COMMUNICATION WITHIN AN INTELLIGENT TRANSPORT SYSTEM
20230230479 · 2023-07-20
Inventors
- Isabelle MORVAN (Chantepie, FR)
- Eric Nassor (Thorigne-Fouillard, FR)
- Julien Sevin (Saint Aubin du Cormier, FR)
- Brice LE HOUEROU (Acigne, FR)
- Lionel TOCZE (Saint Domineuc, FR)
- Hervé Ruellan (Rennes, FR)
Cpc classification
International classification
Abstract
The present invention regards Cooperative Intelligent Transportation Systems, C-ITS. In response to detecting at least one situation involving at least one object detected within an area monitored by the ITS, a Collective Perception Message, CPM, is generated and transmitted. The CPM comprises a reference to the at least one object and an indication to indicate that the at least one object is involved in the at least one situation.
Claims
1. A method of communication in an Intelligent Transport System, ITS, comprising, at an originating ITS station, ITS-S: in response to detecting at least one situation involving at least one object detected within an area monitored by the ITS, generating and transmitting a Collective Perception Message, CPM, wherein the generated CPM comprises a reference to the at least one object and an indication to indicate that the at least one object is involved in the at least one situation.
2. The method of claim 1, wherein the generated CPM comprises at least one perceived object container, the at least one perceived object container comprising a description of the at least one object and a reference to the at least one situation.
3. The method of claim 1, wherein the generated CPM comprises at least one perceived object container and a space addendum container, different from the at least one perceived object container, the at least one perceived object container comprising a description of the at least one object and the space addendum container comprising a reference to the at least one situation.
4. The method of claim 3, wherein the space addendum container further comprises a reference to the at least one object.
5. The method of claim 2, wherein the at least one perceived object container further comprises an object safety level representing a risk level of the corresponding object with regard to the at least one situation.
6. The method of claim 5, further comprising selecting objects involved in the at least one situation, as a function of the object safety level, only selected objects being referenced within the generated CPM.
7. The method of claim 1, wherein the generated CPM further comprises a situation safety level representing a risk level of the at least one situation.
8. The method of claim 7, further comprising determining whether the situation safety level is higher than a situation safety threshold and, in response to determining that the situation safety level is higher than the situation safety threshold, decreasing the minimum time elapsing between two consecutive CPM generation events.
9. The method of claim 1, further comprising disabling any mechanism preventing a same object to be referenced in consecutive CPMs and/or disabling any grouping mechanism, depending on an object safety level and/or a situation safety level.
10. The method of claim 1, further comprising obtaining an identifier of the at least one situation, wherein the generated CPM further comprises the identifier of the at least one situation, the identifier being a situation identifier of a Decentralized Environmental Notification Message, DENM, the DENM comprising information regarding the at least one situation.
11. The method of claim 1, further comprising generating an identifier of the at least one situation, wherein the generated CPM further comprises the generated identifier, the generated identifier being independent from any situation identifier of Decentralized Environmental Notification Messages, DENM.
12. The method of claim 1, further comprising obtaining information regarding the at least one situation, the generated CPM further comprising the obtained information regarding the at least one situation.
13. The method of claim 1, further comprising receiving a Decentralized Environmental Notification Message, DENM, the received DENM comprising the indication to indicate that the at least one object is involved in the at least one situation.
14. The method of claim 1, wherein the indication to indicate that the at least one object is involved in the at least one situation further comprises a reference to at least one second object, the at least one second object being different from the at least one object and being involved in the at least one situation.
15. The method of claim 14, wherein the generated CPM further comprises predicted data associated with the at least one object and predicted data associated with the at least one second object.
16. The method of claim 15, wherein the generated CPM comprises an item of information to indicate that the predicted data associated with the at least one object are linked to the predicted data associated with the at least one second object.
17. A method of communication in an Intelligent Transport System, ITS, comprising, at an originating ITS station, ITS-S: in response to detecting at least one situation involving at least two objects detected within an area, generating and transmitting a Collective Perception Message, CPM, wherein the generated CPM comprises a reference to the at least two objects and a grouping information linking the at least two objects.
18. The method of claim 17, wherein the grouping information associates a predicted path of one of the at least two objects with a predicted path of another one of the at least two objects.
19. A method of communication in an Intelligent Transport System, ITS, comprising, at a receiving ITS station, ITS-S: receiving a Collective Perception Message, CPM, analysing the received CPM and determining, from the received CPM, that at least one object detected within an area monitored by the ITS is involved in at least one situation.
20. The method of claim 19, further comprising determining a predicted behavior of at least one second object from a predicted behavior of the at least one object, the at least one second object being different from the at least one object and the predicted behaviors and a link between the predicted behaviors being received within the received CPM.
21. An Intelligent Transport System, ITS, station, ITS-S, comprising at least one microprocessor configured for carrying out each step of the method of claim 1.
22. A non-transitory computer-readable medium storing a program which, when executed by a microprocessor or computer system in an Intelligent Transport System station, ITS-S, causes the ITS-S to perform each step of the method of claim 1.
Description
BRIEF DESCRIPTION OF THE DRAWINGS
[0062] Further advantages of the present invention will become apparent to those skilled in the art upon examination of the drawings and detailed description. Embodiments of the invention will now be described, by way of example only, and with reference to the following drawings, in which:
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DETAILED DESCRIPTION OF THE DISCLOSURE
[0079] It is noted that 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.
[0080] The embodiments of the present disclosure are intended to be implemented in Intelligent Transportation Systems (ITS).
[0081] According to some embodiments of the present disclosure, a CPM, i.e. a message regularly transmitted by an originating ITS station to share objects and free spaces perceived by its local sensors, comprises additional items of information about a situation concerning the perceived objects. Examples of such situations can be a human presence on the road, a collision risk, a pre-crash situation, traffic jamming, road work, etc. Accordingly, a link between perceived objects and a situation is provided in the CPM to associate these perceived objects with this situation. Several perceived objects may be associated with different situations.
[0082] In addition, the originating ITS station, ITS-S, sending such a CPM can identify whether some of the objects are safety-critical, based on the severity of the situation associated with them, and then may include in priority these objects at the next CPM generation event. Furthermore, a safety level (or safety-critical level) may be associated with some or all of the perceived objects associated with a situation.
[0083] Still according to some embodiments of the present disclosure, such a CPM comprising additional items of information to associate perceived objects with situations may reference one or more DENMs and/or may comprise further items of information to describe the one or more situations with which the perceived objects are associated.
[0084] On the other side, the receiving ITS-S, concerned directly or indirectly by a situation, can obtain additional (compared to known technics) information from the CPM combining objects and situation information. It can then perform a quicker analysis of the situation and then decide earlier to activate the mitigation actions appropriate to the situation (e.g. anti-collision or pre-crash functions, changing of an itinerary, etc.).
ITS System and ITS Station Comprising a Situation Analysis Module
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[0086] According to this example, an ITS station, that may generate and transmit DENMs and CPMs such as DENM 130 and CPM 131, is embedded in a road-side unit, RSU, 110. It is observed that RSUs have generally more powerful resources to analyze a collision risk situation than moving vehicles. For example, an RSU may have a wider field of view than an ITS-S embedded within a vehicle, multiple fields of view, fast access to other information such as traffic conditions, traffic light status, knowledge of objects that populate the monitored area, etc.
[0087] In particular, a wide view of the area monitored by an RSU allows the RSU to detect collisions or risks of collision mixing colliding vehicles that are ITS connected and not connected, and/or when colliding vehicles cannot see each other (e.g. due to an occlusion at an intersection).
[0088] As illustrated, ITS 100 is implemented at an intersection and comprises fixed road side unit 110 and several entities that may carry or embed an ITS station (ITS-S) each, for transmitting and/or receiving ITS messages within the ITS. The several entities may be for example, vehicles 151, 152, 153, and 154 and pedestrian 155.
[0089] Fixed road side unit 110 includes a set of sensors, such as image sensors, here video cameras 120, 121, 122, and 123 and an analytical module to analyze data provided by the sensors, such as situation analysis module 111. Each of the video cameras 120, 121, 122, and 123 is configured to monitor or scan a portion of the area monitored by the RSU (here the road intersection), making it possible to reproduce images of the monitored area. Other sensors such as LIDARs (laser imaging detection and ranging devices) may also be used.
[0090] The sensors are connected to the situation analysis module (e.g. video cameras 120, 121, 122, 123 are connected to situation analysis module 111) so that the situation analysis module processes the stream captured by the sensors/video cameras to analyze the situation. The situation analysis module and the sensors may be separate from or embedded within the same physical road side unit. For example, the situation analysis module may be wire-connected to sensors that may be remote (i.e. not embedded in the road side unit).
[0091] The processing of the data received from the sensors by the situation analysis module, e.g. situation analysis module 111, aims at detecting objects potentially present in the monitored area, referred to as “perceived objects” or “detected objects” hereinafter. Mechanisms to detect such objects are well known by one skilled in the art.
[0092] The situation analysis module is also configured to output a list of the perceived objects respectively associated with corresponding description information referred to as “state vector”. The state vector for a perceived object may include for instance parameters such as a position, a kinematic, temporal information, behavioral or object type classification information, etc.
[0093] Therefore, the situation analysis module may identify, among the perceived objects, Vulnerable Road Users (VRUs) such as pedestrians, cyclists as well as motorcyclists and also persons with disabilities or reduced mobility and orientation. It may also identify objects such as trees, road construction/work equipment (e.g. road barriers), and so on.
[0094] The VRUs may be considered as ITS-S when carrying an ITS equipment, for example an ITS equipment included in a smartphone, a satnav system, a smart watch, or in a cyclist equipment.
[0095] According to the example illustrated in
[0098] In addition, the perceived objects may be classified. For example, if the perceived objects are ITS stations, they can be classified as vehicles, VRUs, RSUs, or any another ITS-S types. Such object type classification may be based for example on predetermined rules, provided during the setting up of road side unit 110, or more generally the ITS-S. ETSI TR 103 562 V2.1.1 defines for instance the categories “unknown”, “vehicle”, “person”, “animal”, and “other”. Of course, other categories, more specific, can be defined.
[0099] According to some embodiments of the present disclosure, the situation analysis module comprises situation analysis functions to analyze the trajectories of the perceived objects, to predict their future trajectories, and to identify possible risks of collision between perceived objects.
[0100] The situation analysis module may also have access to some information about the monitored area, e.g. about the road intersection geometry, that can be used to analyze the monitored area, enabling, for example, to detect the presence of a pedestrian on the roadway outside of the crosswalk area.
[0101] As illustrated in
[0102] By the means of roadside ITS-S 112, RSU 110 can share information relative to the perceived objects. Typically, RSU 110 can share such information with receiving ITS stations by sending ITS messages, particularly the so-called Collective Perception Messages, CPMs, e.g. CPM 131, defined in documents ETSI TR 103 562 and ETSI TS 103 324 and usually sent periodically. Examples of formats of a CPM according to some embodiments of the present disclosure are illustrated in
[0103] By the means of roadside ITS-S 112, RSU 110 can also share information relative to a detected event. Typically, RSU 110 can share such triggered-event information with receiving ITS stations by sending ITS messages, particularly the so-called Decentralized Environmental Notification Messages, DENMs, e.g. DENM 130, defined in document EN 302 167-3.
[0104] More generally, any ITS-S in ITS 100 can share information on the objects it perceives, by sending CPMs, as well as information on itself, by sending so-called Cooperative Awareness Messages, CAMs, defined in document ETSI EN 302 637-2. CAMs may include a position, a kinematic (or dynamics), a unique station identifier, temporal information, behavioral or object type classification information, etc. Similarly, VRU Awareness Messages, VAMs, defined in document ETSI TS 103 300-3, can be sent by VRU ITS-S to share their own position and kinematic or the sharing of the information corresponding to a group of VRUs (VRU cluster).
[0105] The ITS messages are usually broadcast by their originating ITS-S, so that any other ITS-S can exploit them.
[0106] All the messages exchanged over ITS 100 help each ITS-S to have a good level of knowledge of its environment in terms of which objects are present, where and how they behave.
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[0108] For the sake of illustration, it is considered here that the illustrated ITS station is the RSU referenced 110 in
[0109] As mentioned above by reference to
[0110] The perceived objects detected by each sensor are analyzed by sensor data fusion module 230 in order to combine or merge the same objects detected by several sensors. Consideration of similarity between objects from different sensors can be based on their object types, positions, kinetics/dynamics (speed, acceleration), trajectories, etc. A level of confidence may also be computed when scrutinizing the similarities of these information items and the merging can be affected when the level of confidence is high enough.
[0111] Newly perceived objects or updates about already-tracked objects are used to update the environment model 220 of the ITS-S. Received CAMs, VAMs, DENMs, and CPMs from other ITS-S, conveying additional information, can also be used to update environment model 220.
[0112] The environment model is also known as the Local Dynamic Map and contains a list of the perceived objects. Each ITS-S has its own environment model 220.
[0113] An object in environment model 220 is defined together with multiple information items including, for example, all or part of the following: [0114] objectID: is the identifier of the perceived (or detected) object, [0115] timeOfMeasurement: represents the time when the (last) measurement concerning the perceived object was made, [0116] Distance: is the distance between the perceived object and the originating ITS-S. It is determined according to a frame of reference fixed to the originating ITS-S (e.g. RSU 110 in
[0133] According to some embodiments of the present disclosure, the environment model contains a situation list regularly updated by the situation analysis module.
[0134] For example, a CPM sent by an originating ITS-S wishing to share perceived object information includes containers (Perceived Object Containers), each listing such information for the corresponding perceived object.
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[0136] As shown in
[0137] The situation analysis module continuously analyzes the objects of the environment model and the DENMs received from other ITS-Ss so as to detect whether a particular situation is occurring (step 310). This may include analyzing the position of objects on road portions (e.g. detecting human presence on a road), predicting trajectories for the perceived objects, and inferring whether a risk exists, for example a risk of collision between several objects, or even whether a pre-crash situation exists if the predicted time to collision is less than a predetermined threshold, for example 1.5 s.
[0138] In a case in which a particular situation is detected, the originating ITS-S sends a CPM containing an item of information about the perceived objects and the associated situations (step 320), for example using the CPM format illustrated in
[0139] As illustrated in
[0140] In a variant, this may be done by checking whether one of the safety-critical objects as defined in the Perceived Object Containers is too close (in terms of position, speed and dimensions) to itself, to determine an own risk associated with a situation.
[0141] In case of positive determining, the receiving ITS-S can then trigger the appropriate mitigation measures (step 370), depending on the situation type, such as pre-crash functions, changing of the itinerary, emergency brake, etc.
CPM Structure
[0142] According to the disclosure, the structure of the CPMs is modified to comprise items of information describing a link between perceived objects and one or more situations.
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[0144] The illustrated CPM structure, referenced 400, is based on ETSI TS 103 324 Specification (V0.0.22 of May 2021). It comprises an ITS PDU header referenced 405, a “CPM Parameters” field 410, and a “Certificate” 415.
[0145] ITS PDU header 405 is a common header that includes the information of the protocol version, the message type, and the ITS-S identifier (ID) of the originating ITS-S.
[0146] “CPM Parameters” field 410 contains a management container referenced 420, a station data container referenced 430, a perception data container referenced 440 containing a set of sensor information containers referenced 450, a set of perceived object containers referenced 460, and a set of free space addendum Containers referenced 470.
[0147] Each container includes some data elements (DE) and/or data frames (DF). ETSI TS 102 894-2 Specification defines conventional data elements and data frames used in ITS messages.
[0148] Regardless of the type of the ITS-S generating the considered CPM, the management container provides information regarding the station type and the reference position of the originating ITS station. The message can be transmitted either by an ITS station, such as a vehicle, or by a stationary RSU. In case of a CPM generated by a vehicle, the station data container contains the dynamic information of the originating ITS station. It is not optional in case of a vehicle transmitting the CPM. In case of a CPM generated by an RSU, the station data container may provide references to identification numbers provided by the MAP Message (CEN ISO/TS 19091) reported by the same RSU. These references are required in order to match data provided by the CPM to the geometry of an intersection or road segment as provided by the MAP message. It is not required that a RSU has to transmit a MAP message for matching objects to road geometries. In this case, the station data container may be omitted. It is for this reason that the station data container is set as optional.
[0149] Each sensor information container contained in the set of sensor information containers 450 is optional. It provides information about the sensory capabilities of an ITS station. Depending on the station type of the originating ITS station, different container specifications are available to encode the properties of a sensor. The sensor information containers are attached to CPMs at a lower frequency than the other containers, as defined in ETSI TR 103 562. Up to 128 containers of this type may be used in a CPM.
[0150] Each perceived object container contained in the set of perceived object containers 460 is optional. It is composed of a sequence of optional or mandatory data elements (DEs) and/or data frames (DFs) which give a detailed description of the dynamic state and properties of a detected (or perceived) object.
[0151] More precisely, each object has to be described using the dedicated perceivedObject structure referenced 461. The first part of this structure (reference 462) contains data elements and/or data frames as defined by the ETSI TS 103 324 (V0.0.22 of May 2021) and comprises various fields including the following: [0152] objectID: this data element is an identifier assigned to a perceived object. It remains constant as long as the object is perceived by the originating ITS-S, [0153] timeOfMeasurement: this data element corresponds to the time difference for the provided measurement information with respect to the generation delta time stated in the management container, [0154] the distance defined by xDistance, yDistance, and zDistance (optional): it corresponds to the distance between the perceived object and the ITS-S's reference point the in x, y, z-direction of the ITS-S coordinate system, respectively, for the time of measurement, [0155] the speed defined by xSpeed, ySpeed and zSpeed (optional): it corresponds to the speed of the perceived object in the detecting ITS-S's reference system in the x, y, z-direction, respectively, for the time of measurement, [0156] the acceleration (optional) defined by xAcceleration, yAcceleration, and zAcceleration: it corresponds to the acceleration of the perceived object from the ITS-S's reference point in the x, y, z-direction, respectively, for the time of measurement, [0157] the dimension (optional) defined by planarObjectDimension1, planarObjectDimension2, and verticalObjectDimension: it represents the dimension of the perceived object, [0158] objectRefPoint: this is a reference point of the detected object. By default, the reference point is the center point of the perceived object, [0159] objectAge: it is the age of the perceived object, [0160] objectConfidence: it is the confidence level associated with the perceived object. The computation of the object confidence level may be based on a sensor's or merging system confidence, on the binary detection success (i.e. the detection success of the object during the last measurements), and on the object age, [0161] sensorIDList (optional): it is the list of sensor identifiers which provided the measurement data. It refers to sensorID in the sensor information container. If the sensor information container is never provided by the originating ITS-S, the list may be populated with random numbers, where each number is assigned to a sensor of the originating ITS-S, [0162] dynamicStatus (optional): it is a dynamic Status providing the capabilities of the originating ITS-S to move away from the perceived object (for example, it may take one of the values dynamic, hasBeenDynamic, or static), [0163] classification (optional): it provides the classification of the perceived object. It is composed of an object class and possibly a subclass (e.g. vehicle class has subclasses passengerCar, bus, etc.) with a class confidence value, and [0164] matchedPosition, (optional): it indicates the position of the perceived object mapped onto the intersection topology description transmitted in MAP messages.
[0165] Each free space addendum container contained in the set of free space addendum containers 470 is optional. It comprises a sequence of optional or mandatory data elements (DEs) which provide information about free spaces detected by a particular sensor. Each free space comprises various fields such as: [0166] freeSpaceConfidence: is the free space confidence value that applies to the entire area, [0167] freeSpaceArea: is the geometry of the free space area, [0168] sensorIDList is a list of identifiers of the sensors which performed the measurement to indicate the free space, and [0169] shadowingApplies: is a Boolean indicator used to indicate whether a tracing approach should be used to compute a shadowed area behind an object.
[0170] It is noted that collective perception messages as described in TS 103 324 draft V0.0.22 with the items of information contained in data structure 462 for perceivedObject allows the reporting of a list of independent objects.
[0171] According to particular embodiments of the present disclosure, the situation awareness of all ITS-S receiving CPMs is improved by including information about a situation concerning objects reported in a CPM, making it possible to associate objects reported in a CPM with one or more particular situations. In addition, the safety may be improved by identifying objects that are safety-critical so that a situation analysis module (e.g. situation analysis module 240 in
[0172] To that end, the CPMs that are generated and transmitted according to some embodiments of the present disclosure contain additional information to be included in the perceivedObject structure (e.g. perceivedObject structure 461). Such items of information may comprise: [0173] objectSafetyCriticalLevel (e.g. data element 463): is a safety-critical level value assigned to the perceived object, [0174] situationList (e.g. data frame 464): is a list of situations, containing situation identifiers (e.g. situationID data frame 466) and information related to the object analysis in the context of the listed situations (e.g. objectSituationAnalysis data frame 467), and/or [0175] objectStationID (e.g. data frame 468): is an ITS-ID of the perceived object, with the confidence level in the association between the ITS-ID and the perceived object.
[0176] It is noted here that the situationList given per object, described in a CPM, makes it possible to establish a list of objects associated with the same situation. Alternatively, a list of objects may be provided per situation. For example, a LinkedObjectList may be a list of linked objects referred by their CPM objectID.
[0177] The corresponding ASN.1 representation of the data added in the perceived object container 461 may be expressed as follows:
TABLE-US-00001 PerceivedObject : := SEQUENCE { objectID Identifier, . . /** @details objectSafetyCriticalLevel Safety-critical categorization of the perceived object @see SafetyCriticalLevel */ objectSafetyCriticalLevel SafetyCriticalLevel OPTIONAL, . . /** @details situationList List of situations which provided the situation data. @see SituationList */ situationList SituationList OPTIONAL, /** @details objectStationId ITS ID of the detected object with the association confidence level @see ObjectStationId */ objectStationID OPTIONAL -- ITS ID of the object with its confidence . . }
[0178] According to some embodiments, the safety-critical categorization information of data element 463 added in the perceived object container 461 is computed by a situation analysis module. This information can be in the form of level values (as illustrated in Table 1 in the Appendix, wherein the safety-critical level is a value, where 0-value means that the object is not detected as safety-critical and higher values means that the object is detected as safety-critical) or percentage values (as illustrated in Table 2 in the Appendix).
[0179] It is observed that an object can be associated with multiple situations at the same time. As described above, a list of situation identifiers (situationIDs) may be provided in the data frame situationList 464, a situation being composed, for example, of multiple items of information provided in the situation data frame 465.
[0180] As illustrated, a first part of the situation data frame 465, denoted situationID and referenced 466, contains information representing the situation such as an identifier. A second part of the situation data frame 465, denoted objectSituationAnalysis and reference 467, contains information representing the perceived object with respect to that situation.
[0181] Examples of a format of a list of situations (e.g. situationList data frame 464), of situations (e.g. situation data frame 465), of situation identifiers (e.g. situationID data frame 466), of information representing the object with respect to that situation (e.g. objectSituationAnalysis data frame 467), and of identifiers of stations having detected objects (e.g. objectStationID data frame 468) are provided in Tables 3 to 7, respectively, of the Appendix.
[0182] A situation identifier may be determined by a situation analysis module such as situation analysis module 240 in
[0183] If there is no DENM associated with the new situation, then the situation analysis module may create a new situation identifier composed of the ITS-ID of the originating station and of a sequence number. This sequence number may be set to a next unused value each time a new situation is detected by the originating ITS-S.
[0184] Since an object can be associated with multiple situations, the items of information related to an object/situation association can be provided in a data frame such as data frame 467 (i.e. objectSituationAnalysis). It is possible to set a safety-critical level information that is relevant for a specific situation (e.g. situationSafetyCritical Level). In addition, a time-to-situation (e.g. timeToSituation) and a predicted path (e.g. objectPredictedPath) may be estimated based on the object relative distance and relative speed to the situation. When an object is directly concerned by the considered situation, the time-to-situation may then be set to 0. In a variant, the time-to-situation can be representing a time-to-collision value.
[0185] Thanks to the information about the situations associated with a perceived object, it is possible to link the objects concerned by the same situation together instead of having independent reporting of objects by the collective perception service.
[0186] Thanks to the safety-critical level, it would be possible to prioritize the perceived object candidates to be included at a next CPM generation event. For safety-critical situations, it is possible to have a low latency for the next CPM generation event, as described by reference to
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[0188] Back to
[0189] In order to secure V2X communications within the ITS, a public-key-infrastructure (PKI) as defined in the version 1.1.1 of the ETSI TS 102 731 specification may be used, in particular to control the integrity of a message and to authenticate an originating ITS-S. The PKI-based security may be implemented through the use of certificates delivered by a certification authority to the ITS stations.
[0190] Therefore, each ITS message exchanged is made of a non-encrypted message, CPM parameter 410, accompanied with a digital signature and a pseudonym certificate (also referred to as authorization ticket) that validates the authenticity of the originating ITS-S and the integrity of the message, while keeping anonymity of the originating ITS-S. For communicating within the ITS, an ITS-S may comprise one or more authorization tickets, and may use an authorization ticket for communicating.
[0191] Information about the safety-critical categorization of the different entities present in the monitored area, provided for example in the data element 463 of CPM 400 in
[0192] The authorization ticket may therefore comprise indications related to the privileges and authorizations of an originating ITS-S to transmit specific ITS messages, for example CPM 400 comprising data element 463 or data frame 464.
[0193] To that end, an authorization ticket may contain a field called ITS AID, which includes the list of the services that the station is authorized to access and use, as specified in ETSI TR 102 965. In particular, a specific service is dedicated to collective perception service, to indicate that the sender is entitled to send CPMs. The authorization ticket also contains a field called ITS AID service specific permissions (SSP), which indicates specific sets of permissions within the overall permissions indicated by the ITS-AID. Its format is specified in ETSI TS 103 097.
[0194] According to some embodiments of the present disclosure, a SSP is provided, that may be specified in the certificate of CPMs containing a data element or a data frame like data element 463 or data frame 464, as described hereinbefore. An example of such a SSP is illustrated in
[0195] As illustrated, SSP 500 comprises 3 bytes referenced 510, 520, and 530. According to this example, the first byte (byte 510) identifies an SSP version and the second and third bytes (bytes 520 and 530) specify specific permissions.
[0196] Still according to the illustrated example, specific permissions 540 are introduced using the first, second, and third bit of the second byte (byte 520) as follows: [0197] the first bit is set to 1 for indicating a permission for reporting a safety-critical categorization information item (such as data element 463) in the payload of the CPM, otherwise it is set to 0, [0198] the second bit is set to 1 for indicating a permission for reporting a situation information item (such as data frame 464) in the payload of the CPM, otherwise it is set to 0, and [0199] the third bit is set to 1 for indicating a permission for repeating a safety-categorization information item in the payload of the CPM, otherwise it is set to 0.
[0200] As described by reference to
[0201] Of course, other positions and/or values may be contemplated.
[0202] With this permission, the originating ITS-S is allowed to include in its CPM safety-critical objects perceived by other ITS-S and reported through their respective CPMs when these safety-critical objects are associated with a situation detected by the situation analysis module of the originating ITS-S.
[0203] According to some embodiments of the present disclosure, such an SSP may be provided in authorization tickets dedicated to an RSU, which are less likely to be hacked. Of course, according to some embodiments of the present disclosure, such an SSP may be provided within authorization tickets to any type of ITS-S.
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[0205] Like CPM 400 in
[0206] In addition, according to the illustrated embodiment, the perception data container 640 further comprises a set of situation addendum containers referenced 680.
[0207] The situation addendum containers contain information describing situations such as situation 681 with more details than a CPM having a CPM structure such as the one illustrated in
[0208] This additional container could advantageously be used to include information that could also be contained in a separate DENM. This facilitates the analysis of the content of the CPM at the receiving ITS-S since it has the object list and the situation information at the same time.
[0209] According to the illustrated example, situation addendum container 681 comprises: [0210] cpmSituationID data frame: is an identifier of the situation. The cpmSituationID is preferably set to a next unused value each time a new situation or event is detected by the CPM originating ITS-S, [0211] like the situationID defined in reference to
[0219] According to the embodiment illustrated in
[0220] The inclusion of a situation addendum container follows the steps described in reference to
[0221]
[0222] Like CPM 400 in
[0223] In addition, according to the illustrated embodiment, the perception data container 740 further comprises a set of space addendum containers referenced 790.
[0224] Each space addendum container contained in the set of space addendum containers 790 is optional. It comprises a sequence of optional or mandatory data elements (DEs) which provide information of space performed by one or more sensors. More precisely, each space monitored can be reported using the structure referenced 791 comprising for example: [0225] spaceID data element: represents a space identifier assigned to a monitored space as an object which remains constant as long as the space is perceived by the originating ITS-S. Numbers may be assigned as identifiers in an increasing round-robin fashion. When the last identifier in the allowed range has been used, the first counter for the identifier starts from the beginning of the range again, [0226] spaceState data element: a space state value indicates that the monitored space is either in a default state corresponding to the state empty (e.g. equal to 0) or not empty (e.g. value equal to or greater than 1). The default value may be 0. The possible values can also have some variations according to the SpaceType as derived from road type, and [0227] spaceArea data frame: represents the geometry on the area monitored as a space.
[0228] Optionally, space addendum container may also comprise: [0229] spaceOccupancy data element (optional): may be used to report information about the space occupancy. It may be a value used to report on the occupancy as a global evaluation of the monitored space. For the sake of illustration, this value may be the number of vehicles detected in the space monitored by a situation analysis module such as situation analysis module 111 in
[0233] According to the embodiment illustrated in
[0240] The inclusion rules of the space addendum container may be similar to those described for the situation addendum container.
[0241] According to this embodiment, the perceived object container 793 may optionally contain a new data frame denoted spaceList and referenced 794, that can be used to associate a detected (or perceived) object with a space described in a space addendum container. The spaceList content may be similar to the situationList data frame 465 in
CPM Generation and Transmission
[0242]
[0243] As illustrated, a first step is directed to monitoring an area associated with the considered ITS-S (step 800). Such a step may be based on a situation analysis module such as situation analysis module 240 in
[0244] If the situation corresponds to a situation already reported in a DENM (either triggered by the R-ITS-S, e.g. R-ITS-S 112 in
[0245] On the contrary, if the situation does not correspond to a DENM event, a new situation identifier is set (step 830). According to some embodiments, the situation identifier (situationID) is composed of the originating ITS-ID and of a sequence number value (sequenceNumber) assigned to the next unused value each time a new situation or event is detected by the originating ITS-S.
[0246] Next, the situation analysis module obtains the situation type (it can be obtained from the DENM event type if the situation is associated with a DENM) and sets the severity of the situation based on its type (step 840).
[0247] For the sake of illustration, an example of severities of situations is provided in Table 8 of the Appendix. It is not limitative.
[0248] Next, the situation analysis module gets the list of objects concerned by the new situation from the environment model. The corresponding situationID is then added in the situationList for all objects concerned directly or collaterally by the situation.
[0249] For example, by reference to
[0250] Next, the safety-critical level value of each object concerned is computed (step 860) based on the severity of the situation they belong to (obtained in step 840) and on their relevance regarding this situation. In a variant, the relevance of an object regarding a given situation is expressed as a time-to-situation value. The relevance score or the time-to-situation may be computed based on the relative distance and the relative speed of the object to the situation.
[0251] The situation analysis module continuously updates the information about the detected situations for the objects concerned in the environment model. Each time an object is leaving or joining a situation, the situation analysis module re-assesses the safety-critical level of the objects concerned and update the situationList content for the objects concerned.
[0252] When a situation is terminated (e.g. DENM termination event), then the situation analysis module deletes the situationID from the situationList of the objects concerned and re-assesses the object safety-critical levels.
[0253] Next, steps (referenced 870) are carried out to generate a CPM including updated information from the environment model. Such steps may be carried out in a CPM generation module (for example in CPM generation module 260 in
[0254] According to some embodiments, the latency of a next CPM generation event is reduced when a safety-critical situation occurs and corresponding objects are reported in the next CPM.
[0255] An example of such steps for generating a CPM is provided in
[0256]
[0257] It is recalled that according to standard TS 103 324 V0.0.22, the minimum time elapsing between two consecutive CPM generation events should be equal to or larger than a value denoted T_GenCpm that belongs to a range of values defined by a minimum value denoted T_GenCpmMin and a maximum value denoted T_GenCpmMax (i.e. T_GenCpmMin≤T_GenCpm≤T_GenCpmMax), where T_GenCpmMin=100 ms and T_GenCpmMax=1000 ms.
[0258] When T_GenCpm time is elapsed, i.e. when the difference between the present time (denoted T_Now) and the time at which the last CPM was generated (denoted T_LastCpm) is equal to or larger than T_GenCpm (step 900), the CPM generation event time (denoted T_GenEvent) is to set to current time T_Now (step 910). Next, the CPM generation module selects the perceived object container candidates from the environment model (step 920). The objects associated with the same situationID are candidates for inclusion in the same CPM generation event. When an object associated with a situation is marked for transmission in a next CPM, then other objects associated with this situation are also marked for transmission in the next CPM. The object inclusion rules may be those defined in standard TS 103 324 V0.0.22, based on the object classification and the object kinematics. For example, an object that is static (e.g. a vehicle stopped at a traffic light) might be reported only every 1 second. As another example, an object of class person (pedestrian) should be reported every 500 ms. Therefore, if a situation contains a static vehicle and a pedestrian, both objects might be reported every 500 ms.
[0259] However, for safety-critical situations, the latency to report about safety-critical objects should be as low as possible. Accordingly, for situations that are safety-critical, T_GenCpmMin could be a value set to T_GenCpmMinCritical=0 ms (or another value lower than current T_GenCpmMin) and T_GenCpmMax could be a value set to T_GenCpmMaxCritical=100 ms (or another value lower than current T_GenCpmMax).
[0260] According to some embodiments of the present disclosure, the situation analysis module can request, to the CPM Generation module, to generate a CPM generation event when the situation analysis module detects a new situation with a severity higher than a threshold value (denoted SafetyCriticalLevel_Threshold) or when the severity of an existing situation becomes higher than this threshold (step 930). This request can be immediate or after the minimum time delay T_GenCpmMinCritical (with T_GenCpmMinCritical<T_GenCpmMin) elapsed from the last CPM generation event. In such a case, the CPM generation event time T_GenEvent is to set to current time T_Now (step 940). Next, the CPM generation module selects the perceived object container candidates from the environment model (step 950). The perceived object container candidates are preferably those associated with the safety-critical situation. They are preferably ordered by their safety-critical level.
[0261] For the next inclusion request of objects associated with a safety-critical situation, the minimum time elapsed between consecutive CPMs is preferably equal or larger than T_GenCpm where T_GenCpmMinCritical≤T_GenCpm≤T_GenCpmMaxCritical, where T_GenCpmMaxCritical<T_GenCpmMax. Accordingly, in a case of a safety-critical situation, objects concerned by this situation can be reported more frequently.
[0262] After having selected perceived object container candidates (step 920 or 950), a CPM is generated (step 960) and the T_LastCpm value is set to the T_GenEvent value (step 970).
[0263] In a variant, other perceived object candidates not associated with the safety-critical situation can be included in the same CPM generation event based on the object type and kinematics inclusion rules (step 950), for example using the same rules as those used in step 920.
[0264]
[0265] As illustrated, a first step is directed to obtaining the situation list and the object list from the environment model (step 1000).
[0266] Next, a test is carried out to determine whether the situation list comprises at least one situation identifier (step 1005). If the situation list comprises at least one situation identifier, the CPM generation module obtains the next situation ordered by their severity (step 1010), corresponding to the situation having the higher severity level when the algorithm illustrated in
[0267] If the situation is not considered as safety-critical (step 1015), the objects associated with the obtained situation are obtained successively (step 1030). For each object, the inclusion rules defined in TS 103 324 V0.0.22 (in section 6.1.3.2 Perceived Object Container Inclusion Management), based on the object classification and its kinematics since its last inclusion in CPM, are checked (step 1035).
[0268] According to some embodiments of the present disclosure, if an object is to be included in the current generated CPM, then all objects associated with the same situation are selected to be included in the current generated CPM (step 1025). On the contrary, if no object associated with the situation is to be included in the current generated CPM (step 1040), then the next situation (if any) is obtained (step 1050) and the process loops to step 1010 in order to process the next situation.
[0269] Next, after having processed all the obtained situations (step 1005), the objects that are not associated with any situation are examined. To that end, a test is carried out to determine whether there is at least one object not associated with a situation (step 1055) and, if any, a first or a next object is obtained (step 1060). In a preferred embodiment, the objects obtained from the environment model are ordered by the safety-critical level (objectSafetyCriticalLevel). Next, the obtained object is included in the current generated CPM (step 1065) depending on the inclusion rules defined in TS 103 324 V0.0.22 (in section 6.1.3.2 Perceived Object Container Inclusion Management), based on its classification and its kinematics since its last inclusion in a CPM.
[0270] According to some embodiments of the disclosure (not shown in
[0271] 1) safety-critical situations and their objects,
[0272] 2) safety-critical objects not associated with any situation,
[0273] 3) non safety-critical situations and their objects, and
[0274] 4) non safety-critical objects not associated with any situation.
[0275] As described in TS 103 324, only an object with sufficient confidence level and not subject to redundancy mitigation techniques should be selected from the object list for transmission. According to some embodiments of the present disclosure, an object associated with a safety-critical situation or with a safety-critical level higher than the SafetyCriticalLevel_Threshold value should not be subject to any kind of redundancy mitigation techniques consisting, for example, in not including by an originating ITS-S an object in a CPM if this object is already reported by the same or by other ITS-S in order to limit the congestion on the ITS radio channel.
[0276] According to some embodiments of the present disclosure, a VRU object associated with a safety-critical situation or with a safety-critical level higher than the SafetyCriticalLevel_Threshold value should not be subject to any kind of grouping for reporting in CPM and should be reported individually.
[0277] In case the size of the ASN.1 encoded CPM including all perceived object candidates selected for transmission exceeds the MTU_CPM threshold, message segmentation may occur. The objects associated with the same situation are preferable transmitted in the same message segment.
[0278] According to some embodiments of the present disclosure, CPM are generated more frequently when there exist safety-critical situations. Therefore, it is important that only ITS stations able to categorize the safety-critical objects and situations are authorized to process safety report and situation report in CPM. To that end, a R-ITS-S (e.g. R-ITS-S 112 in
Examples of Use Cases
[0279]
Human Presence on the Road Situation
[0280]
[0281] For the sake of clarity and conciseness, the intelligent transportation systems, referenced 1100, is the same or is similar to the one illustrated in
[0282] Like ITS-S 110 in
[0283] Again, ITS 1100 is implemented at an intersection and comprises a fixed road side unit 110 and several entities that may carry or comprise ITS station (ITS-S) each, for transmitting and or receiving ITS messages within the ITS. The several entities may be for example, the vehicles 1151, 1152, 1153, and 1154 and the pedestrians 1155 and 1156. Likewise, fixed road side unit 1110 includes a set of sensors, such as image sensors, here video cameras 1120, 1121, 1122, and 1123, and situation analysis module 1111 to analyze data provided by the sensors.
[0284] By scanning the monitored area, situation analysis module 1111 may perceive the following objects: [0285] objects 1161, 1162, 1163, and 1164 respectively corresponding to the vehicles 1151, 1152, 1153, and 1154 on the roadway, [0286] object 1165 corresponding to pedestrian 1155 on the sidewalk, and [0287] object 1166 corresponding to pedestrian 1156 on the roadway.
[0288] In the illustrated example, pedestrian 1156 on the roadway has a risk of collision with vehicle 1154 that will turn to its right. To cope with this situation, the situation analysis module is able to detect the presence of the pedestrian on the roadway, to analyze the trajectories of the vehicles at the intersection, and to generate CPMs to draw the attention of pedestrian 1156 and vehicle 1154 to the risk.
[0289] As illustrated, road side unit 1110 further comprises a roadside ITS-S, R-ITS-S, 1112 enabling RSU 1110 to share information relative to the perceived objects. Typically, RSU 1110 can share such information with receiving ITS stations by sending CPM 1131. It can also share information relative to a detected event via DENMs 1130.
[0290] Turning to
[0291] Furthermore, situation analysis module 240 can associate relevant objects contained in its environment model 220 to this situation of “Human Presence on the Road” and compute a safety-critical level for objects directly concerned by the situation or collaterally concerned by the situation.
[0292] In the scenario in the
[0293] Table 9 in the Appendix illustrates an example of a situation analysis of the scenario described by reference to
Collision Risk Situation
[0294]
[0295] In this example the originating ITS station, ITS-S, sending DENM 1230 and CPM 1231, is a road-side unit, RSU, referenced 1210. As set forth above, RSUs have advantageously more powerful resources to analyze a collision risk situation than moving vehicles (e.g. a wider field of view, multiple fields of view, fast access to other information, knowledge of objects that populate the monitored area, etc.).
[0296] In particular, RSU 1210 has a better view than other ITS-S of the monitored area allowing RSU 1210 to detect collisions or risks of collision when mixing ITS connected and not connected colliding vehicles, and/or when colliding vehicles cannot see each other (e.g. due to an occlusion at an intersection).
[0297] Like ITS 100 in
[0298] According to the illustrated example, the situation analysis module 1211 may perceive the following objects when scanning the monitored area: [0299] objects 1261, 1262, and 1263 respectively corresponding to vehicles 1261, 1262, and 1263 on the roadway and [0300] objects 1264 and 1265 respectively corresponding to pedestrians 1254 and 1255 on the sidewalk.
[0301] Still according to the illustrated example, the situation analysis module is provided with situation analysis functions to analyze the trajectories of the perceived objects, to predict their future trajectories, and to identify a possible risk of collision between the perceived objects.
[0302] In this example, perceived objects 1262 and 1263 are detected to have a risk to collide at collision position marked 1270 in the center of the road intersection. The situation analysis module is able to predict the trajectories 1271 and 1272 of the two vehicles and to compute a time-to-collision (TTC) information. The time-to-collision value may be representing a risk: [0303] a TTC value that is lower than a first threshold, e.g. 5 seconds, but greater than a second threshold, e.g. 1.5 seconds, means that a risk of collision is detected between the two or more objects and [0304] a TTC value that is lower than the second threshold means that an imminent collision or “pre-crash” situation is detected.
[0305] In the proposed scenario of
[0306] Turning to
[0307] Any trajectory predicting method can be used, including those that optionally use additional information as inputs, such as traffic conditions (traffic jam, traffic light status, speed limits), weather conditions, etc. A predicted trajectory is a set of predicted positions with associated position times defining when it is expected that the object be at the predicted position. A plurality of trajectories can be predicted for one and the same object, for instance by using various trajectory predicting methods.
[0308] Detection of a collision risk can be based on such predicted trajectories: trajectories that cross each other (given a position margin) at the same time (given a time margin) can raise a risk of collision should said time be no later than a first threshold (e.g. 5 s) and later than a second threshold (e.g. 1.5 s), can raise a pre-crash situation should said time be no later than the second threshold, or even raise an accident situation should said time be 0.
[0309] Situation analysis module 240 may decide to trigger a collision warning or pre-crash situation event to DENM generation module 270.
[0310] A collision can involve two or more objects, including one or multiple vehicles, a VRU, an animal, or an object on the road or near the road (tree, road barrier, traffic light, etc.). Those objects are labelled “critical” or “colliding” objects.
[0311] In the scenario described by reference to
[0312] Table 10 in the Appendix illustrates an example of a situation analysis of the scenario described by reference to
Pre-Crash DENM Situation
[0313]
[0314] In this example the originating ITS station, ITS-S, sending DENM 1330, is a vehicle ITS station, V-ITS-S, comprised in vehicle 1353, that has detected an imminent collision (TTC less than 1.5 s) with vehicle 1352, for example using its front-face sensor. DENM 1330 is a DENM for the event type “Collision Risk” with the subtype “Pre-Crash”. This Pre-Crash DENM is being studied by ETSI in the pre-standardization study report TR 103 832, based on Car2Car Communication Consortium document (Triggering Conditions and Data Quality Pre-Crash Information). The Pre-Crash DENM contains information about the objects concerned by the pre-crash situation (called hereafter critical objects), the two critical objects 1372 and 1373 respectively corresponding to vehicles 1352 and 1353.
[0315] In the example illustrated in
[0318] Turning to
[0319] In the scenario illustrated in
[0323] Optionally, it may also comprise ITS-ID (stationID), for example the ITS-ID of vehicle 1352, and the time-to-collision (timeToCollision), for example to the time-to-collision between vehicles 1352 and 1353.
[0324] R-ITS-S 1312 can receive pre-crash DENM 1330 and thanks to its situation analysis module 1311, the RSU can establish a link between objects of the list of critical objects contained in the pre-crash DENM. Object 1362, corresponding to vehicle 1352, is associated with the pre-crash DENM critical object positioned at 1372. The pre-crash DENM critical object positioned at 1373, corresponding to vehicle 1353 is not perceived by the sensor of the R-ITS-S(it is outside the field of view 1322 of camera 1322), but can be added in the list of objects in environment model 220.
[0325] The situation analysis module can then analyze if the other objects contained in its environment model are concerned by the pre-crash DENM situation. Objects 1361 representing the vehicle just in front of vehicle 1352 and objects 1365 representing a VRU just near the pre-crash area are also concerned collaterally by the pre-crash DENM situation and can then be associated with it.
[0326] Table 11 in the Appendix illustrates an example of a situation analysis of the scenario described by reference to
[0327] In the scenarii illustrated in
[0332] For the sake of illustration, roadside ITS-S 1112 (R-ITS-S) can transmit the DENM warning message triggered by situation analysis module 240 using DENM generation module 270. The DENM generation module can be a state-of-the-art DENM (no modification of the DENM format, no modification of the DENM generation rules).
[0333] Roadside ITS-S 1112 (R-ITS-S) can transmit periodically CPMs using CPM generation module 260. At each CPM generation event, the list of candidate objects to be included in the next CPM are obtained from environment model 240. According to some embodiments of the present disclosure, the situation analysis module can trigger a next CPM generation event depending on the safety-critical level of a situation. This makes it possible to reduce the latency to generate CPM containing safety-critical data and then to increase the safety of the road users by improving their situation awareness with a low latency. Still according to some embodiments of the present disclosure, the CPM contains information enabling to identify rapidly which objects are associated with the situation that can correspond to a DENM. Thanks to this additional information, receiving ITS-S can improve their situation awareness more easily, and re-use the analysis already done by the originating ITS-S. This is particularly advantageous when the originating ITS-S is a R-ITS-S having more powerful analysis resources and wider and multiple fields of view.
Linking Predictions to Situations
[0334]
[0335] The perceived object data structure 1461 is included in a CPM, in a perceived object container, for example in perceived object container 460 in
[0336] As illustrated, perceived object data structure 1461 comprises an additional container, referenced 1469, that may be used to provide information about a future state of an object (i.e. about a predicted behavior of the object), denoted a prediction container.
[0337] The data structure of prediction container 1469, referenced 1471, contains a set of information used to describe predicted information associated with a perceived object. Such predicted information may comprise one or several of the following items of information: [0338] Delta Time, which represents the time difference between each pair of consecutive points in a predicted path (e.g. a predicted path can contain points spaced by 100 ms), [0339] list of PredictedPath, which contains one or several predicted paths. The number of paths can be up to 3 for instance. Each predicted path contains: [0340] PathProbability, which represents the probability that an event leading to the predicted path occurs, [0341] list of PathPoints, which lists the points of a predicted path (e.g., the number of points of a predicted path can be up to 10). Each path point may be defined by: [0342] XDistanceOffset, YDistanceOffset, which represent the distances measured from the CPM reference point in the x- and y-directions of the ITS-S coordinate system, respectively, and [0343] optional covariance information for each point can be included with XConfidence, YConfidence and Correlation. [0344] PathDangerousness, which represents a level of dangerousness of the predicted path, [0345] PathPredictability, which represents a level of difficulty to predict the path without any advanced prediction algorithm or local knowledge, [0346] PredictionSendingReason, which represents an added-value of the prediction, which may comprise a reason of sending the predicted paths in the CPM, [0347] PredictedSituationType, which is a type of the situation associated with the predicted path, [0348] PredictedPathID, which is an identifier of a predicted path, [0349] SituationIDList, that makes it possible to associate a predicted path with a situation or a list of situations by referencing the corresponding identifier of the situation (SituationID), that may be described, for example, in the data structure 1465 in
[0351] According to the illustrated example, the situation data structure 1465 is similar to the situation data structure 465 in
[0352] It is possible to set a safety-critical level information that is relevant to a specific situation (e.g. situationSafetyCriticalLevel). In addition, it is possible to set a time-to-situation (e.g. timeToSituation) and a reference to a predicted path or to a set of predicted paths (objectPredictedPathIDList) for which the considered object may be concerned by the situation (i.e., several paths may be predicted for the same object and the same situation). According to some particular embodiments, the set of predicted paths (objectPredictedPathIDList) defined in the situation structure may comprise predicted paths of other objects (i.e., objects different from the one associated with the considered perceived object container), making it possible to link one or more predicted paths of an object to one or more predicted paths of another object. This may lead to reducing the size of CPMs.
[0353] According to some other embodiments, it is possible to include similar items of information (i.e., objectPredictedPathIDList) in a situation data structure similar to situation data structure 666 of
[0354] Still according to particular embodiments, it is possible to omit the situation data structure to reduce the CPM size. Accordingly, linking predicted paths of two objects (through the data element LinkedPredictedPathIDList) creates an implicit situation (the situation being a group of object predicted paths). Tables 13 and 14 in the Appendix illustrate examples of some portions of CPMs transmitting predicted paths of perceived objects and links between some of these predicted paths, without situation data per se.
[0355] Still according to some embodiments, one or more predicted paths associated with one or more objects may be associated with a situation associated with one or more predicted paths of the considered object in the data element situationIDlist (within the predictions data structure, for example within predictions data structure 1471). Accordingly, a link between different predicted paths associated with different perceived objects and with the same situation may be established by analyzing the corresponding perceived object containers. Examples of links between predicted paths associated with perceived objects and situations and between predicted paths associated with perceived objects with each other is illustrated in Table 12.
[0356] According to some particular embodiments, linking one or several predicted paths associated with an object different from the object associated with the considered structure, denoted the first predicted paths, to one or several predicted paths associated with the object associated with the considered structure, denoted the second predicted paths, without any further indication, means that the first predicted paths may result from the second predicted path (i.e., the first predicted paths may become true if the second predicted path becomes true). A particular indication, for example ‘parent’, may be used to indicate that the first predicted paths may lead to the second predicted paths, as apparent from the examples provided in Table 12. According to this example, predicted path 1581 is indicated to be the parent for predicted path 1591 and predicted path 1582 is indicated to be the parent for predicted paths 1592 and 1593.
[0357] Establishing a link between several object predicted paths, for example by establishing a link between these object predicted paths and a situation, makes it possible to reflect the interactions between the objects concerned by the same situation, thus avoiding reporting independently these objects and their predicted paths by the collective perception service. Although a link between different predicted paths of different objects may be established by analyzing several CPMs, two predicted paths should be preferably included in the same CPM if one of these predicted paths refers to the other.
[0358] In order to secure V2X communications within the ITS, a public-key-infrastructure (PKI) as defined in the version 1.1.1 of the ETSI TS 102 731 specification may be used, in particular to control the integrity of a message and to authenticate an originating ITS-S, as described by reference to
[0359] Accordingly, an additional specific permission may be defined within the certificate to be used so as to authorize reporting of predicted information such as data element 1471 in
[0360]
[0361] For the sake of clarity and conciseness, the intelligent transportation systems, referenced 1500, is similar to the one illustrated in
[0362] Like RSU 110 in
[0363] As illustrated, ITS 1500 is implemented on the side of a roadway and comprises stationary road side unit 1510 and several entities that may carry or comprise an ITS station (ITS-S) each, for transmitting and/or receiving ITS messages within the ITS. The several entities may be, for example, the vehicles referenced 1551 and 1552. Like stationary road side unit 110, stationary road side unit 1510 comprises a set of sensors, such as image sensors, here video camera 1520, and situation analysis modules, here situation analysis module 1511, to analyze data provided by the sensors.
[0364] By scanning the monitored area, situation analysis module 1511 may perceive objects 1561 and 1562, respectively corresponding to vehicles 1551 and 1552 on the roadway.
[0365] As illustrated in
[0366] Based on previous vehicle behaviors in the monitored area, the situation analysis module can predict with a certain probability the different possible trajectories of vehicles facing obstacle 1570 on the roadway. For instance, vehicle 1552 may have two possible behaviors: [0367] stopping in front of obstacle 1570 and waiting until vehicle 1551 has passed the area where the obstacle is located, which corresponds to predicted path 1581 (represented with an arrow in a regular dashed line) and [0368] forcing the way and bypassing the obstacle immediately, which corresponds to predicted path 1582 (represented with an arrow in a chain line).
[0369] As apparent from
[0372] According to this scenario, situation analysis module 1511 may create two different situations: [0373] situation 1 (illustrated with regular dashed arrows in
[0375] According to some embodiments, RSU 1510 generates CPM 1531 containing the predicted paths and their relationships by associating the predicted paths with the situations. The content of some portions of this CPM according to a particular embodiment of the disclosure is shown in Table 12.
[0376] According to other embodiments of this disclosure, RSU 1510 generates CPM 1531 containing the predicted paths and their relationships by associating the predicted paths with each other, without referring explicitly to the identified situations 1 and 2 by the situation analysis module, in order to reduce the size of the CPM. The content of some portions of this CPM according to this particular embodiment of the disclosure is shown in Table 13 and in Table 14. It is noted that a main difference between Table 13 and Table 14 is that Table 14 does not comprise any parent indication. Accordingly, as apparent from Table 14, the LinkedPredictedPathIDList associated with a predicted path is empty if this predicted path has no parent.
[0377] According to this invention, DENM 1530 of the Collision Risk type can be triggered for situation 2, in parallel to generating CPM 1531.
[0378] It is observed that using prior art methods leads to transmitting independently all the predicted paths, for each object. Therefore, the receivers have to perform additional analysis to understand what are the global possible predicted scenarios. According to the embodiments described by reference to
Example of a Hardware to Carry Out Steps of the Method of Embodiments of the Present Disclosure
[0379]
[0380] The communication device 1600 may preferably be a device such as a microcomputer, a workstation, or a light portable device embedded in a vehicle or a RSU. The communication device 1600 comprises a communication bus 1613 to which there are preferably connected: [0381] a central processing unit 1611, such as a microprocessor, denoted CPU or a GPU (for graphical processing unit), [0382] a read only memory 1607, denoted ROM, for storing computer programs for implementing some embodiments of the disclosure, [0383] a random access memory 1612, denoted RAM, for storing the executable code of methods according to some embodiments of the disclosure as well as the registers adapted to record variables and parameters necessary for implementing methods according to some embodiments of the disclosure, and [0384] at least one communication interface 1602 connected to the radio communication network over which ITS messages are transmitted. The ITS messages are written from a FIFO sending memory in RAM 1612 to the network interface for transmission or are read from the network interface for reception and writing into a FIFO receiving memory in RAM 1612 under the control of a software application running in the CPU 1611.
[0385] Optionally, the communication device 1600 may also include one or several of the following components: [0386] a data storage means 1604 such as a hard disk, for storing computer programs for implementing methods according to one or more embodiments of the disclosure; [0387] a disk drive 1605 for a disk 1606, the disk drive being adapted to read data from the disk 1606 or to write data onto said disk; [0388] a screen 1609 for serving as a graphical interface with the user, by means of a keyboard 1610 or any other pointing means.
[0389] The communication device 1600 may be optionally connected to various peripherals including perception sensors 1608, 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 1600.
[0390] Preferably the communication bus provides communication and interoperability between the various elements included in the communication device 1600 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 1600 directly or by means of another element of the communication device 1600.
[0391] The disk 1606 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 invention to be implemented.
[0392] The executable code may optionally be stored either in read-only memory 1607, on the hard disk 1604 or on a removable digital medium such as for example a disk 1606 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 1602, in order to be stored in one of the storage means of the communication device 1600, such as the hard disk 1604, before being executed.
[0393] The central processing unit 1611 is preferably adapted to control and direct the execution of the instructions or portions of software code of the program or programs according to some embodiments of 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 non-volatile memory, for example on the hard disk 1604 or in the read only memory 1607, are transferred into the random access memory 1612, 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 invention.
[0394] In a preferred embodiment, the apparatus is a programmable apparatus which uses software to implement the invention. However, alternatively, the present invention may be implemented in hardware (for example, in the form of an Application Specific Integrated Circuit or ASIC).
[0395] Although the present invention has been described hereinabove with reference to specific embodiments, the present invention 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 invention.
[0396] Many further modifications and variations will suggest themselves to those versed in the art upon making reference to the foregoing illustrative embodiments, which are given by way of example only and which are not intended to limit the scope of the invention, that being determined solely by the appended claims. In particular, the different features from different embodiments may be interchanged, where appropriate.
[0397] Certain of the embodiments of the invention described above may 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.
[0398] 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.
APPENDIX
[0399]
TABLE-US-00002 TABLE 1 (example of a format of a safety-critical level expressed as a value, using ASN.1) Descriptive Name objectSafetyCriticalLevel ASN.1 SafetyCriticalLevel ::= INTEGER { representation notSafetyCritical (0), -- the safety critical level is set to 0, no safety-risk detected SafetyCritical (1), -- the detected object has a safety risk } (0..1) or SafetyCriticalLevel ::= INTEGER { notSafetyCritical (0), -- the safety critical level is set to 0, no safety-risk detected lowSafetyCritical (1), -- the detected object has a low level of safety risk mediumSafetyCritical (2), -- the detected object has a medium level of safety risk highSafetyCritical (3), -- the detected object has a high level of safety risk } (0..15) Definition Describes the safety-critical level value of a detected object. This DE is optional. Unit Not applicable
TABLE-US-00003 TABLE 2 (example of a format of a safety-critical level expressed as a percentage value, using ASN.1) Descriptive Name objectSafetyCriticalLevel ASN.1 SafetyCriticalLevel ::= INTEGER { representation notSafetyCritical (0), -- the safety critical level is set to 0, no safety-risk detected onePercent (1), oneHundredPercent (100), unavailable (101) -- In case the level value computation is not available } (0..101) Definition Describes the safety-critical level value of a detected object. This DE is optional. Unit percent
TABLE-US-00004 TABLE 3 (example of a format of a situation list data frame, using ASN.1) Descriptive Name situationList ASN.1 SituationList ::= SEQUENCE SIZE(1..128, ...) OF representation Situation Definition Contains a list of situations associated with the detected object. A detected object can be associated with more than one situation at the same time. This DF is optional. Unit Not applicable
TABLE-US-00005 TABLE 4 (example of a format of a situation data frame, using ASN.1) Descriptive Name situation ASN.1 Situation ::= SEQUENCE { representation situationID SituationID, -- identifier of a situation, can refer to a DENM actionID objectSituationAnalysis ObjectSituationAnalysis OPTIONAL -- object information related to the situation } Definition Represents a situation and the analysis of the object regarding this situation. This DF should include the information: situationID DF objectSituationAnalysis DF Unit Not applicable
TABLE-US-00006 TABLE 5 (example of a format of a situation identifier data frame, using ASN.1) Descriptive Name situationID ASN.1 SituationID ::= SEQUENCE { representation sequenceNumber SequenceNumber, -- Identifier of the situation INTEGER (0..65535) originatingStationID StationID OPTIONAL, -- If the situation is detected from -- another ITS station message (e.g. a DENM), ID of this ITS station originatingMessageType MessageID OPTIONAL -- Type of message used to detect the -- situation (e.g. DENM) } MessageID ::= INTEGER{ denm(1), cpm(15)} (0..255) -- Same messageID codes as in ITS PDU Header Definition Identifier of a situation. The situationID contains at least a sequence number used as an identifier of the situation. It can refer to a DENM ID (actionID: composed of an ITS ID and a sequence number). It is composed of: sequenceNumber DE: The sequenceNumber in the situationID shall be set to a next unused value each time a new situation or event is detected by the originating ITS-S. originatingStationID optional DE: The identifier of an ITS station that has triggered an ITS message used to detect the situation (e.g. ITS ID in a received DENM) originatingMessageType optional DE: Type of the message if the situation is detected from another ITS message (e.g. DENM, CPM) Unit Not applicable
TABLE-US-00007 TABLE 6 (example of a format of an objectSituationAnalysis data frame, using ASN.1) Descriptive Name objectSituationAnalysis ASN.1 ObjectSituationAnalysis ::= SEQUENCE { representation situationSafetyCriticalLevel SafetyCriticalLevel OPTIONAL, timeToSituation TimeToSituation OPTIONAL, objectPredictedPath ObjectPredictedPath OPTIONAL } TimeToSituation ::= INTEGER { directlyConcernedByTheSituation (0), oneSecBeforeSituation (1) } (0..100) Definition Contains information specific to the object for the situation. As an object can belong to multiple situations at the same time, some information concerning the analysis of the object for this situation are then linked to the situation in the DF composed of: situationSafetyCriticalLevel optional DE: safety-critical level of the detected object for the situation. If an object belongs to multiple situations at the same time, this field can be used to specify a different value than the one set at the object level by objectSafetyCriticalLevel. An object could then be set as “not safety- critical” for a first situation and set as “safety-critical” for a second situation. timeToSituation optional DE: representing the relevance of the object to the situation. Expressed as a time to situation in seconds. objectPredictedPath optional DF: set of PathPoints corresponding to the predicted path of the object related to this situation; for a same object multiple paths can be predicted, then the relevant one for the situation can be reported here. Unit Not applicable
TABLE-US-00008 TABLE 7 (example of a format of an obiectStationID data frame, using ASN.1) Descriptive Name objectstationID ASN.1 ObjectStationId ::= SEQUENCE { representation stationID StationID, confidence StationIDConfidence } StationIDConfidence ::= INTEGER { zeroPercent (0), onePercent (1), hundredpercent (100), unknown (101) } (0..101) Definition ITS-ID of the detected objects if available with the confidence level of the association between the detected object and the ITS-ID. This DF is optional. It is authorised to be included in the Perceived Object Container, when the object is a safety-critical object. Unit Not applicable
TABLE-US-00009 TABLE 8 (example of severities of situations) Situation severity Example of situation types 0 Not safety critical situation (e.g. tolling lane) 1 Traffic jam, slow vehicle 2 Stationary vehicle, roadworks, extreme weather conditions 3 Wrong way driving, pre-crash, collision risk, accident, human presence on the road
TABLE-US-00010 TABLE 9 (example of a situation analysis for the scenario described by reference to FIG. 11 Concerned by the pre-crash objectID situation? Safety-critical level Time-to-situation 161 No Not-safety critical for this Not applicable situation 162 No Not-safety critical for this Not applicable situation 163 No Not-safety critical for this Not applicable situation 164 Collaterally Medium 5 s 165 No Not-safety critical for this Not applicable situation 166 Directly High 0 s
TABLE-US-00011 TABLE 10 (example of a situation analysis for the scenario described by reference to FIG. 12) Concerned by the collision risk objectID situation? Safety-critical level Time-to-situation 261 No Not-safety critical Not applicable for this situation 262 Directly High 3 s (time-to-collision) 263 Directly High 3 s (time-to-collision) 264 No Not-safety critical Not applicable for this situation 265 Collaterally Medium 5 s
TABLE-US-00012 TABLE 11 (example of a situation analysis for the scenario described by reference to FIG. 13) Concerned by the pre-crash objectID situation? Safety-critical level Time-to-situation 361 Collaterally Medium 3 s 362 Directly High 0 s 363 Directly High 0 s 364 No Not-safety critical for this — situation 365 Collaterally Medium 2 s
TABLE-US-00013 TABLE 12 (example of content of some portions of a CPM transmitting predicted paths of perceived objects and links between some of these predicted paths and with associated situations) Perceived Object ID 1562 SituationList for 1562 SituationID 1 objectSituationAnalysis 1581 objectPredictedPathIDList SituationID 2 objectSituationAnalysis 1582 objectPredictedPathIDList Prediction container PredictedPathID 1581 with a List of Predicted SituationIDList 1 Paths for 1562 LinkedPredictedPathIDList 1591 Predicted PathID 1582 SituationIDList 2 LinkedPredictedPathIDList 1592, 1593 Perceived object ID 1561 SituationList for 1561 SituationID 1 objectSituationAnalysis 1591 objectPredictedPathIDList SituationID 2 objectSituationAnalysis 1592, 1593 objectPredictedPathIDList Prediction container with PredictedPathID 1591 a List of Predicted Paths SituationIDList 1 for 1561 LinkedPredictedPathIDList 1581-Parent PredictedPathID 1592 SituationIDList 2 LinkedPredictedPathIDList 1582-Parent PredictedPathID 1593 SituationIDList 2 LinkedPredictedPathIDList 1582-Parent
TABLE-US-00014 TABLE 13 (example of content of some portions of a CPM transmitting predicted paths of perceived objects and links between some of these predicted paths) Perceived Object ID 1562 Prediction container PredictedPathID 1581 with a List of Predicted LinkedPredictedPathIDList 1591 Paths for 1562 PredictedPathID 1582 LinkedPredictedPathIDList 1592, 1593 Perceived object ID 1561 Prediction container PredictedPathID 1591 with a List of Predicted LinkedPredictedPathIDList 1581-Parent Paths for 1561 PredictedPathID 1592 LinkedPredictedPathIDList 1582-Parent PredictedPathID 1593 LinkedPredictedPathIDList 1582-Parent
TABLE-US-00015 TABLE 14 (example of content of some portions of a CPM transmitting predicted paths of perceived objects and links between some of these predicted paths) Perceived Object ID 1562 Prediction container PredictedPathID 1581 with aList of Predicted PredictedPathID 1582 Paths for 1562 Perceived object ID 1561 Prediction container PredictedPathID 1591 with a List of Predicted LinkedPredictedPathIDList 1581 Paths for 1561 PredictedPathID 1592 LinkedPredictedPathIDList 1582 PredictedPathID 1593 LinkedPredictedPathIDList 1582