# US 12,759,556 B2 · Method for quantifying object detection performance > Agents: this data is available as JSON from the patentgrep API and MCP server — see https://patentgrep.com/llms.txt - Number: US 12,759,556 B2 - Document ID: US12759556B2 - Status: Granted - Kind: B2 - Application number: 18/050,294 - Filed: 2022-10-27 - Published: 2023-05-11 - Granted: 2026-10-13 - Earliest claimed: 2021-11-09 - Assignees: Volvo Autonomous Solutions AB - Applicants: Volvo Autonomous Solutions AB - Inventors: Linus Hagvall; Fredrik Sandblom - Web page: https://patentgrep.com/patent/US12759556B2 ## CPC - B60W 40/04: Traffic conditions - B60W 60/001: {Planning or execution of driving tasks} - G05D 1/86: Monitoring the performance of the system, e.g. alarm or diagnosis modules - G06F 17/18: for evaluating statistical data {, e.g. average values, frequency distributions, probability functions, regression analysis (forecasting specially adapted for a specific administrative, business or logistic context G06Q10/04)} - B60W 50/0097: {Predicting future conditions} - B60W 2300/12: Trucks; Load vehicles - B60W 2554/80: Spatial relation or speed relative to objects - B60W: CONJOINT CONTROL OF VEHICLE SUB-UNITS OF DIFFERENT TYPE OR DIFFERENT FUNCTION; CONTROL SYSTEMS SPECIALLY ADAPTED FOR HYBRID VEHICLES; ROAD VEHICLE DRIVE CONTROL SYSTEMS FOR PURPOSES NOT RELATED TO THE CONTROL OF A PARTICULAR SUB-UNIT - G05D: SYSTEMS FOR CONTROLLING OR REGULATING NON-ELECTRIC VARIABLES - G06F: ELECTRIC DIGITAL DATA PROCESSING (computer systems based on specific computational models G06N) ## Abstract A method for controlling a vehicle based on a quantified objection detection performance. The method includes obtaining samples of objection detection performance p i i = 1 n - 1 , wherein the objection detection performance comprises a comparison of an estimated object state with a reference object state, selecting a subset of the objection detection performance samples such that the selected samples follow a pre-determined statistical extreme value distribution, parameterizing the pre-determined statistical extreme value distribution based on the selected samples of objection detection performance, quantifying objection detection performance based on the parameterized statistical extreme value distribution, and controlling the vehicle based on the quantified objection detection performance. ## Claims ### Claim 1 A method performed by a control unit for controlling a vehicle based on a quantified object detection performance, the method comprising: obtaining samples of object detection performance p i i = 1 n - 1 , wherein the object detection performance comprises a comparison of an estimated object state with a reference object state, wherein the obtaining comprises obtaining a previously stored set of object detection performance samples, or obtaining a set of object detection performance samples during operation of an ego vehicle, selecting a subset of the obtained object detection performance samples such that the selected samples follow a pre-determined statistical extreme value distribution, wherein the selecting comprises determining a threshold ζ such that the samples of object detection performance in excess of the threshold ζ, {p_i:p_iζ}, follow the pre-determined statistical extreme value distribution, parameterizing the pre-determined statistical extreme value distribution based on the selected samples of object detection performance, quantifying the object detection performance as a bounded model by bounding the performance to lie within a rang −w≤bk≤w with probability greater than 1−y, based on the pre-determined statistical extreme value distribution; and controlling the vehicle based on the quantified object detection performance. ### Claim 2 (depends on 1) The method according to claim 1, comprising measuring a time between exceedances metric indicating the time passed between object detection performance samples exceeding the threshold ζ, and monitoring operational design domain, ODD, based on the time between exceedances metric. ### Claim 3 (depends on 1) The method according to claim 1, wherein the pre-determined statistical extreme value distribution is a Generalized Pareto Distribution, GPD. ### Claim 4 (depends on 1) The method according to claim 1, wherein the pre-determined statistical extreme value distribution is a Generalized Extreme Value distribution, GEV. ### Claim 5 (depends on 1) The method according to claim 1, wherein the samples of object detection performance p i i = 1 n - 1 comprise any of object position, object heading, object longitudinal velocity, object lateral velocity, object longitudinal acceleration, object lateral acceleration, object yaw rate, object motion relative to a road surface object motion relative to a road lane, object motion relative to another object, minimum distance for reliable object classification. ### Claim 6 (depends on 1) The method according to claim 1, wherein a sample of object detection performance is obtained dependent on any of; type of vehicle, type of vehicle combination, vehicle physical dimension parameters, weather condition, road friction, road geometry, and path trajectory geometry. ### Claim 7 (depends on 1) The method according to claim 1, comprising determining a confidence value β associated with the pre-determined statistical extreme value distribution. ### Claim 8 (depends on 7) The method according to claim 7, comprising assessing a sufficiency of gathered object detection performance data for performance quantification based on the confidence value β associated with the pre-determined statistical extreme value distribution. ### Claim 9 (depends on 1) The method according to claim 1, comprising monitoring an operational design domain, ODD, associated with the vehicle by comparing the parameterized pre-determined statistical extreme value distribution to a set of baseline distribution parameters, wherein operation outside the ODD is indicated by a difference between parameterized pre-determined statistical extreme value distribution parameters and the baseline distribution parameters. ### Claim 10 (depends on 1) A non-transitory computer readable medium carrying a computer program comprising program code for performing the steps of claim 1 when said program code is run on a computer or on processing circuitry of a control unit. ### Claim 11 (depends on 1) A control unit for quantifying object detection performance, the control unit being configured to perform the steps of the method according to claim 1. ### Claim 12 (depends on 11) A vehicle comprising a control unit according to claim 11.