# US 12,118,881 B1 · System and method for providing consolidated events in a traffic management control centre using situational awareness > Agents: this data is available as JSON from the patentgrep API and MCP server — see https://patentgrep.com/llms.txt - Number: US 12,118,881 B1 - Document ID: US12118881B1 - Status: Granted - Kind: B1 - Application number: 18/529,040 - Filed: 2023-12-05 - Granted: 2024-10-15 - Earliest claimed: 2023-12-05 - Assignees: Valerann Ltd. - Applicants: Valerann Ltd. - Inventors: Kelvin Choi Tsz Hei; Ran Katzir; Jacob Guy Rainbow - Web page: https://patentgrep.com/patent/US12118881B1 ## CPC - G08G 1/0141: {for traffic information dissemination} - G06F 18/251: {of input or preprocessed data} - G06F 40/40: Processing or translation of natural language (natural language analysis G06F40/20; semantic analysis G06F40/30) - G06N 5/048: Fuzzy inferencing - G06N 7/023: {Learning or tuning the parameters of a fuzzy system} - G08G 1/0108: {based on the source of data} - G08G 1/0125: {Traffic data processing} - G08G 1/0129: {for creating historical data or processing based on historical data} - G08G 1/0133: {for classifying traffic situation} - G08G 1/0112: {from the vehicle, e.g. floating car data [FCD]} - G08G 1/0116: {from roadside infrastructure, e.g. beacons} - G08G 1/012: {from other sources than vehicle or roadside beacons, e.g. mobile networks} - G08G: TRAFFIC CONTROL SYSTEMS (guiding railway traffic, ensuring the safety of railway traffic B61L; arrangement of road signs or traffic signals E01F9/00; radar or analogous systems, sonar systems, lidar systems specially adapted for traffic control G01S13/91, G01S15/88, G01S17/88; {radar or analogous systems, sonar systems, lidar systems specially adapted for anti-collision purposes G01S13/93, G01S15/93, G01S17/93}) - G06F: ELECTRIC DIGITAL DATA PROCESSING (computer systems based on specific computational models G06N) - G06N: COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS ## Abstract A system and method for providing consolidated events in a traffic management control centre may include: obtaining a plurality of raw events, each from an event source and each including at least one of a geographical location, timestamp and event type; validating each of the raw events against a set of rules; terminating the raw events for which validation is not successful and leaving validated events; obtaining context data from a plurality of georeferenced dynamic map layers; matching each of the validated events with the georeferenced dynamic map layers using the at least one of geographical location, timestamp and event type, and enriching each of the validated events with the associated context data to generate enriched events; matching each of the enriched events against a plurality of stored events to generate consolidated events; and providing at least one of the consolidated events to a user. ## Claims ### Claim 1 A method for providing consolidated events in a traffic management control centre, the method comprising: receiving, from a plurality of sensors, a plurality of raw traffic events, wherein each raw traffic event of the plurality of raw traffic events comprises a geographical location, timestamp and event type; validating each of the raw traffic events against a set of fuzzy rules that comprise fuzzy operators with a road-specific ontology; discarding the raw traffic events for which validation is not successful from further processing and leaving validated events; obtaining geographic data from a plurality of georeferenced dynamic map layers; matching each of the validated events with the georeferenced dynamic map layers using the geographical location, timestamp and event type, and enriching each of the validated events with the associated geographic data to generate enriched events; matching each of the enriched events against a plurality of stored events using the geographical location, timestamp and event type, to generate consolidated events; and displaying at least one of the consolidated events on a map on a display in a traffic management control centre, wherein the map is based on the georeferenced dynamic map layers. ### Claim 2 (depends on 1) The method of claim 1, wherein each raw event of the plurality of raw events is obtained from an event source, and wherein matching each of the enriched events against a plurality of stored events comprises: matching each of the enriched events against a plurality of stored events originated from the same event source and having the same event type to generate same-source-same-type fused events; matching each of the same-source-same-type fused events against a plurality of stored events originated from a plurality of other event sources and having the same event type to generate different-source-same-type fused events; and matching each of the different-source-same-type fused events against a plurality of stored events originated from the plurality of event sources and having a different event type to generate the consolidated events. ### Claim 3 (depends on 2) The method of claim 2, comprising: in each step of matching, associating a relevancy score to each of the validated events, enriched events, same-source-same-type fused events, different-source-same-type fused events and consolidated events, based on the matching; and providing the associated relevancy score together with the at least one of the consolidated events. ### Claim 4 (depends on 3) The method of claim 3, wherein providing the at least one of the consolidated events comprises providing the consolidated events with a relevancy score that exceeds a threshold. ### Claim 5 (depends on 2) The method of claim 2, wherein the event source is selected from the list consisting of: a road side camera, a connected vehicle, a navigation service, sensors of an intelligent transportation system (ITS), social media, weather reports and operators event, and wherein the georeferenced dynamic map layers are selected from a list consisting of: asset maps, weather maps, air temperature maps, wind speed maps, humidity maps, visibility maps, traffic maps, speed maps, flow maps, class maps, historical weekly speed maps, historical weekly flow maps, risk maps, collision maps and congestion maps. ### Claim 6 (depends on 1) The method of claim 1, wherein validating and matching is performed using at least one of a binary operator and a knowledge graph, with a road-specific ontology. ### Claim 7 (depends on 1) The method of claim 1, wherein matching is performed using a second set of fuzzy rules that comprise fuzzy operators with a road-specific ontology. ### Claim 8 (depends on 1) The method of claim 1, wherein the road-specific ontology comprises: domains, each representing a continuous property that is related to events; input values that are continuous variables that quantify the property represented by the domain; generics that are membership functions, each defined against a domain and maps an input value into a membership value for a domain; and prepositions that are membership functions defined against a relationship between domains. ### Claim 9 (depends on 8) The method of claim 8, wherein the road-specific ontology further comprises: terms, a categorial variable representing a property that is related to an input value; and term membership functions that map first term value into a second term value or provide a confidence level. ### Claim 10 (depends on 1) The method of claim 1, wherein at least some of the fuzzy rules in the set of fuzzy rules comprise one of time or space fuzzy prepositions. ### Claim 11 (depends on 10) The method of claim 10, wherein each of the fuzzy preposition evaluates relative properties between two events or between an event and a context data item. ### Claim 12 (depends on 10) The method of claim 10, wherein at least one of the fuzzy prepositions is customized to the events or the context data item. ### Claim 13 (depends on 1) The method of claim 1, wherein at least one of the fuzzy rules in the set of rules is of the form IF A B C then D is E where: A is a first event, B is a fuzzy preposition, C is a second event or a context data item, wherein validating and matching comprises evaluating A B C into a truth value and applying the truth value to D is E, where: D is an outcome event and E is a generic that describes a domain in D. ### Claim 14 (depends on 1) The method of claim 1, comprising: providing the at least one consolidated event to a machine learned natural language model to produce a narrative of the at least one consolidated event; and providing the narrative to a user. ### Claim 15 A system for providing consolidated events in a traffic management control centre, the system comprising: a display; a memory; and a processor configured to: receive, from a plurality of sensors, a plurality of raw traffic events, wherein each raw traffic event of the plurality of raw traffic events comprises a geographical location, timestamp and event type; validate each of the raw events against a set of fuzzy rules that comprise fuzzy operators with a road-specific ontology; discard the raw traffic events for which validation is not successful from further processing and leaving validated events; obtain geographic data from a plurality of georeferenced map layers; match each of the validated events with the georeferenced map layers using the geographical location, timestamp and event type, and enrich each of the validated events with the associated geographic data to generate enriched events; match each of the enriched events against a plurality of stored events using the geographical location, timestamp and event type, to generate consolidated events; and display at least one of the consolidated event on a map on a display in a traffic management control centre, wherein the map is based on the georeferenced dynamic map layers. ### Claim 16 (depends on 15) The system of claim 15, wherein each raw event of the plurality of raw events is obtained from an event source, and wherein the processor is configured to match each of the enriched events against a plurality of stored events by: matching each of the enriched events against a plurality of stored events originated from the same event source and having the same event type to generate same-source-same-type fused events; matching each of the same-source-same-type fused events against a plurality of stored events originated from a plurality of other event sources and having the same event type to generate different-source-same-type fused events; and matching each of the different-source-same-type fused events against a plurality of stored events originated from the plurality of event sources and having a different event type to generate the consolidated events. ### Claim 17 (depends on 16) The system of claim 16, wherein the processor is configured to: in each step of matching, associate a relevancy score to each of the validated events, enriched events, same-source-same-type fused events, different-source-same-type fused events and consolidated events, based on the matching; and provide the associated relevancy score to the user together with the at least one of the consolidated events. ### Claim 18 (depends on 17) The system of claim 17, wherein the processor is configured to provide the at least one of the consolidated events by providing the consolidated events with a relevancy score that exceeds a threshold. ### Claim 19 (depends on 16) The system of claim 16, wherein the event source is selected from the list consisting of: a road side camera, a connected vehicle, a navigation service, sensors of an intelligent transportation system (ITS), social media, weather reports and operators event, and wherein the georeferenced map layers are selected from a list consisting of: asset maps, weather maps, air temperature maps, wind speed maps, humidity maps, visibility maps, traffic maps, speed maps, flow maps, class maps, historical weekly speed maps, historical weekly flow maps, risk maps, collision maps and congestion maps. ### Claim 20 (depends on 15) The system of claim 15, wherein the processor is configured to validate and matching using at least one of a binary operator and a knowledge graph, with a road-specific ontology. ### Claim 21 (depends on 15) The system of claim 15, wherein the processor is configured to validate using a second set of fuzzy rules that comprise fuzzy operators with a road-specific ontology. ### Claim 22 (depends on 15) The system of claim 15, wherein the road-specific ontology comprises: domains, each representing a continuous property that is related to events; input values that are continuous variables that quantify the property represented by the domain; generics that are membership functions, each defined against a domain and maps an input value into a membership value for a domain; and prepositions that are membership functions defined against a relationship between domains. ### Claim 23 (depends on 22) The system of claim 22, wherein the road-specific ontology further comprises: terms, a categorial variable representing a property that is related to an input value; and term membership functions that map first term value into a second term value or provide a confidence level. ### Claim 24 (depends on 15) The system of claim 15, wherein at least a portion of the fuzzy rules in the set of fuzzy rules comprise one of time or space fuzzy prepositions. ### Claim 25 (depends on 24) The system of claim 24, wherein each of the fuzzy preposition evaluates relative properties between two events or between an event and a context data item. ### Claim 26 (depends on 24) The system of claim 24, wherein at least one of the fuzzy prepositions is customized to the events or the context data item. ### Claim 27 (depends on 15) The system of claim 15, wherein at least one of the fuzzy rules in the set of rules is of the form IF A B C then D is E where: A is a first event, B is a fuzzy preposition, C is a second event or a context data item, wherein validating and matching comprises evaluating A B C into a truth value and applying the truth value to D is E, where: D is an outcome event and E is a generic that describes a domain in D. ### Claim 28 (depends on 15) The system of claim 15, wherein the processor is configured to: provide the at least one consolidated event to a machine learned natural language model to produce a narrative of the at least one consolidated event; and provide the narrative to the user. ### Claim 29 A method for providing consolidated events in a traffic management control centre, the method comprising: receiving, from a plurality of sensors a plurality of raw traffic events, wherein each raw traffic event of the plurality of raw traffic events comprises a geographical location, timestamp and event type; validating each of the raw traffic events against a set of rules; discarding the raw events for which validation is not successful from further processing and leaving validated events; obtaining geographic data from a plurality of georeferenced dynamic map layers; matching each of the validated events with the georeferenced dynamic map layers using the geographical location, timestamp and event type, and enriching each of the validated events with the associated geographic data to generate enriched events; matching each of the enriched events against a plurality of stored events originated from the same event source and having the same event type to generate same-source-same-type fused events; matching each of the same-source-same-type fused events against a plurality of stored events originated from a plurality of other event sources and having the same event type to generate different-source-same-type fused events; matching each of the different-source-same-type fused events against a plurality of stored events originated from the plurality of event sources and having a different event type to generate the consolidated events; and displaying at least one of the consolidated events on a map on a display in a traffic management control centre, wherein the map is based on the georeferenced dynamic map layers.