# US 11,443,622 B2 · Systems and methods for mitigating a risk of being followed by a vehicle > Agents: this data is available as JSON from the patentgrep API and MCP server — see https://patentgrep.com/llms.txt - Number: US 11,443,622 B2 - Document ID: US11443622B2 - Status: Granted - Kind: B2 - Application number: 16/739,765 - Filed: 2020-01-10 - Published: 2021-07-15 - Granted: 2022-09-13 - Earliest claimed: 2020-01-10 - Assignees: Toyota Motor North America, Inc. - Applicants: Toyota Motor North America, Inc. - Inventors: Farinaz Tehrani; Narendran Narayanasamy - Web page: https://patentgrep.com/patent/US11443622B2 ## CPC - G08G 1/017: identifying vehicles (G08G1/015, G08G1/054 take precedence) - B60Q 9/00: Arrangement or adaptation of signal devices not provided for in one of main groups B60Q1/00 - B60Q7/00 {, e.g. haptic signalling} - G06F 18/21: Design or setup of recognition systems or techniques; Extraction of features in feature space; Blind source separation - G06N 3/088: Non-supervised learning, e.g. competitive learning - G06V 20/56: exterior to a vehicle by using sensors mounted on the vehicle - G08G 1/09626: {where the origin of the information is within the own vehicle, e.g. a local storage device, digital map} - G06V 2201/08: Detecting or categorising vehicles - 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}) - B60Q: ARRANGEMENT OF SIGNALLING OR LIGHTING DEVICES, THE MOUNTING OR SUPPORTING THEREOF OR CIRCUITS THEREFOR, FOR VEHICLES IN GENERAL - G06F: ELECTRIC DIGITAL DATA PROCESSING (computer systems based on specific computational models G06N) - G06N: COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS - G06V: IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING ## Abstract Embodiments of the disclosure relate to systems and methods for mitigating a risk of being followed using information and notification of being followed by a vehicle. In one embodiment, a computer-implemented method for mitigating a risk of being followed includes the step of detecting, by a plurality of sensors built into a first vehicle, a second vehicle that has been following the first vehicle for a period of time. The method further includes the step of presenting a notification in the first vehicle that it is being followed. ## Claims ### Claim 1 A computer-implemented method for mitigating a risk of being followed, comprising: detecting, by a plurality of sensors built into a first vehicle, a second vehicle that has been following the first vehicle for a period of time that exceeds a threshold period of time; and presenting a notification in the first vehicle that it is being followed. ### Claim 2 (depends on 1) The computer-implemented method of claim 1, wherein the detecting is based on one or more attributes of the second vehicle. ### Claim 3 (depends on 2) The computer-implemented method of claim 2, wherein the one or more attributes of the second vehicle are selected from the following: make, model, color, operational parameters, and driver identity. ### Claim 4 (depends on 2) The computer-implemented method of claim 2, wherein the one or more attributes are given different weight values. ### Claim 5 (depends on 1) The computer-implemented method of claim 1, wherein the detecting is based upon an unsupervised machine learning model utilizing a neural network. ### Claim 6 (depends on 1) The computer-implemented method of claim 1 further comprising presenting a notification in the first vehicle. ### Claim 7 (depends on 6) The computer-implemented method of claim 6, wherein the notification occurs based upon exceeding a first predetermined threshold value of the period of time. ### Claim 8 (depends on 1) The computer-implemented method of claim 1 further comprising presenting a prompt for action in the first vehicle. ### Claim 9 (depends on 8) The computer-implemented method of claim 8, wherein the prompt for action occurs based upon exceeding a second predetermined threshold value of likelihood that the second vehicle was present within a predetermined distance of the first vehicle during a previous trip. ### Claim 10 (depends on 8) The computer-implemented method of claim 8, wherein the prompt for action is selected from one or more of the following: move to the side of the road, change destination, and notify law enforcement. ### Claim 11 A vehicle comprising a plurality of sensors, a processor, and a non-transitory computer-readable medium storing instructions that, when executed by the processor, cause the processor to: receive data from the sensors; determine the presence of a following vehicle from the data by determining that the following vehicle has been following the vehicle for a period of time that exceeds a threshold period of time; and present a notification in the vehicle upon determination of the presence of the following vehicle. ### Claim 12 (depends on 11) The vehicle of claim 11, wherein the instructions, when executed by the processor further cause the processor to determine the presence of the following vehicle based on one or more attributes of the following vehicle. ### Claim 13 (depends on 12) The vehicle of claim 12, wherein the one or more attributes of the following vehicle are selected from the following: make, model, color, operational parameters, and driver identity. ### Claim 14 (depends on 12) The vehicle of claim 12, wherein the one or more attributes are given different weight values. ### Claim 15 (depends on 11) The vehicle of claim 11, wherein the instructions, when executed by the processor further cause the processor to determine the presence of the following vehicle utilizing an unsupervised machine learning model utilizing a neural network. ### Claim 16 (depends on 11) The vehicle of claim 11, wherein the instructions, when executed by the processor further cause the processor to present a notification in the vehicle. ### Claim 17 (depends on 16) The vehicle of claim 16, wherein the notification occurs based upon exceeding a first predetermined threshold value of the period of time. ### Claim 18 (depends on 11) The vehicle of claim 11, wherein the instructions, when executed by the processor further cause the processor to present a prompt for action in the vehicle. ### Claim 19 (depends on 18) The vehicle of claim 18, wherein the prompt for action occurs based upon exceeding a second predetermined threshold value of likelihood that the following vehicle was present within a predetermined distance of the vehicle during a previous trip. ### Claim 20 (depends on 18) The vehicle of claim 18, wherein the prompt for action is selected from one or more of the following: move to the side of the road, change route, change destination, and notify law enforcement.