Method for assessing models of vehicle driving style or vehicle usage model detector
First Claim
1. A method of determining the probability that a statistical model describes the driving style of a vehicle observed from time step k to present, said method comprising:
- (a) establishing at least one statistical model of driving style, wherein said at least one statistical model is a conditional probability model that describes probability of conditions at a next time step k+1, given conditions at said time step k;
(b) determining initial probability distribution of said at least one statistical model of driving style,
wherein said (b) determining the initial probability distribution of said at least one statistical model comprises setting vector Π
0 as the initial probability distribution wherein said vector Π
0 is re-determined for each said at least one statistical model;
(c) determining probability of an observed transition being predicted by said at least one statistical model of driving style;
(d) calculating probability of said at least one statistical model explaining driving style of said vehicle observed at said next time step k+1; and
wherein said (c) determining the probability of said observed transition and said (d) calculating probability of said at least one statistical model are iterated for all time steps until present.
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Abstract
The present invention relates to a method of determining the probability that a statistical model describes the driving style or usage of a vehicle observed. By identifying an appropriate statistical model, the driving style can be detected and vehicle operation can be optimized. The method may be applied to both hybrid and non-hybrid vehicles. The method may also be augmented to determine the most probable statistical model of the usage of the vehicle. This allows detection of driving styles that can be classified as urban, highway, aggressive, etc. For example, the augmented method can be used to distinguish between highway and urban driving with a very high certainty.
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Citations
16 Claims
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1. A method of determining the probability that a statistical model describes the driving style of a vehicle observed from time step k to present, said method comprising:
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(a) establishing at least one statistical model of driving style, wherein said at least one statistical model is a conditional probability model that describes probability of conditions at a next time step k+1, given conditions at said time step k; (b) determining initial probability distribution of said at least one statistical model of driving style,
wherein said (b) determining the initial probability distribution of said at least one statistical model comprises setting vector Π
0 as the initial probability distribution wherein said vector Π
0 is re-determined for each said at least one statistical model;(c) determining probability of an observed transition being predicted by said at least one statistical model of driving style; (d) calculating probability of said at least one statistical model explaining driving style of said vehicle observed at said next time step k+1; and wherein said (c) determining the probability of said observed transition and said (d) calculating probability of said at least one statistical model are iterated for all time steps until present. - View Dependent Claims (2, 3, 4, 5, 6)
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7. The A method of determining the probability that a statistical model describes the driving style of a vehicle observed from time step k to present, said method comprising:
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(a) establishing at least one statistical model of driving style, wherein said at least one statistical model is a conditional probability model that describes probability of conditions at a next time step k+1, given conditions at said time step k; (b) determining initial probability distribution of said at least one statistical model of driving style; (c) determining probability of an observed transition being predicted by said at least one statistical model of driving style; (d) calculating probability of said at least one statistical model explaining driving style of said vehicle observed at said next time step k+1; wherein said (c) determining the probability of said observed transition and said (d) calculating probability of said at least one statistical model are iterated for all time steps until present; and wherein said (c) determining the probability of the observed transition being predicted by said statistical model of driving style includes; employing a reference table look up, wherein said table look-up is a two dimensional matrix developed for each said statistical model, wherein there are N said statistical models of driving style, wherein said table look-up has three independent indices;
an index identifying said statistical model, current velocity and previous velocity of said vehicle observed; andemploying a pre-determined matrix M1 to define probability of transitioning from one driving style to another at each sample, wherein M1 is a Markov matrix. - View Dependent Claims (8, 9)
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10. A method of determining the most probable statistical model of driving style of a vehicle observed from time step k to present, said method comprising:
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(a) establishing at least two statistical models of driving style, wherein said at least two statistical models are conditional probability models that describe probability of conditions at a next time step k+1, given conditions at said time step k; (b) determining initial probability distribution of said at least two statistical models of driving style,
wherein said (b) determining the initial probability distribution of said at least two statistical models comprises setting vector Π
0 as the initial probability distribution, wherein said vector Π
0 is pre-determined for each of said at least two statistical models;(c) determining probability of an observed transition being predicted by said at least two statistical models of driving style; (d) calculating probability of said at least two statistical models explaining driving style of said vehicle observed at said next time step k+1; and wherein said (c) determining the probability of said observed transition and said (d) calculating probability of said at least two statistical models are iterated for all time steps until present; and (e) determining which of said at least two statistical models has the highest probability of explaining the driving style observed. - View Dependent Claims (11, 12, 13, 14, 15, 16)
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Specification