Adaptive Kalman filter method for accurate estimation of forward path geometry of an automobile
First Claim
1. An apparatus for dynamically predicting road geometry comprising:
- i. a bank of filters configured to receive current environmental data from at least one sensor and to perform an estimation using the current environmental data, and to provide a filter output from each filter;
ii. a weighting element configured to receive the filter output from the bank of filters and to ascribe a weighted value to each filter output based on a relevance of the filter, thereby providing weighted outputs;
iii. a fusing element configured to fuse the weighted outputs into a single output;
whereby the output describes a vehicle forward path estimate.
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Abstract
The present invention provides a method and apparatus for estimation of vehicle forward path geometry utilizing an adaptive Kalman filter bank and a two-clothoid road model. The invention provides that each of a plurality of Kalman filters, utilizing the latest available measurement vector Yk at time k, estimates the state vector Xk and error covariance matrix Pk. The outputs of filter 504a, 504b, and 504c denoted as as Xkj and Pkj, are provided to a plurality of weighting elements, which calculate weight factors, Wkj 506a, 506b, and 506c for each filter. The weight factor of each filter is the probability that the upcoming road geometry matches the road model hypothesized in the filter. After being assigned a weighted value, the weighted value road models are fused in a fusion element 508, and a weighted output road geometry model is provided.
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Citations
57 Claims
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1. An apparatus for dynamically predicting road geometry comprising:
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i. a bank of filters configured to receive current environmental data from at least one sensor and to perform an estimation using the current environmental data, and to provide a filter output from each filter;
ii. a weighting element configured to receive the filter output from the bank of filters and to ascribe a weighted value to each filter output based on a relevance of the filter, thereby providing weighted outputs;
iii. a fusing element configured to fuse the weighted outputs into a single output;
whereby the output describes a vehicle forward path estimate.- View Dependent Claims (2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19)
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20. A computer program product for dynamically predicting road geometry, the computer program product comprising means, stored on computer readable medium, for:
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i. filtering current environmental data from at least one sensor and performing an estimation using the current environmental data, and outputting a filter output from each filter;
ii. weighing the filter output from the bank of filters and ascribing a weighted value to each filter output based on a relevance of the filter, thereby providing weighted outputs;
iii. fusing the weighted outputs into a single output;
whereby the output describes a vehicle forward path estimate.- View Dependent Claims (21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38)
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39. A method for dynamically predicting road geometry, the method comprising steps of:
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i. filtering current environmental data from at least one sensor and performing an estimation using the current environmental data, and outputting a filter output from each filter;
ii. weighing the filter output from the bank of filters and ascribing a weighted value to each filter output based on a relevance of the filter, thereby providing weighted outputs;
iii. fusing the weighted outputs into a single output;
whereby the output describes a vehicle forward path estimate.- View Dependent Claims (40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57)
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Specification