# US 2018/0023965 A1 · Anomaly estimation apparatus and display apparatus > Agents: this data is available as JSON from the patentgrep API and MCP server — see https://patentgrep.com/llms.txt - Number: US 2018/0023965 A1 - Document ID: US20180023965A1 - Status: Published application - Kind: A1 - Application number: 15/658,199 - Filed: 2017-07-24 - Published: 2018-01-25 - Earliest claimed: 2016-07-25 - Assignees: Denso Corporation - Applicants: Denso Corporation - Inventors: Hideaki MISAWA; Masumi EGAWA; Utsushi SAKAI; Kentaro HITOMI; Yuki SHINOHARA; Kazuhito TAKENAKA; Masataka MORI - Web page: https://patentgrep.com/patent/US20180023965A1 ## CPC - G01C 21/3415: {Dynamic re-routing, e.g. recalculating the route when the user deviates from calculated route or after detecting real-time traffic data or accidents} - G01C 21/3602: {Input other than that of destination using image analysis, e.g. detection of road signs, lanes, buildings, real preceding vehicles using a camera} - G01C 21/367: {Details, e.g. road map scale, orientation, zooming, illumination, level of detail, scrolling of road map or positioning of current position marker} - G01C 21/3697: {Output of additional, non-guidance related information, e.g. low fuel level (G01C21/3679 takes precedence)} - G06N 5/04: Inference or reasoning models - G08G 1/01: Detecting movement of traffic to be counted or controlled (G08G1/07 - G08G1/14 take precedence) - G08G 1/0116: {from roadside infrastructure, e.g. beacons} - G08G 1/0133: {for classifying traffic situation} - G08G 1/0141: {for traffic information dissemination} - G06N 7/01: Probabilistic graphical models, e.g. probabilistic networks - G01C: MEASURING DISTANCES, LEVELS OR BEARINGS; SURVEYING; NAVIGATION; GYROSCOPIC INSTRUMENTS; PHOTOGRAMMETRY OR VIDEOGRAMMETRY (measuring liquid level G01F; radio navigation, determining distance or velocity by use of propagation effects, e.g. Doppler effects, propagation time, of radio waves, analogous arrangements using other waves G01S) - G06N: COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS - 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}) ## Abstract An anomaly estimation apparatus includes a collection section that collects vehicle data, a feature amount calculation section that calculates a feature amount from the vehicle data and stores the feature amount and a place corresponding thereto, an anomaly determination section that determines whether an anomaly occurrence point is present based on the feature amount, an accumulation section that, if the anomaly occurrence point is present, uses the vehicle data at the anomaly occurrence point and an anomaly periphery point to generate estimation data, an information generation section that uses the estimation data to generate causality information representing causality between an anomaly caused at the anomaly occurrence point and an anomaly caused at the anomaly periphery point, and an estimation section that, if the anomaly occurrence point is present, uses the causality information to estimate transition of the anomaly from the anomaly occurrence point to the anomaly periphery point. ## Claims ### Claim 1 An anomaly estimation apparatus, comprising: a collection section that is configured to repeatedly collect vehicle data concerning conditions of a plurality of vehicles for the respective vehicles; a feature amount calculation section that is configured to calculate a feature amount from the vehicle data collected by the collection section and store the feature amount and a place corresponding to the feature amount in a state where the feature amount is associated with the place; an anomaly determination section that is configured to determine whether or not an anomaly occurrence point, at which an anomaly has occurred, is present at a present time based on the feature amount calculated by the feature amount calculation section; an accumulation section that is configured to, if the anomaly determination section determines that the anomaly occurrence point is present at the present time, use the vehicle data at the anomaly occurrence point and the vehicle data at an anomaly periphery point, which is a point around the anomaly occurrence point, to generate estimation data, which is previously set to estimate transition of the anomaly from the anomaly occurrence point to the anomaly periphery point, and store the generated estimation data; an information generation section that is configured to use the estimation data stored by the accumulation section to generate causality information representing causality between an anomaly that has occurred at the anomaly occurrence point and an anomaly that has occurred at the anomaly periphery point; and an estimation section that is configured to, if the anomaly determination section determines that the anomaly occurrence point is present at the present time, use the causality information generated by the information generation section in the past to estimate transition of the anomaly from the anomaly occurrence point, at which it is determined to be present at the present time by the anomaly determination section, to the anomaly periphery point. ### Claim 2 (depends on 1) The anomaly estimation apparatus according to claim 1, wherein the anomaly determination section uses the feature amount calculated by the feature amount calculation section at the present time as a current feature amount, uses the feature amount, which is calculated before the current feature amount is calculated, as a past feature amount, and compares the current feature amount with the past feature amount to determine whether the anomaly occurrence point is present at the present time. ### Claim 3 (depends on 1) The anomaly estimation apparatus according to claim 1, wherein the accumulation section determines that a point in a periphery determination area, which is previously set so as to include the anomaly occurrence point, is the periphery of the anomaly occurrence point. ### Claim 4 (depends on 1) The anomaly estimation apparatus according to claim 1, wherein the estimation section extracts the causality information in the past at the anomaly periphery point having causality with the anomaly that has occurred at the anomaly occurrence point, and obtains information representing the number of extracted anomalies. ### Claim 5 (depends on 1) The anomaly estimation apparatus according to claim 1, wherein the information generation section generates the causality information so as to include at least one of a degree of anomaly indicating a degree of the anomaly caused at the anomaly occurrence point and the feature amount obtained when the anomaly has occurred at the anomaly occurrence point, and the estimation section uses at least one of the degree of anomaly and the feature amount included in the causality information in the past to extract the causality information of an anomaly similar to the anomaly at the anomaly occurrence point, which is determined to be present at the present time by the anomaly determination section, and estimates the transition of the anomaly from the anomaly occurrence point to the anomaly periphery point. ### Claim 6 (depends on 1) The anomaly estimation apparatus according to claim 1, wherein the information generation section obtains anomaly cause information representing a cause of the anomaly that has occurred at the anomaly occurrence point and associates the obtained anomaly cause information with the generated causality information, and when anomaly determination section determines that the anomaly occurrence point is present, the estimation section obtains the anomaly cause information representing the anomaly that has occurred at the anomaly occurrence point, extracts the causality information in the past associated with the same anomaly cause information as the obtained anomaly cause information to estimate the transition of the anomaly from the anomaly occurrence point to the anomaly periphery point. ### Claim 7 (depends on 1) The anomaly estimation apparatus according to claim 1, wherein the data collected by the collection section includes image data of the periphery of the vehicle captured by a camera installed in the vehicle, the information generation section is configured to use the image data to identify a type of the anomaly that has occurred at the anomaly occurrence point, and to associate anomaly type information representing the type of the identified anomaly with the generated causality information, and when the anomaly determination section determines that the anomaly occurrence point is present, the estimation section uses the image data to identify the type of the anomaly that has occurred at the anomaly occurrence point, and extracts the causality information in the past associated with the anomaly type information that is the same as the identified type of the anomaly to estimate the transition of the anomaly from the anomaly occurrence point to the anomaly periphery point. ### Claim 8 (depends on 1) A display apparatus that is installed in the vehicle and obtains estimation information representing a result of the estimation by the estimation section of the anomaly estimation apparatus according to claim 1, wherein the display apparatus uses the anomaly occurrence point, which is determined by the anomaly determination section to be present at the present time, as a current anomaly occurrence point, uses the anomaly periphery point, to which the estimation section estimates that transition of the anomaly is made from the current anomaly occurrence point, as a current anomaly transition point, and displays anomaly position specifying information specifying positions of the current anomaly occurrence point and the current anomaly transition point. ### Claim 9 (depends on 8) The display apparatus according to claim 8, wherein the anomaly position specifying information includes occurrence position information representing the position of the current anomaly occurrence point and transition position information representing the position of the current anomaly transition point. ### Claim 10 (depends on 9) The display apparatus according to claim 9, wherein the display apparatus displays occurrence time specifying information specifying time when an anomaly occurs at the current anomaly transition point. ### Claim 11 (depends on 9) The display apparatus according to claim 9, wherein the display apparatus highlights the current anomaly transition point having a high anomaly transition probability. ### Claim 12 (depends on 9) The display apparatus according to claim 9, wherein the display apparatus displays an avoidance route avoiding the current anomaly occurrence point and the current anomaly transition point. ### Claim 13 (depends on 9) The display apparatus according to claim 9, wherein the display apparatus displays only one the current anomaly transition point at which the vehicle installing the display apparatus is likely to arrive. ### Claim 14 (depends on 8) The display apparatus according to claim 8, wherein the anomaly position specifying information is anomaly area information representing an anomaly area including the position of the current anomaly occurrence point and the position of the current anomaly transition point. ### Claim 15 (depends on 14) The display apparatus according to claim 14, wherein the anomaly area has an anomaly occurrence area including the position of the current anomaly occurrence point and an anomaly transition area including the position of the current anomaly transition point, and the anomaly occurrence area and the anomaly transition area are distinguishably displayed.