# US 2026/0097784 A1 · Systems and methods for evaluating object tracking methodologies > Agents: this data is available as JSON from the patentgrep API and MCP server — see https://patentgrep.com/llms.txt - Number: US 2026/0097784 A1 - Document ID: US20260097784A1 - Status: Published application - Kind: A1 - Application number: 18/905,833 - Filed: 2024-10-03 - Published: 2026-04-09 - Earliest claimed: 2024-10-03 - Assignees: TORC Robotics, Inc. - Applicants: Torc Robotics, Inc. - Inventors: Zhenghan Zhu; Jackson Maury Newton; Haseeb Chaudhry - Web page: https://patentgrep.com/patent/US20260097784A1 ## CPC - B60W 60/001: {Planning or execution of driving tasks} - B60W 40/04: Traffic conditions - B60W 50/06: Improving the dynamic response of the control system, e.g. improving the speed of regulation or avoiding hunting or overshoot - B60W 60/00276: {for two or more other traffic participants} - 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 2554/402: Type - B60W 2554/4041: Position - B60W 2554/4042: Longitudinal speed - B60W 2554/4044: Direction of movement, e.g. backwards - 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 - G06F: ELECTRIC DIGITAL DATA PROCESSING (computer systems based on specific computational models G06N) ## Abstract An autonomous vehicle including one or more sensors, at least one processor, and at least one memory storing instructions, which, when executed by the at least one processor, cause the at least one processor to perform operations including receiving sensor data from one or more sensors of an autonomous vehicle, determining, using a first tracking methodology, methodology tracking based on the sensor data, and receiving ground truth tracking. The at least one processor determining an association between the methodology tracking and the ground truth tracking, determining a joint probability distribution of the methodology tracking and the ground truth tracking, determining a methodology tracking distribution based on the joint probability distribution, determining a ground truth tracking distribution based on the joint probability distribution, and determining a Kullback-Leibler Divergence (KLD) score between the methodology tracking distribution and the ground truth tracking distribution. ## Claims ### Claim 1 An autonomous vehicle comprising: one or more sensors; at least one processor; and at least one memory storing instructions, which, when executed by the at least one processor, cause the at least one processor to perform operations comprising: receiving sensor data from the one or more sensors of the autonomous vehicle; determining, using a first tracking methodology, methodology tracking based on the sensor data; receiving ground truth tracking; determining an association between the methodology tracking and the ground truth tracking; determining a joint probability distribution of the methodology tracking and the ground truth tracking; determining a methodology tracking distribution based on the joint probability distribution; determining a ground truth tracking distribution based on the joint probability distribution; determining a Kullback-Leibler Divergence (KLD) score between the methodology tracking distribution and the ground truth tracking distribution; and controlling operation of the autonomous vehicle based on the KLD score. ### Claim 2 (depends on 1) The autonomous vehicle of claim 1, wherein the methodology tracking includes a first methodology tracking and a second methodology tracking and wherein the KLD score includes a first KLD score and a second KLD score, and wherein the at least one memory storing instructions, which, when executed by the at least one processor, cause the at least one processor to perform operations comprising: determining the first KLD score for the first tracking methodology; determining the second KLD score for the second tracking methodology; comparing the first KLD score and the second KLD score; and based on the comparison, selecting between the first tracking methodology and the second tracking methodology implemented on the autonomous vehicle. ### Claim 3 (depends on 1) The autonomous vehicle of claim 1, wherein the at least one memory storing instructions, which, when executed by the at least one processor, cause the at least one processor to perform operations comprising: optimizing the methodology by optimizing the KLD score. ### Claim 4 (depends on 1) The autonomous vehicle of claim 1, wherein the at least one memory storing instructions, which, when executed by the at least one processor, cause the at least one processor to perform operations comprising: determining the association by: computing a similarity metric between the methodology tracking and the ground truth tracking; and comparing the similarity metric with a threshold. ### Claim 5 (depends on 1) The autonomous vehicle of claim 1, wherein the at least one memory storing instructions, which, when executed by the at least one processor, cause the at least one processor to perform operations comprising: generating an association table that counts a number of times that the methodology tracking is associated with the ground truth tracking. ### Claim 6 (depends on 1) The autonomous vehicle of claim 1, wherein determining the methodology tracking distribution comprises integrating the joint probability distribution over the ground truth methodology tracking. ### Claim 7 (depends on 1) The autonomous vehicle of claim 1, wherein determining the ground truth tracking distribution comprises integrating the joint probability distribution over the methodology ground truth tracking. ### Claim 8 A computer implemented method comprising: receiving sensor data from one or more sensors of an autonomous vehicle; determining, using a first tracking methodology, methodology tracking based on the sensor data; receiving ground truth tracking; determining an association between the methodology tracking and the ground truth tracking; determining a joint probability distribution of the methodology tracking and the ground truth tracking; determining a methodology tracking distribution based on the joint probability distribution; determining a ground truth tracking distribution based on the joint probability distribution; determining a Kullback-Leibler Divergence (KLD) score between the methodology tracking distribution and the ground truth tracking distribution; and controlling operation of the autonomous vehicle based on the KLD score. ### Claim 9 (depends on 8) The computer implemented method of claim 8, wherein the methodology tracking includes a first methodology tracking and a second methodology tracking and wherein the KLD score includes a first KLD score and a second KLD score, and wherein the method further comprises: determining the first KLD score for the first tracking methodology; determining the second KLD score for the second tracking methodology, and comparing the first KLD score and the second KLD score; and based on the comparison, selecting between the first tracking methodology and the second tracking methodology implemented on the autonomous vehicle. ### Claim 10 (depends on 8) The computer implemented method of claim 8, wherein controlling the operation further comprises: optimizing the methodology by optimizing the KLD score. ### Claim 11 (depends on 8) The computer implemented method of claim 8, wherein determining the association further comprises: computing a similarity metric between the methodology tracking and the ground truth tracking; and determining the association by comparing the similarity metric with a threshold. ### Claim 12 (depends on 8) The computer implemented method of claim 8, determining the association comprises: generating an association table that counts a number of times that the methodology tracking matches the ground truth tracking, for each parameter, for each time step, for each actor. ### Claim 13 (depends on 8) The computer implemented method of claim 8, wherein determining the methodology tracking distribution comprises integrating the joint probability distribution over the ground truth tracking. ### Claim 14 (depends on 8) The computer implemented method of claim 8, wherein determining the ground truth tracking distribution comprises integrating the joint probability distribution over the methodology tracking. ### Claim 15 A non-transitory computer-readable medium embodying programmed instructions which, when executed by at least one processor of an autonomous vehicle, cause the at least one processor to perform operations comprising: receiving sensor data from one or more sensors of the autonomous vehicle; determining, using a first tracking methodology, methodology tracking based on the sensor data; receiving ground truth tracking; determining an association between the methodology tracking and the ground truth tracking; determining a joint probability distribution of the methodology tracking and the ground truth tracking; determining a methodology tracking distribution based on the joint probability distribution; determining a ground truth tracking distribution based on the joint probability distribution; determining a Kullback-Leibler Divergence (KLD) score between the methodology tracking distribution and the ground truth tracking distribution; and controlling operation of the autonomous vehicle based on the KLD score. ### Claim 16 (depends on 15) The non-transitory computer-readable medium of claim 15, wherein the programmed instructions cause the at least one processor to perform operations comprising: determining a first KLD score for a first tracking methodology; determining a second KLD score for a second tracking methodology, and comparing the first KLD score and the second KLD score; and based on the comparison, selecting between the first tracking methodology and the second tracking methodology implemented on the autonomous vehicle. ### Claim 17 (depends on 15) The non-transitory computer-readable medium of claim 15, wherein the programmed instructions cause the at least one processor to perform operations comprising: optimizing the methodology by optimizing the KLD score. ### Claim 18 (depends on 15) The non-transitory computer-readable medium of claim 15, wherein determining the association further comprises: computing a similarity metric between the methodology tracking and the ground truth tracking; and determining the association by comparing the similarity metric with a threshold. ### Claim 19 (depends on 15) The non-transitory computer-readable medium of claim 15, determining the association comprises: generating an association table that counts a number of times that the methodology tracking matches the ground truth tracking, for each parameter, for each time step, for each actor. ### Claim 20 (depends on 15) The non-transitory computer-readable medium of claim 15, wherein determining the methodology tracking distribution comprises integrating the joint probability distribution over the ground truth tracking.