Abstract
In this paper, a neural network approach is presented for solving the problem of estimating road grade and vehicle mass, for the case of simulated heavy-duty vehicles (HDVs) driving on highways. After training, and using only signals normally available in HDVs, the (feedforward) neural network provides road grade estimates with an average root mean square (RMS) error of around 0.10 to 0.14 degrees, and mass estimates with an average RMS error of around 1%, when applied to two different test data sets (one with synthetic roads and one based on a real road), not used during the training phase. The estimates obtained outperform road grade and mass estimates obtained with other approaches.
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