SYSTEM AND METHOD FOR STOCHASTICALLY PREDICTING THE FUTURE STATES OF A VEHICLE
First Claim
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1. A method for predicting future states of a vehicle, comprising the steps of:
- selecting a model having n states reflecting dynamic features of the vehicle;
inputting noisy sensor measurements representing a current state of the vehicle to generate (2n +1) sigma points Xi where i=0, . . . 2n, each of the sigma points having n states;
performing (2n+1) integrations, each integration includes propagating the n-states of the respective sigma points Xi through the non-linear function Yi=f(Xi); and
combining the propagated sigma points to generate the predicted future states of the vehicle.
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Abstract
A method for predicting future states of a vehicle including the steps of selecting a model having n states reflecting dynamic features of the vehicle; inputting noisy sensor measurements representing a current state of the vehicle to generate (2n+1) sigma points Xi where i=0, . . . . 2n, each of the sigma points having n states; performing (2n+1) integrations, each integration includes propagating the n-states of the respective sigma points Xi through the non-linear function Yi=f(Xi); and combining the propagated sigma points to generate the predicted future states of the vehicle.
41 Citations
16 Claims
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1. A method for predicting future states of a vehicle, comprising the steps of:
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selecting a model having n states reflecting dynamic features of the vehicle; inputting noisy sensor measurements representing a current state of the vehicle to generate (2n +1) sigma points Xi where i=0, . . . 2n, each of the sigma points having n states; performing (2n+1) integrations, each integration includes propagating the n-states of the respective sigma points Xi through the non-linear function Yi=f(Xi); and combining the propagated sigma points to generate the predicted future states of the vehicle. - View Dependent Claims (3, 4, 5, 6, 7)
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2. The method of claim I, wherein the input sensor measurements include velocity, yaw-rate, and acceleration.
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8. A system for predicting future states of a vehicle comprising:
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a first plurality of sensors, each sensor configured to output a performance measurement of the vehicle representative of the current state of the vehicle; and a processor configured to predict future states of the vehicle based on the sensed performance measurements, the prediction calculated by the processor by generating (2n+1) sigma points Xi where i=0, . . . 2n, each of the sigma points having n states, performing (2n+1) integrations, each integration including propagating the n-states of the respective sigma points Xi through the non-linear function Yi=f(Xi), and combining the propagated sigma points to generate the predicted future states of the vehicle. - View Dependent Claims (9, 10, 11, 12, 13, 14)
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15. A roadside system for predicting future states of vehicles comprising:
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a plurality of sensors configured to sense the state of at least one vehicle; and a processor configured to predict future states of each sensed vehicle based on sensed performance measurements, the prediction calculated by the processor by generating (2n+1) sigma points Xi where i=0, . . . 2n, each of the sigma points having n states, performing (2n+1) integrations, each integration including propagating the n-states of the respective sigma points Xi through the non-linear function Yi=f(Xi), and combining the propagated sigma points to generate the predicted future states of each sensed vehicle; wherein the processor is configured to predict whether a collision involving the sensed vehicles will occur in the future based on the predicted future states of the sensed vehicles, and to transmit instructions to the sensed vehicles to avoid the collision.
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16. A computer readable medium having stored thereon instructions for causing the computer to implement a method for predicting future states of a vehicle, comprising the steps of:
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inputting noisy sensor measurements representing a current state of the vehicle to generate (2n+1) sigma points Xi where i=0, . . . 2n, each of the sigma points having n states; performing (2n+1) integrations, each integration includes propagating the n-states of the respective sigma points Xi through the non-linear function Yi=f(Xi); and combining the propagated sigma points to generate the predicted future states of the vehicle.
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Specification