Method for passing a road irregularity

Abstract

The disclosure relates to a method (100) for passing a road irregularity (7) of a road (6), the road (6) being travelled by a first vehicle (1), the method (100) comprising: - Obtaining (102) detection data indicative of a passing of the road irregularity (7) by a second vehicle (2); - determining (104) passing behavior data indicative of a behavior of passing the road irregularity (7) by the second vehicle (2) based on the detection data; and - providing (106) instruction data to the first vehicle (1) for passing the road irregularity (7) based on the passing behavior data.

Description

TECHNICAL FIELD

[0001] The present disclosure relates to a method for passing a road irregularity of a road, a computer program product, a data processing system, and a vehicle.BACKGROUND ART

[0002] Going over road irregularities such as speed bumps is of high importance for drivers to ensure safety and comfort of the occupants of the vehicle. Specifically, going over or, in other words, passing road irregularities is not merely a comfort concern, but a safety concern. For example, if a vehicle goes over a speed bump too fast, it may lead to an accident. Also, distracted driving may lead to drivers braking abruptly when spotting a speed bump too late. Further, improper driving on speed bumps may lead to vehicle damages, especially if the driver is unaware of their vehicle's ground clearance.SUMMARY

[0003] The problem is at least partially solved or alleviated by the subject matter of the independent claims of the present disclosure, wherein further examples are incorporated in the dependent claims.

[0004] According to a first aspect, there is provided a method for passing a road irregularity of a road, the road being travelled by a first vehicle, the method comprising: obtaining detection data indicative of a passing of the road irregularity by a second vehicle; determining passing behavior data indicative of a behavior of passing the road irregularity by the second vehicle based on the detection data; and providing instruction data to the first vehicle for passing the road irregularity based on the passing behavior data.

[0005] Accordingly, the method provides a first vehicle with the capability to pass a road irregularity based on detected passing behavior of another vehicle, thereby increasing safety and comfort of the first vehicle. Specifically, the first vehicle may adapt its driving depending on the instruction data derived from the passing behavior data of the other vehicle, herein referred to as a second vehicle. Hence, the first vehicle, which may alternatively be referred to as an ego vehicle, may learn from the passing behavior determined based on the detection data, how to pass the road irregularity in the best way. For example, if the second vehicle passed the road irregularity in a safe and comfortable way as determined based on the detection data, the first vehicle may partially or fully mimic the driving behavior of the second vehicle when passing the road irregularity.

[0006] The method may in particular be a computer implemented method. This means that at least one, multiple or all of the steps of the method may be carried out by a data processing system of the first vehicle, which may comprise one or more computers or data processing apparatuses. Different steps may be carried out by the same or by different computers. A computer is herein understood as a data processing apparatus, which can carry out the steps as defined by the method.

[0007] The method may in particular be carried out by a vehicle, in particular the first vehicle or ego vehicle, more particularly by its data processing system. Specifically, a data processing system of the first vehicle or ego vehicle may obtain the detection data, determine the passing behavior data and / or provide the instruction data, in particular to an at least partially autonomous drive system or assisted drive system of the first vehicle or ego vehicle.

[0008] For example, the instruction data may be configured as an instruction for the driver, e.g., to be displayed, for consequently passing the road irregularity. For example, the instruction may be a visual instruction on a vehicle display on how to pass the road irregularity, e.g., with what speed to pass the road irregularity. Alternatively, or additionally, for example, the instruction data may be configured for an at least partially autonomous drive system of the first vehicle for at least partially autonomously controlling the first vehicle. In this case, the first vehicle may at least partially be autonomously controlled to pass the road irregularity. For example, the first vehicle's speed and / or positioning relative to the road irregularity may be controlled by the at least partially autonomous drive system. The instruction data may in particular be determined based on the passing behavior data. Also, the method may comprise executing the instruction data.

[0009] Specifically, the first vehicle may be an at least partially autonomous vehicle with an at least partially autonomous driving system or assisted drive system. At least partially autonomous means that one or more assisted drive functions may be provided to the driver, e.g., lane positioning assistant, cruise control, and similar. In particular, the first vehicle may be a fully autonomous vehicle, where the driver may not need to provide any input for driving the vehicle.

[0010] According to an example, the method may further comprise: obtaining first vehicle characteristic data indicative of at least one vehicle characteristic of the first vehicle; and determining second vehicle characteristic data indicative of at least one vehicle characteristic of the second vehicle based on the detection data; wherein the providing of the instruction data is further based on the first vehicle characteristic data and the second vehicle characteristic data. Hence, similar, same, or different vehicle characteristics between the first vehicle and the second vehicle may be considered by the method. Accordingly, it may be possible to provide, in particular determine, more accurate instruction data for the first vehicle taking into account similar, same, or different vehicle characteristics between the ego vehicle and the further vehicle that has passed the road irregularity. The first vehicle characteristic data may in particular be stored on a data storage of the first vehicle. The second vehicle characteristic data may in particular be determined from the detection data.

[0011] According to an example, the providing of the instruction data may comprise comparing the first vehicle characteristic data to the second vehicle characteristic data. Moreover, the method may comprise that the instruction data is determined for controlling the first vehicle such that it at least partially mimics the passing behavior of the second vehicle for passing the road irregularity based on the comparison of the first vehicle characteristic data to the second vehicle characteristic data. Specifically, by at least partially mimicking the passing behavior of the second vehicle under consideration of similar, same or different vehicle characteristics, the first vehicle may comfortably and safely pass the road irregularity after observing that the second vehicle passed the road irregularity comfortably and safely. At least partially mimicking may for example relate to either one or more of: mimicking the speed, the positioning of the vehicle with respect to the road irregularity, the trajectory when passing the road irregularity, and similar. The more the first vehicle characteristic data and the second vehicle characteristic data correspond to each other or are similar to each other, in particular in at least one vehicle characteristic, e.g., the vehicle model (such as sedan or truck), the more the passing behavior of both vehicles may be similar. In case of similarity and a good passing behavior of the second vehicle is determined, the first vehicle may be mimicking the second vehicle's passing behavior, e.g., substantially in full, or at least partially, e.g., going sideways, frontwards and / or in terms of speed. Specifically, the determining of the passing behavior data may include a determination of how well the second vehicle passed the road irregularity in terms of safety and / or comfort, in order to determine whether or not, in particular by what degree, to mimic the passing behavior of the second vehicle and / or potentially any further vehicles, whose passing behaviors for that road irregularity have been determined.

[0012] According to an example, the at least one vehicle characteristic of the first vehicle and / or of the second vehicle may be one or more from: a vehicle model, a vehicle model year, a vehicle type, a vehicle size, a vehicle weight, a vehicle ground clearance, a number of vehicle occupants, or an allocation of occupied vehicle seats. For example, in case of similar or same vehicle models, vehicle types, vehicle sizes, vehicle weight and / or vehicle ground clearance, the passing behavior of the second vehicle may be substantially determined to be reproducible by the first vehicle using instruction data to mimic the driving of the second vehicle by the first vehicle, e.g., drive with the same speed, drive the same trajectory, and similar. For other vehicle models, for example, a mimicking may only be done partially or may need to be adapted based on the differences between the vehicle characteristics. For example, a first vehicle with more weight or less ground clearance than the second vehicle may need to go over a speed bump with less speed than the second vehicle, while the trajectory in going over the speed bump, e.g., sideways rather than frontwards, may be mimicked.

[0013] According to an example, the determining of the second vehicle characteristic data may comprise: determining second vehicle characteristic data indicative of a vehicle model as vehicle characteristic of the second vehicle based on the detection data; and determining one or more further vehicle characteristics of the second vehicle characteristic data based on the vehicle model, wherein the one or more further vehicle characteristics are from: a vehicle model year, a vehicle type, a vehicle size, a vehicle weight, or a vehicle ground clearance.

[0014] Hence, for determining the second vehicle characteristic data, first the vehicle model may be determined and then, based thereon, further vehicle characteristics may be obtained, which may be known for the determined vehicle model. For example, such one or more further vehicle characteristics may be determined from a database, to which the first vehicle or data processing system may have access, e.g., stored on a data storage of the first vehicle and / or stored remotely on a server and accessed through the Internet.

[0015] According to an example, the passing behavior may at least comprise a speed of the second vehicle, a travel direction of the second vehicle, and / or a movement pattern of a movement of the second vehicle around at least one axis of the second vehicle. The passing behavior may also comprise multiple travel directions in the form of a passing or driving trajectory or similar. The travel direction may for example be determined from the detection data by the visible area of the second vehicle, e.g., whether the second vehicle goes frontwards and / or sideways. The movement pattern may in particular be of a movement of the second vehicle around a longitudinal axis, transverse axis, and a vertical axis of the second vehicle. In particular, the movement pattern may be relating to a movement of the second vehicle, in particular its chassis or body, relative to the road, e.g., yawing, rolling, and pitching. Based on the movement pattern, it may be derived whether the second vehicle comes into contact or close to contacting the road irregularity, for example. Also, from the movement pattern, it may be possible to determine in how war the vehicle chassis or body moves and therefrom derive whether that causes discomfort to the occupants of the second vehicle. For example, if the movement pattern is low for a certain speed and travel direction, thus indicating a high comfort, the instruction data may be provided such that they at least partially or mostly mimic the second vehicle's passing behavior to also achieve a high comfort in the first vehicle.

[0016] According to an example, the detection data may be from at least one camera of the first vehicle, wherein the passing behavior data may be determined based on computer vision. Also, the first vehicle may have a camera system with multiple cameras. By means of computer vision, the first vehicle may analyze the pictures captured from the one or more cameras and therefrom determine the passing behavior of the second vehicle. Alternatively, or additionally, the detection data may be captured by other means, including radar detection, lidar detection or any other detection means that the first vehicle may be equipped with, e.g., for determining the speed and / or location of the second vehicle.

[0017] According to an example, the method may further comprise: determining road irregularity data indicative of the road irregularity based on the detection data, wherein the detection data is indicative of a sign and / or a marking indicative of the road irregularity, and / or based on the passing behavior data; wherein the providing of the instruction data is further based on the road irregularity data. Hence, for example, the road irregularity may be determined by detecting a sign or a marking on the road indicating the road irregularity. Alternatively, or additionally, based on the passing behavior data indicative of the passing behavior of the second vehicle, the first vehicle may be able to derive from that that there must be a road irregularity and potentially which type that road irregularity has, e.g., a speed bump, pothole, or object on the road. Consequently, when determining the road irregularity data, the instruction data may be provided to pass the road irregularity indicated by the road irregularity data.

[0018] According to an example, the road irregularity data may be determined based on the passing behavior data, wherein the road irregularity data is indicative of a position, geometry and / or size of the road irregularity. For example, based on the passing behavior, in particular the speed of the second vehicle, the travel direction of the second vehicle, and / or the movement pattern of the second vehicle, and / or the one or more vehicle characteristics of the second vehicle, the method may be able to determine the position, geometry and / or size of the road irregularity, even without seeing it when it is partially or fully obstructed by the second vehicle. Hence, the road irregularity with its relevant parameters, such as but not only position, geometry and / or size, may be determined and consequently used to provide instruction data for high driving comfort and safety of the first vehicle even without having vision on the road irregularity by one or more cameras of the first vehicle.

[0019] According to an example, the road irregularity may be one from: a speed bump, a pothole, and an object on the road. The object may for example be any road object, any vehicle part, e.g., a tire, or any other object such as an animal or a tree.

[0020] According to an example, the detection data may be further indicative of a passing of the road irregularity by a third vehicle. The passing behavior data may be further indicative of a behavior of passing the road irregularity by the third vehicle based on the first data. Having more passing behavior data from other vehicles available by observing their passing behavior, the instruction data may be determined such that a higher comfort and safety is ensured for the first vehicle. For example, in the case of a speed bump as road irregularity, the method may include observing further vehicles passing the speed bump, e.g., multiple vehicles on the opposing lanes, or on multiple lanes, e.g., one on opposing direction and one in front of first vehicle.

[0021] According to an example, the method may comprise providing two or more instruction data for controlling the first vehicle when (or for) passing the road irregularity based on the passing behavior data. Further, the method may comprise selecting one from the two or more instruction data based on at least one from (or two or more from): a driver awareness of the driver of the first vehicle, a distance of the first vehicle from the road irregularity, an activity of one or more occupant of the first vehicle, or a position and speed of a third vehicle travelling the road. Accordingly, the instruction data and hence the control of the first vehicle for passing the road irregularity may be selected based on one or more factors relating to the occupants of the first vehicle or relating to traffic safety with regard to a third vehicle, for example. For example, if the driver awareness of the driver is low during assisted or at least partially autonomous driving of the vehicle or the activity of one or more occupants inside the vehicle is sleeping or resting, instruction data with a lower speed to pass the road irregularity may be selected instead of one with a higher speed. Alternatively, for example, if a third vehicle behind the first vehicle is travelling at a high speed or close to the first vehicle, instruction data with a passing behavior having a higher speed may be selected, to reduce the risk of a rear accident because the third vehicle or their driver may not see the road irregularity ahead of them.

[0022] According to an example, the detection data may be further indicative of a third vehicle approaching the road irregularity. The method may further comprise providing a notification indicative of the road irregularity to the third vehicle and / or wherein the providing of the instruction data may be further based on a position and speed of the third vehicle indicated by the detection data. Hence, the third vehicle may be informed about the road irregularity, further increasing road safety by ensuring that the third vehicle knows about the road irregularity. The provision of the notification may be through any communication technique such as but not limited to Car2Car communication or via a cloud server. Alternatively, or additionally, the providing, in particular determining, of the instruction data may be depending on the position and speed of the third vehicle. For example, if the third vehicle is travelling behind the first vehicle at a high speed and / or close to the first vehicle, instruction data with a passing behavior having a higher speed may be determined or selected, to reduce the risk of a rear accident because the third vehicle or their driver may not see the road irregularity ahead of them.

[0023] According to a second aspect, there is provided a computer program product comprising instructions which, when executed by a data processing system, cause the data processing system to carry out the method of the first aspect of this disclosure.

[0024] The computer program product may be a computer program as such or a product such as a computer readable medium having stored the computer program thereon.

[0025] According to a third aspect, there is provided a data processing system comprising means for carrying out the method of the first aspect of this disclosure. The means may for example include the computer program product.

[0026] According to a fourth aspect, there is provided a vehicle comprising the data processing system of the third aspect of this disclosure.

[0027] The vehicle may in particular be a road vehicle, such as a car or truck, for example.

[0028] It is noted that the above examples may be combined with each other irrespective of the aspect involved. Accordingly, the method may be combined with structural features and, likewise, the computer program product, data processing system and the vehicle may be combined with features described above with regard to the method.

[0029] These and other aspects of the present disclosure will become apparent from and elucidated with reference to the examples described hereinafter.BRIEF DESCRIPTION OF DRAWINGS

[0030] Examples of the disclosure will be described in the following with reference to the following drawings. Figure 1shows a schematic illustration of a method for passing a road irregularity of a road; Figure 2shows a schematic illustration of a first vehicle with vehicle components for carrying out the method of Fig. 1; Figure 3shows a schematic driving scenario with the first vehicle of Fig. 2 and a sequence of a second vehicle 2 during the passing of a road irregularity, the first vehicle carrying out the method of Fig 1; Figure 4shows another schematic driving scenario with the first vehicle of Fig. 2 and further vehicles, the first vehicle carrying out the method of Fig. 1; Figure 5shows a table with exemplary data obtained by the method of Fig. 1 during the driving scenario of Fig. 4; and Figure 6shows a schematic sequence of a movement pattern of a second one of the further vehicles of Fig. 4. DETAILED DESCRIPTION

[0031] The Figures are merely schematic representations and serve only to illustrate examples of the disclosure. Identical or equivalent elements are in principle provided with the same reference signs.

[0032] Fig. 1 shows a method 100 for passing a road irregularity 7 of a road 6 by a first vehicle 1 (see Fig. 3). The method 100 may in particular be computer-implemented and be carried out by a data processing system 20 of the first vehicle 1 (see Fig. 2).

[0033] As shown in Fig. 2, the data processing system 20 may comprise a data processing apparatus 22 and a computer program product 24 containing instructions which may be executable by the data processing apparatus 22 to carry out the method 100.

[0034] Specifically, as seen in Fig. 3, the first vehicle 1 may be using a camera system 10 with one or more cameras 12 to detect and / or observe a second vehicle 2 when passing the road irregularity 7, e.g., a speed bump as shown in Fig. 3. The passing behavior of the second vehicle 2 when passing the road irregularity 2 is shown exemplary as a sequence of vehicle positions in Fig. 3. In the example of Fig. 3, the second vehicle 2 is travelling in front of the first vehicle 1 but alternatively could be on the opposing lane as shown in the example of Fig. 4.

[0035] Using the camera system 10, the data processing system 20 may, in a first step 102 of method 100, obtain detection data indicative of the passing of the road irregularity 7 by the second vehicle 2 in Fig. 3, including for example pictures and / or video from the passing.

[0036] In a second step 104 of method 100, the passing behavior data indicative of the behavior of passing the road irregularity 7 by the second vehicle 2 may be determined by the data processing system 20 using the detection data. For example, the passing behavior data may comprise a speed of the second vehicle 2 when passing the road irregularity 7, a travel direction or trajectory of the second vehicle 2 when passing the road irregularity 7 as shown in Fig. 3 by the sequence of the second vehicle 2 during the passing of the road irregularity 7, and / or a movement pattern of a movement of the second vehicle 2 around at least one axis of the second vehicle 2 when passing the road irregularity 7, in particular a vertical movement as exemplary shown in Fig. 6.

[0037] Then, in a third step 106 of method 100, the method 100 may comprise providing or, in particular, determining instruction data for the first vehicle 1 for passing the road irregularity 7 using the passing behavior data. For example, in this step, the method 100 may compare first vehicle characteristic data indicative of vehicle characteristics of the first vehicle 1 to second vehicle characteristic data indicative of vehicle characteristics of the second vehicle 2 as may be determined using the detection data. Based on a level of similarity of the vehicle characteristics and if the second vehicle 2 passed the road irregularity safely and with high comfort, the instruction data may be configured such that the first vehicle 1 may at least partially mimic the second vehicle 2 when going over the road irregularity 7 in form of the speed bump in this example.

[0038] Consequently, in a fourth step 108 of method 100, the driver and / or the first vehicle 1 may be executing the instruction data when passing the road irregularity 7. For example, the instruction data may be comprising an instruction to the driver on how to pass the road irregularity 7, e.g., at what speed or how the trajectory of driving is to look like to ensure safe and comfortable passing. Alternatively, however, the first vehicle 1 may be autonomously passing the road irregularity 7, for the purpose of which the instruction data may be comprising a control instruction for an autonomous or assisted drive system of the first vehicle 1.

[0039] Fig. 4 shows another driving scenario exemplary involving a third vehicle 3, fourth vehicle 4 and a fifth vehicle 5. In step 102 of method 100, the data processing system may be obtaining detection data indicative of the passing of the road irregularity 7 by every one of the further vehicles 2, 3, 4, 5, and consequently determine the passing behavior data and instruction data in steps 104, 106. Accordingly, the instruction data may advantageously be determined based on the passing behaviors of different vehicles 2, 3, 4, 5, which may have different vehicle characteristics and / or passing behaviors as shown in Fig. 5

[0040] Specifically, Fig. 5 shows the vehicle model, ground clearance and occupancy of the vehicles 2, 3, 4, 5 as exemplary vehicle characteristics, which may have been determined based on the detection data, for example. Also, Fig. 5 exemplary shows passing behavior data in the form of a speed at the road irregularity 7, and a vehicle movement pattern, in particular a vertical movement pattern, such as exemplary illustrated as a sequence of vehicle movement pattern from left to right in Fig. 6. Also, the method 100 includes consideration of the vehicle area detection in terms of the perspective that the detection data was recorded from by the camera system 10.

[0041] As exemplary seen in Fig. 5, every vehicle 2, 3, 4, 5 has a different passing behavior for passing the road irregularity 7. The method 100 may accurately determine how it would, based on its own vehicle characteristics, pass the road irregularity 7. Taking the result in terms of comfort and safety seen in the detection data for all the vehicles 2, 3, 4, 5, the method 100 may assess in steps 104 and 106 of method 100, how to best pass the road irregularity 7 with respect to occupant safety and comfort.

[0042] In the particular example of Fig. 5, the vehicle movement pattern for vehicles 4, 5 with speeds of 10km / h and 15 km / h for larger vehicle models such as pickup and truck, is rather intense compared to the one of the sedan type vehicles 2, 3 with less movement in vertical direction at less speed of 5 km / h and thus safer. If the first vehicle 1 has occupants, they will also experience a higher comfort when travelling over the road irregularity 7 like vehicles 2, 3 compared to vehicles 4, 5. Accordingly, the first vehicle 1 may make a determination for the instruction data, taking into account the aforesaid, e.g., in dependence on its own vehicle model, ground clearance and / or occupancy, to at least partially mimic any one or more of the passing behaviors of the vehicles 2, 3, 4, 5.

[0043] In particular, it may be possible to categorize the passing behaviors into two or more categories of passing behavior, e.g., ideal, good, not good or similar, relating to the safety and / or comfort with which a road irregularity 7 has been passed. The first vehicle 1 may then select the ideal category and / or combine the passing behaviors from different, e.g., ideal and / or good, categories for mimicking the passing behavior of these categories for its own passing behavior, e.g., pass the road irregularity 7 with a speed of 5 km / h like vehicles 2 and 3 in the example of Figs. 4 and 5.

[0044] Generally, road irregularities 7 such as speed bumps, lane marks, traffic signs or alike may be determined from the detection data. If such detection is not available due to, e.g., snow, the first vehicle 1 may be able to detect road irregularities 7 by detecting the passing behaviors of the further vehicles 2, 3, 4, 5 such as speed variation and up and down movements at the lowest speed location.

[0045] Also, when detecting a road irregularity 7, the method 100 may be able to differentiate different types of road irregularities 7, e.g., pothole and speed bump, from one another considering the passing behavior and / or vehicle characteristics of the further vehicles 2, 3, 4, 5. Specifically, the method 100 may include detecting any one of the further vehicles 2, 3, 4, 5 as a bounding box around the respective vehicle 2, 3, 4, 5 and determine at least their vertical movement for estimating whether the road irregularity 7 is a pothole or speed bump, for example.

[0046] Also, besides determining instruction data for its own passing behavior by using method 100, it may also be possible to adapt the driving of the first vehicle 1 in case one of the further vehicles 2, 3, 4, 5 shows unsafe passing behavior, e.g., approaches or passes a road irregularity 7 too fast, thereby preventing an accident with such further vehicles 2, 3, 4, 5. Apart from that, the first vehicle 1 may be configured to notify any one of the further vehicles 2, 3, 4, 5 about the detected road irregularity 7, e.g., by wireless communication such as vehicle2vehicle communication, via a cloud server or, for example, by projecting, in particular with light onto the street, a notification, e.g., to another vehicle.

[0047] As used herein, the word "comprising" does not exclude other elements or steps, and the indefinite article "a" or "an" does not exclude a plurality. The mere fact that certain measures are recited in mutually different dependent claims does not indicate that a combination of these measures cannot be used to advantage. Further, as used herein, the phrase "at least one" or similar, e.g., "one or more of', in reference to a list of one or more entities should be understood to mean at least one entity selected from any one or more of the entities in the list of entities, but not necessarily including at least one of each and every entity specifically listed within the list of entities and not excluding any combinations of entities in the list of entities. This definition also allows that such entities may optionally be present other than the entities specifically identified within the list of entities to which the phrase "at least one" or similar refers, whether related or unrelated to those entities specifically identified. Thus, as a non-limiting example, "at least one of A and B" (or, equivalently, "at least one of A or B" or, equivalently "at least one of A and / or B" or, equivalently "one or more of A and B", "one or more of A or B", or "one or more of A and / or B") may refer, in one example, to at least one, optionally including more than one, A, with no B present (and optionally including entities other than B); in another example, to at least one, optionally including more than one, B, with no A present (and optionally including entities other than A); in yet another example, to at least one, optionally including more than one, A, and at least one, optionally including more than one, B (and optionally including other entities). In other words, the phrases "at least one," "one or more," and "and / or" are open-ended expressions that are both conjunctive and disjunctive in operation. For example, each of the expressions "at least one of A, B, and C," "at least one of A, B, or C," "one or more of A, B, and C," "one or more of A, B, or C," and "A, B, and / or C" may mean A alone, B alone, C alone, A and B together, A and C together, B and C together, A, B, and C together, and optionally any of the above in combination with at least one other entity.

[0048] As used herein, the phrase "being indicative of" may for example mean "reflecting" and / or "comprising". Accordingly, an entity, element and / or step referred to herein as "being indicative of [...]" can be synonymously or interchangeably used herein with one, two or all of said entity, element and / or step "comprising [...]" and said entity, element and / or step "reflecting [...]".

[0049] Further, as used herein, phrases such as "based on", "related" or "relating", "associated" and similar are not to be seen exclusively in terms of the entities, elements and / or steps to which they are referring, unless otherwise stated. Instead, these phrases are to be understood inclusively, unless otherwise stated, in that, for example, an entity, element or step referring by any of these phrases or similar, e.g., being "based on", an or another entity, element or step, does not exclude that the respective entity, element or step may be further or also "based on" any other entity, element or step than the one to which it refers.

[0050] Any designation of methods, steps and elements as first, second, etc. or similar as provided herein is merely intended to make the methods, their steps and elements referenceable and distinguishable from one another. By no means does the designation of methods, steps and elements constitute a limitation of the scope of this disclosure. For example, when this disclosure describes a third step of a method, a first or second step of the method do not need to be present yet alone be performed before the third step unless they are explicitly referred to as being required per se or before the third step. Moreover, the presentation of methods or steps in a certain order is merely intended to facilitate one example of this disclosure and by no means constitutes a limitation of the scope of this disclosure. Generally, unless no explicitly required order is being mentioned, the methods and steps may be carried out in any feasible order. Specifically, the terms first, second, third or (a), (b), (c) and the like in the description and in the claims are used for distinguishing between similar elements and not necessarily for describing a sequential or chronological order. It is to be understood that the terms so used are interchangeable under appropriate circumstances and that the examples of the disclosure described herein are capable of operation in other sequences than described or illustrated herein.

[0051] In the context of the present disclosure any numerical value indicated is typically associated with an interval of accuracy that the person skilled in the art will understand to still ensure the technical effect of the feature in question. As used herein, the deviation from the indicated numerical value is in the range of ± 10%, and preferably of ± 5%. The aforementioned deviation from the indicated numerical interval of ± 10%, and preferably of ± 5% is also indicated by the terms "about" and "approximately" used herein with respect to a numerical value.

[0052] Any reference signs in the claims should not be construed as limiting the scope of the claims.LIST OF REFERENCE SIGNS

[0053] 1first vehicle 2second vehicle 3third vehicle 4fourth vehicle 5fifth vehicle 6road 7road irregularity 10camera system 12camera 20data processing system 22data processing apparatus 24computer program product 100method 102-108steps

Claims

1. A method (100) for passing a road irregularity (7) of a road (6), the road (6) being travelled by a first vehicle (1), the method (100) comprising: - obtaining (102) detection data indicative of a passing of the road irregularity (7) by a second vehicle (2); - determining (104) passing behavior data indicative of a behavior of passing the road irregularity (7) by the second vehicle (2) based on the detection data; and - providing (106) instruction data to the first vehicle (1) for passing the road irregularity (7) based on the passing behavior data.

2. The method (100) of claim 1, wherein the method (100) further comprises: - obtaining first vehicle characteristic data indicative of at least one vehicle characteristic of the first vehicle (1); and - determining second vehicle characteristic data indicative of at least one vehicle characteristic of the second vehicle (2) based on the detection data; wherein the providing (106) of the instruction data is further based on the first vehicle characteristic data and the second vehicle characteristic data.

3. The method (100) of claim 2, wherein the providing of the instruction data comprises comparing the first vehicle characteristic data to the second vehicle characteristic data, wherein the instruction data is determined for controlling the first vehicle (1) such that it at least partially mimics the passing behavior of the second vehicle (2) for passing the road irregularity (7) based on the comparison of the first vehicle characteristic data to the second vehicle characteristic data.

4. The method (100) of claim 2 or 3, wherein the at least one vehicle characteristic of the first vehicle (1) and / or of the second vehicle (2) is one or more from: a vehicle model, a vehicle model year, a vehicle type, a vehicle size, a vehicle weight, a vehicle ground clearance, a number of vehicle occupants, or an allocation of occupied vehicle seats.

5. The method (100) of any one of claims 2 to 4, wherein the determining of the second vehicle characteristic data comprises: - determining second vehicle characteristic data indicative of a vehicle model as vehicle characteristic of the second vehicle (2) based on the detection data; and - determining one or more further vehicle characteristics of the second vehicle characteristic data based on the vehicle model, wherein the one or more further vehicle characteristics are from: a vehicle model year, a vehicle type, a vehicle size, a vehicle weight, or a vehicle ground clearance.

6. The method (100) of any one of the previous claims, wherein the passing behavior at least comprises a speed of the second vehicle (2), a travel direction of the second vehicle (2), and / or a movement pattern of a movement of the second vehicle (2) around at least one axis of the second vehicle (2).

7. The method (100) of any one of the previous claims, wherein the detection data is from at least one camera (12) of the first vehicle (1), and wherein the passing behavior data is determined based on computer vision.

8. The method (100) of any one of the previous claims, wherein the method (100) comprises: - determining road irregularity data indicative of the road irregularity (7) based on the detection data, wherein the detection data is indicative of a sign and / or a marking indicative of the road irregularity (7), and / or based on the passing behavior data; wherein the providing (106) of the instruction data is further based on the road irregularity data.

9. The method (100) of claim 8, wherein the road irregularity data is determined based on the passing behavior data, and wherein the road irregularity data is indicative of a position, geometry and / or size of the road irregularity (7).

10. The method (100) of any one of the previous claims, wherein the road irregularity (7) is one from: a speed bump, a pothole, and an object on the road (6).

11. The method (100) of any one of the previous claims, wherein the detection data is further indicative of a passing of the road irregularity (7) by a third vehicle (3), and wherein the passing behavior data is further indicative of a behavior of passing the road irregularity (7) by the third vehicle (3) based on the first data.

12. The method (100) of any one of the previous claims, wherein the method (100) comprises providing two or more instruction data for controlling the first vehicle (1) when passing the road irregularity (7) based on the passing behavior data, and wherein the method (100) further comprises: selecting one from the two or more instruction data based on at least one from: a driver awareness of the driver of the first vehicle (1), a distance of the first vehicle (1) from the road irregularity (7), an activity of one or more occupants of the first vehicle (1), or a position and speed of a third vehicle (3) travelling the road (6).

13. A computer program product (24) comprising instructions which, when executed by a data processing system (20), cause the data processing system (20) to carry out the method (100) of any one of the previous claims.

14. A data processing system (20) comprising means for carrying out the method (100) of any one of claims 1 to 12.

15. A vehicle (1) comprising the data processing system (14) of claim 14.

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