Platoon Merging Distance Prediction Using A Neural Network Vehicle Speed Model

Heavy-duty vehicle platooning has been an important research topic in recent years. By driving closely together, the vehicles save fuel by reducing total air drag and utilize the road more efficiently. Often the heavy-duty vehicles will catch-up in order to platoon while driving on the common stretch of road, and in this case, a good prediction of when the platoon merging will take place is required in order to make predictions on overall fuel savings and to automatically control the velocity prior to the merge. The vehicle speed prior to platoon merging is mostly influenced by the road grade and by the local traffic condition. In this paper, we examine the influence of road grade and propose a method for predicting platoon merge distance using vehicle speed prediction based on road grade. The proposed method is evaluated using experimental data from platoon merging test runs done on a highway with varying level of traffic. It is shown that under reasonable conditions, the error in the merge distance prediction is smaller than 8%. (C) 2017, IFAC (International Federation of Automatic Control) Hosting by Elsevier Ltd. All rights reserved.