SYSTEM AND METHOD FOR PROVIDING DRIVER BEHAVIOR CLASSIFICATION AT INTERSECTIONS AND VALIDATION ON LARGE NATURALISTIC DATA SETS
First Claim
1. A warning system configured to predict whether a vehicle will come to a stop at an intersection before a first time, comprising:
- at least one sensor configured to measure vehicle data of the vehicle, wherein the vehicle data comprises;
a speed of the vehicle, an acceleration of the vehicle and a distance from the vehicle to the intersection; and
at least one processor coupled to the at least one sensor configured to;
receive vehicle data measured by the at least one sensor at a plurality of times during a time window, wherein the vehicle data comprises a plurality of measurements of each of;
the speed of the vehicle;
the acceleration of the vehicle; and
the distance from the vehicle to the intersection;
generate a prediction of whether the vehicle will or will not stop at the intersection before the first time based on the vehicle data measured during the time window; and
at a second time, the second time being before the first time and approximately equal to a time at which the time window ends, provide an indication that the vehicle will not stop at the intersection before the first time based upon the prediction,wherein generating the prediction comprises using a classification model, the classification model configured to indicate whether the vehicle will or will not stop at the intersection before the first time based on a plurality of input parameters, andwherein the plurality of input parameters comprises a speed, an acceleration and a distance to an intersection.
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Accused Products
Abstract
A system and method for predicting whether a vehicle will come to a stop at an intersection is provided. Generally, the system contains a memory; and a processor configured by the memory to perform the steps of: generating a prediction of whether the vehicle will or will not stop at the intersection before a first time based on vehicle data measured during a first time window; and at a second time, the second time being before the first time and approximately equal to a time at which the time window ends, providing an indication that the vehicle will not stop at the intersection before the first time based upon the prediction, wherein generating the prediction comprises using a classification model, the classification model configured to indicate whether the vehicle will or will not stop at the intersection before the first time based on a plurality of input parameters, and wherein the plurality of input parameters are selected from the group consisting of speed, acceleration, and distance to the intersection.
46 Citations
3 Claims
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1. A warning system configured to predict whether a vehicle will come to a stop at an intersection before a first time, comprising:
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at least one sensor configured to measure vehicle data of the vehicle, wherein the vehicle data comprises;
a speed of the vehicle, an acceleration of the vehicle and a distance from the vehicle to the intersection; andat least one processor coupled to the at least one sensor configured to; receive vehicle data measured by the at least one sensor at a plurality of times during a time window, wherein the vehicle data comprises a plurality of measurements of each of; the speed of the vehicle; the acceleration of the vehicle; and the distance from the vehicle to the intersection; generate a prediction of whether the vehicle will or will not stop at the intersection before the first time based on the vehicle data measured during the time window; and at a second time, the second time being before the first time and approximately equal to a time at which the time window ends, provide an indication that the vehicle will not stop at the intersection before the first time based upon the prediction, wherein generating the prediction comprises using a classification model, the classification model configured to indicate whether the vehicle will or will not stop at the intersection before the first time based on a plurality of input parameters, and wherein the plurality of input parameters comprises a speed, an acceleration and a distance to an intersection.
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2. A device for predicting whether a vehicle will come to a stop at an intersection before a first time, wherein the device comprises:
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a memory; and a processor configured by the memory to perform the steps of; generating a prediction of whether the vehicle will or will not stop at the intersection before the first time based on vehicle data measured during a first time window; and at a second time, the second time being before the first time and approximately equal to a time at which the time window ends, providing an indication that the vehicle will not stop at the intersection before the first time based upon the prediction, wherein generating the prediction comprises using a classification model, the classification model configured to indicate whether the vehicle will or will not stop at the intersection before the first time based on a plurality of input parameters, and wherein the plurality of input parameters are selected from the group consisting of speed, acceleration, and distance to the intersection.
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3. A method of producing a classification model for predicting whether a vehicle will stop at an intersection before a signal at the intersection indicating a stopping condition is presented, comprising:
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obtaining vehicle data for a plurality of vehicles, the vehicle data for at least a first vehicle comprising; an indication of whether the first vehicle stopped at the intersection before a first signal indicating a stopping condition was presented at the intersection; and a plurality of values measured during at a plurality of times during a time window prior to the first signal indicating the stopping condition, the plurality of values comprising a plurality of each of; a speed of the first vehicle; an acceleration of the first vehicle; and a distance from the first vehicle to the intersection; training a classification algorithm to, based on a plurality of inputs, generate a probability that a vehicle will stop at the intersection before a signal at the intersection indicating a stopping condition is presented, wherein the plurality of inputs comprises; the vehicle data for the plurality of vehicles; and the duration of the time window; and combining the trained classification algorithm with a probabilistic classifier to produce a classification model, wherein the probabilistic classifier determines whether a vehicle will or will not stop at the intersection before a signal at the intersection indicating a stopping condition is presented based on a respective probability for the vehicle produced by the classification algorithm.
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Specification