A method for semantic information interaction of traffic entities

Abstract

The present invention discloses a method for semantic information interaction among traffic entities. The method collects status information of traffic scenes and traffic entities, identifies category labels of each traffic entity in the traffic scene, and identifies relationship labels between entities. The method establishes a traffic entity interaction language framework based on logical language and ontology theory, semantically expresses traffic entities and the relationships between entities, and forms logical statements for semantic information interaction. The method also constructs a traffic entity semantic information interaction architecture, forms an information interaction process and an information hierarchy, and realizes semantic information interaction among traffic entities. The method of the present invention, when applied to the transportation field, helps improve the current traffic situation, ensure traffic safety, and improve travel efficiency. It has significant practical significance for breaking down information barriers among traffic entities, promoting the formation of collaborative and comprehensive cognition among multiple traffic entities, and promoting effective collaboration among traffic entities.

Description

Technical Field

[0001] The present invention belongs to the technical field of transportation, and in particular relates to a method for interacting with semantic information of traffic entities. Background Art

[0002] Information exchange between traffic entities is an important way to achieve information sharing and coordination among them. With the continuous advancement of transportation system autonomy, the problem of information blockage between traffic entities has become increasingly apparent. To break down information barriers and effectively increase the speed and efficiency of information exchange, it is urgent to explore an effective method for semantic information exchange among traffic entities.

[0003] In existing technologies, information exchange between traffic entities is mostly implemented through protocols, with data structures defining information content for specific traffic scenarios. However, communication protocols only serve to transmit information and cannot meet the requirements for collaborative cognitive semantic interoperability among traffic entities. Moreover, they are designed for limited and relatively independent traffic scenarios, making it difficult to support interaction in multiple scenarios and expand to new scenarios. The information they can convey is relatively fixed, and their semantic expression capabilities are limited, making it impossible to fully express the semantic elements within the traffic scene and the intentions of traffic actions. Furthermore, different manufacturers and systems often design different data standards for the same traffic scenario, resulting in diverse interaction methods and the formation of interaction barriers.

[0004] Semantic information exchange among multiple agents is a key area of ​​research in the field of multi-agent systems and has widespread applications across various intelligent fields. Its primary goal is to enable information sharing among multiple agents, and it involves two key components: semantic information expression and information interaction. Semantic information expression refers to the structured or formalized representation of the state information of agents and their environment. Information interaction involves exchanging information among agents according to a pre-defined interaction process, achieving semantic understanding of the information, and performing related operations. Currently, the concept and method of semantic information exchange among multiple agents have not been applied in the transportation field.

[0005] As transportation systems become increasingly autonomous, the demand for sufficient and accurate traffic status information is growing. Equipping a single transportation entity with excessive sensor equipment is costly, while overcoming the communication barriers between multiple entities presents a significant challenge. Therefore, a rational method for semantic information exchange among transportation entities is urgently needed to provide complete and accurate traffic information to support the development of autonomous transportation systems.

[0006] Existing technologies have limitations when it comes to semantic information exchange among traffic agents. This is primarily due to their inability to fully express the semantic elements within diverse traffic scenarios, nor to convey the agents' intentions. Therefore, a method for semantic information exchange among traffic agents is urgently needed to improve the efficiency and security of information exchange among traffic agents, break down interaction barriers, reduce resource waste, promote collaborative and comprehensive cognition among multiple traffic agents, and facilitate effective collaboration among traffic agents. Summary of the Invention

[0007] The present invention aims to provide a method for interacting with semantic information of traffic entities, which is characterized by comprising the following steps:

[0008] Step S1: Collecting traffic scene and traffic subject status information, identifying the categories, attributes, and relationship labels of each traffic subject in the traffic scene;

[0009] Step S2: Establish a traffic subject interaction language framework based on logical language and ontology theory, express the state information of traffic subjects and the relationship between subjects in a semantic way, and construct content sentences for semantic information interaction;

[0010] Step S3: Construct the semantic information interaction process of traffic subjects, establish the semantic interaction information hierarchy, form semantic interaction messages, and realize the semantic information interaction of traffic subjects.

[0011] Preferably, in step S1, the status information from different sources is integrated into the same form through a fusion algorithm model, the key elements of the traffic subject are identified and marked through an identification algorithm model, and corresponding category labels are assigned to them; based on the category labels of the traffic subjects and the real physical relationship, the relationship labels between the objects in the traffic scene are constructed.

[0012] Preferably, step S2 includes:

[0013] Step S21: establishing interactive sentence grammar rules;

[0014] Step S22: creating a traffic subject interaction six-tuple;

[0015] Step S23: Constructing semantic information interaction sentences.

[0016] Preferably, the step S21 includes:

[0017] Step S211: defining symbols, where the symbols include variables, constants, attributes, relations, mapping functions, quantifiers, logical connectives, and axioms;

[0018] Step S212: Establish interactive language syntax rules.

[0019] Preferably, in step S22, the traffic subject interaction six-tuple includes: a class set, a class attribute set, an instance object set, a relationship set, a mapping function set, and an axiom set.

[0020] Preferably, the class set includes: measurement class, carrier class, equipment class, location class, time class, status class, and event class;

[0021] The in-class attribute set includes: inherent attributes and dynamic attributes;

[0022] The instance object set includes: each specific traffic subject, state and event involved in the interaction domain;

[0023] The relationship set includes: relationships between classes, between classes and instances, and between instances and instances;

[0024] The mapping function set includes: functions that map from multiple classes to a certain other class, and functions that map from multiple instances to a certain other instance;

[0025] The axiom set should include: traffic rules stipulated by law and driving behavior experience.

[0026] Preferably, step S3 includes:

[0027] Step S31: Constructing a traffic subject semantic information interaction process;

[0028] Step S32: establishing a traffic subject semantic information hierarchy;

[0029] Step S33: Fusing the interactive sentences to form a semantic interactive message.

[0030] Preferably, the step S31 includes: the traffic subject A expects to achieve the goal G1, forms a specific intention I1, decides to take a semantic interaction behavior Act1, forms the behavior into a message M, and encodes the message M so that it meets the requirements of the underlying communication protocol and service, and transmits it to the traffic subject B; after receiving the message, the traffic subject B decodes the message M, and according to the pre-defined specifications and its own goals G2 and intentions I2, takes a semantic interaction behavior Act2 and chooses whether to reply to the message N, and can also perform certain operations O, and then repeats this process.

[0031] Preferably, the step S32 includes creating a traffic subject semantic information hierarchy structure with "message frame - message body - message parameters" as the hierarchy:

[0032] Step S321: define a message frame, which contains one or more message bodies;

[0033] Step S322: Define the message body and its parameters. The message body includes a set of one or more message parameters and contains semantic information interaction statements. Traffic entities achieve information sharing and collaborative cognition by transmitting message frames, parsing the message body, and obtaining interaction statements.

[0034] Step S323: Constructing a semantic information interaction behavior representation method.

[0035] Preferably, in step S323, the semantic information interaction behaviors of the traffic subject are divided into three categories: information interaction, collaborative control, and error handling, and the semantic information interaction behaviors correspond to corresponding parameters in the message parameters.

[0036] The beneficial effects of the present invention are:

[0037] The present invention provides a method for semantic information interaction among traffic subjects. By designing a traffic subject interaction language framework based on logical language and ontology theory, a traffic subject interaction sextuple is created, traffic scenes and status information of traffic subjects are semantically expressed, and semantic information interaction statements are formed. The method also creates a traffic subject semantic information interaction architecture, constructs an information interaction process, establishes an interactive information hierarchy, and fuses interactive statements to form interactive semantic messages, thereby realizing semantic information interaction among traffic subjects. The method of the present invention can provide accurate interactive information for traffic subjects, and realize information sharing and collaborative cognition among traffic subjects. Applying the concept and method of multi-agent information interaction to the field of transportation will help improve the current traffic situation and increase transportation travel efficiency. It has important practical significance for breaking down information barriers among traffic subjects, promoting the formation of collaborative and comprehensive cognition among multiple traffic subjects, and promoting effective collaboration among traffic subjects. BRIEF DESCRIPTION OF THE DRAWINGS

[0038] Figure 1 A flow chart of a method for interacting with semantic information of traffic entities according to the present invention;

[0039] Figure 2 A schematic diagram of the structure of a method for interacting with semantic information of traffic entities according to an embodiment of the present invention;

[0040] Figure 3 This is a schematic diagram of a reverse overtaking scenario according to an embodiment of the present invention;

[0041] Figure 4 A schematic diagram of the traffic subject semantic information interaction process according to an embodiment of the present invention;

[0042] Figure 5 A schematic diagram of the hierarchical structure of semantic information of traffic entities according to an embodiment of the present invention;

[0043] Figure 6 Schematic diagram of vehicle-to-vehicle semantic information interaction in a reverse overtaking scenario according to an embodiment of the present invention. DETAILED DESCRIPTION

[0044] The present invention will be further described in detail below with reference to the accompanying drawings and taking a reverse overtaking scenario as an example.

[0045] like Figure 1 The method for interacting with semantic information of traffic entities disclosed in the present invention includes the following steps:

[0046] Step S1: Collecting traffic scene and traffic subject status information, identifying the categories, attributes, and relationship labels of each traffic subject in the traffic scene;

[0047] Step S2: Establish a traffic subject interaction language framework based on logical language and ontology theory, express traffic subjects and the relationships between subjects in a semantic way, and form the content of semantic information interaction;

[0048] Step S3: Construct the semantic information interaction process of traffic subjects, establish the semantic interaction information hierarchy, form semantic interaction messages, and realize the semantic information interaction of traffic subjects.

[0049] In this embodiment, the state information of traffic scenes and traffic subjects is collected, and the categories, attributes and relationship labels of each traffic subject in the traffic scene are identified; based on the traffic scenes and traffic subjects, by establishing interactive language grammar rules, a traffic subject interactive language framework based on logical language and ontology theory is constructed to express the state information semantically as logical statements; a traffic subject semantic information interaction architecture is created, an information interaction process is constructed, an interactive information hierarchy is established, and interactive logical statements are integrated to form semantic interactive messages, ultimately realizing traffic subject semantic information interaction. The overall structure of the method is as follows: Figure 2 shown.

[0050] Taking the reverse overtaking scenario as an example, an interactive experiment was conducted, in which vehicles exchanged information with each other and verified the status information obtained by interacting with the roadside unit, and finally completed the reverse overtaking scenario. Figure 3 The following is a reverse overtaking scenario, as detailed below:

[0051] The host vehicle HV is in Lane 1 and attempts to overtake the vehicle in front of it, FV1, in the opposite direction. The host vehicle HV first changes lanes to the left into Lane 2, then accelerates until the rear of the host vehicle HV completely clears the front of FV1, maintaining a safe distance from it. At the same time, the host vehicle HV avoids colliding with the distant vehicle RV in the opposite lane. The host vehicle HV then merges into Lane 1 without colliding with the vehicles in front of it, FV1, FV2, or the distant vehicle RV. Throughout this process, the host vehicle HV continuously exchanges information with other vehicles and the roadside infrastructure (RSUs).

[0052] By applying the traffic subject semantic information interaction method disclosed in the present invention, accurate interaction information can be provided to traffic subjects through the semantic expression and interaction of traffic scenes and traffic subject status information, thereby realizing information sharing and collaborative cognition among traffic subjects.

[0053] In step S1, the traffic scene and traffic subject status information are collected to identify the category, status, and relationship labels of each traffic subject in the reverse overtaking scene:

[0054] In this embodiment, sensors capture real-time status information about traffic scenes and traffic entities. A fusion algorithm model integrates this information from different sources into a unified format. Simultaneously, a recognition algorithm model identifies and labels key elements such as vehicles, lanes, and roadside facilities, assigning them corresponding category labels. Subsequently, based on the category labels of traffic entities and their real-world physical relationships, relationship labels are constructed between objects in the traffic scene, for example, associating a vehicle with its lane. Professionals should understand that the selection of sensors, fusion algorithms, and recognition algorithms must be flexibly adjusted to the specific traffic scenario and are not specifically limited by this embodiment.

[0055] The steps of establishing a traffic subject interaction language framework based on logical language and ontology theory in step S2 include:

[0056] Step S21: establishing interactive sentence grammar rules;

[0057] Step S22: creating a traffic subject interaction six-tuple;

[0058] Step S23: Constructing semantic information interaction logic statements.

[0059] In this embodiment, based on the acquired traffic subject categories, states, and relationship labels between subjects, they are extracted and expressed by means of defined symbols and syntactic rules, and by constructing traffic subject interaction sextuples, so that the interaction relationships between traffic subjects in different traffic scenarios are clearly expressed in the form of logical statements, which are used for semantic information understanding and interaction between different traffic subjects, and to realize information sharing.

[0060] The step S21 of establishing the interactive statement grammar rules includes:

[0061] Step S211: defining symbols, including variables, constants, attributes, relations, mapping functions, quantifiers, logical connectives, and axioms;

[0062] Step S212: Establish interactive language syntax rules.

[0063] In the reverse overtaking scenario of this embodiment, traffic entities such as vehicles, lanes, roadside devices, etc., as well as their attributes, relationships, traffic rules, and other information need to be represented using appropriate symbols and statements.

[0064] 1) Define symbols

[0065] First, define the symbols required for logical representation. These symbols generally include variables, constants, attributes, relationships, mapping functions, quantifiers, logical connectives, and axioms, as shown in Table 1:

[0066] Table 1 Symbol Definition Table

[0067]

[0068]

[0069] 2) Establish the syntactic rules of the interaction language

[0070] Establish the syntactic representation rules of the interaction language as follows:

[0071] For attributes: <attribute name>(<item>)

[0072] For relationships: <relationship name>(<item list>)

[0073] For mapping functions: <f_function name>(<item list>)

[0074] For axioms:

[0075] Where the item represents a variable or a constant. In the axiom, y,... can be omitted. The interaction statements are separated by a half - angle comma ",", and the line breaks between statements are not distinguished.

[0076] According to the syntactic representation rules of the interaction language, use the defined variables, constants, attributes, relationships, quantifiers, logical connectives, and axioms to create a traffic entity interaction sextuple.

[0077] The steps of creating a traffic entity interaction sextuple in step S22 include:

[0078] The traffic entity interaction sextuple includes: a class set, an intra - class attribute set, an instance object set, a relationship set, a mapping function set, and an axiom set;

[0079] In this embodiment, that is, in the reverse overtaking scenario, the class set specifically includes the Measurement class, Vehicle class, Lane class, Equipment class, Time class, State class, and Event class. They and their sub - classes are shown in Table 2:

[0080] Table 2: Categories of reverse overtaking scenarios

[0081]

[0082]

[0083] In this embodiment, the intra-class attribute set specifically includes attributes of the vehicle class, lane class, and equipment class. Some of the attributes are shown in Table 3:

[0084] Table 3. Some of the intra-class attributes included in the reverse overtaking scenario

[0085]

[0086] The object set instantiated in this embodiment is specifically shown in Table 4:

[0087] Table 4: Table of instantiated objects included in the reverse overtaking scenario

[0088]

[0089]

[0090] The relationship set included in this embodiment is specifically shown in Table 5:

[0091] Table 5 Relationship table included in the reverse overtaking scenario

[0092]

[0093] In this embodiment, the mapping function set includes a function called f_ros_fv_distance(HV, FV1, FV2) to calculate the minimum inter-vehicle distance between FV1 and FV2 required for the host vehicle HV to overtake in the opposite direction; a function called f_ros_rv_distance(HV, RV) to calculate the minimum inter-vehicle distance between HV and the oncoming vehicle RV in the opposite lane when HV is overtaking in the opposite direction; and a function called f_vehicle_distance(Vehicle, Vehicle) to calculate the actual inter-vehicle distance between the two vehicles. In actual use, the mapping function can be viewed as a relationship that returns a numerical value.

[0094] In this embodiment, the axioms include the following three:

[0095] 1)

[0096] Explanation: The speed of vehicle x should be less than the maximum speed of lane y.

[0097] 2)

[0098] Explanation: Vehicles x, y, and z are in the same lane a. Z is in front of Y, and Y is in front of X. The distance between y and Z is less than the minimum distance required for X to overtake in the opposite direction. In this case, X issues a warning for overtaking in the opposite direction.

[0099] 3)

[0100] Explanation: Vehicles x and y are in the same lane a, z is in the opposite lane b, y is in front of x, and the distance between x and z is less than the minimum distance required for x to overtake in the opposite direction, then x issues a wrong overtaking warning.

[0101] The step of creating a traffic subject interaction statement in step S23 includes:

[0102] In this embodiment, based on the interactive language syntax representation rules, defined variables, constants, attributes, relations, mapping functions, quantifiers, logical connectives, axioms, and interactive six-tuples are used to create traffic subject interaction statements for describing traffic status information in a reverse overtaking scenario. The format is shown in Table 6:

[0103] Table 6 Some interactive statements in the reverse overtaking scenario

[0104]

[0105]

[0106]

[0107] The steps of constructing the traffic subject semantic information interaction architecture in step S3 specifically include:

[0108] Step S31: Constructing a traffic subject semantic information interaction process;

[0109] Step S32: establishing a traffic subject semantic information hierarchy;

[0110] Step S33: Fusing the interactive sentences to form a semantic interactive message.

[0111] Construct the basic process that traffic entities should meet for semantic information interaction, create an interactive information hierarchy with "message frame-message body-message parameters" as the level, build a semantic information interaction behavior representation method, integrate the interactive logic statements created in the previous steps to form interactive messages, and finally realize semantic information interaction between traffic entities.

[0112] The traffic subject semantic information interaction process constructed in step S31 is as follows: Figure 4 The specific instructions are as follows:

[0113] Figure 4 There are two traffic subjects: traffic subject A and traffic subject B, where A is the initiator of the conversation and sends the message first. The overall process of semantic interaction between traffic subjects is as follows: traffic subject A hopes to achieve goal G1, so it forms a specific intention I1 and decides to take semantic interaction behavior Act1, forms this behavior into message M, and encodes message M so that it meets the requirements of the underlying communication protocol and service, and transmits it to traffic subject B; after receiving the message, traffic subject B decodes the message M and, based on pre-defined specifications and its own goals G2 and intentions I2, takes semantic interaction behavior Act2 and chooses whether to reply to message N. It may also perform certain operations O, and then repeats this process. Those skilled in the art should know that the appropriate underlying communication protocol and service should be selected according to the specific traffic scenario, and this is not specifically limited in this embodiment.

[0114] The step of establishing the traffic subject semantic information hierarchy structure in step S32 includes:

[0115] Step S321: define a message frame;

[0116] Step S322: Define the message body and its parameters.

[0117] Step S323: Constructing a semantic information interaction behavior representation method.

[0118] In this embodiment, the message frame contains one or more message bodies, which contain a series of message parameters and semantic information interaction statements. Traffic entities achieve information sharing and collaborative cognition by transmitting message frames, parsing message bodies, and obtaining interaction statements. The structure of semantic information interaction message frames, message bodies, and message parameters is as follows: Figure 5 shown.

[0119] 1) Define the message frame

[0120] First, define the message frame used for communication transmission, as shown in Table 7

[0121] Table 7 Message frame representation table

[0122]

[0123]

[0124] 2) Define the message body and its parameters

[0125] The message body shall contain one or more message parameters, as shown in Table 8. Message body parameters support extension. In addition to the message parameters specified in Table 7, users are free to define message body parameters when targeting specific implementations. The names of these non-standard additional parameters must be prefixed with the string "X-".

[0126] Table 8 Message body parameters

[0127]

[0128]

[0129] The structure specification of the message body is shown in Table 9

[0130] Table 9 Message body structure specification table

[0131]

[0132] 3) Constructing a method for representing semantic information interaction behavior

[0133] In this embodiment, the information interaction behaviors of traffic entities are abstracted from various traffic scenarios and divided into three types of interaction: information interaction, collaborative control, and error handling. These interactions include notification, query, request, repeated execution, acceptance, rejection, failure, and confusion. These semantic interaction behaviors correspond to the values ​​of the Perform parameter in the message parameters, as shown in Table 10:

[0134] Table 10 Semantic information interaction behavior table

[0135]

[0136]

[0137] In this embodiment, different semantic interaction behaviors determine the number and type of parameters in the message body. The structural specifications of each type of message body are shown in Table 11. For simplicity, it is assumed that both parties of the interaction know the message language and the ontology representing the meaning of the symbols. The parameters Language and Ontology are omitted in each type of message body:

[0138] Table 11 Standard table of message body structure corresponding to semantic interaction behavior

[0139]

[0140]

[0141]

[0142] The step of fusing interactive sentences to form semantic interactive messages in step S33 includes:

[0143] In this embodiment, based on the established semantic information hierarchy of traffic agents, interaction statements are integrated to construct a semantic interaction message body, which is then combined to form a message frame for semantic information exchange between traffic agents in a reverse overtaking scenario. It is assumed that all interacting agents in this scenario know the message language and the ontology representing the meaning of the symbols. That is, the parameters "Language" and "Ontology" are omitted from the message body. The message body for interaction between traffic agents in a reverse overtaking scenario is as follows:

[0144] 1) Vehicle-to-vehicle interaction, as shown in Table 12:

[0145] Table 12 Vehicle-to-vehicle interaction message table

[0146]

[0147]

[0148]

[0149]

[0150] 2) Interaction between vehicle and roadside unit: as shown in Table 13:

[0151] Table 13 Vehicle and roadside unit interaction message table

[0152]

[0153]

[0154] In order to verify the effectiveness of the semantic information interaction method for traffic entities disclosed in the present invention, a simulation experiment was designed taking the reverse overtaking scenario as an example to simulate and verify whether the semantic information interaction method for traffic entities disclosed in the present invention can improve traffic travel efficiency and ensure traffic safety.

[0155] In this embodiment, according to a method for semantic information interaction of traffic entities disclosed in the present invention, a traffic entity interaction simulation scenario interaction verification is designed. Based on the Python environment, taking the reverse overtaking scenario as an example, the state information of the traffic entity is semantically expressed to form interactive messages, and an interactive simulation experiment is conducted to verify the effectiveness of the proposed method, thereby improving traffic efficiency and ensuring traffic safety.

[0156] The specific simulation experiment process in this embodiment is as follows:

[0157] (1) Experimental environment

[0158] In the Windows system environment, experiments are conducted using Python as the programming language.

[0159] (2) Experimental process

[0160] First, a simulation environment for the reverse overtaking scenario is built. The reverse overtaking scenario includes two lanes in both directions, Lane 1 and Lane 2. In the main lane Lane 1, the main vehicle HV that will perform reverse overtaking is set, the front vehicle FV1 that is closer to the HV in the same lane, the front vehicle FV2 that is farther away, and the far vehicle RV that is set in the opposite lane Lane 2. The specific situation is as follows: Figure 2 As shown. The length of each vehicle is set to 4.5m, the initial speed is set to 10m / s, and the acceleration of the vehicle speed change is 2.5m / s. 2 . The time for the main vehicle HV to change lanes and merge is 2s, which ensures that the vehicle will not experience dangerous situations such as uncontrolled lateral movement during the lane change or merging process. The safe distance between vehicles traveling in the same direction is 10m, and the safe distance between vehicles traveling in the opposite direction is 20m. The selected safety distance is relatively small in order to test the status under extreme scenarios. The initial vehicle distance between the main vehicle HV and the preceding vehicle FV1 is set to 15m, and the initial vehicle distance between the preceding vehicles FV1 and FV2 is randomly generated between 20m and 30m according to a uniform distribution. The initial longitudinal vehicle distance between the main vehicle HV and the preceding vehicle FV1 is randomly generated between 140m and 180m according to a uniform distribution. A total of 100 cases of two different vehicle distances are generated.

[0161] Next, the entire reverse overtaking process is constructed. Reverse overtaking is divided into three steps: the lane change process, in which the host vehicle HV changes lanes from the main lane, Lane 1, to the opposite lane, Lane 2; the overtaking process, in which the host vehicle HV overtakes the preceding vehicle, FV1; and the lane change process, in which the host vehicle HV returns from the opposite lane, Lane 2, to the main lane, Lane 1. If the distance between the host vehicle HV and the distant vehicle, RV, or between the host vehicle HV and the preceding vehicle, FV2, falls below the safe vehicle distance, the host vehicle HV issues a risk warning but continues the reverse overtaking process. Only after the entire process is completed and the host vehicle HV does not collide with the distant vehicle, is the reverse overtaking considered complete.

[0162] In scenarios without semantic information exchange, vehicle status information relies on the driver's judgment. Only when the longitudinal distance between the host vehicle (HV) and the remote vehicle (RV) is less than 80 meters does the remote vehicle (RV) begin to decelerate to 5 m / s. Only when the host vehicle (HV) is overtaking does the front vehicle (FV1) begin to decelerate to 8 m / s. In scenarios with semantic information exchange, however, semantic information flows between vehicles. The host vehicle (HV) sends a deceleration request to the remote vehicle (RV) from the outset, which decelerates to 8 m / s at the beginning of the scenario. Simultaneously, the host vehicle (HV) sends a deceleration request to the front vehicle (FV1) to facilitate overtaking. Upon receiving the request, the front vehicle (FV1) decelerates to 8 m / s.

[0163] Then, an interactive message is constructed to realize information interaction. The present invention discloses a method for interactive semantic information of traffic entities to construct interactive statements, forming a vehicle-to-vehicle interactive message body as shown in Table 12 above, including deceleration request messages from the main vehicle HV to the front vehicle FV1 and the remote vehicle RV. With the help of the established interactive message, a reverse overtaking simulation experiment is completed. Information interaction scenarios such as Figure 6 shown.

[0164] (3) Analysis of experimental results

[0165] The above scenario verification shows that when overtaking on the wrong side of the road, vehicles can achieve mutual avoidance through information exchange between vehicles, achieving effective coordination and improving road safety. The final simulation results are shown in Table 14. In the 100 case verifications, without semantic information exchange, the main vehicle successfully overtook 87 times and issued risk warnings 79 times. After semantic information exchange, the main vehicle successfully overtook in all the simulation scenarios, and the number of risk warnings dropped to 45 times.

[0166] Table 14 Summary of simulation experiment results

[0167]

[0168] The data shows that this embodiment, through the transmission of semantic interaction messages, enables semantic information exchange between traffic entities in reverse overtaking scenarios, ultimately increasing the number of successful reverse overtaking attempts and significantly reducing the number of safety risk alarms. Furthermore, in scenarios without semantic information exchange, the remote vehicle (RV) needed to reduce its speed to 5 m / s for the host vehicle (HV) to complete the reverse overtaking. However, after semantic information exchange, a speed reduction of 8 m / s was sufficient, demonstrating that this method can improve traffic efficiency.

[0169] The foregoing is merely a preferred embodiment of the present invention. Those skilled in the art will readily appreciate that the present invention is susceptible to various modifications and variations. Clearly, the described embodiments represent only a portion of the present invention, not all of it. Other embodiments resulting from simple modifications, equivalent variations, or improvements made by those skilled in the art without inventive effort, without departing from the scope of the present invention, are also within the scope of protection of the present invention.

Claims

1. A method for interacting with semantic information of traffic entities, characterized in that: The steps include: Step S1: Collecting traffic scene and traffic subject status information, identifying the categories, attributes, and relationship labels of each traffic subject in the traffic scene; Step S2: Establish a traffic subject interaction language framework based on logical language and ontology theory, express the state information of traffic subjects and the relationship between subjects in a semantic way, and construct content sentences for semantic information interaction; Step S21: establishing interactive sentence grammar rules; Step S22: creating a traffic subject interaction six-tuple, including: a class set, a class attribute set, an instance object set, a relationship set, a mapping function set, and an axiom set; The class set includes: measurement class, vehicle class, equipment class, location class, time class, status class and event class; The in-class attribute set includes: inherent attributes and dynamic attributes; The instance object set includes: each specific traffic subject, state and event involved in the interaction domain; The relationship set includes: relationships between classes, between classes and instances, and between instances and instances; The mapping function set includes: functions that map from multiple classes to a certain other class and functions that map from multiple instances to a certain other instance; The axiom set includes: traffic rules stipulated by law and driving behavior experience; Step S23: constructing semantic information interaction sentences; Step S3: Construct the semantic information interaction process of traffic subjects, establish the semantic interaction information hierarchy, form semantic interaction messages, and realize the semantic information interaction of traffic subjects.

2. The method for interacting with traffic subject semantic information according to claim 1, characterized in that: In step S1, the state information from different sources is integrated into the same form through the fusion algorithm model, the key elements of the traffic subject are identified and marked through the recognition algorithm model, and corresponding category labels are assigned to them; based on the category labels of the traffic subjects and the real physical relationship, the relationship labels between the objects in the traffic scene are constructed.

3. A method for interacting with traffic subject semantic information according to claim 1, characterized in that: The step S21 includes: Step S211: defining symbols, where the symbols include variables, constants, attributes, relations, mapping functions, quantifiers, logical connectives, and axioms; Step S212: Establish interactive language syntax rules.

4. A method for interacting with semantic information of traffic entities according to claim 1, characterized in that: The step S3 comprises: Step S31: Constructing a traffic subject semantic information interaction process; Step S32: establishing a traffic subject semantic information hierarchy; Step S33: Fusing the interactive sentences to form a semantic interactive message.

5. A method for interacting with semantic information of traffic entities according to claim 4, characterized in that: The step S31 includes: traffic subject A expects to achieve goal G1, forms intention I1, decides to take semantic interaction behavior Act1, forms the semantic interaction behavior into message M, and encodes message M so that it meets the requirements of the underlying communication protocol and service, and transmits it to traffic subject B; after receiving the message, traffic subject B decodes message M, and according to the pre-defined specifications and its own goal G2 and intention I2, takes semantic interaction behavior Act2 and chooses whether to reply to message N, and performs certain operations O at the same time, and then repeats this process.

6. A method for interacting with semantic information of traffic entities according to claim 4, characterized in that: The step S32 includes creating a traffic subject semantic information hierarchy structure with "message frame-message body-message parameters" as the hierarchy: Step S321: define a message frame, which contains one or more message bodies; Step S322: Define the message body and its parameters. The message body includes a set of one or more message parameters and contains semantic information interaction statements. Traffic entities achieve information sharing and collaborative cognition by transmitting message frames, parsing the message body, and obtaining interaction statements. Step S323: Constructing a semantic information interaction behavior representation method.

7. A method for interacting with semantic information of traffic entities according to claim 6, characterized in that: In step S323, the semantic information interaction behaviors of the traffic subject are divided into three categories: information interaction, collaborative control, and error handling. The semantic information interaction behaviors correspond to the corresponding parameters in the message parameters.

Images