# US 12,112,635 B2 · Process and system for local traffic approximation through analysis of cloud data > Agents: this data is available as JSON from the patentgrep API and MCP server — see https://patentgrep.com/llms.txt - Number: US 12,112,635 B2 - Document ID: US12112635B2 - Status: Granted - Kind: B2 - Application number: 16/794,884 - Filed: 2020-02-19 - Published: 2021-08-19 - Granted: 2024-10-08 - Earliest claimed: 2020-02-19 - Assignees: GM Global Technology Operations LLC - Applicants: GM Global Technology Operations LLC - Inventors: Prakash Mohan Peranadam; Mohamed A. Layouni; Meng Jiang; Shengbing Jiang; Jiyu Zhang; Erik B. Golm - Web page: https://patentgrep.com/patent/US12112635B2 ## CPC - G08G 1/162: {event-triggered} - H04W 4/029: Location-based management or tracking services - G06N 20/00: Machine learning - G08G 1/0112: {from the vehicle, e.g. floating car data [FCD]} - G08G 1/012: {from other sources than vehicle or roadside beacons, e.g. mobile networks} - G08G 1/0133: {for classifying traffic situation} - G08G 1/0145: {for active traffic flow control} - G08G 1/166: {for active traffic, e.g. moving vehicles, pedestrians, bikes} - H04W 64/006: {with additional information processing, e.g. for direction or speed determination} - 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}) - H04W: WIRELESS COMMUNICATION NETWORKS (broadcast communication H04H; communication systems using wireless links for non-selective communication, e.g. wireless extensions H04M1/72) - G06N: COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS ## Abstract A process for local traffic approximation through analysis of cloud data is provided. The process includes, within a computerized traffic flow estimation controller of a host vehicle, operating programming to monitor a planned navigational route of the host vehicle, identify along the planned navigational route a road section including cross-traffic, monitor cloud data related to a mobile cellular device, analyze the cloud data to identify traffic posing a hazardous condition to the host vehicle within the road section, and generate a vehicle alert to a driver of the host vehicle based upon the identified traffic. ## Claims ### Claim 1 A process for local traffic approximation through analysis of cloud data, comprising: within a computerized traffic flow estimation controller of a host vehicle, operating programming to: monitor a planned navigational route of the host vehicle; detect along the planned navigational route a road section including cross-traffic which may pose an increased hazard to the host vehicle as compared to other road sections along the planned navigational route, wherein the road section including cross-traffic comprises a road section including merging traffic or an intersection of a first roadway along the planned navigational route upon which the host vehicle is planned to travel and a second roadway; monitor the cloud data related to a mobile cellular device disposed within a second vehicle, wherein the second vehicle is disposed within the road section including the cross-traffic and along the second roadway that is spaced from the planned navigational route; analyze the cloud data related to the mobile cellular device disposed within the second vehicle to identify traffic posing a hazardous condition to the host vehicle within the road section including the cross-traffic; and generate a vehicle alert to a driver of the host vehicle based upon the identified traffic. ### Claim 2 (depends on 1) The process of claim 1, wherein monitoring the cloud data includes monitoring global positioning system data for the mobile cellular device; and wherein analyzing the cloud data includes identifying the second vehicle approaching the road section based upon movement of the mobile cellular device identified through the cloud data. ### Claim 3 (depends on 1) The process of claim 1, wherein monitoring the cloud data includes monitoring roaming cellular data acquired from a plurality of cellular towers; and wherein analyzing the cloud data includes identifying the second vehicle as approaching the road section based upon movement of the mobile cellular device identified through the cloud data. ### Claim 4 (depends on 3) The process of claim 3, wherein identifying the second vehicle approaching the road section based upon movement of the mobile cellular device includes estimating a speed of the mobile cellular device based upon a frequency that the mobile cellular device switches between the plurality of cellular towers or monitoring global positioning service data. ### Claim 5 (depends on 1) The process of claim 1, further comprising analyzing the cloud data to identify a number of vehicles approaching the road section. ### Claim 6 (depends on 1) The process of claim 1, wherein analyzing the cloud data to identify the traffic posing the hazardous condition includes filtering out data related to a pedestrian not posing the hazardous condition to the host vehicle. ### Claim 7 (depends on 1) The process of claim 1, wherein analyzing the cloud data to identify the traffic posing the hazardous condition includes filtering out redundant data related to a plurality of mobile cellular devices collocated in a single vehicle. ### Claim 8 (depends on 1) The process of claim 1, further comprising operating programming to: monitor data from an on-board sensor of the host vehicle, wherein the on-board sensor includes at least a LIDAR sensor; and compare the data from the on-board sensor of the host vehicle to the cloud data; and wherein generating the vehicle alert to the driver of the host vehicle is further based upon the comparing indicating that the cloud data is related to the second vehicle obscured from the on-board sensor by an object located between the second vehicle and the on-board sensor. ### Claim 9 (depends on 1) The process of claim 1, further comprising operating programming to command autonomous braking based upon the vehicle alert. ### Claim 10 (depends on 1) The process of claim 1, further comprising: calculating a time to meet for the second vehicle and the host vehicle; and identifying the second vehicle as posing the hazardous condition based upon the time to meet. ### Claim 11 (depends on 1) The process of claim 1, further comprising applying a machine learning algorithm to estimate traffic intensity for the road section. ### Claim 12 (depends on 11) The process of claim 11, further comprising operating programming to warn the driver based upon the estimated traffic intensity for the road section. ### Claim 13 A system for local traffic approximation through analysis of cloud data, comprising: a computerized traffic flow estimation controller of a host vehicle, operating programming to: monitor a planned navigational route of the host vehicle; detect along the planned navigational route a road section including cross-traffic which may pose an increased hazard to the host vehicle as compared to other road sections along the planned navigational route, wherein the road section including cross-traffic comprises a road section including merging traffic or an intersection of a first roadway along the planned navigational route upon which the host vehicle is planned to travel and a second roadway; monitor the cloud data related to a mobile cellular device disposed within a second vehicle, wherein the second vehicle is disposed within the road section including the cross-traffic and along the second roadway that is spaced from the planned navigational route; analyze the cloud data related to the mobile cellular device disposed within the second vehicle to identify traffic posing a hazardous condition to the host vehicle within the road section including the cross-traffic; and generate a vehicle alert to a driver of the host vehicle based upon the identified traffic. ### Claim 14 (depends on 13) The system of claim 13, further comprising an on-board sensor of the host vehicle operable to provide data related to an environment around the host vehicle, wherein the on-board sensor includes at least a LIDAR sensor; and wherein the computerized traffic flow estimation controller further includes programming to: monitor data from the on-board sensor; compare the data from the on-board sensor to the cloud data; and identify the second vehicle as being hidden by an object located between the second vehicle and the on-board sensor based upon the comparing indicating that the second vehicle is obscured from the on-board sensor. ### Claim 15 (depends on 13) The system of claim 13, wherein the programming to monitor the cloud data related to the mobile cellular device includes programming to monitor global positioning service data. ### Claim 16 (depends on 13) The system of claim 13, wherein the programming to monitor the cloud data related to the mobile cellular device includes programming to monitor roaming cellular data acquired from a plurality of cellular towers. ### Claim 17 A process for local traffic approximation through analysis of cloud data, comprising: within a computerized traffic flow estimation controller of a host vehicle, operating programming to: monitor a planned navigational route of the host vehicle; detect along the planned navigational route a road section including cross-traffic which may pose an increased hazard to the host vehicle as compared to other road sections along the planned navigational route, wherein the road section including cross-traffic comprises a road section including merging traffic or an intersection of a first roadway along the planned navigational route upon which the host vehicle is planned to travel and a second roadway; monitor the cloud data related to a mobile cellular device disposed within a second vehicle, wherein the second vehicle is disposed within the road section including the cross-traffic and along the second roadway that is spaced from the planned navigational route, the cloud data including one of global positioning service data or roaming cellular data acquired from a plurality of cellular towers; analyze the cloud data related to the mobile cellular device disposed within the second vehicle to identify the second vehicle as posing a hazardous condition to the host vehicle within the road section including the cross-traffic; and generate a vehicle alert to a driver of the host vehicle based upon the second vehicle. ### Claim 18 (depends on 17) The process of claim 17, wherein analyzing the cloud data to identify the second vehicle as posing the hazardous condition includes filtering out data related to a pedestrian not posing the hazardous condition to the host vehicle. ### Claim 19 (depends on 17) The process of claim 17, wherein analyzing the cloud data to identify the second vehicle as posing the hazardous condition includes filtering out redundant data related to a plurality of mobile cellular devices collocated in a single hidden vehicle. ### Claim 20 (depends on 17) The process of claim 17, further comprising applying a machine learning algorithm to estimate traffic intensity for the road section.