Systems and methods for multi-sensor fusion using permutation matrix track association
First Claim
1. A fusion tracking system for associating disparate tracks from multiple sensor inputs for observed objects, comprising:
- one or more processors;
a memory communicably coupled to the one or more processors and storing;
a sensor module including instructions that when executed by the one or more processors cause the one or more processors to, in response to receiving a first input from a first sensor and a second input from a second sensor, generate the disparate tracks including first sensor tracks and second sensor tracks for the observed objects that correspond to the first input and the second input; and
a fusion module including instructions that when executed by the one or more processors cause the one or more processors to identify correlations between the first sensor tracks and the second sensor tracks by computing association likelihoods between the first sensor tracks and the second sensor tracks within a permutation matrix according to an objective cost function,wherein the fusion module further includes instructions to control a vehicle according to the correlations by generating unified tracks from the first sensor tracks and the second sensor tracks using the correlations, and wherein the unified tracks include trajectories of the observed objects that are comprised of inputs from separate sensors.
2 Assignments
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Accused Products
Abstract
System, methods, and other embodiments described herein relate to associating disparate tracks from multiple sensor inputs for observed objects. In one embodiment, a method includes, in response to receiving a first input from a first sensor and a second input from a second sensor, generating the disparate tracks including first sensor tracks and second sensor tracks for the observed objects that correspond to the first input and the second input. The method includes identifying correlations between the first sensor tracks and the second sensor tracks by computing association likelihoods between the first tracks and the second tracks within a permutation matrix according to an objective cost function. The method includes controlling a vehicle according to the correlations.
9 Citations
18 Claims
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1. A fusion tracking system for associating disparate tracks from multiple sensor inputs for observed objects, comprising:
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one or more processors; a memory communicably coupled to the one or more processors and storing; a sensor module including instructions that when executed by the one or more processors cause the one or more processors to, in response to receiving a first input from a first sensor and a second input from a second sensor, generate the disparate tracks including first sensor tracks and second sensor tracks for the observed objects that correspond to the first input and the second input; and a fusion module including instructions that when executed by the one or more processors cause the one or more processors to identify correlations between the first sensor tracks and the second sensor tracks by computing association likelihoods between the first sensor tracks and the second sensor tracks within a permutation matrix according to an objective cost function, wherein the fusion module further includes instructions to control a vehicle according to the correlations by generating unified tracks from the first sensor tracks and the second sensor tracks using the correlations, and wherein the unified tracks include trajectories of the observed objects that are comprised of inputs from separate sensors. - View Dependent Claims (2, 3, 4, 5, 6, 7)
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8. A non-transitory computer-readable medium storing instructions for associating disparate tracks from multiple sensor inputs for observed objects and that when executed by one or more processors cause the one or more processors to:
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in response to receiving a first input from a first sensor and a second input from a second sensor, generate the disparate tracks including first sensor tracks and second sensor tracks for the observed objects that correspond to the first input and the second input; identify correlations between the first sensor tracks and the second sensor tracks by computing association likelihoods between the first sensor tracks and the second sensor tracks within a permutation matrix according to an objective cost function; and control a vehicle according to the correlations including generating unified tracks from the first sensor tracks and the second sensor tracks using the correlations, and wherein the unified tracks include trajectories of the observed objects that are comprised of inputs from separate sensors of the vehicle. - View Dependent Claims (9, 10, 11)
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12. A method of associating disparate tracks from multiple sensor inputs for observed objects, comprising:
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in response to receiving a first input from a first sensor and a second input from a second sensor, generating the disparate tracks including first sensor tracks and second sensor tracks for the observed objects that correspond to the first input and the second input; identifying correlations between the first sensor tracks and the second sensor tracks by computing association likelihoods between the first sensor tracks and the second sensor tracks within a permutation matrix according to an objective cost function; and controlling a vehicle according to the correlations including generating unified tracks from the first sensor tracks and the second sensor tracks using the correlations, and wherein the unified tracks include trajectories of the observed objects that are comprised of inputs from separate sensors of the vehicle. - View Dependent Claims (13, 14, 15, 16, 17, 18)
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Specification