Method, device, and computer program for monitoring proper operation of sensors within an intelligent transport system

Abstract

Method of detecting misbehaviour in an intelligent transport system (ITS). A plurality of objects located in a given area are identified 300 over at least one time period, the objects being identified from data obtained from at least one sensor of at least one infrastructure ITS station (ITS-S). During each of the at least one time periods, a plurality of ITS messages reporting objects perceived by at least one moving ITS-S in the given area, are received 310. For each of the at least one time periods, a perception indicator is determined 340 as a function of a number of objects belonging to the reported perceived objects and / or to the identified objects. Misbehaviour is then detected 350 as a function of the determined perception indicator for the at least one time periods. The ITS may be a cooperative intelligent transport system (C-ITS) and the infrastructure ITS-S may be a road side unit (RSU).

Description

FIELD OF THE DISCLOSURE The present disclosure relates generally to Intelligent Transport Systems (ITSs) and more specifically to Cooperative Intelligent Transport Systems (C-ITSs). BACKGROUND OF THE DISCLOSURE Cooperative Intelligent Transport Systems (C-ITSs) are an emerging technology for future transportation management that aims at improving road safety, traffic efficiency and driver experience. Intelligent Transport Systems (ITS), as defined by the European Telecommunications Standards Institute (ETSI), include various types of communication such as: - communications between vehicles (e.g., car-to-car), and - communications between vehicles and stationary stations (e.g., car-to-infrastructure). C-ITSs are not restricted to road transport as such. More generally, C-ITS may be defined as the use of information and communication technologies (ICT) for rail, water, and air transport, including navigation systems. Such various types of C-ITS generally rely on radio services for communication and use dedicated technologies. Such C-ITSs are subject to standards, specified for each country and / or territory where C-ITSs are implemented. Today, in Europe, the European Telecommunications Standards Institute is in charge of the elaboration of the specifications forming the standards to which C-ITSs are subjected. Cooperation within C-ITSs is achieved by exchange of messages, referred to as ITS messages, between ITS stations (denoted ITS-Ss). The ITS-Ss may be vehicles, Road Side Units (RSUs), Vulnerable Road Users (VRUs) carrying an ITS equipment (for instance included in a smartphone, a GPS device, a smart watch, or in a cyclist equipment), or any other entities or infrastructure equipped with an ITS equipment, as well as central subsystems (back-end systems and traffic management centres). As observed above, C-ITSs may support various types of communications, for instance between vehicles (vehicle-to-vehicle or “V2V”), referring to all kinds of road users, e.g., car-to-car, or between vehicles and stationary stations such as vehicle-to-infrastructure or“V2l”, and infrastructure-to-vehicle or“l2V”, e.g., car-to-infrastructure. Such exchanges of messages may be performed via a wireless network, referred to as “V2X” (for “vehicle” to any kind of devices) networks, examples of which may include 3GPP LTE- Advanced Pro, 3GPP 5G, or IEEE 802.11p technology (3GPP, LTE, and IEEE are Registered Trade Marks). Exemplary ITS messages include Collective Perception Messages (CPMs), Cooperative Awareness Messages (CAMs), Vulnerable Road Users Awareness Messages (VAMs), and Decentralized Environmental Notification Messages (DENMs). An ITS-S sending an ITS message is referred to as an “originating” ITS-S and an ITS-S receiving an ITS message is referred to as a “receiving” ITS-S. VAMs and CAMs are generally periodically sent with a period varying depending, for example, on the speed of the originating ITS-S. It is noted that ETSI TS 103 324 (v2.1.1 of June 2023) defines the Collective Perception Service through which an ITS-S having on-board sensor systems detects objects in its vicinity and transmits, using broadcast CPMs, description information (e.g. dynamics such as position and / or kinematic information) thereof. The CPMs are sent periodically with a period from 100 ms to 1 s depending for example on the speed of the objects sensed by the originating ITS-S. It is recalled here that EN 302 637-2 (V1.4.1 of April 2019) standard defines the Cooperative Awareness Basic Service, that may be used by an ITS-S to transmit, using broadcast CAMs, its ego-vehicle dynamics (e.g., its position and speed). The CAMs are generally periodically sent with a period varying from 100 milliseconds to 1 second depending, for example, on the speed of the originating ITS-S. It is also to be noted that EN 302 637-3 (V1.3.1 of April 2019) standard defines the Decentralized Environmental Notification Basic Service, that may be used by an originating ITS-S to send, using broadcast DENMs, notifications to other ITS-Ss, such as warnings or alerts. Such a message notifies an event (e.g., a road hazard, driving environment information, traffic condition information, etc.) detected by the originating ITS-S. Each ITS station has an environment model called a Local Dynamic Map (LDM) that is regularly updated with highly dynamic data to locate vehicles, pedestrians, bicycles, etc., in the vicinity of the ITS station. The LDM is updated using information from on-board sensors and completed with information from received ITS messages such as awareness messages containing the ego-position and the speed of connected vehicles (CAMs) and / or of connected Vulnerable Road Users (VAMs) and such as Collective perception messages (CPMs) containing the perceived objects (e.g. vehicles, motorbikes, bicycles or pedestrians) from sensor-equipped ITS stations. CPMs improve the local perception ability (larger field of view, non-connected objects etc.). It is observed that cooperative ITS messages are not encrypted when exchanged over the V2X network. To guarantee authentication of the transmitting or originating ITS station and data integrity of the ITS messages, a Public Key Infrastructure (PKI) providing digital certificates to the ITS stations is implemented. The PKI mechanism alone cannot address all cyber threats. For instance, misbehaving entities in possession of valid certificates can still transmit tampered data. Consequently, additional security mechanisms need to be deployed at the ITS stations to detect misbehaving entities, and to report them to a Misbehaviour Authority (MA) in case of detection. In the scope of the disclosure, it is important to note that a misbehaving entity is not necessarily malicious, it may be a malfunctioning entity. Indeed, it is difficult to determine whether a station is malicious or “only” malfunctioning, but in both cases, it is important to make its misbehaviour visible. TS 103 759 (V2.1.1 of January 2023) defines the Misbehaviour Reporting Service, by which an ITS Station (ITS-S) or an end entity may provide misbehaviour reports to the MA of the C-ITS Cooperative-ITS Credential Management System (CCMS). In particular, it defines the report format, which is dependent on the application in which a misbehaviour has been detected. As stated above, some of the ITS stations, denoted infrastructure ITS-Ss, are associated with the infrastructure. Such ITS stations, for example ITS stations comprising road side units, play a particular role since they generally have more sensors and much greater computing power than ITS stations of vehicles or of VRUs. In addition, they may have knowledge about their environment (e.g., the layout of the roads, the location of the cameras to which they are connected, the locations of road signs, etc.). Accordingly, ITS stations associated with the infrastructure are particularly adapted for performing monitoring tasks, for example for locating and tracking vehicles and vulnerable road users using videos acquired through stationary cameras. However, it may happen that a stationary camera has an issue which prevents traffic monitoring. For instance, a person may move the camera so that it records a different area. Alternatively, a person may obfuscate the camera to prevent it from acquiring images of the scene. Numerous other issues may occur, for example due to a bug of the software running on the camera or to an action made by a person (e.g., a physical action or an action carried out by a hacker attacking the camera). In all these cases, the camera can no longer be used to achieve a given monitoring task. Consequently, such issues should be detected and reported to people in charge of traffic monitoring. Various solutions exist to determine that a camera has been tampered with or is no more working as expected. For instance, methods based on an analysis of the image background make it possible to determine that a camera has been moved. As another example, methods based on an analysis of image histograms make it possible to determine that a camera has been obfuscated. Such methods have their pros and cons, but none is perfect. Therefore, there is a need to improve detecting and / or reporting of malfunctioning entities in C-ITS, in particular to improve detecting and / or reporting of malfunctioning sensors such as cameras and / or lidars. SUMMARY OF THE DISCLOSURE The present disclosure has been devised to address one or more of the foregoing concerns. In this context, there is provided a solution to monitor proper operation of sensors within an intelligent transport system. According to a first aspect of the disclosure, there is provided a method, in an Intelligent Transport System, ITS, of detecting misbehaviour, the ITS comprising at least one infrastructure ITS station, ITS-S, and at least one moving ITS-S, the method comprising: identifying, over at least one time period, a plurality of objects located in a given area, the objects being identified from data obtained from at least one sensor of the at least one infrastructure ITS-S, receiving, during each of the at least one time period, from the at least one moving ITS-S, a plurality of ITS messages reporting objects perceived by the at least one moving ITS-S in the given area, determining, for each of the at least one time period, a perception indicator, the perception indicator being determined as a function of a number of objects belonging to the reported perceived objects and / or to the identified objects, and detecting misbehaviour as a function of the perception indicator determined for the at least one time period. Accordingly, the method of the disclosure makes it possible to detect and / or report malfunctioning sensors in C-ITS, in particular malfunctioning cameras and / or lidar of an infrastructure ITS-S, and / or to detect and / or report errors in reporting perceived objects, using Collective Perception Messages (CPMs) and / or Cooperative Awareness Messages (CAMs), to avoid hazardous situations, leading to improved trust in ITS messages in general. The at least one time period may comprise at least a first and a second time periods. According to some embodiments, the perception indicator is determined as a function of a number of objects belonging to the reported perceived objects and to the identified objects. Still according to some embodiments, the perception indicator is further determined as a function of a number of the reported perceived objects. Still according to some embodiments, the perception indicator is a ratio of the number of objects belonging to the reported perceived objects and to the identified objects to the number of the reported perceived objects. An object belonging to the reported perceived objects and the identified objects may be an object identified by the at least one infrastructure ITS-S and perceived by the at least one mobile ITS-S substantially at a same time and substantially at a same location. Substantially at the same time may mean in a given time frame and substantially at the same location may mean at a distance from each other lower than a given threshold. Still according to some embodiments, the perception indicator is a ratio of the number of reported perceived objects not belonging to the identified objects to the number of reported objects plus the ratio of the number of identified objects not belonging to the reported perceived objects to the number of identified objects. This value is comprised between zero and two. A value of zero means that the different sources of data are fully correlated, whereas a value of two means that they are not at all correlated. The first ratio reflects whether a large share of reported perceived objects cannot be associated with identified objects, and the second ratio reflects whether a large share of identified objects cannot be associated with reported perceived objects. Still according to some embodiments, the perception indicator further depends on at least one characteristic of the given area and / or of the at least one time period. Still according to some embodiments, detecting misbehaviour comprises comparing the perception indicator with a predetermined threshold. Still according to some embodiments, detecting misbehaviour comprises comparing a difference between the perception indicator determined for the first time period and the perception indicator determined for the second time period with a threshold. Still according to some embodiments, the ITS messages reporting objects perceived in the given area are received from a plurality of moving ITS-Ss. Still according to some embodiments, the ITS messages comprise at least one Cooperative Awareness Message, CAM, and / or at least one Collective Perception Message, CPM. Still according to some embodiments, the at least one time period lasts for at least a predetermined amount of time, for example five minutes. Still according to some embodiments, at least one of the number of reported perceived objects and of the number of identified objects is greater than a predetermined number of objects, for example fifty objects. Still according to some embodiments, the method further comprises generating and transmitting a misbehaviour report. Still according to some embodiments, the infrastructure ITS-S comprises a Road Side Unit, RSU and / or the at least one moving ITS-S is embedded within a vehicle or is carried by a Vulnerable Road User. The at least one sensor may be a camera, a lidar, and / or a radar. Still according to some embodiments, the method is carried out in the at least one infrastructure ITS-S. Still according to some embodiments, the method further comprises setting up the at least one sensor, the first time period comprising the setting up or the first time period following the setting up within a maximum period of time. According to another aspect of the disclosure there is provided a device comprising a processing unit configured for carrying out each of the steps of the method described above. This aspect of the disclosure has advantages similar to those mentioned above. At least parts of the methods according to the disclosure may be computer implemented. Accordingly, the present disclosure may take the form of an entirely hardware embodiment, an entirely software embodiment (including firmware, resident software, micro-code, etc.) or an embodiment combining software and hardware aspects that may all generally be referred to herein as a "circuit", "module" or "system". Furthermore, the present disclosure may take the form of a computer program product embodied in any tangible medium of expression having computer usable program code embodied in the medium. Since the solutions of the present disclosure can be implemented in software, the solutions of the present disclosure can be embodied as computer readable code for provision to a programmable apparatus on any suitable carrier medium. A tangible carrier medium may comprise a storage medium such as a floppy disk, a CD-ROM, a hard disk drive, a magnetic tape device or a solid-state memory device and the like. A transient carrier medium may include a signal such as an electrical signal, an electronic signal, an optical signal, an acoustic signal, a magnetic signal or an electromagnetic signal, e.g., a microwave or RF signal. BRIEF DESCRIPTION OF THE DRAWINGS Further advantages of the present disclosure will become apparent to those skilled in the art upon examination of the drawings and detailed description. Embodiments of the disclosure will now be described, by way of example only, and with reference to the following drawings, in which: Figure 1 illustrates an example of an ITS system in which some embodiments of the present disclosure may be implemented; Figure 2 illustrates an example of an infrastructure ITS station in which some embodiments of the present disclosure may be implemented; Figure 3 illustrates an example of steps of a method for detecting misbehaviour with regard to perception of objects in a field of view of sensors of an infrastructure ITS-S or with regard to reporting of objects perceived by mobile ITS-Ss in this field of view; Figure 4 illustrates an example of steps of a process for associating selected V2X-obtained objects, obtained in a given time frame, with sensor-obtained objects; Figure 5 illustrates an example of steps of a method for triggering a report such as a misbehaviour report upon determining that perception of objects in the field of view of the infrastructure ITS-S sensors or reporting of perceived objects in this field of view by other ITS-S behave improperly; Figure 6 illustrates the misbehaviour report format, as defined in the ETSI Misbehaviour Reporting Service; and Figure 7 is a schematic representation of an example of a communication ITS-S device configured to implement some embodiments of the present disclosure. DETAILED DESCRIPTION OF THE DISCLOSURE The names of the lists and elements (such as data elements) provided in the following description are only illustrative. Embodiments are not limited thereto and other names could be used. According to some embodiments of the disclosure, it is determined whether the objects identified in response to analysing images acquired by a camera connected to an infrastructure ITS station (or more generally in response to analysing data acquired by any sensor used to perceive an environment, such as a camera, a lidar, or a radar) correspond to perceived objects reported in ITS messages received from various moving ITS stations such as ITS stations associated with vehicles and / or with vulnerable road users (i.e., V2X agents). If the objects identified in response to analysing data acquired by a sensor of the infrastructure ITS station do not correspond to perceived objects reported in ITS messages received from the moving ITS stations, a misbehaviour report may be generated. For the sake of illustration, the infrastructure ITS station may comprise a Road Side Unit (RSU) that is installed nearby one or more cameras used to monitor the traffic in the field of view of the cameras, the RSU being able to receive V2X messages such as CPMs and or CAMs emitted by vehicles passing by. Intelligent Transport System Figure 1 illustrates an example of an ITS system 100 in which some embodiments of the present disclosure may be implemented. According to this example, a road having four lanes 105-1 to 105-4 is considered. This road may for instance be a motorway, with two lanes for vehicles going in one direction (e.g., lanes 105-1 and 105-2) and two lanes for vehicles going in the opposite direction (e.g., lanes 105-3 and 105-4). For the sake of illustration, the vehicles located on lanes 105-1 and 105-2 move from left to right (e.g., vehicles 110-1, 110-2, and 110-3) while vehicles located on lanes 105-3 and 105-4 move from right to left (e.g., vehicles 110-4 and 110-5). Still for the sake of illustration, it is assumed that the area represented in Figure 1 is monitored by ITS station 115 that comprises a Road Side Unit (RSU), ITS station 115 being referred to as an infrastructure ITS-S. In turn, the RSU is associated with camera 120 having the field of view denoted 125 that encompasses, at a given time, all the illustrated vehicles but vehicle 110-5. It is to be understood that the RSU may be associated with a set of sensors, such as one or more cameras, one or more lidars, and / or one or more radars. These sensors are used to monitor the traffic, especially to identify vehicles, pedestrians, and possibly other kinds of objects. Such monitoring is carried thanks to Analytics Module 130. Analytics Module 130 may be used to obtain information regarding the traffic in the monitored area and may also be used to determine a list of identified objects that may be described in CPMs like CPM 135 emitted by ITS-S 115. In addition, ITS-Ss of vehicles travelling through the area monitored by ITS-S 115 and its surroundings may emit ITS messages (i.e., V2X messages), such as CAMs and / or CPMs. In particular, an ITS-S of a vehicle, also referred to as a moving ITS-S, travelling through the area monitored by ITS-S 115, may generate and transmit CAMs to share its location within this area and / or CPMs to share its perception of its local environment (comprising at least a portion of the area monitored by ITS-S 115), for example a list of perceived objects. Such messages may be received by ITS-S 115, which may provide them to Analytics Module 130. As a consequence, Analytics Module 130 may obtain information regarding the monitored area from the sensors associated with the RSU of ITS-S 115 (i.e., as perceived by the infrastructure ITS-S) and from ITS-S of vehicles travelling through the area monitored by ITS-S 115 and its surroundings (i.e., as perceived and reported by moving ITS-Ss). Based on these different perceptions of the objects located in the monitored area, Analytics Module 130 may check the consistency of information received. Architecture of an infrastructure ITS station Figure 2 illustrates an example of an infrastructure ITS station in which some embodiments of the present disclosure may be implemented. As illustrated, Analytics Module 130 of ITS-S 115 is connected to one or more sensors, for example to cameras 110 and 200 and to lidar 205. When being connected to an Analytics Module of an ITS-S comprising a RSU, the sensors that are connected to the Analytics Module, that may comprise cameras, lidar, and / or radar, are located near the area to be monitored, for example nearby the road comprising lanes 105-1 to 105-4 in Figure 1, so as to make it possible to monitor the traffic in this area. The objects detected by each sensor connected to Analytics Module 130 are analysed by sensor data fusion module 210 of the Analytics Module. The sensor data fusion module may merge two or more objects detected by several sensors if the confidence level that they are the same objects is high enough, for example above a given threshold. Object merging may be done by comparing the type, position, speed, trajectory, etc. of the objects. For the sake of clarity, it is assumed that the considered one or more sensors are calibrated, i.e. the absolute position of objects they perceive can be determined (for instance as a latitude and a longitude, and possibly an altitude too). Newly detected objects or updates about tracked objects are then used to update the environment model referenced 215. The environment model contains a list of objects, comprising information for each tracked object such as the followings: - a unique object identifier (ID) to track the object, - a time of measurement for the object, a list of the sensors that have perceived the object, - the object position, - the object relative distance and speed from the ITS-S reference position and an associated confidence level, - a type of the object (e.g., a vehicle, a pedestrian, etc.) with an associated confidence level, and an associated ITS station identifier (ID) with an associated confidence level. Based on environment model 215, CPMs may be generated and emitted by ITS Module 220 to report objects that are perceived by the ITS-S. Such messages may be transmitted periodically. They contain a list of objects perceived by the ITS-S, that are described in the environment model. In addition, ITS Module 220 receives V2X messages that may be provided to the Issue Detection Module 225. According to some embodiments of the disclosure, only V2X messages of given types are provided to Issue Detection Module 225, for example CPMs and CAMs, only CPMs, or only CAMs. Vulnerable Road User Awareness Messages (VAMs), that provide information about Vulnerable Road Users such as pedestrians or cyclists, may also be provided to Issue Detection Module 225. As illustrated, Issue Detection Module 225 is also connected to the Environment Model 215. Therefore, Issue Detection Module 225 may check whether objects described in V2X messages (such as objects described in CPMs or CAMs) correspond to objects perceived by the ITS-S comprising the Issue Detection Module. When the ITS is operating normally, most of the objects that are reported in V2X messages and that are located in the field of view of the sensors of the infrastructure ITS-S (i.e., the ITS-S comprising the Issue Detection Module in this case) should correspond to objects perceived by these sensors. If this is not the case, there is a misbehaviour regarding perception of objects in the field of view of the sensors of the infrastructure ITS-S or regarding reporting of the perceived objects. It is noted here that misbehaviour may represent any kind of issues, malfunctioning, hacking, etc., that may be intentional or not. As described hereafter, upon detecting such a misbehaviour, the Issue Detection Module may perform a specific analysis to further characterize the detected misbehaviour, for instance to determine whether the misbehaviour comes from the infrastructure ITS-S sensors or from V2X messages. In parallel or after having characterized the detected misbehaviour, the latter may be reported to a competent authority or to one or more ITS-Ss, for example ITS-Ss located in the field of view of the sensors of the infrastructure ITS-S or nearby, for instance through a misbehaviour report which may be emitted as an ITS message, or through a direct notification or alert in an application such as an application used for traffic monitoring. As an example, the architecture of a moving ITS station may be similar to the one illustrated in Figure 2, except that it does not comprise any issue detection module like issue detection module 225. Detecting misbehaviour Figure 3 illustrates an example of steps of a method for detecting misbehaviour with regard to perception of objects in a field of view of sensors of an infrastructure ITS-S or with regard to reporting of objects perceived by mobile ITS-Ss in this field of view. As illustrated, a first step is directed to identifying objects (or recognising objects, i.e., identifying the type of objects and determining some of their characteristics) from data received from calibrated sensors connected to an infrastructure ITS-S (step 300), using video analytics. If there is a single calibrated sensor (e.g., a single camera), the identified objects are the objects perceived by this sensor. Alternatively, if multiple calibrated sensors are used, a fusion step may be performed so that the same objects, when perceived by different sensors, are reported only once in the list of identified objects. For the sake of clarity and as described with reference to Figure 2, it is assumed that sensors are calibrated, which makes it possible to determine the position of each identified object. The objects identified from data obtained from at least one sensor are also referred to as sensor-identified objects. Next, in parallel, or before step 300, characteristics of objects perceived by other ITS-Ss, that are in the field of view of the calibrated sensors used during step 300 for identifying objects, and that are reported in V2X messages, are obtained from these V2X messages (step 310). It is observed that the sensors of the infrastructure ITS-S being calibrated, their field of view is known. According to some particular embodiments, the field of view may be more precisely defined by an operator of the infrastructure through an application, for example to adapt the field of view to a given region of interest from an applicative point of view. When V2X messages reporting perceived objects are received, the description of the perceived objects comprising at least their position, a measurement time, and a type (e.g., as described in CAMs or CPMs), it can be checked whether the reported perceived objects are identified by the infrastructure ITS-S in the field of view of at least one sensor to which it is connected. For the sake of illustration, the position of a reported perceived vehicle is comprised in the Basic container of a CAM, within the referenceposition field. According to some embodiments of the disclosure, additional information regarding the vehicle may be used to better characterize the trajectory of the vehicle (as further described). Such additional information may be obtained from the High Frequency container (e.g., the speed or heading) and / or from the Low Frequency container (e.g., the pathHistory, which describes a list of recent positions). Still for the sake of illustration, some characteristics of objects may be obtained from the perceivedObjectContainer of CPMs, through the list named perceivedObjects. The position and confidence of the measurements are indicated for each object. The objects obtained at step 310 may be referred to as V2X-obtained objects. Next, after having obtained characteristics of objects perceived by other ITS-Ss, that are in the field of view of the calibrated sensors used during step 300 for identifying objects, the V2X-obtained objects may be selected based on their measurement time and a given time frame (step 320). For instance, the time frame may have a duration of one second (and the process may be performed every second as illustrated with the dashed line and described hereafter). All the V2X-objects for which at least one measurement has been obtained in this time frame are then selected. The time frame may be chosen based on the type of the considered V2X messages, which may be emitted at different frequencies. The time frame may also be chosen based on the output frequency of the Analytics Module (i.e., the frequency at which the module generates a description of the environment, it being noted that the Analytics Module does not necessarily process all the inputs from sensors - it may for instance process one frame every 200 ms even though a camera generates one frame every 20 ms, in order to limit the required amount of processing power). According to some embodiments, the time frame is larger than the emitting period of V2X agents and Analytics Module output period, so that data from each sensor have been obtained and processed during the considered time frame. If more than one measurement is obtained for a given object and a given sensor in the considered time frame, it may be either possible to keep only the latest measurement in the time frame, or to keep all the measurements and to associate the object with a list of measurements (each measurement comprising at least a position and a measurement time). As described hereafter, using multiples measurements in relation to a given object makes it possible to rely on object trajectories in the process of associating V2X-obtained objects with sensor-obtained objects. According to some embodiments, even measurements outside of the time frame may be considered in this process provided they are available (as described hereafter, this may be useful to better characterize the motion of the considered objects). Back to Figure 3, it is determined, at step 330, after having identified objects from data received from sensors (step 300) and after having selected V2X-obtained objects (step 320), whether the selected V2X-obtained objects may be associated with the identified objects (or sensor-obtained objects). This step is described in more detail with reference to Figure 4. Next, a perception indicator is determined (step 340). According to some embodiments, the perception indicator corresponds to the number of selected V2X-obtained objects associated with sensor-obtained objects. According to some other embodiments, the perception indicator corresponds to an association ratio representing the ratio of the number of selected V2X-obtained objects associated with sensor-obtained objects to the total number of selected V2X-obtained objects. The perception indicator may be determined for a time frame or for several time frames. When being determined for a time period representing several time frames, for example a time period of 5 to 10 minutes, the perception indicator may correspond to the perception indicator mean value over the time period, to the minimum value, to the maximum value, etc., that is to say to single value, or to a set of values, for example a set of association ratios where each association ratio corresponds to each time frame, making it possible to analyse the association ratio behavior. Then, a consistency check of the perception indicator is carried out (step 350) to determine whether perception of objects in the field of view of the infrastructure ITS-S sensors or reporting of perceived objects behave properly. According to some embodiments, checking whether perception of objects in the field of view of the infrastructure ITS-S sensors or reporting of perceived objects behave properly comprises checking whether the association ratio (i.e., the ratio of selected V2X-obtained objects successfully associated with sensor-obtained objects to the total number of selected V2X-obtained objects) is nominal. As described hereafter, checking consistency of the perception indicator may be based on the value of the perception indicator with regard to one or several thresholds or with regard to one or more reference values corresponding to a nominal operation. Checking consistency of the perception indicator may also be based on the variation of the value of the perception indicator over time. As illustrated, a report such as a misbehaviour report may be triggered (step 360) if it is determined that perception of objects in the field of view of the infrastructure ITS-S sensors or reporting of perceived objects does not behave properly. It is observed that reference values corresponding to a nominal operation may be obtained after the initial setup of the system. Indeed, at setup time, it may be assumed that an operator reviews the results obtained and checks the amount of V2X-obtained objects that can be associated with sensor-obtained objects. If this amount is too low, there may be an issue with the setup of the system. The operator in charge of this should be able to fix this issue, whereupon nominal data can be acquired, from which reference values may be determined. The nominal data stored after the initial set up may comprise lists of V2X-obtained objects (comprising object’s features), lists of sensor-obtained objects (also comprising object’s features), corresponding perception indicators, some conditions of the environment such as weather conditions, the geographical location, the corresponding month, the corresponding day of the week, the corresponding time of the day, etc. The review made by an operator does not necessarily consists in a detailed check of all the objects associated with each other. Instead, an Issue Detection Module (e.g., Issue Detection Module 225 in Figure 2) may automatically determine association between objects and display V2X-obtained objects, sensor-obtained objects, and corresponding associations, for instance as an overlay of an image obtained through a camera. The operator in charge of checking that behaviour is nominal may then validate that the setting of the system is appropriate. By recording more data, and assuming that no specific issue occurs during this period, more nominal data can be acquired, which can then be used to determine reference values. It is also observed that some of the V2X-obtained objects may not be associated with sensor-obtained objects. As a matter of fact, some vehicles that generate V2X messages have positioning means with a low accuracy, and possibly other defects that may lead to unreliable coordinates for the objects they describe (e.g. an inaccurate calibration of their sensors). In such cases, association may fail. As another example, association may fail for an object that is present in the field of view of a sensor, but that is not perceived by the sensor due to an obfuscation by another object that is in between them (e.g., a large vehicle that obfuscates a pedestrian). The amount of association failure typically depends on the considered ITS station’s sensors and on the contents of the scene. By considering a large amount of nominal data (e.g. data based on recordings along several days), a better understanding of nominal data may be obtained, which allows determining the nominal distribution of association ratio. Based on this, it may be possible to determine that, below a given ratio value, the situation is abnormal. As described above and according to some embodiments, the perception indicator may correspond to the ratio of the number of selected V2X-obtained objects associated with sensor-obtained objects to the total number of selected V2X-obtained objects. In such a case, determining misbehaviour may consist in determining whether the ratio is below a given threshold or is below a given threshold for a given time period. Other parameters may also be used. For example, a misbehaviour report may be triggered if the ratio is below a given threshold and if the selected V2X-obtained objects is above another given threshold. For the sake of illustration, a misbehaviour report may be triggered if the ratio remains below 30% for the last 10 minutes and if at least 20 V2X-obtained objects have been selected during these 10 minutes. Still according to some embodiments, the amount of time spent below a given threshold to trigger a misbehaviour report may depend on the threshold. For example, it may be 10 minutes for a threshold of 30%, but 2 minutes for a threshold of 5%. In a particular embodiment, a user interface associated with the Analytics Module (e.g., Analytics Module 130 in Figure 2) may allow a user to display the nominal distribution of association ratio and to define his / her own criteria for triggering reports based on this distribution, for example by indicating thresholds directly on the distribution and associating with them a sufficient time period (time spent below said threshold) and a sufficient number of V2X-obtained objects during this period. Still according to some embodiments, other parameters like the month, the day of the week, the time of the day, the weather conditions, or the geographical situation of the infrastructure ITS station may be taken into consideration for triggering or not a misbehaviour report. Figure 4 illustrates an example of steps of a process for associating selected V2X-obtained objects, obtained in a given time frame, with sensor-obtained objects. As illustrated, a first step is directed to obtaining a selected V2X-obtained object (step 400). Next, at step 410, a match is searched for among sensor-obtained objects. This search is based on the properties of each object, comprising at least the object position and measurement time. These properties may also comprise other features such as an object type and a speed. It is noted that since the accuracy of the different sensors may not be perfect and since the measurement times may differ from one sensor to another (either the sensors of the infrastructure ITS station or the sensors used by an ITS-S in vehicles to analyse their environment and to generate V2X messages), it cannot be assumed that a given V2X-object with a certain position at a certain time may be matched with a sensor-obtained object having the exact same position at the exact same time. Instead, it should be checked whether a sensor-obtained object is reasonably close to the considered V2X-object at considered measurement time. Similarly, if the speed of the object is considered, it should be checked whether the speed is reasonably similar (the speed may either be a numerical value, for example expressed in km / h, but it may also be a vector with a direction, in which case the similarity check should be made based on all the components of the vector). Different methods may be used to check whether a selected V2X-obtained object may be associated with a sensor-obtained object. As an example, machine learning may be used based on training data that associate V2X-obtained objects with sensor-obtained objects. An advantage of such a method is that it does not require implementing a matching algorithm that has to take into account the differences in measurements times and the differences in accuracy for the different sensors. Another method may consist in considering only sensor-obtained objects for which at least two positions have been obtained at different measurement times, in order to enable the creation of a function that approximates the position of said object as a function of time (the simplest way to do that is to perform an interpolation, and possibly to extrapolate the positions before the first measured position and after the last measured position). Once such functions have been calculated for sensor-obtained objects, it may be checked whether a given V2X-object located at a given position at a given measurement time is within a given range from a corresponding sensor-object at that time. To increase accuracy, and if at least two measurements are available for a given V2X-obtained object, this check may be done for different times, to check whether the trajectory of a given V2X-obtained object corresponds to the one of a sensor-obtained object. Next, at step 420, it is checked whether a matching sensor-object is found for the considered V2X-obtained object. If a matching sensor-object is found for the considered V2X-obtained object, this V2X-obtained object is associated with the corresponding sensor-obtained object (step 430). As suggested with the dashed line, the process is repeated for all the V2X-obtained object. According to some embodiments, when the steps illustrated in Figure 4 have been carried out for a given selected V2X-obtained object and if a corresponding sensor-obtained object is associated with the considered V2X-obtained object, the associated object may be stored along with the representation of the V2X-obtained object. Therefore, if the same V2X-obtained object is to be processed again, for example if it is selected in another time frame, such as the next one, step 410 may start by checking whether previously associated sensor-obtained object still matches the characteristics of considered V2X-obtained objects. In most cases, this should be the case, which saves time in the search process. Figure 5 illustrates an example of steps of a method for triggering a report such as a misbehaviour report upon determining that perception of objects in the field of view of the infrastructure ITS-S sensors or reporting of perceived objects in this field of view by other ITS-S behave improperly. It is noted that the report may take various forms. For instance, the report may take the form of a V2X message such as a V2X message adapted to represent a misbehaviour report. As another example, the report may be an alert that is displayed in a user interface to notify a user that a problem is occurring. As a preliminary remark, it should be noted that the steps illustrated in Figure 5 are executed only if an inconsistency of the perception indicator is detected (e.g., step 350 in Figure 3), for example if the association ratio is not nominal. In other words, the steps illustrated in Figure 5 are executed only if an issue has been detected. While a mere indication of generic failure may be provided in such a case, indicating that there is an undetermined issue in a given location monitored by a given ITS station, the issue coming from the station itself (possibly from its sensors) or from the traffic (i.e. the station works nominally, but the V2X messages are different from usual, for instance because there is an attack by hackers or because the traffic is very different), the steps of Figure 5 aim at providing further information, to indicate whether the issue comes from the station or from the V2X data. Optionally, it may be possible to define that only some kinds of issues should be reported. As another remark, it is noted that while it could be valuable to determine the cause of an issue, this is generally difficult: consequences of issues can be observed, but different causes may have the same consequences. For instance, a camera that detects no object (whereas it usually does) may have been obfuscated, but it may also have been moved or zoomed in a way that prevents any detection. In such a case, specific strategies could be defined to further assess whether a camera has been obfuscated or zoomed or moved, but this is not the topic of this disclosure. As illustrated, a first step is directed to obtaining nominal data for sensor-obtained objects and V2X-obtained objects. As described previously, such data may be stored following the initial setup of the system. According to some embodiments, the obtained nominal data may be filtered to keep only the nominal data that correspond to a context similar to the current one, for instance based on the month of the year, the day of the week, the time of the day, the current weather conditions, etc. Such a filtering step may be particularly relevant if it has been observed that the perception indicator such as the ratio of the number of selected V2X-obtained objects associated with sensor-obtained objects to the total number of selected V2X-obtained objects depends on such parameters. Next, a test is carried out to determine whether recent V2X-obtained objects are similar to the nominal V2X-obtained objects obtained previously (step 510). For the sake of illustration, this step may comprise comparing the positions and directions of V2X-obtained objects with nominal positions and directions. For example, if it is observed that many V2X-obtained objects have positions in areas where there are normally no objects, it may reflect an issue. This may happen, for instance, if hackers send fake V2X messages with objects that do not correspond to actual objects. This may also happen if there is an accident or temporary work leading vehicles to go through an unusual way. Other criteria may also be checked in addition to the positions of V2X-obtained objects. For instance, the number of objects described through V2X messages in a given amount of time may be compared to the nominal one in order to identify outlier values. Next, if it is determined that recent V2X-obtained objects are similar to nominal ones, another test is carried out to determine whether recent sensor-obtained objects are similar to nominal sensor-obtained objects (step 520). As described with reference to step 510, the positions and directions of objects may be evaluated, as well as the number of detected objects in a given amount of time. For instance, if a camera, a lidar, or a radar has been moved, its field of view is different, and thus, the positions of detected objects are also different compared to nominal ones, as well as their directions. As another example, if a camera, a lidar, or a radar has been obfuscated, it does not detect any object. If it is determined that recent V2X-obtained objects are similar to nominal ones (step 510) but that recent sensor-obtained objects are not similar to nominal sensor-obtained objects (step 520), it may be considered that the issue comes from one of ITS station’s one or more sensors and the report type is set to sensor issue (step 540), possibly along with specific data further describing the type of issue (e.g. based on the observed consequences of considered issue, for instance “unexpected areas for objects” or “no object detected”). On the contrary, if it is determined that recent V2X-obtained objects are similar to nominal ones (step 510) and that recent sensor-obtained objects are similar to nominal sensor-obtained objects (step 520), it means that both V2X-obtained objects and sensor-obtained objects appear to be nominal, but that the association ratio is not. In such a case, the report type is set to global issue (step 550), possibly along with specific data further describing the type of issue. Ideally, such issue should not occur, but practically, it may happen in case of invalid implementation, for instance because of edge cases with a low amount of objects or short period of time. Keeping that kind of issue as detected and reported allows being aware of such problem in the implementation in case they happen. Likewise, if it is determined that recent V2X-obtained objects are not similar to nominal ones (step 510) and that recent sensor-obtained objects are not similar to nominal sensor-obtained objects (step 520), the report type is set to global issue (step 550), possibly along with specific data further describing the type of issue. On the contrary, if it is determined that recent V2X-obtained objects are not similar to nominal ones (step 510) but that recent sensor-obtained objects are similar to nominal sensor-obtained objects (step 520), the report type is set to V2X issue (step 560), as the issue seems to come from V2X data. As illustrated, steps 540, 550, and 560 are followed by step 570, where the report is transmitted. Example of misbehaviour Back to Figure 1, a simple example is provided, it being noted that in view of the very low number of perceived vehicles, this example is not representative of real situations. If it is assumed that after the initial setup, during a first time period, infrastructure ITS-S identifies vehicles 110-1, 110-3, and 110-4, and that during the same timeframe vehicle 110-1 is reported in a CAM and vehicles 110-2 and 110-3 are reported in aCPM. If it assumed that sensor-obtained objects 110-1 and 110-3 are associated with V2X-obtained objects 110-1 and 110-3, the association ratio is 2 / 3 (the number of associated objects being equal to 2 and the number of reported (and presumably selected) V2X-obtained objects being equal to 3). If it is assumed that, during a second time period, infrastructure ITS-S identifies vehicles 110-1, 110-2, and 110-3, and that during the same time frame vehicle 110-2 is reported in a CAM and vehicles 110-1 and 110-3 are reported in a CPM, and if it assumed that sensor-obtained objects 110-1, 110-2, and 110-3 are associated with V2X-obtained objects 110-1, 110-2, and 110-3, the association ratio is 1 (the number of associated objects being equal to 3 and the number of reported (and presumably selected) V2X-obtained objects being equal to 3). In such a case, the perception indicator corresponding to the association ratio of the second time period being greater than the perception indicator corresponding to the association ratio of the first time period, that is deemed to correspond to the nominal operation, it is considered that the operation during the second time period is normal. If it is assumed that, during a third time period, infrastructure ITS-S identifies only vehicle 110-2 and that during the same time frame vehicle 110-3 is reported in a CAM and vehicles 110-1 and 110-2 are reported in a CPM, and if it assumed that sensor-obtained object 110-2 is associated with V2X-obtained object 110-2, the association ratio is 1 / 3 (the number of associated objects being equal to 1 and the number of reported (and presumably selected) V2X-obtained objects being equal to 3). In such a case, the perception indicator corresponding to the association ratio of the third time period is 30%. Since this is lower than the perception indicator corresponding to the association ratio of the first time period, that is deemed to correspond to the nominal operation, it is considered that the operation during the third time period is abnormal. If it is assumed that, during a fourth time period, infrastructure ITS-S identifies vehicles 110-1, 110-2, and 110-4, and that during the same time frame vehicle 110-4 is reported in a CPM, and if it assumed that sensor-obtained object 110-4 is associated with V2XX-obtained object 110-4, the association ratio is 1 (the number of associated objects being equal to 1 and the number of reported (and presumably selected) V2X-obtained objects being equal to 1). In such a case, the perception indicator corresponding to the association ratio of the fourth time period is greater than the perception indicator corresponding to the association ratio of the first time period, that is deemed to correspond to the nominal operation, but since the number of V2X-obtained objects is too low (i.e. below a given threshold), it is considered that the operation during the fourth time period is abnormal. A misbehaviour report is generated and transmitted during the third and the fourth time periods. Misbehaviour report According to some embodiments, a generated report, for example a misbehaviour report generated at step 360 in Figure 3, may be a specific type of misbehaviour report, that may be generated by the infrastructure ITS station itself. For the sake of illustration, the report may be targeted to nearby V2X agents, so that they may be aware of a current issue, or to an infrastructure operator such as the one in charge of considered infrastructure ITS station. Figure 6 illustrates the misbehaviour report format, as defined in the ETSI Misbehaviour Reporting Service. According to the illustrated format, the misbehaviour report is a message that comprises, after decryption, a version 605, a generationTime 610, an observationLocation 615 referring to the place where the misbehaviour was detected, and finally a report 620. As illustrated, the report contains an aid (application id) 625 and a content data field 630. For each application, a set of possible observations with their target properties (e.g., a speed or a security level) are defined. In addition, the list of possible non V2X evidences (evidences that are not ITS messages) is defined by the application. For example, in the context of the disclosure, the fact that V2X-obtained objects located in the field of view of the considered ITS station’s sensors are weakly correlated to the objects perceived by these sensors may be indicated. This is achieved thanks to Information Object Class and Information Object Set concepts. The aid selects the set of observations and non V2X evidence specific to an application. Content field 630 refers to the content data field which contains the observations 635, the v2xPduEvidence 645, and the sequence of nonV2xPduEvidence 655. In the context of the disclosure, V2X evidence may in particular be V2X messages recently obtained. However, as it may not be possible to include a large number of messages, V2X evidence may also be let empty. Alternatively, V2X messages that describe objects that could not be associated with sensor-obtained objects (e.g., as described by reference to step 420 in Figure 4) may be indicated as V2X evidence. On the other hand, non V2X evidence may be described, especially thanks to properties that describe the type of issue (e.g. “sensor issue” and “V2X issue”). In addition, the objects perceived by the sensors but that have not been associated with V2X-obtained objects may also be described as non V2X evidence. According to some embodiments, observations 635 is composed of a sequence of ObservationsByTarget 640. This data type contains all of the observations related to a particular "target" property, such as a speed or a security level. In the context of the disclosure, the target property may be defined, for example, as “RoadsideDetection”, with possible observations defined for the different types of reports defined in reference to Figure 5, for instance “Sensorissue”, “V2Xlssue”, and “Globallssue”, for example as follows: - tgtld that identifies the "target" of the observation, e.g., the speed. This identifier is drawn from an application-specific Information Object Set of observations by target and - observations that contains all the observations related to this target. The observations are drawn from the provided Information Object Set. According to some embodiments, v2xPdu Evidence 1145 is composed of a sequence of V2xPduStream 1150. This data type contains PDU stream from a single sender, for example as follows: - v2xPdus is the PDU stream, i.e., a series of PDUs for the same AID sent by the same sender (where "sent by the same sender" means "signed by the same certificate"). The PDUs are ordered in chronological order of reception by the reporter. All PDUs in this field are of the same type, i.e., correspond to the same IdObsPdu. This field preferably always contains a "subject PDU", i.e., a PDU that is the subject of the observations. Additional PDUs may be included depending on which observations appear in the observations field. A specification of an observation is expected to include a specification of which PDUs are to be included in this field, - certificate (optional) contains the certificate that signed the PDUs if it is not explicitly included in one of the PDUs. For ETSI PDUs (i.e., PDUs with the psid field in the security headerinfo equal to an ITS-AID assigned to ETSI as per ETSI TS 102 965) the certificate may be of type EtsiTs103097Certificate as specified in ETSI TS 103 097 (there is no need to include the entire certificate chain from the ITS station up to the Root CA, just the ITS station certificate is enough, as the MA is expected to have the rest of the certificates in the chain). It is noted that if the sender certificate changes, PDUs signed by the new certificate and included in this report is in a separate V2xPduStream instance within the v2xPduEvidence field, - subjectPdulndex identifies which PDU within the v2xPdus sequence is the "subject PDU", i.e., the PDU associated with the observations, and - id identifies the type of the PDU, meaning in this case what protocol headers are included from the stack. According to some embodiments, nonV2xPduEvidence 1155 is composed of a sequence of NonV2xPduEvidenceltem 1160. This data type contains evidence, which may be referenced by one or more observations, for example as follows: - id identifies the evidence type. - evidence contains the evidence. Example of a hardware to carry out steps of the method of embodiments of the present disclosure Figure 7 is a schematic representation of an example of a communication ITS-S device configured to implement some embodiments of the present disclosure. It may be either an ITS-S embedded in a vehicle or in a road side entity. The communication device 700 may preferably be a device such as a microcomputer, a workstation or a light portable device. The communication device 700 comprises a communication bus 713 to which there are preferably connected: a central processing unit 711, such as a microprocessor, denoted CPU or a GPU (for graphical processing unit); - a read-only memory 707, denoted ROM, for storing computer programs for implementing some embodiments of the disclosure; - a random access memory 712, denoted RAM, for storing the executable code of methods according to embodiments of the disclosure as well as the registers adapted to record variables and parameters necessary for implementing methods according to embodiments of the disclosure; and - at least one communication interface 702 connected to the radio communication network over which ITS messages are transmitted. The ITS messages are written from a FIFO sending memory in RAM 712 to the network interface for transmission or are read from the network interface for reception and writing into a FIFO receiving memory in RAM 712 under the control of a software application running in the CPU 711. Optionally, the communication device 700 may also include the following components: a data storage means 704 such as a hard disk, for storing computer programs for implementing methods according to one or more embodiments of the disclosure; - a disk drive 705 for a disk 706, the disk drive being adapted to read data from the disk 706 or to write data onto said disk; a screen 709 for serving as a graphical interface with the user, by means of a keyboard 710 or any other pointing means. The communication device 700 may be optionally connected to various peripherals including perception sensors 708, such as for example a digital camera, each being connected to an input / output card (not shown) so as to supply data to the communication device 700. Preferably the communication bus provides communication and interoperability between the various elements included in the communication device 700 or connected to it. The representation of the bus is not limiting and in particular the central processing unit is operable to communicate instructions to any element of the communication device 700 directly or by means of another element of the communication device 700. The disk 706 may optionally be replaced by any information medium such as for example a compact disk (CD-ROM), rewritable or not, a ZIP disk, a USB key or a memory card and, in general terms, by an information storage means that can be read by a microcomputer or by a microprocessor, integrated or not into the apparatus, possibly removable and adapted to store one or more programs whose execution enables a method according to the disclosure to be implemented. The executable code may optionally be stored either in read-only memory 707, on the hard disk 704 or on a removable digital medium such as for example a disk 706 as described previously. According to an optional variant, the executable code of the programs can be received by means of the communication network, via the interface 702, in order to be stored in one of the storage means of the communication device 700, such as the hard disk 704, before being executed. The central processing unit 711 is preferably adapted to control and direct the execution of the instructions or portions of software code of the program or programs according to the disclosure, which instructions are stored in one of the aforementioned storage means. On powering up, the program or programs that are stored in a nonvolatile memory, for example on the hard disk 704 or in the read-only memory 707, are transferred into the random access memory 712, which then contains the executable code of the program or programs, as well as registers for storing the variables and parameters necessary for implementing the disclosure. In a preferred embodiment, the apparatus is a programmable apparatus which uses software to implement the disclosure. However, alternatively, the present disclosure may be implemented in hardware (for example, in the form of an Application Specific Integrated Circuit or ASIC). Although the present disclosure has been described herein above with reference to specific embodiments, the present disclosure is not limited to the specific embodiments, and modifications will be apparent to a skilled person in the art which lie within the scope of the present disclosure. Many further modifications and variations will suggest themselves to those versed in the art upon referring to the foregoing illustrative embodiments, which are given by way of example only and which are not intended to limit the scope of the disclosure, that being determined solely by the appended claims. In particular, the different features from different embodiments may be interchanged, where appropriate. Each of the embodiments of the disclosure described above can be implemented solely or as a combination of a plurality of the embodiments. Also, features from different embodiments can be combined where necessary or where the combination of elements or features from individual embodiments in a single embodiment is beneficial. In the claims, 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 different features are recited in mutually different dependent claims does not indicate that a combination of these features cannot be advantageously used.

Claims

1. A method, in an Intelligent Transport System, ITS, of detecting misbehaviour, the ITS comprising at least one infrastructure ITS station, ITS-S, and at least one moving ITS-S, the method comprising:identifying, over at least one time period, a plurality of objects located in a given area, the objects being identified from data obtained from at least one sensor of the at least one infrastructure ITS-S,receiving, during each of the at least one time period, from the at least one moving ITS-S, a plurality of ITS messages reporting objects perceived by the at least one moving ITS-S in the given area,determining, for each of the at least one time period, a perception indicator, the perception indicator being determined as a function of a number of objects belonging to the reported perceived objects and / or to the identified objects, anddetecting misbehaviour as a function of the perception indicator determined for the at least one time period.

2. The method of claim 1, wherein the at least one time period comprises at least a first and a second time periods.

3. The method of claim 1 or claim 2, wherein the perception indicator is determined as a function of a number of objects belonging to the reported perceived objects and to the identified objects.

4. The method of any one of claims 1 to 3, wherein the perception indicator is further determined as a function of a number of the reported perceived objects.

5. The method of any one of claims 1 to 4, wherein the perception indicator is a ratio of the number of objects belonging to the reported perceived objects and to the identified objects to the number of the reported perceived objects.

6. The method of claim 5, wherein an object belonging to the reported perceived objects and the identified objects is an object identified by the at least one infrastructure ITS-S and perceived by the at least one mobile ITS-S substantially at a same time and substantially at a same location.

7. The method of any one of claims 1 to 4, wherein the perception indicator is a ratio of the number of reported perceived objects not belonging to the identified objects to the number of reported objects plus the ratio of the number of identified objects not belonging to the reported perceived objects to the number of identified objects.

8. The method of any one of claims 1 to 7, wherein the perception indicator further depends on at least one characteristic of the given area and / or of the at least one time period.

9. The method of any one of claims 1 to 8, wherein detecting misbehaviour comprises comparing the perception indicator with a predetermined threshold.

10. The method of claim 2 or of any one of claims 3 to 8 depending on claim 2, wherein detecting misbehaviour comprises comparing a difference between the perception indicator determined for the first time period and the perception indicator determined for the second time period with a threshold.

11. The method of any one of claims 1 to 10, wherein the ITS messages reporting objects perceived in the given area are received from a plurality of moving ITS-Ss.

12. The method of any one of claims 1 to 11, wherein the ITS messages comprise at least one Cooperative Awareness Message, CAM, and / or at least one Collective Perception Message, CPM.

13. The method of any one of claims 1 to 12, wherein the at least one time period lasts for at least a predetermined amount of time.

14. The method of any one of claims 1 to 13, wherein at least one of the number of reported perceived objects and of the number of identified objects is greater than a predetermined number of objects.

15. The method of any one of claims 1 to 14, further comprising generating and transmitting a misbehaviour report.

16. The method of any one of claims 1 to 15, wherein the infrastructure ITS-S comprises a Road Side Unit, RSU and / or wherein the at least one moving ITS-S is embedded within a vehicle or is carried by a Vulnerable Road User.

17. The method of any one of claims 1 to 16, wherein the at least one sensor is a camera, a lidar, and / or a radar.

18. The method of any one of claims 1 to 17, wherein the method is carried out in the at least one infrastructure ITS-S.

19. The method of claim 2 or of any one of claims 3 to 18 depending on claim 2, further comprising setting up the at least one sensor, the first time period comprising the setting up or the first time period following the setting up within a maximum period of time.

20. A computer program product for a programmable apparatus, the computer program product comprising a sequence of instructions for implementing each of the steps of the method according to any one of claims 1 to 19 when loaded into and executed by the programmable apparatus.

21. A non-transitory computer-readable storage medium storing instructions of a computer program for implementing each of the steps of the method according to any one of claims 1 to 19.

22. A device for an Intelligent Transport System, ITS, station, the device comprising a processing unit configured for carrying out each of the steps of the method according to any one of claims 1 to 19.

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