Road grade auto-mapping
First Claim
1. A method of modeling a road characteristic over a region in which a host vehicle is driven, comprising the steps of:
- operating the host vehicle on roadways having a characteristic that varies over a plurality of predetermined ranges;
generating a succession of characteristic values while operating the host vehicle at a predetermined rate, wherein each characteristic value identifies a respective predetermined range then being encountered by the vehicle;
updating a Markov chain road-characteristic model stored in an optimizer controller in response to the succession of characteristic values, wherein the model represents respective elements of probability in a matrix of transition events from each predetermined range to a respective next-in-succession range, wherein the optimizer controller stores a current state and a previous state of the matrix;
periodically testing a convergence of the current state of the matrix with the previous state of the matrix using KL divergence;
if convergence is found then using the current state of the matrix to identify a powertrain control policy optimized for the characterized road characteristic; and
adjusting operation of a powertrain of the host vehicle using the identified control policy within a powertrain controller in the host vehicle;
wherein each element of the matrix has a value π
i,j representing a weighted frequency of transition events from a first respective characteristic value to a second respective characteristic value divided by a weighted frequency of transition events initiating from the first respective characteristic value, so that the matrix successively approximates the road characteristic of the region.
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Abstract
Road grade is modeled over a region in which a vehicle is driven on roadways having a grade that varies over a plurality of predetermined grade ranges. A succession of grade values are generated while operating the vehicle at a predetermined rate, wherein each grade value identifies a respective grade range then being encountered. A Markov chain road-grade model is updated in response to the succession of grade values, wherein the model represents respective elements of probability in a matrix of transition events from each predetermined grade range to a respective next-in-succession grade range. Each element of the matrix has a value πi,j representing a weighted frequency of transition events from a first respective grade value to a second respective grade value divided by a weighted frequency of transition events initiating from the first respective grade value, so that the matrix successively approximates the road grade of the region.
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Citations
18 Claims
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1. A method of modeling a road characteristic over a region in which a host vehicle is driven, comprising the steps of:
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operating the host vehicle on roadways having a characteristic that varies over a plurality of predetermined ranges; generating a succession of characteristic values while operating the host vehicle at a predetermined rate, wherein each characteristic value identifies a respective predetermined range then being encountered by the vehicle; updating a Markov chain road-characteristic model stored in an optimizer controller in response to the succession of characteristic values, wherein the model represents respective elements of probability in a matrix of transition events from each predetermined range to a respective next-in-succession range, wherein the optimizer controller stores a current state and a previous state of the matrix; periodically testing a convergence of the current state of the matrix with the previous state of the matrix using KL divergence; if convergence is found then using the current state of the matrix to identify a powertrain control policy optimized for the characterized road characteristic; and adjusting operation of a powertrain of the host vehicle using the identified control policy within a powertrain controller in the host vehicle; wherein each element of the matrix has a value π
i,j representing a weighted frequency of transition events from a first respective characteristic value to a second respective characteristic value divided by a weighted frequency of transition events initiating from the first respective characteristic value, so that the matrix successively approximates the road characteristic of the region. - View Dependent Claims (2, 3, 4, 5, 6, 7, 8, 9)
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10. Apparatus for a host vehicle that is driven in a region over roadways exhibiting a road characteristic that varies over a plurality of predetermined ranges, comprising:
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a powertrain controller for adjusting control parameters of powertrain components of the host vehicle; a road monitor generating a succession of characteristic values while operating the host vehicle at a predetermined rate, wherein each characteristic value identifies a respective range then being encountered by the host vehicle; and an optimizer including a Markov chain road-characteristic model that is updated in response to the succession of characteristic values, wherein the model represents respective elements of probability in a matrix of transition events from each predetermined range to a respective next-in-succession range, wherein the optimizer stores a current state and a previous state of the matrix, and wherein each element of the matrix has a value π
i,j representing a weighted frequency of transition events from a first respective characteristic value to a second respective characteristic value divided by a weighted frequency of transition events initiating from the first respective characteristic value, so that the matrix successively approximates the road characteristic of the region;wherein the optimizer periodically tests a convergence of the current state of the matrix with the previous state of the matrix, wherein if convergence is found then the optimizer uses the matrix to identify a powertrain control policy optimized for the characterized road characteristic, and wherein the powertrain controller adjusts the control parameters using the identified control policy. - View Dependent Claims (11, 12, 13, 14, 15, 16, 17, 18)
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Specification