Patent Publication Number: US-2022219721-A1

Title: Methods, apparatuses and computer programs for automated vehicles

Description:
PRIORITY CLAIM 
     This patent application claims priority to European Patent Application No. 21150775.1, filed 8 Jan. 2021, the disclosure of which is incorporated herein by reference in its entirety. 
     SUMMARY 
     Illustrative embodiments relate to methods, apparatuses and computer programs for at least semi-autonomously operated transportation vehicles, and, in particular, to adjustments being made to the at least semi-autonomous operation of the transportation vehicles based on a prediction of a driving behavior of other at least semi-autonomously operated transportation vehicles. 
    
    
     
       BRIEF DESCRIPTION OF THE DRAWINGS 
       Disclosed embodiments will be described in more detail below with reference to the accompanying figures, in which: 
         FIGS. 1 a  and 1 b    show flow charts of examples of a disclosed method for a first transportation vehicle, suitable for adjusting an at least semi-autonomous operation of a first transportation vehicle; 
         FIG. 1 c    shows a block diagram of an example of a disclosed apparatus for a first transportation vehicle, suitable for adjusting an at least semi-autonomous operation of a first transportation vehicle; 
         FIG. 1 d    shows a schematic diagram of an interaction between a first transportation vehicle and a second transportation vehicle; 
         FIG. 2 a    shows a flow chart of an example of a disclosed method for an at least semi-autonomously operated transportation vehicle; and 
         FIG. 2 b    shows a block diagram of an example of a disclosed apparatus for an at least semi-autonomously operated transportation vehicle. 
     
    
    
     DETAILED DESCRIPTION 
     The research of automated (e.g., autonomously or semi-autonomously operated) transportation vehicles is a field of research and development. In some scenarios, the future of mobility is linked with transportation vehicles becoming simultaneously connected and automated. It is foreseen that in the near future more and more automated vehicles (AVs) will drive on public roads. 
     In general, different levels of automation are distinguished, e.g., SAE (Society of Automotive Engineers) Level 1-5. For example, German patent application DE 10 2015 205 135 discloses different levels of automation that are available for use in vehicles, depending on a scenario the vehicle is in. For example, the level of automation currently available can be transmitted to other vehicles in the aforementioned patent application. A similar concept is used in US patent application US 2017/0158116, where a vehicle provides a notification regarding whether the vehicle is under manual, semi-automated or fully automated driving mode. US patent application US 2019/0324451 discloses a concept where an automation level is adjusted based on the surrounding traffic. In German patent application DE 10 2018 128 892, a level of automation of a second vehicle can be adapted based on the actions of a first vehicle. 
     In international patent application WO 2019/185217, information about advanced driver assistance features being supported by a vehicle is transmitted from one vehicle to another, which enables the latter vehicle to have a greater understanding of a driving behavior of the former vehicle, enabling the latter vehicle to make adjustments to its speed or direction to avoid a collision with the former vehicle. In US 2015/0149019, a vehicle uses a signal transmitted by another vehicle to determine that the latter vehicle is at least semi-autonomously operated. In this case, the former vehicles takes action to autonomously operate the former vehicle. 
     In DE 2017 222874, a vehicle receives a message from another vehicle, with the message indicating an autonomous driving system being used, e.g., a version or manufacturer therefore, which enables the former vehicle to predict driving actions of the former vehicle, and to initiate a driving maneuver thereupon. In US 2018/335785, a vehicle predicts the behavior of another vehicle, e.g., based on whether the other vehicle is operated autonomously or not, and adapts its autonomous or semi-autonomous control in response to the predicted behavior. 
     The AVs are able to communicate via the network or even through direct communication with each other or with infrastructure. This enables the AVs to use cooperation to increase safety and efficiency. 
     There may be technical value to provide an improved concept for automated transportation vehicles, which considers the different driving behaviors of the transportation vehicles. 
     Disclosed embodiments are based on the finding that autonomously or semi-autonomously operated vehicles (also denoted automated vehicles, AVs, as introduced above) behave differently compared to human-driven transportation vehicles. For example, transportation vehicles may behave differently depending on their level of automation. In addition to different levels of automation, different software versions with distinct features are used by the AVs. Consequently, among a group of AVs, the automation level, available features, and software versions of the automated operation may be distinct, leading to different driving behaviors of different AVs. Such different driving behaviors may complicate the operation or cooperation of AVs, as these different AVs share the road and potentially cooperate with each other. To enable or improve a prediction, by an AV, of a driving behavior of other AVs, the AV may receive wireless messages from the other AVs, which indicate the automation capabilities of the other AVs (which may in turn depend on the level of automation, software version, available features, driving scenario etc.). This information may be used by the AV, to enable cooperation, improve the AVs perception and to enable predicting the behavior of other transportation vehicles. Consequently, the automation capabilities that are broadcast by the other AVs are used, by the AV, to predict the driving behavior of the other AVs, and, eventually, to adjust the automated/at least semi-autonomous operation of the AV. 
     Various exemplary embodiments of the present disclosure provide a method for a first transportation vehicle. The method is suitable for adjusting an at least semi-autonomous operation of the first transportation vehicle based on a prediction of a driving behavior of one or more second transportation vehicles. The first and the one or more second transportation vehicles are at least semi-autonomously operated transportation vehicles (also denoted “automated vehicles, or AVs, in the context of the present disclosure. The method comprises receiving one or more wireless messages from the one or more second transportation vehicles. The one or more wireless messages comprise information on one or more automation capabilities the one or more second transportation vehicles are capable of. The method comprises predicting the driving behavior of the one or more second transportation vehicles based on the information on the one or more automation capabilities the one or more second transportation vehicles are capable of. The method comprises adjusting the at least semi-autonomous operation of the first transportation vehicle based on the prediction of the driving behavior of the one or more second transportation vehicles. Receiving the information on the automation capabilities of the second transportation vehicle may enable or improve the prediction of the driving behavior of the second transportation vehicles, which may in turn lead to suitable adjustments of the automated driving of the first transportation vehicle. 
     In addition to the information on the automation capabilities, various other pieces of information may be used to convey more accurately the likely driving behavior of the second transportation vehicles. 
     For example, the one or more wireless messages may (further) comprise information on a manufacturer and a version of the one or more automation capabilities the one or more second transportation vehicles are capable of. Accordingly, the driving behavior of the one or more second transportation vehicles may be predicted based on the information on the manufacturer and the version of the one or more automation capabilities the one or more second transportation vehicles are capable of. The behavior of the automated driving of the second transportation vehicles may also depend on the manufacturer and/or a software version of the automation capabilities, as different manufacturers may implement slightly different driving behaviors, which may also evolve over time. 
     The one or more wireless messages may (further) comprise information on a maximal automation level and/or on a currently applied automation level of the one or more second transportation vehicles. The driving behavior of the one or more second transportation vehicles may be predicted based on the information on the maximal automation level and/or on the currently applied automation level of the one or more second transportation vehicles. As pointed out above, automated transportation vehicles may behave differently depending on their level of automation. 
     In addition to autonomous operation, possible cooperation between transportation vehicles may also influence their driving behavior. For example, the one or more wireless messages may comprise information on one or more cooperation capabilities the one or more second transportation vehicles are capable of and/or information on one or more cooperative driving maneuvers currently executed by the one or more second transportation vehicles. The driving behavior of the one or more second transportation vehicles may be predicted based on the information on the one or more cooperation capabilities the one or more second transportation vehicles are capable of and/or the information on the one or more cooperative driving maneuvers currently executed by the one or more second transportation vehicles. By considering possible or actually executed cooperative driving maneuvers, the prediction of the driving behavior may be improved. 
     Some transportation vehicles have different performance modes, such as an “economy mode” that prioritizes energy use, a “comfort mode” that seeks to reduce abrupt movements, or a “sport mode” that aims at improving the acceleration behavior of the transportation vehicle. These performance modes may also have an influence on the driving behavior of the second transportation vehicles. For example, the one or more wireless messages may comprise information on a driving performance setting currently used by the one or more second transportation vehicles. The driving behavior of the one or more second transportation vehicles may be predicted based on the information on the driving performance setting currently used by the one or more second transportation vehicles. 
     In some scenarios, transportation vehicles may also broadcast their driving intention to other transportation vehicles, e.g., to initiate a cooperative driving behavior. For example, the one or more wireless messages may comprise information on a driving intention of the one or more second transportation vehicles. The driving behavior of the one or more second transportation vehicles may be predicted based on the information on the driving intention of the one or more second transportation vehicles. For example, knowledge of the intended driving behaviors may improve the prediction of the driving behavior of the one or more second transportation vehicles. 
     There are various properties of the at least semi-autonomous operation of the first transportation vehicle that can be adjusted based on the predicted driving behavior. For example, adjusting the at least semi-autonomous operation of the first transportation vehicle may comprise adjusting a currently applied automation level of the first transportation vehicle. For example, depending on the predicted driving behavior of the one or more second transportation vehicles, different levels of automation may be possible. 
     In some cases, overall driving safety may be improved if some or all transportation vehicles concerned operate at the same level of automation. The method may comprise determining a common automation level that is suitable for use by the first transportation vehicle and at least a subset of the one or more second transportation vehicles based on the predicted driving behavior of the one or more second transportation vehicles. The method may comprise transmitting information on the determined common automation level to at least the subset of the one or more second transportation vehicles. 
     In some disclosed embodiments, adjusting the at least semi-autonomous operation of the first transportation vehicle may comprise selecting at least one transportation vehicle of the one or more second transportation vehicle to contact to perform a coordinated driving maneuver. The method may comprise transmitting a cooperation message to the selected at least one transportation vehicle. For example, based on the predicted driving behavior of the other transportation vehicles, transportation vehicles can be identified that are suitable for cooperation, e.g., as the predicted driving behavior can be suitably combined with the driving behavior of the first transportation vehicle. 
     In various disclosed embodiments, adjusting the at least semi-autonomous operation of the first transportation vehicle may comprise adjusting one of a velocity of the first transportation vehicle and a minimal distance of the first transportation vehicle relative to the one or more second transportation vehicles. Depending on the predicted driving behavior of the one or more second transportation vehicles, a higher or lower velocity, and/or a higher or lower minimal distance may be advisable, e.g., to take possible erratic driving behavior into consideration due to a likely change of automation level of one of the one or more second transportation vehicles. 
     The information is received from the one or more second transportation vehicle via the one or more wireless messages. For example, the one or more wireless messages may be cooperative awareness messages, CAMs. In other words, the information that is used for predicting the driving behavior of the one or more second transportation vehicles may be included within the CAMs. Alternatively, separate messages may be used. In other words, the one or more wireless messages may be wireless messages that are received in addition to the cooperative awareness messages from the one or more second transportation vehicles. 
     In addition to receiving the wireless messages, the first transportation vehicle may also broadcast its own information on its automation capabilities. In other words, the method may comprise transmitting a wireless message to the one or more second transportation vehicles, the wireless message comprising information on one or more automation capabilities the first transportation vehicle is capable of. For example, the wireless message may further comprise one or more of information on a manufacturer of the one or more automation capabilities the first transportation vehicle is capable of, information on a version of the one or more automation capabilities the first transportation vehicle is capable of, information on a maximal automation level of the first transportation vehicle, information on a currently applied automation level of the first transportation vehicle, information on one or more cooperation capabilities the first transportation vehicle is capable of, information on one or more cooperative driving maneuvers currently executed by the first transportation vehicle, information on a driving performance setting currently used by the first transportation vehicle, and information on a driving intention of the first transportation vehicle. Consequently, the information may be transmitted to the second transportation vehicles, and used by the second transportation vehicles to predict the driving behavior of the first transportation vehicle (similar to the prediction being performed by the first transportation vehicle). 
     Various exemplary embodiments of the present disclosure relate to a corresponding apparatus for the first transportation vehicle, suitable for adjusting the at least semi-autonomous operation of the first transportation vehicle based on a prediction of a driving behavior of the one or more second transportation vehicles. The one or more second transportation vehicles are at least semi-autonomously operated transportation vehicles. The apparatus comprises an interface for communicating with the one or more second transportation vehicles. The apparatus comprises a control module, configured to perform the above method. 
     Various exemplary embodiments of the present disclosure relate to a method for an at least semi-autonomously operated transportation vehicle (e.g., a second transportation vehicle as introduced above). The method comprises transmitting a wireless message to one or more further transportation vehicles. The wireless message comprises information on one or more automation capabilities the transportation vehicle is capable of. For example, the wireless message may further comprise one or more of information on a manufacturer of the one or more automation capabilities the transportation vehicle is capable of, information on a version of the one or more automation capabilities the transportation vehicle is capable of, information on a maximal automation level of the transportation vehicle, information on a currently applied automation level of the transportation vehicle, information on one or more cooperation capabilities the transportation vehicle is capable of, information on one or more cooperative driving maneuvers currently executed by the transportation vehicle, information on a driving performance setting currently used by the transportation vehicle, and information on a driving intention of the transportation vehicle. For example, the information may be used by the one or more further transportation vehicles to predict the driving behavior of the transportation vehicle. 
     Various exemplary embodiments of the present disclosure relate to a corresponding apparatus for the at least semi-autonomously operated transportation vehicle. The apparatus comprises an interface for communicating with one or more further transportation vehicles, and a control module, configured to perform the above method. 
     Various exemplary embodiments of the present disclosure relate to a computer program having a program code for performing at least one of the above methods, when the computer program is executed on a computer, a processor, or a programmable hardware component. 
     Various example embodiments will now be described more fully with reference to the accompanying drawings in which some example embodiments are illustrated. In the figures, the thicknesses of lines, layers or regions may be exaggerated for clarity. Optional components may be illustrated using broken, dashed or dotted lines. 
     Accordingly, while example embodiments are capable of various modifications and alternative forms, exemplary embodiments thereof are shown by way of example in the figures and will herein be described in detail. It should be understood, however, that there is no intent to limit example embodiments to the particular forms disclosed, but on the contrary, example embodiments are to cover all modifications, equivalents, and alternatives falling within the scope of the disclosure. Like numbers refer to like or similar elements throughout the description of the figures. 
     As used herein, the term, “or” refers to a non-exclusive or, unless otherwise indicated (e.g., “or else” or “or in the alternative”). Furthermore, as used herein, words used to describe a relationship between elements should be broadly construed to include a direct relationship or the presence of intervening elements unless otherwise indicated. For example, when an element is referred to as being “connected” or “coupled” to another element, the element may be directly connected or coupled to the other element or intervening elements may be present. In contrast, when an element is referred to as being “directly connected” or “directly coupled” to another element, there are no intervening elements present. Similarly, words such as “between”, “adjacent”, and the like should be similarly interpreted. 
     The terminology used herein is for the purpose of describing particular embodiments only and is not intended to be limiting of example embodiments. As used herein, the singular forms “a,” “an” and “the” are intended to include the plural forms as well, unless the context clearly indicates otherwise. It will be further understood that the terms “comprises,” “comprising,” “includes” or “including,” when used herein, specify the presence of stated features, integers, operations, elements or components, but do not preclude the presence or addition of one or more other features, integers, operations, elements, components or groups thereof. 
     Unless otherwise defined, all terms (including technical and scientific terms) used herein have the same meaning as commonly understood by one of ordinary skill in the art to which example embodiments belong. It will be further understood that terms, e.g., those defined in commonly used dictionaries, should be interpreted as having a meaning that is consistent with their meaning in the context of the relevant art and will not be interpreted in an idealized or overly formal sense unless expressly so defined herein. 
       FIGS. 1 a  and 1 b    show flow charts of examples of a method for a first transportation vehicle  100 , suitable for adjusting an at least semi-autonomous operation of the first transportation vehicle based on a prediction of a driving behavior of one or more second transportation vehicles  200  (transportation vehicles  100 ;  200  are shown in connection with  FIGS. 1 c , 1 d  and 2 b   ). In general, the method may be performed by the first transportation vehicle, e.g., by an apparatus  10  of the first transportation vehicle  100 . In some cases, at least subsets of the methods may also be performed by one of the one or more second transportation vehicles  200 , e.g., by an apparatus  20  of the second transportation vehicle  200 . On the other hand, the transportation vehicle  100  may alternatively or additionally perform the method shown in connection with  FIG. 2 a   . The one or more second transportation vehicles, and also the first transportation vehicle, are at least semi-autonomously operated transportation vehicles. The method comprises receiving  110  one or more wireless messages from the one or more second transportation vehicles. The one or more wireless messages comprise information on one or more automation capabilities the one or more second transportation vehicles are capable of. The method comprises predicting  120  the driving behavior of the one or more second transportation vehicles based on the information on the one or more automation capabilities the one or more second transportation vehicles are capable of. The method comprises adjusting  130  the at least semi-autonomous operation of the first transportation vehicle based on the prediction of the driving behavior of the one or more second transportation vehicles. 
       FIG. 1 c    shows a block diagram of an example of a corresponding apparatus  10  for the first transportation vehicle  100 , suitable for adjusting an at least semi-autonomous operation of the first transportation vehicle. The apparatus comprises an interface  12  for communicating with the one or more second transportation vehicles. The apparatus further comprises a control module  14 , coupled to the interface  12 , and configured to perform the method shown in connection with  FIGS. 1 a    and/or  1   b.    
     For a better overview of the scenario,  FIG. 1 d    is provided.  FIG. 1 d    shows a schematic diagram of an interaction between the first transportation vehicle  100  and one of the second transportation vehicles  200 . The first transportation vehicle  100  comprises the apparatus  10 . The second transportation vehicle  200  comprises an apparatus  20  (e.g., as introduced in connection with  FIG. 2 b   ). For example, the apparatus  20  may be implemented similar to the apparatus  10  of  FIGS. 1 c    and/or  1   d . For example, the transportation vehicle  100 , and/or the transportation vehicle  200 , may be a land vehicle, a road vehicle, a car, an off-road vehicle, a transportation vehicle, a truck or a lorry. 
     The following description relates to the method of  FIGS. 1 a    and/or  1   b  and to the corresponding apparatuses and transportation vehicles of  FIGS. 1 c    and/or  1   d  (and likewise a corresponding computer program). Features introduced in connection with the method may likewise be applied to the corresponding apparatuses/vehicles and/or computer program (and vice versa). 
     Various exemplary embodiments of the present disclose relate to a method, an apparatus and a computer program for a first transportation vehicle, suitable for adjusting an at least semi-autonomous operation of the first transportation vehicle based on a prediction of a driving behavior of one or more second transportation vehicles  200 . In this context, the term “at least semi-autonomously operated” indicates that at least one of the sub-systems of the transportation vehicle that are used to control/steer the movement of the transportation vehicle are autonomously operated. For example, according to SAE standard J3016, six levels of automated (or autonomous) driving are distinguished, from level 0 to level 5. The six levels are denoted level 0 (no automation, driver operates the transportation vehicle), level 1 (driver assistance, driver operates the transportation vehicle with the help of assistive systems that provide longitudinal or lateral guidance), level 2 (partial automation, driver assistance systems provide assistance with longitudinal or lateral guidance), level 3 (conditional automation, automated driving with the expectation, that the driver has to resume control in an instance), level 4 (high automation, automated driving without the expectation, that the driver has to resume control in an instance) and level 5 (full automation in any scenario that the driver is also capable of). For example, at least semi-autonomously operated may indicate an automation level of at least level 2 or at least level 3). In the context of the present application, the terms autonomous and automated are used interchangeably. For example, the term “at least semi-autonomously operated” may be understood as “at least partially automated”. 
     The method adjusts the at least semi-autonomous operation of the first transportation vehicle based on the predicted driving behavior of the one or more second transportation vehicles, which are in turn predicted based on the one or more wireless messages received from the one or more second transportation vehicles. For example, the one or more wireless messages may be wireless messages that are periodically transmitted by, and received from, the one or more second transportation vehicles. For example, the one or more wireless messages may be wireless messages that are directly received from the one or more second transportation vehicles, e.g., so-called device-to-device (D2D) and, in particular, vehicle-to-vehicle (V2V) messages. For example, the one or more wireless messages may be broadcast by the one or more second transportation vehicles, e.g., as D2D/V2V messages. In some cases, the direct communication may be aided by a relay, e.g., a road-side communication relay that re-broadcasts the one or more wireless messages. In this case, the terms D2D or V2V may still apply. 
     In V2V communication, one wireless message being used to periodically convey information between transportation vehicles is a so-called Cooperative Awareness Message (CAM). For example, the one or more wireless messages may be cooperative awareness messages. CAMs include a position, a heading, a transportation vehicle type, a timestamp etc. of the transportation vehicle transmitting the CAM. In addition, the CAMs may now include the various pieces of information attributed to the one or more wireless messages in the context of the present disclosure. Alternatively (or additionally), the one or more wireless messages may be wireless messages that are received in addition to the cooperative awareness messages from the one or more second transportation vehicles. For example, a mixed scenario may be used, wherein some pieces of information are included in the CAMs, and some information is transmitted via additional wireless messages. 
     As pointed out before, the one or more wireless messages include various pieces of information that can be useful in predicting the driving behavior of the one or more second transportation vehicles. In particular, the one or more wireless messages comprise the information on the one or more automation capabilities the one or more second transportation vehicles are capable of. Likewise, the driving behavior of the one or more second transportation vehicles are predicted based on the information on the one or more automation capabilities the one or more second transportation vehicles are capable of. For example, the information on the one or more automation capabilities the one or more second transportation vehicles are capable of may indicate, which automation capabilities are available, or being used, for the at least semi-autonomous operation of the one or more transportation vehicles. For example, the information on the one or more automation capabilities the one or more second transportation vehicles are capable of may comprise a bit vector indicating for each of a set of pre-defined automation capabilities whether the respective second transportation vehicle is capable of performing the automation capabilities (e.g., currently or in general). Alternatively, the information on the one or more automation capabilities the one or more second transportation vehicles are capable of may include a listing of the automation capabilities that the respective second transportation vehicle is capable of. 
     In some cases, the information on the one or more automation capabilities the one or more second transportation vehicles are capable of may be derived from other information, e.g., a manufacturer of the second transportation vehicle, a software version of the respective automation capabilities, and/or a level of automation of the second transportation vehicle. For example, to achieve a given level of automation, a transportation vehicle by a given manufacturer may provide a set of required capabilities (and optionally also a set of optional capabilities). These sets of required (or optional) capabilities may change over time, so that, for example, a transportation vehicle (by a manufacturer) capable of semi-autonomous operation on level 3 in 2020 has different capabilities than a transportation vehicle capable of semi-autonomous operation on level 3 in 2025. Therefore, the manufacturer, software version and/or level of automation may be used to determine which set of required capabilities applies. Consequently, the one or more wireless messages may comprise information on a manufacturer and a version of the one or more automation capabilities the one or more second transportation vehicles are capable of. Additionally or alternatively, the one or more wireless messages comprise information on a maximal automation level and/or on a currently applied automation level of the one or more second transportation vehicles. Consequently, the driving behavior of the one or more second transportation vehicles may be predicted based on the information on the manufacturer and the version of the one or more automation capabilities the one or more second transportation vehicles are capable of and/or based on the information on the maximal automation level and/or on the currently applied automation level of the one or more second transportation vehicles. For example, the information on the manufacturer and the version of the one or more automation capabilities the one or more second transportation vehicles are capable of and/or the information on the maximal automation level and/or on the currently applied automation level of the one or more second transportation vehicles may include or indicate the information on the one or more automation capabilities the one or more second transportation vehicles are capable of. In this context, the first transportation vehicle may distinguish between automation capabilities that are available “in general”, and automation capabilities that are currently being used, e.g., based on the currently used level of automation. 
     Another factor in determining the likely driving behavior of the one or more second transportation vehicles is a “performance mode” the one or more second transportation vehicles are driven in. For example, the “performance mode” or “driving performance setting” may be one of a “sport mode/sport driving performance setting”, which may include a more intense acceleration, a “comfort mode/driving performance setting”, which may guide the operation of the transportation vehicle towards fewer abrupt movements, an “economy mode/driving performance setting”, which may reduce an energy use of the respective second transportation vehicle, or a “balanced mode”. Depending on the driving performance setting, the driving behavior of the respective second transportation vehicle may vary, and the prediction of the driving behavior may be adjusted accordingly. In other words, the one or more wireless messages may comprise information on a driving performance setting currently used by the one or more second transportation vehicles. For example, the information on a driving performance setting currently used by the one or more second transportation vehicles may indicate which driving performance setting is currently being used by the respective second transportation vehicle. The driving behavior of the one or more second transportation vehicles may be predicted based on the information on the driving performance setting currently used by the one or more second transportation vehicles. 
     Another factor is the actual driving intention of the respective second transportation vehicles. In many cases, the driving intention of the one or more second transportation vehicles can be derived (by the first transportation vehicle) base on their position, velocity and heading that is included in the CAM received from the respective transportation vehicle, and used to predict the driving behavior of the one or more second transportation vehicles. In some cases, however, information on the driving intention may be included in the one or more wireless messages. For example, the one or more wireless messages may comprise information on a driving intention of the one or more second transportation vehicles. The driving behavior of the one or more second transportation vehicles may be predicted based on the information on the driving intention of the one or more second transportation vehicles. For example, the information on the driving intention may be received as part of a cooperation request, or independent of any planned cooperation of the respective second transportation vehicle. 
     As pointed out above, the behavior of the one or more second transportation vehicles may also depend on possible cooperation between transportation vehicles. For example, at an intersection, cooperation between transportation vehicles may be used to perform a “convoy start”, where the transportation vehicles being queued at the intersection start simultaneously and in a coordinated manner. Such cooperation capabilities (and actual cooperation being performed), or lack thereof, have a major influence on the driving behavior of the respective transportation vehicles, leading to an adjustment of the predicted driving behavior of the one or more second transportation vehicles. For example, the one or more wireless messages may comprise information on one or more cooperation capabilities the one or more second transportation vehicles are capable of and/or information on one or more cooperative driving maneuvers currently executed by the one or more second transportation vehicles. The driving behavior of the one or more second transportation vehicles may be predicted based on the information on the one or more cooperation capabilities the one or more second transportation vehicles are capable of and/or the information on the one or more cooperative driving maneuvers currently executed by the one or more second transportation vehicles. 
     In the above description, merely the abstract term “adjusting  130  the at least semi-autonomous operation of the first transportation vehicle based on the prediction of the driving behavior of the one or more second transportation vehicles” has been used. In practice, various facets of the at least semi-autonomous operation of the first transportation vehicle may be adjusted. In this context, the term “at least semi-autonomous operation of the first transportation vehicle” may indicate that the operation of the first transportation vehicle being adjusted relates to the operation, and thus movement, of the first transportation vehicle. For example, the prediction of the driving behavior of the one or more second transportation vehicles may be used to adjust how the first transportation vehicle moves in at least semi-autonomous operation. In some cases, the prediction of the driving behavior may be used to cease the at least semi-autonomous operation altogether, yielding control of the transportation vehicle to the driver of the transportation vehicle. 
     For example, adjusting the at least semi-autonomous operation of the first transportation vehicle may comprise adjusting  138  one of a velocity of the first transportation vehicle and a minimal distance of the first transportation vehicle relative to the one or more second transportation vehicles. Both parameters may be based on how predictable, and/or abrupt, the movement of the one or more second transportation vehicles are predicted to be. In scenarios where the movement of the one or more second transportation vehicles is predicated with a high degree of certainty (e.g., on a near-empty highway), a higher velocity and/or a smaller minimal distance may be chosen. 
     Another factor that can be adjusted, as hinted above, the level of automation being used by the first transportation vehicle. For example, based on how predictable, and/or abrupt, the movement of the one or more second transportation vehicles are predicted to be, different levels of automation may be chosen. In other words, adjusting the at least semi-autonomous operation of the first transportation vehicle comprises adjusting  132  a currently applied automation level of the first transportation vehicle. For example, in an ambiguous scenario, a lower level of automation may be chosen (e.g., level 3), and the driver may be warned that they may be required to take over control on short notice. As this automation level may also apply to other transportation vehicles, the determined automation level may be shared among the transportation vehicles occupying the same (or adjacent) portions of the road. For example, the method may comprise determining  134  a common automation level that is suitable for use by the first transportation vehicle and at least a subset of the one or more second transportation vehicles based on the predicted driving behavior of the one or more second transportation vehicles, and transmitting  135  information on the determined common automation level to at least the subset of the one or more second transportation vehicles. For example, the common automation level may be determined similar to the currently applied automation level of the first transportation vehicle, e.g., based on how predictable and/or ambiguous the driving of the one or more second transportation vehicles is. 
     Finally, the adjustment of the at least semi-autonomous operation of the first transportation vehicle may be related to cooperation being performed between the first transportation vehicle and the one or more second transportation vehicles. For example, adjusting the at least semi-autonomous operation of the first transportation vehicle may comprise selecting  136  at least one transportation vehicle of the one or more second transportation vehicle to contact to perform a coordinated driving maneuver, and transmitting  137  a cooperation message to the selected at least one transportation vehicle. For example, the predicted driving behavior, and/or the cooperation capabilities included in the one or more wireless messages may be used to identify a transportation vehicle among the one or more second transportation vehicles that is suitable for cooperation with the first transportation vehicle. 
     As has been pointed out above, the first transportation vehicle may be both recipient and transmitter of wireless messages. Indeed, the same type of wireless message received by the first transportation vehicle may also be transmitted from the first transportation vehicle to the one or more second transportation vehicles. Accordingly, the method may comprise transmitting (e.g., broadcasting) a wireless message  140  to the one or more second transportation vehicles, the wireless message comprising information on one or more automation capabilities the first transportation vehicle is capable of. For example, the wireless message transmitted by the first transportation vehicle may comprise one or more of information on a manufacturer of the one or more automation capabilities the first transportation vehicle is capable of, information on a version of the one or more automation capabilities the first transportation vehicle is capable of, information on a maximal automation level of the first transportation vehicle, information on a currently applied automation level of the first transportation vehicle, information on one or more cooperation capabilities the first transportation vehicle is capable of, information on one or more cooperative driving maneuvers currently executed by the first transportation vehicle, information on a driving performance setting currently used by the first transportation vehicle, and information on a driving intention of the first transportation vehicle. 
     In various disclosed embodiments, the interface  12  may correspond to any method or mechanism for obtaining, receiving, transmitting or providing analog or digital signals or information, e.g., any connector, contact, pin, register, input port, output port, conductor, lane, etc. which allows providing or obtaining a signal or information. An interface may be wireless or wireline and it may be configured to communicate, i.e., transmit or receive signals, information with further internal or external components. The interface  12  may comprise further components to enable according communication in the mobile communication system, such components may include transceiver (transmitter and/or receiver) components, such as one or more Low-Noise Amplifiers (LNAs), one or more Power-Amplifiers (PAs), one or more duplexers, one or more diplexers, one or more filters or filter circuitry, one or more converters, one or more mixers, accordingly adapted radio frequency components, etc. The interface  12  may be coupled to one or more antennas, which may correspond to any transmit and/or receive antennas, such as horn antennas, dipole antennas, patch antennas, sector antennas etc. The antennas may be arranged in a defined geometrical setting, such as a uniform array, a linear array, a circular array, a triangular array, a uniform field antenna, a field array, combinations thereof, etc. In some examples the interface  12  may serve the purpose of transmitting or receiving or both, transmitting and receiving, information, such as information, input data, control information, further information messages, etc. 
     As shown in  FIGS. 1 c  and 1 d   , the respective interface  12  is coupled to the respective control module  14  at the apparatus  10 . For example, the control module  14  may be implemented using one or more processing units, one or more processing devices, one or more processors, any method or mechanism for processing, such as a processor, a computer or a programmable hardware component being operable with accordingly adapted software. In other words, the described functions of the control module  14  may as well be implemented in software, which is then executed on one or more programmable hardware components. Such hardware components may comprise a general-purpose processor, a Digital Signal Processor (DSP), a micro-controller, etc. 
     In disclosed embodiments, communication, i.e., transmission, reception or both, may take place among apparatuses  10 / 20  or transportation vehicles  100 / 200  directly. Such communication may make use of a mobile communication system. Such communication may be carried out directly, e.g., by Device-to-Device (D2D) communication. Such communication may be carried out using the specifications of a mobile communication system. An example of D2D is direct communication between transportation vehicles, also referred to as Vehicle-to-Vehicle communication (V2V), car-to-car, Dedicated Short Range Communication (DSRC), respectively. Technologies enabling such D2D-communication include 802.11p, 3GPP systems (4G, 5G, NR and beyond), etc. 
     In disclosed embodiments, the interface  12  can be configured to wirelessly communicate in the mobile communication system. To do so radio resources are used, e.g., frequency, time, code, and/or spatial resources, which may be used for wireless communication with a base station transceiver as well as for direct communication. The assignment of the radio resources may be controlled by a base station transceiver, i.e., the determination which resources are used for D2D and which are not. Here and in the following radio resources of the respective components may correspond to any radio resources conceivable on radio carriers and they may use the same or different granularities on the respective carriers. The radio resources may correspond to a Resource Block (RB as in LTE/LTE-A/LTE-unlicensed (LTE-U)), one or more carriers, sub-carriers, one or more radio frames, radio sub-frames, radio slots, one or more code sequences potentially with a respective spreading factor, one or more spatial resources, such as spatial sub-channels, spatial precoding vectors, any combination thereof, etc. For example, in direct Cellular Vehicle-to-Anything (C-V2X), where V2X includes at least V2V, V2-Infrastructure (V2I), etc., transmission according to 3GPP Release 14 onward can be managed by infrastructure (so-called mode 3) or run in a UE. 
     More details and facets of the method, apparatus, transportation vehicle and computer program are mentioned in connection with the proposed concept or one or more examples described above or below (e.g.,  FIGS. 2 a  to 2 b   ). The method, apparatus, transportation vehicle and computer program may comprise one or more additional optional features corresponding to one or more facets of the proposed concept or one or more examples described above or below. 
       FIG. 2 a    shows a flow chart of an example of a method for an at least semi-autonomously operated transportation vehicle  100 ;  200 . For example, the method may be performed by the transportation vehicle  100 ;  200 . The method comprises transmitting  140  a wireless message to one or more further transportation vehicles  100 ;  200  (e.g., the first transportation vehicle or one or more second transportation vehicles) of  FIGS. 1 a  to 1 c   .  FIG. 2 b    shows a block diagram of an example of a corresponding apparatus  20  for the at least semi-autonomously operated transportation vehicle. The apparatus may comprise an interface  22  for communicating with one or more further transportation vehicles, and a control module  24 , configured to perform the method of  FIG. 2 a    and/or the method of  FIG. 1 a    and/or  1   b . The control module  24  is coupled to the interface  22 . 
     For example, the wireless message may correspond to one of the one or more wireless messages introduced in connection with  FIGS. 1 a  to 1 d   . Consequently, the wireless message comprises information on one or more automation capabilities the transportation vehicle is capable of. The wireless message may comprise one or more of information on a manufacturer of the one or more automation capabilities the transportation vehicle is capable of, information on a version of the one or more automation capabilities the transportation vehicle is capable of, information on a maximal automation level of the transportation vehicle, information on a currently applied automation level of the transportation vehicle, information on one or more cooperation capabilities the transportation vehicle is capable of, information on one or more cooperative driving maneuvers currently executed by the transportation vehicle, information on a driving performance setting currently used by the transportation vehicle, and information on a driving intention of the transportation vehicle. For example, the transportation vehicle may be one of the one or more second transportation vehicles  200  of  FIGS. 1 a  to 1 d   , or the first transportation vehicle of  FIGS. 1 a  to 1 d   . Accordingly, the transportation vehicle may also be configured to perform the method of  FIG. 1 a    and/or  1   b , and the apparatus  20  may be implemented similar to the apparatus  10  of  FIG. 1 c    and/or  1   d . However, in some cases, some of the features of the method and apparatus of  FIGS. 1 a  to 1 d    may be optional for the method and/or apparatus of  FIGS. 2 a    to  2   b.    
     In various disclosed embodiments, the interface  22  may correspond to any method or mechanism for obtaining, receiving, transmitting or providing analog or digital signals or information, e.g., any connector, contact, pin, register, input port, output port, conductor, lane, etc. which allows providing or obtaining a signal or information. An interface may be wireless or wireline and it may be configured to communicate, i.e., transmit or receive signals, information with further internal or external components. The interface  22  may comprise further components to enable according communication in the mobile communication system, such components may include transceiver (transmitter and/or receiver) components, such as one or more Low-Noise Amplifiers (LNAs), one or more Power-Amplifiers (PAs), one or more duplexers, one or more diplexers, one or more filters or filter circuitry, one or more converters, one or more mixers, accordingly adapted radio frequency components, etc. The interface  22  may be coupled to one or more antennas, which may correspond to any transmit and/or receive antennas, such as horn antennas, dipole antennas, patch antennas, sector antennas etc. The antennas may be arranged in a defined geometrical setting, such as a uniform array, a linear array, a circular array, a triangular array, a uniform field antenna, a field array, combinations thereof, etc. In some examples the interface  22  may serve the purpose of transmitting or receiving or both, transmitting and receiving, information, such as information, input data, control information, further information messages, etc. 
     As shown in  FIG. 2 b   , the respective interface  22  is coupled to the respective control module  24  at the apparatus  10 . For example, the control module  24  may be implemented using one or more processing units, one or more processing devices, one or more processors, any method or mechanism for processing, such as a processor, a computer or a programmable hardware component being operable with accordingly adapted software. In other words, the described functions of the control module  24  may as well be implemented in software, which is then executed on one or more programmable hardware components. Such hardware components may comprise a general-purpose processor, a Digital Signal Processor (DSP), a micro-controller, etc. 
     In exemplary embodiments, communication, i.e., transmission, reception or both, may take place among apparatuses  10 / 20  or transportation vehicles  100 / 200  directly. Such communication may make use of a mobile communication system. Such communication may be carried out directly, e.g., by Device-to-Device (D2D) communication. Such communication may be carried out using the specifications of a mobile communication system. An example of D2D is direct communication between transportation vehicles, also referred to as Vehicle-to-Vehicle communication (V2V), car-to-car, Dedicated Short Range Communication (DSRC), respectively. Technologies enabling such D2D-communication include 802.11p, 3GPP systems (4G, 5G, NR and beyond), etc. 
     In exemplary embodiments, the interface  22  can be configured to wirelessly communicate in the mobile communication system. To do so radio resources are used, e.g., frequency, time, code, and/or spatial resources, which may be used for wireless communication with a base station transceiver as well as for direct communication. The assignment of the radio resources may be controlled by a base station transceiver, i.e., the determination which resources are used for D2D and which are not. Here and in the following radio resources of the respective components may correspond to any radio resources conceivable on radio carriers and they may use the same or different granularities on the respective carriers. The radio resources may correspond to a Resource Block (RB as in LTE/LTE-A/LTE-unlicensed (LTE-U)), one or more carriers, sub-carriers, one or more radio frames, radio sub-frames, radio slots, one or more code sequences potentially with a respective spreading factor, one or more spatial resources, such as spatial sub-channels, spatial precoding vectors, any combination thereof, etc. For example, in direct Cellular Vehicle-to-Anything (C-V2X), where V2X includes at least V2V, V2-Infrastructure (V2I), etc., transmission according to 3GPP Release 24 onward can be managed by infrastructure (so-called mode 3) or run in a UE. 
     More details and facets of the method, apparatus, transportation vehicle and computer program are mentioned in connection with the proposed concept or one or more examples described above or below (e.g.,  FIG. 1 a  to 1 d   ). The method, apparatus, transportation vehicle and computer program may comprise one or more additional optional features corresponding to one or more facets of the proposed concept or one or more examples described above or below. 
     Various exemplary embodiments of the present disclosure relate to an automation level and/or cooperation broadcast message. For example, the automation level and/or cooperation broadcast message may correspond to the wireless message(s) introduced in connection with  FIGS. 1 a  to 2 b   . As has been pointed out above, various kinds of information may be shared by an AV (e.g., second transportation vehicles and/or first transportation vehicles) to facilitate prediction of the driving behavior of the AV by other AVs (e.g., the first transportation vehicle). AVs may share this information via direct communication through a broadcast message, for example. For example, the information may be transmitted as a new broadcast message or it might be contained within an existing vehicular broadcast message as, e.g., Cooperative Awareness Message (CAM). In some scenarios, the information may be distributed via road-side infrastructure (which may be used as a relay). 
     The automation &amp; cooperation broadcast message may contain one or more of the following features: AV level (automation capabilities), such as the SAE level and/or the software version, cooperation capabilities, AV features, AV status and action (e.g., a maneuver currently performed by the AV, such as AV is parking, turning, etc.), a timestamp, an ID of the transportation vehicle itself and model (type, dimensions), and a position of the transportation vehicle itself. 
     For example, an AV A may determine the possible automation level and cooperation even if it is not using it, e.g., by predicting the driving behavior of other AVs, adjusting the level of automation of AV A, and/or cooperating with the other AVs. 
     AV A may share its automation related information, such as its SAE level (for example), software version, automation features/capabilities, and/or an estimation of a possible automation level. For example, a visual notification of the possible automation level may be provided to a driver of the transportation vehicle, e.g., as a warning, so the driver can take over operation of the transportation vehicle. In some cases, different modes (sport, comfort) may be considered. 
     For example, AV A (or the other AVs) may share their cooperation related information, such as cooperation capabilities (platooning, maneuver coordination), or detected possible cooperation. 
     For example, AV A may share its status and actions (e.g., what is the transportation vehicle doing in a) and/or b), i.e., status of automation or cooperation. Other entities, e.g., transportation vehicles receiving one of the above pieces of information from AV A, may use the information to predict the behavior of AV A, adapt their behavior based on AV A, and/or evaluate whether cooperation is possible. 
     In the following, some examples are given. In a first example, a convoy start is performed at a traffic light (as a cooperation example). A first AV may queue with other transportation vehicles (e.g., AVs) on front of a traffic light. The (e.g., all of the) AVs may share their automation &amp; cooperation broadcast message. The first AV can verify if the transportation vehicle(s) in front of it is/are able to support a convoy start (cooperation capabilities) to improve the fuel and traffic flow efficiency. 
     In a second example, AV driving mode adaptation is performed (as a cooperation example). A first AV drives on a street with other transportation vehicles (AVs). The (e.g., all of the) AVs are sharing their automation &amp; cooperation broadcast message. The first AV can verify if the transportation vehicle(s) in its surrounding are driving with a distinct automation methods and then adapt its own driving behavior to it to increase fuel and traffic flow efficiency. 
     In a third example, an initiation of any kind of cooperation is performed (as a cooperation example). A first AV drives on a street with other transportation vehicles (e.g., AVs). The (e.g., all of the) AVs are sharing their automation &amp; cooperation broadcast message. The first AV can verify if other transportation vehicle(s) providing any kind of cooperation and it may initiate a certain kind of cooperation if needed. 
     For example, AV A may correspond to one of the second transportation vehicles and/or to the first transportation vehicle introduced in connection with  FIGS. 1 a  to 1 c   , and the “other” AVs (e.g., the first AV) may correspond to the first transportation vehicle introduced in connection with  FIGS. 1 a    to  1   c.    
     More details and facets of the automation level and/or cooperation broadcast message are mentioned in connection with the proposed concept or one or more examples described above or below (e.g.,  FIG. 1 a  to 2 b   ). The automation level and/or cooperation broadcast message may comprise one or more additional optional features corresponding to one or more facets of the proposed concept or one or more examples described above or below. 
     As already mentioned, in disclosed embodiments the respective methods may be implemented as computer programs or codes, which can be executed on a respective hardware. Hence, another exemplary embodiment is a computer program having a program code for performing at least one of the above methods, when the computer program is executed on a computer, a processor, or a programmable hardware component. A further exemplary embodiment is a computer readable storage medium storing instructions which, when executed by a computer, processor, or programmable hardware component, cause the computer to implement one of the methods described herein. 
     A person of skill in the art would readily recognize that operations of various above-described methods can be performed by programmed computers, for example, positions of slots may be determined or calculated. Herein, some disclosed embodiments are also intended to cover program storage devices, e.g., digital data storage media, which are machine or computer readable and encode machine-executable or computer-executable programs of instructions where the instructions perform some or all of the operations of methods described herein. The program storage devices may be, e.g., digital memories, magnetic storage media such as magnetic disks and magnetic tapes, hard drives, or optically readable digital data storage media. The exemplary embodiments are also intended to cover computers programmed to perform the methods described herein or (field) programmable logic arrays ((F)PLAs) or (field) programmable gate arrays ((F)PGAs), programmed to perform the above-described methods. 
     The description and drawings merely illustrate the principles of the disclosure. It will thus be appreciated that those skilled in the art will be able to devise various arrangements that, although not explicitly described or shown herein, embody the principles of the disclosure and are included within its spirit and scope. Furthermore, all examples recited herein are principally intended expressly to be only for pedagogical purposes to aid the reader in understanding the principles of the disclosure and the concepts contributed to furthering the art, and are to be construed as being without limitation to such specifically recited examples and conditions. Moreover, all statements herein reciting principles and embodiments of the disclosure, as well as specific examples thereof, are intended to encompass equivalents thereof. 
     When provided by a processor, the functions may be provided by a single dedicated processor, by a single shared processor, or by a plurality of individual processors, some of which may be shared. Moreover, explicit use of the term “processor” or “controller” should not be construed to refer exclusively to hardware capable of executing software, and may implicitly include, without limitation, Digital Signal Processor (DSP) hardware, network processor, application specific integrated circuit (ASIC), field programmable gate array (FPGA), read only memory (ROM) for storing software, random access memory (RAM), and non-volatile storage. Other hardware, conventional or custom, may also be included. Their function may be carried out through the operation of program logic, through dedicated logic, through the interaction of program control and dedicated logic, or even manually, the particular technique being selectable by the implementer as more specifically understood from the context. 
     It should be appreciated by those skilled in the art that any block diagrams herein represent conceptual views of illustrative circuitry embodying the principles of the disclosure. Similarly, it will be appreciated that any flow charts, flow diagrams, state transition diagrams, pseudo code, and the like represent various processes which may be substantially represented in computer readable medium and so executed by a computer or processor, whether or not such computer or processor is explicitly shown. 
     Furthermore, the following claims are hereby incorporated into the detailed description, where each claim may stand on its own as a separate exemplary embodiment. While each claim may stand on its own as a separate exemplary embodiment, it is to be noted that—although a dependent claim may refer in the claims to a specific combination with one or more other claims—other disclosed embodiments may also include a combination of the dependent claim with the subject matter of each other dependent claim. Such combinations are proposed herein unless it is stated that a specific combination is not intended. Furthermore, it is intended to include also features of a claim to any other independent claim even if this claim is not directly made dependent to the independent claim. 
     It is further to be noted that methods disclosed in the specification or in the claims may be implemented by a device having methods or mechanisms for performing each of the respective operations of these methods. 
     LIST OF REFERENCE SIGNS 
     
         
           10  Apparatus 
           12  Interface 
           14  Control module 
           20  Apparatus 
           22  Interface 
           24  Control module 
           100  First transportation vehicle 
           110  Receiving one or more wireless messages 
           120  Predicting a driving behavior 
           130  Adjusting an operation of the transportation vehicle 
           132  Adjusting an automation level 
           134  Determining a common automation level 
           135  Transmitting information on the common automation level 
           136  Selecting a transportation vehicle for cooperation 
           137  Transmitting a cooperation message 
           138  Adjusting a velocity and/or minimal distance 
           140  Transmitting a wireless message 
           200  Second transportation vehicle