# US 11,451,457 B2 · Sensor delay time estimation device, sensor delay time estimation method and recording medium > Agents: this data is available as JSON from the patentgrep API and MCP server — see https://patentgrep.com/llms.txt - Number: US 11,451,457 B2 - Document ID: US11451457B2 - Status: Granted - Kind: B2 - Application number: 17/121,075 - Filed: 2020-12-14 - Published: 2021-09-30 - Granted: 2022-09-20 - Earliest claimed: 2020-03-30 - Assignees: Kabushiki Kaisha Toyota Chuo Kenkyusho - Applicants: Kabushiki Kaisha Toyota Chuo Kenkyusho - Inventors: Daiki Mori; Yoshikazu Hattori; Hideki Sugiura - Web page: https://patentgrep.com/patent/US11451457B2 ## CPC - G01S 19/48: by combining or switching between position solutions derived from the satellite radio beacon positioning system and position solutions derived from a further system - H04L 43/0858: {One way delays} - G01S 5/16: using electromagnetic waves other than radio waves - H04L 67/12: specially adapted for proprietary or special-purpose networking environments, e.g. medical networks, sensor networks, networks in vehicles or remote metering networks - G01S: RADIO DIRECTION-FINDING; RADIO NAVIGATION; DETERMINING DISTANCE OR VELOCITY BY USE OF RADIO WAVES; LOCATING OR PRESENCE-DETECTING BY USE OF THE REFLECTION OR RERADIATION OF RADIO WAVES; ANALOGOUS ARRANGEMENTS USING OTHER WAVES - H04L: TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION (arrangements common to telegraphic and telephonic communication H04M) ## Abstract A sensor delay time estimation device that includes: a memory; and a processor coupled to the memory, wherein the processor is configured to: acquire an observable required to estimate a vehicle state from a plurality of sensors that detect and output the observable, each of the plurality of sensors being different, and based on the observable for a fixed time interval acquired from each of the plurality of sensors, estimate a time delay from detection to acquisition of the observable for each of the plurality of sensors, and estimate a vehicle state for the fixed time interval. ## Claims ### Claim 1 A sensor delay time estimation device, comprising: a memory; and a processor coupled to the memory, wherein the processor is configured to: acquire a plurality of observables from a plurality of sensors, each of the plurality of sensors being different, and each of the plurality of sensors detecting and outputting a respective observable from among the plurality of observables, and estimate, based on the observables for a period of time, a time delay from detection to acquisition of observables other than one reference observable for the period of time, wherein the reference observable is output by a reference sensor of the plurality of sensors, the reference sensor having a minimum delay time from detection to acquisition of its respective observable with respect to other sensors from among the plurality of sensors. ### Claim 2 (depends on 1) The sensor delay time estimation device of claim 1, wherein the processor is configured to compute a predicted value of a state quantity indicating a vehicle state at a next timing, based on an interrelationship between time and the vehicle state and based on the observable of the reference sensor. ### Claim 3 (depends on 2) The sensor delay time estimation device of claim 2, wherein the processor is configured to estimate the vehicle state and a delay time of a sensor other than the reference sensor, based on a deviation between the observables of the plurality of sensors taking into account an error in a predicted value of the vehicle state and the delay time in the period of time, and the observables of the plurality of sensors computed from the state quantity. ### Claim 4 (depends on 1) The sensor delay time estimation device of claim 1, wherein the reference sensor is an inertial measurement unit (IMU). ### Claim 5 (depends on 1) The sensor delay time estimation device of claim 1, wherein data output by the plurality of sensors is discrete and non-continuous, and the processor is configured to interpolate the discrete and non-continuous data to generate continuous data. ### Claim 6 (depends on 5) The sensor delay time estimation device of claim 5, wherein the processor is configured to interpolate the discrete and non-continuous data by using a Gaussian process regression. ### Claim 7 A sensor delay time estimation method, comprising: acquiring a plurality of observables from a plurality of sensors, each of the plurality of sensors being different, and each of the plurality of sensors detecting and outputting a respective observable from among the plurality of observables; and estimating, based on the observables for a period of time, a time delay from detection to acquisition of observables other than one reference observable for the period of time, wherein the reference observable is output by a reference sensor of the plurality of sensors, the reference sensor having a minimum delay time from detection to acquisition of its respective observable with respect to other sensors from among the plurality of sensors. ### Claim 8 (depends on 7) The sensor delay time estimation method of claim 7, further comprising computing a predicted value of a state quantity indicating a vehicle state at a next timing, based on an interrelationship between time and the vehicle state and based on the observable of the reference sensor. ### Claim 9 (depends on 8) The sensor delay time estimation method of claim 8, further comprising estimating the vehicle state and a delay time of a sensor other than the reference sensor, based on a deviation between the observables of the plurality of sensors taking into account an error in a predicted value of the vehicle state and the delay time in the period of time, and the observables of the plurality of sensors computed from the state quantity. ### Claim 10 (depends on 7) The sensor delay time estimation method of claim 7, wherein the reference sensor is an inertial measurement unit (IMU). ### Claim 11 (depends on 7) The sensor delay time estimation method of claim 7, wherein data output by the plurality of sensors is discrete and non-continuous, and the method further comprises interpolating the discrete and non-continuous data to generate continuous data. ### Claim 12 (depends on 11) The sensor delay time estimation method of claim 11, further comprising interpolating the discrete and non-continuous data by using a Gaussian process regression. ### Claim 13 A non-transitory recording medium that records a program that is executable by a computer to perform a sensor delay time estimation processing, the sensor delay time estimation processing comprising: acquiring a plurality of observables from a plurality of sensors, each of the plurality of sensors being different, and each of the plurality of sensors detecting and outputting a respective observable from among the plurality of observables; and estimating, based on the observables for a period of time, a time delay from detection to acquisition of observables other than one reference observable for the period of time, wherein the reference observable is output by a reference sensor of the plurality of sensors, the reference sensor having a minimum delay time from detection to acquisition of its respective observable with respect to other sensors from among the plurality of sensors. ### Claim 14 (depends on 13) The non-transitory recording medium of claim 13, wherein the sensor delay time estimation processing further includes computing a predicted value of a state quantity indicating a vehicle state at a next timing, based on an interrelationship between time and the vehicle state and based on the observable of the reference sensor. ### Claim 15 (depends on 14) The non-transitory recording medium of claim 14, wherein the sensor delay time estimation processing further includes estimating the vehicle state and a delay time of a sensor other than the reference sensor, based on a deviation between the observables of the plurality of sensors taking into account an error in a predicted value of the vehicle state and the delay time in the period of time, and the observables of the plurality of sensors computed from the state quantity. ### Claim 16 (depends on 13) The non-transitory recording medium of claim 13, wherein the reference sensor is an inertial measurement unit (IMU). ### Claim 17 (depends on 13) The non-transitory recording medium of claim 13, wherein data output by the plurality of sensors is discrete and non-continuous, and the processing further comprises interpolating the discrete and non-continuous data to generate continuous data. ### Claim 18 (depends on 17) The non-transitory recording medium of claim 17, further comprising interpolating the discrete and non-continuous data by using a Gaussian process regression.