Platoon Merging Distance Prediction using a Neural Network Vehicle Speed Model

Abstract 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 efciently 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 infuenced by the road grade and by the local trafc condition. In this paper, we examine the infuence 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 trafc. It is shown that under reasonable conditions, the error in the merge distance prediction is smaller than 8%.