (v A) compares and generates the lane change recommendation (24) from the exit lane (12) to the destination lane (14) as soon as a deviation between the vehicle's own speed (v E ) and the exit lane speed (v A ) reaches a predetermined limit, and where - the exit lane speed (vA) is made plausible by comparison with at least one other known average speed of a further lane adjacent to the exit lane (12) and / or the destination lane (14). [2] Method according to claim 1, wherein the lane change assistant (34) generates the lane change recommendation (24) as soon as the vehicle's own speed (vE) is closer to an average known target lane speed (vZ) than to the initial lane speed (vA). [3] Method according to one of the preceding claims, wherein the lane change assistant (34) generates the lane change recommendation (24) when it is detected that the target lane (14) is clear at least within a target area (26). [4] Method according to one of the preceding claims, wherein the lane change assistant (34) generates the lane change recommendation (24) when the target lane (14) is assigned to at least one predetermined lane category. [5] Method according to one of the preceding claims, wherein the lane change assistant (34) generates the lane change recommendation (24) when at least one distance between the motor vehicle (10) and at least one other motor vehicle (16) in the exit lane (12) is less than a predetermined minimum distance. [6] Method according to one of the preceding claims, wherein the lane change assistant (34) generates the lane change recommendation (24) when it is detected that another motor vehicle (16) is approaching the motor vehicle (10) from behind at a predetermined minimum speed. [7] Method according to one of the preceding claims, wherein the lane change assistant (34) generates the lane change recommendation (24) when it is detected that the motor vehicle (10) is overtaken at least once by another motor vehicle (16) on the target lane (14), in particular within a predetermined time interval. [8] Method according to claim 2 or one of claims 3 to 7 with reference to claim 2, wherein the target lane speed (vZ) is made plausible by comparison with at least one other known average speed of a further lane adjacent to the exit lane (12) and / or the target lane (14). [9] Lane change assist system (20) configured to perform a method according to any of the preceding claims. [10] Motor vehicle (10) with a lane change assistance system (20) according to claim 9.

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